'Adolescent Physical Activity Correlates' (APAC) Questionnaires

Development and Construct Validation of the ‘Adolescent Physical Activity Correlates’ (APAC) Questionnaires

Con Burns BA (Phys Ed), MA. 2012

Thesis submitted in fulfilment of the requirements for the Degree of Doctor of Philosophy at the Department of Physical Education and Sport Sciences, University of Limerick.

Candidate Supervisor: Dr. Ciaran MacDonncha

Submitted to the University of Limerick, May 2012

Do mo chlann go léir, gabhaim buíochas daoibh

ABSTRACT

DEVELOPMENT AND VALIDATION OF THE ‘ADOLESCENT PHYSICAL ACTIVITY CORRELATES’ (APAC) QUESTIONNAIRE

Con Burns BA (Phys Ed.), MA.

The primary aim of this research was to develop and validate a tool to measure the correlates of physical activity among adolescent males and females. Secondary aims were to investigate the correlate profiles and objectively measured activity levels of Irish adolescents. Correlates of physical activity were measured using the ‘Adolescent Physical Activity Correlate’ (APAC) questionnaire, developed for this research based on previously validated tools. Physical activity levels were assessed using Actigraph triaxial accelerometers. Five separate research studies were undertaken which examined correlates of activity, levels of activity and validity of the questionnaire. Study one which was exploratory in nature, provided some evidence to support the construct validity of the ‘APAC’ questionnaire. Study two used a series of focus groups to further inform key facilitators and barriers to physical activity among Irish adolescents. Study three measured correlate profiles and activity levels among Irish adolescents using a questionnaire which had construct validity. Males reported a higher correlate profile and had higher levels of activity compared to females. Key correlates identified in the research were participation in sport, physical activity stage of change, self-efficacy and peer support. Study four examined activity levels and correlate profiles of sport participants and non-sport participants. Sport participants were found to have higher levels of activity and a more positive physical activity correlate profile than non-sport participants. Study five assessed the validity and reliability of the ‘APAC’ questionnaires. Principal component analysis revealed an interpretable 13 factor solution for males and 14 factor solution for females. Levels of internal consistency reached an acceptable level, and evidence exists of convergent validity. The final ‘APAC’ questionnaires explained between 38 – 42% of the variance in male and female MVPA. In conclusion the ‘APAC’ questionnaires were found to be valid reliable tools to measure correlates of activity among adolescents.

Declaration

The substance of this thesis is the original work of the author and due reference and acknowledgement has been made, where necessary, to the work of others. No part of this thesis has already been submitted for any degree and is not being concurrently submitted in candidature for any degree.

Candidate: Supervisor:

______

Con Burns Dr. Ciaran MacDonncha

Date: Date:

______

Examination Board

Chairman: Dr. Ross Anderson Internal Examiner: Prof. Alan Donnelly External Examiner: Dr. Catherine Woods

iii

Acknowledgements

I would sincerely like to thank the following people for their assistance, help and encouragement in the completion of this work

• My supervisor Dr. Ciaran MacDonncha for his guidance, direction and encouragement over the last five years, I greatly appreciate all your help.

• Dr. John Murphy for all his help with the statistical analysis in this research.

• Dr. Áine MacNamara for her help with the qualitative analysis.

• John Vaughan for his time and expertise in developing the data reduction programmes for the accelerometer.

• Dr. Ted Vaughan for his assistance in formatting the thesis.

• The principals, PE teachers and students in the schools for their assistance throughout the research.

• Pat Daly and the GAA for their support throughout the project

• Mr. Jim Walsh, Head of Department and other staff members in the Department of Social and General Studies in Cork Institute of Technology, for being so understanding and supportive throughout my research.

• Irene Hogan, Tracy Higgins and Jonathon Lerner for their assistance in data collection and analysis.

• Recreation and Leisure students from Cork Institute of Technology who helped in the data collection.

• Members of staff from Dublin City University for use of the accelerometers.

• My parents and family for their support over the last number of years and throughout my life.

• To Annmarie, as we embark on our future lives together, thanks for everything.

Table of Contents

Declaration iii Acknowledgements v Table of Contents i List of Figures xv List of Tables xvii Chapter 1 Introduction and Objectives 1 1.1 Introduction 1 1.2 Overview of the Research 3 1.3 Aims and Hypotheses of the Research 4 Chapter 2 Literature Review 7 2.1 Introduction and Overview 7 2.2 Theoretical Models Explaining Physical Activity 8 2.2.1 Definition and explanation of key terms 8 2.2.2 Theoretical Background 11 2.2.3 Health Belief Model 13 2.2.4 Theory of Reasoned Action and Theory of Planned Behaviour 15 2.2.5 Transtheoretical Model 18 2.2.6 Social Cognitive Theory 21 2.2.7 Social Ecological Model 23 2.2.8 Strengths and Limitations of Models of Behaviour Change 27 2.2.9 Role of cross- sectional correlate studies 28 2.2.10 Review of theoretical models: Implications for current research 29 2.3 Review of the Reviews on Correlates of Activity 31 2.4 Psychological Influences on Physical Activity 37 2.4.1 Self-Efficacy and Change Strategies 38 2.4.2 Physical Activity Stage of Change 42 2.4.3 Physical Self-Perception 43 2.4.4 Enjoyment 45 2.4.5 Enjoyment of PE 48 2.4.6 Perceived Benefits/ Outcome Expectancy Value 50 2.4.7 Perceived School Climate 51 2.4.8 Perceived Barriers to Physical Activity 51

vii

Table of Contents

2.4.9 Psychological correlates literature; implications for current research 53 2.5 Social Influences on Physical Activity 55 2.5.1 Family Influences 57 2.5.2 Peer Influence 59 2.5.3 Family and Peer Influence 60 2.5.4 Social correlates literature; implications for current research 61 2.6 Environmental Influences on Physical Activity 62 2.6.1 Access to Facilities 68 2.6.2 Perceived Environment 70 2.6.3 Transportation barriers 72 2.6.4 Weather 73 2.6.5 Environmental correlates literature; implications for current research 73 2.7 Behavioural Influences on Physical Activity 75 2.7.1 Screen Time Inactivity/ Sedentary Leisure Habits 76 2.7.2 Participation in Organized Sport 79 2.7.3 Correlates of Sport Participation 80 2.7.4 Barriers to Sport Participation 82 2.7.5 Behavioural correlates literature; implications for current research 83 2.8 Personal and Biological Influences on Physical Activity 84 2.8.1 Gender 85 2.8.2 Age 87 2.8.3 Social Class and Socio Economic Status 89 2.8.4 Overweight/ Obesity 91 2.8.5 Personal/ biological literature; implications for current research 96 2.9 Explained Variance in Physical Activity 97 2.10 Physical Activity 99 2.10.1 Recommended Level of Physical Activity 99 2.10.2 Levels of Physical Activity 102 2.10.3 Levels of Active Transportation 105 2.10.4 Tracking of Physical Activity 106 2.10.5 Physical Activity literature: Implications for current research 107 2.11 Measuring Physical Activity 107 2.11.1 Self-Report Questionnaires 107 2.11.2 Heart -rate Monitors 109 2.11.3 Doubly Labelled Water 109 2.11.4 Measuring physical activity: implications for current research 110 2.12 Measuring Physical Activity using Accelerometers 111 2.12.1 Accelerometer Validation Studies 112 2.12.2 Epoch 114 2.12.3 Number of Monitoring Days 115 2.12.4 What Constitutes a Day? 116 2.12.5 Non-wear Time 117 2.12.6 Spurious Data 118 2.12.7 Measured Aspects of Physical Activity 118 viii

Table of Contents

2.12.8 Procedural Issues 119 2.12.9 Field-based Protocol 120 2.12.10 Data Analysis 120 2.12.11 Promotion of Compliance 120 2.12.12 Accelerometer literature; implications for current research 122 2.13 Factor Analysis 126 2.13.1 Historical Overview 127 2.13.2 Approaches to Factor Analysis 127 2.13.3 Factor Analysis Procedure 129 2.13.4 Suitability of Data for Factor Analysis 129 2.13.5 Generation of the Correlation Matrix 130 2.13.6 Factor Extraction 131 2.13.7 Factor Rotation and Interpretation 132 2.13.8 Scale Development from Factor Analysis 133 2.13.9 Validity and Reliability using Factor Analysis 134 2.13.10 Factor Analysis and Correlates of Physical Activity 135 2.13.11 Factor Analysis; implications for current research 136 2.14 Measures of the Correlates of Physical Activity 142 2.14.1 Measures of Psychological Correlates of Physical Activity 142 2.14.2 Measures of social correlates of physical activity 147 2.14.3 Measures of Environmental Correlates of Physical Activity 147 2.14.4 Measurement of the Correlates of Physical Activity; implications for the current research 149 2.15 Conclusion 150 Chapter 3 Correlates of Physical Activity among Adolescent Females: Influence of Year in School and Physical Activity Stage of Change 151 3.1 Abstract 151 3.2 Introduction 152 3.3 Methods 154 3.3.1 Subjects 154 3.3.2 Instruments and Procedure 155 3.3.3 Data Analysis 156 3.4 Results 157 3.4.1 Influence of Year in School on Correlates of Physical Activity 157 3.4.2 Influence of Stage Physical Activity Change on Correlates of Physical Activity 162 3.5 Discussion 168 3.6 Conclusion 171 3.6.1 Implications for School Health 172

Table of Contents

Chapter 4 A qualitative study of factors influencing physical activity among Irish adolescent males and females 173 4.1 Abstract 173 4.2 Introduction 174 4.3 Methods 177 4.3.1 Sampling Procedure 177 4.3.2 Focus Group Structure 177 4.3.3 Procedure 181 4.3.4 Data Analysis 183 4.3.5 Ensuring Trustworthiness 184 4.4 Results 185 4.4.1 Perceived Benefits and Facilitators to Physical Activity and Sport Participation 185 4.4.2 Perceived Barriers to Physical Activity and Sport Participation 191 4.5 Discussion 197 4.5.1 Modifications to the ‘APAC questionnaire’ 200 4.6 Conclusion 202 Chapter 5 Correlates of physical activity among Irish adolescents 203 5.1 Abstract 203 5.2 Introduction 204 5.3 Methods 206 5.3.1 Sampling Procedure 206 5.3.2 Correlates of Physical Activity Questionnaire 207 5.3.3 Physical Activity Protocol 210 5.3.4 Instrument Quality and Dependability 210 5.3.5 Promotion of Compliance 210 5.3.6 Inclusion Criteria 211 5.3.7 Measured Aspects of Physical Activity 212 5.3.8 Accelerometer Data Analysis 214 5.3.9 Stature and Body Mass 215 5.3.10 Statistical Analysis 215 5.4 Results 217 5.5 Discussion 229 5.6 Conclusion 233

Table of Contents

Chapter 6 Levels and Correlates of Physical Activity among Adolescent Sport Participants and non-Sport Participants. 235 6.1 Abstract 235 6.2 Introduction 236 6.3 Methodology 238 6.3.1 Study Sample 238 6.3.2 Correlates of Physical Activity Questionnaire 240 6.3.3 Physical Activity Protocol 243 6.3.4 Instrument Quality and Dependability 243 6.3.5 Promotion of Compliance 243 6.3.6 Inclusion Criteria 244 6.3.7 Measured Aspects of Physical Activity 245 6.3.8 Accelerometer Data Analysis 248 6.3.9 Stature and Body Mass 248 6.3.10 Statistical Analysis 248 6.4 Results 250 6.5 Discussion 257 6.6 Conclusion 261 Chapter 7 Development and Validation of the Male and Female Adolescent Physical Activity Correlates (APAC) Questionnaires 263 7.1 Abstract 263 7.2 Introduction 264 7.3 Methods 268 7.3.1 Sampling Procedure 268 7.3.2 Adolescent Physical Activity Correlates (APAC) Questionnaire 269 7.3.3 Physical Activity 272 7.3.4 Stature and Body Mass 272 7.3.5 Statistical Analysis 272 7.4 Results 277 7.4.1 Interpretation and Naming of Factors 278 7.4.2 Internal Consistency Reliability 287 7.4.3 Validity 288 7.5 Discussion 293 7.6 Conclusion 296 Chapter 8 Discussion 297 8.1 Introduction 297

Table of Contents

8.2 Physical Activity 297 8.2.1 Methodological Difficulties with Accelerometer Research 297 8.2.2 Activity Counts among Irish Adolescents 299 8.2.3 Time Spent in Activity of Different Intensities 300 8.2.4 Irish Adolescents Achieving the Recommended Levels of Activity 302 8.2.5 Sedentary Inactivity and Health Implications 304 8.3 Relationship between Correlate Scales and Physical Activity 305 8.4 Key Correlates of Activity 307 8.4.1 Personal/ Biological Correlates 307 8.4.2 Psychological Correlates 308 8.4.3 Social Correlates 310 8.4.4 Environmental Correlates 311 8.5 ‘APAC’ Questionnaire 313 8.6 Limitations 314 Chapter 9 Conclusions and Recommendations for Future Research 317 9.1 Conclusion 317 9.2 Recommendations for Future Research 318 References 321 Appendix A Information Sheets and Consent Forms A-1 A.1 Introduction A-1 A.2 Principal’s letters and consent forms A-1 A.2.1 Principals letters A-1 A.2.2 Principal Consent Form A-5 A.3 Information Sheets and Consent Forms A-6 A.3.1 Information Sheets and Consent Forms - Questionnaire A-6 A.3.2 Information Sheets and Consent Forms – Focus groups A-11 A.3.3 Information Sheets and Consent Forms – Accelerometer A-16 A.3.4 Physical Activity Accelerometer information handout A-21 A.4 Data Cleaning Procedure A-23 Appendix B B-1 Accelerometer protocol B-1 B.1 Introduction B-1 B.2 Summary Output Sheet for Participants B-2 B.3 Analysis of Non-wear Time B-3 B.3.1 Sorting of Non-wear Time B-3 xii

Table of Contents

B.3.2 Generation of Non-wear Minutes Summary B-5 B.4 Analysis of Total Counts B-6 B.4.1 Total Counts for Qualifying Days B-6 B.4.2 Counts Number of Qualifying Days B-7 B.5 Analysis of Activity Data B-7 B.5.1 Filter out Non-qualify Days B-7 B.5.2 Creation of Moderate, Vigorous and Moderate/ Vigorous Tabs B-8 B.5.3 Summary of Moderate and Vigorous Analysis B-12 B.6 Analysis of Bout Data B-13 B.6.1 Generation of Moderate Bouts B-13 B.6.2 Generation of Vigorous Bouts B-16 B.6.3 Generation of Moderate/ Vigorous Bouts B-19 B.6.4 Summary of Moderate Bouts B-23 B.6.5 Summary of Vigorous Bouts B-24 B.6.6 Summary of Moderate/ Vigorous Bouts B-25 B.6.7 Mean data for Activity Bouts B-26 B.7 Analysis of Low/ Sedentary Data B-27 B.7.1 Low / Sedentary Data B-27 B.7.2 Identification of Non-Wear in Sedentary Data B-30 B.7.3 Summary of Sedentary Data B-31 B.7.4 Mean Sedentary and Low Activity Data B-32 Appendix C Principal Component Analysis C-1 C.1 Introduction C-1 C.2 Factor Loadings of Complete Psychological Correlates for Males C-2 C.3 Factor Loadings of Complete Psychological Correlates for Females C-4 C.4 Factor Loadings of Complete Social Correlates for Males C-6 C.5 Factor Loadings of Complete Social Correlates for Females C-6 C.6 Factor loadings of Complete Environmental Correlates for Males C-7 C.7 Factor loadings of Complete Environmental Correlates for Females C-8 C.8 Male ‘APAC’ Questionnaire: Factors and Items C-9 C.9 Female ‘APAC’ questionnaire: Factors and Items C-11 C.10 Original Questionnaire C-14 C.11 Male Adolescent Physical Activity Correlate Questionnaire C-28 C.12 Female Adolescent Physical Activity Correlate Questionnaire C-38

xiii

List of Figures

Chapter 2

Figure 2.1: Health Belief Model...... 14 Figure 2.2: Theory of Reasoned Action and Planned Behaviour...... 18 Figure 2.3: Stages of Change...... 21 Figure 2.4: Social Cognitive Theory...... 23 Figure 2.5 Social Ecological Theory...... 26 Figure 2.6 Theoretical Model used in the current research...... 30 Figure 2.7: Factor rotation and interpretation...... 133

Chapter 3

Figure 3.1: Psychological, social and environmental Z scores across class year in school ...... 160 Figure 3.2: Psychological, social and environmental Z scores across physical activity stage of change ...... 163

Chapter 4

Figure 4.1: Stratified sampling framework for the psychological domain ...... 180

List of Tables

Chapter 2

Table 2.1: Summary of reviews of the correlates of physical activity...... 36 Table 2.2: Recommended levels of physical activity for adolescents...... 101 Table 2.3: Levels of physical activity among adolescents...... 104 Table 2.4: Actigraph Accelerometer cut points for adolescents...... 121 Table 2.5: Studies using factor analysis in questionnaire development...... 140

Chapter 3

Table 3.1: Description, internal consistency and reliability of the correlates of physical activity ...... 159 Table 3.2: Group Means, Standard Deviations, F Values, Effect Sizes and Univariate Analysis for Year in School ...... 161 Table 3.3: Group Means, Standard Deviations, F Values, Effect Sizes and Univariate Analysis for Stage of Change ...... 164 Table 3.4: Pearson correlations between potential correlates of physical activity and physical activity stage of change ...... 166 Table 3.5: Results of Regression Analysis Explaining Physical Activity Stage of Change ...... 167

Chapter 4

Table 4.1: Core questions for focus groups ...... 181 Table 4.2: Summary of psychological, social and environmental factors positively influencing physical activity ...... 186 Table 4.3: Sticky dots analysis of the factors positively influencing physical activity 187 Table 4.4: Summary of psychological, social and environmental factors negatively influencing physical activity ...... 192 Table 4.5: Sticky dots analysis of the factors negatively influencing physical activity 193

xvii

Chapter 5

Table 5.1: Description of ‘Correlates of Physical Activity Questionnaire’ ...... 209 Table 5.2: Descriptive statistics, chi square analysis and one way between groups analysis of variance of the correlates of physical activity among male and female participants ...... 218 Table 5.3: Descriptive statistics, chi square analysis and independent sample t tests of physical activity levels between male and female participants ...... 219 Table 5.4: Pearson and Spearman Correlation Coefficents for MVPA and log transformed VPA with Personal, Psychological, Social and Environmental Correlates...... 222 Table 5.5: Results of Regression Analysis Explaining mean minutes in MVPA and log transformed VPA for males ...... 223 Table 5.6: Results of Regression Analysis Explaining mean minutes in MVPA and log transformed VPA for females ...... 224 Table 5.7: Means, Standard Deviations, F Values, Effect Sizes and Univariate Analysis among different levels of male MVPA activity ...... 225 Table 5.8: Means, Standard Deviations, F Values, Effect Sizes and Univariate Analysis among different levels of female MVPA activity ...... 227

Chapter 6

Table 6.1: Description of ‘Correlates of Physical Activity Questionnaire’ ...... 242 Table 6.2: Personal/ biological characteristics and sport participation levels among male and female participants ...... 252 Table 6.3: Levels of physical activity among sport participants and non-participants . 253 Table 6.4: Group means, standard deviations, F values, effect size and univariate analysis for sport participation ...... 255 Table 6.5: Odds ratio and confidence intervals for correlates of physical activity by level of male and female sport participation ...... 256

Chapter 7

Table 7.1: Description of ‘Correlates of Physical Activity Questionnaire’ ...... 271 Table 7.2: Factor loadings of the data reduced psychological correlates for males ..... 280 Table 7.3: Factor loadings of the data reduced psychological correlates for females . 281 Table 7.4: Factor loadings of the data reduced social correlates for males ...... 283 Table 7.5: Factor loadings of the data reduced social correlates for females ...... 283 Table 7.6: Factor loadings of the data reduced environmental correlates among males ...... 283 xviii

Table 7.7: Factor loadings of the data reduced environmental correlates among females ...... 284 Table 7.8: Description and internal consistency of the male Adolescent Physical Correlate Questionnaire...... 285 Table 7.9: Description and internal consistency of the female Adolescent Physical Correlate Questionnaire...... 286 Table 7.10: Internal consistency of the APAC questionnaires...... 287 Table 7.11: Pearson correlations between the APAC questionnaire, activity levels and BMI for males...... 289 Table 7.12: Pearson correlations between the APAC questionnaire, activity levels and BMI for females...... 290 Table 7.13: Results of Regression Analysis explaining MVPA and log transformed VPA among males...... 291 Table 7.14: Results of Regression Analysis explaining MVPA and log transformed VPA among females...... 292

xix

Chapter 1

Introduction and Objectives

1.1 Introduction

Physical inactivity has been associated with various health problems while physical activity has been identified as playing a role in the prevention of many chronic diseases (Dishman & Sallis, 1994). Sedentary individuals have been found to have higher risk of developing CHD (Morris et al. , 1953; Paffenbarger & Hale, 1975; Morris et al. , 1980; Blair et al. , 1989; Lakka et al. , 1994), stroke (Abbott et al. , 1994; Lee & Blair, 2002), Type 2 diabetes (Tuomilehto et al. , 2001), cancer (Giovanucci et al. , 1995; Friedenreich & Thune, 2001), osteoporosis (Heinonen et al. , 1995) as well as an increased risk of all cause mortality (Hardman & Stensel, 2005). Establishing a clear association between childhood physical activity and future health problems is difficult as it may take many years for a hypo kinetic health problem to manifest (Biddle et al. , 2004). However the relationship between physical activity and childhood and adolescent health have been found to be weakly related (Eisenmann, 2004). This relationship between physical activity and health has been found in many areas including increased aerobic fitness (Twisk et al. , 2000) and bone mass (McKenzie et al. , 1996). Physical activity has also been found to reduce CVD risk factors such as obesity (Bar Or & Baranowski, 1994), low HDL cholesterol (Armstrong & Simons- Morton, 1994), elevated blood pressure (Craig et al. , 1996) and components of metabolic syndrome (Kahle et al. , 1996). Physical inactivity has been associated with

Aims and Objectives increased psychological health complaints among Canadian children (Janssen et al. , 2004). Participation in organized sport has also been associated with numerous benefits for adolescents. Adolescents involved in organized sport report higher perceptions of general health and well being (Fisher et al. , 1996; Steptoe & Butler, 1996; Ferron et al. , 1999; Pate et al. , 2000; Steiner, 2000; Harrison & Narayan, 2003; Michaud et al. , 2006) and lower levels of health threatening behaviour (Ferron et al. , 1999; Pate et al., 2000; Steiner, 2000; Michaud et al. , 2006). In a study of Swiss adolescents, various indicators of perceived health and satisfaction with school/ professional life are positively correlated with a higher level of sport participation. Organized sport participation has also been correlated with higher physical fitness levels among adolescents (Hoffman et al. , 2005) and higher perceived self image (Ferron et al. , 1999; Harrison & Narayan, 2003; Michaud et al. , 2006). Sports programmes may promote positive health behaviours and deter negative health behaviours by placing a premium on health and fitness as prerequisites to optimal sports performance (Pate et al. , 2000). It may also be possible that participation in organized sport promotes health by placing youth in pro- social environments during time periods that they would be in danger of participating in problem behaviours (Pate et al. , 2000). Because regular physical activity is associated with both short and long term health benefits efforts encouraging adolescents to adopt a physically active lifestyle or remain physically active is of critical importance (Vu et al. , 2006).

Recent data indicates that sizeable proportions of adolescents fail to meet the recommended guidelines for physical activity (Ruiz et al., 2011; Woods et al ., 2010). Furthermore, participation in regular physical activity has been found to dramatically decrease in adolescence (Butcher et al. , 2008), this trend has been found to be most pronounced among females (Kahn et al. , 2008). Due to the fact that levels of physical activity have been found to track from adolescence into adulthood (Telama et al. , 1997), the effective promotion of physical activity in adolescence may result in long term increases in physical activity and improvements in health.

Measured variables that promote or inhibit attitudes towards and engagement in physical activity are termed ‘correlates of physical activity’ (Bauman et al. , 2002).

Aims and Objectives

Physical activity is influenced by various personal/ biological, psychological, social and environmental correlates (Biddle et al. , 2005; Elder et al. , 2007). To develop effective interventions to promote physical activity, the relationship between these factors and physical activity need to be well understood (Sallis et al. , 2000). Due to difficulties associated with self report measures of physical activity, it has been recommended that correlate studies use objective measures of physical activity as the criterion measure (Kelly et al. , 2010), however limited Irish research has used this methodological approach.

The value of scientific data depends on the precision with which variables under consideration are observed and measured (Dunn et al. , 1999). Using valid and reliable measures of the correlates of physical activity reduces measurement error and strengthens the conclusions that can be drawn (McMinn et al. , 2009). Currently a number of individual scales have been developed to measure specific correlates of activity, these scales have been found to display elements of validity and reliability. No previous research has attempted to develop a valid and reliable questionnaire to comprehensively measure the personal, psychological, social and environmental correlates of adolescent physical activity.

1.2 Overview of the Research

This thesis is divided into separate distinct sections. Chapter two provides an extensive overview of the literature relating to correlates of physical activity, levels of adolescent activity, measurement of physical activity and the development of scales to measure correlates of physical activity. The series of related research studies which were undertaken are written up in chapter’s three to seven. These chapters are in a paper ready format, however, having some necessary additional comments for presentation within a PhD thesis.

Chapter three uses the ‘Adolescents Physical Activity Correlates’ (APAC) questionnaire developed by the researcher based on previously validated tools. This

Aims and Objectives questionnaire is used to assess the influence of year in school and physical activity stage of change on the correlate profile (scores on correlate scales) of adolescent females. Data on both genders was gathered and analysed, but for the purpose of this PhD and subsequent publications, the analysis for females only is presented. This study was used to pilot the ‘APAC’ questionnaire and to explore the key correlates of activity among Irish adolescent females. Chapter four presents the findings from a qualitative study. This research provides in-depth analysis of the barriers and facilitators of activity among adolescent males and females. The findings of this research are also used to further develop the ‘APAC’ questionnaire, to ensure that key correlates influencing adolescent’s physical activity are adequately measured.

In chapter five, the ‘APAC’ questionnaire was administered to a stratified sample of Irish adolescents. The questionnaire was used to measure the correlates of physical activity and to assess the relationship between the correlates of activity and objectively measured activity among male and female adolescents. Chapter six compares the correlate profile and activity levels of sport participants and non-sport participants. An extra sample of elite sport participants were sampled and used for comparative purposes in this study. Chapter seven assess the reliability and validity of the ‘Adolescent Physical Activity Correlate’ (APAC) questionnaire.

Chapter eight discusses the main findings from the five research studies, and compares the findings with other national and international research. Chapter nine provides an overall conclusion for the research as well as recommendations for future research. Chapter ten includes references used in the research using the Harvard referencing style.

1.3 Aims and Hypotheses of the Research

The aims of this research were to: • Develop and validate ‘Adolescent Physical Activity Correlate’ (APAC) questionnaires for male and female adolescents.

Aims and Objectives

• Measure the correlates of physical activity among adolescents of different ages, genders and among sport participants and non-participants. • Assess the relationship between correlates of physical activity and levels of activity among adolescents. • Measure physical activity levels of Irish adolescents using objective measure of physical activity, and to compare these results to recommended levels of physical activity and previous research.

The key questions and hypotheses of this research are (stated as null hypotheses): • Ho 1 = There will be no significant difference in correlate profiles among adolescent females from different years in school (Chapter 3). • Ho 2 = There will be no significant difference in correlate profiles among adolescent females in different physical activity stages of change (Chapter 3). • KQ1 = Do differences exist in qualitatively assessed facilitators to physical activity among adolescents of different gender, age and activity levels (Chapter 4). • KQ2 = Do differences exist in qualitatively assessed barriers to physical activity among adolescents of different gender, age and activity levels (Chapter 4). • Ho 3 = Correlate profiles will not be significantly different among male and female participants (Chapter 5). • Ho 4 = Levels of physical activity will not be significantly different among male and female adolescents (Chapter 5). • Ho 5 = There will be no relationship between correlate profile scores and objectively measured physical activity (Chapter 5). • Ho 6 = Levels of physical activity will not be significantly different among sport participants and non-participants (Chapter 6). • Ho 7 = Correlate profiles will not be significantly different between elite sport participants, sport participants and non-participants (Chapter 6). • KQ 3 = Do the ‘Adolescent Physical Activity Correlate’ (APAC) questionnaires have construct and convergent validity (Chapter 7)

Aims and Objectives

Chapter 2

Literature Review

2.1 Introduction and Overview

To effectively promote physical activity in youth the correlates of activity i.e. variables which promote or inhibit physical activity levels, need to be well understood (Sallis et al. , 2000; Sallis et al. , 2002; Whitt-Glover et al. , 2009; Van Dyck et al. , 2010). O’Sullivan (2002) believes that no one variable or category of variable is anticipated to account for most of the variance in children’s physical activity. Physical activity levels in adolescents appear to be influenced by a host of biological, psychological, social and environmental factors (Poulsen & Ziviani, 2004; Sallis & Kerr, 2006). Strategies to increase physical activity are likely to be most effective when they alter all of these factors (Sallis & Kerr, 2006).

Physical activity has been found to decrease in adolescence; this trend being most pronounced among females. There would appear to be a need for targeted physical activity promotion during adolescence and in particular in the transition from adolescence to early adulthood (De Roiste & Dinneen, 2005). However as Kimm et al. (2000) argue the specific factors associated with the decline in activity during adolescence remain largely unknown.

This review of literature can be divided into five main sections. Section one reviews theoretical models which attempt to explain physical activity behaviour.

Literature Review

Section two provides an overview of relevant literature relating to the psychological, social, environmental, behavioural and personal/ biological correlates of physical activity. Section three provides a critical analysis of the literature relating to measurement of physical activity. Section four examines factor analysis literature and assesses its use in questionnaire development and refinement. Finally, section five reviews various tools which have been developed to measure the correlates of physical activity. A brief section outlining how the review has guided the current research is provided at the end of each section. For this review of literature computer searches of PubMed and SportDiscus were conducted for studies published after 1995. The following keywords were used correlates, determinants, models of behaviour change, adolescent behaviour, attitudes, exercise psychology, social environment, self efficacy, barriers, enjoyment, support, physical activity, sport, accelerometer, questionnaire development, factor analysis. The titles and abstracts of the papers were screened for suitable literature. To be included in the correlates review a study had to meet the following criteria; subjects were healthy young people in the age range of 4 – 18 years, the dependent variable was physical activity and variables had to be tested for their association with physical activity. Relevant unpublished theses were also included in the review.

2.2 Theoretical Models Explaining Physical Activity

This section comprises of (i) a definition of key terms associated with theoretical models attempting to explain physical activity behaviour; (ii) a critical review of relevant models and theories which attempt to explain physical activity behaviour and (iii) an outline and justification of how existing theories influenced the development and implementation of the current study.

2.2.1 Definition and explanation of key terms

Prior to reviewing the models and theories of explaining physical activity and physical activity behaviour change it is important to firstly discuss key terms associated with this

Literature Review literature. Theories and models are formulations of underlying principles of a certain observed phenomena that have been verified to some degree and are used to explain and predict behaviour (Dishman et al. , 2004; Nigg & Paxton, 2008). In psychology of physical activity and sport distinctions are sometimes made between the terms theory and model; these distinctions however are not consistently observed in the field (Nigg & Paxton, 2008). Similar to Nigg & Paxton (2008) we have chosen use the two terms interchangeably in this review. Theoretical models guide both our current and future understanding of health behaviour, as well as providing direction for our research and intervention development. As a metaphor, each model or theory provides a different roadmap of health behaviour territory and different theories describe the territory differently (Redding et al. , 2000). Components of theory quality include (i) parsimony i.e. the ability to explain things in as simple a fashion as possible while maintaining completeness; generalizability i.e. transferability, application from one situation to the next and from one population to the next and (iii) productivity i.e. ability to drive experimentation and produce further knowledge relating to a phenomenon (Nigg & Paxton, 2008). Bauman et al. (2002) stress the importance of clearly distinguishing among determinants and correlates of physical activity. Within cross sectional research many studies have identified significant associations between a range of personal, social and environmental variables and levels of physical activity. These are usually correlational studies and might report for example that social support or self-efficacy is associated with physical activity behaviour (Bauman et al. , 2002). Cross- sectional studies therefore can highlight associations or correlations between variables and physical activity but do not prove cause and effect. For example, although physical activity may be related to self-efficacy, we cannot be sure if low levels of physical activity cause low self-efficacy or if low self-efficacy causes low levels of physical activity. Determinants of physical activity are most appropriately defined as causal factors and variations in these factors are followed systematically by variations in physical activity behaviour (Bauman et al. , 2002). In public health causal factors might be health interventions that are deliberate efforts to increase physical activity. Therefore an effective intervention that increases physical activity self-efficacy and subsequently positively effects physical activity may lead to the conclusion that physical activity self-

Literature Review efficacy is a determinant of physical activity. It is recommended that the term determinant be used with greater precision and not be used to describe physical activity correlates (Bauman et al. , 2002). For research on physical activity to progress systematically the mechanisms of action must be studied (Bauman et al. , 2002). Mediators of physical activity have been defined as variables that are hypothesised to fall in the causal pathway (Baranowski et al. , 2003; Dishman et al. , 2004; TAAG, 2004). There may be a single mediator between a program (intervention) and an outcome (physical activity) or a series of mediators that intervene and are causally related in sequence between the program and outcome (Bauman et al. , 2002). Therefore, if self efficacy is a mediator of physical activity, it implies that exposure to a physical activity program which results in a change in self efficacy would lead to increases in physical activity. The mediating variable model has been proposed as a framework both for designing interventions and for understanding how interventions work to promote change in physical activity (Baranowski et al. , 2003). An increased understanding of the mediators of physical activity will provide feedback that can lead to further systematic improvements in intervention efficacy (Bauman et al. , 2002). In the current study potential mediators were the biological, psycho – social and environmental variables which were hypothesised to influence physical activity levels. The cross sectional nature of the current study provides correlation information between correlates and physical activity. Moderators of physical activity are variables that modify the effect of an intervention or another variable on physical activity, e.g. the effects of an intervention may be much greater for males than females, therefore effect sizes for males and females may be different from each other and also different from the overall effect size (Bauman et al. , 2002; Dishman et al. , 2004). This is recognised as an interaction in statistical methods and can be dealt with by stratifying the data by levels of moderator and re examining the effects e.g. activity levels among males and females (Bauman et al. , 2002). A better understanding of moderators can help to tailor interventions to the needs of specific subgroups of people (Bauman et al. , 2002). In the current study moderators were identified as biological factors such as gender, age, physical activity stage of change and level of sport participation. Differences in hypothesised mediators

Literature Review are assessed across specific moderators, thus developing correlate profiles among sub groups of adolescents. A confounder is a predictor of the outcome, but is also associated with exposure, for example age or gender imbalance in a study design may confound the relationship between correlates of physical activity and levels of activity (Bauman et al. , 2002). The stratified sampling procedure used in the current studies ensured the participating populations were not confounders in the research. The concept of a moderator differs from a confounder. The confounder distorts the observed association e.g. unequal sample sizes in gender and age could distort the findings relating to correlates and physical activity (Bauman et al. , 2002). On the other hand, a moderator produces different estimates of the association at different levels of the variable. For example, correlates of activity would be expected to differ across age and gender (moderators) (Bauman et al. , 2002). It is possible for a potential correlate to be a moderator, mediator or confounder in different situations. If for example, enjoyment is a mediator in one situation it implies that exposure to a physical activity program results in a change in enjoyment that results in an increase in physical activity (Bauman et al. , 2002). In other situations, enjoyment could be a moderator (effect modifier). In this instance exposure to the intervention for people at different levels of enjoyment would produce different effects e.g. those with high levels of enjoyment may be more likely to change their levels of physical activity compared to those with low levels of physical activity enjoyment (Bauman et al. , 2002). It would also be possible for enjoyment to have some confounding aspects such that people with high enjoyment are more likely to participate in interventions (Bauman et al. , 2002). The consistent use of terms and additional research on mediators and moderators of physical activity will improve our ability to understand and influence physical activity (Bauman et al. , 2002).

2.2.2 Theoretical Background

Identifying the factors that influence physical activity behaviour in youth is an important pre requisite for designing effective interventions for this population (Baranowski et al. , 1997). Models and theories that identify these influences of

Literature Review behaviour change can contribute to an understanding of how people adopt and maintain positive health behaviours (Naidoo & Wills, 2001). To assist in the organisation of approaches and theories a classification system has been developed which allows types of theories to be clustered together (Biddle et al. , 2008). These theories have been categorised as belief – attitude theories e.g. theory of planned behaviour, health belief model; competence based theories e.g. self efficacy theory, SCT; control based theory e.g. self determination theory; stage based theory e.g. transtheoretical model or hybrid models e.g. socio ecological model (Biddle et al. , 2008). Operant psychology focuses on considering the behaviour primarily from the individual level perspective. Operant psychology (behaviour modification) models have been dominant in guiding the design of health behaviour change interventions (Elder et al. , 2007). The operant basis for behaviour change has been demonstrated to be central in the promotion of physical activity. In a meta-analysis of 127 intervention studies, the contributions of behaviour modification, cognitive behaviour change (e.g. self monitoring, goal setting), health education and curriculum on effect sizes for physical activities were examined. It was concluded that behaviour modification had an average of a 0.9 effect size, more than four times greater than that for physical education curricula interventions and 9 times that of cognitive behaviour change and health education interventions (Dishman & Buckworth, 1996). However, human behaviour has been found to be influenced by conditions under which people live as well as individual level attributes (Cohen et al. , 2000; Naidoo & Wills, 2001). Therefore, the targeting of individual behaviour solely is unlikely to be effective unless it acknowledges how people’s behaviour may be an influenced by the environment in which they live (Cohen et al. , 2000; Naidoo & Wills, 2001). Guiding frameworks in health promotion have emphasised the need to intervene in domains beyond the psychological and social to develop supportive environments, healthy public policies and a reorientation of health services (Cohen et al. , 2000; Elder et al. , 2007). Recent theoretical advancements such as the Social Ecological Model are characterised by targeting multiple levels of influence in the promotion of physical activity participation. This review will examine both individual and multilevel theories and models which aim to explain physical activity.

Literature Review

2.2.3 Health Belief Model

The Health Belief Model (Figure 2.1)which was originally developed by Rosenstock (1966) and further developed by Becker (1974) has been used to predict protective health behaviour such as screening, vaccination and compliance with medical advice (Naidoo & Wills, 2001). This model is based on the premise that the likelihood of adopting behaviour appropriate to the prevention or control of some disease depends on the individuals perception of a threat to personal health and a conviction that the recommended action will reduce the threat (Maiman & Becker, 1974). Therefore, this model proposes that for behaviour change to occur individuals must have an incentive to change (cue), feel threatened by current behaviour, feel that the benefits to change would outweigh the costs and they must feel competent to change (Naidoo & Wills, 2001). The construct of self efficacy or persons confidence in ability has been added to the model (Rosenstock et al. , 1988) perhaps allowing it to account for habitual behaviours such as physical activity. A number of criticisms of the Health Belief Model and its application to physical activity exist. Firstly, the Health Belief Model is a psycho social model which explains health behaviour based on individuals attitudes and beliefs, it does not take into account other influences on behaviour such as environmental and public policy factors (Janz & Becker, 1984). Secondly, the Health Belief Model assesses behaviour based on disease prevention. This narrow perspective does not take cognisance that individuals may engage in physical activity for other reasons than disease prevention, for example, aesthetic appearance, intrinsic enjoyment of the activity (Biddle & Nigg, 2000; Nutbeam & Harris, 2004). Thirdly, based on the Health Belief Model individuals who are ‘at risk’ of various hypo kinetic diseases, who receive support and who are aware of the benefits of physical activity should be regularly active. Research carried out by Heinzelman & Bagley (1970) provides some support for the Health Belief Model in explaining physical activity among an ‘at risk’ population. This study reported that the exercise participation of middle aged men prone to CHD was related to their perception that exercise involvement would reduce heart disease (Heinzelmann & Bagley, 1970). However, contrary to expectations of the Health Belief Model, a separate study reports that young adults who learned that a family member experienced a heart attack or stroke were not more likely to initiate weight loss or regular physical activity (Kip et al. ,

Literature Review

2002). Therefore, the Health Belief Model may only be most effective among older groups who would perceive a greater threat of hypo kinetic diseases. Bauman et al. (2002) identified a number of variables which were tallied with adult physical activity. The effectiveness of individual theories at explaining these variables were assessed. In this review, the Health Belief Model failed to receive clear support in literature on adult physical activity correlates with only 25% of the correlates being explained by the Health Belief Model. Due to the limitations of the health belief model in explaining health promoting behaviours such as physical activity (Biddle & Nigg, 2000) and as most adolescents do not perceive themselves to be ‘at risk’ of hypo kinetic diseases this approach did not influence the development and implementation of the current study.

Figure 2.1: Health Belief Model

Literature Review

2.2.4 Theory of Reasoned Action and Theory of Planned Behaviour

The Theory of Reasoned Action (TRA) and the Theory of Planned Behaviour (TPB) have been identified as particularly efficacious social cognitive theoretical frameworks that help explains people’s behaviours (Godin, 1994). The TRA was developed by Ajzen and Fisbein (1980) to explain human behaviour that is under ‘voluntary’ control. This theory is established on the assumption that intention to act is the most immediate determinant of behaviour, and that factors influencing behaviour will be mediated through behavioural intention. Intentions are assumed to capture the motivational factors that influence behaviour. They are indications of how hard people are willing to try, of how much of an effort they are planning to exert in order to perform the behaviour (Ajzen, 1991). As a general rule the stronger the intention to engage in behaviour the more likely should be its performance (Ajzen, 1991). Behavioural intentions are influenced by attitudes and subjective norms. Attitudes have been defined as an individual’s favourable or unfavourable evaluation of an attitude object or target behaviour (Ajzen, 1991). Attitudes are often viewed as multidimensional with affective (emotional), instrumental (cognitive), functional (behavioural) aspects. Subjective norms relate to what ‘significant others’ do and expect and the degree to which the person wants to conform and be like others (Naidoo & Wills, 2001). The subjective norm construct is generally found to be weak predictor of intentions. This is partly attributable to a combination of poor measurement and the need for expansion of the normative component (Armitage & Conner, 2001). This may also be consistent with the notion that participation in physical activity relies more on personal motivation judgements and is less subject to the influence of pressure from others (Godin et al. , 1993). The TPB (Figure 2.2) is an extension of the TRA made necessary by the original models limitations in dealing with behaviours over which people have incomplete volitional control (Ajzen, 1991). The performance of most behaviour depends to some degree on such non motivational factors as availability of requisite opportunities and resources e.g. time, money, skills, and co-operation of others (Ajzen, 1991). Researchers comparing the TRB and TRA in physical activity context have demonstrated that TPB is superior to the TRA in accounting for the variance in intention (Godin et al. , 1993). Hagger et al . (2002) assessed relations between

Literature Review behaviour, intentions, attitudes, subjective norms, perceived behavioural control, self efficacy and past behaviour across studies using TRA and TPB in a physical activity context. A total of 72 studies were included in the meta analysis. Results indicated that the TRA and TPB both demonstrated good fit, but the TPB accounted for more variance in physical activity intention and behaviour. The TPB has been found to be superior as perceived behavioural control has been shown to have a large effect on physical activity intentions and behaviour (Armitage & Conner, 2001; Hagger et al. , 2002). It has been assumed that those with strong internal locus of control will see themselves as more able to act decisively and will undertake preventive health actions or change to more healthy behaviours (Wallston et al. , 1978; Naidoo & Wills, 2001). Measurement of perceived behavioural control has attracted considerable attention in TPB literature (Jackson et al. , 2003). Conceptually Ajzen (1991) has aligned the concept of perceived behavioural control with Bandura’s self efficacy concept. Several authors have identified perceived behavioural control as a multidimensional construct comprising two distinct constructs namely perceived control and self efficacy. Perceived control assesses whether behaviour is considered to be under the individuals voluntary control, whereas self efficacy refers to perceived difficulty or ease in performing behaviour (Terry & O'Leary, 1995; Jackson et al. , 2003). Support from this multidimensional construct is provided by Trafimov et al . (2002) who using factor analysis on studies using TPB, found control items to load on one factor and easy/ difficult items (self efficacy) to load on a second factor. Furthermore, the author also found different correlations with intentions and behaviour and (i) reported control and (ii) easy/ difficult (self efficacy) items (Trafimow et al. , 2002). Furthermore, a meta analytic review of the TRA and TPB in a physical activity context carried out by Hagger et al. (2002) found that self efficacy explained unique variance in intention to be active. The structural components of the TRA and TPB have some support in physical activity research (Bauman et al. , 2002; Hagger et al. , 2002); however some limitations exist in the application of these theories. Trost et al. (2002) assessed the suitability of the TRA and TPB for physical activity, in this study participants completed questionnaire measuring attitudes, subjective norms, perceived behavioural control and intentions for physical activity. Physical activity was assessed using a 3 day physical

Literature Review activity recall questionnaire. Among 1,030 white adolescent girls 17% of the variance in intentions was contributed by subjective norms and attitude, with intentions accounting for only 8% of the variance in physical activity. The addition of perceived behavioural control and self efficacy to the TRA significantly improved the prediction of intentions with 40% of the variance explained while 10% of the variance in MVPA was explained (Trost et al. , 2002). The relatively weak link between intention and MVPA observed suggests that other external variables such as personality and belief based constructs have independent effects on intentions and behaviour that are not mediated by attitudes, subjective norms, perceived behavioural control and intention (Hagger & Chatziarantis, 2008). Other limitations to the TRA and TPB is that the authors of the TRA and TPB do not deal with the processes of behaviour change, nor do they specify the procedures that change those mediating variables resulting in behaviour change (Baranowski et al. , 2003). A further criticism of the TPB is its failure to mediate the influence of past behaviours on future participation in activity. In a meta- analysis of the TRA and TPB in a physical activity context TPB was found to explain 44.5% of variance in physical activity intention and 27.4% variance in activity behaviour. However, past behaviour which was also included in the analysis was found to account for the majority of the variance previously accounted for by the TRA and TPB constructs (Hagger et al. , 2002). Due to the limitations outlined broader more comprehensive models were examined to influence and inform the current research. Elements of the TRA and TPB were included in both the current analysis and are encapsulated in broader models explaining physical activity. In the current questionnaire, affective attitudes relating to physical activity were assessed using scales which measured enjoyment and enjoyment of PE. Instrumental attitudes were measured using belief statements relating to physical activity and corresponding value statements. Perceived behavioural control was not included in the analysis, however this construct has been viewed as conceptually related to Bandura’s concept of self efficacy which was included. Participant’s intention to be active was also assessed in the current research.

Literature Review

Figure 2.2: Theory of Reasoned Action and Planned Behaviour

2.2.5 Transtheoretical Model

The Transtheoretical Model of Behaviour Change (TTM) was developed by Prochaska and Di Clemente (1983) to help understand the process of smoking cessation, it was later adapted for physical activity research (Marcus et al. , 1992; Marcus & Simkin, 1993). While the focus of the TTM is on promoting change in behaviour several of the constructs employed (e.g. pros, cons, self efficacy) imply a model for understanding behaviour (Baranowski et al., 2003). The TTM as applied to physical activity consists of the stages of change, processes of change, decisional balance (pros and cons) and self efficacy (Prochaska & DiClemente, 1992). In the stages of change (Figure 2.3), change is presented as a dynamic process that occurs over time. As an individual changes their behaviour they progress through a series of stages from pre-contemplation to maintenance (Marcus et

Literature Review al. , 1992). People are thought to progress through these stages at various rates, often moving back and forth along the continuum a number of times before attaining the goal of maintenance (USDHHS, 1996). Therefore, change is better described as spiralling or cyclical rather than linear (Prochaska & DiClemente, 1992). The process of change can be described as the ‘how’ part of the change equation. There are two elements in the processes of change, (i) experiential processes, referring to emotions and feelings experienced by the individual in pursuit of change and (ii) behavioural processes are the processes whereby environmental events and behaviours such as stimulus control and reinforcement control generate information (Prochaska & DiClemente, 1992; Dishman et al. , 2004). Self efficacy is based on the premise that if a person feels confident in their abilities to perform a desired behavior in a specific setting, then they are more likely to engage in that activity (Bandura, 1977). Self efficacy is an important predictor of progress through the stages of change as more efficacious individuals are more likely to habitually adopt and maintain behavior change. Studies that have examined exercise stage and self efficacy for exercise, have found that in general self efficacy is lowest in the early stages and higher in each adjacent stage (Janis & Mann, 1977; Dishman et al. , 2004). Decisional balance is based on decision theory developed by Janis and Mann (1977), in this theory perceived costs (cons) and benefits (pros) to oneself and significant others are considered important influences on behavior change (Dishman et al. , 2004). Pros tend to increase up through the action stage and then level off, while cons decrease with each subsequent stage. Most of the evidence also indicates that the balance between pros and cons for exercise shifts during the preparation stage, which is consistent with several other behaviours (Dishman et al. , 2004). Stages of change and processes of change are the components of the TTM which have been used to develop and implement interventions. Different strategies have been applied based on an individual’s stage of change e.g. cognitive strategies relating to benefits of physical activity have been used for individuals in the pre contemplation stage of change. Those in the preparation or action stage may benefit more from behavioural strategies such as reinforcement management and stimulus control (Dishman et al. , 2004).

Literature Review

Despite its appeal as a model for exercise behaviour change and its application in several interventions studies, some uncertainty remains about whether the TTM has stages and processes relevant to understanding exercise behaviour change (Dishman et al. , 2004). Criticisms of the TTM include that readiness to change does not explain why people have difficulty maintaining a change. Even among individuals unexpected changes can interrupt or end an exercise program, for example various factors such as relocation, exam pressure, and medical events can disrupt the continuity and create new activity barriers (Dishman et al. , 2004). Furthermore, in a review of studies using the TTM, Baranowski et al. (2003) concludes that although the relationship expected among the variables in the TTM in regard to physical activity have been found in some studies, only 45% were supported in a large longitudinal study. Hypothesised relationships concerning self efficacy were the most likely relationships to be confirmed (Baranowski et al. , 2003). A number of potential correlates included in the current study such as individual’s readiness to change, use of change strategies, self efficacy, perceived benefits and barriers of activity are rooted in the TTM. However the TTM is a model attempting to explain behaviour change, which may be useful for the development of stage matched interventions. The current study adopts a broader approach in an attempt to explain correlates of physical activity behaviour.

Literature Review

Figure 2.3 Stages of Change

2.2.6 Social Cognitive Theory

Researchers and practitioners have come to recognise the limitations of focusing on individual behaviour change and have moved towards approaches that respond to the reciprocity between the health behaviour, individual and the environment (Green et al. , 1996). In 1986 Bandura officially launched the Social Cognitive Theory (SCT), which developed from Social Learning Theory (Figure 2.4). The SCT defines human behaviour as a triadic dynamic and reciprocal interaction of personal factors, behaviour and the environment (Bandura, 1986). SCT recognises that the interaction between the three factors is deeply complex (Ryan et al. , 2006) and will differ based on the individual; the particular behaviour being examined and the specific situation where the behaviour occurs(Bandura, 1986). While the SCT upholds the behaviourist notion i.e. that positive experiences will mediate behaviour, it contends that behaviour is strongly influenced antecedently

Literature Review through cognitive processes. The SCT strong emphasis on ones cognitions suggests that the mind is an active force that constructs ones reality, selectively encodes information, performs behaviour on the basis of values and expectations and imposes structure on its own actions (Jones, 1989). The SCT therefore, assesses influences from an individual perspective and assesses individual’s cognitions, attitudes and beliefs to physical activity and individuals perceptions of the environment. The person – behaviour interaction involves the bi – directional influences of thoughts, emotions and biological properties of one’s actions (Bandura, 1986). A central tenet of the SCT is the concept of self efficacy. A person must believe in his/her capability to perform behaviour (i.e. person must have efficacy) and must perceive incentive to do so, i.e. a person’s positive expectations from performing behaviour must outweigh negative expectations (USDHHS, 1996; Baranowski et al. , 2003). Additionally the person must value the outcomes that he/ she will gain as a result of performing specific behaviour action (USDHHS, 1996). The behaviour - person interaction is represented as activity that is carried out which will influence ones thoughts and emotions relating to future physical activity. Bi directional interaction also occurs between environment and personal characteristics (Bandura, 1986). Human expectations and beliefs are developed and modified by social influences and perceived environmental influences. Therefore, support e.g. modelling, vicarious learning, social persuasion, and perceived environment can positively influence participation in physical activity. Different individuals can also evoke different reactions from their social environment as a result of their physical characteristics, for example gender, age, perceived competence may influence time spent in physically active environments. The final interaction in the SCT is between the behaviour and the environment. It is hypothesised that an individual’s preferences and competencies will determine aspects of the environment to which they are exposed. Behaviour is in turn modified by the environment in which a person resides i.e. facilities in an individual’s neighbourhood may influence physical activity participation among individuals. Some support for the application of the SCT to physical activity can be found in a study of college students. A structural equation model with SCT constructs accounted for 55% of the variance in physical activity among college students (Rovniak et al. ,

Literature Review

2002). A criticism of the SCT is that even though it includes the environment in the construct of reciprocal determinism, many applications of the SCT are limited to individual’s cognitions and perceptions of environmental influences (TAAG). The current study draws heavily on elements within the SCT, for example personal perceptions of physical activity e.g. enjoyment, barriers may influence and mediate levels of physical activity. Similarly, past experiences of physical activity will inform cognitive and personal variables. The importance of perceived environmental influences on behaviour is also assessed. A detailed presentation of the application of the SCT in the current study is provided in section 2.2.10.

Figure 2.4 Social Cognitive Theory

2.2.7 Social Ecological Model

The term ecology pertains broadly to the interrelations between organisms and their environment (Stokols, 1992). From its early roots in biology the ecological paradigm has evolved in several disciplines (sociology, psychology, economics, public health) to provide a general framework of understanding of peoples transactions with their physical and socio cultural surroundings (Stokols, 1996). A criticism of most theories and models of behaviour change is that they emphasise individual behaviour change processes and pay little attention to socio

Literature Review cultural and environmental influences on behaviour (McLeroy et al. , 1988). The Social Ecological Model (SEM) of health behaviour provides an understanding of the multiple levels of influence on behaviour (Stokols, 1996; Gorely, 2005; McLaren & Hawe, 2005; Bauman & Bull, 2007). Ecological models are distinguished by their explicit inclusion of environmental and policy variables that are expected to influence behaviour (Sallis et al. , 2006). The increased popularity of the ecological approach stems from a growing recognition that most public health challenges are too complex to be understood from single level of analysis (Stokols, 1996; Baranowski et al. , 2003; McLaren & Hawe, 2005). The broad social ecological perspective is a valuable development on previous individual behavioural research models to a more holistic understanding of health (Woods et al. , 2004). For example physical activity investigators often use objective measures of physical activity that provide detailed information on duration, frequency and intensity of activity but do not reveal the purpose or place of the behaviour. Similarly transportation researchers may use travel diaries to indicate where and how people travel and what they do at each destination but they do not measure leisure physical activity. Leisure researchers measure time and place of activities but have limited interest in quantifying physical activity. Combinations of these methods may be required to improve understanding of how people use environments for physical activity (Sallis et al. , 2006). Various SE models have been proposed, yet there is currently no consensus on any one specific model (Bauman & Bull, 2007). Ecological approaches provide general framework for explaining behaviour rather than a series of specific constructs or variables (Gorely, 2005). Generally SE models depict layers of influence on physical activity (Dahlgren & Whitehead, 1991; Pikora et al. , 2003; Gorely, 2005; Sallis et al. , 2006). McElroy et al . (1988) proposed an ecological model that encompasses several layers of influence on health behaviour. These layers of influences were intrapersonal factors, interpersonal factors, institutional factors, community factors and public policy (Figure 2.5). The advantage of dividing the environment into different levels of influence is that it forces attention on the potential for greater understanding and improved intervention at each level (Gorely, 2005). It is hypothesised that interventions that simultaneously influence these multiple levels may be expected to lead to greater

Literature Review and longer lasting changes in health behaviour (Stokols, 1992; USDHHS, 1996). The TAAG intervention for example is an example of an intervention using a SEM approach. The overall purpose of this intervention was to create environments at school and in the community that facilitate physical activity, enhance social support in those environments and provide participants with the motivation and skills to participate in activity in all settings (Elder et al. , 2007). It is important to note that an ecological approach does not exclude models and theories that have been used to focus on individual, social and psychological correlates of activity. Rather it draws on these theories to add specificity to the explanation of how and why a particular factor may influence behaviour (Gorely, 2005) A primary difference between the SCT and SEM is that although the SCT (Bandura, 1986) includes the environment in the construct of reciprocal determinism many applications of SCT are limited to individual’s cognitions and perceptions of environmental influences. SEM approaches are characterized by an emphasis on external environmental and policy influences on behaviour (USDHHS, 1996; Elder et al. , 2007). As a new field of research a large number of different characteristics of the environment have been identified and explored (Bauman & Bull, 2007). These include single and aggregated constructs at varying levels of scale from local neighbourhood such as a few streets through to characteristics assessed at the suburb or city level and can include e.g. level of connectivity, distance to destinations, access to open spaces (Bauman & Bull, 2007). The SEM also incorporates objectively assessed characteristics as well as subjective (perceptions) assessments of the environment such as perceived safety, perceived levels of traffic, aesthetic qualities, and access to facilities. In conjunction with physical environmental influences the SEM incorporates analysis of the transactions between behaviour and the specific social environments e.g. social support, school climate (Stokols, 1992; Stokols, 1996; Cohen et al. , 2000). Therefore, interventions using an SEM approach will attempt to create socio physical environmental conditions that support and promote effective and sustainable behaviour change (Kothari et al. , 2007). A major strength of the SEM is the comprehensive nature of the model which provides some assistance to articulate the complexities of health determinants and the environmental influences on health (Green et al. , 1996). The SE perspective allows a

Literature Review broader analysis of the transactions between the individual and collective behaviour, and the various constraints and resources for physical activity that exist in socio physical environments (Stokols, 1996). Limitations of the ecological approach as applied to physical activity are that ecological interventions require the integration of knowledge from several different disciplines and close co-ordination among persons and groups from various sections in the community. Furthermore, multilevel assessments and interventions can be quite expensive and identifying the most promising strategies among the many options is difficult (Sallis et al. , 2006). The current study draws on elements within the ecological approach to assess aspects of the social environment (peer, family influence, school climate, social norms) and physical environment (home, school, community) in which adolescents live. The justification of the theoretical approach and the use of the SEM in the current study is outlined in section 2.2.10.

Policy factors

Community factors

Institutional factors

Interpersonal

Intrapresonal

Figure 2.5 Social Ecological Theory

Literature Review

2.2.8 Strengths and Limitations of Models of Behaviour Change

Limitations of individual behaviour change models are that a person’s efforts to modify their own health practices are often impeded by external economic social and cultural constraints (Stokols, 1996; Dishman et al. , 2004). Low educational status, lack of time, money and energy can derail efforts to improve individual’s health. Furthermore while interventions based on individual models of behaviour change have led to effective changes in physical activity these findings should be viewed with caution. The effect sizes for many of these physical activity interventions has been found to be small to moderate, furthermore, maintenance of physical activity following these programmes is poor (Sallis et al. , 2006). Any expectation that programs with moderate and temporary effects that reach small numbers of people will create population wide increases in physical activity is unreasonable (Sallis et al. , 2006). Health promotion and physical activity promotion based on environmental enhancement strategies are clearly a crucial adjunct to individually focused lifestyle modification programmes (Stokols, 1996). Clearly, behaviour and ecology based problems require behaviour and ecology based solutions (Baranowski et al. , 2003). A further limitation of most current behaviour change models is that efforts to persuade increased physical activity and other health behaviours may go unheeded if that person is unready or unmotivated to enact suggested behaviours (Stokols, 1996). The TTM is the only model which assesses individual’s readiness to change. It would seem reasonable that this measure would be included in correlate studies to assess individual’s readiness for physical activity. Bauman et al. (2002) reviewed the effectiveness of various behavioural theories to predict correlates of activity. Even though this research was carried out with adults it provides some interesting insights into different behavioural theories and physical activity. In this research the number of variables which were consistently associated with adult physical activity was identified. The effectiveness of the individual theories at explaining these variables were tallied. The percentage of variables supported was found to range from 25% in the HBM to 100% of the TTM. Both the TPB (67%) and the SCT (70%) were also highly supported. Thus, only the health belief model failed to receive clear support on literature on adult physical activity correlates. In this review Bauman et al. (2002) does make reference to an ecological model however few of the

Literature Review correlates included in the analysis were in the ecological domain. The most remarkable finding in this review was the large number of correlates that were not associated with a specific theory but were consistently associated with physical activity (Bauman et al. , 2002). It was found that no current theory accounted for 15 variables that were associated with physical activity. Therefore, current behavioural theories do not appear to be extensive enough to include all correlates of activity. Baranowski et al (1999) findings concur somewhat with this, the author concludes that the predictiveness of most of these models of physical activity has been modest (r 2 < 0.3) which suggests there are substantial limitations to these models (Baranowski et al. , 1999). There are a number of possible interpretations for future work based on these finding. One perspective is that current existing theories do not adequately explain a multitude of important correlates, therefore new more extensive theories need to be developed that account for these existing variables (Bauman et al. , 2002; Baranowski et al. , 2003). An alternative perspective proposed by Bauman (2002) which holds merit is that physical activity is too complex a behaviour to be encompassed by a single theory. Perhaps the most effective approach at the current stage of understanding would best be served by a continued application of existing theories supplemented by creative thinking to evaluate influencing variables that are outside of current theories (Bauman et al. , 2002). It could be argued that a broad overarching ecological framework which bridges several theoretical approaches as used in the TAAG study may be the most comprehensive approach currently in use in the explanation and promotion of physical activity.

2.2.9 Role of cross- sectional correlate studies

In some sense behavioural research on physical activity is in its infancy. More effective programs will not be designed until there is a better understanding of why people partake in physical activity (Baranowski et al. , 2003). Bauman et al, (2002) identifies both practical and theoretical uses of correlate studies in increasing understanding of physical activity. Practically correlate studies generate hypotheses about possible causal relationships and about potential mediators. Once the correlate research has identified the most influential causative mediators, research should be focused on developing and

Literature Review testing procedures for changing these variables (Baranowski et al. , 2003). Theoretically correlate studies can test predictions derived from theory and produce results that eventually lead to modifications in theory (Bauman et al. , 2002).

2.2.10 Review of theoretical models: Implications for current research

The current study used the Social Cognitive Theory and Ecological Theory to provide theoretical underpinning to the research, this approach is similar to previous research (Woods et al. , 2009). Using this approach the influences of personal attributes such as psychological dispositions and physical characteristics in conjunction with multiple facets of both the physical environment (e.g. access to facilities, perceived environmental safety and aesthetics) and the social environment (e.g. school climate, societal norms, peer influences) on physical activity were assessed. This approach was used in an attempt to provide a holistic representation of the possible individual and collective correlates of adolescent physical activity. The theoretical model used in the current study highlights the multilevel nature of the approach used in the research (Figure 2.6). The SCT examined the possible demographic, physical and perceived psychological correlates of physical activity. The SEM informed the analysis of the physical and social environmental correlates of physical activity. A key tenet in the social ecological model is the use of different measurement tools including objective measures to assess the physical environment. For example, many health researchers are incorporating direct observation and powerful Geographical Information Systems (GIS) software to facilitate more sophisticated and detailed analysis of the links between physical activity and environmental variables (Leslie et al. , 2007). No objective measures of the physical environment are used in the current study and this has been identified as a limitation of the research. However, despite not being objectively assessed, the access to facilities measure which assessed facilities in an adolescent’s neighbourhood is somewhat of a factual measure which should not be subject to self reported bias and which should provide similar information as objective tools. The social environmental a adopted a multilevel approach assessing influences in school, home and community.

Literature Review

Demographic Physical Psychological Age, Gender, School Height, Weight, Stage of change, Change strategy,

year, Medical BMI, Screen time Self efficacy, Perceived competence, condition, Urban/ inactivity Enjoyment of physical activity,

rural, SES, Ethnicity, Enjoyment PE, Intention to be active Intact family Perceived benefits/ barriers, Value

Person

SCT

Behaviour Environment

Physical activity, Stage of change

Physical Social

Transport, Access to School climate, Boys facilities, Perceived influence, Girls norms, environment (safety, Active groups of friends, aesthetics, Encourage peers, Social connectivity) support peers, Social support family

Figure 2.6 Theoretical model used in the current research

Literature Review

2.3 Review of the Reviews on Correlates of Activity

This section outlines the reviews which have examined the correlates of activity among adolescents. These reviews were used in conjunction with an analysis of theoretical models to identify correlates for inclusion in the current study. The three comprehensive reviews included in this section are those carried out by Sallis et al . (2000), Van der Horst et al . (2007) and Biddle et al . (2005). A summary of the results of these reviews is presented in Table 2.1. The following sections provide a critical analysis of the consistencies and limitations of these reviews. A number of methodological consistencies exist across all three reviews which allow direct comparisons to be made between reviews. Firstly, the Sallis et al . (2000) reviewed adolescent physical activity correlate studies from 1970 – 1998 and included a total of 54 studies in the review. Van der Horst et al. (2007) elaborated on the Sallis et al. (2000) study and using a similar methodological approach reviewed studies published from 1999 to January 2005. A total of 40 adolescent related studies are included in this analysis. Therefore, when these reviews are analysed together correlates of adolescent physical activity across 94 separate studies from 1970 to 2005 are reviewed. Biddle et al. (2005) also adopted a similar methodological approach on studies of adolescent girls between 1999 and 2003; a total number of 51 studies are included in this review. While some of the studies of Biddle et al. (2005) may also included in the Van der Horst et al. (2007) review, it is included in this literature review due to the importance in identifying key correlates of physical activity among adolescent girls. Secondly, to be included in the reviews studies had to meet the following criteria: subjects had to be adolescents, the dependent variable was a measure of overall physical activity and the variables had to be tested for their association with the dependent variable. All three reviews only included variables if they were studied on three or more occasions (Sallis et al. , 2000; Biddle et al. , 2005; Van der Horst et al. , 2007). This inclusion criteria permits analysis of correlates across the three reviews. Thirdly, each of the reviews used the same categorisation of correlates based on the social ecological model. Correlates were categorised either (i) behavioural attributes and skills, (ii) demographic/ biological, (iii) psychological, (iv) social cultural or (v)

Literature Review environmental correlates of activity (Sallis et al. , 2000; Biddle et al. , 2005; Van der Horst et al. , 2007). Furthermore, reviews classified correlates as having a positive, negative, indeterminate or no relationship with physical activity. Similar levels of association were used to assess the relationship between physical activity and various potential correlates. In the Sallis et al . (2000) review between 0 – 33% association with correlates was classified as no association, between 34 – 59% were classified as indeterminate association while 60 - 100% were classified as positive or negative association. The Van der Horst et al. (2007) review categorised 0- 49% as no association, 50% as indeterminate and 51-100% as positive or negative association. Biddle et al. (2005) also classified studies as being related to or not being related to physical activity based on the 60% criteria identified by Sallis et al. (2000). The direction and strength of the relationship was also identified in this review and classified as small, moderate and large (Cohen, 1977). Fourthly, studies which reported the findings of males and females separately were included as two independent samples in analysis of results in both the Sallis et al. (2000) and Van der Horst et al. (2007) reviews. This allows some comparisons of the correlates of physical activity among adolescent males and females. The review of Biddle et al. (2005) further adds to our knowledge of the correlates among adolescent females. Finally, both Sallis et al. (2000) and Biddle et al . (2005) assessed the quality of the physical activity measure using the same protocol. Studies were categorised as measuring physical activity using (i) a physical activity self report non valid measure, (ii) a self report valid measure or (iii) an objective measure. No information relating to the quality of physical activity data is provided by Van der Horst et al . (2007). It could be argued, that the inclusion of non valid self reported physical activity research studies may be a limitation of these reviews and future reviews should concentrate on more validated measures. A number of limitations exist of the research methodologies used in these reviews. Firstly, it could be argued that due to the differences in physical activity patterns (Kimm et al. , 2000) and evidence of differences in correlates of activity (Rees et al. , 2006; Vu et al. , 2006) separate detailed analyses of the correlates among males and females is warranted. Both Sallis et al. (2000) and Van der Horst et al . (2007) report some consistent findings relating to the correlates of activity when both male and

Literature Review female data is assessed. SES, BMI/ skinfolds, goal orientation, enjoyment, smoking, parental activity, family support, peer support were found to have similar associations with physical activity in both reviews. When the findings of Biddle et al. (2005) are compared the only correlates which were found to have consistent findings were gender and family support. Other possible explanations for the inconsistency in findings may be explained by different populations, different measures of physical activity, different measures of correlates of activity and different statistics used (Sallis et al. , 2000; Biddle et al. , 2005; Van der Horst et al. , 2007). For example, the reviews did not distinguish between MVPA and general physical activity or activity at different settings. Therefore, differences in correlates associated with low, moderate, vigorous physical activity and home/ school physical activity could not be established (Van der Horst et al. , 2007). More stringent criteria could be used to increase the comparability of results in the reviews. A second limitation is that despite using a comprehensive approach to gather studies it may be possible that some relevant studies were not included. Differences existed in the databases which were used to search for studies, i.e. Sallis et al. (2000) searched Medline and PsycINFO, Van de Horst et al. (2007) searched Pub Med and PsycINFO while Biddle et al. (2005) searched Medline, Web of Science and PsycINFO. The different key words used in the studies may also have resulted in articles not being found in the retrieval process. Furthermore, bias against publishing negative findings may also influence results (Van der Horst et al. , 2007). Another limitation of the reviews of the correlates of adolescent physical activity is the lack of studies which have included the influence of environmental factors on physical activity. The only environmental correlates included across the three reviews are availability of facilities, opportunities to exercise and media influence. Only one environmental correlate was included in the Van der Horst et al (2007) review and no environmental correlate was studied sufficiently in to be included in the Biddle et al. (2005) review. Despite the focus in the literature on physical environmental correlates of physical activity, few studies have examined environmental correlates of physical activity in adolescents and more research is warranted in this area (Van der Horst et al. , 2007). This may be particularly pertinent given the effectiveness of an ecological approach to modify health behaviours. Furthermore, few objective measures of the

Literature Review physical environment are included, for example in the Van der Horst et al. (2007) review five studies examined the association between physical activity and availability/ proximity to facilities. Only one of these studies used objective measurement of environmental variables. A fourth limitation of the reviews is that most of the research on correlates of physical activity involved cross sectional designs. Therefore, only the directions of the associations can be established, no conclusions relating to cause and effect can be established (Van der Horst et al. , 2007). To gain a further insight into the determinants of change in physical activity levels or decline in physical activity during adolescence more prospective and intervention studies are needed (Van der Horst et al. , 2007). Based on the review of reviews (Table 2.1) correlates were identified for inclusion in the original ‘Adolescent Physical Activity Questionnaire’ (APAC) (Chapter 3). Other correlates were added to the questionnaire from the results of focus group interviews (Chapter 4). Literature supporting the inclusion of specific correlates is provided in subsequent sections. Personal and biological influences that were included in the questionnaire were gender, age, ethnicity, SES, parental occupation and BMI. Levels of parental education were not included in the questionnaire due to possible difficulties participants may have in reporting parental education, the researcher also felt questions relating to parental occupation and SES provided sufficient information relating to the parents. Behavioural correlates included in the research were time spent in screen time inactivity, PE enjoyment and current involvement in organised sport. No measure of previous sports activity was included in the questionnaire. This may be perceived as a limitation of the questionnaire; however the current study focused primarily on modifiable correlates of activity that could be targeted in future interventions. In an extensive review of literature very limited research has assessed the influence of sensation seeking on levels of physical activity. Due to the limited support for this correlate it was not included in the ‘APAC questionnaire’. Psychological correlates included in the questionnaire based on the reviews (Table 2.1) were self efficacy, intention to be active, perceived barriers, outcome expectancy value, perceived competence, body image and fun/ enjoyment. A scale used in the TAAG (2004) study which was adapted from the attitude questionnaire (Motl et al. , 2000) and the Amherst study (Sallis et al. , 2002) was used to compute attitude

Literature Review towards physical activity and values associated with physical activity. Therefore, attitude toward physical activity (in chapters 3 -7 this correlate is termed ‘perceived benefits’) were also assessed in the current research. Goal orientation was not assessed in the current research, contemporary perspective theories of achievement motivation assert that there are two predominant goal perspectives achievement situations, these have been characterised as task and ego orientations (Kilpatrick et al. , 2003). A number of tools were reviewed which assessed goal orientation such as the Goal Orientation in Exercise Scale (GOES) scale (Kilpatrick et al. , 2003) and the Task and Ego Orientation in Sport Questionnaire (Duda, 1989). The author concluded that the nature and content of these scales was similar to the physical self perception. For example sample items from the GOES scale were; I can do better than my friends, I learn a new skill by trying hard. In an attempt to keep the questionnaire as succinct as possible, therefore goal orientation was not included in the questionnaire. Social variables which have been found to be related to physical activity and were included in the correlates questionnaire were peer support, encouragement of peers for physical activity and family support. The family support scale examines sibling activity with the participants therefore this correlate was included as part of this scale. Limited research has been conducted on the environmental correlates of physical activity. In the Biddle et al . (2005) review no physical environmental correlate was studied enough to be included in the analysis. Three physical environmental variables were included in the Sallis et al. (2000) review. Opportunities to exercise were the only correlate which was found to be positively associated with physical activity in this review. No physical environmental variables were found to be associated with physical activity in the Van der Horst et al. (2007) review. It may well be that these reviews have not found clear associations between environmental variables and physical activity as there is a dearth of research assessing environmental correlates which met the inclusion criteria pre 2006 (Biddle et al. , 2005; Van der Horst et al. , 2007). Furthermore, measurement difficulties may also impact on the association between environmental correlates and physical activity. Most studies on environmental determinants of physical activity rely on self reports of environmental factors. This represents perceived rather than actual features of the environment; it is recommended that future research employ more objective measures of environmental correlates (Van der Horst et al. , 2007). The

Literature Review

lack of environmental correlates included in the current study may be identified as a limitation of the research. Environmental correlates included were transport barriers, access to facilities and perceived environment. Due to the limited research on the environmental correlates of activity reported in these reviews (Table 2.1) specific reviews which evaluated environmental correlates were assessed. This review is presented in section 2.6.

Table 2.1: Summary of reviews of the correlates of physical activity Variable Name Sallis et al. (2000) Van der Horst et al. (2007) Biddle et al. (2005) a Number of studies Number of studies Number of studies showing an association b showing an association showing an association (+/-) (+/-) (+/-) Biological Variables Male 27/28 + 12/12 + 22/24 + Age 2/27 19/27 -- 1/10 5/10 0 7/11 -- Caucasian 10/14 + 6/13 0 6/7 + SES 3/9 1/9 0 3/8 0 3/3 + Parent education ND 4/6 + 3/4 + BMI/ skinfolds 2/21 6/21 0 3/7 0 6/8 + Psychological Variables Attitude/ expectancy value 3/7 0 2/3 + ND Self –efficacy 7/13 + 14/17 + 10/10 + Intention 6/8 + 2/4 ? ND Perceived barriers 5/15 0 1/14 6/14 0 3/3 -- Perceived benefits 11/29 0 1/7 00 Perceived competence 2/3 + 2/6 0 4/5 + Goal orientation/ motivation 5/6 + 4/5 + ND Self perception/ body image 3/7 0 3/8 0 6/7 + Fun/ Enjoyment 0/5 00 3/8 0 7/8 + Depression 3/4 -- 2/7 0 ND Behavioural Variables ND ND Smoking 6/15 0 1/3 0 3/4 -- Television/ sedentary time 3/3 -- 1/5 0 1/6 3/6 0 PE/ school sports 1/3 0 2/2 + ND Previous physical activity 11/12 + ND ND Community sports 7/7 + ND 4/4 + Sensation seeking 3/3 + ND ND Social Variables Parental activity 9/27 0 2/8 0 6/12 ? Family influences 2/3 + 10/15 + 7/8 + Friends support 4/4 + 5/7 + 3/6 ? Sibling physical activity 4/4 + ND ND Parental help in activity 3/4 + ND ND Environmental Variables Availability of facilities 1/8 00 2/6 0 ND Opportunities to exercise 2/3 + ND ND a Review of females only bWhen more than 75% of the associations were in a similar direction, this was coded as + (positive), -- (negative), or 00 (no association).When 50 – 75% of the associations were in a similar direction, this was coded as +, -- or 0. When exactly 50% of the associations were in a positive or inverse direction, or if there was considerable lack of consistency it was coded as ? (Inconclusive); ND = not described in review

Literature Review

2.4 Psychological Influences on Physical Activity

This section provides an overview of reviews of literature which have examined psychological correlates of activity. A review of individual psychological correlates is presented in the subsequent sub sections.

Biddle et al. (2005) reviewed literature on the correlates for participation in physical activity among adolescent girls. Perceived competence was associated with physical activity in four of the five studies reviewed with most effects being small. Similarly, a small to moderate association was reported between self-efficacy and participation in ten of the ten studies reviewed. Seven of the eight studies which assessed enjoyment showed a positive relationship with physical activity, with effects found to be small to moderate. Physical activity was also found to be positively correlated with perceptions of body attractiveness and overall self-worth, all three studies were found to have small to moderate association. Perceived barriers were found to be negatively associated with physical activity in all three studies which were reviewed. The author believes that to promote physical activity, environments and opportunities should be created whereby adolescent’s girls should be able to develop perceptions of competence and confidence. This then is likely to lead to higher levels of enjoyment, better physical self-worth and greater persistence (Biddle et al. , 2005).

In a separate review of the literature examining the correlates of physical activity among children and adolescents, Sallis et al. (2000) reports some similar results. Of the 17 psychological variables reviewed among 13-18 year olds, the only ones with consistent and positive associations with physical activity were perceived competence, intention to be active, achievement orientation (academic success) and depression (inverse). Factors which were found to have indeterminate relationship included self- efficacy with seven of the studies reviewed significantly correlated to levels of physical activity, whereas in six of the studies reviewed no significant correlation was reported (53%). Similarly, perceived physical appearance/ body image (three studies reporting positive associations; four studies reporting no association, 42%), knowledge of exercise/health (four positive; three no association, 57%) and attitudes/ outcome expectation (three positive; four no association, 42%) were found to have indeterminate

Literature Review associations with physical activity. The differing methodological approaches and measurement tools used in these studies may partly explain the inconsistent results detailed in these studies.

Van der Horst et al . (2007) reviewed correlates of activity and included studies published between January 1999 and January 2005. Twenty eight studies examined ten psychological correlates of adolescents’ physical activity. Attitude, self efficacy and goal orientation/ motivation were found to be positively associated with physical activity. No association was found between perceived benefits, self perception, fun/enjoyment and depression. Evidence linking physical activity and intention, perceived barriers and sport competence was found to be inconclusive (Van der Horst et al. , 2007).

A systematic review of literature on the barriers and facilitators to children’s physical activity was undertaken by Brunton et al . (2003). Children aged six to eleven years identified 14 distinct but interrelated factors which they perceived facilitated physical activity. These factors were found to cluster around four main themes. ‘Fun and enjoyment’ which is classified as a psychological theme was the most common theme cited for participation in sport and physical activity. The ‘perceived health benefits’ associated with being active was a second psychological theme which was identified in this review. Other themes identified were having a ‘supportive encouraging family ‘and ‘access to opportunities and facilities and choice of activities’ (Brunton et al. , 2003). These themes will be reviewed in the social and environmental influences sections.

2.4.1 Self-Efficacy and Change Strategies Self efficacy, a central component of Bandura’s social cognitive theory has been advanced as an important personal determinant of human behaviour (Ryan et al. , 2006). Perceived self efficacy is defined as the belief in ones capabilities to organise and execute the courses of action required to manage prospective situations (Bandura, 1994).

Literature Review

A strong sense of self efficacy enhances human accomplishment and personal well being in many ways. People with high assurance in their capabilities approach difficult tasks as challenges to be mastered rather than as threats to be avoided (Bandura, 1994). In contrast people who doubt their capabilities shy away from difficult tasks which they view as personal threats (Bandura, 1994). The application of self efficacy to research on physical activity centres on the hypotheses that strong belief in one’s ability to be active relates to participation in physical activity (Ryan et al. , 2006). Therefore, individuals who are dissatisfied with current exercise levels, who have positive outcome expectancies about physical activity, who adopt challenging goals and believe in their ability to achieve goals (self efficacy) would presumably have optimal motivation for adopting and maintaining physical activity (Dishman et al. , 2004). Self efficacy has been found have four main sources of influence (Bandura, 1997). The most effective way of creating strong sense of efficacy is through mastery experiences. Successes build robust belief in one’s personal efficacy while failure undermines it (Bandura, 1994). A second influence on self efficacy is vicarious experiences provided by social models. Seeing people similar to oneself succeed by sustained effort raises observers beliefs that they too possess the capabilities to master comparable activities (Bandura, 1994). Social persuasion is a third way of strengthening people’s beliefs that they have what it takes to succeed. People who are persuaded that they possess the capabilities to master given activities are likely to mobilize greater effort and sustain it than if they harbor self doubts (Bandura, 1994). Finally, emotional or perceived signs of coping can influence self efficacy e.g. lowered perceived exertion after increasing fitness (Bandura, 1997). In the Sallis et al. (2000) review the evidence for self efficacy as a correlate of adolescent activity was found to be equivocal. Closer examination of the self efficacy scales used in the reviewed studies shows that a number of different types or components of self efficacy were examined. For example one study of high school students combined questions about engaging in regular physical activity with questions about overcoming barriers to physical activity (Reynolds et al. , 1990). Another study in this review included a self efficacy scale measuring belief in ability to be active relative to peers (DiLorenzo et al. , 1998). A third study examined self efficacy for seeking support for physical activity, for overcoming barriers to physical activity and for being

Literature Review active despite competing activities such as watching TV (Pate et al. , 1997). The self efficacy scale used in the current study attempts to rectify problems with using different self efficacy measures by using a unidimensional scale (Motl et al. , 2000). This scale combines barriers efficacy items with items measuring self efficacy for seeking support, being active despite competing activities and engaging the task of being regularly active. Differences in the types of self efficacy measured may be contributing to the inconsistency in self efficacy among adolescents (Ryan et al. , 2006).

Physical activity self-efficacy has been related to participation in structured and unstructured physical activity. Anderson et al. (2007) assessed structured physical activity and the psychosocial correlates among 2,791 middle school girls participating in the ‘Trial of Activity for Adolescent Girls’ (TAAG) study. In this study, self-efficacy was measured using a version of a previously validated scale developed by Saunders et al. (1997). It was reported that girls who participated in structured physical activity reported higher physical activity self-efficacy (OR = 3.44, 95% CI = 1.72-6.92) compared to girls who did not participate in structured physical activity (Anderson et al ., 2007).

In a review of literature examining the determinants of change in physical activity among children and adolescents; higher scores on physical activity self-efficacy were associated with smaller declines in levels of physical activity in adolescence (Craggs et al. , 2011). Further support for the importance of self-efficacy on physical activity is provided in a recent study of 1,518 adolescent girls from 24 secondary schools in Australia. This study assessed levels of activity using accelerometery and psychological, behavioural social and environmental correlates of physical activity. It found a relationship between self-efficacy and accelerometer counts, indicating higher levels of efficacy associated with higher levels of activity (Lubans et al. , 2011). This is in agreement with a Canadian adolescent and adult study which used a telephone survey and IPAQ questionnaire (N = 5,167). In this study self-efficacy and intention to be active were found to be positively associated with physical activity among participants (15-24 yrs, OR = 1.73; 95% CI. 1.37-2.19) (Pan et al., 2009).

Literature Review

These findings are in agreement with the Irish ‘Switch off Get Active Study’ on 9-11 year old children. In this study physical activity self-efficacy was also measured by the scale developed by Saunders et al. (1997), and physical activity was measured using the ‘Previous Day Physical Activity Recall’ questionnaire. Males and females in the highest tertile for physical activity participation were found to have significantly higher physical activity self-efficacy when compared to those in the lowest tertile for physical activity (1.54 ± 0.3 V 1.41 ± 0.4; P = .01 boys) (1.54 ± 0.05 V 1.41 ± 0.3; P < .05 girls) (Harrison et al. , 2006)

Due to the association between physical activity self-efficacy and physical activity interventions have aimed to enhance physical activity self-efficacy and assess its impact on physical activity. Dishman et al. (2004) carried out a randomized control trial among 2,087 adolescent girls in the ‘Lifestyle Education for Activity Program’ (LEAP). Physical activity self-efficacy was measured using the scale developed by Saunders et al. (1997). The intervention was found to have a direct effect on increasing physical activity self-efficacy and also to have a subsequent effect on increasing physical activity levels. Therefore it was concluded that manipulation of self-efficacy results in increased physical activity among adolescent girls (Dishman et al. , 2004).

Change strategies are described as the use of cognitive and behavioural strategies to adopt and maintain regular physical activity (TAAG, 2004). As previously outlined self efficacy is based on the hypotheses that adolescents who have confidence in their ability to be physically active will perceive fewer barriers relating to physical activity. Self efficacy is believed to influence physical activity through various change strategies (e.g. thoughts, goals, plans) that support physical activity (Dishman et al. , 2005). Dishman et al. (2005) conducted a confirmatory factor analysis on a measure of change strategies for physical activity in a cross sectional sample of 6 th and 8 th grade girls. It was found that, change strategies (behavioural and cognitive) mediated the association of self efficacy with physical activity in both samples i.e. the use of change strategies are possible mechanisms by which self efficacy influences physical activity. The author concludes that the change strategies measure warrants further study as a mediator of the influence of efficacy on physical activity.

Literature Review

2.4.2 Physical Activity Stage of Change

The physical activity stage of change is a component of the Trans theoretical model of behaviour change (Prochaska & DiClemente, 1992). In this model an individual is classified from: 1 = pre contemplation, to 5 = maintenance, based on their cognitive and behavioural readiness to begin physical activity.

Haas & Nigg (2009) assessed the validity of the physical activity stage of change measure using a self report measure of vigorous, moderate and light activity and sedentary behaviour derived from Godin’s Leisure Time Exercise Questionnaire. Among two separate samples of children aged 10-12 (Sample 1, N = 513; Sample 2, N = 253), one way ANOVA’s revealed significant differences in both samples between physical activity stage of change scores and self reported vigorous activity (sample 1, F = 15.9, P < .05; sample 2, F = 12.8, P < . 05). Significant differences were also recorded between stage of change scores and moderate activity in both samples (sample 1, F = 8.54, P < 05 ; sample 2, F = 4.72, P < . 05). No significant association was reported in this study between the physical activity stage of change score and levels of light activity and sedentary inactivity (Haas & Nigg, 2009).

Similar findings are reported by Lee et al. (2001) in a cohort of 819 Canadian adolescents. This study also used Godin’s Leisure Time Exercise Questionnaire and a measure of the physical activity stage of change. Separate ANOVA’s were conducted between physical activity stage of change and self reported strenuous, moderate and mild activity. Both strenuous and moderate activity were found to vary by stage (strenuous, F = 32.53, P < . 05; moderate F = 10.99, P < . 05). Furthermore, a composite physical activity index developed in this study was also found to vary as a function of stage F = 23.37, P < . 05, with participants in pre contemplation and contemplation reporting less exercise than those in preparation. In addition, those in preparation reported less exercise than those in action and maintenance (Lee et al. , 2001). Other similar research has also reported an association between physical activity stage of change and moderate and vigorous self reported activity (De Bourdeaudhuij et al. , 2005). Currently there is a dearth of research which has assessed the relationship between the stage of change measure and objectively measured adolescent physical

Literature Review activity. Future research should explore this association. Physical activity stage of change has also been related with a more positive correlate profiles (Kim et al., 2004).

Efforts to use the trans theoretical model in the development of interventions have been found to be effective (Marcus & Forsyth, 2003). This framework focuses on ‘patient treatment matching’, where intervention strategies are matched to the physical activity stage of an individual or group. Marcus et al. (1998) conducted a RCT trial in which sedentary participants were randomly assigned to a tailored intervention group or a control group. Participants in the intervention groups significantly increased the number of minutes of physical activity per week (Marcus et al. , 1998). The increase in physical activity participation was maintained at the 12 month follow up (Bock et al. , 2001).

2.4.3 Physical Self-Perception

There is general agreement that self perception is a multifaceted, hierarchical and dynamic construct (Karterliotis, 2008). Shavelson et al . (1976) in a review of literature proposed a model where general self concept is at the apex of a hierarchy and is influenced by academic self concept, social self concept, emotional self concept and physical self concept (Shavelson et al. , 1976). The Self Perception Profile for Children (Harter, 1982) was developed based on the Perceived Competence Scale for Children (Harter, 1982). Similar to the model proposed by Shavelson et al. (1976) it is a multidimensional scale consisting of 5 specific subscales (scholastic competence, social acceptance, athletic competence, physical appearance, behavioural conduct). The APAC questionnaire used in the current study includes the athletic competence and physical appearance subscales. Perceived athletic competence is classified as participant’s perception of their ability to do well at sports, learn new outdoor games readily and prefer to play new sports than merely watch others play (Harter, 1982). Physical self perception examined individual’s satisfaction with their personal appearance (Harter, 1985).

Literature Review

Higher levels of physical self-perception have been found to be positively associated with levels of physical activity participation. The ‘US Growing Up Today Study’ (GUTS) followed a cohort of over 8,000 US children and adolescents between 1997-1999. It reported positive associations between physical activity and self- perception of competence (Stein et al. , 2007). A modified version of the Harter ‘Self Perception Profile for Children’ was used in this study, this tool measures child’s self perceptions in separate domains i.e. social acceptance, athletic ability, scholastic skill and global self-worth. For both males and females, linear regression models showed that increases in physical activity were positively associated with changes in social and athletic self-perception scores ( P < . 001) . No association was found with a change in either scholastic or global self-perception. Therefore the author concludes that those who increased physical activity felt more competent in their athletic ability and perceived themselves to be more socially accepted (Stein et al. , 2007).

Further support for the influence of self-perception on physical activity is provided by Kahn et al. (2008), in a study of 12,812 US adolescents. This study used the same measurement of competence and found that athletic and social self-worth was associated with MVPA at baseline, similar to Stein et al. (2007), no association was evident between scholastic and global self-worth (Kahn et al. , 2008). Similar findings are reported in a longitudinal study carried out by Jose et al. (2011) among children and adolescents aged (7-15 years) as part of the 1985 ‘Australian Health and Fitness Survey’. In this study, data relating to perceived sports competency was measured using a scale developed by the researcher. At follow up in 2004-2006 participants reported activity levels between the ages of 15-29 retrospectively using the ‘Historical Leisure Activity Questionnaire’. For females, perceived sports competency in childhood and adolescence was significantly associated with habitual activity in adolescence and adulthood (RR = 1.88, 95% CII = 1.39, 2.55).

Findings from Irish research are in agreement. In a random sample of 1,602 Irish 11-12 year olds from 62 schools, O’ Sullivan et al. (2002), investigated physical self- perception of competence. Items from Harter’s (1982) ‘Perceived Competence Scale for Children’ and the ‘Physical Self Perception Profile’ (PCPP-C) (Fox & Corbin, 1989) which related to sport and physical activity, were adapted and used in this study. A

Literature Review significant and positive association was observed between levels of physical activity and physical self-perception of competence scores for both boys and girls ( P < . 01). It was found that the largest proportion of children with high self-perception (44%), was among the highly active group and the largest proportion of those with low self perception (46%), were in the low moderate activity group. The underlying interpretation is that children who are confident about their movements will choose to be active, will do so in an assured manner and will be more likely to persist (O’Sullivan 1997). It should be noted however, that this study used a self report measure of physical activity based on the ‘Previous Day Physical Activity Recall’. Use of objective measures would further support these findings among Irish adolescents.

The evidence linking physical activity physical self perception is equivocal, with some physical activity correlate reviews of literature reporting an indeterminate relationship (Sallis et al. , 2000; Van der Horst et al. , 2007), while a separate review reports a positive association with adolescent girls (Biddle et al. , 2005). The initial questionnaire which was piloted in chapter three did not include a measure of physical self perception. Physical self perception was identified as being an important correlate in the focus group interviews in chapter four and was therefore included in the APAC questionnaire.

2.4.4 Enjoyment Enjoyment can be described as a positive affective state that reflects feelings such as pleasure, liking and fun (Scanlon & Simons, 2000). It can be conceptualised as the perceived reinforcing value of physical activity (TAAG, 2004). The Surgeon General Report on Physical Activity and Health suggested that enjoyment may be the major reason that young people engage in physical activity (USDHHS, 1996), yet enjoyment has seldom been studied (Sallis et al. , 2000). Various descriptive research studies have identified the importance of enjoyment for participation in physical activity. Among Irish children (N=1,602) aged 11-12 years, students rated a series of nine statements relating to sport and physical activity participation as very important, important or not important. The main self reported

Literature Review reasons for participating in physical activity were found to have fun (66%), to stay healthy (55%), be good at the activity (40%), and spend time with friends (35%) and to please parents (20%) (O Sullivan, 2002). Connor (2003) investigated the primary reasons for participation in sport among Irish adolescents, the findings were found to mirror those of O Sullivan (2002). It was found that enjoyment (37%), because friends play (28%) and to keep fit (21%) were the primary reasons for participation in physical activity. Intervention studies can target and assess the impact of potential mediators on physical activity, e.g. enjoyment is a mechanism through which interventions are believed to influence physical activity behaviours (Marcus & Forsyth, 2003). This type of research design can provide support for the mediating influence of a correlate on activity. Correlational studies generate hypotheses about possible causal relationships and about potential mediators that can be targeted in intervention studies (Bauman et al. , 2002). Some correlational research has found an association between exercise enjoyment, enjoyment of PE and physical activity. This provides some support for the hypotheses that exercise enjoyment may be a mediator of physical activity. In a correlational analysis of the baseline data from the TAAG study, the influence of enjoyment of physical activity and PE on structured activity was investigated (Barr- Anderson et al. , 2007). Physical activity enjoyment was measured using questions adapted from the Physical Activity Enjoyment Scale (PACES) (Kendzierski & DeCarlo, 1991), while factors influencing enjoyment of physical education was measured using a validated tool developed by Motl et al. (2001). Interestingly, no association was reported between physical activity enjoyment and participation in structured physical activity (OR= 0.76; 95% CI 0.38-1.52). The author however did report that higher self reported levels of enjoyment of physical education classes was associated with higher participation in structured physical activity (OR=1.97; CI 1.25 – 3.1) (Barr-Anderson et al. , 2007). A separate correlational study also provides some support for the possible mediating influence of enjoyment on physical activity. In this study carried out by Vira and Raudsepp (2000) the relationship between (i) achievement goals, (ii) beliefs about sport success and sport emotions were correlated with MVPA among Estonian adolescents. In this study it was found that active males and active females reported higher enjoyment scores compared to participants in the low active groups (Viira &

Literature Review

Raudsepp, 2000). Future interventions targeting exercise enjoyment will provide further insight into the mediating role of enjoyment on physical activity. Research has in general reported limited associations between exercise enjoyment and physical activity (Sallis et al. , 2000). For example, Neumark –Sztainer et al. (2003) evaluated the effectiveness of a school based obesity prevention physical education program among a cohort of inactive girls. In this study, associations between physical activity and a range of factors was assessed over an eight month period. The two strongest and most consistent factors associated with change in physical activity were time constraints and support for physical activity from peers, parents and teachers. No association was reported between changes in physical activity and levels of enjoyment (Neumark - Sztainer et al. , 2003). Some research has found that exercise enjoyment can act as a moderator of physical activity, i.e. a variable that can be used to divide participants into subgroups for whom the intervention works differently (Marcus & Forsyth, 2003). In a study using the PACES measure of enjoyment, a significant three way interaction between time, baseline enjoyment and group assignment ( P < .01) showed that baseline enjoyment moderated the effect of the intervention on vigorous activity (Schneider & Cooper, 2011). Within the intervention group girls with low enjoyment of physical activity at baseline increased vigorous activity from pre to post intervention. However girls with high baseline exercise enjoyment showed no change. No changes were evident among control groups. Therefore, adolescent girls were found to respond differently to a physical activity promotion intervention depending on their baseline levels of exercise enjoyment. The author concludes that girls with low enjoyment of exercise may benefit most from a physical education based intervention to increase physical activity (Schneider & Cooper, 2011). Similarly, Sallis (1999) assessed correlates of activity across grades 4-12. Enjoyment of PE was a variable which had strong and consistent associations with the children’s physical activity index, this generalized across sub groups. The author concludes that this variable in conjunction with afternoon time for sports and family support should be targeted for change to promote physical activity in all groups of young people (Sallis et al. , 1999). Research has also suggested that enjoyment of physical education may be an antecedent to physical activity i.e. important preceding motivational factor for

Literature Review participation in physical activity. The theory of reasoned action (Ajzen & Fishbein, 1980) and the theory of planned behaviour (Ajzen, 1991) provides support for this assertion. In these theories behavioural intention is directly influenced by an individual’s attitude towards behaviour. Therefore, positive attitudes towards physical activity should result in motivation for participation in activity. It has been argued that if physical educators are able to increase student’s perceived competence and subsequent enjoyment of their experiences in physical education, these affective outcomes will transfer into motivation to adopt physically active lifestyle outside of school (Sallis & McKenzie, 1991). Wallhead and Buckworth (2004) analysed five studies from the Sallis et al. (2000) review that assessed attitude to PE and its influence on physical activity. In these studies, among girls, the most commonly occurring correlates of physical activity behaviour were self confidence related to athletic or sport skills and attitudes towards PE. This relationship was most pronounced among females. Therefore, for many girls it seems a positive or negative experience in PE has a strong influence on their motivation and willingness to be involved in physical activity (Wallhead & Buckworth, 2004). Considering the significant decrease in physical activity in female adolescents, physical educators must pay greater attention to the needs of these population of students and consider modifying existing PE programs to cater for the needs of adolescent girls e.g. more lifetime activities such as individual games, dance over other team game related activities (Wallhead & Buckworth, 2004).

2.4.5 Enjoyment of PE The United Nations International Charter for Physical Education and Sport states that ‘every human being has a fundamental right of access to physical education and sport, which are essential for the full development of his/her personality’ (UNESCO, 1978). Furthermore, schools have been identified as effective settings for the promotion of health and physical activity (Naidoo & Wills, 2001). In a study of Irish primary school children approximately half of the children (50.7%) interviewed reported to have PE lessons at least once per week and less than one third (30.6%) had PE twice weekly. Worryingly however 12% of children had no regular PE lessons. Furthermore this study reports that analysis of combined PE index

Literature Review scores (PEI) and the physical activity index (PAI) demonstrated a significant positive relationship between children’s PE experiences in school and recreational activity outside of school ( P < 0.05). In conjunction, those with higher levels of PE are more likely to be highly active than those with limited PE experience (p=0.000) (O Sullivan, 1997). The importance of quality PE is identified by Sohun et al. (2001). This study investigated PE provision in post primary schools and examined whether different levels of PE provision had an impact on lifestyle, physical activity habits, fitness levels and psychological constructs. In a study of 32 post primary schools it was found that female adolescents attending schools which were classified as having above average physical education provision were more physically active than females in the average or below average schools ( P < 0.05). Furthermore both males and females attending schools of above average PE scored significantly better on cardio respiratory test compared to adolescents in the other PE groups ( P < 0.05). Therefore, quality physical education appears to have a positive effect on levels of physical activity and fitness. Worryingly, in a study of principals and PE teachers in Ireland it was found that over half of the responses recorded referred to low and declining levels of student participation in physical activity generally and physical education in particular (MacPhail & Halbert, 2005). The study reports 2 trends, firstly that drop out increases with age and secondly dropout is more prevalent among girls. This coincides with the findings of Connor (2003). Encouragingly however both principals and teachers believe that students in Irish schools value PE (89% and 87% respectively) (McPhail and Halbert, 2005). Problems to the effective implementation of physical education have been outlined. At the Second World Summit on Physical Education in 2005, it was outlined that since 2005 PE time has decreased in 16% of countries, it has increased in 24% of countries and has remained the same in 60% of countries. These figures represent a worrying trend of decreasing time allocation from 2000 to 2005 and this despite international advocacy supported by an overwhelming medical, scientific, economic, social and cultural case for adequately timetabled PE programs (Hardmann & Marshall, 2000).

Literature Review

Other problems associated with the development of physical education are the perception that it is merely a recreational activity with little educational value. The World Summit on Physical Education reports that in many regions of the world PE is perceived as being a non- productive educational activity, less important to a successful future than academic subjects (Hardmann & Marshall, 2000). Therefore, despite the problems associated with the promotion and development of physical education, evidence has found that quality physical education can positively affect levels of physical activity.

2.4.6 Perceived Benefits/ Outcome Expectancy Value Most social learning theories are based on an expectancy-value framework in which behaviour is determined largely by expected outcomes and the value places on them (TAAG, 2004). This construct is often operationalised as a cost/ benefit or barriers/ benefits assessment (TAAG, 2004).

Having positive expectations about the benefits of being physically active have been found to predict levels of physical activity. Heitzler et al. (2006) investigated the correlates of (1) organized sport and (2) unorganized physical activity among 3,114 parents and children (aged 9-13) in the ‘Youth Media Campaign Longitudinal Survey’. Outcome expectancies in this study were measured using a scale developed by Saunders et al. (1997). The author reports that having positive outcome expectancies was found to predict participation in both organized sport (OR = 1.37; 95% CI 1.25-1.51) and unorganized physical activity (OR = 1.23; 95% CI 1.10-1.37). Furthermore it was found that parental perceptions that children’s participation in physical activity is important are related to both organized (1.73; 95% CI 1.56-1.92) and free time physical activity (1.24; 95% CI 1.1-1.4). The author believes that ‘interventions and messages targeted directly to 9-13 year old children should leverage these findings by using images and language that communicate the social and physical benefits to physical activity’ (Heitzler et al. , 2006)

Literature Review

2.4.7 Perceived School Climate As adolescents spend considerable amounts of time at school the potential effect of school climate on physical activity is great (TAAG, 2004). Currently, there is limited research which has assessed the influence on school climate on levels of habitual activity. School climate in the current research assesses perceived support for physical activity from PE teachers and other teachers. In a factor analysis study examining school climate influences on adolescent female physical activity, a direct association was found between the factor assessing teacher influence and self reported physical activity. The author concludes that school climate for females is a measurable construct and preliminary evidence suggests a relationship with physical activity (Birnbaum et al. , 2005).

2.4.8 Perceived Barriers to Physical Activity

Barriers to physical activity have been classified as intrapersonal, interpersonal and structural (Crawford et al. , 1991). Intrapersonal barriers include individual factors such as personal beliefs, perceptions and attitudes. Interpersonal barriers arise from social interaction e.g. lack parental/ peer support, poor relationship with coach. Structural barriers relates to external elements such as lack of money, lack of time, facilities, transportation etc. (Crawford et al. , 1991; De Roiste & Dinneen, 2005).

Kimm et al. (2006) focused on the perceptions of 2,379 adolescent girls who were not regularly active. Barriers to physical activity were assessed using a 10 item questionnaire developed by the author. Those reporting weekly activity frequencies as ‘sometimes’ or ‘rarely’ were surveyed on 3 consecutive years from ages 16 or 17. It was found that 60% cited lack of time as the leading barrier to activity for all 3 years. Other frequently cited barriers to activity included ‘I’m too tired’ and ‘they don’t interest me’. Robbins et al. (2003) also investigated the perceived barriers to physical activity among inactive girls. A questionnaire developed by Garcia et al. (1998) was used whereby individuals rated each statement on a 5 point likert scale. Similar to Kimm et al. (2006) the author also reports that ‘not being motivated to be active’ (2.68; 58.5%) was a

Literature Review common barrier to physical activity. Interestingly this study found that ‘being self conscious about my looks when I exercise’ (2.80; 57.2%) was also a commonly cited barrier to physical activity. The author emphasises the importance for health care professionals to be aware of self-consciousness as a barrier to physical activity when encouraging girls of this age to increase physical activity. These findings lend support to the hypothesis of Stevens et al. (2005). She believed that compared to boys, adolescent girls preferred different activities, tended to participate in physical activity for different reasons and may face different barriers. Therefore to be successful, girls’ interventions should be designed specifically for girls.

Male influence has also been identified as a barrier to adolescent girls’ participation in physical activity. In a qualitative study, using 80 interviews and 24 focus groups (13 focus groups with boys, N = 100 ; 11 focus groups with girls, N = 77) from Trial of Activity for Adolescent Girls (TAAG) study, Vu et al. (2006) found that the top barrier for physical activity among adolescent girls was boys. More than half the girls interviewed discussed how boys hindered their ability to be physically active by taunting, name calling etc. According to adolescent boys, the main physical activity barriers girls faced were themselves, not wanting to get dirty, being embarrassed and shy. However this study also found that boys were identified as a main motivator by 68% of girls and 100% of boys to girls’ participation in physical activity.

Brunton et al. (2003) reviewed literature examining perceived barriers and facilitators to children’s (6-11 years) physical activity. Five studies which examined children’s views of the barriers to physical activity were reviewed. A total of twenty distinct but often interrelated barriers were identified which clustered around 3 underlying themes. The first main theme identified was lack of time and preference to do other things (intrapersonal barrier). Secondly, lack of family and peer support was reported as a barrier to participation in physical activity (interpersonal barrier). Finally, restricted access to opportunities for participation in sport and exercise was identified by children in 4 studies (structural barriers). The main environmental restrictions to physical activity identified related to cost, distance, lack of safe means of travel and availability of facilities.

Literature Review

Similar findings are reported by Heitzler et al. (2006) who investigated correlates of physical activity among 9-13 year old children. This study used a telephone survey of parent and child pairs from the Youth Media Campaign Longitudinal Survey (N = 3114). Validated tools developed by Sallis et al. (1992), Saunders et al. (1997), Trost et al. (2002) to measure correlates of physical activity were used in this study. Factors identified by children that negatively correlated with physical activity were found to be: (i) no one to be active with, (ii) preferring to watch TV and (iii) feeling too busy. In a recent Irish study a sample of (N = 4,122) Irish adolescents, identified the primary barriers to participation in activity. Similar to previous research, lack of time was identified as a primary barrier by both males and females. Other barriers related to lack of perceived competence and not liking the activity. Fear of injury was also identified among adolescent males (Woods et al., 2010).

2.4.9 Psychological correlates literature; implications for current research A lack of consistency has been reported in the psychological correlates of physical activity research. In the review of review analysis (Table 2.1), self efficacy was the only psychological correlate which was consistently associated with physical activity in all three reviews i.e. association found in more than 50% of the studies. Even within this correlate the findings are not universal, for example in the Sallis et al. (2000) review, the association between self efficacy and physical activity was found in seven of the thirteen studies reviewed. The association between self efficacy and activity was found in each of the ten studies reviewed in the Biddle et al . (2005) review of adolescent girls. Given these finding it may be hypothesised that self efficacy is a more salient correlate among adolescent females, however the Sallis et al. (2000) and Van der Horst et al. (2007) reviews report no association between self efficacy and physical activity in numerous female only samples. This lack of consistency makes the identification of psychological correlates problematic. The same methodological approach was used in the Sallis et al . (2000) and the Van der Horst et al. (2007) reviews allowing an overview correlate research between

Literature Review

1970 and 2005. When data from the Sallis et al. (2000) and Van der Horst et al., (2007) are combined together it provides an overview of the correlates of adolescent activity literature which has been carried out from 1970 - 2005. Cumulatively in both of these reviews an association (relationship found in greater than 50% of studies) has been found with adolescent physical activity and the following correlates; self efficacy (20 of the 30 studies; 66%), expectancy value (five of the ten studies; 50%), intention (eight of the twelve studies; 75%), goal orientation or motivation (nine of the eleven studies, 81%). A number of correlates were found to have no association (>50% of studies) or an indeterminate association (association in 50% of studies) with physical activity in these reviews (Sallis et al. , 2000; Van der Horst et al. , 2007). These findings however are not in agreement with those presented in a review of correlates among adolescent females (Biddle et al. , 2005). For example no clear association was found between perceived competence and physical activity (four out of nine studies; 44%) in the Sallis et al. (2000) and the Van der Horst et al. (2007) reviews. However, Biddle et al. (2005) reports an association between this correlate and activity in four of the five studies (80%) which were carried out among adolescent girls. Similarly barriers to physical activity (11 of the 29 studies; 38%), self perception/ body image (six of the fifteen studies; 40%), enjoyment (three of the thirteen studies; 23%) were not found to be related to physical activity across the Sallis et al. (2000) and the Van der Horst et al. (2007) reviews. In contrast, in the Biddle et al. (2005) review perceived barriers (ten of the ten studies, 100%), self perception/ body image (six of the seven studies; 86%) enjoyment (seven of the eight studies; 88%) were found to be important correlates. Therefore it may be that different correlates may be more important for females or that difference in correlate measures, physical activity measures and sample groups in these studies can result in differences in findings. The analysis and review of randomised control trials can assist in the identification of the mediating effect that correlates may have on physical activity. Such evidence has been reported in this review of psychological correlates relating to self efficacy (Dishman et al. , 2004), physical activity stage of change (Marcus et al. , 1998) and self perception (Stein et al. , 2007).

Literature Review

Based on the literature review; self efficacy, intention to be active, perceived barriers, perceived benefits/ attitude, outcome expectancy value, perceived competence, body image/ self perception, physical activity stage of change, exercise and PE enjoyment were identified for inclusion in the original ‘APAC questionnaire’. To minimise methodological issues the instruments to measure these correlates had been subject to previous reliability and validity assessments (Section 2.14). Furthermore, objective measurements of physical activity were used in the current research, to minimise issues relating to subjective measures of activity.

2.5 Social Influences on Physical Activity

This section provides an overview of the findings relating to social influences from relevant reviews of literature. Individual analysis of peer and parental influences are provided in the subsequent sub sections. Social support can be defined as a ‘system by which an individual receives assistance, reinforcement and or stimuli in daily living’ (TAAG, 2004). This psychosocial construct has been strongly implicated in health behaviour initiation and maintenance.

In a review of literature of the correlates of participation, Biddle et al. (2005) examined the effect of peer support, family support and parental involvement on physical activity levels among adolescent girls. It was found that peer support was found to have an indeterminate effect on adolescent girl’s physical activity with three studies reporting a positive effect and three studies reporting no effect. However, family support was found to a have a small to moderate effect on physical activity levels in seven of the eight studies reviewed. Participation in physical activity by the mother was found to show an indeterminate relationship with physical activity levels of female offspring (four positive; three no effect). For the father, three of the five studies were found to show a small to moderate effect. The author argues this falls just within the margin of 60% suggested by Sallis et al. (2000) to be classified as a positive association. The author argues that with so few studies, conclusions are necessarily cautious.

Literature Review

Sallis et al. (2000) in a similar review of literature investigates social influences on male and female adolescent physical activity. The findings are generally in agreement with Biddle et al. (2005). The effects of parental physical activity levels on adolescent physical activity were found to be indeterminate (nine of 27 studies reporting positive associations; 18 studies reporting no association; 33%). However, sibling physical activity was found to be positively associated with adolescent physical activity (four positive; 100%). However, measures of parental support (two positive, one no association; 67%), direct help from parents (three positive, one no association; 75%), and support from ‘significant others’ (four positive; 100%) were consistently related to adolescent physical activity level. The influence of peers on physical activity appears inconclusive, with support from peers being a positive influence in two studies and having no relationship in three studies (40%). Furthermore, peer modelling of physical activity was found to be unrelated to physical activity (five studies, no effect). Similarly, there was no association with teacher or coach support or modelling (two studies positive, four studies no effect; 33%). Subjective norms, or perceived attitudes of significant others were often studied, but this variable was found to be indeterminate (six studies positive, five studies no effect; 55%).

Thirteen studies examined social variables as correlates of activity in the Van der Horst et al. (2007) review. Family influences and friend support were found to be positively associated with physical activity. No association was found between parental activity and adolescent physical activity (Van der Horst et al. , 2007). Craggs et al. (2011) reviewed prospective quantitative studies investigating change in physical activity and determinants of change in children and adolescents. A total of 46 studies were included in the analysis, 31 of these used self-reported physical activity. Higher scores on social support measures were found to be consistently associated with smaller declines in levels of physical activity among 14-18 year old adolescents, further supporting the importance of social support in promoting and maintaining physical activity.

Literature Review

2.5.1 Family Influences

Socialization from the family unit is one of the major influences on health behaviour patterns (Connor, 2003). O’Sullivan (2002) argues that a pre-requisite for children’s participation in sport is a positive attitude and support from the parents. Parents can positively influence adolescent’s physical activity not alone by encouragement and behaviour reinforcement but also by elements such as role modelling, provision of transport and participating in activities with children and adolescents (O' Sullivan, 2002; Connor, 2003).

A comprehensive study of leisure time pursuits of Irish adolescents (12-18 years) was carried out by Connor (2003). In conjunction with a questionnaire a total of 312 adolescents were interviewed in this study. A common theme which emerged from the interviews was the influence exerted by the family unit on physical activity

“It was a tradition, it was in the family. Everyone in my family and area either played hurling or camogie ”. (Female aged 14).

Similarly Vu et al. (2006) used focus groups in the ‘Trial of Activity for Adolescent Girls’, and found that the majority of adolescent girls identified the family to be the most positive influential factor in their decisions to be physically active.

The influence of family supports it also evident in an Irish ESRI study which investigated sports participation and social disadvantage among 3,000 Irish adults in Ireland. It was found that parental participation and coming from a ‘sporty family’ positively affected levels of sport participation. It was found that among participants in which both parents played sport, 69% were found to participate in sport. Among the group whose father only participated in sport more than half (53%) participated in sport. The group in which neither parent participated in sport was found to have the lowest participation (36%) rate in sport (Lunn, 2007).

Similarly, a previous ESRI study carried out by Fahey and colleagues, identified the importance of parental interest in physical activity and sport. In a study of 3,883 Irish primary school children in 5 th and 6 th classes, mothers’ participation in sport (P <

Literature Review

.05), fathers’ participation in sport ( P < . 05), were found to have a significant positive association with participation in both extra curricular sport and participation in sports’ clubs outside of school. Similarly among 3,527 children and adolescents in Irish secondary schools it was reported that both mothers’ and fathers’ sports participation has a substantial effect on the student’s level of sports participation with a club ( P < .001 for both), and fathers’ sports participation has a statistically significant effect on extra-curricular participation in the school ( P < . 001) (Fahey et al. , 2005).

Research has reported that Irish adolescents receive support and encouragement for physical activity from their parents. De Roiste and Dinneen (2005) in a study of 2,260 adolescents report that 61% of participants agreed that their family encouraged them to join new clubs. In addition, 86% of individuals reported that their parents gave them permission to participate in activities that they would like to do.

Support and encouragement from parents has been identified as exerting a positive impact on levels of children and adolescens’ physical activity. Heitzler et al. (2006) analyzed data from 3,114 children (aged 9-13) and their parents in the Youth Media Campaign Longitudinal Survey (YMCLS). This study used previously validated correlate scales developed by Sallis et al. (1992), Saunders et al . (1997), and Reynolds et al . (1990). A number of parental support measures were found to positively predict levels of physical activity. Specifically parental beliefs scale (OR = 1.22; 95% CI 1.12- 1.33); parental barriers (inverse) (OR = 0.72; 95% CI 1.12-1.33); parents who perceived it as important that their children participated in physical activity (OR = 1.73; 95% CI 1.56-1.92), parents who attended sports events (OR = 1.31; 95% CI 1.19-1.43), parents who provided transport for physical activity (OR = 1.21; 95% CI 1.11-1.33) and perception of it ‘being important to be active in free time’ were found to predict children’s participation in organized sport. No association was reported between parents participating in physical activity with the child and levels of physical activity. Surprisingly the researcher reported a negative association between parents coaching a child’s sports team and activity levels among the child (OR = 0.55; 95% CI 0.43-0.6). The only variables found to be associated with children’s free time physical activity were parents participating in physical activity with the child (OR = 1.24; 95% CI 1.10- 1.40) and parental perception of the importance of physical activity (OR = 1.07; 95% CI

Literature Review

1.03-1.11). Furthermore, among children and adolescents, perceived parental support was found to be associated with participation in organized sport (OR = 1.65; 95% CI 1.45-1.88). However, no association was reported between perceived parental support and free time physical activity. Heitzler et al. (2006) believes that researchers and programme planners should continue to qualitatively explore the variables associated with parental perceived importance of physical activity and how that translates into parental support or actual participation by the child.

It appears that parental support and coming from a ‘sporty family’ can positively affect participation in physical activity. Sallis et al. (2000) believes that the strong relation of social support from parents suggests that parents still play important roles in their adolescent’s lives.

2.5.2 Peer Influence The role of parents in children’s activity behaviour is multifaceted and the extent of influence fluctuates as children move from childhood through adolescence(O' Sullivan, 1997). Connor (2003) concurs, believing as children approach secondary school level, peer group, siblings and school assume greater importance in the sport socialization of the adolescent. Peers may positively influence physical activity participation through verbal encouragement and role modelling (Hohepa et al. , 2006).

The importance of peers’ influence on physical activity was highlighted by Sallis et al. (2002); in a study of 781 US children and adolescents. In regression analysis, peer support was found to be the only significant correlate of objectively monitored physical activity in multiple subgroups. The importance of peer support was identified in the younger and older groups. This is particularly interesting as developmental theory predicts that family influences should decrease with age while peer influences increase with age, it might be however that peer influence may be a salient correlate in children and adolescents (Sallis et al. , 2002). Similarly Kelly et al. (2010) in the TAAG study reports that the friends’ support was consistently associated with MVPA and VPA in all ethnic subgroups. The author argues that social support/

Literature Review social modeling interventions may be very important for promoting physical activity among all subgroups of girls in this age group (Kelly et al. , 2010).

Peers have been found to exert a positive influence in a number of ways. Co - participation i.e. being active with peers was identified as an important peer influence in a study of 535 US adolescents by Tergerson et al. (2002). This study found that the most helpful cue to physical activity for both male and female participants was ‘having a friend to be active with me’. Further support for the importance of co-participation is provided by O’ Sullivan (2002). It was reported that those individuals who are highly integrated into their social network were found to be more active than those who have less contact with friends and/ or those who have difficulty relating to others. It was found that 40% of those classified as highly integrated were in the highly active group compared to 17% of the poorly integrated.

Using qualitative interviews and focus groups from the Trial of Activity for Adolescent Girls, Vu et al. (2006) found that the majority of girls participating in the interviews had positive perceptions of girls who were physically active. It was found that terms such as ‘good’ or ‘cool’ were used to describe these girls. In contrast, common themes from boys’ and girls’ focus groups discussions included physically active girls being described as tomboys and too aggressive. “ It kind of makes me feel uncomfortable if a girl is more athletic than I am ” (Group of 7 th grade boys). Interestingly, both girls and boys reported unfavourable views of girls who were physically inactive. The majority of girls and boys considered inactive girls to be either “lazy ” or “ fat ”.

2.5.3 Family and Peer Influence

Davison (2004) investigates the influence of both family and peer support on physical activity among US adolescents. The ‘Activity Support Scale’ (ACTS) was modified and used in this study. This scale measured general familial, peer and sibling support. Results from a series of ANCOVA’s found that both boys and girls in the high activity group reported significantly higher levels of paternal logistic support (boys

Literature Review

F=9.36, P < . 01; girls F=10.33, P < . 01) peer support (boys F=23.32, P < . 01; girls F= 11.09, P < . 01) and sibling support (boys 8.58, P < . 01; girls 11.38, P < . 01) than adolescents in the low activity group. Interestingly, differences were found to exist in the type of support offered by parents. Mothers were found to provide more logistic support while fathers were found to provide more modelling ( P < . 001 for both).

Similarly King et al. (2008) reports that adolescents who received parental encouragement to exercise and who had an exercising friend, engaged in significantly more days of physical activity in the previous week than did their counterparts. Furthermore, perceived benefits were found to differ significantly based on whether the respondent received parental encouragement and had a friend who exercised (King et al. , 2008).

2.5.4 Social correlates literature; implications for current research Consistent associations have been reported between social correlates of physical activity and levels of activity among adolescents. When the results of the Sallis et al. (2000) and the Van der Horst et al. (2007) reviews of the correlates of physical activity are compiled, clear consistent associations between peer and parental support and levels of activity among adolescents are evident. Family influences were found to be positively associated with physical activity (12 of the 18 studies; 67%), while parental help was also found to be a positive influence (three of the four studies; 75%) on activity. Furthermore, sibling physical activity was found to be related to activity in four of the four studies reviewed (100%). Friend support was also positively associated with adolescent activity in nine of the eleven studies (82%) which evaluated this relationship. No clear association was found between parental activity and adolescent activity with ten of the thirty five studies reviewed identifying this association (29%). These findings are further supported by a review of social correlates among adolescent females (Biddle et al. , 2005). Limited randomised control trials have specifically targeted social support in an attempt to increase physical activity. Some evidence of the mediating role of social support however is provided in prospective studies of children and adolescents. In a

Literature Review review of prospective quantitative research, adolescents with higher perceived social support scores were found to be associated with smaller declines in physical activity during adolescence (Craggs et al. , 2011). Modifications to perceptions of social support may therefore have a positive impact on physical activity levels in adolescence. Clear associations have also been reported in cross sectional correlation research between parental and peer support and physical activity. Social support variables identified for inclusion in the original ‘APAC questionnaire’ were social support peers, encouraging peers to be active and social support family. Both parental help and sibling activity which were identified as important correlates in the review were included in the family social support scale. Details of the reliability and validity information relating to these scales are provided in section 2.14.

2.6 Environmental Influences on Physical Activity

The following section presents the findings of reviews of literature which examined a range of environmental influences on adolescent physical activity. Specific environmental influences which were assessed in various research studies are presented in subsequent sub sections. Owen et al. (2000) believes that of the determinants of physical activity, environmental influences are the least understood but arguably the most important class of determinant that should be targeted through public health intervention. Many theories e.g. Social Cognitive Theory, refer to and highlight the importance of the environment as influencing levels of physical activity (Owen et al. , 2000). For example using the Social Cognitive Theory physical activity participation is dependent on environmental (e.g. well maintained sidewalks) and individual (e.g. self efficacy) variables. Therefore environments perceived as unsafe or lacking in accessible equipment might be negatively associated with beliefs of personal efficacy and the reduction in efficacy beliefs might in turn be associated with reduced physical activity (Mota et al. , 2006). Biddle et al. (2005) argues that structural developments (buildings, lack of walkways etc.) have led to the development of “activity toxic environments” in which physical

Literature Review activity and active transportation are unlikely to occur. Recent attention linking the built environment to physical activity in adults suggests an investigation into the relationship between the built environment and physical activity in children could guide future interventions (Davison & Lawson, 2006). The physical or built environmental has come to the forefront of public health research in the past five years leading to a surge of research on environmental attributes and their associations with physical activity (Davison & Lawson, 2006). As outlined in section 2.3 of this thesis, the questionnaire used in the current study was developed primarily based on reviews of the correlates of physical activity (Sallis et al. , 2000; Biddle et al. , 2005; Van der Horst et al. , 2007). Seven variables were assessed in the physical environment category in the Sallis et al. (2000) review; however only three of these correlates had three or more comparisons and were therefore included. Opportunities to exercise were found to be the only correlate which had had consistently positive associations (Sallis et al., 2000). Five studies examined the association between physical activity and availability or proximity to sports facilities in the Van der Horst et al . (2007) review. Only one of these studies used objective measures of the environment. No association between availability and adolescent’s physical activity was found (Van der Horst et al. , 2007). Similarly, no association between environmental correlates and activity was reported in the Biddle et al. (2005) review. The dearth of research studies which have analysed the environmental correlates of activity among adolescents pre 2006 has resulted in the limited inclusion of environmental correlate studies in these reviews (Sallis et al. , 2000; Biddle et al. , 2005; Van der Horst et al. , 2007). A review of literature of children and adolescent environmental correlates of physical activity carried out by Davison and Lawson (2006) was also consulted prior to the development of the ‘APAC questionnaire’ in the current study. Bauman and Bull (2007) carried out a synthesis of review papers which investigated the potential environmental determinants of physical activity and walking in children and adults. An understanding of these ‘factors can inform the development of more effective interventions and direct policy action that will assist in the development of promising areas for intervention to change the environment which may positively influence physical activity at the population level’ (Bauman & Bull, p4). The

Literature Review search strategy used identified 13 review papers that explored the relationship between physical environments and physical activity or walking. Only one of these reviews cited in this review was found to focus on children and adolescents (Davison & Lawson, 2006). This review of children and adolescents included searches of PubMed, PsychInfo, EBSCO and CINAHL. Search terms included physical activity, exercise, recreation, sport, walking, cycling, transport and active commuting. A total of 23 studies assessing environmental correlates among children and adolescents ages 3-18 years met the inclusion criteria. The results of this review were presented under the sub headings (i) transport infrastructure; (ii) recreational infrastructure; and (iii) local conditions within communities. The most consistent pattern of findings was evident for transport infrastructure, followed by recreational infrastructure with the least consistent pattern of results reported for local conditions (Davison & Lawson, 2006). The following section provides a justification of the environmental correlates included in the current APAC questionnaire based on the review of Davison & Lawson (2006). Four transport infrastructure correlates were found to be associated with adolescent physical activity in this review. Firstly, the presence and condition of footpaths were found to be positively associated to physical activity in three out of four studies (75%) (Davison & Lawson, 2006). In the perceived environment scale included in the APAC questionnaire items were included which assessed (i) the availability of footpaths on streets in the neighbourhood and (ii) the availability of walking and cycling tracks in the neighbourhood. A second transport infrastructure correlate relating to physical activity was access to destinations, this association was found in three out of four studies (75%) (Davison & Lawson, 2006). The APAC questionnaire included a transportation barriers scale which attempted to assess this construct. This scale assessed participant’s ease of access to and home from physical activities at school and elsewhere. The access to facilities scale in the APAC questionnaire provides further information relating to ease of access to and from various recreational facilities. Various road hazards such as traffic speed, traffic density, and pedestrian/ cyclist safety were assessed in three studies in the review of literature (Davison & Lawson, 2006). All three studies (100%) found a negative association between such hazards and physical activity (Davison & Lawson, 2006). Items within the perceived environment scale included in the APAC questionnaire assessed key elements of road hazards such

Literature Review as traffic density and safety for active transportation. The final transport infrastructure correlate reported by Davison & Lawson (2006) was the presence of controlled crossings. Two studies (100%) which examined the presence of controlled crossings (e.g. presence of lights, crossings) reported positive associations with physical activity. Based on the criteria identified by previous reviews (Sallis et al. , 2000; Biddle et al. , 2005; Van der Horst et al. , 2007) correlates needed to be included in three studies to be considered a salient correlate of activity. Therefore, based on these criteria this correlate was not included in the current APAC questionnaire; future research is warranted to assess the impact of this correlate. Results on recreational infrastructure reported that a significant positive association existed between adolescent physical activity and proximity of parks and playgrounds in three of five studies (60%), and recreational areas in eight of ten studies (80%) (Davison & Lawson, 2006). This correlate is measured in the access to facilities scale in the APAC questionnaire. This scale provides a composite score of access to various recreational facilities including public parks. Studies investigating school related physical activity revealed that three out of four studies (75%) identified a negative association between distance to school and children’s physical activity. When active commuting is assessed, three out of three (100%) of studies found that children and adolescents who live close to schools were more likely to actively commute to school (Davison & Lawson, 2006). The ‘APAC questionnaire’ does not include information relating to distance to school, this may be identified as a limitation of the current study. Furthermore, while no direct association between active transportation and overall habitual activity is presented by Davison & Lawson (2006) this analysis did find that those living close to school were more likely to commute. No evaluation on active commuting on overall levels of physical activity is provided in the reviews of Sallis et al. (2000), Biddle et al . (2005) and Van der Horst et al . (2007). Evidence exists however which identifies those who actively commute to school as being more habitually physically active (Cooper et al. , 2003; Lee et al. , 2008). The Irish CSPPA study reports that 40% of Irish adolescents actively commute to school, journey durations in this study were found to be on average 15 minutes (Woods et al. , 2010). Therefore, adolescent who commute to and from school may accumulate a significant portion of the recommended levels of physical activity. Furthermore, the age related

Literature Review decreases seen in other forms of physical activity do not exist in active commuting making, thus making it a unique form of physical activity maintained throughout adolescence (Woods et al. , 2010). The lack of assessment of active transportation in the current study can be identified as a methodological limitation in the current research. The final recreational infrastructure correlate, home equipment was found to be related to adolescent physical activity in only two of six studies (33%). This correlate therefore was not included in the APAC questionnaire. The results relating to local conditions and children and adolescent physical activity were least consistent in the review (Davison & Lawson, 2006). No effects were found for perceived safety in the neighbourhood with two studies showing negative associations and eight showing no association (20%). In contrast to perceived safety, three out of three studies (100%) identified a significant negative association between crime or area deprivation and children’s physical activity. Weather was found to be inconsistently associated with activity in children and adolescents in the four studies which assessed its impact (33%). The perceived environment scale in the APAC questionnaire assessed perceptions of crime in the participant’s neighbourhood. One cannot assume that the association between physical environment and physical activity among adults are applicable to children (Davison & Lawson, 2006). As highlighted by Krizek et al. (2004) children in contrast to adults spend large parts of their day at school, have considerable time for recreation and are more likely to accumulate physical activity through play, are not able to drive and are subject to restrictions placed on them by adults (Krizek et al. , 2004). Nonetheless, due to the limited reviews on environmental correlates of activity among adolescents cognisance should be taken of key adult findings. In a review of reviews of environmental correlates of physical activity and walking, eleven review papers revealed consistent associations between access to physical activity facilities, convenient and proximate access destinations, high residential density, land use mix and urban walkability scores. There were also reasonably consistent associations between perceived safety, exercise equipment, footpaths and physical activity participation. Less clear associations were noted for aesthetic features of environment, parks and perceived crime (Bauman & Bull, 2007). The ‘APAC questionnaire’ includes items relating to urban access to facilities, access to destinations, land use mix, perceived safety, footpaths and aesthetic features.

Literature Review

To better understand how built environmental factors can influence physical activity there is a need to identify and to document objectively specific attributes of the community environment (Leslie et al. , 2007). This objective documenting of differences in environmental attributes between localities (e.g. density, land use mix, street connectivity and access to services) should be helpful in identifying factors associated with physical activity. Davison & Lawson (2006) argue that participants perception of their environment should also be measured in conjunction with objective measurements, as people’s perceptions may in fact motivate their behaviour more than the true nature of the situation (Davison & Lawson, 2006; Wong et al. , 2011). GIS is a computer based tool for the capture, storage, manipulation, analysis, modelling, retrieval and graphic presentation of spatially referenced information (Leslie et al. , 2007). GIS uses sophisticated databases and software to analyse data by location revealing hidden patterns, relationships and trends that may not be apparent using spreadsheets or other statistical approaches (Leslie et al. , 2007; Wong et al. , 2011). GIS can be conceptualised as a series of layers of information e.g. population, road network, land use, shopping centre locations with each observation in each layer tied to specific points and areas on the earth’s surface via a specific co-ordinate system (Leslie et al. , 2007). The current research did not have access to this technology; therefore this measure of the built environment is not included in this thesis. A recent systematic review assessed GIS measured environmental correlates of active school transportation (Wong et al. , 2011). Built environment may influence travel demand across three general dimensions; density, diversity and design. Results relating to density found that in 15 of the 17 studies reported a negative association between distance to school and active school transport. Of the 15 studies which examined diversity/ land use mix, three studies found positive associations with active school transport. Studies which examined associations between street designs in particular intersection and dead end densities reported null hypotheses in 18 of the 20 studies. One of the five studies included found an association between school transport and aesthetics. The author concludes however, that the inconsistent use of spatial concepts limits the ability to draw conclusions about the relationship between objectively measured environmental attributes and active school transportation and that future research is warranted (Wong et al. , 2011).

Literature Review

Foster & Hillsdon (2004) presents the results of a systematic review of the studies that used environmental interventions to increase health enhancing physical activity. In this review it is argued that the environment is a critical part in any health promoting activity and indeed forms one of the core elements of the World Health Organizations Ottawa Charter (WHO, 1986). Studies which aimed to change the physical environment by creating new health enhancing physical activity facilities, policies, and supporting mechanisms, all report some positive improvements in levels of activity, active commuting or fitness. The author concludes that there is a dearth of research in this area and more is warranted (Foster & Hillsdon, 2004). Limitations of the environmental correlates included in the current research are presented in section 2.6.4.

2.6.1 Access to Facilities Both the Social Cognitive Theory and Social Ecological Model hypothesise that access to different facilities can have direct effects on levels of physical activity and can result in changes in individual perceptions, which can influence physical activity (TAAG, 2004). A study which examined the effect of effect of community design and access to recreational facilities on adolescent physical activity was carried out with 799 adolescents. Physical activity was measured using an accelerometer and community design and access to recreational facilities variables were derived using geographic information systems (GIS). For girls, statistically significant bivariate correlations were found for total min/day of moderate to vigorous activity with the number of recreational facilities (r = 0.11; P < . 05), number of parks (r=0.14; P < . 05) and intersection density (r = -0.14 P < . 01) (Norman et al. , 2006) .

Similarly, Motl et al. (2005) investigated the effects of :(i) perceived equipment accessibility in the home (bicycles, balls), and in the community (e.g. playgrounds, parks, gyms) and (ii) neighbourhood safety on physical activity. In this study the measure of perceived environment included four items that were rated on a five point scale with anchors of: 1=Disagree a lot and 5=Agree a lot. Data was collected in the Spring semester of 1999 (baseline) and 2000 (follow up) among adolescent girls in 24

Literature Review high schools. At baseline there was a statistically significant relationship between equipment accessibility and physical activity. However perceived neighbourhood safety did not exhibit effects on physical activity among adolescent girls. No significant longitudinal effect was found in this study (Motl et al. , 2005).

These findings are supported by McCormack et al. (2007) who found that the availability of recreational destinations predicted levels of physical activity among 1,355 adults. In this study, respondents reported physical activity over the previous 2 weeks as well as the name and address of the recreational facilities used. Using MapInfo and ArcInfo the distance in meters from each respondent’s home to the recreational facilities was analyzed. It was found that the likelihood of using a local pay recreational destination increased for each additional local pay facility within their neighbourhood (OR 1.51; 95% CI 1.32- 1.73). Similar trends were found with local free destination, however this association was not as strong (OR 1.12; 95% CI 1.03- 1.2). Respondents using the neighbourhood facilities were also more likely to achieve the recommended levels of moderate and vigorous physical activity (McCormack et al. , 2007).

Similarly, Scott et al. (2007), examined the relationship between the number and proximity of physical activity facilities and respondent perceptions among 1,367 girls participating in the ‘Trial of Activity for Adolescent Girls’ (TAAG) study. In this study girls reported whether 9 different types of recreational facilities were easily accessible. It was found that individual perceptions of facility accessibility were associated with higher levels of physical activity. Dowda et al. (2007) also reports an association between facilities and levels of physical activity. Among 1,556 girls in the TAAG study, physical activity was measured using accelerometers and residential areas were geocoded. Girls, who lived near one or more commercial physical activity facilities, had higher non school moderate to vigorous physical activity their counterparts who did not have easy access to commercial physical activity facilities ( P < . 05). Heitzler et al. (2006), Evenson et al . (2006) concur with these findings.

Therefore, it appears that proximity to and number of leisure facilities positively effects participation in physical activity. Worryingly however, Irish adolescents perceive that there is very inadequate provision of suitable facilities. For example, De

Literature Review

Roiste and Dinneen (2005) report that the majority of the sample, 59%, believed that there was very little leisure provision in their area, while a further 32% were undecided.

Connor (2004) makes an interesting observation; he argues that there appears to be gaps in the facility provision for particular niches or groupings during the adolescent years. This view was evident in some of the focus groups ‘there are a good few things for people younger but nothing for people our age’. He reports that these adolescent consider themselves to have outgrown youth clubs and no longer want engage in activities with their parents.

2.6.2 Perceived Environment Perceived physical environment conditions are defined as an individual’s personal representation and evaluation of external structural factors that influence their behaviour (Woods et al. , 2009). Perceived environmental barriers are aspects of the environment that an individual views as a hindrance to being physically active, whereas perceived environmental enablers are aspects of the environment the individual views as helpful to being active (Evenson et al. , 2006). To date few studies have explored the independent effects of these environmental factors on physical activity among adolescents (Sallis et al. , 2000; Evenson et al. , 2006). Physical environmental factors were assessed under the broad categories of perceived safety, aesthetics and facilities near the home (TAAG, 2004; Evenson et al. , 2006). The items included in the measurement of environmental influences were based on the TAAG study (TAAG, 2004).

A number of studies have found physical activity to be effected by neighbourhood safety and levels of crime. In a study carried out by among a cohort of university students, regular cycling was negatively associated with perception of traffic safety and positively associated with high safety from bicycle theft, many friends cycling, high emotional satisfaction and high mobility. Safety concerns appear to strongly affect levels of active transportation. Those who felt there were traffic safety concerns with cycling to university were 45% less likely to cycle regularly compared to

Literature Review those who rated traffic safety concerns as low (Titze et al. , 2007). Similarly in the TAAG study, Evenson and colleagues reported that girls who agreed with the statement it ‘is safe to walk or jog in my neighbourhood’ were more than twice as likely to be categorized as active compared to girls who disagreed with this statement (OR = 2.14; 95% CI 1.31 -3.48). Active girls were also found to respond more favourably to other items relating to neighbourhood safety i.e. ‘safe to cycle in my neighbourhood’ (OR = 1.45 95% CI 0.85-2.47) and ‘my neighbourhood streets are well lit at night’. No association was found between activity levels and items relating to traffic volume, crime and seeing other children playing (Evenson et al. , 2006).

Using data from the 1996 ‘National Longitudinal Study of Adolescent Health’ with 17,766 US adolescents, Gordon Larrson et al. (2000), investigated the area lived in and the effect it might have on physical activity levels. In this study, adolescents living in areas with high neighbourhood crime levels were found least likely to be in the highest category of moderate to vigorous physical activity (OR 0.77 ; 95% CI 0.66- 0.91). Similarly Heitzler et al. (2006), in the ‘Youth Media Campaign Longitudinal Survey’ of 3,114 pairs of adults and children investigated perceived safety and participation in organized and unorganized physical activity. It was found that perceived neighbourhood safety (asked in one question) was associated with participation in free time physical activity (OR 1.29; 95% CI 1.15-1.46) in unorganized sport. No association was reported between perceived safety and organized sport. This may be due to the structured adult controlled nature of organized sport which is generally conducted in safe environments.

Schools have also been found to have potential to promote and increase levels of physical activity. Scott et al. (2006), investigated the effect weekend school accessibility had on levels of physical activity among 1,556 participants in 407 schools in the TAAG study. The author reports that schools represented 44% of potential sites for physical activity. Only 57% of schools were found to be accessible with opportunities for activity on the Saturday visited. Furthermore, approximately one third of the schools, (34%), were found to be inaccessible on the Saturday visited. The number of locked schools was also associated with significantly higher body mass index (each additional locked school was associated with 3% increase in BMI; P < . 05). It

Literature Review was argued that given the tremendous potential recreational resource that school grounds represent, public health advocates should seek to raise the profile of schools as sites for physical activity by offering organized recreational programmes at these locations. Similarly Saunders et al. (2006) also argues that collaborative relationships between schools and community agencies may be an effective strategy to overcome various barriers.

Sallis et al. (2001), investigated the physical activity levels at school during free time among 1,081 middle school students. Physical activity was measured using SOPLAY system of observing physical activity before school, during lunch and after school. Overall, very low levels of physical activity were reported during unstructured time, with only 2% of girls and 6% of boys being physically active during unstructured time in school. The author also found that girls and boys were more likely to be physically active when there was adult supervision and where there were structural improvements to the physical environment e.g. basketball nets to the facilities.

2.6.3 Transportation barriers Transportation difficulties to and from physical activity venues may be a barrier to participation in those activities (TAAG, 2004). This measure assessed difficulty level in getting home from after school activities at school, to after school activities in a location other than school, and home from after school activities from a location other than school. A study of ethnically diverse students from 24 middle schools and their parents assessed the influence of parent transportation to increasing youth physical activity. Parent transportation for physical activity significantly contributed to girls total physical activity ( P = 0.001) and their participation in sports/activity lessons ( P = 0.001). Transportation marginally contributed to boys total physical activity, but significantly to their participation in sports/ activity lessons ( P = 0.001). Therefore, it was concluded that parent provision of transport to activity locations is associated with out of school physical activity (Hoefer et al. , 2001).

Literature Review

2.6.4 Weather

It would be fair to assume that levels of physical activity would be higher during the summer months in Ireland when the weather is more conducive to outdoor physical activity. Livingstone (2003), agrees with this viewpoint, he believes that one of the main inconsistencies in the measurement of childhood activity levels is that seasonal variability in patterns of physical activity is rarely accounted for.

The ‘Switch Off Get Active’ study provides evidence to support this view. In this study of 9-11 year olds, Irish children’s physical activity was measured using the ‘Previous Day Physical Activity Recall Questionnaire’ validated by Weston et al. (1997). In this study it was found that average daily moderate to vigorous activity increased from 92 minutes per day (1.5 hr/day) in February to 153 minutes per day (2.6 hr/day) in June ( P < . 001). The number of weekly bouts of vigorous activity also increased from 12.8 in February to 18.1 in June ( P < . 001) (Burns, 2004). However De Roiste and Dinneen (2005) report that 68% of Irish adolescents perceive that weather is not a barrier to leisure participation.

2.6.5 Environmental correlates literature; implications for current research Environmental correlates of activity are understudied and more research is warranted in this area. The only correlate which was found to be related to physical activity in the combined analysis of the Sallis et al. (2000) and the Van der Horst et al. (2007) reviews was opportunities to exercise with two of the three studies reporting an association (66%). A review of adolescent environmental correlates of activity is presented by Davison & Lawson (2006); a justification of the environmental correlates included in the ‘APAC questionnaire’ which are based on this review is presented in section 2.6. The correlate scales identified for inclusion in the current analysis were perceived environment, access to facilities and transport barriers. The dearth of literature on environmental correlates which has been included in the reviews of iiterature (Sallis et al. , 2000; Biddle et al. , 2005; Davison & Lawson, 2006; Van der Horst et al. , 2007) results in few environmental correlates being included

Literature Review in the current research. This can be identified as limitation of the current study. While the environmental correlates identified by Davison & Lawson (2006) were included in the current ‘APAC questionnaire’, many of these were assessed with single items, and formed a composite scale. A more in depth assessment of these correlates may provide further details of environmental influences on adolescent physical activity. A recent study has assessed the psychometric properties of the ‘Neighbourhood Environment Walkability Scale for Youth’ (NEWS-Y) and explored its relationship with physical activity (Rosenberg et al. , 2009). In this study adolescents completed NEWS-Y surveys assessing perceived land mix diversity, recreation facility availability, pedestrian/ automobile traffic safety, crime safety, aesthetics, walking/ cycling facilities, street connectivity and residential density. These sub scales were found to have acceptable test retest reliability (ICC range .56 - .87). Numerous measures of physical activity such as being active in a park, walking to a park, shops and school were found to be related to multiple environmental attributes in all participant groups (Rosenberg et al. , 2009). Furthermore, an overall neighbourhood environment score which was calculated was related consistently to the neighbourhood based activities of being active in a park, walking to a park, walking to shops and walking to school. Therefore, these findings illustrate how activity friendly environments can contribute to youth physical activity by supporting active recreation and active transportation (Rosenberg et al. , 2009). A further study attempted to provide further validation for this tool. A total of 878 parents of children completed the NEWS and International Physical Activity Questionnaire (IPAQ). GIS measurements were also used to develop one mile street network buffers around participant’s residences. Except for trees and traffic, concordance between NEWS and GIS measures were found to be significant with weak to moderate effect sizes (r = -.09 - .036) (Adams et al. , 2009). These research studies were not published prior to the development of the ‘APAC questionnaire’; therefore this tool was not included in the analysis. Future research should attempt to incorporate this comprehensive tool into a physical activity correlates questionnaire. To better understand how built environmental factors can influence physical activity there is a need to identify and to document objectively specific attributes of the community environment that may be influential (Leslie et al. , 2007). Recent developments in assessing the environment for physical activity include the use of

Literature Review

Geographic Information Systems (GIS). Objectively documenting differences in environmental attributes between localities (e.g. density, land use mix, street connectivity, access to services) should be helpful in identifying factors that may influence physical activity (Leslie et al. , 2007). The current study did not have access to this technology; therefore no objective measure of environmental correlates is included in the current research. It is hoped that as an addition to the current research a future study could use this technology to objectively assess local conditions surrounding each participating school included in the current study. A third limitation relating to environmental correlates of activity is the lack of a measure of active transportation to school. Evidence has reported higher levels of habitual activity among those who use active transportation to and from school (Cooper et al. , 2003). Future research should incorporate active transportation as a possible correlate of adolescent physical activity.

2.7 Behavioural Influences on Physical Activity

Relevant reviews of literature which have examined behavioural influences on physical activity are presented in this section. Findings relating to specific behavioural influences are presented in subsequent sub sections.

Biddle et al. (2005) reviewed the correlates of participation among adolescent girls. A specific domain of influence investigated was behavioural influences on physical activity. It was found that screen time inactivity had an indeterminate relationship with physical activity across six studies, (three studies suggesting a negative relationship, one positive, two no association). A moderate to large effect was found for participation in organized sport, i.e. those in organized sport were more likely to be more physically active than those who did not participate in organized sport, (four of four studies).

In a previous review by Sallis et al. (2000), consistently positive associations were found for participation in community sports and overall levels of physical activity (seven studies with a positive association, 100%). It was found that cigarette smoking (six negative, nine no association, 40%) had an indeterminate effect on physical

Literature Review activity, while alcohol use (two negative, 11 no association, 15%) and healthy diet (four positive, 12 no association; 25%) were found to be unrelated to physical activity. Screen time inactivity was found to have an interesting relationship with physical activity. Weekly screen time inactivity was found to be unrelated to physical activity (three negative, nine no association, 25%), however screen time inactivity after school and on weekends was found to be consistently and inversely related to adolescent physical activity (three negative; 100%).

The behavioural variables included in the Van der Horst et al. (2007) were sedentariness/ screen time, smoking, and physical education/ sport school activity. There was a positive association between physical education/ school sports and physical activity. No association was found between sedentariness/ screen time and physical activity. The evidence linking smoking and adolescent physical activity was inconclusive (Van der Horst et al. , 2007).

2.7.1 Screen Time Inactivity/ Sedentary Leisure Habits Owen et al. (2000) argued that understanding and influencing physical activity levels in whole populations requires a strong focus on the determinants of inactive lifestyles (Owen et al. , 2000). He argued that there should be a research focus on sedentary behaviours as a distinct class of variable. Physical activity and screen time inactivity are recognized as two distinct albeit related behaviours (Gordon-Larsen et al. , 2000). Truly sedentary behaviour generally encompasses sitting or clear ‘inactivity’ (Biddle et al. , 2004). There are many types of sedentary behaviour engaged in by adolescents such as motorised transportation, sitting and talking, homework (Biddle et al. , 2004). Screen time inactivity viewing has been identified as the most prevalent sedentary behaviour with estimates for young people suggesting 1.8 – 2.8 hours per day, depending on age and gender (Marshall et al. , 2006). It is recommended that the exposure to sedentary screen time inactivity should be < 120 minutes per day (ADHA, 2004). Few Irish children (1%) spend less than two hours daily sitting viewing TV, videos or playing on the computer (Woods et al. , 2010). The current study focuses on screen time inactivity

Literature Review and assesses time spent (i) watching TV and (ii) time engaged in video or computer games.

The correlates of sedentary leisure habits and physical activity were examined using previously validated tools in the Teens Eating for Energy and Nutrition at School Study (N = 3,878) (Schmitz et al. , 2002). It was found that perceptions of higher academic rank predicted higher physical activity and lower sedentary leisure habits among males and females. Furthermore, depressive symptomatology, i.e. those who scored higher in depression scales, was found to predict higher sedentary leisure habits but did not affect physical activity. Among girls higher self- reported value of health, appearance and achievement also predicted higher physical activity and lower sedentary leisure habits. In addition, among girls, higher parental educational level was found to be inversely associated with sedentary leisure habits. No association was found with physical activity. In this study the author concludes that the determinants of physical activity and sedentary leisure habits appear to differ from each other. A clear understanding of both of these behaviours is important in developing effective intervention strategies for instilling lifelong healthy physical activity habits (Schmitz et al. , 2002).

Some evidence exists which supports the displacement hypotheses, (Mutz et al. , 1993) i.e. that screen time inactivity may take the place of or displace physical activity. A study of 671 ninth grade US children used the self report previous day physical activity recall (PDPAR) questionnaire developed by Weston et al. (1997) to measure time spent in active and sedentary pursuits (Robinson & Killen, 1995). This study reports weak but significant negative relationships between time children spent watching TV and physical activity i.e. those who watched most TV tended to be the least active (r = -0.09; P < . 05). Furthermore, Crespo et al. (2001), using ‘National Health and Nutrition Examination Survey’ (NHANES) data, reports that those who watched most TV tended to be the least active ( P < . 05 girls; P < . 01 boys). The recent CSPPA research lends further support to this hypothesis. Among post primary youth those who achieved the recommended 60 minutes of MVPA daily were found to have significantly lower levels of sedentary activity compared to those who did not meet the recommendations (4.3 hrs v 3.9 hrs; P < . 05) (Woods et al ., 2010)

Literature Review

Similarly, very active children have been found to engage in lower levels of screen time inactivity. The Irish ‘Take Part’ study investigated a question on TV consumption among Irish adolescents (N = 939; age 15-17). These questions were separated into weekdays and weekends and students had to choose the frequency with which they partake on a scale of one to nine. It was reported that 46% of adolescents who are regularly active tended to watch more than two hours TV whereas 59% of those who were not regularly active watched more than two hours ( P < . 05) (Woods et al. , 2004). Therefore an alternative approach to increasing physical activity among children is to reduce access to sedentary behaviours which compete with being active (Epstein & Roemmich, 2001).

Some research refutes the so-called displacement hypotheses of Mutz et al. (1993). Biddle (2004) argues that while watching TV may prevent someone from being active at that time it is not inversely related over weeks/months. A meta analysis of 24 studies of young children correlated TV viewing and physical activity and concluded that the relationship is at best described as small (TV and physical activity r = -0.096) (Biddle, 2004).

Further supportive evidence of this viewpoint may be found in a Belgian/Luxembourg study of 1,028 children aged 6-12. In this study, Guillaume et al. (1997), report no relationship between screen time inactivity and physical activity. Data from ‘Youth Risk Behaviour Survey’ (CDC, 2000), also reports no association between weekly TV time and time spent in moderate and vigorous physical activity. Similar findings are reported from the third National Health and Nutrition Examination Survey (NHANES III) (N = 2,791). Males were found to participate in more vigorous physical activity than females ( P < . 001. In conjunction with this males were also found to participate in higher levels of screen time inactivity when compared to females (P < .001) (Dowda et al. , 2001).

A similar trend is evident in the Irish ‘Switch Off Get Active Study’ among 312 children aged 9-11. Boys were found to have a greater desire for and consumption of screen time inactivity (boys 200 ± 6.1 mins per day; girls 150 ± 5.4 mins per day, P < .001). Paradoxically, they also had higher levels of vigorous (14.8 ± 0.9 boys; 7.5 ± 0.9

Literature Review girls, P < . 001) and moderate to vigorous bouts of physical activity ( P < . 05). These findings appear to contradict the displacement hypothesis, i.e. that screen time recreational pursuits are in direct competition with more active recreational pursuits and a direct cause of falling activity levels. However, when male and female data is analyzed separately, the boys in the highest tertile for screen time inactivity had lower levels of moderate to vigorous (88.3 ± 7 vs 118 ± 9 minutes daily; P < . 01) and fewer bouts of vigorous activity compared to boys (P = .02) in the lowest tertile. This finding lends some support to the displacement hypotheses, at least in boys. When those who spend most and least time in screen time inactivity are compared, other interesting findings emerge. Both the girls and boys with the highest screen time inactivity had a lower self-efficacy for physical activity ( P < . 05) and lower perceived ability to manage without TV ( P < . 05). Therefore these findings portray a subgroup of children for whom inactive screen time pursuits predominates their leisure time. It may be that screen time pursuits do not necessarily displace physical activity but high levels of screen time inactivity serve as a marker for individuals with low interest in and self- efficacy for active pursuits (Burns, 2004).

It appears that the displacement hypothesis has limited support and that screen time inactivity in young people is not strongly associated with the amount of physical activity they undertake (Biddle et al. , 2004).

2.7.2 Participation in Organized Sport Sport participation is defined as a form of physical activity that involves competition. In general, sport is a competitive activity undertaken in the context of rules defined by a regulator agency (Bouchard et al. , 2007)

Organized sport has been found to contribute a substantial portion of adolescent’s physical activity (Katzmarzyk & Malina, 1998; Booth et al. , 2004). Organized sport has accounted for between 55-65% of adolescents’ daily moderate and vigorous physical activity (MVPA) in self-report studies (Katzmarzyk & Malina, 1998; Booth et al. , 2004) and 23% of daily MVPA in a study using accelerometers with 6-12

Literature Review year old males (Wickel et al. , 2007). Adolescent’s who participate in organized sport have been found to have higher levels of overall physical activity and to engage in more moderate and vigorous activity compared to those not involved in sport, (Pate et al. , 2000; Booth et al. , 2004; Santos et al. , 2004; Woods et al. , 2010). Furthermore, youths involved in organized sport were found to have higher levels of activity on days when they engaged in organized sport compared to days they were not engaged in organized sport (Wickel et al. , 2007). It is therefore important to study trends and correlates of sport participation during adolescence, as this is a period of life in which many individuals engage in health habits which will last throughout adulthood (Michaud et al. , 2006). Currently, few studies have examined the correlates of sport among adolescents (Toftegaard-Støckel et al. , 2010).

2.7.3 Correlates of Sport Participation

A number of research studies have found participation in organized sport to be influenced by a multitude of personal/ biological correlates. Age has been found to influence sports’ participation with lower levels of sport and physical activity participation evident in older adolescents (Pate et al. , 2000; Santos et al. , 2004; Michaud et al. , 2006; Johnston et al. , 2007; Toftegaard-Støckel et al. , ; Woods et al. , 2010). This trend has been found to be most pronounced among females (Fisher et al. , 1996; Steptoe & Butler, 1996; Katzmarzyk & Malina, 1998; Pate et al. , 2000; Sallis et al. , 2000; Booth et al. , 2004; Michaud et al. , 2006; Toftegaard-Støckel et al.2010 ). Males engage in higher levels of sport participation compared to females (Fisher et al. , 1996; Pate et al. , 2000; Booth et al. , 2004; Michaud et al. , 2006; Toftegaard-Støckel et al. , 2010; Woods et al. , 2010). A recent ESRI publication (2008) highlights interesting trends in Irish sport participation, the proportion of boys who take up a sport is very much higher than girls in every year up to the age of 10, (male compared to females @ age 10, OR = 9.8 ± 0.16). The gender gap in sport participation closes somewhat in second level school when more girls take up sport (male compared to females @ age 15, OR = 3.6 ± 0.12), this trend is not maintained with females tending to cease participation in team sport in late adolescence. This results in a pattern of sport

Literature Review participation, rising in the mid teenage years when the clear majority of people play some kind of sport and falling sharply in the late teens (males compared to females @ age 20, OR = 10.94 ± 0.16) (Lund, 2008). Furthermore, this study reports that the fall off in sporting activity in the late teens and on to adulthood is almost entirely due to people dropping out from team sport. Individual sports are played much later in adulthood and the proportion playing them does not decline much with age (Lunn & Layte, 2008). The author concludes that sports’ policy needs to recognize the trend towards individual sports such as swimming and jogging and to devote a greater share of its efforts to promoting and supporting these increasingly popular activities (Lunn & Layte, 2008).

Higher socio-economic status, (SES), has also been found to be associated with participation in organized sports among adolescents (Steptoe & Butler, 1996; Ferron et al. , 1999; Pate et al. , 2000; Booth et al. , 2004; Santos et al. , 2004; Michaud et al. , 2006; Johnston et al. , 2007; Dollman & Lewis, 2010; Toftegaard-Støckel et al 2010.). It has been hypothesized that there may be a more positive attitude towards sport in families from higher SES (Ferron et al. , 1999; Dollman & Lewis, 2010). Interestingly, in a recent Irish study social class was found to exert no influence on extracurricular sport (Woods et al. , 2010).

Other correlates which are related to organized sport participation include parental interest in and level of sport and activity. In a Danish study adolescents who reported parents as being active were almost twice as likely to participate in sport compared to those who perceived their parents as inactive (Toftegaard-Støckel et al. , 2010). Lund et al. (2008) report that fathers participation in organized sport was associated with higher levels of participation among siblings (OR @ 15 years = 1.4 ± 0.1). This trend was not evident with mother’s participation. Other social correlates associated with participation in organized sport include parental education, (Ferron et al. , 1999; Santos et al. , 2004; Michaud et al. , 2006), and being from intact families (Ferron et al. , 1999; Michaud et al. , 2006).

A recent nationally representative Irish study has identified that 73% of Irish post primary adolescents play sport or physical activity at least once a week (Woods et

Literature Review al. , 2010). Participation in extra-school sport (males Beta = .228, P < . 01; females Beta = .278, P < . 01) and extra-curricular sport (males Beta = .139, P < . 01; females Beta = .073, P < .01)were found to significantly influence the number of days that post primary pupils reached the 60 minutes of MVPA (Woods et al. , 2010). Logistic regression analysis revealed that important significant factors predicting sport participation include family support, friends support, will power and skill. Lack of time and lack of competence were identified as barriers to participation (Woods et al. , 2010).

Similarly, in a study of Australian youth aged 10-15 years predictors were derived from the ‘Children’s Physical Activity Correlates Scale’ (CPAC). Predictors relating to perceived outcomes, parental support and perceived barriers were positively associated with sport participation among male and female participants (Dollman & Lewis, 2010).

2.7.4 Barriers to Sport Participation

De Roiste and Dinneen (2005) investigated the reasons for dropout of sport among 2,260 Irish 12-18 year olds. The following key reasons were found: (i) loss of interest (23%); (ii) time of activity didn’t suit (15%); (iii) didn’t like the leader/ coach (11%), 4. perception that their skill level was not good enough (9%) were the main reasons cited for dropping out. Further reasons identified in the focus groups as to why girls drop out of physical activity included, ‘ starting to smoke and drink’, people you hang out with don’t like sport and that has an influence on you’, ‘you start college and then you’re too busy ’. The authors argue that the drop off from sport evident in this study suggests that adherence to physical activity cannot be easily predicted and is a complex multivariant issue.

Sirard et al. (2006) study on 1,692 children and adolescents mirrors these findings. A modified version of the ‘Participation Motivation Questionnaire’ (PMQ) developed by Ewing et al. (1990), was used in this study to identify factors associated with sports programme participation and attrition. Factor analysis of the attrition scale produced 3 barriers regardless of gender or participation status. Lack of interest,

Literature Review coaching problems and lack of time were found to be the primary factors inhibiting participation in physical activity.

2.7.5 Behavioural correlates literature; implications for current research When the findings of the Sallis et al. (2000) and the Van der Horst et al. (2007) reviews are compiled together, a number of behavioural correlates were found to be consistently related to physical activity among adolescents. Participation in organised sport was found to be associated with physical activity in seven of the seven studies reviewed (100%), while participation in PE and school sport were also associated with physical activity in three of the five studies reviewed (60%). Previous physical activity was also related to current physical activity levels in eleven of the twelve studies reviewed (92%). Some evidence was also found between TV viewing and physical activity participation (50%), while no association was reported between smoking and physical activity participation (39%). Sensation seeking was found to be a potential correlate with an association reported with physical activity in three studies (100%). Considerable cross sectional research identifies sport participants to have higher levels of habitual physical activity. Limited research has examined the correlates of sport participation; however correlates which have been found to be related to physical activity also appear to be related to sport participation. Furthermore, trends in sport participation appear to be similar to trends in habitual physical activity (Lunn & Layte, 2008). Relatively inconsistent cross sectional data exists relating to the influence of screen time inactivity on physical activity. It appears that screen time inactivity may have an association with physical activity; however research has reported that this association may be weak (Biddle et al. , 2004). The current study assessed sport participation and screen time inactivity as behavioural correlates of physical activity. No measure of previous physical activity or sport participation was measured in the current research. The current research attempted to focus on modifiable current correlates of activity; however the exclusion of this correlate may be perceived as a weakness of the current study.

Literature Review

2.8 Personal and Biological Influences on Physical Activity

This section outlines the key findings relating to personal and biological correlates of activity from relevant reviews of literature. Individual personal and biological correlates are reviewed in subsequent sub sections.

The effects of gender and age on physical activity appear clear. In a review of literature on the correlates of physical activity among adolescent girls, it was found that 22 of the 24 studies reviewed showed girls to be less active than boys with two studies showing no difference (Biddle et al. , 2005). Similarly studies assessing the effect of age on physical activity confirmed a small to moderate age related trend of less activity among older adolescents in seven of 11 studies reviewed. Other biological factors found to influence physical activity included body mass index which was found to be negatively related to physical activity in all but two of eight studies, with all effects being small. Socioeconomic status variables were also found to be associated with participation in physical activity. In all three samples, higher family income/socioeconomic status were associated with higher physical activity among girls. Finally parental education was also positively associated with girls’ physical activity in three of four studies (Biddle et al. , 2005).

The majority of these findings concur with the results of Sallis et al. (2000) in a review of correlates of physical activity in children and adolescents. Sallis found that 27 of the 28 studies reviewed found boys to be more active than girls. Age was found to be negatively associated with physical activity in 27 of the comparisons (six studies no association; 70%). Sallis et al. (2000), however, found that adolescent body weight and adiposity were indeterminate, (six negative i.e. lower body weight associated with increased physical activity, 13 no association, two positive). The association between physical activity and socio-economic status was also found to be indeterminate, which also differs from the findings of Biddle et al. (2005). Three of the studies reviewed found that higher socio-economic status was associated with increased physical activity. However no association was found in six studies (three positive, six no association, 33%) (Sallis et al. , 2000).

Literature Review

Twenty four studies examined personal and biological correlates in the Van der Horst et al. (2007) review. This review found a positive association between physical activity and gender and parental education with physical activity. No association between socio-economic status or BMI and physical activity was found. Age and ethnicity influences were found to be inconclusive (Van der Horst et al. , 2007).

2.8.1 Gender

In a study examining physical activity among US adolescents, large disparities in the achievement of physical activity recommendations were observed among males and females across all ethnic groups (Whitt-Glover et al. , 2009). Similarly, data from the ‘Take Part Study’ of Irish 15-17 found that significantly more females than males did not meet the recommended level of physical activity of 60 mins of moderate to vigorous physical activity 4 times per week (70% females V 58% males; P < . 001) (Woods et al. , 2009). The findings of Lalor and Baird (2006) among Irish adolescents (N = 988) from Kildare also concurs with this. It was found that 21% of males were active for ten hours or more per week while only 7% of females achieved this threshold. Nilsson et al. (2009), also reports that males were significantly more active than females in a study of adolescents from four European countries; in this study physical activity was measured using accelerometer.

Males have also reported higher scores on physical activity correlate scales than females. For example, Sallis et al. (1999), examined modifiable psychological and social correlates of physical activity among a group of 4 th and 5 th grade students (370 girls; 362 boys), over 20 months. Child reported psychometric characteristics were primarily developed by the author. It was found that at baseline boys reported higher attitudes towards physical activity ( P < . 001), to getting sweaty ( P < . 001), competence in physical activity ( P < . 001) and body image (P = 0.03). Boys were also found to receive more support through transportation ( P = .03) and parents paying sports fees ( P = .001). No difference was found in attitudes to physical education ( P = .45), self concept ( P = .23), intention to be active (P = 0.81), parental encouragement ( P = 0.32) and parent being active with the child ( P =0.06) (Sallis et al. , 1999). Similarly, in a

Literature Review study of 832 Taiwanese adolescents, girls were found to report lower self-efficacy, less perceived benefits and more perceived barriers than males. Females also reported less social support from peers compared to their male counterparts (Wu et al. , 2003). Similar findings are reported by Garcia et al. (1995), indicating a more positive attitude towards as well as engagement in physical activity among male participants.

In conjunction with this it appears that different factors influence male and female participation in physical activity (Tergerson & King, 2002). Gender specific sport participation and attrition factors were identified by Sirard et al. (2006) among 1,692 children aged 10-15. A modified version of the ‘Participation Motivation Questionnaire’ was used to identify factors associated with sports programme participation. Three factors were identified for sports’ programme participation for both genders; these were similar to the findings in previous research, however some gender differences were reported. For boys the factors identified for sports’ programme participation were found to be: (i) competition; (ii) social benefits and (iii) fitness. For girls the factors associated with sports participation were: (i)social and skill benefits; (ii)competition and (iii) fitness. It was found that for boys the competition factor loaded first whereas the social and skill benefits factor loaded first for girls. Therefore it appears that middle school boys have a stronger attraction to the competitive aspects of sports programmes whereas girls are drawn more to the social facets of participation, this is in agreement with the findings of Connor (2003) who found that girls are particularly keen to have a friend to go along with them to their activity. Clearly the identification of gender specific factors associated with participation is important in the promotion of long term physical activity.

Burrows et al. (1999), also reports that boys more often than girls identified ‘fun’ as a motivating factor for participation in exercise. Girls were more likely to identify ‘keeping in shape and weight control’ as motivating factors. The author concluded that this may support the idea that boys participate in physical activity more for intrinsic factors i.e. reward of the activity itself, while girls may be motivated by extrinsic motivators i.e. as a means to achieving another goal. It appears therefore, that girls prefer different activities and tend to participate in physical activity for dissimilar

Literature Review reasons than boys and may face different barriers, indicating that targeted interventions should be designed specifically for girls (Vu et al. , 2006).

Higher levels of physical activity among males may be partly explained by the support received. Connor (2003) argues that theNunnaare are agents of socialization and cultural factors that encourage and predispose males more than females to physical activity. In particular, he feels that parents and significant others appear to be more encouraging to boys to be physically active. In the focus groups, it was found that girls frequently talked about their brothers being given greater latitude to pursue their sporting interests. In general it was found that girls appeared to do more homework, watch more soaps on TV and read more magazines. Adolescent boys were much more likely to engage in casual outside activities, such as soccer on the green. Furthermore, it was perceived among adolescents that facilities and sports access appears much better for boys. To overcome this barrier, Connor (2003), recommends strong structural back- up is needed to help increase girls’ opportunities for sports’ participation. Similarly, a strong educational programme could possibly help to banish some of the stereotypical myths about the nature of sport and its suitability for both genders (Connor, 2003).

2.8.2 Age

Research has consistently found that physical activity is negatively associated with age in adolescence; this trend has been found to be most pronounced among females. In a US study which used the ‘Previous Day Physical Activity Recall Questionnaire’ to measure adolescent activity at baseline and four years later, it was found that that vigorous physical activity declined from 45.4% in 8 th grade to 34.1% in 12 th grade (Pate et al. , 2007). This supports previous findings of 2,672 young people aged 4-18 years, which reports that almost all children aged 4-6 years were identified by their parents as being fairly active or very active (96% boys; 93% girls). However this was found to decline with age, among 15 -18 year olds only 44% boys and 31% of girls were found to be moderately active for at least 1 hour per day (Gregory, 2000). These findings are in agreement with a recent Irish study (Woods et al., 2010).

Literature Review

The rate of decline in physical activity has been found to be most pronounced among females. A longitudinal ‘Growth and Health Study’ (NGHS) among 2,379 black and white girls followed from ages 9-10 years to 18-19 years, highlighted the decline in physical activity among adolescent females. The results indicated a steep decline in levels of reported activity from baseline to year ten as indicated in the three day self report diary and by the caltrac accelerometer. At baseline the respective median activity scores for black and white girls were 27.3 and 30.8 MET-times per week and declined to 0 and 11.0 by year ten of the study ( P < . 001) (Kimm et al. , 2002). Further studies have reported a decline in activity in adolescence (Dovey & Chalmers, 1998; Rowland, 1998; Kimm et al. , 2002; Booth et al. , 2004; Eisenmann et al. , 2004).

While a large body of research exists on the correlates of physical activity in youth, relatively few studies have examined possible age related differences in correlates. Many changes occur in adolescence that may affect physical activity levels and correlates of physical activity. One such study of 1,033 males and 962 females aged between 12-19 years examined the possible age related differences in physical activity correlates (Schaben et al. , 2006). Specifically, parental influence was measured using a previously validated tool developed by Welk et al. (2003), perceived competence was measured using elements of Harter (1982) ‘Perceived Athletic Competence Scale’, while outcome expectations for physical activity was measured using ‘Children’s Attraction to Physical Activity’ instrument developed by Brustad et al. (1993). Results indicated that high school youth had lower levels of physical activity ( P < . 001) and lower levels on the psychosocial correlates than middle school youth (Schaben et al. , 2006).

Similarly, in a study tracking levels and correlates of activity among children and adolescents in the year prior to and following the transition from elementary to junior high school; it was found that decreases in efficacy, social support and expectations to be physically active occurred among males. Females also reported decreases in social support for physical activity; they also reported fewer active role models and were less likely to perceive that the benefits of activity outweighed the barriers following the transition (Garcia et al. , 1998). These findings lend support to the belief that positive attitudes and levels of physical activity decline during adolescence.

Literature Review

2.8.3 Social Class and Socio Economic Status Class positions are seen as deriving from social relations in economic life or more specifically from employment relations (Goldthorpe, 2000). It is, therefore in economic life that the implications for individuals of the class positions that they hold should be most immediately apparent (Goldthorpe, 2000). Studies have repeatedly found strong relationships between occupational status using classifications such as manual v non manual labour and diverse health indicators (Marmot et al. , 1978) and levels of activity (Van der Horst et al. , 2007). Inaccuracies in measuring SES could affect research findings and conclusions with implications for practice and policy (Braveman et al. , 2005). Despite expert consensus that SES is complex and multi factorial most health studies use a single socioeconomic variable measured at a single period and level (Braveman et al. , 2005). Occupation is frequently used as a measure of SES in Europe while income or education is a more common measure in the US (Braveman et al. , 2005). Researchers should select socioeconomic factors systematically, considering whether economic resources, education, occupation, socioeconomic factors earlier in life and neighbourhood socioeconomic conditions at any lifer stage could plausibly be relevant to physical activity (Braveman et al. , 2005). The Irish CSO measure of parental occupation was used to classify individuals based on socio economic status (CSO, 2008). The classification adhered to the international occupation classification ISCO Com (CSO, 2008). The code to which a person’s occupation is classified is determined by the kind of work he or she performs in earning a living irrespective of the place in which, or the purpose for which, it is performed (CSO, 2008). Participants were classified to one of ten specific socio economic groups. In addition a residual group “All others classified gainfully employees and unknown” was used where sufficient details were not provided. The classification aims to bring together persons with similar social and economic statuses.

Participation in physical activity shows clear social class differentials. These parallel the social differentials in risk of several chronic diseases (US Dept of Health and Human Services, 1996). Connor (2003), believes that a sporting ethos still permeates the higher social class groupings and is often alien to the lifestyle of the

Literature Review lower social class groupings. In a study of 3,315 Irish adolescents, it was found that students from higher socio-economic groups had greater access to a wider variety of sport and physical activity in their neighbourhoods, more access to transport, increased money for activities and knowledge of a greater variety of activities than their working class counterparts. For example, in individual sports it was found that 66% of those in the upper class participated in individual sports while only 43% from working class participated in individual sports (Connor, 2003). This finding is supported in a comprehensive review of barriers and facilitators to children’s physical activity. Brunton et al. (2003), concludes that families with more money and spare time are better able to overcome structural barriers that may affect physical activity.

Evidence of this association can be seen from data from the 1996 ‘National Longitudinal Study of Adolescent Health’ on 17,766 US adolescents enrolled in US middle and high schools. In this study, Gordon Larsen et al . (2000), found that high family income was associated with increased moderate to vigorous physical activity (OR 1.43; 95% CI 1.22-1.67) and decreased inactivity (OR 0.7; 95% CI 0.59-0.82). Similarly, in a phone survey interview study with 6,125 adolescents, the results of the logistic regression indicated that for both genders compliance with recommended daily 60 mins MVPA per day was significantly associated with having higher household incomes (Butcher et al. , 2008).

The effect of income on levels of physical activity has also been investigated in a recent ESRI survey (2007) using data from 3,000 Irish adults. Levels of physical activity were analyzed based on quartiles of income. Among individuals in the lowest quartile for income 35% of the males and 20% of the females were found to have played sport in the previous 12 months. The findings are somewhat different for those in the highest quartile for income. In total 71% of the males and 49% of the females reported playing sport in the previous 12 months. When subgroups are analyzed the effects are even more pronounced. Those in the highest quartile for income and with an educational degree were found to be five times more likely to be classified as active compared to those in the lowest quadrant for income and with a junior cert qualification. Furthermore, 21% of the Irish population reported never playing sport, 34% of these

Literature Review were in the lowest income group with only 9% being in the highest income group (Lunn, 2007).

Interestingly, O’ Sullivan’s (2002) findings on social class and physical activity are equivocal. When social class groups were collapsed into higher and lower social classes, the differences in physical activity were found to be only of borderline significance ( P < . 05). This study however did report that adolescents in the higher social classes tended to receive more parental support to participate in physical activity. Among children in the lower social classes, 42% reported receiving little parental support; however of those in the high social category, only 28% reported receiving little parental support. Woods et al. (2004), in a study of 939 Irish adolescents also found that no difference was associated between social class and activity level. In a separate study Woods et al. (2010) found no difference in the proportion of children across all social class categories, who reached the recommended levels of activity. Furthermore, no association was found between minutes of PE and participation in extracurricular sport. However, the higher a child’s social class the more likely they were to participate in extra school sports and physical activity clubs. It appears, therefore, that the influence of social class may need further research to assess its impact on physical activity among Irish adolescents.

2.8.4 Overweight/ Obesity Obesity is defined as an excessively high amount of body fat or adipose tissue in relation to lean body mass (Dishman et al. , 2004). To be practical, assessment of obesity must reflect excess body fat and still be simple to use. Body mass index (BMI) is expressed as body weight in kilograms divided by the square of height in metres (kg/m 2), is a weight for height index that meets this criteria (Bellizzi & Dietz, 1999; Campbell et al. , 2002). Body mass index has been found to correlate with other methods of measuring body fat in children and adolescents (Pietrobelli et al. , 1998). McCarthy et al . (2003), however has expressed reservations with the use of BMI as a means of classifying children as overweight and obese. Trends in waist circumferences during the past 10-20 years have greatly exceeded those of BMI, indicating that BMI is a poor

Literature Review proxy for central fatness. Furthermore, BMI is a weight for height measure that does not take into account different levels of maturation in adolescence (McCarthy et al. , 2003). Overweight and obesity classifications used in the current study were based on the International Obesity Task Force (IOTF) standards. These age and gender specific standards are based on the adult standards of 25 kg/m 2 and 30 kg/m 2 classification of overweight and obesity. Cole et al. (2000) examined national surveys from six countries (Brazil, Britain, Hong Kong, Netherlands, Singapore and US). A set of BMI values, which were defined to pass through the adult BMI cut off points of overweight and obesity at age 18 were calculated for males and females at six monthly intervals from age 2 years (Cole et al. , 2000). The theoretical advantage of this approach is that it attempts to create absolute BMI cut off points during childhood that equate to adult cut off points (Roberts & Dallal, 2001). Other cut off points have been developed based on the Centre for Disease Control (CDC) age and sex specific BMI percentile charts. The reference population in the development of these growth charts was based on US nationally representative samples from 1963-1994. Based on these charts it is possible to rank the position of an individual by indicating what percent of the reference population the individual would equal or exceed (Kuczmarski & Flegal, 2000). Current US recommendations for diagnosing overweight suggest that subjects with a BMI over the 95 th percentile should be considered overweight and undergo in-depth medical assessment. Subjects above the 85 th percentile should be considered at risk of overweight and referred for a second level screening (Crespo et al. , 2001). These reference values are based on US national survey data and are intended to be used for US children and adolescents (Flegal et al. , 2001), therefore the more relevant IOTF cut off points were used in the current research. Other methods such as waist circumference measures have attracted much recent attention as an indicator of fatness and health risks in children and adolescents (Krebs et al. , 2007). The interest in waist circumference stems from research which links accumulated visceral adipose tissue to increase health risks and metabolic disorders in children and adults (Krebs et al. , 2007). In a review of 474 healthy adolescents BMI and waist circumference measures were found to show strong positive correlation (r = 0.68 - 0.73; P < 0.0001) with percentage body fat in both sexes. Himes (2009) argues that there is little evidence that measures of body fat such as skinfolds, waist circumference

Literature Review are sufficiently practicable or provide appreciable added information than that provided from BMI in the identification of children and adolescents who are overweight or obese (Himes, 2009). BMI has become the most common indicator used to assess overweight and obesity in a wide variety of settings. Although it is not a perfect surrogate for total body fatness and not without its technical limitations, BMI has been recommended the most appropriate single indicator of overweight and obesity in children and adolescents (Himes, 2009). Furthermore, due to the fact that the researcher had previous experience of using BMI in research studies, and to allow comparison with most other similar studies which have used BMI this correlate was used in the current research (Van der Horst et al. , 2007).

Some evidence indicates that physical activity is correlated with higher levels of overweight and obesity. In a review of literature carried out by Fogelholm & Kukkonen- Harjula (2000) the most consistent finding was that a large volume of physical activity was associated with less weight gain in prospective observational studies if the level of activity was assessed in two conditions: only at the end of the follow up period (four studies negative association between physical activity and weight gain; one study no association), or as a change from baseline to follow up (seven studies negative association, two studies no association). When physical activity measured at baseline was compared to changes in weight between baseline and follow up, the findings were found to be indeterminate (three studies negative association; four studies no association; three studies positive association). Similarly, in a separate review, Saris et al. (2003) reports that from 13 prospective studies it was found that 11 showed an inverse relationship between physical activity level and increase in body mass index, body fat, weight or percent overweight/obese.

Some cross sectional studies have also found a dose response association between levels of physical activity and obesity among children and adolescents. For example Hernandez et al. (2003), studied activity levels among Mexican children and found the odds of obesity were reduced by 36% among children in the most active quartile for physical activity, compared to those in the least active quartile ( P < . 001). Data from US ‘National Longitudinal Survey of Adolescent Health’ (1995/96) with 12,759 participants, also found that the odds of being overweight decreased with high

Literature Review levels of moderate/vigorous activity among white boys (OR 0.81 ;95% CI 0.67-0.87), and girls (OR 0.84; 95% CI 0.69-0.87), non-Hispanic black boys (OR 0.86; 95% CI 0.76-0.98) and girls (OR 0.88; 95% CI 0.78-0.99) and Hispanic boys (OR 0.9; 95% CI 0.83-0.97) and girls (OR 0.91; 95% CI 0.84-0.99) (Gordon-Larsen et al. , 2002). Similarly, in the TAAG study, 6 th grade girls with an average of 12.8 minutes MVPA (15 th percentile), were 2.3 times more likely to be overweight than girls with 34.7 mins MVPA (85 th percentile) (Stevens et al. , 2007). It appears that high levels of moderate and vigorous activity may be related to lower levels of overweight and obesity.

Participation in organized and unorganized sport has also been associated with lower levels of overweight and obesity. Data from the first wave of the ‘National Longitudinal Survey of Children and Youth’ in Canada provided information on 7,216 children aged 7-11 years. This study reported that involvement in organized and unorganized sport was negatively associated with overweight (10-24% reduced risk respectively) and obesity (23-43% reduced risk) (Tremblay, 2003) .

Cross-sectional studies examining behaviour patterns of obese children have found that obese children tend to participate in less moderate and vigorous activity than their normal weight counterparts. Maffeis et al. (1998) found obese children spent less time performing play and physical exercise compared to normal weight children (1767 ± 66 and 199 ± 52 min /week respectively; P < . 05). Similarly, in a study of 133 non- obese and 54 obese 6 th grade children (mean age 11.4 ± 0.6), obese children were found to exhibit significantly lower daily accumulations of daily moderate and vigorous activity (Trost et al. , 2001). Obese children were also found to display lower levels of physical activity self-efficacy, less likely to report parents as active and were involved in fewer community organizations promoting physical activity (Trost, 2001).

The association between physical activity and obesity however is unclear with some research reporting no link between obesity and childhood activity. In the Third ‘National Health Examination Survey’ (NHANES), Crespo (2001) found that the prevalence of obesity, according to weekly participation in vigorous activity, showed no trends for boys and girls. Beunen et al. (1992), also found no link between self-reported participation in sport and skin fold thickness among Belgian adolescents. Maffeis et al.

Literature Review

(1998),) Bar Or and Baranowski (1994), Ekeuland et al. (2002), Johnson et al. (2000) and Salbe et al. (2002), also report no link between physical activity and overweight and obesity.

Lower activity levels appear to be a cause, rather than a consequence, of overweight and obesity. Davies (1995) measured energy expenditure of 77 infants using the doubly labeled water method. The amount of energy expended in physical activity was calculated and the percentage of body fat was also determined. None of the infants were obese, but as physical activity levels fell the percentage of body fat rose. This negative relationship between body fatness and activity was significant and appears to confirm that lower activity levels are a cause rather than a consequence of overweight and obesity.

Obesity has rapidly increased among genetically stable populations which therefore points to environmental causes (Ebbeling et al. , 2002). Children are exposed to a range of influences from a foetal stage onwards which increase their risk of becoming overweight, e.g. consumption of energy dense foods and decline of physical activity (Warren et al. , 2003) These influences are embedded in a child’s social surroundings and may be described as an obesogenic environment (IOTF, 2003). Biddle et al. (2004) argues that physical inactivity and not energy intake is the primary cause of obesity. The justification is that while obesity has been increasing among children, food intake has actually been decreasing. Cavadinin et al. (2000), in a review of adolescent food intake trends from 1965-1996, using United States Department of Agriculture Surveys, showed that total energy intake decreased by 17% over a 30 year period, a period during which obesity was increasing. This apparent paradox was also reported by Heini & Weisnier (1997) from the second and third NHANES. They found that average total calories and average fat intake decreased from 1977-1987, while average BMI increased from 25.3 kg/m2 to 26.3 kg/m2 during this period. This finding is consistent with food surveys in Great Britain, where average food per capita energy intake declined by 20% from 1970 to 1990, while the prevalence of obesity doubled from 1980-1991 (Prentice, 1995). Therefore, it appears that exposure to an obesogenic environment in which physical inactivity is central may explain the large increase in levels of obesity.

Literature Review

2.8.5 Personal/ biological literature; implications for current research Analysis of the Sallis et al. (2000) and Van der Horst et al. (2007) reviews of personal/ biological correlates of activity report some clear associations with physical activity. Gender was found to be related to physical activity in 29 of the 30 studies reviewed (97%); an association between age and activity was found in 24 of the 37 studies included (65%) (Sallis et al. , 2000; Van der Horst et al. , 2007). Some evidence is also identified between ethnicity and activity with 16 of the 27 studies reporting an association (59%). Parental education was included as a correlate in four of the six studies included in these reviews (67%). A number of personal/ biological correlates were found to have no clear association in the Sallis et al. (2000) and Van der Horst et al. (2007) reviews; however they have been found to have a strong association in a review of adolescent females (Biddle et al. , 2005). For example, measures of socio economic status were found to be related to activity in 35% of the Sallis et al. (2000) and Van der Horst et al. (2007) review. The Biddle et al. (2005) review of correlates among adolescent females, however reports this association in each of the three studies (100%) included. Similarly, BMI was found to be associated with nine of the twenty eight studies included in the Sallis et al. ( 2000) and the Van der Horst et al . (2007) reviews (32%), but was found to be related to activity in six of the eight studies (75%) included in the Biddle et al. (2005) review. Personal/ biological correlates of physical activity have been identified as potential moderators of activity, with different correlate profiles evident across age, gender and BMI classification. Based on these analyses gender, age, ethnicity, SES, parental occupation and BMI were included in the ‘APAC questionnaire’. Parental education was not included due to difficulties participants may have in providing this information; furthermore it was deemed parental occupation provided sufficient information relating to participants parents.

Literature Review

2.9 Explained Variance in Physical Activity

A number of studies have used regression analysis to assess the ability of individual correlates to explain the variance of physical activity levels and thus identifying key correlates of activity. The ‘Trial of Activity for Adolescent Girls’ used a valid questionnaire to measure correlates of activity and measured MVPA using accelerometery among 1,180 adolescent girls. The overall explained variance in the TAAG study was 30-35% for MVPA and 26-30% for VPA among US adolescent females (Kelly et al. , 2010). Ethnic differences were found on the correlates significantly influencing physical activity. Among Hispanic girls perceived transportation barriers (+: P = 0.01) were significantly and positively related to physical activity. Among black girls, BMI (-: P = 0.005) and social support (+: P = 0.006) were significant correlates associated of MVPA. Among white girls BMI (-: P < . 001), barriers (-: P = 0.012), social support from friends (+: P = 0.01), participation in sport (+: P = 0.009) were significant correlates of MVPA in the regression analysis (Kelly et al. , 2010).

Trost and colleagues (1999) also examined the psycho-social and environmental correlates of objectively measured activity in diverse population of youths (mean age 11.4 ± 0.6 years). It should be noted that this study used a smaller sample than the TAAG study (N = 198) and a shorter questionnaire compared to the TAAG study, which may partly explain the lower explained variance. Among boys the final model was found to be significantly associated with VPA [F (6,88) = 3.06; P =0.009] explaining 12% of the variance. Significant correlates for boys VPA were: self-efficacy, social norms regarding physical activity and involvement in community sport. For MPA, social norms regarding physical activity and involvement in community sport were significant correlates explaining 13% of the variance. The final model was found to be significant for male MPA [F (5, 89) = 3.89; P = 0.003].

For girls, self-efficacy was the only significant predictor of VPA. The final model was significantly associated with daily VPA [F (4, 98) = 3.66 ; P = 0.008], explaining 10% of the variance. The final model was also found to be significant for

Literature Review female MPA [F (5, 97) = 2.83; P = 0.02] explaining 8% of the variance. No single variable was found to be significant at the .05 level.

In a similar study by Sallis and colleagues (2002), the correlates of activity were examined in young people in grades 1 – 12, with separate regression analyses conducted for separate sub-groups. Physical activity was assessed in 781 young people via parent report and a subsample (N = 200) wore an accelerometer for seven days. The findings of the older groups are presented here as the 12 – 18 year groups are the focus of this research. All of the regressions were found to be significant in this analysis. Among males in grade 7 – 9, a total of 36% of the variance in vigorous physical activity based on parent report was explained for by the correlate scales. Among this group use of recreation time (+: P = 0.01) and peer support (+: P < . 01) were significant correlates. Among males in grades 10 -12, 30% of the variance of vigorous activity based on parent report is explained. Significant contributors to the model were age (-: P < . 01) and ethnicity (minorities were higher). Among females in grade 7 – 9, 24% of the variance was explained. Use of recreational time (+: P < . 01) and peer support (+: P < .01) were found to be significant in the regression. Among females in grades 10 -12, 25% of vigorous activity based on parent report was explained in the regression analysis. Use of recreational time (+: P < . 01) and family support (+: P = 0.04) were found to be significant in this analysis. The relationship between objectively measured physical activity and each potential correlates was also examined in this study. Among older males perceived co-ordination (r = 0.37), peer influences (r = 0.32) and use of recreational time (r = 0.36) were significantly correlated with vigorous activity. Park frequency (r = 0.27) was found to be the only significant correlate related to female vigorous activity. The relatively small sample size which used objective measures of physical activity limit the generalisability of these findings.

Self report studies have been found to have higher explained variance than objective measures (Sallis et al. , 1999; Sallis et al. , 2002; Loucaides et al. , 2007). In a study of children in grades 4-12 which used interview to assess levels of activity, the range of variance explained was between 18- 59% (Sallis et al. , 1999). A possible explanation for the stronger association in self report methods may be attributable to shared methods variance, i.e. self reported thoughts and feelings about physical activity

Literature Review may correspond more closely to self reported physical activity than objective measures of physical activity (Trost et al. , 1999; Kelly et al. , 2010) . In this study, Sallis and colleagues identified three variables which had a strong consistent association with physical activity: (i) use of afternoon time for sports and physical activity, (ii) enjoyment of physical education and (iii) family support for physical activity (Sallis et al ., 1999).

Due to the limited number of studies using objective measured of physical activity in regression analysis, it is difficult to draw conclusions. It may be concluded, however that due to the complexity of physical activity behaviour and the multitude of factors influencing this behaviour, attempts to explain activity have been only moderately effective. Research has been more effective at identifying specific correlates which have been found to be related to activity, than explaining overall variation in activity. To date, self-efficacy, social support and participation in sport appear to be important correlates of physical activity.

2.10 Physical Activity

2.10.1 Recommended Level of Physical Activity Physical activity is defined as bodily movement produced by skeletal muscle contraction that requires energy expenditure. Physical activity is characterised by features including frequency, intensity, timing and type (Dishman et al. , 2004). Recent Irish guidelines recommend that Irish children and adolescent should engage in 60 minutes of moderate and vigorous physical activity daily (Dept of Health, 2009). Moderate intensity activity has been defined as when the heart is beating faster than normal and breathing is harder than normal. Broad international agreement exists on this guideline across nations; however some minor differences exist on the type of activity recommended. Various recommended levels of activity for adolescents are outlined in Table 2.2. Pate et al. (1995) assessed research which has evaluated the influence of continuous and intermittent activity on health and fitness. In this review, the authors

Literature Review conclude that intermittent bouts activity confers substantial health and fitness benefits (Pate et al. , 1995). Given that lack of a time is a commonly cited barrier to physical activity a recommendation which allows individuals to accumulate shorter bouts of activity throughout the day rather than having to put aside a continuous time slot is intuitively appealing (Murphy et al. , 2009). Similarly, a separate review has identified a further three studies that made direct comparisons from the benefits accrued from single continuous bouts of activity or intermittent bouts accrued over the course of a day (Hardman, 1999). Similarly, in a more recent review it was found that where improvements in cardio vascular fitness were found no difference was evident between bouts and continuous exercise (Murphy et al. , 2009). For other health outcomes such as adiposity, blood lipids, psychological well being there was insufficient evidence to determine whether bouts of exercise is as effective as continuous exercise (Murphy et al. , 2009). This study also reviewed seven short term studies in which at least one health related outcome was measured during the 0 – 48 hours after a single continuous bout of exercise and a number of short bouts of equivalent total duration. Many of the studies of such short term effects considered the plasma triglyceride response to a meal following either accumulated short or continuous bouts of exercise. Collectively these studies suggest shorter bouts of exercise may be as effective as reducing post prandial lipaemia (Murphy et al. , 2009). Further support for the effectiveness of short bouts of exercise is provided by Jackicic et al. (1995). In a study of overweight women short exercise bouts were found to enhance exercise adherence and weight loss compared to continuous exercise. Similar changes to cardio respiratory fitness were found when compared to longer bouts (Jakicic et al. , 1995). Further research is required to determine if shorter bouts (less than 10 minutes) of physical activity confer health benefits; therefore a bout was defined as 10 minutes in the current research (Murphy et al. , 2009).

100

Literature Review

Table 2.2: Recommended levels of physical activity for adolescents

Group Recommendation

Dept of Health and Children (2009) Irish All children and young people should be active, at a moderate to vigorous level, for at least 60 Physical Activity Guidelines minutes every day.

Include muscle-strengthening, flexibility and bone-strengthening exercises 3 times a week.

International Conference on Physical Activity 1. All adolescents should be active each day as part of play, games, sport, work, transportation or Guidelines (1993) (Sallis & Patrick, 1994) recreation in the context of family, school and community activities

2. Three or more sessions of moderate/ vigorous activity per week of at least 20 minutes duration

Health Education Authority All children and youth should participate in physical activity that is of at least moderate intensity for Recommendations (Cavill et al. , 2001) an average of one hour per day. While young people should be active every day, the amount of physical activity can appropriately vary from day to day in type, setting, intensity, duration and amount. All children and youth should participate at least twice per week in physical activities that enhance and maintain strength in the musculature of the trunk and upper arm girdle.

(Strong et al. , 2005) School age youth should participate every day in 60 minutes or more of moderate to vigorous physical activity that is enjoyable and developmentally appropriate.

American Heart Association (2004) 30 mins moderate intensity activity most days of the week

30 mins vigorous activity 3-4 days per week

101

Literature Review

2.10.2 Levels of Physical Activity

Studies investigating levels of physical activity are faced with numerous methodological dilemmas. Firstly, some authors have examined activity of all intensities whereas others have examined only moderate and vigorous physical activity. This disregards all low intensity activity which Hussey et al. (2001) found constituted a large portion of children’s activity. A second methodological constraint is based on the criteria used in assessment. Some studies use 60 minutes of moderate and vigorous activity and assess the number of participants reaching this threshold (Troiano et al. , 2008), whereas others have assessed the number of participants who are active for bouts at specific intensities (Kelleher et al. , 2003) . These differences make comparisons between various studies difficult.

Other methodological constraints in measuring adolescent physical activity include different measurement tools used by researchers to quantify physical activity among adolescents (self report v objective measures). Furthermore, different methodological protocols used by researchers using objective measures of physical activity can result in very different findings relating to physical activity (Sallis et al. , 2000). As a result of these methodological problems, it is often difficult to compare studies. For example, Riddoch et al. (2004), found that over 60% of European 15 year old females attained the recommended 60 minutes of moderate and vigorous activity daily. In a US study, Troiano et al. (2008) found that only 3% achieved these criteria.

Table 2.3 outlines the findings of recent studies measuring levels of adolescent physical activity. No large scale Irish study has used objective measure of physical activity and analysed the data using similar protocols used in large scale international studies. Therefore, it is difficult to assess the activity levels of Irish adolescents compared to adolescents from other countries. It appears, however, that Irish activity levels may be lower than our European counterparts and similar to US adolescents (Table 2.3).

102

Literature Review

Research has found that there appears to be a subgroup of children and adolescents who participate in little or no physical activity. Murphy et al. (1994) examined the amount of adolescents who did not achieve 60 minutes moderate/ vigorous physical activity in the previous 7 days. It found that 52% of girls reported being involved in less than 60 minutes moderate/vigorous physical activity in the 7 days preceding the questionnaire. Similarly Mairs (1999), in a study of 240 girls in a single sex post primary school in Northern Ireland, found that 59% of 11 year olds did not participate in non-compulsory activity. These findings are higher than HBSC data (Kelleher et al. , 2003), which found that 8% of boys and 14% of girls are exercising less than weekly. The recent ‘Children Sport Participation and Physical Activity’ Study (CSPPA) however reports a different trend. This study sampled 4,122 second levels students and found that 93% of post primary children are achieving the 60 minutes of MVPA once per week, while 65% achieve 60 minutes 3 times per week (Woods et al. , 2010).

International studies have also found a considerable proportion of children who participate in very low levels of physical activity. The ‘Youth Risk Behaviour Surveillance System’ (CDC, 2000), found that 35% of children did not participate in vigorous activity 3 or more days per week. Forty five per cent of those who were questioned were not part of any team/club. The findings of the US ‘Youth Media Campaign Longitudinal Survey’ of children 9-13 years appear similar. A total of 61% of the sample population did not participate in any organised physical activity outside of school, while 22.6% did not engage in any free time physical activity (Duke et al. , 2003).

103

Literature Review

Table 2.3: Levels of physical activity among adolescents

Country Age Recommended level of Activity % Achieved Recommended Level Source

US National Health and 12-19 60 mins MVPA per day 11.9% 12-15 yr; 10% 16-19yr males Troiano et al. (2008) Nutrition Examination Survey 2 N = 611 3.4% 12-15 yr; 5.4% 16-19 yr females

European Youth Heart Study 9-15 60 mins MVPA per day 81.9% 15 year old boys Riddoch et al. (2004) (Denmark, Portugal, Estonia, Norway) 2 N = 2,185 62.0% 15 year old girls

Irish Children Sport 12-18 60 mins MVPA 15% males Woods et al. (2010) Participation and Physical Activity Study (CSPPA) 1 9% females

Irish study 1 11-12 4 bouts of physical activity per week 91.3% males O’ Sullivan (2002)

N = 1,602 77.9% females

Irish HBSC 1 10-17 4 bouts of weekly moderate/ 53% 12-14 year olds (Kelleher et al. , 2003) vigorous physical activity N = 5,712 35% 15-17 year olds

N Ireland 1 11-18 4 bouts of moderate/vigorous 50% boys Riddoch (1990)

N = 3,211 activity per week 30% girls

Take Part Irish Study 15-17 (a) > 4 days for 60 mins per day (a) 42% males ; 30% females Woods et al. (2004) years N = 939 (b) > 5days 60 mins per day (b) 20%

1 Use of self-report of questionnaire to measure physical activity; 2 Use of accelerometer to measure physical activity

104

Literature Review

2.10.3 Levels of Active Transportation

Active transportation, i.e. walking or cycling, is an important opportunity for physical activity and appears to vary considerably worldwide, depending on factors such as infrastructure, weather and tradition. US children report low levels of active transportation to school. In a study of 1,656 children from 5-15 years, Bricker et al. (2003), found that only 4.2% walked to school. Interestingly of those living less than one mile from the school only 19% walked.

Results are quite different in parts of Europe. Sixty percent of school children cycle to school in Denmark, similarly in Sweden 80% of children walk/cycle to school (Bratteby et al. , 1998). Fox et al. (2003) reports that 70% of Dutch children cycle to school. This in part may explain the low prevalence of overweight and obesity in these countries. For example in Holland 12% of 7-11 year old children were found to be overweight or obese (Fredriks et al. , 2000).

UK and Irish children and adolescents appear relatively inactive in travelling to school compared to European counterparts. Woods and colleagues (2010) report that 38% of adolescent Irish females and 43% of adolescent Irish males commuted to school via active transportation. Journey duration for active commuters was found to take 15 minutes (Woods et al., 2010). The Irish ‘Take Part’ (Woods et al. , 2004) study revealed that 34% of 15-17 year olds walked to school while 5% cycled. Among a slightly younger population of 9-11 year olds, Irish children’s levels of active transportation were found to be lower. Twenty percent of the population was found to walk to school while only two percent were found to cycle. The younger age profile of this population and the safety implications may explain the lower levels of active transportation in this sample (Burns, 2004).

The impact of active transportation is evident in a study of 1,596 adolescent girls from the TAAG study. In this study travel by walking to school was identified by 14% of participants before school and 18% after school. Those who reported walking before and after school had 13.7 more minutes (95% CI 1.2-26.3) of total physical activity and 4.7 more minutes (95% CI 2.2-7.2) of MVPA than girls who did not (Saksvig et al. , 2007) .

105

Literature Review

Similarly, Cooper et al. (2003) report that children who tend to walk to school are more active than those who travel to school by car and bus. In a sample of 10 year old children from the US, those who walked to school were 65% more active than those who did not (Cooper et al., 2003). Therefore, it appears that active transportation may be important in the accumulation of recommended daily levels of physical activity.

2.10.4 Tracking of Physical Activity

Data relating to tracking of physical activity from childhood into adulthood is inconclusive. Researchers have hypothesized that an active child is more likely to become an active adult, and by implication, the carry over effect of childhood physical activity could indirectly impact on adult health status. Conversely, if levels of physical activity exhibit stability over time, then children initially observed to be inactive would become inactive adults with an elevated risk of chronic disease mortality and morbidity (Blair, 1989; Murphy et al. , 1994).

Studies have found that physical activity tends to track from childhood to adulthood. Telama et al. (1997) in the ‘Young Finns Study’ examined the extent to which being physically active at 9, 12, 15 and 18 years predicted physical activity 9 and 12 years later. Correlations after 9 years (1980-1989) and 12 years (1980-1992) were with the exception of one group significant but low (r =0.18-0.29). Similarly, Van Reusal et al. (1993), in a Belgian study, reports that 73% of those who were very active in youth were active at age 30 ( P < . 001).

A number of factors appear to effect tracking of activity from childhood into adulthood. Children who participate in competitive sport tend to be more likely to be physically active as adults. In the ‘Young Finns Study’ competitive sport ( P < . 001 for boys; P < . 05 for girls) and grade in physical education were the best predictors of continued participation in physical activity (Telama et al. , 1997). Similarly Kraut et al. (2003) in ‘Cardiovascular Occupational Risk Factors in Israel Study’ (CORDIS) reported

106

Literature Review that participation in organised school age sporting activities predicted physical activity in adults (OR 3.55, 95% CI 2.97-4.23).

However some authors argue that the data to support the contention that adult physical activity patterns are established during childhood are not convincing (Malina, 2001). Unfortunately there are relatively few rigorously conducted longitudinal studies of sufficient duration to allow an informed evaluation of the phenomenon. Overall these longitudinal studies reveal low to moderate inter-age tracking for physical activity in childhood into adulthood (Raitakari et al. , 1994; Twisk et al. , 1998). In a review of the literature, Malina et al. (2001), report weak correlations (r = 0.09-0.37) in a number of studies that tracked physical activity from childhood into adulthood.

Therefore, it appears that levels of physical activity in adolescence are related to activity in adulthood, however the correlations are found to decrease as the length of time increases.

2.10.5 Physical Activity literature: Implications for current research The recently developed Dept of Health and Children (2009) Irish Physical Activity Guidelines were adopted for use in the current study. Due to the evidence linking intermittent bouts of exercise with health and fitness benefits the current study examined physical activity accumulated in ten minute bouts (Pate et al. , 1995). A rigorous review of literature of physical activity measurements tools and protocols is presented in the following sections to minimise measurement error.

2.11 Measuring Physical Activity

2.11.1 Self-Report Questionnaires

Some self-report questionnaires have been found to be valid tools in measuring activity among children over ten years of age. Simons Morten et al. (1994), assessed the

107

Literature Review ability of children in 3 rd and 5 th grades to recall activity on the previous day. These self- report tools were validated with both a Caltrac accelerometer and heart rate monitor. Correlations with monitored minutes were between r = 0.50 and 0.72 for heart rate monitors and between r = 0.55 and 0.80 for Caltrac accelerometer.

One form of self-report questionnaire, the ‘Previous Day Physical Activity Recall’ questionnaires, have established validity based on concurrent observation with both motion sensors (r = 0.77) and heart rate monitors ( r = 0.63) and have established test retest reliability (r = 0.98) with adolescents in grades 7-12 (Weston et al. , 1997). Habitual physical activity questionnaires have also been used by authors in this area where children are asked about their weekly activity in a general questionnaire (Crespo et al. , 2001).

For self-report questionnaires both reliability and validity increases with age (Sallis et al. , 1993). The use of these instruments is especially problematic in children because of cognitive limitations and because their activity patterns tend to be sporadic in terms of intensity and duration (Armstrong & Bray, 1991). Sallis et al. (1993) concluded that recall questionnaires should be used only with children who are older than ten years and when the time from the physical activity to the report, i.e recall interval, is as short as possible to enhance validity. Self-report questionnaires have been widely used due to their feasibility in large studies.

Problems and difficulties have been associated with self-report questionnaires. Westertop (1999) believes that because diaries and recalls rely on memory, they are subject to misrepresentation, particularly socially desirable responding. Ainsworth et al. (2000), argues that self report questionnaires are useful for recalling organised sport, however activities of light to moderate activity e.g. household tasks, general informal play with friends, are more difficult to recall accurately. Self-report methods do not tend to be sensitive enough to record these activities, yet children, perform large amounts of such activities characterized by numerous short bursts of activity lasting mere seconds interspersed with similar periods of recovery (Bailey et al. , 1995). Another problem outlined by Guillaume et al. (1997), is that when self-report methods are used, difficulties

108

Literature Review often arise among children attempting to estimate the intensity and duration of physical activity and to remember this retrospectively.

2.11.2 Heart -rate Monitors

Heart rate monitoring is based on the premise of a linear relationship between heart rate to physical exertion (Trost, 2007). Riddoch and Boreham (1995) reviewed 13 studies that used heart rate monitors to determine activity levels in children and concluded that heart rate monitoring can provide valid estimates of energy expenditure at higher exercise intensities. Therefore, they are useful in determining moderate and vigorous activity but not as accurate for low intensity activities, which is an important part of children’s physical activity (Hussey et al. , 2001). Other difficulties associated with the use of heart rate moitors are the influence that emotion, environmental condition and fitness levels of different adolescents can have on heart rate (Hiilloskorpi et al. , 1999).

However, advantages of heart rate monitoring include the objective nature of data collection and its use as a relatively inexpensive measure of physical activity. Treuth et al. (1998) believes the enhanced precision afforded by a combination of heart rate monitors and accelerometers holds promise for future research.

2.11.3 Doubly Labelled Water

“Currently the most socially acceptable and powerful technique for providing an objective measure of total energy expenditure is DLW” (Livingstone, 2003).

Doubly labelled water measurement involves the participant drinking stable non- radioactive isotopes which contain oxygen and hydrogen. Measuring the amounts of excretion of these isotopes can give an estimate of carbon dioxide production and thus energy expenditure.

109

Literature Review

Bratteby et al. (1998) argues that it is now possible to take valid measurements of total energy expenditure through the DLW method in free-living subjects without influencing their daily lives. Going et al. (1999), concurs and believes some of the advantages of this method is its safety and accuracy. As a technique it is non-intrusive and measurements are performed over longer periods than other techniques (typically one to two weeks).

The doubly labelled water technique does not provide information on behavioural aspects, such as type of activity, frequency, duration and intensity (Kohl et al., 2000; Troiano et al., 2001). Furthermore, because of the cost of the stable oxygen isotope, DLW is not feasible for use in population studies.

2.11.4 Measuring physical activity: implications for current research

The principal barrier to measuring children’s physical activity is the lack of a valid measurement instrument that can capture the full complexity of childhood activity (Riddoch et al . 2004). Techniques for measuring physical activity can be classified as objective (e.g. doubly labelled water, motion sensors, heart rate monitors) and subjective (questionnaires, observation techniques) (Livingstone et al. , 2003).

Due to the nature of children’s physical activity i.e. short bursts of activity of mixed intensity (Harro & Riddoch, 2000; Ward et al. , 2005) and the possibility of over-estimation and recall bias associated with subjective measurement techniques (Freedson et al. , 2005; Troiano, 2005; Metzger et al. , 2008), the use of more objective measures have been recommended (Ekelund et al. , 2004; Freedson et al. , 2005). These objective studies are likely to be better placed when examining correlates of physical activity (Nilsson et al. , 2009).

110

Literature Review

2.12 Measuring Physical Activity using Accelerometers

Accelerometers are objective motion sensors which are unobtrusive, easy to use and measure body movement in terms of accelerations. There measurements can be used to estimate physical activity intensity and duration (Troiano, 2005; Rowlands & Eston, 2007). Accelerometers measure bodily accelerations using one or more piezoelectrical acceleration sensors. An accelerometer contains a piezoelectrical element and a seismic mass housed in an enclosure. When the sensor undergoes acceleration the seismic mass causes the piezoelectrical element to experience deformation. These changes cause a variable output voltage to be generated which is proportional to the acceleration applied; these values are stored for analysis (Chen & Bassett, 2005). Accelerometers can be uniaxial and measure in a single axis, or triaxial, measuring acceleration in 3 planes separately as well as a providing a composite measure (Rowlands & Eston, 2007).

The Actigraph accelerometer is one of the most widely used accelerometers (Rowlands & Eston, 2007), it is a small (1.5 x 1.44 x .70 inches; 3.8 x 3.7 x 1.8 cm), light (27 grams), instrument which records acceleration information as an activity count. This can be used to provide objective estimates of the intensity and the duration of bodily movement (Catellier et al. , 2005; Matthews et al. , 2008). The Actigraph accelerometers have been found to be feasible to use in large scale studies such as the US ‘National Health and Nutrition Examination Survey’ (Matthews et al. , 2008; Troiano et al. , 2008) and the ‘European Heart Study’ (Riddoch et al. , 2004). Validation studies of Actigraph accelerometers have been performed among young people using doubly labelled water (Ekelund et al. , 2001), indirect calorimetry (Trost et al. , 1998; Freedson et al. , 2005; Schmitz et al. , 2005), whole body calorimetry (Puyau et al. , 2002; Treuth et al. , 2004 a) and direct observation (McClain et al. , 2008).

A variety of other accelerometers have also been used in the measurement of physical activity. For example, the ActivPAL uniaxial accelerometer, measures time spent sitting/ lying, standing and stepping from its placement on the anterior thigh. A recent study has found the ActivPAL to be an acceptable measure of objective physical activity in

111

Literature Review adolescent females (Harrington, 2010); furthermore the ActivPAL provides information on sedentary levels among individuals (Harrington, 2010).

Despite the advantages and widespread use of accelerometers to measure physical activity, a number of limitations exist. Firstly there is no universally accepted methodological model for data collection and analysis (Pate et al. , 2006; McClain et al. , 2008). The discrepancies that exist between data analysis methods can result in large differences in outcomes such as in moderate and vigorous physical activity (Masse et al. , 2005; Pate et al. , 2006; Sirard et al. , 2008). Until clear guidelines are developed, it will be difficult to compare findings across studies that use accelerometers (Masse et al. , 2005). Another limitation of accelerometer use is that they have been found to underestimate energy expenditure in weight bearing activities, cycling, upper body movements, and ambulation upstairs or on an incline (Trost, 2001; Ekelund et al., 2004; Schmitz et al., 2005). Finally, despite being able to measure physical activity triaxially, there is currently no means of analysing this data with the Actigraph accelerometer. Some evidence exists to suggest that triaxial accelerometers are more valid that uniaxial accelerometers, however, differences have been found to be small (Eston et al., 1998; Trost et al., 1998; Vanhelst et al., 2011). Nonetheless, to get an accurate measure of activity levels, activity in all planes should be recorded and analysed.

2.12.1 Accelerometer Validation Studies

Accelerometer output is in activity counts, while some studies have used these counts to analyse habitual physical activity, most research has attempted to calibrate these counts with energy expenditure to give meaning to the output (Ward et al. , 2005; Rowlands & Eston, 2007). A number of studies have developed count thresholds which relate to different intensities of energy expenditure. This process called calibration is achieved by comparing counts with some known standard e.g. indirect calorimetry, that has been obtained from specific equations designed for this purpose (Ward et al. , 2005). These count thresholds separate movement into level of exertion (sedentary, light, moderate, vigorous) (Ward et al. , 2005). 112

Literature Review

Table 2.4 summarises the differences in threshold cut points developed for adolescents. Large differences are evident across the count thresholds (Matthews, 2005). Possible explanations for these differences include the different activities which have been used to develop these thresholds (Rowlands & Eston, 2007) as well as different calorimetry methods, age range of the subjects and the number of subjects per age range (Freedson et al. , 2005). Another difficulty in defining threshold cut points for a population is the large individual factors which add to the imprecise estimates of energy expenditure. For example, Ekeuland et al . (2003) found that individuals’ counts per minute ranged from 400 to 2,600 while walking at 4 km per hour. Therefore, published equations can predict mean energy expenditure at group levels but are less accurate in individuals, individual calibration is necessary if exact measurement is required (Melanson & Freedson, 1995; Ward et al. , 2005).

The Freedson age specific equation has been widely used to analyse accelerometer data. In this equation activity MET values are used in the development of count thresholds. Many adolescent and children studies using this equation have categorised moderate intensity as 3METS based on adult intensity classification. This classification may lead to an over estimation of time spent in moderate and vigorous activity. Trost et al. (2005), compared the predictive value of three equations to predict energy expenditure using indirect calorimetry. It was found that the Freedson 3 MET equation classifications significantly overestimated mean energy expenditure during normal walking (29.4%), brisk walking (23.5%) and slow running (15.9%). Children’s resting energy expenditure is higher than an adult MET and has been found to range from 3.8 ml/kg/min (Treuth et al. , 2004 a) to 5.9 ml/kg/min (Harrell, 2005). Therefore the application of the adult 3 MET value as a measure of moderate intensity children does not seem appropriate and a higher value would appear to be more suitable (McMinn et al. , 2010).

The TAAG (2004) study has used the MET value of 4.6 to classify adolescent girls as moderately or vigorously active based on the validation study carried out by Treuth et al. (2004 a). When this criterion is applied to activity data it was found that only 1% of participants achieved the recommended 60 mins of moderate and vigorous activity per day. However when the criteria is changed to 3 METs 88% of participants achieved the

113

Literature Review recommended levels of physical activity (Pate et al. , 2006). Other studies that have used the 3 MET classifications have resulted in high levels of moderate and vigorous physical activity. In a study by Guinhoya et al. (2006), using the Freedson 3 MET’s to classify moderate intensity activity; he found that 100% of the participants achieved 60 mins per day in moderate and vigorous activity. When the Puyau equation was applied to the same data, only 8.7% achieved this threshold. Similarly, Reilly et al. (2008), reports more than 4 hours of moderate and vigorous activity among young children when the 3 MET Freedson equation is used compared to 28 mins and 41 mins using the Treuth and Puyau equations respectively. Riddoch et al. (2004) also reports very high levels of moderate and vigorous activity among children when the Freedson 3 METs equation was applied in a large scale European study. Guinhoya et al. (2006) argues that the high levels of activity in the studies that have used 3 METs as criteria does not make sense based on the rising levels of childhood obesity.

The use of 4 METS may be a more appropriate classification of moderate intensity among children and adolescents. In a study to define threshold values for accelerometers, Treuth et al. (2004 a), found that 4 METS distinguished brisk walking from slow walking, this is in agreement with the calibration study of Riddoch et al. (2007). Further support for the use of 4 METs is provided by McLain et al. (2008), who compared 3 cut points with a direct observation method during a PE class. It was found that the Freedson age specific equation was the most accurate equation to predict physical activity based on direct observation. Importantly, in this analysis, 4 METs was used to classify individuals as being moderately active. Therefore, although 3 METS have been used to classify MVPA in children and adolescents it has now been recommended that a higher threshold (4 METs) be used to denote MVPA in children (McMinn et al. , 2010).

2.12.2 Epoch

An epoch represents the amount of time (secs) over which movement data (counts) are summed and stored (McClain et al. , 2008). The choice of epoch should be based on the

114

Literature Review population being studied, the amount of memory available and the activity being assessed (Rowlands & Eston, 2007).

Children’s physical activity occurs in short, sporadic bursts with high intensity activity often lasting only a few seconds (Bailey et al. , 1995; Ward et al. , 2005; Baquet et al. , 2007). Most studies have recorded physical activity in 30 seconds or 1 minute epochs (Riddoch et al. , 2004; Troiano et al. , 2008). However this is known to underestimate moderate and vigorous activity in children by smoothing the data (Nilsson et al. , 2002; Ward et al. , 2005; Rowlands et al. , 2006; Riddoch et al. , 2007). For example, if a study used a 1 minute epoch and a child was vigorously active for 30 seconds and then lightly active for 30 seconds the averaged count for that minute would reflect a more moderate intensity (Chen & Bassett, 2005). McLain et al. (2008), compared levels of physical activity in a PE class using accelerometers at different epochs and by direct observation and reported that recording data in 5 second epochs was more accurate than longer epochs (Rowlands & Eston, 2007; McClain et al. , 2008). The longer epochs used in previous research of children and adolescents’ can be identified as a limitation of these studies.

2.12.3 Number of Monitoring Days

The number of days that participants need to wear an accelerometer to get an accurate measure of habitual physical activity has been examined by Trost et al. (2000). For both children and adolescents, 7 days of monitoring produced acceptable estimates of MVPA (ICC =0.76-0.86) and accounted for significant differences in weekday and weekend day physical activity. These findings are supported by Treuth et al. (2003) and Ward et al. (2005). However Janz et al. (1995), found that 4 days of monitoring among 7- 15 year olds may be sufficient with an ICC of 0.75-0.78.

Difficulties arise in getting participants to wear an accelerometer for a full 7days. The question arises how many days’ data is enough to be included in the data analysis? In general researchers have set the minimum criteria for inclusion in the analysis as being 4 full days (Sirard et al. , 2008; Troiano et al. , 2008), or 3 full days when the accelerometer

115

Literature Review was worn (Ekelund et al. , 2004; Masse et al. , 2005; Riddoch et al. , 2007). Physical activity patterns tend to differ on weekdays as opposed to weekends (Trost et al. , 2000; Rowlands & Eston, 2007). Therefore, some researchers have set criteria for inclusion as 4 full days of monitoring, one of which must be a weekend day (Riddoch et al. , 2004). Some researchers have used as little as one full day of monitoring to be included in the data analysis (Treuth et al. , 2007; Matthews et al. , 2008). Mattocks et al. , (2008)in a study of almost 6,000 children aged 11 years, concluded that three days with at least 600 minutes a day recorded gave adequate reliability (r = 0.7) and power (>90%) for accelerometer measured physical activity. Although weekend day was not specified in order to fulfil validity criteria in this study, 90% of children had at least 1 weekend day.

Some researchers have eliminated the omitted data from the first day of data collection to reduce the impact of reactivity (higher levels of activity as a result of wearing the accelerometer) (Pate et al. , 2006). It has been found by Riddoch et al. (2007) that activity counts on day 1 were on average 17 counts higher than subsequent days, indicating a very small reactive effect. The author does not regard as being meaningful.

No difference was found between weekday and weekend physical activity levels in some research, therefore participants were not required to have sufficient wear time on a weekend day to be included in the analysis (Mattocks et al. , 2008; Troiano et al. , 2008). Similarly, Steele et al. (2010) investigated physical activity patterns among children on weekdays and weekend days and reported no difference for weekday versus weekend day physical activity.

2.12.4 What Constitutes a Day?

The minimum amount of accelerometer wear time in a specific day for that day to be included in the analysis has been found to vary. For example the minimum criteria has been set at 600 minutes of wear time per day by some researchers (Ekelund et al. , 2004; Anderson et al. , 2005; Penpraze et al. , 2006; Riddoch et al. , 2007; Troiano et al. , 2008).

116

Literature Review

Other researchers have found that eight hours (Eiberg et al. , 2005) and six hours (Treuth et al. , 2007) of recorded activity was a sufficient measure of habitual physical activity.

While all research studies have set criteria as to what is the minimum amount of wear time in a ‘standard day’, few studies have set limits to a standard day, e.g. 12 hours. The implications of this are that participants with 10, 12 and 14 hours of activity per day will be compared together and it may lead to an underestimation of activity among those who wear the accelerometer for shorter periods of time. Masse et al. (2005), argues that the periods of the day when the accelerometer is removed is probably not associated with much moderate and vigorous physical activity, however if the main interest is resting/ light intensity activity it may be important to standardise this.

The TAAG study (2004) has developed criteria for maximum and minimum wear time in a day using the 70%; 80% rule. In this study a day was defined as the time when 70% of the participants wore the accelerometer (13 hours during the week; 8 hours weekend). Each day was broken into blocks e.g. 9am-2pm; and for the raw physical activity score to be included, 80% of the data within each block was required. If participants did not reach the 80% criteria in a specific block, the missing data was imputed based on the EM algorithm. Girls who failed to provide at least one day with a minimum of six hours of data were excluded, as the accuracy of imputation was deemed too imprecise (TAAG, 2004; Treuth et al. , 2007).

2.12.5 Non-wear Time

Classification of non-wear time, i.e. time when the accelerometer has been removed, has been set at 60 minutes of consecutive zeros (Metzger et al. , 2008; Troiano et al. , 2008) while others have used 30 minutes (Sirard et al. , 2008); 20 minutes (Anderson et al. , 2005; Catellier et al. , 2005; Pate et al. , 2006), and 10 minutes (Eiberg et al. , 2005; Riddoch et al. , 2007).

117

Literature Review

In other studies attempts have been made to impute data, i.e. to use observed data to predict activity levels for segments of the day when the monitor was not worn. Imputation may work better on weekdays than weekend days because there is higher correlation among activity levels on weekdays (Catellier et al. , 2005). Imputation using the EM algorithm found to accurately predict missing values in the TAAG study (Catellier et al. , 2005) and used in subsequent studies by Metzger et al. (2008); Pate et al. (2006) and Treuth et al . (2007). Other similar imputation techniques have been used by Kang et al. (2009), to predict missing data.

2.12.6 Spurious Data

Accelerometers may malfunction and therefore it is very important to be able to identify spurious data (Masse et al. , 2005). Spurious data has been defined as 10 minute counts of continuous identical variables that were not zero (Masse et al. , 2005; Metzger et al. , 2008) and a 1 minute count of greater than 20,000 counts per minute (Masse et al. , 2005).

2.12.7 Measured Aspects of Physical Activity

Counts per minute have been analysed as it is the only accelerometer variable that has been rigorously validated during free living conditions using doubly labelled water (Ekelund et al. , 2001; Riddoch et al. , 2004; Sirard et al. , 2008). Mean counts per minute are calculated by dividing the sum of activity counts for a valid day by the number of minutes of wear time in that day. Daily and weekly mean counts have been calculated and used in large scale studies (Troiano et al. , 2008).

Mean daily and weekly minutes of low, moderate and vigorous physical activity have been used in research studies using various cut points that have been developed (Table 2.4). For example the Freedson age specific cut points that equate to 4 METs and for

118

Literature Review moderate and 7 METs for vigorous minutes of moderate and vigorous physical activity were used as in the NHANES study (Troiano et al. , 2008).

Increasing number of studies are reporting the time spent in bouts of activity that are typically 5, 10, 15 or 20 minutes long (Janz et al. , 2006). Ward et al. (2005) recommends that the number and lengths of MVPA bouts as well as total duration of MVPA should be reported. Modified moderate and vigorous bouts have been classified as seven minutes out of a ten minute period in which activity levels are greater than or equal to the moderate threshold. This allows for natural breaks in activity e.g. stopping at traffic lights, waiting for the ball to be retrieved etc. (Metzger et al. , 2008). A bout has been deemed to be over when 3 consecutive minutes are below the threshold, thus allowing for normal breaks in physical activity (Masse et al. , 2005). Large cross sectional studies use one minute epochs to categorise bouts of moderate and vigorous intensity physical activity (Riddoch et al. , 2007; Troiano et al. , 2008).

With the increase in levels of obesity, especially in children, measuring inactivity or sedentary behaviour is of increasing interest to researchers. There is a need for cut points that indicate sedentary behaviours to be further developed (Ward et al. , 2005). Sedentary time has been defined by Mathews et al. (2008), as the amount of time accumulated below 100 counts per minute during periods when the monitor is worn. Sedentary time has been expressed as proportion of monitor wearing time (%) and as total duration (mins / day) (Mathews et al ., 2008). The 100 count per minute was developed by Treuth et al. (2004 a) and is in agreement with the findings of Zhang et al. (2003).

2.12.8 Procedural Issues

Many accelerometer studies have used different procedural and analysis techniques making it very difficult to compare different studies. It is important that a standardised protocol be developed for accelerometers (Welk, 2005). Publishing decisions on rules relating to how data is cleaned, collapsed and analysed will facilitate the analysis process and allow comparison among studies (Ward et al. , 2005).

119

Literature Review

2.12.9 Field-based Protocol

Studies that employed multiple accelerometers to estimate physical activity energy expenditure report marginal improvements in explanatory power. These small improvements do not justify participants being burdened with wearing more than one accelerometer (Trost et al. , 2005). Studies have also examined the optimal placement of the accelerometer on the body (Bouten et al. , 1997; Welk et al. , 2000; Nilsson et al. , 2002). It is generally accepted that the hip or lower back is the most effective positioning of the accelerometer. The right side of the body may be the most convenient place as most people are right handed (Ward et al. , 2005).

2.12.10 Data Analysis

Many data reduction programmes exist to condense large amounts of data to more manageable and reportable outputs. Programmes such as MahUffe (Steele et al. , 2009), excel macro programmes (Ekelund et al. , 2004; Treuth et al. , 2004 a), and SAS programmes (Troiano et al. , 2008), have been used to reduce the data.

2.12.11 Promotion of Compliance

To promote compliance among participants a number of initiatives have been developed. These include phone calls, text messages, activity logs, tip/ information sheets, incentives contingent on compliance and showing a sample output of information before the commencement of the study (Trost et al., 2005; Ward et al., 2005).

120

Literature Review

Table 2.4: Actigraph Accelerometer cut points for adolescents

Author Age Type of Activities Assessment Sed. Light Moderate Vigorous Measure

Freedson et al. 6-17yrs Treadmill walking, running Indirect <2,580 2,580-5,668 >5,669 (1997) a Calorimetry

Freedson et al. 6-16 yrs Regression Equation based Aggregate of <1400 1400-5700 5700-10000 (2005) a on equations developed by previous Treuth et al. , (2004) and studies which Puyau et al. , (2002). used calorimetry

Puyau et al. (2002) 6-16 yrs Sedentary, Low, moderate, Indirect <800 <3200 <8200 ≥8200 vigorous activities Calorimetry

Treuth et al. 13-14 yrs Sedentary, Low, moderate, Indirect <100 101-2999 3000-5200 >5200 vigorous, activities Calorimetry (2004 a)

Ekelund et al. (2004) 9-10yrs Intensity categories based on Aggregate of <500 500-1999 2000-3000 ≥3000 previous thresholds previous studies

Mattiocks et al. 12 yrs Sedentary, Low, moderate, Indirect <3581 3581-6130 >6130 (2007) vigorous activities calorimetry a (For the purpose of the Freedson equation calculations based on a14 year old, with 4 MET classifying moderate intensity physical activity

121

Literature Review

2.12.12 Accelerometer literature; implications for current research

Regular physical has been associated with numerous health benefits in adolescence and adults. Various tools have been used to measure physical activity, furthermore, various protocols have been used to analyse activity data in studies using the same measuring tools. This has resulted in difficulties comparing levels of activity among different studies. Self- report studies suggest that levels of activity among Irish adolescents are low compared to international adolescents. There is currently a dearth of research which has objectively measured physical activity among Irish adolescents. Accelerometers have been found to be valid and reliable tools to measure physical activity. The collection of accelerometer measured activity levels among Irish adolescents would considerably add to the body of knowledge relating to activity levels among Irish adolescents.

The following physical activity protocol was developed for use in the current study. Physical activity was measured using Actigraph triaxial accelerometers (GT3X, Fort Walton Beach, FL). The distribution and the collection of the accelerometers were carried out by the primary researcher, who visited each school and personally distributed the accelerometers during an information briefing session. This is in agreement with Ward et al . (2005) who recommend face to face distribution and collection of monitors as being the most effective dissemination technique. Participants were asked to wear the accelerometer over their right hip for a period of 7 days, and to remove it when swimming or bathing. To promote compliance among participants a number of initiatives were used, these initiatives were adopted from Trost et al . (2005) and Ward et al . (2005). Participants whose parents provided consent received a text message to their mobile phone each morning to remind them to put on their accelerometer. Levels of wear time have been found to be lower on weekends, (Masse et al. , 2005; Treuth et al. , 2007; Sirard et al. , 2008) therefore a reminder text message was sent each morning and afternoon on weekend days. A second initiative was that an information sheet was developed for participants with relevant facts and frequently asked questions relating to accelerometers. This information sheet was disseminated at the initial information session. A third initiative to promote compliance was that an I-pod shuffle was raffled in each school among those that participated. To avoid

122

Literature Review a degree of reactivity (i.e. increased activity levels during the week) it was stressed to participants that eligibility for the raffle was based on wear time as opposed to activity levels. A fourth initiative was that each participant was provided with a summary sheet of their weekly levels of physical activity after participating in the study. This strategy was used as a means of providing PE teachers and students with some important information on their levels of physical activity. The author was aware that this procedure may have led to a degree of reactivity; however it was felt that it was necessary to provide participants with some feedback after wearing the accelerometer for a week. To be included in the analysis participants were required to wear the accelerometer for a minimum of 600 minutes on 4 separate days. Wear time was calculated by subtracting non wear time from 24 hours. Classification of non wear time (i.e. time when the accelerometer has been removed) was measured using 1 minute epochs and defined by an interval of at least 60 consecutive minutes of zero activity, with allowance for 1-2 min of counts between 0 -100 (Metzger et al. , 2008; Troiano et al. , 2008). Periods of non wear time were considered over when (i) 3 consecutive minute of 1 – 100 were recorded; or (ii) a minute with a score of greater than 100 was recorded. This follows the procedure recommended by Troiano et al. (2008) in the NHANES study. Accelerometers may malfunction and therefore it is very important to be able to identify spurious data (Masse et al. , 2005). The current study identified spurious data as 10 minute counts of continuous identical variables that were not zero (Masse et al. , 2005; Metzger et al. , 2008) and a 1 minute count of greater than 20,000 counts per minute (Masse et al. , 2005). Furthermore, mean daily counts were analysed and questionable data (n=1) were removed from the analysis. Some researchers have omitted data from the first day of data collection to reduce the impact of reactivity (higher levels of activity as a result of wearing the accelerometer) (Pate et al. , 2006). It has been found that activity counts on day one were on average 17 counts higher than subsequent days indicating a very small reactive effect, and the author does not regard as being meaningful (Riddoch et al. , 2007). Therefore, the first day of accelerometer wear was included in the analysis in the current study. No difference was found between weekday and weekend physical activity levels in the current study (mean weekday moderate/ vigorous activity = 44.7 ± 20.2 mins; mean

123

Literature Review weekend moderate/ vigorous activity = 44.4 ± 30.2 mins), therefore participants were not required to have a weekend day to be included in the analysis. This follows the criteria of Troiano et al. (2008) and Mattocks et al. (2008). Similarly, Steele et al . (2010) investigated physical activity patterns among children on weekdays and weekend days and reported no difference for weekday versus weekend day physical activity. Outcome variables calculated for the current study were, (1) mean counts per minute, (2) mean daily minutes in sedentary, low, moderate and vigorous activity, (3) mean daily minutes in moderate and vigorous activity bouts and (4) estimates of adherence to recommended physical activity guidelines. Counts per minute were analysed in the current study as it is the only accelerometer variable that has been rigorously validated during free living conditions using doubly labelled water (Ekelund et al. , 2001; Riddoch et al. , 2004; Sirard et al. , 2008), furthermore mean counts per minute evaluates raw data scores without the use of any external criteria. Mean counts per minute were calculated by dividing the sum of activity counts for a valid day by the number of minutes of wear time in that day. A weekly mean count per minute was computed based on each valid day, similar to Troiano et al. (2008). Time spent in physical activity of different intensities is based on the application of count thresholds derived from calibration studies that classify accelerometer output to measured activity energy expenditure. Accelerometer counts during wear time were classified as sedentary, low, moderate or vigorous intensity. Sedentary behaviour was calculated based on minutes accumulated below 100 counts per minute during periods when the monitor was worn expressed as mean minutes per day. This threshold was developed in a calibration study carried out among adolescent girls (Treuth et al. , 2004 a) and has been widely used (Healy et al. , 2007; Healy et al. , 2008; Matthews et al. , 2008). Moderate and vigorous activity count thresholds were calculated using the age specific equations developed by the Freedson group as published by Trost et al, (2002). METs=2.757 + (0.0015 x counts min -1) – (0.08957 x age [yr]) – (0.000038 x counts min -1 x age [yr]). Thresholds for moderate activity of 4 METs and vigorous activity of 7 METs were used as these values adjust for the higher resting energy expenditure of children and youth and have been found to equate to moderate and vigorous intensity activity among adolescents (Treuth

124

Literature Review et al. , 2004; Riddoch et al. , 2007; McClain et al. , 2008). Low intensity activity was calculated as time spent over the sedentary threshold and under the moderate threshold i.e. 100 counts per minute - 3.9 METs (Healy et al. , 2007). Due to the nature of activity among youths (i.e. short bursts of intermittent activity at different intensities (Bailey et al. , 1995; Ward et al. , 2005) data was collected at 5 second epochs to avoid underestimation of time in moderate and vigorous activity that may occur when longer epochs are used (Nilsson et al. , 2002; Ward et al. , 2005; Rowlands et al. , 2006; Riddoch et al. , 2007), for example if a participant was to run vigorously for a period of 10 seconds and to stand for a period of 50 seconds a one minute epoch may categorise this as low intensity activity, which would not be a correct representation of the activity pattern . Therefore, for a 14 year old participant moderate intensity activity was classified as between 215 – 472 counts per 5 second epoch. Time spent in activity of a defined intensity was determined by summing 5 second epochs where counts met criteria for that intensity. Increasing number of studies are reporting the time spend in bouts of activity that are typically 5, 10, 15 or 20 minutes long (Janz et al. , 2006). Therefore, the current study calculated the mean daily time in modified moderate and vigorous bouts across all valid days. Modified moderate and vigorous bouts were established when 7 minutes out of a 10 minute period had activity levels greater than or equal to the moderate threshold. A bout was terminated by 3 consecutive minutes below the threshold (Troiano et al. , 2008). This is in agreement with Masse et al. (2005) who recommend that bout data reduction methods should to take account of normal breaks in physical activity. Bouts of physical activity were computed using one minute epochs as youths activity is classified by short bouts of intermittent activities of varying intensities (Rowlands & Eston, 2007), therefore, mean scores across each minute would provide a more accurate picture of whether a person is involved in bouts of physical activity. An example of this would be if children were playing a game of soccer and they were vigorously active for 40 seconds and they in low levels of activity for 20 seconds waiting for the ball to be retrieved they would not have achieved 70% of time in moderate and vigorous activity. The cumulative mean score across 1 minute epochs allows a more lenient threshold which would cater for such sporadic short bursts of activity with rest periods. In conjunction with this, all other large cross sectional studies use

125

Literature Review

1 minute epochs to categorise bouts of moderate and vigorous intensity physical activity (Riddoch et al. , 2007; Troiano et al. , 2008). Adherence to recommended physical activity guidelines were analysed by estimating the proportion of adolescents achieving 420 mins of moderate and vigorous activity during the week (Dept. of Health and Children, 2009). Prevalence was calculated both by including all moderate and vigorous 5 second epochs and by restricting inclusion to bouts.

2.13 Factor Analysis

Quality of scientific data is reliant on the precision with which variables under consideration are observed and measured (Dunn et al. , 1999). Measurement in the field of physical education (PE) and exercise science includes many instruments for which the structure of the measure is abstract and unobservable, yet exists in theory, e.g. self-efficacy. Factor analysis techniques can assist in the development and evaluation of these instruments (Goodwin, 1999).

Using factor analysis, the scale developer starts with a large number of individual scale items and seeks to reduce the number of items while simultaneously classifying them into coherent factors (Nie et al. , 1975; Steiger, 1994; Goodwin, 1999; Pallant, 2005). Each factor which will be identified will consist of a cluster of variables that are highly correlated among themselves but poorly correlated with items in other factors (Portney & Watkins, 2000; McMinn et al. , 2009). A strength associated with factor analysis is that it allows the researcher to superimpose order on a complex phenomenon (Portney & Watkins, 2000), such as correlates of physical activity.

126

Literature Review

2.13.1 Historical Overview

Goodwin (1999) provides an historical overview of the development of factor analysis techniques. Pearson (1901) first proposed a procedure for factor analysis deriving his formulae from the geometry of multidimensional space. However, it was Spearman’s work on general intelligence that has been credited with the development of factor analytic techniques (Goodwin, 1999). Other landmarks in the development of factor analysis include Hotelling’s (1933) development of Principal Component Analysis, a widely used method in the development of components which are similar to factors and Thurstone’s development of Multiple Factor Analysis in 1931 (Thurstone, 1931).

2.13.2 Approaches to Factor Analysis

Factor analysis consists of 2 main approaches: (i) exploratory factor analysis and principal component analysis which is used in the early stage of research to gather information about interrelationships among a set of variables and; (ii) confirmatory factor analysis which is a more complex set of techniques used later in the research process to test theories concerning the structure underlying a set of variables (Pallant, 2005). Exploratory factor or component analysis should be viewed as an unrestricted procedure, i.e. the bias of the researcher cannot affect the end result, whereas, confirmatory approaches are an attempt to fit the data to a preconceived model. This may result in a confirmatory bias as a consequence of the failure of the investigator to specify the best model (Velicier & Jackson, 1990). This review will concentrate on exploratory factor analysis and principal component analysis which are most suitable for data reduction and scale development.

Factor analysis encompasses a wide variety of related techniques. A technique often used interchangeably with factor analysis is principal component analysis. Both factor analysis and principal component analysis attempt to produce a smaller number of linear combinations of the original variables in a way that accounts for most of the variability in the pattern of correlations. There are however, a number of differences between principal components analysis and factor analysis (Pallant, 2005; Tabachnik & Fidell, 2007). The

127

Literature Review defining characteristic that distinguishes between factor analysis and component analysis is that, in principal factor analysis only the variability of items that are in common with other items is used (communality), whereas in principal component analysis all the variability in an item is used in the analysis (common and unique variance) (Costello & Osbourne, 2005). Communalities are calculated by squaring the correlation of an item with all other items measured and are estimates of the amount of variance of an item that is explained by other items (Nie et al. , 1975). Therefore, principal factor analysis can be thought of as a technique by which a minimum number of hypothetical variables are specified in such a way, that after controlling for those hypothetical variables, all the remaining correlations would be zero- i.e. all shared variance grouped together and accounted for (Nie et al. , 1975).

Principal components analysis is a relatively straightforward method of transforming a given set of variables into a new set of composite variables or principal components (Nie et al. , 1975). The researcher does not require any assumptions about the general structure of the data. One simply asks what would be the best linear combination of variables to explain maximum variation, i.e. what is the best way of organising the variables to explain most of the data (Nie et al. , 1975).

The choice between principal components analysis and principal factor analysis is not obvious as both serve similar purpose and share many important mathematical characteristics (Velicier & Jackson, 1990). It has been hypothesised that as principal component analysis uses both the common and unique variance of items, this may lead to loadings on factors that are systematically inflated and therefore factor analysis techniques may be more suitable (Snook & Gorusch, 1989). Velicier (1990) however, disagrees with this hypothesis. Using empirical and sample data-sets he reports that differences between the two approaches are small and should not have any effect in practice in conclusions regarding which factor loadings are salient.

One possible source of discrepant solutions between component analysis and factor analysis may be in the overestimation of factors to extract (Velicier & Jackson, 1990). The Kaiser criteria, which is widely used, has been criticised as retaining too many components.

128

Literature Review

This results in over extraction, which can result in observed differences between factor analysis and component analysis solutions (Zwick & Velicier, 1986; Velicier & Jackson, 1990). The use of other more rigorous and accurate procedures for determining the number of factors to extract in principal component analysis has been recommended e.g. Cattell’s scree plot and Horns parallel analysis (Velicier & Jackson, 1990).

2.13.3 Factor Analysis Procedure

Munro et al. (2005), identifies five steps in factor analysis. Firstly, data needs to be collected and analysed for its suitability. Secondly, it is necessary to generate a correlation matrix. Thirdly factors are extracted, fourthly the data is rotated and interpreted and finally the analysis is used in the construction or modification of a scale or using the factor scores in future analysis.

2.13.4 Suitability of Data for Factor Analysis

To ensure data is suitable for carrying out factor analysis there are a number of recommendations have been provided relating to: (i) sample size and, (ii) strength of relationship among variables/ items. There is little consensus on how large a sample should be for factor analysis. Comrey and Lee (1992) classify 100 subjects as poor, 200 as fair, 300 as good, 500 as very good while 1000+ subjects was classified as an excellent sample size for factor analysis. Similarly, Tabachnik and Fiddell (1998), recommend having at least 300 cases for factor analysis.

Other researchers suggest that it is not the overall sample size that is of concern rather the ratio of subjects to items. Cattell (1978), recommends 3-6 subjects per item in the questionnaire with a minimum of 250 subjects. Other authors recommend a ratio of at least 10 subjects for each variable (Everitt, 1975; Nunnally, 1978; Munro, 2005). Limitations with small sample size are based in the logic that such samples provide correlation coefficients that are less reliable due to the influence of relationships based on random 129

Literature Review patterns, and also tend not to generalise as well as those from larger samples (Goodwin, 1999; Costello & Osbourne, 2005; Pallant, 2005).

Other authors have identified the importance of strong data which is an important criterion to assess data suitability for factor analysis. Strong data has been categorised as having uniformly high communalities across items, (i.e. a large portion of the variance of each item is explained by the factor structure), without cross loadings (i.e. not loading at 0.32 or higher on 2 or more factors), with 5 or more strongly loaded items (0.5 or better) on each factor (Tabachnik & Fidell, 1998; MacCullum et al. , 1999; Costello & Osbourne, 2005). If the data fail to meet these criteria larger sample size may be needed (Costello & Osbourne, 2005). Field (2005), reviews many suggestions relating to sample size necessary for factor analysis and recommends that an appropriate sample size consists of over 300 cases with communalities after extraction above 0.5.

Other criteria of sampling adequacy include Kaiser Meyer Olkin (KMO) with Kaiser (1974) recommending values greater than 0.5 as acceptable. This analysis assesses the patterns of correlates and values close to one signify a compact pattern of correlates so that factor analysis should yield distinct and reliable factors (Field, 2005). Bartletts tests of Sphericity is used to test for an adequate level of correlation between items and significance (P < . 05) indicates that factor analysis is appropriate (Pallant, 2005).

2.13.5 Generation of the Correlation Matrix

The item correlation matrix should also be examined to assess the suitability of the data for factor analysis . For effective factor analysis, items should have some relationship with each other and should correlate together (above 0.3). Items that correlate highly together should be merged or removed to avoid issues associated with multicollinearity (items correlated above 0.9) (Field, 2005).

130

Literature Review

2.13.6 Factor Extraction

The third step in factor analysis is to extract the set of factors (Munro, 2005). Difficulties arise in deciding the number of factors that best describe the underlying relationship among the variables. The researcher has to try to balance conflicting needs, i.e. the need to find simple solutions with as few factors as possible and the need to explain as much of the variance in the original data set as possible. Both over extraction and under extraction of factors retained can have deleterious effects on results (Costello & Osbourne, 2005).

A number of statistical techniques such as Kaiser’s criterion, Cattells scree test and Horns parallel analysis can be of assistance in deciding the number of factors to retain (Tabachnik & Fidell, 2007). Using Kaiser’s criterion or the eigenvalue rule, only factors with an eigenvalue of >1.0 or greater are retained for further investigation. The eigenvalue can be calculated by squaring the loading of each item in a factor and summing the totals (Nie et al. 1975). This eigenvalue represents the amount of total variance explained by that factor. In essence, unless a factor extracts at least as much equivalent of one original variable, this factor is excluded. Kaiser’s criterion has been recommended to be used with less than 30 variables and with communalities after extraction > 0.7 or with a sample size of > 250 and mean communality of > 0.6 (Field, 2005). There is broad consensus in the literature that this is among the least accurate methods for selecting the number of factors to retain (Velicier & Jackson, 1990).

Another approach which can assist in factor extraction is Cattells scree test (Cattell, 1966). This involves plotting each of the eigenvalues of the factors and inspecting the plot to find a point at which the shape of the curve changes direction and becomes horizontal. It is recommended that all factors above the elbow or break in the plot be examined as these factors account for most of the variance (Costello & Osbourne, 2005). It should be noted however, the scree tests can be unclear if there are data points clustered together near the bend. If this is the case, it is recommended to run multiple factor analysis and set the number of factors to retain manually (Costello & Osbourne, 2005). The scree plot has been recommended for studies with a sample size of > 200 (Field, 2005).

131

Literature Review

The third procedure that assesses the number of factors for extraction is Horn’s parallel analysis (Horn, 1965). This technique is based on the comparison of eigenvalues from the data set with a randomly generated data set. Factor retention is based on only those eigenvalues that exceed the corresponding values from the random data set. This approach to identifying the correct number of components to retain has been found to be the most accurate, (Lance et al. , 2006), as Kaiser’s criterion and Catell’s scree test tend to overestimate the number of factors to retain (Zwick & Velicier, 1986)

2.13.7 Factor Rotation and Interpretation

Once the factors have been determined, the fourth step in factor analysis involves rotation and interpretation of the factors (Munro, 2005). The purpose of rotation is to achieve a simpler and theoretically more meaningful factor pattern (Nie et al. , 1975; Meyers et al. , 2006). All variables are located in a 4 dimensional space however it may be difficult to see where certain variables load as they may be equi-distance from axes (factors) e.g. in figure 2.7, V1 loads on factor 1 and factor 2. The rotation of the axes can maximise the loadings of each variable on one of the axes, while minimizing the loading on other axes (Portney & Watkins, 2000; Field, 2005). For example, in figure 1, V1 is rotated to load strongly on factor 1. This process does not change the underlying solution, but merely makes the interpretation of the analysis easier (Tabachnik & Fidell, 2007).

The exact choice of rotation depends on whether it is hypothesised that the factors are related (Field, 2005). Orthogonal rotation maintains the perpendicular nature of the axes thus assuming that factors are independent entities and are not correlated with other factors (Figure 2.7). Varimax is the most common orthogonal rotation as the variance of loading within factors is maximised. Therefore, the interpretation of each factor is made easier as the variables that correlate with that factor are more clearly identified (Munro, 2005). Oblique rotations allow for axis to change their orientation to each other during rotation. Due to the fact that the axes may be closer than 90 degrees, it is possible for factors to correlate together (Costello & Osbourne, 2005). In situations where the correlation between variables is clear, both rotations result in similar findings (Tabachnik & Fidell, 1998). 132

Literature Review

Orthogonal factors are mathematically simpler to handle while oblique factors are empirically more realistic (Nie et al. , 1975).

Munro (2005), believes the essence of interpreting factor analytic results is the process of identifying from the rotated factor matrix which variables go with which factor. The naming of these factors should be influenced by the pivotal terms, i.e. items that load most heavily within any factor (Tabachnik & Fidell, 1998). The optimal outcome has been termed ‘simple structure’, where all items load strongly on only one factor with no cross loading across factors.

Figure 2.7: Factor rotation and interpretation

2.13.8 Scale Development from Factor Analysis

The final step in factor analysis involves the construction of scales or factor scores to use in further analysis. Scale construction usually involves refining and reducing the number of items in a scale or questionnaire. Items that were not conceptually meaningful, because they don’t measure the same construct as the cluster of items that formed the factor, should not be considered in the interpretation (MacNamara & Collins, in press). In conjunction with this, items that load simply but with a low loading (Sherwood et al. , 2004; Ommundsen et al. , 2006; MacNamara & Collins, in press) and items with complex loading, should be examined to see if they fit logically in the factor. If they do not meet this criteria 133

Literature Review these items should be removed (Saunders et al. , 1997; Sherwood et al. , 2004; MacNamara & Collins, in press).

Other studies focus on further analysis, e.g. regression based on a factor score. An individual factor score is calculated by multiplying each variable value by a weighting (factor loading), and then summing the weighted scores for all the variables within the factor.

2.13.9 Validity and Reliability using Factor Analysis

Measures with acceptable levels of validity and reliability reduce measurement error and strengthen conclusions that can be drawn from research (McMinn et al. , 2009). Factor analysis is only recently seen to play an important role in reliability and validity (Goodwin, 1999). Cronbach alpha scores have been used to assess the internal reliability of factors within questionnaires which have been factor analysed (McMinn et al. , 2009; MacNamara & Collins, in press). Cronbach alpha involves splitting the questionnaire into all possible halves, calculates the scores, correlates them and averages for all splits (Field, 2005).

McMinn (2009) argues that validation of a measure of physical activity correlates in youth is problematic. There is no established gold standard with which to compare new measures. Some validation studies of child report questionnaires have used alternative sources such as parents and teachers as a criterion measure for comparison. Other validation studies have used factor analysis to validate the content and structure of measurement tools (Motl et al. , 2001; Motl et al. , 2003). It should be noted however, that while factor analysis can be used to reduce a set of questions on a particular construct or to guide the grouping of individual variables into constructs, this does not verify whether the new questionnaire is measuring what it is supposed to (McMinn et al. , 2009). Other authors have used regression analysis to assess how effective the factor analysed questionnaire is at explaining the variance in the phenomenon of interest e.g. physical activity, thus testing the construct validity of the questionnaire, however this may be counter intuitive as it is investigating whether the variables in question are associated with physical activity before

134

Literature Review being clear that the questionnaire is actually measuring these variables (McMinn et al. , 2009). As there does not appear to be one ideal method of validating a questionnaire on the correlates of physical activity using a combination of methods is likely to give the best estimates of validity (McMinn et al. , 2009).

2.13.10 Factor Analysis and Correlates of Physical Activity

Physical activity research has been based on the socio-ecological model of health which posits that activity is influenced be various psychological, social, environmental and personal correlates (Sallis et al. , 2000; Elder et al. , 2007). An important aspect of the research into the correlates of physical activity is the use of valid reliable tools, both of physical activity and of the correlates being investigated (McMinn et al. , 2009). Using measures with acceptable levels of validity and reliability reduces measurement error and strengthens conclusions that can be drawn (McMinn et al. , 2009). There is currently a dearth of research however, which has attempted to develop a valid, reliable and comprehensive tool to measure these influences on physical activity.

Studies which used factor analysis to develop valid and reliable tools to measure the correlates of physical activity among adolescents are described in Table 2.5. Much of the research which has been carried out with adolescents has focused on developing individual specific scales. Studies which have focused on factor analysing more than one scale have examined each scale separately and attempted to assess the factor structure of each specific scale (Table 2.5). No research has attempted to factor analyse and refine a comprehensive questionnaire measuring a broad range of psychological social and environmental correlates of physical activity. Furthermore, no study has attempted to assess the internal reliability and criterion validity of such a questionnaire. One study which used this approach applied factor analysis in the development of a broad, comprehensive measure of psychological, social and environmental variables involved in developing excellence in sport (MacNamara & Collins., in press). A list of 160 items was reduced using expert panel, cognitive interviews, pilot test and factor analysis resulting in an interpretable 59 item, 6 factor solution with good levels of internal consistency. 135

Literature Review

2.13.11 Factor Analysis; implications for current research

Factor analysis techniques can be used in the development and evaluation of scales. It does this by analysing a large number of individual items and looking for groups of items which cluster together. Scale developers can identify the important items explaining the majority of the variance which can be used to refine and reduce the number of items in a scale. Factor analysis techniques consist of a series of steps; initially data must be assessed for its suitability for factor analysis. Factors must be identified, rotated and interpreted. The results can then be used in the development and refinement of a scale.

The following procedure was adopted and used in the current research. Principal component analyses (PCA) was used in preference to exploratory factor analysis as the purpose of the analysis was to give an empirical summary of the data set, for which principal component analysis is more suitable (Nie et al. , 1975; Tabachnik & Fidell, 2007). Data was rotated to achieve a simpler and more meaningful factor pattern. In the initial analyses both orthogonal (varimax) and oblique (direct oblimin) rotations were run. Both sets of analyses provided similar results, which is to be expected when correlations were found to exist between items (Tabachnik & Fidell, 2007). Orthogonal rotations are presented in the analyses as they are simpler to handle (Nie et al. , 1975), and are more widely used in factor analysis and principal component analysis literature. A number of othogoonal rotations exist, the current study used varimax rotations which are the most common orthogonal rotation as the variance of loading within factors is maximised, thus the interpretation of each factor is made easier as the variables that correlate with that factor are more clearly identified (Munro, 2005).

136

Literature Review subjects per item (Munro, 2005). Other criteria of sampling adequacy that were applied include the Kaiser- Meyer- Olkin measure of sampling adequacy (KMO) which assesses patterns of items. Higher values above the recommended .05 level and closer to 1 signify a compact pattern of correlates where factor analysis would be deemed suitable (Field, 2005; Tabachnik & Fidell, 2007). Bartletts test of Sphericity is used to test for an adequate level of correlation between items, with P < .05 recommended for factor analysis (Field, 2005; Tabachnik & Fidell, 2007). Item total correlations were also examined to assess the suitability of the data for factor analysis. It is recommended that items should correlate between 0.3 and 0.9; i.e. have some relationship yet avoiding issues associated with multicollinearity (Field, 2005). Separate principal component analyses were carried out for psychological, social and environmental correlates of physical activity in the current study. The current study is based on the socio ecological model of health which posits that physical activity is based on separate personal/ biological, psychological, social and environmental correlates. It would appear logical therefore, to examine each of these domains of influences separately and to identify the important factors within each domain of influence. In conjunction with this, as previously outlined, for data to be considered suitable for factor analysis a degree of correlation is recommended between items (Field, 2005). Higher item total correlations were observed in items within a homogenous category e.g. social support from peers items were found to correlate with social support from family items, these items did not correlate as favorably with items from items from psychological and environmental domains. A further justification for separate analysis was to assist in managing and interpreting the data. An initial principal component analysis was carried out with all psychological, social and environmental variables included. Due to the large quantity of items difficulties arose in identifying the correct number of factors to extract and in interpreting the data. Furthermore, most other factor analysis research has used individual scales or a small number of scales from a specific category of influence (Saunders et al. , 1997; Motl et al. , 2001; Sherwood et al. , 2004). In conjunction, as outlined in the previous section, some researchers have recommended a high ratio of questionnaire items to participants to assess the suitability of the data for principal component analysis (Cattell, 1978; Munro, 2005). Separate psychological, social and environmental analyses increase the item participant

137

Literature Review ratio in the current study further supporting the suitability of the data for component analysis. No analyses were carried out on the personal/ biological correlates of physical activity due to the unsuitability of this data for principal component analysis. Much of the data gained in the personal/ biological category were diverse factual pieces of information which did not correlate together e.g. age of student, ethnicity etc. Furthermore most items consisted of single items which did not cluster with other personal/ biological items in a factor analysis. Due to the non modifiable nature of much of this data e.g. gender, age and the importance of this information in providing context and demographic details of the participants in future studies questions relating to personal/ biological information from the original questionnaire were retained in the final APAC questionnaires. A number of statistical techniques such as Cattells scree test and Horns parallel analysis were used to decide the number of factors to retain from the analyses (Tabachnik & Fidell, 2007). Data from Kaiser’s criterion also were examined; however these data were examined with caution as previous researchers have been critical of this technique overestimating the number of factors to extract in principal component analysis (Zwick & Velicier, 1986; Velicier & Jackson, 1990). In all analyses the factor extraction techniques were used as a guide on the possible number of factors to extract. Multiple analyses were run with the number of factors to retain being set manually e.g. if the criteria recommended a 3 factor solution, analyses were run extracting 2, 3 and 4 factors to identify the cleanest factor structure (Costello & Osbourne, 2005). The cleanest factor structure was determined using item loadings above 0.3, with no or fewest complex loadings (Field, 2005) and which was conceptually meaningful and interpretable. After extraction of factors, efforts were made to reduce the number of items in the questionnaire. Currently, there is a lack of criteria and guidelines relating to questionnaire data reduction techniques. The following protocol was developed based on the procedure used by MacNamara & Collins (in press). Items that loaded simply (i.e. loading on one factor) but with low loading less than .40 were removed from the analyses (Comrey & Lee, 1992; MacNamara & Collins, in press). Items with a loading of greater than .40 were examined and those that were not conceptually meaningful i.e. they did not measure the

138

Literature Review same construct as the cluster of items that formed the factor, were not considered in the analyses. Strong data has been identified as having no or few complex loaded items (Field, 2005) therefore; more stringent criteria were applied to items with complex loading (i.e. items with loading of > 30 on more than one factor). Items which loaded on more than one factor and did not have a loading of greater than .50 on either factor were removed from the analyses. Complex loaded items with a loading of greater than .50 were examined to see if they fitted logically into the factor structure based on the wording of the item and the label given to the factor, where this was not the case these items were removed. Conceptually meaningful items with simple loadings of greater than .40 and complex loaded items with a factor loading of greater than .50 were further examined. A number of trial factor analyses were run with lower loaded items removed in an attempt to find the factor structure of best fit; i.e. reduced number of items which explained most of the variance. Where pairs of items within the same factor were found to be repetitive, the item with the lower loading was removed. Factors which did not have acceptable levels of internal consistency, (Cronbach alpha > .7) (Tabachnik & Fidell, 2007) were also not considered in the interpretation. After data reduction techniques were applied to the data, a second set of principal components analyses were used to confirm the integrity of factor structure (Henson et al. , 2004; MacNamara & Collins, in press).

139

Literature Review

Table 2.5: Studies using factor analysis in questionnaire development

Author Participants Scales factor Type of factor Factor Other validity and Results of the study analyzed analysis Extraction and reliability measures data reduction used

Ommundsen et al. 760 adolescents Locations specific Principal Eigenvalue <1 Validity of these self Physical activity occurs in (2006) aged 9-15 in forms of physical component reported measures three separate contexts Norway activity analysis with examined through varimax rotation correlations with School commuting, Items with a objective measures of loading of less physical activity Informal games at school than 0.5 removed assessed using accelereomtery Organized sport s and leisure time.activity

Sherwood et al . African American Scales measuring Principal Scree plot Internal consistency Sub scales derived were: (2004) girls aged 8-10 component criterion reliability Self concept analysis with Activity Preference varimax rotation Test retest reliability Self-efficacy Positive Expectancies Items cross Validity assessed by Outcome loading and items correlating measures Negative Expectancies for expectancies where highest with physical activity physical activity loading less than measured by Preferences 0.3 removed acceleroemter and BMI

Saunders et al. 442 fifth grade Three theory Principal Scree plot Internal consistency Social influences scale (1997) students (approx based component criterion reliability contained a single factor. 10-11 years) questionnaires and analysis with measure of after varimax rotation Test retest reliability Self-efficacy contained 3 school physical factors: support seeking, activity Items assigned to Validation by barriers and positive a factor if its correlating scale scores alternative. loading was 0.35 with intention to be after

140

Literature Review

and had no school physical activity loading of 0.3 or Beliefs scale contained 2 higher on another factors, social outcomes factor and physical activity outcomes.

Motl et al, (2003) Two cohorts (N = Self Motivation Exploratory Solutions Correlation between self 9 positively worded items 955 and 1,797) of and Physical principal axis extracted based report physical activity, of the questionnaire can be adolescent girls, Activity Inventory factor extraction on expectations BMI, team sport used to measure self (mean age 13.7) for Children with oblique of the factor involvement and motivation in children rotation and structure, developed confirmatory eignvalues, scree questionnaire. factor analysis plot, correlations and factor pattern Structural Equation interpretability Modeling

Motl et al. (2001) Adolescent girls Enjoyment of Exploratory Solutions Structural Equation PACES scale is a vlaid (N = 1,797) (mean physical principal axis extracted based Modeling measure of enjoyment age 13.7) education, factor extraction on expectations among adolescent girls physical activity with oblique of the factor and sport rotation and structure, involvement confirmatory eignvalues, scree Cross validation of final factor analysis plot, correlations solution in independent and factor pattern sample interpretability

(Pirasteh et al. , Adolescent Iranian Psycho-social and Principal Eigenvalue and Internal consistency Scales which were 2008) girls (N = 512) environmental component scree plot and reliability translated were found to aged 14-17 measures of analysis with parallel analysis suitable to measure correlates of either oblique or Test retest reliability psychosocial correlates in physical activity varimax rotation Iranian girls dependent correlation between items

141

Literature Review

2.14 Measures of the Correlates of Physical Activity

As the research field searches to identify factors related to adolescents’ physical activity, better and more encompassing measurement tools need to be developed (McGuire, 2002). Moreover, as studies are developed, a conscious effort should be made to use uniform measures so that cross study comparisons can be made (McGuire, 2002).

2.14.1 Measures of Psychological Correlates of Physical Activity

A large number of scales have been developed to assess various psychological correlates of physical activity. The ‘PACES’ scale was developed to measure physical activity enjoyment using college aged students (Kendzierski & DeCarlo, 1991). This initial 18 items scale was developed and refined to a 16 item scale, with a 5 point likert scale based on the findings of a series of focus groups with eighth grade girls (Motl et al. , 2001). The scale was found to be uni – dimensional, i.e. consisting of one substantive factor supporting the factorial validity of the scale (Motl et al. , 2001). Scores on the enjoyment scale were found to be associated with other measures, physical activity, sport involvement and enjoyment of physical education providing some evidence of convergent validity for the scale (Motl et al. , 2001). Follow up analyses demonstrated that enjoyment mediated the effect of a school-based intervention to increase physical activity among adolescent girls (Dishman et al. , 2002); indicating that the PACES scale is a valid measure of physical activity enjoyment among adolescent girls.

A separate measure of ‘Enjoyment of Physical Education’ has been developed with black and white adolescent girls (Motl et al. , 2001). Confirmatory factor analysis has demonstrated adequate factorial validity of the scale and scores on the scale have been shown to influence physical activity enjoyment as measured by the modified PACES (Motl et al. , 2001). A shortened one item scale has been developed for use in the Trial of Activity for Adolescent Girls study (TAAG, 2004).

142

Literature Review

‘Self-Efficacy for Physical Activity’ was initially developed as a 17 item scale for adolescents (Reynolds et al. , 1990; Sallis et al. , 1992). This scale was further developed by Saunders et al. (1997) for use with preadolescent youths. It was found to be a multidimensional construct consisting of sub scales (factors), measuring support seeking, barriers, and positive alternatives (Saunders et al. , 1997). Motl et al. (2000) further modified this questionnaire with adolescent girls. Items were re phrased to identify a subset of items that best tapped the constructs under investigation (Motl et al. , 2000). This resulted in an eight item scale measured on five point likert scale. Confirmatory factor analysis provided evidence of a uni-dimensional measure of the refined self- efficacy scale. This scale was found to have acceptable internal consistency with a Cronbach alpha of 0.78 across two cohorts and a test retest reliability of 0.66 after a one year period (Motl et al. , 2000). The factor structure of this scale was further supported in factor analysis with both black and white adolescent girls (Dishman et al. , 2002).

The ‘Perceived Benefits’ scale was initially developed as a 16 item scale (Saunders, 1986). In a follow up analysis this scale was found to consist of a two factors namely, social outcomes and physical outcomes. This scale was found to be reliable (Cronbach alpha > 0.7) and to be correlated with intention to be active, thus providing some evidence of the convergent validity of the scale. A similar ‘Attitude Questionnaire’ was developed by Motl et al. (2000) to examine beliefs about the consequences of being physically active and a corresponding positive or negative evaluation of the consequences. The 22 belief statements were rated on a 5 point likert scale anchored by 1=Disagree a lot and 5=Agree a lot; value statements were rated on a 5 point likert scale ranging from 1=Very bad to 5=Very good. The attitude items were calculated as a product of the belief and corresponding value score. Analysis of the factorial validity of the scale with structural equation modelling revealed the original set of 22 items could be reduced to eight (Motl et al. , 2000), this solution was found to fit acceptably and similarly among samples of both black and white adolescent girls (Dishman et al. , 2002). Cronbach alpha’s for internal consistency across two cohorts was 0.73 and the test retest intraclass correlation coefficient was 0.64. The benefits scale which was used in the TAAG (2004) study was adapted from the ‘Attitude Questionnaire’ (Motl et al. , 2000) and the ‘Amherst study’ (Sallis et al. ,

143

Literature Review

2002). Separate scores were computed for benefits relating to physical activity and values associated with physical activity in this study.

A ‘Change Strategies Scale’ has been developed which investigates the use of cognitive and behavioural strategies to promote physical activity. In the ‘GRAD study’ of physical activity promotion among college students, assessments were developed which linked to the change strategies taught in the curriculum. Ratings of frequency explained small significant amounts of variance in physical activity at post test, providing some evidence of convergent validity (Saelens et al. , 2000). Further evidence of convergent validity has also been reported with change strategies found to mediate the association of self-efficacy with physical activity in a sample of 6 th and 8 th grade girls (Dishman et al. , 2005). When this scale was factor analyzed it was found to load on two factors, i.e. behavioural and cognitive (Dishman et al. , 2005). In a more recent study however, items on the change strategy scale loaded on a single factor among adolescent Iranian girls (Pirasteh et al., 2008). More research is warranted to assess the most appropriate factor structure of the scale (Pirasteh et al. , 2008). Acceptable levels of reliability have been reported with this scale (Cronbach alpha 0.78) (Dishman et al. , 2005; Pirasteh et al. , 2008).

The ‘Barriers to Physical Activity’ scale was originally developed by Sallis et al. (1989) but has been modified and developed over time into a 25 item measure (Calfas et al. , 1994; Sallis et al. , 1999). Evidence of reliability and validity of the scale exist with Sallis et al . (1999) reporting a 1 week test retest reliability of (r = 0.79) among a cohort of college students. The scale was also found to demonstrate some construct validity as it was found to be significantly and inversely correlated with vigorous exercise in adults (r = - 0.22) (Sallis et al. , 1989) and to change significantly with changes in exercise (Sallis et al. , 1992). This scale was further developed by Heesch et al. (2006) who performed Rasch modelling, a method to thoroughly evaluate the properties of scales, to evaluate the scale. Item characteristic curves were developed which highlight the probability of participants selecting each response option of an item. Each response option is represented as a curve and its probability of being selected changes over the Rasch scale continuum. For a scale that targets all the respondents well, all response option curves should have peaked within a range of -3.00 to +3.00 logits, signifying that each option had a greater probability than all

144

Literature Review other options of being selected at some point along the Rasch scale continuum. On the 5 point ‘Perceived Barriers to Physical Activity Scale’ the ‘often’ response was found not to peak for 8 items and barely peaked for 2 items. Therefore, Heesch et al. (2006), recommends that this be used as a 4 point scale as respondents appeared to have difficulty in differentiating between the sometimes and often options. Heesch et al. (2006) also reported that the barriers scale was not uni-dimensional, i.e. the eigenvalue plot revealed the possibility of two separate factors being examined in the scale. On the scale four items were found to represent time demands externally imposed by other individuals, time demand barriers, and the other items represented perceived barriers internal to the individual (Heesch et al. , 2006).

‘Intention to be physically active’ has been measured using a single item developed by Godin and Shepherd (1986). The theory of reasoned action provided the theoretical framework for this tool, which has been found to be associated with measures of self- efficacy, social influences and benefits of physical activity (Saunders et al. , 1997).

‘Perceived Athletic Competence’ has been measured using a subscale (factor) from the ‘Perceived Competence Scale for Children’ (Harter, 1982; Harter, 1985). For each statement participants choose the statement that best described them and indicates whether it was ‘somewhat true’ or ‘very true’ for them. Internal consistency of the perceived competence scale has been found to be satisfactory with Cronbach alphas of 0.83 (Harter, 1982) and 0.81 (Muris et al. , 2003) in separate studies. Test retest stability of the athletic competence subscale over 4 week interval was also found to be high with intraclass correlation coefficient (ICC > 0.80) (Harter, 1985; Muris et al. , 2003). Evidence of the construct validity of the scale was also evident as the scale correlated in a meaningful way with child, parent and teacher reports of psychopathology and personality (Muris et al. , 2003). This scale was factor analysed with a group of 8-10 year old children in the GEMS study. Perceived physical competence was found to be best described as a single factor scale, with acceptable levels of internal consistency (Cronbach apha = 0.69) and the test retest coefficient (r = 0.51) was moderate (Sherwood et al. , 2004).

145

Literature Review

Body image has been measured using the ‘Physical Appearance’ subscale from the ‘Self Perception Profile for Children’ (Harter, 1985). This scale has been found to load on one factor, to have acceptable levels of internal consistency in US and European samples of children and adolescents (Cronbach alpha > 0.7) (Boivin et al. , 1992; Van Dongen-Melman et al. , 1993; Schumann et al. , 1999). Some evidence of the construct validity of this scale has been found with scores on the physical appearance scale significantly related to a ‘Body Areas Satisfaction Scale’ in a large sample of over 11,000 Norwegian adolescents (Wichstraum, 1995).

The ‘Physical Activity Stage of Change’ measure developed by Marcus (1992), categorises individuals based on their readiness to be physical active from stage 1=pre contemplation to stage 5=maintenance. This tool has been found to display some convergent validity, being associated with levels of self reported measures of physical activity among children and adolescents (Lee et al. , 2001; Berry et al. , 2005; Haas & Nigg, 2009) . The stage of change has also been associated with correlates of physical activity such as self-efficacy, processes of change and decisional balance among New Zealand adolescent youth (Nigg & Courneya, 1998; Prapavessis et al. , 2004; Berry et al. , 2005). A Korean study has also reported that measures of perceived benefits and barriers were found to differ across physical activity stages of change (Kim, 2004) . In a follow up analysis, it was found that scores on the trans theoretical model were found to predict exercise stage transition six months later, providing some internal consistency of the measure (Prapavessis et al. , 2004). Jackson et al. (2007), further supports the construct validity of the stage of change measure which was found to be associated with markers of health and wellbeing such as cardio-respiratory fitness, body fat and cholesterol.

The ‘Perceived School Climate Scale’ was a scale developed for adolescent girls as part of the Trial of Activity for adolescent Girls Study (TAAG) (Birnbaum et al. , 2005). An initial scale of 14 items was reduced to five items using confirmatory factor analysis. These five items were found to load on two factors, perceptions about teachers’ and boys’ behaviours. Perceptions about teachers were found to detect a positive significant association with self reported physical activity providing some evidence of convergent validity.

146

Literature Review

2.14.2 Measures of social correlates of physical activity

The social influences scales were developed by Reynolds et al. (1990) and Sallis et al. (1992). Saunders et al. (1997) found social support items on the scale to load on a single factor with pre-adolescent youth and to explain 37% of the variance. A separate study of adolescent females reports that a two factor solution, namely peer influence and family influence, may be a more appropriate with this group (Pirasteh et al. , 2008). Due to the fact that in adolescence peer influence may become a more salient influence, a two factor solution may be more appropriate for adolescents (Sallis et al. , 2002; De Roiste & Dinneen, 2005). Two week test retest reliability of the family and peer scales were strong (intraclass correlation coefficient = 0.88; 0.86 respectively) (Prochaska et al. , 2002). Cronbach’s alpha for the family scale was 0.77, and for peers was 0.81 (Prochaska et al. , 2002).

2.14.3 Measures of Environmental Correlates of Physical Activity The built environment can be measured in a number of ways; these include (i) indirect measures where aspects of the environment are assessed through data gained from secondary sources such as GIS or street network data; (ii) direct measures where built environmental features are assessed through in person audits such as walking through built environments and (iii) through intermediate measures assessing individual perceptions of neighbourhood characteristics (Gasevic et al. , 2011). Further development of environmental audit tools are essential for conducting direct assessments and enhancing knowledge regarding the built environment and the association with physical activity (Gasevic et al. , 2011). The current study employs intermediate measures to assess environmental influences on physical activity. Due to the lack of previously developed questionnaires investigating environmental influences on physical activity new scales were developed for the TAAG study (Evenson et al. , 2006). The items in these scales were modified from existing questionnaires designed for adults such as the Amherst parent questionnaire, (Sallis et al. , 2002) the Neighbourhood Environment Walkability Scale (Saelens et al. , 2003) and a recreational environmental scale (Sallis et al. , 1997). The ‘Amherst Reliability Study’

147

Literature Review provides some reliability information relating to items used in these scales. In this study the average interval between test and retest was 16 days, the sample (n = 60) was from grades 6 - 8. An index of student reported environmental variables had a reliability of r = 0.92 (TAAG, 2004). The parent reported environmental variables were found to have the following reliabilities; neighbourhood safety (r = 0.86), access to parks/gyms (r = 0.77), park distance (r = 0.70) and park use frequency (r = 0.68) (TAAG, 2004). Three separate scales were developed to investigate Perceived Environment, Transportation and Access to Facilities in the TAAG study (Evenson et al. , 2006). The perceived environment factors were assessed under three broad categories perceived safety, aesthetics and facilities near the home. For perceived safety of the environment, six items assessed environmental safety; one item assessed environmental aesthetics, while three items assessed facilities (Evenson et al. , 2006). In the ‘Access to Facilities Measure’ participants were provided with a list of 15 facilities and asked if it was to get to and from these facilities (Evenson et al. , 2006). These were scored by adding up the total number of facilities that the participant knew of near their home. The ‘Transportation Barriers Scale’ consisted of three items; these items investigated an individual’s difficulty in getting to and from physical activity venues. These three scales were used in a study which evaluated the test retest reliability and the relationship with physical activity between these environmental correlates and physical activity (Evenson et al. , 2006). A total of 480 adolescent females completed both test and retest surveys. Overall the 24 individual items on safety, aesthetics, facilities near home and transportation mostly indicated fair to moderate reliability (kappa range 0.28 – 0.68 among adolescent girls. The scale measuring access to facilities indicated substantial reliability (ICC range 0.74 – 0.82); these reliability results were generally similar to or higher than reliability reported in other youth studies (Evenson et al. , 2006). Scales were found to display some evidence of convergent validity with some items on all three scales found to be associated with self reported physical activity and active transportation to school using logistic regression (Evenson et al. , 2006). Girls who agreed with the statements it is safe to walk or jog in my neighbourhood; girls reporting more trees, interesting things to look at and lack of garbage were more likely to report physical activity than girls not reporting these characteristics. Similarly, girls with bicycle or walking trails were more likely to

148

Literature Review report physical activity than their counterparts not reporting these facilities (Evenson et al. , 2006). Further evidence is recommended to assess the reliability, factor structure and validity of these scales using an objective measure of physical activity (Evenson et al. , 2006). A critical review of the limitations of the environmental correlates included in the current study is presented in section 2.6.5. The recently developed ‘Neighbourhood Environment Walkability Scale for Youth’ (NEWS – Y) has been found to be reliable with adolescents. This scale provides a comprehensive measure of environmental correlates assessing perceived land use mix – diversity, recreation facility availability, pedestrian / automobile traffic safety, crime safety, aesthetics, walking cycling facilities, street connectivity, land use mix-access and residential density (Rosenberg et al. , 2009). This scale was not developed for adolescents prior to the development of the ‘APAC questionnaire’ therefore it is not included in the current study. Future research should take cognisance of this tool to measure the environmental correlates of adolescent activity. The inclusion of objective measures of environmental correlates such as GIS measurements would also provide further quality in measures of environmental correlates of activity.

2.14.4 Measurement of the Correlates of Physical Activity; implications for the current research A variety of scales have been developed to measure the correlates of activity. The scales presented in this research have been found to have evidence of reliability and validity among adolescents. The ‘APAC questionnaire’ was constructed using these previously developed tools (Table 3.1). Further additions were made to the correlates questionnaire based on focus group interviews (Chapter 4). Factor analysis techniques are a possible way to assist in the further development and refinement of the ‘APAC questionnaire’ into a more succinct and meaningful tool.

149

Literature Review

2.15 Conclusion

In depth analysis of the correlates of physical activity is important in the development of a successful intervention to increase levels of physical activity (Baranowski et al. 1998; Young et al. 2006). As Kimm et al. (2002) argue, the factors associated with the decline in activity during adolescence remain largely unknown and further research is warranted in this area.

A number of correlates have been found to positively and negatively influence attitudes towards and engagement in physical activity. It is important to have valid and reliable measures of these correlates of physical activity. Support for the construct validity of an ‘Adolescent Physical Activity Correlate Questionnaire’ could be gained by using factor analysis and by assessing the relationship between the questionnaire and objective measures of physical activity.

This study was planned with the intention of further exploring the correlates of activity among Irish adolescents and developing a comprehensive instrument to measure the correlate profile of adolescents.

150

Chapter 3

Correlates of Physical Activity among Adolescent Females: Influence of Year in School and Physical Activity Stage of

Change

3.1 Abstract

Background : Female levels of physical activity have been found to dramatically decrease during adolescence. The reasons for this decline remain largely unknown. The purpose of the current study was to analyse the physical activity correlate profile among females from different years in school and across different physical activity stages of change.

Methods : Correlates of physical activity and physical activity stage of change were assessed among 871 Irish adolescent females in years 1 through 6 in secondary school (mean age 15.28 ± 1.8) using a questionnaire based on the Social Cognitive Theory and

151

Correlates of female activity

Ecological Theory. Multivariate Analysis of Variance (MANOVA) was used to identify whether differences in correlates of physical activity could be detected in (a) adolescent females of different ages and (b) those classified in different stages of change.

Results: Females in more senior years in school and in earlier physical activity stages of change reported significantly less positive physical activity correlate profiles than females in junior years and in later physical activity stages of change. Analysis of variance and regression analysis revealed perceived competence, peer social support and intention to be physically active (partial eta range .21- .25) to be the most important predictors of physical activity stage of change.

Conclusion: The present study supports the construct validity of the stages of change of exercise adoption. It provides information on the interaction between the physical activity stage of change and correlate profile of Irish adolescent females and should be considered when designing future physical education and physical activity programmes.

Keywords : Stages of change; Transtheoretical Model; Adolescents; Physical Activity

3.2 Introduction

Participation in regular physical activity has been found to be negatively associated with age in adolescence (Butcher et al. , 2008); this trend has been found to be most pronounced among females (Kahn et al. , 2008; Pfeiffer et al. , 2009). Due to the various health benefits associated with regular physical activity (Harrison & Narayan, 2003; Strong et al. , 2005) and the likelihood that active adolescents become active adults (Telama et al. , 1997) efforts encouraging adolescents to adopt a physically active lifestyle or to remain physically active are of significant importance.

Measured variables that promote or inhibit attitudes towards and engagement in physical activity are classified as ‘correlates of physical activity’ (Bauman et al. , 2002). An

152

Correlates of female activity extensive body of research which has investigated the correlates of physical activity among adolescents reveals that physical activity is influenced by various biological, psychological, social and environmental correlates (Sallis et al. , 2000; Biddle et al. , 2005). To effectively promote physical activity in youth these correlates need to be well understood (Sallis et al. , 2002; Barr-Anderson et al. , 2007). Due to various methodological differences however, many studies investigating correlates of physical activity report conflicting findings and lack comparability. Limited research has explored the influence of age on correlates of physical activity among adolescents, and further research is required (Garcia et al. , 1998; Sallis et al. , 2002; Gyurcsik et al. , 2006; Schaben et al. , 2006). Adolescence is a time when a number of maturational changes occur which may alter perceptions of and influences on physical activity (Sallis et al. , 2002; Schaben et al. , 2006); with research reporting that older adolescents report lower values for psychosocial correlates such as social support and self-efficacy than younger adolescents (Garcia et al. , 1998; Sallis et al. , 2002; Gyurcsik et al. , 2006; Schaben et al. , 2006).

The stage of change concept from the transtheoretical model has been used extensively in the study of health and physical activity behaviour. The model incorporates 5 distinct stages which categorise individuals as inactive, contemplating activity or at different levels of activity (Prochaska & DiClemente, 1992). The physical activity stage of change measure (Marcus et al. , 1992) has been found to correlate with self reported moderate and vigorous physical activity (Lee et al. , 2001; Haas & Nigg, 2009) and psychological correlate scales among children and adolescents, (Kim, 2004; Prapavessis et al., 2004) i.e. those in earlier stages of readiness reported less exercise and lower correlate profiles than those in later stages. The physical activity stage of change has also been found to correlate with objectively measured cardio respiratory fitness among adults (Jackson et al. , 2007), however, to the author’s knowledge no similar study has been carried out among adolescents. It has been concluded; that the physical activity stage of change measure provides feasible, cost effective and efficient methods of predicting and measuring changes in physical activity levels and physical activity correlate profiles in large scale studies (Kim, 2004; Prapavessis et al. , 2004; Jackson et al. , 2007).

153

Correlates of female activity

In an attempt to account for decreasing physical activity in adolescent females the purpose of the current study was to (1) analyse the physical activity correlate profile of adolescent females in year 1 to year 6 in Irish second level schools and (2) investigate the relationship between physical activity correlate profiles and physical activity stage of change among adolescent females.

3.3 Methods

3.3.1 Subjects

Participants in the current study were females from 12 secondary schools in the Republic of Ireland. The questionnaires were administered between September and December 2008 by trainee physical education teachers from the University of Limerick. The schools that were invited to participate in the project were schools where the trainee teachers were involved in teaching practice placement. The trainee teachers received training on the administration of the questionnaire.

Participants and their parent/guardian provided written informed consent before data collection. Within each school, random samples of 25 students from each class year were invited to participate in the study. The response/participation rate in the current study was 60% (12 schools X 6 years/grades X 25 students per grade, equals a total of 1,800 students of whom 1,087 participated. Response rate = 1,087/ 1,800 X 100). Four mixed schools participated in the study; however data from males (n = 216) was not included in the analysis. Twenty five per cent of the females participating in the current study were from mixed schools (n = 222). No significant difference on measured variables was found among females from mixed and single sex schools; therefore, data from all female participants is presented together. This resulted in 871 females with a mean age of 15.28 ± 1.8 years completing the questionnaire.

154

Correlates of female activity

3.3.2 Instruments and Procedure A physical activity correlate profile questionnaire which was influenced by the work of the Trial of Activity for Adolescent Girls Study (TAAG, 2004) and Sallis et al . (1999) was developed for use in the current study. Constructs from the questionnaire were organised in 4 blocks: personal/ biological, psychological, social and environmental. Details of the psychological, social and environmental correlates are provided in Table 1. Variables in the personal/ biological block were: age, having older brothers/ sisters, ethnicity, medical conditions, socio economic status and previous/ current sports participation. The Irish Central Statistics Office (CSO) measure of parental occupation was used to classify individuals based on socio economic status (SES). The classification adhered to the international occupation classification ISCO Com (CSO, 2008). The code to which a person’s occupation is classified is determined by the kind of work he or she performs in earning a living irrespective of the place in which, or the purpose for which, it is performed (CSO, 2008). Participant’s parents were classified to one of ten specific socio economic groups. In addition a residual group “All others classified gainfully employees and unknown” was used where sufficient details were not provided. The classification aims to bring together persons with similar social and economic statuses. Challenges associated with measurement of socioeconomic status are presented in section 2.8.3. Sport participation is defined as a competitive activity undertaken in the context of rules defined by a regulator agency (Bouchard et al. , 2007). This definition of sport was simplified to ‘organised structured activity that is played or done according to rules and involves winning or losing’ for the purpose of the current research. Sport participation was measured using a single item with participants responding yes/no to the statement ‘I currently play sport competitively’. A limitation of the measure of sport participation in the current study is that it assessed only ‘current’ participation in sport. Previous participation in sport participation which has been identified as an important correlate of activity was not included in the current research (Van der Horst et al. , 2007). One school expressed concern with the personal nature of the socio economic status questions therefore these questions were excluded from the questionnaire in this school. Socioeconomic status information is provided for 672 participants. . The constructs measured in the questionnaire were created based on the Socio-Ecological Model which

155

Correlates of female activity posits that multiple domains of variables influence physical activity (Elder et al. , 2007). The questionnaire was administered in one 40 minute class by the trained data collectors. Initial instruction was provided and the questionnaire was completed in a systematic fashion i.e. section by section.

In the current study the variables used were found to have acceptable consistency, the Cronbach’s alpha ranged from .74- .91. Reliability of the questionnaire was analysed using one week test re-test reliability with a sub-sample of 62 participants (mean age 14.63 ± 1.18 years). Intraclass correlation coefficients (ICC) ranged from .71 - .94. Reliability and internal consistency for the current study are shown in Table 3.1.

3.3.3 Data Analysis The procedure used in the cleaning of data and replacing missing data is included in appendix A.4. This procedure was followed for each study in this thesis. Z scores were calculated for all correlate scales to accommodate different scales and to provide standardized scores across scales which could be compared. In each block of correlates i.e. psychological, social, environmental, the relevant z scores were summed and the mean z score for each block was calculated. The mean of the summed z scores enabled comparison of composite psychological, social and environmental correlate scores among participants from different years in school and different physical activity stage of change.

Analysis of Variance and Multiple Regression were used to investigate the relationship between physical activity stage of change and attitudinal features of physical activity across year at school (6 levels) and physical activity stage of change (5 levels). Appropriate use of multiple regression and analysis of variance presupposes that certain distributional characteristics of the variables involved, are at least approximately achieved. Therefore, preliminary analysis was conducted to examine violation of the assumptions of normality, univariate and multivariate outliers, linearity and multicollinearity. Extreme outliers were identified in the data and removed from the analysis. Scales that were not normal were transformed (Tabachnik & Fidell, 2007) and scales that were not normal and were not sensitive to transformation were removed from the initial analysis and analysed 156

Correlates of female activity non-parametrically using Kruskall Wallis and Mann Whitney U tests. No difference was found in analyses using transformed variables and original non – transformed variables, therefore it was decided to report results based on the original non transformed data.

Differences in correlate scores among (i) year group in school and (ii) participants at each physical activity stage of change were initially analysed using multivariate analysis of variance (MANOVA). When significant differences were found univariate analysis of variance (ANOVA) and Tukey post hoc analysis were used to investigate the nature of these differences. To lower the probability of a Type 1 error due to multiple comparisons a conservative alpha of .01 was used in the analysis.

Hierarchial linear regression was conducted between physical activity stage of change as dependent variable and correlate scores as independent variables. Personal/ demographic factors and correlate scales that were bivariately correlated with self reported physical activity stage of change were included in the regression analysis ( P < . 05). Blocks of variables were created and entered into regressions in a standard order.

All statistical analyses were computed using Predictive Analytics Software (PASW) PASW statistical software package, version 17.0.

3.4 Results

3.4.1 Influence of Year in School on Correlates of Physical Activity

The Mean of the summed Z scores for psychological, social and environmental correlates of physical activity across year in school are shown in Figure 3. 1. A graded decrease was evident with a less positive correlate profile evident in more senior years in school. Means and standard deviation of correlate scores are presented in Table 3.2. Personal/ biological factors which were not found to be related to year in school and physical activity stage of change (older sister, ethnicity, medical condition) are not reported. A one way between groups multiple analysis of variance (MANOVA) revealed a statistically significant difference between year in school on the combined dependent variables, F (70, 3898) = 3.35; P = .000; Wilks Lambda = .76; partial eta squared = .05.

157

Correlates of female activity

When the results for the dependent variables were analysed separately 11 of the 14 correlate scales were significantly influenced by year in school ( P < . 01). Despite reaching significance, when the guidelines suggested by Cohen (1977) are applied, the effect size of year in school on correlates of physical activity were found to be small with partial eta squared ranging from .02 to .11. Post hoc Tukey analysis (Table 3.2) revealed a clear trend across all correlate scales with participants in more senior years in school reporting a significantly less positive correlate profile than their junior counterparts.

158

Correlates of female activity

Table 3.1: Description, internal consistency and reliability of the correlates of physical activity Variable Name Des cription Items Cronbach Test Re - References [range] alpha test (ICC) Physical Activity Stage of change Stage of change; 1= Currently physically active, 4 [1,2] n/a 0.90 Marcus & Rossi (1992) 4=Regularly active for past 6 months Psychological Variables Change strategies Cognitive and behavioural change strategies; 1= 9 [1,5] 0.72 0.91 TAAG (2004); Dishman et al . (2005) never, 5 = very often Self -efficacy Self -efficacy ; 1= Disagree a lot, 5= Agree a lot 8[1,5] 0.81 0.91 Saunders et al. (1997); Motl et al. (2000); TAAG (2004) Perceived c ompetence Athletic co -ordination and peer related physical 2 [1,5] 0.82 0.92 Harter (1982) activity; 1=much less than others, 5=much more than others Enjoyment of activity Enjoyment of physical activity; 1= Disagree a 16 [1,5] 0.84 0.92 Heesch et al. (2006); Motl et al. (2000) lot, 5= Agree a lot: reverse scoring Kendzeirski & De Carlo (1991) Perceived b arriers Perceived Barriers to Physical Activity; 22 [1,4] 0.91 0.90 Heesch et al. (2006); Sallis et al. (2002 1=Never, 4=Very Often Perceived b enefits Perceived benefits of physical activity; 1= 9[1,5] 0.75 0.83 Sallis et al. (2002); Motl et al . (2000); TAAG Disagree a lot, 5 = Agree a lot (2004) Enjoyment of PE Enjoyment of PE; 1= disagree a lot; 5= agree a 1 [1,5] n/a 0.92 Heesch et al. (2006); Sallis et al. (2002) lot TAAG (2004); Motl et al. (2001) Intention to be active Intention to be active on most days; 1=sure I 1 [1,5] n/a 0.71 Godin & Shephard (1986) will not be active, 5= sure I will be active Social Variables Social support peers Social Support from peers; 1= none, 5= 4 [1,5] 0.74 0.91 Sallis et al. (1999); TAAG (2004) everyday Sallis et al. (1999); TAAG (2004) Social support f amily Social support from family; 1= none, 5 = 5 [1,5] 0.78 0.84 Sallis et al. (1999); TAAG (2004) everyday Sallis et al. (1999); TAAG (2004) Environmental Variables Transport Transportation to and from physical activities; 3 [1,4] 0.75 0.94 Evenson et al. (2006; TAAG (2004) 1= Not at all difficult, 4= impossible Perceived environment Perceived environmental suitability for physical 10 [1,5] 0.77 0.88 Evenson et al. (2006; TAAG (2004) activity; 1=disagree a lot, 5= agree a lot Access to f acilities Ease of access to physical activity facilities 14 [1,3] n/a 0.80 Evenson et al. (2006; TAAG (2004) 1=yes, 2= no

159

Correlates of female activity

0.5 0.4 0.3 0.2 0.1 Psychological 0

Z scores Z -0.1 Social -0.2 Environmental -0.3 -0.4 -0.5 1 2 3 4 5 6 Year

Figure 3.1: Psychological, social and environmental Z scores across class year in school

160

Correlates of female activity

Table 3.2: Group Means, Standard Deviations, F Values, Effect Sizes and Univariate Analysis for Year in School Variable Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 F η2 Pairwise N = N = 193 N = 166 N = 138 N = 66 N = 177 N = 127 Comparison a Mean Age 13.08±0.5 14.05± 0.5 15.26 ±0.5 16.01 ± 0.3 16.72 ±0.7 17.82±0.5 Personal/ Biological Socioeconomic status c 3.6 ± 1.6 3.7 ± 2.2 3.9 ± 1.9 3.3 ± 1.9 4.0 ± 1.9 4.0 ± 2.5 1.5 NS Older brother 1.6 ± 0.5 1.6 ± 0.5 1.6 ± 0.5 1.6 ± 0.5 1.6 ± 0.5 1.5 ± 0.5 1.3 NS Sport p articipation 1.9 ± 0.3 1.9 ± 0.2 1.9 ± 0.3 1.9 ± 0.3 1.9 ± 0.3 1.9 ± 0.3 0.7 NS Stage of c hange 4.2 ± 1.2 4.2 ± 1.2 3.8 ± 1.4 4.1 ± 1.2 3.5 ± 1.4 3.4 ± 1.4 10.8** .06 6,5<1,2,4; 6<3;

Psychological Variables Change strategy 31.5 ± 7.0 31.0 ± 6.0 29.0 ± 6.8 29.3 ± 7.0 28.5 ± 6.7 27.6 ± 7.2 7.0** .04 6,5<1,2; 3<1 Self -efficacy 30.2 ± 6.7 29.6 ± 5.9 28.6 ± 6.5 27.3 ± 7.2 27.1 ± 6.7 26.5 ± 6.5 7.4** .04 6,5<1,2; 4<1 Perceived competence 6.5 ± 1.7 6.4 ± 1.7 5.8 ± 1.8 6.0 ± 2.0 5.9 ± 2.1 5.9 ± 1.9 3.7** .02 6,5< 1; 3<1 Perceived b arriers b 45.9 ± 12.6 49.3± 12.2 50.6± 13.6 51.6 ± 12.7 54.1 ± 13.6 53.0± 12.9 8.7** .05 1,2<5;1<3,4,6; Enjoyment of PE 4.3 ± 1.0 4.3 ± 1.1 3.8 ± 1.4 3.8 ± 1.3 3.7 ± 1.4 3.4 ± 1.6 12.2** .07 6,5<1,2; 6<3; 3<2,1 Intention to be a ctive 3.7 ± 1.0 3.5 ± 0.9 3.2 ± 1.0 3.3 ± 1.0 3.2 ± 1.0 3.3 ± 1.0 6.4** .04 6,5,4,3<1; Enjoyment of activity 68.7 ± 10.3 66.9± 10.5 66.5± 11.8 63.8 ± 11.8 65.0± 11.2 66.5±10.2 3.2* .02 5,4<1 Perceived b enefits 38.9 ± 5.8 38.5 ± 5.4 38.0 ± 6.3 37.0 ± 5.3 38.3 ± 5.2 38.5 ± 5.0 1.2 NS

Social Variables Social support peers 12.1 ± 3.2 11.5 ± 3.2 10.3 ± 3.7 10.2 ± 3.6 8.9 ± 3.9 9.2 ± 3.3 21.3** .11 6,5<1,2; 5<3; 4,3<1 Social support f amily 16.9 ± 3.9 16.2 ± 3.8 14.9 ± 4.5 14.3 ± 4.9 13.7 ± 4.9 13.7 ± 4.3 14.9** .08 6,5<1,2; 4,3<1

Environmental Variables Transport barriers b 4.8 ± 1.6 4.9 ± 1.5 5.0 ± 1.7 5.1 ± 1.6 5.4 ± 1.9 5.2 ± 1.8 2.8 NS Access to f acilities b 19.0 ± 2.8 18.8 ± 2.8 19.3 ± 2.9 18.7 ± 2.6 19.8 ± 2.8 19.5 ± 3.2 2.5 NS Perceived environment 34.9 ± 7.8 35.2 ± 7.8 33.9 ± 7.6 32.9 ± 7.7 31.8 ± 8.3 33.2 ± 8.6 4.2* .03 5<1,2 Notes: *P<0.01; **P<0.001 . a Mean Difference for the Tukey HSD pairwise comparisons. b Scales negatively marked higher scores indicate less favourable attitude towards physical activity c Lower scores indicate higher socio-economic status

161

Correlates of female activity

3.4.2 Influence of Stage Physical Activity Change on Correlates of Physical Activity

The physical activity stage of change frequency distributions were: pre contemplation (N = 35, 4%), contemplation (N = 191, 22%), preparation (N = 84, 10%), action (N = 123, 14%) and maintenance (N = 434, 50%). Figure 3.2 shows the mean Z scores for psychological, social and environmental correlates across physical activity stage of change. The mean Z scores display a less favourable correlate profile among those in the pre contemplation and contemplation stages than those in the higher stages of change.

Means and standard deviations of correlate scores in each physical activity stage of change are presented in Table 3.3. Multivariate analysis of variance (MANOVA) with correlate scores as dependent variables and stage of change of physical activity as an independent variable yielded significance F(60, 3171)= 8.52, P = .000; Wilks Lambda = .60, partial eta squared = .14. Follow up univariate analysis of variance (ANOVA) revealed that mean scores on each of the 13 correlate scales differed significantly across stages of physical activity change. The personal/ biological variables age and sports participation were also found be significantly different across stage of physical activity change; however socio-economic status and having an older brother were not found to differ across physical activity stage of change. The effect size of the correlates were found to be medium to large with partial eta squared ranging from.02 to .25 (Table 3.3). In all psycho-social correlate scales, Tukey post hoc analysis revealed that individuals in the pre contemplation and contemplation stages reported significantly less positive correlate scores compared to those in the maintenance stage.

162

Correlates of female activity

1 0.8 0.6 0.4 0.2 0 -0.2 Z scores Z -0.4 Psychological -0.6 -0.8 Social -1 Environmental

Stage of Change

Figure 3.2: Psychological, social and environmental Z scores across physical activity stage of change

163

Correlates of female activity

Table 3.3: Group Means, Standard Deviations, F Values, Effect Sizes and Univariate Analysis for Stage of Change a Variable PC M±SD C M±SD P M±SD A M ± SD M M±SD F η2 Pairwise Comparison N= N = 35 N = 191 N = 84 N = 123 N = 434

Personal/Biological Variables Age 16.2 ± 1.8 15.8 ± 1.6 15.0± 1.8 15.0 ± 2.1 14.9 ± 1.8 10.2** .05 M,A

Psychological Variables Change strategy 21.5 ± 6.7 25.7 ± 6.3 28.3 ± 5.1 29.4 ± 5.8 32.1 ± 6.4 50.0** .19 PC, C

Social Variables Social support peers 7.1 ± 2.8 8.0 ± 3.4 9.2 ± 3.3 10.6 ± 3.3 12.0 ± 3.3 57.2** .21 PC,C

Environmental Variables Transport barriers b 5.4 ± 1.8 5.4 ± 1.7 5.3 ± 1.7 4.9 ± 1.8 4.9 ± 1.7 4.0* .02 M

164

Correlates of female activity

Hierarchical multiple regression using non transformed variables was used to assess the ability of the correlate scale to predict physical activity stage of change. All of the 13 correlate scales were found to be significantly related to physical activity stage of change using Pearson bivariate correlations and were included in the regression analysis (correlations ranged from r = .13 to r = .48, P < . 01) (Table 3. 4). Biological/ personal factors that were found to be significantly related to physical activity stage of change i.e. socio-economic status, age, having an older brother and sport participation were also included in the regression analysis. Personal/ biological variables explained 10% of the variance in physical activity stage of change score. Total variance explained from the complete model was 37 %, F (17, 718) = 26.6, P < . 01. Sport participation (B =.11, P < .001), perceived competence (B =.19, P < . 001), intention to be active (B=.19, P < . 001) and social support from peers (B =.17, P < . 001) were correlate scales that were significantly associated with physical activity stage of change (Table 3.5).

165

Correlates of female activity

Table 3.4: Pearson correlations between potential correlates of physical activity and physical activity stage of change SOC CS SE PC PB EPE IPA EPA P Ben SSP SSF Trans Access Perc Env Stage of Change (SOC) 1

Personal/ Biological Variables Age -.2* Socioeconomic status -.1 * Sport p articipation .33* Older b rother .07 *

Psychological Variables Change strategy (CS) .42* Self -efficacy (SE) .40* .56* Perceived c ompetence (PC) .48* .48* .47* Perceived b arriers (PB) -.43* -.43* -.52* -.52* Enjoyment of PE (EPE) .28* .36* .34 * .32* -.42* Intention to be active (IPA) .48* .5* .54* .48* -.52* .35* Enjoyment of a ctivity (EPA) .37* .56* .52* .47* -.55* .46* .49* Perceived b enefits (P Ben) .2* .38* .34* .21* -.24* .25* .26* .46*

Social Variables Social support peers (SSP) .47* .49* .45* .43* -.44* .37* .47* .46* .30* Social support f amily (SSF) .42* .48* .49* .41* -.48* .32* .44* .40* .29* .56*

Environmental Variables Transport barriers(Trans) -.13* -.16* -.22* -.15* .25* -.15* -.16* -.16* -.13* -.21* -.23* Access to f acilities (Access) -.21* * -.23* * -.26* * -.22* * .27* -.11* -.19* -.17* -.17* -.23* -.21* .29* Perceived e nvironment (Perc Env) .22* * .23* * .29* * .17* * -.22* .13* .22* .19* .19* .31* .23* -.16* -.40* 1 *P < . 05

166

Correlates of female activity

Table 3.5: Results of Regression Analysis Explaining Physical Activity Stage of Change ß P Adj R 2 R square change Personal/ Biological Variables .1 .1 Age -.07 .02 Socio -economic status -.02 .5 Older brother .06 .04 Sport p articipation .11 .000*

Psychological Variables .35 .26 Change s trategies .07 .08 Self -efficacy .01 .8 Perceived c ompetence .19 .000 * Perceived b arriers -.07 .09 Enjoyment of PE -.003 .9 Intention to be a ctive .19 .000* Enjoyment of a ctivity -.03 .52 Perceived b enefits -.03 .5

Social Variables .37 .03 Social support p eers .17 .000* Social support f amily .07 .08

Environmental Variables .37 .002 Transport .02 .6 Access to facilities -.02 .5 Perceived e nvironment .03 .34 ß= beta *P < . 01

167

Correlates of female activity

3.5 Discussion

Previous studies have reported that levels of physical activity decrease with age among adolescent females (Butcher et al. , 2008; Kahn et al. , 2008; Pfeiffer et al. , 2009). The current study reports that physical activity stage of change is negatively influenced by age (mean age for those in the precontemplation stage was 16.2 ± 1.8 years compared to mean age of 14.9 ± 1.8 years in the maintenance stage). The current study reports that a similar trend exists with the correlates of physical activity among adolescent females. Females from more senior years in school reported significantly less positive psychological, social and environmental physical activity correlates than their junior counterparts. The effect size for this relationship was found to be small; however the trend appears clear and gradual across class year in school. These finding lend support to the work of Schaben et al . (2006), who reports lower scores on psychosocial correlates measuring perceived competence, parental influence and attraction to physical activity among high school youth compared to middle school youth, and Gyurcsik et al. (2006) who reports that physical activity barriers reported per student increased as grade in school increased.

Two environmental variables (access to facilities, access to transportation) and the perceived benefits scales were found not to be influenced by year in school. It may be hypothesised that knowledge of facilities, access to transport and perceived benefits of physical activity are factually based constant influences on physical activity that would not be expected to differ among participants from year 1 to year 6. Correlate scales that were found to differ among participants from different class years in school measured perceptions of, support and efficacy for activity. Other research has found that environmental influences did not strongly predict levels of physical activity among children and adolescents of different ages (Sallis et al. , 1999; Schaben et al. , 2006). This study supports the conclusion that the less positive physical activity correlate profiles which are evident in more senior years in schools are primarily accounted for by interpersonal (social support factors) and intrapersonal variables (enjoyment, confidence, competence and perceived barriers).

168

Correlates of female activity

Participants in lower physical activity stages of change (pre contemplation, contemplation) were found to have less positive correlate profiles compared to those in the higher physical activity stages of change. The trend was found to be clear with Tukey analysis indicating all psycho-social correlate scores for pre contemplators and contemplators to be significantly different from those in the maintenance stage of change. These findings have some support in previous research (Kim, 2004; Prapavessis et al. , 2004) that has examined self-efficacy, benefits and barriers on physical activity stage of change. The current study expands on this research and reports that other correlates associated with support, enjoyment and competence are also differentiated across physical activity stages of change. These findings, in conjunction with previous research (Kim, 2004; Prapavessis et al. , 2004; Jackson et al. , 2007) provide some construct validity for physical activity stage of change as a model for describing exercise behaviour change.

One of the key issues within the physical activity literature is the dramatic collapse in levels of activity in adolescent females. This study identifies some key factors associated with this dramatic decline. Previous and/ or current sport participation was found to be the only biological / personal factor which significantly predicted physical activity stage of change. Perceived competences, intention to be physically active and social support from peers were found to be the most important predictors of physical activity stage of change in univariate and multivariate analysis. Multiple regression analysis also found that these correlate scores significantly predicted scores on physical activity stage of change. Previous studies highlight the relationship between physical activity and self perception of competence (Stein et al. , 2007; Kahn et al. , 2008). Stein et al . (2007) used linear regression models to show that increases in adolescent physical activity were positively associated with changes in social and athletic self – worth. Participation trends have found that females tend to drop out of organised sport in adolescence and are more likely to participate in unorganised health related activities e.g. walking. A possible explanation for this may be that in adolescence females become more conscious of their lack of physical competence which may result in a transition from organised skill related activities to more health related activities.

169

Correlates of female activity

The importance of peers on physical activity was also identified in previous studies (Sallis et al. , 2002; Hohepa et al. , 2006; King et al. , 2008), however this finding has not been equivocal (Biddle et al. , 2005). It has been hypothesised that peer influence increases throughout adolescence as family influences decrease (Sallis et al. , 2002; De Roiste & Dinneen, 2005). The current study however, lends support to the belief that peer influence on physical activity is important throughout adolescence (Sallis et al. , 2002; De Roiste & Dinneen, 2005) , this finding is supported by both the ANOVA and regression analysis. Peers may influence physical activity participation through verbal encouraging and role modelling (Hohepa et al. , 2006) and appear to have a powerful influence on adolescent girl’s participation in physical activity.

Previous studies have reported self-efficacy to effectively differentiate individuals at different physical activity stages of change (Kim, 2004; Prapavessis et al. , 2004; Berry et al. , 2005). In the current study self-efficacy was found to be higher among those in the later stages of physical activity change, however, it was not found to explain a significant portion of the variance in the regression analysis. Intention to be active however which is rooted in efficacy was found to be significant in the regression analysis. Participants in the maintenance stage reported significantly higher self-efficacy and intention to be active than those in each of the other stages. Interventions to increase physical activity should attempt to focus on activities that adolescent females feel comfortable and confident participating in and should incorporate elements of peer support.

Multiple regression analysis revealed that the majority of the variance in physical activity stage of change (35%) was explained by psychological correlate scores. Similarly, previous research reports that 37% of the physical activity stage of change score was accounted for by psychological variables (Kim, 2004). Between 24% and 59% of physical activity levels have been explained by correlate scales (Sallis et al. , 1999; Sallis et al. , 2002; Loucaides et al. , 2007). Psychological variables have been consistently found to predict a large portion of the variance in physical activity levels (Sallis et al. , 1999; Sallis et al. , 2002). The current study shows a similar trend in physical activity stage of change variance; there are a number of reasons which may explain this. Firstly, after controlling for biological/ personal factors psychological factors were entered into the second block of the

170

Correlates of female activity regression equation. Elements of both social and environmental domains may be seen to act through the psychological domain (Sallis et al. , 2002). For example in the psychological ‘perceived barrier scale’ questions pertain to ‘not having anyone to be active with’ (social) and lack of a convenient place to be active (environmental). Furthermore, it should be noted that physical activity stage of change is primarily psychological measure of readiness for physical activity change, and would therefore be expected to associate with other psychological variables.

Strengths of the current study are that a comprehensive questionnaire based on previously validated tools was used, and, that a relatively large representative sample from each year of secondary school participated. One of the main aims to the current study was to assess the potential of the stage of change to predict correlate profile; however, a limitation of the study was the self report nature of the physical activity stage of change which may lead to misclassification due to social desirability bias. Future studies should examine the effectiveness of the stage of change to predict objectively measured physical activity in adolescents.

3.6 Conclusion

To stem the age related decrease in adolescent physical activity knowledge of the key correlates which account for the decease is essential. In this study significant variation in correlate profile has been found across class group membership, and physical activity stage of change of Irish adolescent. Previous sport participation, perceived competence, social support from peers and intention to be physical activity were identified as correlates which explained significant amounts of the variance in physical activity stage of change. It is logical to assume that positive changes in correlate profile should result in increased levels of physical activity.

171

Correlates of female activity

3.6.1 Implications for School Health

Participants in more senior years in school and in earlier physical activity stages of change reported less favourable correlate profiles, this trend was most evident across psychological and social correlates. Perceived competence, social support from peers and intention to be active were found to be the strongest predictors of physical activity stage of change. Therefore, interventions and attempts to promote school physical activity should avoid activities which require high levels of technical competence and should focus on activities that adolescent females feel comfortable participating in. School physical activity programmes should also encourage joining and attending exercise sessions with friends. Peers were found to exert a strong influence among adolescent females and efforts should be made to promote activities to groups of friends.

The physical activity stage of change measure was found to have some construct validity. This tool can provide PE teachers with a short cost effective evaluation and monitoring tool which could be used at the start and end of the school year to evaluate the PE programme. This tool could also be used to evaluate readiness for physical activity change which could assist in the development of appropriate stage matched physical activity interventions in schools.

Human Subjects Approval Statement The University of Limerick institutional review board approved the data collection instruments and procedures.

172

Chapter 4

A qualitative study of factors influencing physical activity among Irish adolescent males and females

4.1 Abstract

Background : Knowledge of the correlates of physical activity is important in the effective promotion of activity. The purpose of the current study was to use the socio- ecological approach to explore the factors promoting and inhibiting attitudes towards and engagement in physical activity among Irish adolescents.

Methods : A total of 247 Irish adolescents from six secondary schools participated in one of 32 focus groups. Groups were separated by year in school, gender and level of activity; each focus group investigated separate elements of the socio-ecological model. A standardized semi – structured schedule with key questions and prompts was used. Quantitative analysis was used to validate the findings of the qualitative analysis.

Results : Key factors promoting participation in physical activity centred on peer and parental support, fun and enjoyment and physical benefits. Key factors inhibiting

173

Qualitative Analysis participation in physical activity included the organisation and structure of physical activity; in particular lack of student input and choice in physical education (PE), mixed gender PE and sport becoming too serious with age were identified as negatively influencing participation among adolescents. Other barriers included lack of peer support, lack of perceived competence and lack of time. Females tended to perceive low competence levels in skill related activities and gravitated towards more individual health related physical activities, males tended to be more intrinsically motivated by physical activity and sport.

Conclusion : Future physical activity promotional strategies should attempt to modify the current structures of sport and school physical activity to cater for adolescents of different interests and motivations. Adolescents should be empowered to choose activities they would like to engage in, activities should be structured so that participants feel comfortable and competent to participate and should incorporate elements of social support.

Keywords : Physical Activity; Youth; Barriers; Benefits; Qualitative

4.2 Introduction

Physical activity is associated with both short and long term health benefits (Janssen et al. , 2005; Strong et al. , 2005), therefore efforts encouraging adolescents to adopt a physically active lifestyle or to remain physically active are critical (Vu et al. , 2006). In Ireland, as in other countries, there are rising concerns about levels of activity and inactivity among young people with low levels of adolescents reaching the recommended levels of activity (Harrington et al. , 2008; Woods et al. , 2010). Due to the fact that lifestyle behavioural patterns are formed during childhood and adolescence (Telama et al. , 1997) it is particularly important to devise effective strategies to promote physical activity among adolescents (Grieser et al. , 2006).

174

Qualitative Analysis

The socio-ecological model of health posits that numerous domains influence physical activity (Elder et al. , 2007). Measured variables that are associated with physical activity are classified as ‘correlates of physical activity’ (Bauman et al. , 2002) and have been categorised as biological/ personal, psychological, social, and environmental correlates (Sallis et al. , 2000). Research has reported relationships between a number of correlates and levels of physical activity. However only between 8 -37% of the variance in objectively measured adolescent activity has been explained by these correlates (Trost et al. , 1999; Pfeiffer et al. , 2009; Kelly et al. , 2010). These findings suggest that the search for new correlates should continue (Humbert et al. , 2008). Qualitative methods which offer an in-depth insight into individual’s experiences and perceptions of the factors which promote and inhibit participation in physical activity and sport may be a useful means to achieving this (Allender et al. , 2006). A targeted approach, with separate focus groups examining individual categories of correlates (psychological, social, and environmental) may be beneficial in gaining a greater understanding to motives and barriers for activity.

Currently, strategies for promotion of physical activity are often developed from a researcher’s perspective with little involvement from the target population (Hohepa et al. , 2006; Humbert et al. , 2008). Engaging and consulting with adolescents can provide the researcher with a greater insight and in-depth understanding of the factors associated with participation in adolescent physical activity (Coleman et al. , 2008; Humbert et al. , 2008) which has the potential to improve the efficacy of future efforts to increase adolescent physical activity (Hohepa et al. , 2006; Rees et al. , 2006; Vu et al. , 2006; Coleman et al. , 2008).

Evidence suggests that in adolescence correlates of activity differ by gender (Brunton et al. , 2003; Rees et al. , 2006; Vu et al. , 2006), age (Gyurcsik et al. , 2006; Schaben et al. , 2006)and level of activity (Mulvihill et al. , 2000; Coleman et al. , 2008). To gain an understanding of factors influencing and to be able to effectively promote physical activity among different sub groups of adolescents attitudes and preferences specific to each of these sub groups need to be identified (Sallis et al. , 2002; Gittelsohn et al. , 2006; Grieser et al. , 2006; Whitt-Glover et al. , 2009; Kelly et al. , 2010; Salmon, 2010). Limited

175

Qualitative Analysis studies have explored the correlates of activity among different sub groups of adolescents (Eime et al. , 2010).

Few qualitative studies have comprehensively examined the correlates of activity among Irish adolescents. Previous international research has explored adolescent perceptions of physical activity in particular in relation to perceived barriers and benefits (O'Dea, 2003; Wilson et al. , 2005; Allender et al. , 2006; Grieser et al. , 2006; Hohepa et al. , 2006; Rees et al. , 2006; Humbert et al. , 2008; Eime et al. , 2010). In reviews of literature the primary facilitators to physical activity were health and fitness benefits, fun and enjoyment, social support and skill acquisition (Allender et al. , 2006; Rees et al. , 2006) Barriers to physical activity and sport identified by adolescent youth were lack of perceived competence, dislike of structured activity, negative peer influence, feeling self conscious, negative experience during school physical activity, time and facilities and parental constraints (Allender et al. , 2006; Rees et al. , 2006). Further research is required to investigate if these factors are important correlates of activity among Irish adolescents.

One of the aims of the current study is to use formative research to identify key correlates of physical activity and sport participation among different sub groups of Irish adolescents. Formative research uses qualitative and quantitative methods to provide information for researchers, which could be used to develop future instruments (Gittelsohn et al. , 2006) to measure correlates of physical activity among Irish adolescents. The use of multiple methods in formative research is seen as accruing benefits such as (i) informational convergence (data triangulation); (ii) appropriateness i.e. instruments and interventions both culturally and geographically appropriate and (iii)obtaining detailed information about the attributes of the target group for whom the instrument will be designed (Gittelsohn et al. , 2006).

176

Qualitative Analysis

4.3 Methods

4.3.1 Sampling Procedure

Participants (n = 247; 46% female; 54% male) were from six secondary schools (two male schools, two female schools, two mixed schools) in Cork, a county in southern Ireland. Within each school the physical education teacher (PE) was asked to nominate a Year 1 (12-13 years) and a Year 4 (15-16 years) class group to participate in the focus group interviews. The primary researcher met with the physical education teacher in each school to explain the criteria for selection that ensured the youth selected would represent both sexes and have diverse levels of activity. This criterion was used to purposefully select (Thomas et al. , 2005) participants from Year 1 (N = 129) and Year 4 (N = 118) in each of the six secondary schools.

4.3.2 Focus Group Structure A total of 32 focus group interviews, each with separate groups of participants, were conducted in the participating schools premises at a time suitable for both the school and participants. The focus group interviews took place between January and April 2009. Each focus group interview lasted approximately 50 minutes and included between 6 – 10 participants. Cognisance was taken of the key findings in the previous study (Chapter 3) in the development of the focus group structure and content. Due to the multitude of possible factors from different domains of influence which have been found to be related to measures of physical activity (Sallis et al. , 2000), a targeted approach was used in the research design to facilitate in depth analysis of correlates across different domains of influence Therefore, in the current study separate focus groups examined (i) psychological, (ii) social, and (iii) environmental domains of influences on physical activity. The previous research presented in chapter 3 of this thesis also highlighted differing relationships in correlates across year in school and physical activity stage of change. These relationships were also reported in the literature with differences in correlates of activity

177

Qualitative Analysis reported among males and females and among younger versus older adolescents (Sallis et al. , 1999; Sallis et al. , 2000). Therefore, participants were stratified based on gender (male, female), year group in school (year 1 v year 4) and level of activity (low activity v high activity; based on PE teacher classification). Each separate focus group interview was conducted with a homogenous group of participant’s e.g. male, year 1, high activity. Homogenous focus group membership was also used to enhance participants’ comfort in participating (Miles & Huberman, 1984). An example of the stratified sampling framework used for the focus groups investigating psychological correlates is provided in Figure 4.1. This sampling framework was also used for the focus groups investigating the social and environmental correlates. A minimum of eight focus group interviews were carried out in each of the domains i.e. psychological, social, and environmental. If the research team deemed that theoretical saturation i.e. point when you are not gaining new insights (Krueger & Casey, 2000); was not reached a second focus group was carried out with other participants from the same strata.

A total of 32 focus group interviews, each with separate groups of participants, were conducted in the participating schools premises at a time suitable for both the school and participants. The focus group interviews took place between January and April 2009. Each focus group interview lasted approximately 50 minutes and included between 6 – 10 participants. Due to the multitude of possible factors influencing physical activity a targeted approach was used in the research design based on the socio-ecological model of health (Elder et al, 2009). Separate focus groups examined (i) psychological, (ii) social, and (iii) environmental domains of influences on physical activity. Participants were stratified based on gender (male, female), year group in school (year 1 v year 4) and level of activity (low activity v high activity; based on PE teacher classification). Each separate focus group interview was conducted with a homogenous group of participant’s e.g. male, year 1, high activity. This procedure was followed as differences in correlates of activity have been found among males and females and among younger versus older adolescents (Sallis et al., 1999; Sallis et al., 2000). Homogenous focus group membership was also used to enhance participants’ comfort in participating (Miles & Huberman, 1984). An example of the stratified sampling framework used for the focus groups investigating psychological correlates is provided in Figure 4.1. This sampling framework was also used for the focus 178

Qualitative Analysis groups investigating the social and environmental correlates. A minimum of eight focus group interviews were carried out in each of the domains i.e. psychological, social, and environmental. If the research team deemed that theoretical saturation i.e. point when you are not gaining new insights (Krueger & Casey, 2000); was not reached a second focus group was carried out with other participants from the same strata.

179

Qualitative Analysis

Psychological Correlates

Male Female

Year 1 Year 4 Year 1 Year 4

High levels of Low levels of High levels of Low levels of High levels of Low levels of High levels of Low level of activity activity actiivty activity activity activity Activity Activtiy

Figure 4.1: Stratified sampling framework for the psychological domain

180

Qualitative Analysis

4.3.3 Procedure Focus group discussions were based on a standardised, semi structure interview schedule. The focus group interviews used broad, open ended questions to elicit as wide a range of responses as possible. It was hoped that ‘extra’ correlates would be identified using this approach which could be added to the original ‘APAC questionnaire’ developed in chapter 3. Experts in the field of qualitative research and physical activity assisted in the development of the focus group structure and content. Focus groups interviews in each domain i.e. psychological, social and environmental, were piloted with six groups of students that represented the ages and grades involved in the study. No major changes were to the interview guide as a result of the pilot interviews. Key questions used in the focus group interviews are outlined in Table 4.1.

Table 4.1: Core questions for focus groups Type of Focus Group Core Questions Psychological What are the main benefits of physical activity for you? What are the main barriers of physical activity for you? Social Who helps and assists you to be physically active? Who acts as a barrier for you to be physically active? Environmental How does your neighbourhood promote physical activity? What in your neighbourhood is not conducive to physical activity

Each focus group began with an introduction to the topic, explanation of purpose of the study, and ice breaker activities. Participants were then provided with a post- it and asked to write down two factors that positively influenced their physical activity from the specific domain of influence being examined in that focus group interview i.e. psychological, social or environmental (Table 4.1). Factors which were identified were then relayed to the group and placed on a flip chart by the research assistant. Prompts for discussion based on previous literature were used where necessary (Sallis et al. , 1999; Motl et al. , 2001; Sallis et al. , 2002; Heesch et al. , 2006; Ogilvie, 2008)

181

Qualitative Analysis

The discussion in each focus group was based on the factors on the flip- chart with each factor being discussed systematically. A moving debate (McEvoy, 2008) was used to structure the discussion. In this debate numbers from 1 - 5 were placed on the ground and participants were asked to rate the importance of each factor on their physical activity (1 = not at all important to 5 = very important) by standing at a number. Participants were then asked why they had chosen such a position and were also given the opportunity to move based on the views expressed in the discussion. This follows the procedure used in a previous qualitative study (McEvoy, 2008).

At the end of the discussion each participant was allocated three sticky dots and invited to place these on the flip chart alongside the factors perceived as being most important for their physical activity. Participants could decide to place all their sticky dots on one factor or divide their sticky dots between two or three factors, thus allowing a weighting of the most important factors (McEvoy, 2008). A similar procedure was followed in the discussion of the barriers to physical activity.

All focus group interviews were conducted by the primary researcher who has knowledge of working with secondary school youth through teaching and coaching experience and who is currently undertaking doctoral research on the correlates of adolescent activity. The primary researcher received extensive training on the delivery and analysis of focus group discussions prior to beginning the research. Four research assistants’ were recruited and provided with training on the administration and analysis of the focus group interviews. Two research assistants assisted in the moderating each of the focus group interviews, this involved using a flip chart to record influences on physical activity identified by the group, recording results of the walking likert scale, distribution and recording the results of the sticky dots analysis.

The University of Limerick institutional research ethics review board approved the data collection instruments and procedures. Each parent/ guardian and participant completed a written informed consent before data collection.

182

Qualitative Analysis

4.3.4 Data Analysis

Focus groups were recorded and transcribed verbatim. Data from focus group interviews were content analysed based on the procedure of Côté et al (1993). Content analysis organizes the raw data into interpretable and meaningful themes and categories employing either a deductive or inductive approach (Scanlon et al. , 1989; Patton, 2002). The current study employed an inductive approach which allowed themes to emerge from the data rather than fitting data into predetermined categories as would occur using a deductive approach (Goetz & LeCompte, 1981; Weiss et al. , 1991; Patton, 2002). Transcripts were analysed using the ATLAS.ti software programme for analyzing text based data.

The process of content analysis began during the early stages of data collection, after the completion of the first six focus groups i.e. two psychological, social and environmental. At this stage the transcripts were read and re-read independently by the research team consisting of: (i) the primary researcher, (ii) four research assistants, and (iii) a research consultant who had extensive experience in focus group research. Tags were created which aimed to adequately represent words or quotes that relate to the same central meaning i.e. meaning units (Lincoln & Guba, 1984; Graneheim & Lundman, 2004). Tags were assigned a provisional name and were recorded in a codebook. Using the constant comparative method the tags evolved, were added to, and combined with other tags with similar meanings during the analysis process.

Tags with similar meanings were then clustered together (Patton, 1980; Weiss et al. , 1991), and an overarching label which captures the substance of a group of similar tags was created to identify lower- order themes (Miles & Huberman, 1984). This resulted in the creation of distinct yet flexible categories which served as an organizing system (Tesch, 1990). Categories were then further analyzed and refined to ensure data in each category were similar to each other yet distinct from other categories of data (Côté et al. , 1993). Theoretical saturation was reached when the categorising of new data fitted into the existing organising system without the emergence of new categories (Côté et al. , 1993). The next step involved analysing the lower- order themes to identify similarities in order to

183

Qualitative Analysis further combine themes and work towards higher- order themes (Weiss et al. , 1991). The analysis continued to build upwards until it was not possible to locate further underlying uniformities to create higher- level themes (Miles & Huberman, 1984; Scanlon et al. , 1989). Criteria used in the classification of meaningful higher order themes were that each theme was inclusive i.e. adequately captures all lower order themes that comprise it and that themes were mutually exclusive or distinct from each other (Patton, 1980). The research team met periodically to discuss the analysis and to gain consensus validation on the names and definition of higher and lower order themes.

4.3.5 Ensuring Trustworthiness

A number of strategies were employed to ensure the trustworthiness (validity) of the data. Guba (1981) has identified four criteria related to trustworthiness in qualitative research. Specifically a research study should have credibility (internal validity), transferability (external validity), dependability (reliability) and confirmability (objectivity) (Guba, 1981). A comprehensive methodological and data analysis procedure was developed (Côté et al. , 1993; Shenton, 2004) based on previously published research to support the trustworthiness of the current study (Scanlon et al. , 1989; Weiss et al. , 1991). The use of triangulation through quantitative analysis to support the qualitative analysis also assisted in providing a more comprehensive understanding and increasing all aspects of trustworthiness of the research (Weber, 1990; Shenton, 2004). To ensure richness i.e. quality and depth in responses, and to assist with transferability of the research, both participant triangulation and site triangulation were employed with a wide subgroup of participants from different school types participating in the study (Patton, 2002). The use of a comprehensive research team and consultation with experts in the field assisted in reviewing all transcript interpretations of data assisted in the dependability and credibility of the study (Graneheim & Lundman, 2004). When this process resulted in analytic disagreement both researchers presented their interpretations until a plausible explanation was agreed upon (Sparkes, 1998). This procedure suggested a high degree of congruence

184

Qualitative Analysis with only a small percentage of interpretations (less than 10%) requiring discussion which were subsequently resolved.

4.4 Results

Qualitative and quantitative results are presented across four subgroups of participants, (i) year 1, male; (ii) year 4, male; (iii) year 1, female; (iv) year 4, female. Differentiation between inactive and active participants is not presented due to the subjective nature of this classification, however, where appropriate, reference is made to these subgroups. The prevalence of recurring higher order themes across the subgroups is presented as a percentage of the population. For example, a theme identified by all four subgroups of participants was assigned 100%. Data from the quantitative sticky dots analysis is presented as a percentage of importance placed on each higher-order theme by participants in specific sub groups.

4.4.1 Perceived Benefits and Facilitators to Physical Activity and Sport Participation

Benefits and facilitators to physical activity centred around nine higher order themes across the psychological, social and environmental domains. Seven of these themes were identified and discussed in all four sub groups of focus groups with two higher order themes being discussed in three of the four subgroups (Table 4.2). The strength of these associations across all subgroups of participants is highlighted in the sticky dots analysis in Table 4.3. Exemplar quotations will be used in the text to support the findings.

185

Qualitative Analysis

Table 4.2: Summary of psychological, social and environmental factors positively influencing physical activity Soci o-ecological Higher order themes Lower order themes domain Social influences Parental Influence (100%) Logistical support Active with parents Verbal encouragement Skill acquisition support Peer influence (100%) Social belonging Peer support Coaches and Teachers influence Quality coaching/ teaching (100%) Siblings support Other family member (100%) Other family member support

Psychological Physical benefits (100%) Health and well being influences Physical appearance Improved performance Fun and Enjoyment (100%) Comfort and competence Socialising Goal Setting (75%) Achievement Skill development

Environmental Access to facilities (100%) Access to outdoor facilities influences Access to indoor facilities Pets (75%) Pets

186

Qualitative Analysis

Table 4.3: Sticky dots analysis of the factors positively influencing physical activity Socio -ecological domain Higher order themes Male Year 1 Male Year 4 Female Year 1 Female Year 4 Percentages % % % % Overall N = 78 50 63 56 Social influences N = (23) (13) (18) (16) Parental influence 40.5 43.6 48.1 51.4 Peer influence 20.3 2.6 33.3 36.2 Coaches and Teachers 20.3 30.8 7.5 0 Other family members 2.8 15.4 11.1 12.4 Other 17.1 7.6 0 0

Psychological influences N = (26) (20) (20) (23) Physical benefits 53.8 24.0 36.7 57.9 Fun and Enjoyment 11.5 49.2 40.0 41.0 Goal Setting 22.9 21.8 3.3 0 Other 11.8 5.0 20 1.1

Environmental influences N = (29) (17) (25) (17) Access to facilities(outdoor) 70.1 69.0 58.7 60.1 Access to facilities(indoor) 11.1 9.8 35.2 28.6 Pets 4.4 0 0 8.1 Other 4.1 7.5 6.1 3.2

187

Qualitative Analysis

Key positive social influences on physical activity participation were peers, parents, coaches/ teachers and other family members. Parental support was found to be an important facilitator among all sub groups of participants (Table 4.4), however the nature of the support was found to differ across gender. Males identified fathers as having an important role in advising them on their sport participation and assisting them in skill acquisition. For example, one, active Year 1 male described how his father “practices with me, he used to play himself and he practices the skills of the games with me”. In contrast mothers were found to provide more logistic support for males by transporting them to events, attending games as a spectator, and getting gear ready. Females described the support they received from parents in terms of accompanying them on health related activities such as walking, and their parents providing verbal encouragement to be active for health and well being:

Encouragement to me to go out walking, she comes with me. That’s important for me [yr 1, female, less active]

Peer support was found to be particularly important for female physical activity. Physical activity was seen as an opportunity to spend more time with friends; it was also felt by the study participants that physical activity was more enjoyable when with friends. For example an active female from Year 1 believed that “sport is meant to be fun and you have most fun when you are hanging around with your friends. It’s not really that much fun if you are not with your friends”. Peer support was found to be provided through co- participation (i.e. engaging in an activity together), verbal encouragement, teaching new skills, being active with participants and introducing participants to new sports. In particular, females articulated that spending time with friends was a strong motivator to be active and without friends they would find it difficult to remain active, for example, “we like to do sport but we do it so we can hang out and talk to our friends” [yr 1, female, less active]. Among males participation in physical activity and sport was identified as being important for social belonging:

My friends are always playing soccer and I wouldn’t feel part of the group if I didn’t go [yr 4, male, more active]

188

Qualitative Analysis

Coaches, teachers and other family members were also identified as exerting a positive influence on physical activity. Coaches and teachers positively influenced and promoted participation in physical activity by assisting in skill development, providing diverse physical activity opportunities, providing verbal encouragement and organizing fun sessions, this relationship was more evident among males (Table 4.4). Participants who were given a choice in the activities that were covered in PE class identified it as being positive and beneficial for their motivation and enjoyment of physical activity. For example a Year 4, active male articulated “he (PE Teacher) sits down with us at the start of the year and we get some choices of the stuff we do in PE. He picks some of the activities and we pick some, I think that is good”. Siblings who were interested in sport were found to positively influence physical activity through skill acquisition support, acting as role models and being active with participants:

My brothers because they all play sport and you want to be like them, they are like another trainer for me, if I am not training during the week they will train with me [yr 1, male, active]

Perceived psychological benefits associated with physical activity were physical benefits, fun and enjoyment and setting and striving to achieve goals. All sub groups of participants articulated the physical benefits associated with physical activity however, clear gender differences were evident. Among females the physical benefits were related to improved aesthetic appearance and to avoid becoming overweight. Some females, particularly in the older groups made reference to not particularly enjoying physical activity and undertaking it for the sake appearance and health. For example a Year 4, active female described “exercise is exercise like you just do it. It doesn’t really interest me I just do it to keep healthy”. Males reported a desire to be active to improve physical fitness levels which would lead to improved physical performance, a Year 1, active male, felt it was “important to be fit so that you can play properly and be good at sport” [yr 1, male, more active]. All groups of participants made reference to feeling positive after exercise and that it provided ‘time out’ from stressors in their daily lives:

189

Qualitative Analysis

you’re studying or something the sport would like take your mind off the hard work and you need a break as well [yr 1, female, less active]

Fun and enjoyment emerged as an important benefit for participation in physical activity and sport, when participants were probed as to what makes an activity fun and enjoyable different sub groups identified different factors. Males especially those in the ‘active’ groups were found to perceive themselves as being comfortable and competent in physically active pursuits, which resulted in the activity being fun and enjoyable. For example one, active Year 1 male said “I get enjoyment when I am good at sport, it makes me feel good”. In contrast considerable differences were evident among females and ‘less active’ males with social factors such as spending time with friends being a more salient factor associated with fun and enjoyment:

Me and my friends have great fun when we are doing it and it wouldn’t be fun without them [yr 4, female, active]

More active males in particular were more likely to be more intrinsically motivated by the physical activity or sport. The setting of goals relating to skill development and skill acquisition were regularly talked about as being an important factor for participation in physical activity and sport. This trend was not clearly evident among females (Table 4.4).

I go to get better that’s the main reason if my friends are there its better when they are there but I just go to practice [yr 1, male, active].

High levels of competence in physical activity was also associated with popularity among peers as identified by a Year 1, less active male “if you are good at sport lots of people would want to be your friend, but if you were bad at sport it would be hard to make new friends”.

Key environmental influences identified were ease of access to facilities, and positive influence of pets on physical activity. Facilities were not seen to be a major positive or negative influence on physical activity. Facilities were perceived to provide a more diverse range of physical activity options however this was not believed to affect overall levels of participation. Green spaces, sports pitches were the most popular sites for

190

Qualitative Analysis activity this was especially important for males, who tended to participate in competitive and recreational team based activities in these settings, “because that’s where me and all my friends hang out and play soccer and stuff” [yr 1, male, more active]. Females in the older groups were more likely to participate in health related activities in these settings e.g. walking, cycling, aerobics than younger females. A number of females also identified having a pet which was identified as being a positive influence on physical activity.

I have 2 dogs and I take them walking nearly every day [yr 1, female, active].

4.4.2 Perceived Barriers to Physical Activity and Sport Participation

Barriers to participation in physical activity centred around 11 higher order themes across the psychological, social and environmental domains. Eight of these themes were identified and discussed among all four subgroups of participants with three themes being discussed among three of the subgroups (Table 4.4). The strength of these associations across subgroups of participants is highlighted in the sticky dots analysis in Table 4.5.

191

Qualitative Analysis

Table 4.4: Summary of psychological, social and environmental factors negatively influencing physical activity Socio -ecological domain Higher order themes Lower order themes Social influences Parental Influence (75%) Academic pressure Pressure to succeed in sport Peer influence (100%) Inactive friends Social belonging Girlfriend/ Boyfriend influence Coaches/ Teachers (100%) PE teacher influence Coaches influence

Psychological influences Lack of enjoyment (100%) Lack of perceived competence Self conscious Sport structure Lack of time (100%) School time constraints Work and other time constraints Other interests (100%) Preference for inactive pursuits Lack of motivation (100%) Lack of motivation

Environmental influences Neighbourhood structure (100%) Safety Lack of facilities Lack of opportunities Weather (100%) Transportation barriers School physical activity (75%) Weather PE structure School sport Transition to secondary school Sport structure (75%) Sport structure

192

Qualitative Analysis

Table 4.5: Sticky dots analysis of the factors negatively influencing physical activity Socio -ecological domain Higher order themes Male Year 1 Male Year 4 Female Year 1 Female Year 4 % % % % 78 50 63 56

Social influences N = (23) (13) (18) (16) Parental Influence 0 11.1 8.8 4.2 Peer influence 42.6 51.9 59.3 73.2 Coaches and Teachers 38.5 21.1 13.1 11.3 Other 18.9 9.5 18.8 11.1

Psychological influences N = (26) (20) (20) (23) Lack of enjoyment 16.2 29.3 39.2 46.7 Lack of time 20.4 35.2 21.5 12.3 Other interests 39.3 20.6 24.1 25.5 Lack of motivation 13.6 12.4 12.9 13.1 Other 3.9 2.5 3.3 2.4

Environmental influences N = (29) (17) (25) (17) Neighbourhood structure 52.1 58.8 39.4 33.1 Weather 12.6 14.2 16.4 21.8 School physical activity 18.1 0 17.1 15.6 Sport structure 0 16.4 20.6 23.0 Other 17.2 10.6 6.5 6.5

193

Qualitative Analysis

Key social influences negatively associated with participation in physical activity were parents, peers and coaches/ teachers. Some parents were found to negatively influence physical activity and sport participation by being critical of the time demands associated with physical activity and sport participation; it was felt that the time engaging in physical activity could be spent in more academic pursuits.

she (mother) thinks I play too much and that I should be spending more time studying [yr 4, male, active]

Peers were also found to negatively influence participation in physical activity. All subgroups groups of participants made reference to having different groups of friends e.g. school friends, friends living in neighbourhood and ‘other’ friends. In particular females articulated that some or all of these groups of friends were not interested in physical activity and sport and when they spent time with these friends it tended to be in sedentary pursuits, for example, a Year 1, active female identified having “two groups of friends, the friends in my park are not into sport at all but I go to camogie (Irish sport) and I have another group of friends there who are much more active”. Social belonging was also a peer related barrier with participants involved in sport making reference to the fear of “missing out” and friends complaining when they were involved in organised sport:

your friends are always moaning that your always at matches and stuff and your never around [yr 4, female, active]

Coaches who were too serious and over emphasised winning were perceived as a deterrent to some participants; this was particularly evident among males in the ‘less active groups’, for example, one Year 4, less active male said “they (coaches) put too much pressure on you and you get fed up of it”. Participants were critical of PE teachers who did not provide student choice and variety of activities, a Year 1, less active male described the PE programme as boring due as “they don’t do the physical activities that I am interested in, we only do a few sports and I don’t have any interest in them”. Homework was also talked about as a negative influence on physical activity levels among participants:

194

Qualitative Analysis

teachers because they give us loads of homework and it’s too late to go out after [year 1, male, active]

Key psychological barriers to physical activity were lack of enjoyment, lack of time, other interests and lack of motivation (Table 4.3). Lack of perceived competence in physical activity was found to be strongly associated with lack of enjoyment; this trend was most pronounced among females. A Year 1, less active female explained that “all my friends were good at it and then you feel like really frustrated because everyone is better than you”. In conjunction, females also identified feeling self conscious when they were involved in mixed gender physical activity in the PE and recreational sport settings which negatively influenced participation in physical activity. Participants particularly in the older ‘less active’ groups made reference to organized sport participation becoming more serious with age, which resulted in reduced enjoyment for sports participation. For example, a Year 4, less active male believed that “when we are young it’s all about fun but when you get to our age everything is about winning”. Increased time demands with the sport, not getting game time, lack of social teams and pressure from coaches, other players, and parents were other factors associated with reduced enjoyment from sport. For example a Year 4, active female talked about “the pressure drips down to the players from coaches and mentors”, while a Year 4, less active female identified too much focus on “winning, it’s too competitive, like you want to go out and have some fun with people who don’t take it too serious”.

Lack of time was identified as a barrier to physical activity and sport participation among all subgroups of participants. Part time jobs, school work and home related duties were seen to be negatively impacting on time when participants could be active:

you have school and then you have homework every evening and if you had an exam the next day you would have to study for that. [yr 4, female, less active]

A number of participants in all subgroups talked about a preference for sedentary pursuits such as screen time inactivity and hanging out with friends rather than physically active pursuits. Lack of personal motivation was also identified as a factor negatively influencing participation in activity. Participants made reference to

195

Qualitative Analysis

“being too tired” after school, “sitting down in front of the TV and getting stuck there”, and “getting lazy”:

You prefer to do other things other than exercising, like the internet and stuff especially when you get older. [yr 4, female, less active]

Environmental barriers that emerged from the analysis centred on neighbourhood structure, weather, school physical activity and sport structure (Table 4.3). Inaccessibility of facilities, lack of transportation, neighbourhood safety, traffic and inclement weather were identified as making physical activity more difficult. Safety issues were of particular concern to female participants, for example a Year 4, less active female described her neighbourhood as “not really safe to walk where I live but if you were in a group it would be fine. There are gangs around sometimes at night. They might be out drinking and stuff”.

School physical activity was identified as negatively influencing physical activity in a number of focus groups. A number of older participants were critical that PE was only offered in their school for the first 3 years of their 6 year school programme. Some of these participants were of the opinion that as they were not actively involved in sport, PE would be particularly beneficial for them, for example a Year 4, less active male pointed out that “we don’t have any physical activity classes after third year. I like being active but I don’t play any sport, so for me PE is very important. I think it would be a good idea to have PE after third year”. Mixed gender PE was a commonly reported barrier among females, it was felt that males were very rough, tended to show off and to dominate within the PE class. Issues relating to perceived competence were compounded more among females in this setting, a Year 1, less active female believed that “boys like it when sport and games in PE are serious, girls just think why would I get involved in that, it’s way too serious”.

Some participants felt their sporting interests were not catered for as part of the extracurricular sports programme in the school, which resulted in non participation in extracurricular sport; my sports like rugby aren’t in the school. (yr 1, male, less active). The transition from primary to secondary school was also identified as a negative factor associated with ceasing participation in extracurricular school sport and non school sport. This transitionary time was associated with development of new groups of friends

196

Qualitative Analysis and unfamiliar surroundings which often resulted in reduced participation in physical activity and sport:

The friend who did play with me went to a different school and I’m friends with different people now who don’t play. (yr 1, female, less active)

4.5 Discussion

Similar to previous research this qualitative investigation has demonstrated the importance of family and friends in shaping motivation for and levels of physical activity (Allender et al. , 2006; Hohepa et al. , 2006; Rees et al. , 2006; Coleman et al. , 2008; Eime et al. , 2010). This influence has been found to manifest in elements such as co participation, verbal encouragement and logistic support. Future interventions should target joining and maintaining physical activity with peers especially among females due to the strong peer influence among females. Future interventions should also take cognisance of the importance of parental influence and should attempt to incorporate parental support as an integral part of the intervention.

Perceived competence was identified as being important for enjoyment and participation in skill related activity (Ryan, 1997; Allender et al. , 2006; Eime et al. , 2010). Females tended to report low levels of competence, enjoyment and feeling self conscious performing skill related physical activity in front of others. This resulted in older females tending to gravitate away from skill related activities to more individual activities carried out for extrinsic health related benefits e.g. slim body shape (Mulvihill et al. , 2000; Allender et al. , 2006; Grieser et al. , 2006; Hohepa et al. , 2006; Humbert et al. , 2008; Eime et al. , 2010), this is in agreement with Harter’s Theory of Competence (Harter, 1982).

Males perceived themselves as having higher levels of competence in physical activity and sport and were more comfortable participating in skill related activities (Hohepa et al. , 2006; Humbert et al. , 2008; Eime et al. , 2010). Physical activity and sport were seen as a more integral part of life among males (Coleman et al. , 2008) with physical activity competency and sport participation being associated with enjoyment,

197

Qualitative Analysis prestige, popularity and social belonging, this is in agreement with previous research (Jago & Baranowski, 2004; Hohepa et al. , 2006).

A previous study has also found that competence and enjoyment motives were highly correlated; with body improvement motives were not associated with enjoyment (Ryan, 1997). Participants were more likely to maintain exercise for enjoyment and competence (intrinsic) factors than for body improvement related motives (Ryan, 1997). Therefore, lack of competence and enjoyment in physical activity among females may partly explain the decrease in physical activity during adolescence among females (Kimm et al. , 2000). Due to the importance of perceived competence and enjoyment in physical activity future promotional strategies should provide male and female adolescents with a range of activities that are perceived as fun and enjoyable and participants feel comfortable and competent to participate in.

Despite the importance of promoting physical activity to make the healthy choice the easier choice the current structure and organisation of sport and school physical activity appear to have a negative influence on participation. Organised sport was perceived as becoming much more serious with age in adolescence, this resulted in many adolescent’s not feeling comfortable to participate (Mulvihill et al. , 2000; Grieser et al. , 2006; Eime et al. , 2010). Adolescents who want to participate in sport at a social and recreational level do not appear to be catered for in current sporting structures. This is in agreement with previous research which reported that older groups of adolescents desired decreased competition and more non competitive less structured activity that they could choose how intensely they were active (Humbert et al. , 2008). Future interventions should develop, implement and evaluate social and recreational sports and games which emphasize participation and fun. Due to the strong positive and negative influence peers can have on participation this intervention should aim to encourage groups of friends to be active together.

A number of issues were identified with the structure of school sport which negatively influenced attitudes towards and engagement in physical activity. Participants expressed a desire to have an input into the activities engaged in during PE class(Rees et al. , 2006), while those who had some input into the activities identified it as making PE more enjoyable. Adolescence is a period of transition in young people’s lives when more independence is sought(De Roiste & Dinneen, 2005); empowering

198

Qualitative Analysis adolescents to have an input to the activities may provide a sense of ownership of what is covered which might positively influence interest and participation in physical education.

The importance of ensuring positive physical education experiences for adolescents is vital as negative experiences during school PE were found to strongly discourage participation in physical activity among female girls (Coakley & White, 1992). Factors associated with negative experiences in PE in the current study included issues relating feeling self conscious performing skilled activities in front of peers, lack of perceived competence and tendencies of males to dominate in PE class. Previous research has identified the possible benefits of single sex, non competitive PE classes(Allender et al. , 2006; Eime et al. , 2010), the findings of the current study are in agreement with this.

In two of the participating schools physical education was offered only up to year 3 of the 6 year programme; due to the fact that active adolescents tend to become active adults (Telama et al. , 1997)and physical activity levels decline rapidly in adolescence (Woods et al. , 2010) it would appear logical that regular PE throughout the 6 years in school is important in the promotion of health and well being among Irish adolescents. In conjunction with this, due to the lack of time due to various commitments cited by adolescents further research should investigate the efficacy of offering more flexible, less structured physical activity opportunities during the school day. This might offer youth opportunities to be active daily on ‘their terms’ but not require the time commitments of organized sports (Humbert et al. , 2008).

Transition and change within adolescent’s lives were found to negatively influence physical activity, which is in agreement with previous research (Allender et al. , 2006; Humbert et al. , 2008). A number of participants identified the transition from primary to secondary school as a time when they ceased participation in physical activity and organised sport, positive influences have been identified as important in becoming and staying involved in physical activity during this transition (Allender et al. 2006). The physical activity stage of change measure(Marcus et al. , 1992)may be a useful cost effective tool that could be easily used by PE teachers to measure the physical activity stage of change of students who have made this transition and to assist in the development of appropriate stage matched interventions. Further research is

199

Qualitative Analysis warranted on the efficacy of programmes targeting the promotion and maintenance of physical activity during this transitionary time.

Access to facilities was not found to be a major barrier among participants. Self determination and personal motivation for activity were found to be more salient factors. Structural changes such as more cycle lanes and walkways were identified as being important to increasing levels of active transportation; however the general perception was that if an individual wanted to be active they could be active.

Limitations of the current study included that all the participants were from Cork a region in Southern Ireland therefore the findings may not be nationally representative. As with all focus group research there was a fear of socially desirable responses, due to adolescent males and females discussing potentially sensitive issues relating to being self conscious when being active, peer influences etc. All qualitative work is subjective and reliant on the researcher’s interpretation of the data; however we attempted to minimize this issue by having two researchers code the data.

Strengths of the current study included using a structured focus group with a clear methodological framework to facilitate in depth analysis on correlates among different subgroups of adolescents. The use of the quantitative ‘sticky dots analysis’ provided further validation of the results and assigned a value of importance on the key correlates. Future research should use the key factors identified in the current research to develop a comprehensive correlate questionnaire relevant to Irish adolescents. This questionnaire should be administered to a nationally representative sample of Irish adolescents and should form the basis of future interventions.

4.5.1 Modifications to the ‘APAC questionnaire’ Based on the findings from the current study the ‘APAC questionnaire’ was modified and further developed. Details of these scales and the reliability and validity data relating to these scales are presented in section 2.14. Teacher’s influence and perceived general school climate was identified as having the potential to influence participation in physical activity both positively and negatively (Table 4.2; Table 4.4). A tool which assessed the influence of school climate

200

Qualitative Analysis and teacher support for physical activity was included in the ‘APAC questionnaire’ (Birnbaum et al. , 2005). Having inactive friends was also identified as barrier to participation in activity among adolescent girls, furthermore among older girls participation physical activity was not seen as the ‘norm’ (Table 4.4). A tool was included which assessed girls norms relating to physical activity (Birnbaum et al. , 2005). Participants also made reference to having different groups of friends e.g. school friends, friends in the neighbourhood (Table 4.4). Often some of these groups participated in physical activity while other groups were not active. A simple measure was developed by the researcher and included in the APAC questionnaire which assessed participant’s engagement in activity with different groups of friends. Being physically active for improved aesthetic appearance and to avoid becoming overweight was also identified in the current research (Table 4.2). Furthermore participants believed that being overweight would limit opportunities for and comfort in physical activity. The ‘Physical Appearance’ subscale from the ‘Self Perception Profile for Children’ (Harter, 1985) was included in the questionnaire. Body mass index was also included as a measure of overweight and obesity among adolescents. A critical review of relevant literature relating to this procedure is provided in section 2.8.4. Active participants in the current study were found to display a positive attitude towards physical activity (Table 4.2; Table 4.4). The outcome expectancy value was also included in the ‘APAC questionnaire’ as an indication of attitude towards physical activity and subsequent impact on levels of activity (Ajzen & Fishbein, 1980; Motl et al. , 2000). This provided a positive/ negative evaluation of the consequences of being active. Participation in sedentary pursuits, in particular screen time inactivity was also identified as a potential correlate which negatively influences physical activity (Table 4.4). A screen time inactivity measure was included in the APAC questionnaire. This was assessed using a simple tool developed by the researcher. Participants were asked to identify time spent (i) watching TV and (ii) engaged in computer related activity on weekdays and on weekends. No reliability or validity information exists for this tool. Finally, the nature of physical activity and sport was identified as being taken too seriously (Table 4.4) which may have a negatively consequence on adolescent activity. An item was included in the perceived barriers scale to assess this potential correlate. This item asked the participant to identify how often they felt ‘physical actively being played too seriously’ affected their participation in physical activity.

201

Qualitative Analysis

4.6 Conclusion

The current study provides a comprehensive insight into the key factors influencing subgroups of Irish adolescents. Physical activity promotional strategies among Irish adolescents should empower adolescents to choose activities they would like to engage in, should focus on activities that participants feel comfortable and competent to participate in and should incorporate elements of social support. The physical activity needs of a sub group of older Irish adolescents who are no longer engaging in organized sport due to a variety of factors are not being catered for by sporting organizations and schools. More social recreational activities should be developed for this group.

202

Chapter 5

Correlates of physical activity among Irish adolescents

5.1 Abstract

Background : To effectively promote physical activity a need exists to better understand the factors influencing physical activity. The purpose of this study was to identify the personal, psychosocial and environmental correlates of physical activity among Irish adolescent males and females.

Methods : A total of 1,114 (48% males; 52% females) participants from 27 schools completed a supervised questionnaire measuring potential correlates of physical activity. Physical activity was measured on 7 days using Actigraph accelerometers with a subsample of the population (N = 303). Analysis of variance and hierarchical linear regressions were used to analyze the potential correlates of physical activity.

Results : Males were found to have higher levels of physical activity (P < . 01) and have a more positive psycho-social correlate profile than females. Psycho-social correlate scales were also found to discriminate across categories of activity, i.e. those in the more active categories of MVPA were found to have a more positive psycho-social correlate profile. Participation in competitive sport, physical activity stage of change, self-efficacy,

203

Correlates among adolescents intention to be active and elements of peer support (+, P < . 05 for all) were significant correlates of moderate and vigorous physical activity (MVPA) and vigorous activity (VPA) among adolescent males and females. Correlate scales were found to explain between 24 - 33% of the variance in MVPA and 28 – 37% of the variance in VPA for males and females.

Conclusion: Elements of peer support, physical activity efficacy and promotion of sport participation should be targeted in physical activity interventions.

Keywords: Adolescents; Physical Activity; Exercise behaviour; accelerometry

5.2 Introduction

The relationship between physical activity and health in youth has been well documented (Strong et al. , 2005). Despite the health benefits associated with regular physical activity, data indicate that sizeable percentages of adolescents fail to meet the guidelines for physical activity, with lower adherence rates among adolescent females compared to males (Pate et al. , 2006; Troiano et al. , 2008; Lytle et al. , 2009; Nilsson, 2009; Whitt-Glover et al. , 2009; Dumith et al. , 2010; Woods et al. , 2010). Data from the 2003 - 2004 National Health and Nutrition Examination Survey (NHANES) found that 8% (12% males; 3% females) of adolescent participants were found to achieve the recommended 60 minutes of moderate to vigorous activity per day (Troiano et al. , 2008).

Measured variables that are associated with physical activity are classified as ‘correlates of physical activity’ (Bauman et al. , 2002). The present study used Social Cognitive Theory and Ecological Theory to explore the correlates of activity (Woods et al. , 2009). Understanding these influences on adolescent physical activity and how they vary by age and sex is important in the development and evaluation of effective targeted interventions (Sallis et al. , 2002; Whitt-Glover et al. , 2009; Kelly et al. , 2010; Salmon, 2010).

204

Correlates among adolescents

Physical activity correlate studies have identified numerous important predictors of adolescent physical activity. Sallis et al. (2000) reviewed correlate studies of children and adolescents. Variables that were consistently associated with adolescent physical activity included sex (male), age (inverse), perceived competence, previous/ current sport participation, social support and opportunities to exercise. Limited data has examined the correlates of physical activity among Irish adolescents; however a recent study has identified perceived competence, social support and intention to be active as important correlates among adolescent females (Burns et al. 2011a). No consistent pattern has emerged in studies comparing the influence of gender on correlates of physical activity and more research is warranted in this area (Sallis et al. , 2002; Schmitz et al. , 2002; Tergerson & King, 2002; Wu et al. , 2003).

Kelly et al. (2010) has investigated the correlates of objectively measured physical activity among females from different ethnic groups in the TAAG study. Correlates of physical activity were found to vary across racial groups. Social support from peers was the only variable which was found to be consistently associated with physical activity in all subgroups for both MVPA and VPA. Similarly Sallis et al. (2002) found that peer support was the only significant correlate of objectively monitored activity in adolescents of different age groups. A limitation in the majority of adolescent correlate studies is that they rely solely on self reported measures of physical activity (Sallis et al. , 2002; Kelly et al. , 2010). More correlate studies using objective measures of physical activity have been recommended, as results indicate that self report studies of physical activity correlates may overestimate the association (Trost et al. , 1999; Sallis et al. , 2002; Kelly et al. , 2010). Furthermore, to the author’s knowledge no previous study has comprehensively examined correlates of activity across different levels of activity using an objective measure of physical activity.

Therefore, the purpose of the current study was to explore the variation in personal, psycho-social and environmental correlates of physical activity among male and female adolescents and across different levels of activity, and to assess the relationship of these correlates with objectively measured MVPA and VPA.

205

Correlates among adolescents

5.3 Methods

5.3.1 Sampling Procedure Participants in the current study were adolescents attending second level schools in Cork, a county in southern Ireland. A list of schools in the Cork region from the Department of Education and Skills website (2008) were stratified by school type (secondary, community, vocational, comprehensive); school location (urban/ rural); and by gender (single sex/ co-educational). Schools on the Department of Education and Science website (2008) classified schools as ‘Cork city’ or ‘Cork county’ schools. Schools classified as ‘city’ schools were categorised as urban schools in the current research while ‘county’ schools were categorised as rural schools (Department of Education and Skills, 2008). A cross sectional sample of the schools in the Cork region were then randomly chosen and invited to participate in the study. In total 27 schools participated in the study with a junior class (year 1-3) and a senior class (year 4-5) in each school participating. Four schools from the initial sample declined the invitation to participate and were replaced by alternative schools from the same strata.

The targeted sample was 80% of the selected group of approximately 40 students at each of the 27 schools (i.e. 40 adolescents X 27 schools = 1,080). The sample size exceeded the recruitment goal and a total of 1,114 adolescents (48% males; 52% females) participated in the study. The year group with the smallest representation in the sample was year 5 which accounted for 15% of the sample while the largest representation was year 1 with 26% of overall participants. . Participants in year 6 in Irish secondary schools were not invited to participate in the study due to the exam commitments associated with this year.

The University of Limerick institutional research ethics review board approved the data collection instruments and procedures. Each parent/ guardian completed a written informed consent and each adolescent completed a written assent before data collection.

206

Correlates among adolescents

5.3.2 Correlates of Physical Activity Questionnaire The correlate of physical activity questionnaire was primarily influenced by the Trial of Activity for Adolescent Girls Study (TAAG, 2004) , Sallis et al . (2002) and focus group research carried out by the primary author (Burns et al. , 2011). Correlates of physical activity were organised in 4 blocks: personal/ biological, psychological, social and environmental. Details of the psychological, social and environmental correlates are provided in Table 5.1. Variables in the personal/ biological block included: age (years, months), ethnicity, residence (urban, rural), family structure (living with one parent; both parents), screen time inactivity (hours) and socio economic status. The Irish CSO measure of parental occupation was used to classify individuals based on socio economic status (CSO). The classification adhered to the international occupation classification ISCO Com (CSO, 2008). The code to which a person’s occupation is classified is determined by the kind of work he or she performs in earning a living irrespective of the place in which, or the purpose for which, it is performed (CSO, 2008). Participant’s parents were classified to one of ten specific socio economic groups. In addition a residual group “All others classified gainfully employees and unknown” was used where sufficient details were not provided. The classification aims to bring together persons with similar social and economic statuses. Challenges associated with measurement of socioeconomic status are presented in section 2.8.3. Behavioural correlates in included were current sport participation and engagement in screen time inactivity. In general, sport is a competitive activity undertaken in the context of rules defined by a regulator agency (Bouchard et al. , 2007). This definition of sport was simplified to ‘organised structured activity that is played or done according to rules and involves winning or losing’ for the purpose of the current research. Sport participation was measured using a single item with participants responding yes/no to the statement ‘I currently play sport competitively’. A limitation of the measure of sport participation in the current study is that it assessed only ‘current’ participation in sport. Previous participation in sport participation which has been identified as an important correlate of activity was not included in the current research (Van der Horst et al. , 2007). Screen time inactivity was assessed using a simple tool developed by the researcher.

207

Correlates among adolescents

Participants were asked to identify time spent (i) watching TV and (ii) engaged in computer related activity on weekdays and on weekends.

The questionnaire was administered in one 40 minute class period by the primary researcher and trained data collectors. Initial instruction was provided and the questionnaire was completed in a systematic fashion i.e. section by section. One correlate scale was not found to have acceptable internal consistency of Cronbach alpha > 0.7. This scale was retained however as it consisted of only three items which may have led to a low internal consistency. All other scales were found to have Cronbah alpha’s (range 0.71 – 0.93) (Table 5.1).

208

Correlates among adolescents

Table 5.1: Description of ‘Correlates of Physical Activity Questionnaire’ Variable Name Description Items Cronbach References [range] alpha Psychological Variables Stage of change Physical activity stage of change; 1= Currently physically active, 4[1 ,2] n/a Marcus &Rossi (1992) 4=Regularly active for past 6 months Change strategies Cognitive and behavioural change strategies; 1= never, 5 =very often 9 [1,5] 0.84 TAAG (2004); Dishman & Motl (2005) Self –efficacy Self-efficacy; 1= Disagree a lot, 5= Agree a lot 8 [1,5] 0.83 Saunders et al. (1997); Motl et al. (2000); TAAG (2004) Perceived competence Perceived competence at games and sport; Positive and negative 7 [1,4] 0.80 Harter (1982) statements – really true for me; sort of true for me Body Image Body image – Positive and negative statements – relay true for me sort of 6[1,4] 0.92 Harter (1985) true for me Enjoyment of activtiy Enjoyment of physical activity; 1= Disagree a lot, 5= Agree a lot: reverse 16 [1,5] 0.91 Heesch et al. (2006); Motl et al. (2000) scoring Kendzeirski & De Carlo (1991) Perceived barriers Perceived Barriers to Physical Activity, 1=Never, 4=Very Often 22 [1,4] 0.91 Heesch et al. (2006); Sallis et al. (2002) Enjoyment of PE Enjoyment of PE; 1= disagree a lot; 5= agree a lot 1 [1,5] n/a TAAG (2004); Motl et al. (2001) Intention to be active Intention to be active on most days; 1=sure I will not be active, 5= sure I 1 [1,5] n/a Godin & Shephard (1986) will be active Perceived benefits Perceived benefits about being active; 1= Disagree a lot, 5 = Agree a lot 9[1,5] 0.86 Sallis et al. (2002); Motl et al. (2000); TAAG (2004) Outcome expectancy value Importance on outcome of being active; 1= Disagree a lot, 5 = Agree a 9[1,5] 0.93 Sallis et al. (2002); Motl et al. (2000); lot TAAG (2004) School climate: teachers* Perceptions of teacher support; 1= Disagree a lot, 5 = Agree a lot 2[1,5] 0.84 TAAG (2004) Girls norms* Perceptions of girls physical activity; 1= Disagree a lot, 5 = Agree a lot 1[1,5] n/a TAAG (2004) Boys influence on girls* Perceived influence of boys on girls activity;1= Disagree a lot,5 =Agree a 3[1,5] 0.61 TAAG (2004) lot Social Variables Active groups of friends Active with different groups of friends; 1=Yes; 2 = No 3[1,2] 0.6 Burns et al. (2010) Encourage peers to be active Encouragement of peers; 1= never; 5 = every day 1[1,5] n/a TAAG (2004) Social support peers Social Support from peers; 1= none, 5= everyday 3 [1,5] 0.75 Sallis et al. (1999); TAAG (2004) Social support family Social support from family; 1= none, 5 = everyday 5 [1,5] 0.85 Sallis et al. (1999); TAAG (2004) Environmental Variables Transport Transportation to and from physical activities; 1= Not at all difficult, 4= 3 [1,4] 0.78 Evenson et al. (2006; TAAG (2004) impossible Access to facilities Ease of access to physical activity facilities 14 [1,3] 0.73 Evenson et al. (2006); TAAG (2004) Perceived environment Perceived environmental suitability for physical activity; 1=disagree a 10 [1,5] 0.71 Evenson et al. (2006); TAAG (2004) lot, 5= agree a lot

209

Correlates among adolescents

5.3.3 Physical Activity Protocol

Physical activity was measured using Actigraph triaxial accelerometers (GT3X, Fort Walton Beach, FL) with a subsample (N = 303) of the participants. The distribution and the collection of the accelerometers were carried out by the primary researcher, who visited each school and personally distributed the accelerometers during an information briefing session. This is in agreement with Ward et al . (2005) who recommends face to face distribution and collection of monitors as being the most effective dissemination technique. Participants were asked to wear the accelerometer over their right hip for a period of 7 days, and to remove it when swimming or bathing.

5.3.4 Instrument Quality and Dependability

Monitor comparability should not be assumed and it has been recommended that new monitors be checked for accuracy against a movement standard (Ward et al. , 2005). Prior to the beginning of the study the accelerometers instrument quality and dependability was assessed with a group of first year students from Cork Institute of Technology. Participants wore an accelerometer (i) during a multistage 20 metre shuttle test and (ii) for a 3 day period while an activity/ inactivity diary was kept. One accelerometer did not download data and was returned to the manufacturer. In total 20 accelerometers were used in the data collection.

5.3.5 Promotion of Compliance

To promote compliance among participants a number of initiatives were used, these initiatives were adopted from Trost et al . (2005) and Ward et al . (2005). Participants whose parents provided consent received a text message to their mobile phone each morning to remind them to put on their accelerometer. Levels of wear time have been found to be lower on weekends, (Masse et al. , 2005; Treuth et al. , 2007; Sirard et al. , 2008) therefore a reminder text message was sent each morning and afternoon on weekend days.

210

Correlates among adolescents

A second initiative was that an information sheet was developed for participants with relevant facts and frequently asked questions relating to accelerometers. This information sheet was disseminated at the initial information session. A third initiative to promote compliance was that an I-pod shuffle was raffled in each school among those that participated. To avoid a degree of reactivity (i.e. increased activity levels during the week) it was stressed to participants that eligibility for the raffle was based on wear time as opposed to activity levels.

A fourth initiative was that each participant was provided with a summary sheet of their weekly levels of physical activity after participating in the study. This strategy was used as a means of providing PE teachers and students with some important information on their levels of physical activity. The author was aware that this procedure may have led to a degree of reactivity; however it was felt that it was necessary to provide participants with some feedback after wearing the accelerometer for a week.

5.3.6 Inclusion Criteria

To be included in the analysis participants were required to wear the accelerometer for a minimum of 600 minutes on 4 separate days. Wear time was calculated by subtracting non wear time from 24 hours. Classification of non wear time (i.e. time when the accelerometer has been removed) was measured using 1 minute epochs and defined by an interval of at least 60 consecutive minutes of zero activity, with allowance for 1-2 min of counts between 0 -100 (Metzger et al. , 2008; Troiano et al. , 2008). Periods of non wear time were considered over when (i) 3 consecutive minute of 1 – 100 were recorded; or (ii) a minute with a score of greater than 100 was recorded. This follows the procedure recommended by Troiano et al. (2008) in the NHANES study. A total of 233 (77%) of the participants (N = 109 male; N = 124 female) had sufficient wear time to be included in the analysis.

211

Correlates among adolescents

2005; Metzger et al. , 2008) and a 1 minute count of greater than 20,000 counts per minute (Masse et al. , 2005). Furthermore, mean daily counts were analysed and questionable data (N = 1) were removed from the analysis.

Some researchers have omitted data from the first day of data collection to reduce the impact of reactivity (higher levels of activity as a result of wearing the accelerometer) (Pate et al. , 2006). It has been found that activity counts on day 1 were on average 17 counts higher than subsequent days indicating a very small reactive effect, and the author does not regard as being meaningful (Riddoch et al. , 2007). Therefore, the first day of accelerometer wear was included in the analysis in the current study.

No difference was found between weekday and weekend physical activity levels in the current study (mean weekday moderate/ vigorous activity = 44.7 ± 20.2 mins; mean weekend moderate/ vigorous activity = 44.4 ± 30.2 mins), therefore participants were not required to have a weekend day to be included in the analysis. This follows the criteria of Troiano et al. (2008) and Mattocks et al. (2008). Similarly, Steele et al . (2010) investigated physical activity patterns among children on weekdays and weekend days and reported no difference for weekday versus weekend day physical activity.

5.3.7 Measured Aspects of Physical Activity

Outcome variables calculated for the current study were, (1) mean counts per minute, (2) mean daily minutes in sedentary, low, moderate and vigorous activity, (3) mean daily minutes in moderate and vigorous activity bouts and (4) estimates of adherence to recommended physical activity guidelines.

Counts per minute were analysed in the current study as it is the only accelerometer variable that has been rigorously validated during free living conditions using doubly labelled water (Ekelund et al. , 2001; Riddoch et al. , 2004; Sirard et al. , 2008), furthermore mean counts per minute evaluates raw data scores without the use of any external criteria. Mean counts per minute were calculated by dividing the sum of activity counts for a valid day by the number of minutes of wear time in that day. A

212

Correlates among adolescents weekly mean count per minute was computed based on each valid day, similar to Troiano et al. (2008).

Time spent in physical activity of different intensities is based on the application of count thresholds derived from calibration studies that classify accelerometer output to measured activity energy expenditure. Accelerometer counts during wear time were classified as sedentary, low, moderate or vigorous intensity. Sedentary behaviour was calculated based on minutes accumulated below 100 counts per minute during periods when the monitor was worn expressed as mean minutes per day. This threshold was developed in a calibration study carried out among adolescent girls (Treuth et al., 2004 a) and has been widely used (Healy et al. , 2007; Healy et al. , 2008; Matthews et al. , 2008).

Moderate and vigorous activity count thresholds were calculated using the age specific equations developed by the Freedson group as published by Trost et al, (2002).

METs=2.757 + (0.0015 x counts min -1) – (0.08957 x age [yr]) – (0.000038 x counts min -1 x age [yr]).

Thresholds for moderate activity of 4 METs and vigorous activity of 7 METs were used as these values adjust for the higher resting energy expenditure of children and youth and have been found to equate to moderate and vigorous intensity activity among adolescents (Treuth et al. , 2004; Riddoch et al. , 2007; McClain et al. , 2008). Low intensity activity was calculated as time spent over the sedentary threshold and under the moderate threshold i.e. 100 counts per minute - 3.9 METs (Healy et al. , 2007). Due to the nature of activity among youths (i.e. short bursts of intermittent activity at different intensities (Bailey et al. , 1995; Ward et al. , 2005) data was collected at 5 second epochs to avoid underestimation of time in moderate and vigorous activity that may occur when longer epochs are used (Nilsson et al. , 2002; Ward et al. , 2005; Rowlands et al. , 2006; Riddoch et al. , 2007), for example if a participant was to run vigorously for a period of 10 seconds and to stand for a period of 50 seconds a one minute epoch may categorise this as low intensity activity, which would not be a correct representation of the activity pattern . Therefore, for a 14 year old participant moderate intensity activity was classified as between 215 – 472 counts per 5 second epoch. Time

213

Correlates among adolescents spent in activity of a defined intensity was determined by summing 5 second epochs where counts met criteria for that intensity.

Increasing number of studies are reporting the time spend in bouts of activity that are typically 5, 10, 15 or 20 minutes long (Janz et al. , 2006). Therefore, the current study calculated the mean daily time in modified moderate and vigorous bouts across all valid days. Modified moderate and vigorous bouts were established when 7 minutes out of a 10 minute period had activity levels greater than or equal to the moderate threshold. A bout was terminated by 3 consecutive minutes below the threshold (Troiano et al. , 2008). This is in agreement with Masse et al. (2005) who recommends that bout data reduction methods should to take account of normal breaks in physical activity. Bouts of physical activity were computed using 1 minute epochs as youths activity is classified by short bouts of intermittent activities of varying intensities (Rowlands & Eston, 2007), therefore, mean scores across each minute would provide a more accurate picture of whether a person is involved in bouts of physical activity. An example of this would be if children were playing a game of soccer and they were vigorously active for 40 seconds and they in low levels of activity for 20 seconds waiting for the ball to be retrieved they would not have achieved 70% of time in moderate and vigorous activity. The cumulative mean score across 1 minute epochs allows a more lenient threshold which would cater for such sporadic short bursts of activity with rest periods. In conjunction with this, all other large cross sectional studies use 1 minute epochs to categorise bouts of moderate and vigorous intensity physical activity (Riddoch et al. , 2007; Troiano et al. , 2008).

Adherence to recommended physical activity guidelines were analysed by estimating the proportion of adolescents achieving 420 mins of moderate and vigorous activity during the week (Dept. of Health and Children, 2009). Prevalence was calculated both by including all moderate and vigorous 5 second epochs and by restricting inclusion to bouts.

5.3.8 Accelerometer Data Analysis A series of data reduction macros in Visual Basic for Applications was used to analyse the data (Appendix B). This was adapted based on the NHANES accelerometer analysis

214

Correlates among adolescents

(Troiano et al. , 2008). To provide validity to the macros a number of days wear time were analysed manually and fictional data was developed and analysed. Analysis of the missing accelerometer data was not undertaken in this study, as this information was not presented in previous literature.

5.3.9 Stature and Body Mass Stature (mm) and body mass (kg) (Seca Leicester Height Meter and Seca Digital Floor Scales, Bodycare, UK) were recorded barefoot and with excess bulky clothing removed. Children were categorised as normal weight, overweight or obese using the International Obesity Task Force definitions (Cole et al. , 2000).An evaluation and review of BMI as a measurement of overweight and obesity is provided in section 2.8.4.

5.3.10 Statistical Analysis

Means and standard deviations were calculated for the correlates of physical activity and accelerometer measured physical activity. Frequencies and chi square analysis were used to investigate gender differences in categorical correlate scales. A one way between groups multivariate analysis of variance was used to explore gender differences on correlates of physical activity. To lower the probability of a Type 1 error due to multiple comparisons a conservative alpha of .01 was used in these analyses. Regression analyses were used to investigate how much variance in moderate and vigorous physical activity and vigorous physical activity can be explained by the correlate scales.

Appropriate use of multiple regression and analysis of variance presupposes that certain distributional characteristics of the variables involved, are at least approximately, met. These include that all variables are normally distributed; that the variance of errors in prediction across all levels of the classificatory/independent variables is equal (homoscedasticity assumption) and that the relationship between all variables is linear. Therefore, preliminary analysis was conducted to examine violation of the assumptions of normality, univariate and multivariate outliers, linearity and multicollinearity. Extreme outliers were identified in the data and removed from the analysis. Scales that were not normal were transformed (Tabachnik & Fidell, 1998) and

215

Correlates among adolescents scales that were not normal and were not sensitive to transformation were removed from the initial analysis and analysed non-parametrically using Kruskall Wallis and Mann Whitney U tests. No difference was found in analyses using transformed variables and original non – transformed variables, therefore it was decided to report results based on the original non transformed data. Vigorous physical activity data was skewed therefore data for this variable was log transformed for the analysis.

Hierarchical linear regressions were conducted with data from the subsample of participants who had sufficient accelerometer wear time (N = 233) and correlates of physical activity. In the regression analyses moderate and vigorous physical activity and vigorous activity were the dependent variables and correlate scores were the independent variables. Personal/ demographic factors and correlate scales that were bivariately correlated with moderate and vigorous physical activity and log transformed vigorous activity were included in the regression analysis ( P < . 1). Blocks of variables were created and entered into regressions in a standard order. Personal variables were entered first to adjust for non modifiable correlates and to assess whether other variables explained extra unique variance than explained by personal variables. Due to the small number of participants reporting non Irish ethnicity (males N = 6; females N = 8) this variable was also excluded from the regression analysis due to difficulties in drawing definitive conclusions. Psychological variables were entered second, followed by social and environmental variables.

Four groups of activity were created based on mean daily minutes of moderate and vigorous activity (Group 1 least active – Group 4 most active). Multivariate analyses of variance (MANOVA) were used to investigate differences in correlate scores across category of MVPA (4 groups). Scales that were significantly correlated to MVPA were included in the MANOVA. When significant differences were found ( P < .01) univariate analysis of variance (ANOVA) and Tukey post hoc analysis were used to investigate the nature of these differences.

All statistical analyses were computed using Predictive Analytics Software (PASW) PASW statistical software package, version 18.0.

216

Correlates among adolescents

5.4 Results

Means and standard deviations for correlates of physical activity are presented in Table 5.2. Due to the categorical nature of some personal/ biological correlates these are presented as percentages of males and females in each category, and analyzed using chi square analyses. A Chi square test for independence (with Yates Continuity Correction) indicated a significantly higher level of sport participation among males compared to females, χ 2 (1, N = 1,091) = 39.14, P < . 001, phi = -.19. A one way between groups multivariate analysis of variance was performed to investigate gender differences on the correlates of physical activity. A total 22 dependent variables were included with gender as the independent variable. There was a statistically significant difference between males and females on the combined dependent variables, F (22, 880) = 10.89, P < . 001; Wilks Lambda=.79; partial eta squared =.21. When the dependent scales are analyzed separately using a conservative alpha of P < . 01; scores on 13 of the 22 potential correlates were found to differ significantly across gender. Eta squared values were found to range from .01 to .07. Based on the guidelines proposed for interpreting these values (.01 = small effect; .06 = moderate effect; .14 large effect) the effect sizes (Cohen’s f) were found to be small to medium (Cohen, 1977) (Table 5.2). Across all significant scales males were found to display a more positive physical activity correlate profile than females. The clearest differences between male and female participants were among psychosocial correlates, no gender differences were evident in environmental correlates. The correlates with the strongest gender differences were perceived barriers, body image, perceived competence and social support (Table 5.2).

217

Correlates among adolescents

Table 5.2: Descriptive statistics, chi square analysis and one way between groups analysis of variance of the correlates of physical activity among male and female participants Variable Male Female F η2 Total N = N = 521 N = 568 Accelerometer N = N = 109 N = 124 Personal Variables Irish ethnicity% a 91.6 91.6 Intact family% 87.0 85.2 Play sport competitive% a 80.4 63.0** Age 15.1 ± 1.5 14.9 ± 1.6 3.3 NS BMI 21.0 ± 3.5 21.5 ± 3.7 1.7 NS Socio-economic status 3.4 ± 2.6 3.2 ± 2.5 0.9 NS Screen time 14.2 ± 4.2 13.7± 4.3 1.2 NS Psychological Variables Stage of change 4.2 ± 1.2 3.6 ± 1.3 43.0** .05 Change strategy 27.4 ± 6.7 25.8 ± 6.5 10.7* .01 Self -efficacy 29.6 ± 6.3 28.3 ± 6.7 9.3* .01 Perceived competence 20.5 ± 4.2 18.3 ± 4.5 55.2** .06 Body image 19.1 ± 4.0 16.6 ± 4.8 62.4** .07 Enjoyment of activity 69.6 ± 9.4 66.7 ± 10.4 17.7** .02 Perceived barriers c 47.1 ± 13.1 54.7 ± 13.8 72.5** .08 Enjoyment of PE 4.4 ± 1.1 4.0 ± 1.3 24.8** .03 Intention to be active 3.8 ± 1.0 3.4 ± 1.0 36.4** .04 Perceived benefits 37.7 ± 5.9 37.5 ± 5.4 0.16 NS Outcome expectancy value 37.5 ± 7.9 38.3 ± 6.4 4.5 NS School climate teachers bc n/a 5.2 ± 2.4 Girls norms b n/a 2.4 ± 1.1 Boys influence on girls bc n/a 8.5 ± 3.0 Social Variables Active groups of friends c 3.7 ± 0.9 4.2 ± 1.1 36.4** .04 Encourage peer to be active 3.0 ± 1.2 2.6 ± 1.1 32.8** .04 Social support peers 9.6 ± 2.8 8.4 ± 2.8 45.0** .05 Social support family 16.4 ± 4.9 15.4 ± 4.8 8.6* .01 Environmental Variables Transport c 4.9 ± 1.7 5.0 ± 1.7 1.3 NS Access to facilities c 20.6 ± 3.2 20.2 ± 3.1 3.9 NS Perceived environment 34.4 ± 7.4 34.0 ± 7.0 0.5 NS

*P < . 01; ** P < . 001 . a Categorical variables analyzed using chi square analysis b Correlate scale relevant for females only c Scales negatively marked higher scores indicate less favourable attitude towards activity

Levels of physical activity are presented in table 5.3, mean daily minutes in MVPA for males was 52.8 ± 20.5 minutes compared to 37.8 ± 15.4 minutes for females; t (229) = 6.37, P < . 001. Males were found to accumulate significantly higher mean daily MVPA, VPA, bouts of MVPA and mean accelerometer counts per minute than females ( P < . 001). A significantly higher proportion of males were found to achieve the recommended 60 minutes of MVPA on 7 days of the week (420 mins MVPA per week) ( P < . 01).

218

Correlates among adolescents

Table 5.3: Descriptive statistics, chi square analysis and independent sample t tests of physical activity levels between male and female participants . Variable Male Female Total N = N = 521 N = 568 Accelerometer N = N = 109 N = 124

Activity Levels Mean daily sedentary (mins) 518.8 ± 79.1 582.9 ± 85.2** Mean daily low activity (mins) 269.3 ± 66.7 263.1 ± 51.9 Mean daily MVPA (mins) 52.8 ± 20.5 37.8 ± 15.4** Mean daily VPA (mins) 9.6 ± 8.8 4.7 ± 4.1** Mean daily MVPA in bouts (mins) 28.4 ± 19.1 14.9 ± 13.8** Mean daily number of bouts 1.7 ± 1.1 0.9 ± 0.07** Mean counts per minute 427.2 ± 144.9 328.9 ± 97.7** Achieve recommended thresholds of MVPA Achieve mean 7 day MVPA >420 mins/ wk (%) 34.3 8.1** Achieve mean 7 day MVPA >360 mins/ wk (%) 53.7 18.5** Achieve mean 7 day MVPA >300 mins/ wk (%) 66.7 33.9** Achieve mean 7 day MVPA >240 mins/ wk (%) 82.4 49.2** Achieve mean 7 day MVPA >180 mins/ wk (%) 90.7 76.6** Achieve mean 7 day MVPA >120 mins/ wk (%) 96.3 98.4 Achieve mean 7 MVPA >60 mins/ wk (%) 99.1 100 Achieve recommended thresholds of MVPA in bouts Achieve mean 7 day bouts of MVPA >420 mins/ wk (%) 8.3 1.6 Achieve mean 7 day bouts of MVPA >360 mins/ wk (%) 15.7 3.2** Achieve mean 7 day bouts of MVPA >300 mins/ wk (%) 24.1 4.0** Achieve mean 7 day bouts of MVPA >240 mins/ wk (%) 32.4 9.7** Achieve mean 7 day bouts of MVPA >180 mins/ wk (%) 49.1 14.5** Achieve mean 7 day bouts of MVPA >120 mins/ wk (%) 70.4 34.7** Achieve mean 7 day bouts of MVPA >60 mins/ wk (%) 82.4 61.3**

*P < . 01; ** P < . 001 . a Categorical variables analyzed using chi square analysis

Correlation coefficients were calculated between males and females physical activity and the physical activity correlate scales (Table 5.4). From the personal variables participation in competitive sport (+) was found to be significantly related to male and female MVPA and VPA. Irish ethnicity (+) was found to be significantly related to male MVPA and VPA and female VPA, while socio economic status (+) was found to be related to female MVPA. Age was positively associated with MVPA and negatively associated with VPA in females. Physical activity stage of change (+), self- efficacy (+), perceived barriers (-) and intention to be active (+) were psychological variables significantly related to both male and female MVPA and VPA. Psychological variables associated with only male MVPA and VPA were change strategy (+), perceived competence (+) and enjoyment of physical activity (+). Social variables associated with both male and female MVPA and VPA were social support from peers

219

Correlates among adolescents

(+) and encouraging peers to be active (+). Among males social support from family (+) was also significant for both levels of activity, whereas having active group of friends (+) was associated with female activity. The only environmental variables associated with MVPA and VPA were access to facilities and perceived environment, this relationship was evident among males only.

Hierarchical multiple regressions were used to assess the ability of the correlate scales to predict male and female MVPA and VPA. Correlate scales which were found to be related to moderate and vigorous physical activity or vigorous activity were included in the regression analysis ( P < . 1). Total variance in MVPA explained by the correlates was 33%, F (15, 88) = 2.9, P < . 01 for males and 24%, (10, 107) = 3.4, P < .01 for females. Among males’ sport participation, intention to be active, encouraging of peers to be active, social support from peers and perceived environment were found to be significant predictors of MVPA (Tables 5.5). Among females, age and encouraging of peers to be active were significantly related to MVPA (Table 5.6). Explained variance in log transformed VPA among males was 28%, F (13, 90) = 2.7, p = .003 and among females was 37% (14, 103) = 4.3, P < . 001. Physical activity stage of change and intention to be active were found to be significantly related to VPA among males (Table 5.5), among females sport participation and age were significant predictors of VPA (Tables 5.6).

Participants were categorised into groups based on mean daily minutes in moderate and vigorous activity (Group 1 least active – Group 4 most active). Means and standard deviations of correlate scores among males from different groups of activity are presented in Table 5.7. Scales that were found to correlate with MVPA were included in the MANOVA as dependent variables and category of physical activity (4 groups) was used as independent variable. The overall model was found to be significant among males F(48, 208)= 1.64, P =.01; Wilks Lambda = .39, partial eta squared = 0.27. Follow up univariate analysis of variance (ANOVA) revealed that mean scores on seven of the correlate scales differed significantly across category of MVPA for males. The effect sizes of the correlates were found to be medium to large with partial eta squared ranging from.12 to .2 (Table 5.7). Post hoc tukey analysis revealed that males in the least active group were less likely to participate in organised sport, were in the earlier physical activity stages of change, and had lower levels of

220

Correlates among adolescents enjoyment, intention to be active and social support from peers and family than males in more active groups.

Means and standard deviations of correlate scores among females from different groups of activity are presented in Table 5.8. Multivariate analysis of variance (MANOVA) with correlate scores as dependent variables and level of physical activity as an independent variable was found to be significant among females F(27, 291)= 1.5, P = .046; Wilks Lambda = .68, partial eta squared = 0.19. Follow up univariate analysis of variance (ANOVA) revealed that mean scores on 3 of the correlate scales differed significantly across category of MVPA. The effect sizes of the correlates were found to be medium to large with partial eta squared ranging from.12 to .2 (Table 5.8). Post hoc tukey analysis revealed that females in the least active group had lower self-efficacy and provided and perceived less support from peers than those in the more active groups.

221

Correlates among adolescents

Table 5.4: Pearson and Spearman Correlation Coefficents for MVPA and log transformed VPA with Personal, Psychological, Social and Environmental Correlates. Variable Male Male Female Female MVPA VPA MVPA VPA Accelerometer sample N = 109 N = 124 Personal Variables Ethnicity a -.28** -.24* .05 -.19* Intact family a -.006 .01 -.06 .05 Play sport competitively a .36** .29** .17# .42** Age .11 .09 .15# -.43* BMI a -.06 .05 .10 .17 Socio-economic status .005 .002 .16# -.11 Screen time -.09 .03 .006 -.06 Psychological Variables Stage of change .37** .40** .24** .35** Change strategy .26** .32** .04 .11 Self -efficacy .3** .25** .29** .25* Perceived competence .24** .22* .09 .29** Body image .02 .04 -.14 -.17# Enjoyment of activity .24* .31** .13 .2* Perceived barriers c -.28** -.22* -.17# -.26** Enjoyment of PE -.02 -.05 .1 .23* Intention to be active .41** .39** .2* .23** Perceived benefits .18# .15 .06 .09 Outcome expectancy value .16 .21* .009 .003 School climate teachers bc n/a n/a -.03 -.1 Girls norms b n/a n/a .01 -.05 Boys influence on girls bc n/a n/a -.03 .1 Social Variables Active groups of friends c -.25** -.15 -.24** -.24** Encourage peer to be active .29** .22* .36** .27** Social support peers .21* .22* .23** .34** Social support family .27** .27** .06 .40** Environmental Variables Transport c -.16 -.13 -.14 -.11 Access to facilities c -.23* -.18* .07 .07 Perceived environment .27** .16 .01 -.1 # P < . 1, * P < . 05; ** P < . 01 . a Spearman correlation b Correlate scale relevant for females only c Scales negatively marked higher scores indicate less favourable attitude towards

222

Correlates among adolescents

Table 5.5: Results of Regression Analysis Explaining mean minutes in MVPA and log transformed VPA for males Variable MVPA VPA

ß P R2 R 2 ß P R2 R 2 change change Personal Variables .13 .13 .10 .10 Intact family - - Play sport competitively .25 .02 .14 .19 Age - - BMI - - Socio-economic status - - Screen time - - Psychological Variables .25 .12 .27 .17 Stage of change .18 .10 .25 .03 Change strategy .01 .93 .12 .35 Self -efficacy -.05 .73 -.18 .22 Perceived competence -.05 .65 -.06 .65 Body image - - Enjoyment of activity -.09 .52 .13 .33 Perceived barriers c -.09 .46 .05 .68 Enjoyment of PE - Intention to be active .25 .03 .25 .04 Perceived benefits .03 .75 - Outcome expectancy value - .13 .20 School climate teachers bc n/a n/a Girls norms b n/a n/a Boys influence on girls bc n/a n/a Social Variable .29 .04 .28 .008 Active Groups of friends c -.06 .60 - Encourage peer to be active .27 .045 .07 .59 Social Support peers -.31 .048 -.16 .31 Social Support family -.01 .94 .04 .75 Environmental Variables .33 .04 .28 .005 Transport c - Access to facilities c -.05 .63 -.08 .45 Perceived environment .20 .048 ß= beta - = variable not entered: P >.1 from Pearson correlation bCorrelate scale relevant for females only c Scales negatively marked higher scores indicate less favourable attitude towards activity

223

Correlates among adolescents

Table 5.6: Results of Regression Analysis Explaining mean minutes in MVPA and log transformed VPA for females Variable MVPA VPA

ß P R2 R2 ß P R2 R2 change change Personal Variables .09 .09 .32 .32 Intact Family - - Play sport competitively -.01 .91 .25 .02 Age .23 .009 -.32 <.001 BMI - - Socio-economic status .14 .10 Screen time - - Psychological Variables .17 .08 .36 .04 Stage of change .11 .29 .18 .07 Change strategy - - Self -efficacy .15 .19 .05 .65 Perceived competence - .03 .77 Body image - .02 .81 Enjoyment of activity - -.18 .15 Perceived barriers c .05 .68 -.03 .83 Enjoyment of PE - .04 .68 Intention to be active -.07 .58 -.03 .78 Perceived benefits - - Outcome expectancy value - - School climate teachers bc - - Girls norms b - - Boys influence on girls bc - - Social Variables .24 07 .37 .009 Active groups of friends c -.14 .14 -.02 .87 Encourage peer to be active .31 .01 -.005 .96 Social support peers -.06 .62 .03 .78 Social support family .11 .34 Environmental Variables Transport c - Access to facilities c - Perceived environment - Abbreviations: ß= beta Note - = variable not entered: P >.1 from Pearson correlation b Correlate scale relevant for females only c Scales negatively marked higher scores indicate less favourable attitude towards activity

224

Correlates among adolescents

Table 5.7: Means, Standard Deviations, F Values, Effect Sizes and Univariate Analysis among different levels of male MVPA activity

Variable Group 1 Group 2 Group 3 Group 4 F η2 Pairwise Comparison (least active) (most active)

Personal Variables Intact family Play sport competitively 0.4 ± 0.5 0.8 ±0.4 0.8 ±0.5 0.9 ±0.3 7.3** .18 1<2.3.4 Age BMI Socio-economic status Screen time Psychological Variables Stage of change 3.3 ± 1.3 4.1 ± 1.3 4.1 ± 1.2 4.6 ± 0.9 4.7* .13 1<4 Change strategy 23.3± 7.3 27.9 ± 5.6 25.6 ± 6.8 28.6 ± 5.7 4.3* .12 1<4 Self -efficacy 26.2 ± 6.0 30.3 ± 5.6 29.8 ± 4.4 30.6 ± 5.4 3.3 NS Perceived competence 18.9 ± 4.8 21.9 ± 2.7 20.0 ± 5.0 21.6 ± 3.1 2.9 NS Body image Enjoyment of activity 64.6 ± 10.4 74.7 ± 4.5 68.2 ± 11.6 71.2 ± 6.7 5.4* .14 1<2.4; 3<2 Perceived barriers bc 49.3 ± 12.0 46.1 ± 9.5 44.4 ± 12.2 42.5 ± 10.3 1.8 NS Enjoyment of PE Intention to be active 3.1 ± 1.0 4.0 ± 0.9 3.8 ± 0.7 4.3 ± 0.7 8.0** .2 1<2.3.4 Perceived benefits 35.0 ± 5.2 39.9 ± 4.7 37.2 ± 6.1 37.7 ± 4.5 3.5 NS Outcome expectancy value School climate teachers bc n/a n/a n/a n/a n/a n/a Girls norms b n/a n/a n/a n/a n/a n/a Boys influence on girls bc n/a n/a n/a n/a n/a n/a Social Variables Active groups of friends c 4.3 ± 1.1 3.7 ± 0.7 3.7 ± 0.8 3.6 ± 0.9 2.6 NS Encourage peer to be active 2.4 ± 1.2 3.3 ± 1.0 3.2 ± 1.1 3.2 ± 1.2 3.7 NS Social support peers 7.7 ± 3.0 10.7 ± 1.9 9.5 ± 3.2 9.3 ± 2.7 5.3* .14 1<2 Social support family 12.5 ± 5.4 18.1 ± 3.6 16.3 ± 4.7 16.5 ± 4.1 6.0* .16 1<2.3.4 Environmental Variables

225

Correlates among adolescents

Transport c Access to facilities c 21.7 ± 2.8 20.6 ± 2.7 20.8 ± 2.7 20.0 ± 2.9 2.2 NS Perceived environment 32.5 ± 6.5 37.3 ± 6.7 36.5 ± 7.6 37.8 ± 5.1 3.0 NS

Activity Levels (accelerometer) Mean daily sedentary (mins) 557.4 ± 89.8 528.9 ± 60.7 503.1 ± 79.3 491.6 ± 70.5 1>4 Mean daily low activity(mins) 252.0 ± 58.4 257.2 ± 66.8 280.0 ± 59.2 287.7 ± 76.8 NS Mean daily MVPA (mins) 27.1 ± 8.6 44.5 ± 4.6 58.3 ± 3.7 79.5 ± 8.9 1<2,3,4; 2<3,4; 3<4 Mean Daily VPA (mins) 3.6 ± 3.1 6.0 ± 2.6 11.7 ± 6.5 16.7 ± 11.8 1,2<3,4; 3<4 Mean daily MVPA in bouts (mins) 7.7 ± 5.9 20.2 ± 8.2 31.8 ± 7.9 52.1 ± 14.4 1<2,3,4; 2<3,4; 3<4 Mean counts per minute 271.5 ± 66.5 357.5 ± 55.0 477.0 ± 79.0 570.6 ± 84.3 1<2,3,4; 2<3,4; 3<4 *P < . 01, ** P < . 001 b Correlate scale relevant for females only c Scales negatively marked higher scores indicate less favourable attitude towards activity

226

Correlates among adolescents

Table 5.8: Means, Standard Deviations, F Values, Effect Sizes and Univariate Analysis among different levels of female MVPA activity

Variable Group 1 Group 2 Group 3 Group 4 F η2 Pairwise (least active) (most active) Comparison

Personal Variables Intact family Play sport competitively 1.9 ± 0.3 1.9 ± 0.3 1.9 ± 0.2 1.8 ± 0.4 1.5 NS Age 0.5 ± 0.5 0.6 ± 0.5 07 ± 0.4 0.8 ± 0.4 1.5 NS BMI Socio-economic status 2.2 ± 1.1 3.8 ± 3.4 3.5 ± 3.2 3.8 ± 3.5 2.0 NS Screen time Psychological Variables Stage of change 3.4 ± 1.4 3.5 ± 1.2 3.6 ± 1.3 4.1 ± 1.0 2.3 NS Change strategy Self –efficacy 25.8 ± 7.7 28.6 ± 5.4 30.3 ± 5.8 32.3 ± 5.2 5.2* .12 1<.3.4 Perceived competence Body image Enjoyment of physical activity Perceived barriers c 53.6 ± 14.0 53.9 ± 12.5 51.5 ± 13.7 47.6 ± 13.0 1.4 NS Enjoyment of PE Intention to be active 3.2 ± 0.8 3.7 ± 0.9 3.6 ± 0.9 3.8 ± 0.7 2.1 NS Perceived benefits Outcome expectancy value School climate teachers bc Girls norms b Boys influence on girls bc Social Variables Active groups of friends c 4.3 ± 1.1 4.1 ± 1.0 3.9 ± 1.0 3.5 ± 0.7 3.4 Encourage peers to be active 2.1 ± 1.2 2.8 ± 1.0 3.1 ± 1.0 3.5 ± 0.8 9.0** .2 1<3.4; 2<4 Social support peers 8.0 ± 3.1 8.7 ± 3.1 9.2 ± 2.3 10.5 ± 2.8 3.9* .1 1<4 Social support family Environmental Variables

227

Correlates among adolescents

Transport c Access to facilities c Perceived environment

Activity Levels (accelerometer) Mean daily sedentary (mins) 593.8 ± 71.2 593.4 ± 84.7 569.3 ± 69.5 575.1 ± 109.2 NS Mean daily low activity(mins) 241.1 ± 47.0 250.3 ± 42.6 285.8 ± 44.9 275.9 ± 59.8 1<3,4; 2<3 Mean daily MVPA (mins) 20.7 ± 3.0 29.2 ± 2.3 40.9 ± 4.5 59.4 ± 9.2 1<2,3,4; 2<3,4; 3<4 Mean Daily VPA (mins) 2.7 ± 1.8 3.1 ± 2.7 6.2 ± 4.5 6.5 ± 5.0 1,2 <3,4 Mean daily MVPA in bouts 3.9 ± 3.8 9.0 ± 6.1 15.8 ± 7.9 30.2 ± 16.3 1,2 <3,4; 3<4 (mins) Mean counts per minute 237.2 ± 49.5 278.5 ± 45.3 380.9 ± 76.2 416.0 ± 70.1 1<2,3,4; 2<3,4

* P < . 01; ** P < . 001. b Correlate scale relevant for females only c Scales negatively marked higher scores indicate less favourable attitude towards activity

228

Correlates among adolescents

5.5 Discussion

Similar to previous research (Riddoch et al. , 2007; Troiano et al. , 2008; Nilsson, 2009) the current study found adolescent males to be more active than their female counterparts. Males were also found to have a more positive physical activity correlate profile on psycho-social correlates than females. Similar findings are reported by Wu (2003) who reported that adolescent girls reported lower self-efficacy, less perceived benefits and more perceived barriers than adolescent males (Wu et al. , 2003). Therefore, males appear to perceive more intrapersonal and interpersonal supports for activity than females.

Psycho-social correlate scales were found to display a clear relationship with MVPA and VPA across all analyses. In the Pearson correlation analysis all correlations were in the expected direction. A number of correlates were found to be significantly related to MVPA and VPA in the regression analyses. Participants in the lower categories of MVPA were also found to display a less favourable psycho-social correlate profile in the analysis of variance compared to participants in the more active groups. These findings provide some construct validity for the correlate scales used in the current study. Environmental correlates were not found to be strongly associated with physical activity; this is in agreement with previous reviews (Sallis et al. , 2000; Biddle et al. , 2005).

A set of 26 correlates were found to explain between 24 - 33% of MVPA and 28 - 37% of VPA in the regression analysis. A similar TAAG study has explained between 30 – 35% of the variance for MVPA and 26 – 30% for VPA among US adolescent females (Kelly et al. , 2010). Other studies have reported between 8% and 37% of variance of objectively measured physical activity (Trost et al. , 1999; Pfeiffer et al. , 2009). Self report studies have been found to have higher explained variance than objective measures (Sallis et al. , 1999; Sallis et al. , 2002; Loucaides et al. , 2007). A possible explanation for this stronger association may be attributable to shared methods variance, i.e. self reported thoughts and feelings about physical activity may correspond more closely to self reported physical activity than objectively measured physical activity (Trost et al. , 1999; Kelly et al. , 2010) . It can be concluded that due to the

229

Correlates among adolescents complexity of physical activity behaviour and the multitude of factors influencing this behaviour attempts to explain activity have been only moderately effective. Research has been more effective at identifying key specific correlates which have been found to relate to activity.

Among males correlates which were found to be significantly related to MVPA were also related to VPA. Among females however, a number of variables which were not found to be strongly correlated with MVPA were found to be highly correlated with VPA . In particular age, sport participation, perceived competence and social support from family were found not to be associated with MVPA but strongly associated with VPA. It could be hypothesised therefore that adolescent females who engage in substantial vigorous physical activity are more likely to be younger, participate in sport, have higher perceived competence and receive more support from their family than those not engaging in regular VPA.

A number of correlates such as competitive sport, physical activity stage of change, peer support, self-efficacy and intention to be active were found to be important correlates of adolescent male and female MVPA and VPA. Participation in competitive sport was found to be positively related to male and female activity in the correlation and regression analysis. Previous research has also reported that adolescent’s who participate in organized sport have higher levels of overall physical activity and engage in more moderate and vigorous activity compared to those not involved in sport (Pate et al. , 2000; Booth et al. , 2004; Santos et al. , 2004; Nilsson, 2009). The promotion of sport participation among adolescents appears to be an effective strategy in increasing overall levels of physical activity.

The physical activity stage of change measure which assessed an individual’s readiness for physical activity change was found to be correlated with MVPA and VPA among both males and females. In the regression analysis this variable was found to be important in both male and female VPA. Participants in the higher categories of activity were also found to be in higher physical activity stages of change. The physical activity stage of change has previously been found to correlate with self reported moderate and vigorous physical activity (Lee et al. , 2001; Haas & Nigg, 2009) and psychological correlate scales among children and adolescents, (Kim, 2004; Prapavessis et al. , 2004) i.e. those in earlier stages of readiness reported less exercise and lower correlate profiles

230

Correlates among adolescents than those in later stages. The physical activity stage of change has also been found to correlate with objectively measured cardio respiratory fitness among adults, (Jackson et al. , 2007). To the author’s knowledge no study has investigated the relationship between the physical activity stages of change and objectively measured physical activity among adolescents. Despite not being an easily modifiable correlate the relationship between stage of change and MVPA and VPA is important as it can been concluded; that the physical activity stage of change measure provides feasible, cost effective and efficient methods of predicting and measuring physical activity levels and physical activity correlate profiles in large scale studies (Kim, 2004; Prapavessis et al. , 2004; Jackson et al. , 2007).

Elements of peer support (encouraging peers to be active and social support peers) were found to be associated with male and female MVPA and VPA. Post hoc Tukey analysis revealed that participants in the lowest group of MVPA were found to have lower perceived peer support than those in the most active groups. The importance of peers on physical activity was also identified in previous studies (Sallis et al. , 2002; Tergerson & King, 2002; Hohepa et al. , 2006; King et al. , 2008; Lytle et al. , 2009). In the TAAG study ‘friend support’ was the only variable consistently associated accelerometer measured MVPA and VPA in adolescent females from different ethnic backgrounds(Kelly et al. , 2010) . Similarly, Sallis et al (2002) also found that peer support was the only significant correlate of objectively measured physical activity in all subgroups. It would be logical therefore, that interventions to promote physical activity should have the social support and social modelling as key elements.

Scales measuring elements of perceived efficacy i.e. confidence in ability to be active were found to correlate with male and female physical activity. Among males intention to be physical active was found to be a strong predictor of MVPA and VPA in the regression analysis. Self-efficacy was also found to be significantly correlated with male and female MVPA and VPA. Numerous studies have identified the importance of self-efficacy as a correlate to physical activity, (Trost et al. , 1999; Wu et al. , 2003; Lytle et al. , 2009). Self-efficacy perceptions are derived from 4 principal sources, past performance, modeling, verbal persuasion and physiological state (Bandura, 1986). Therefore, interventions aimed at increasing physical activity self-efficacy should provide enjoyable developmentally appropriate activities that enable all to experience

231

Correlates among adolescents success, provide opportunities for youth to observe influential others (e.g. coach, parent) perform physical activity, verbally encourage participation and reduce anxiety by reducing competition (Trost et al. , 1999). It has been found that interventions targeting self-efficacy can have positive effects on physical activity (Dishman et al. , 2004).

Among females age was found to be inversely related to VPA in the correlation and regression analysis, therefore, older adolescent females were found to engage in significantly lower levels of vigorous physical activity than their younger peers. It should be noted that this trend was not evident in MVPA with age correlating positively with MVPA. Despite being a relatively small sample group this is a positive finding, previous research has found that MVPA has been inversely related to age in adolescent girls (Troiano et al. , 2008; Nilsson, 2009). Future large scale studies should be carried out to assess the levels of objectively measured MVPA and VPA among Irish adolescent girls.

Limitations of this study included the cross sectional nature of the study which prevents us from inferring causal relationship between correlates and physical activity. Furthermore because we did not assess physical activity using self report methods we were unable to compare correlates of physical activity when measured by accelerometer and self report methods. Another limitation of this type of research is the danger of multicollinearity among correlate scales. Despite testing for multicollinearity, it might still be possible that the explained variance of individual scales may partly be explained by related scales. For example some of the variance of self-efficacy may be explained by the intention to be active scale. Future research should use factor analysis to develop a clear physical activity correlate questionnaire.

Other limitations of the research include the exclusion of the sixth year pupils from the research, due to exam commitments associated with this group. Future research should include this group, to provide a complete analysis across each year in Irish secondary school and to assess the impact of exam commitments on perceptions of and levels of activity. A further limitation of the research was the limited number of objective physical activity data which was collected due to the limited number of accelerometers which the researcher was able to access.

232

Correlates among adolescents

Strengths of the current study include the use of a comprehensive questionnaire based on previously validated tools and focus groups analysis. Scales used in the study were found to have acceptable internal consistency, these scales were also found to be reliable (Burns et al. , 2011a). Furthermore, correlate studies using objective measures of physical activity have been rare and this research adds value to this body of knowledge.

5.6 Conclusion

To effectively promote levels of adolescent physical activity a knowledge of the key correlates associated with physical activity is important. A number of correlates were found to be related to both male and female physical activity. Sport participation, physical activity stage of change, peer support and self-efficacy were correlates strongly associated with physical activity. Age was negatively associated with VPA among females. Cognisance of these variables should be taken in attempts to promote physical activity among adolescents.

233

Correlates among adolescents

234

Chapter 6

Levels and Correlates of Physical Activity among Adolescent Sport Participants and non-Sport Participants.

6.1 Abstract

Background: Participation in organized sport has been associated with higher levels of regular physical activity in adolescents. Limited research has examined if the traditional correlates of physical activity apply to sports participation; such research would help inform the nature of sport related intervention programmes for adolescents.

Method: A cross sectional random sample of 1,114 adolescents (15.0 ±1.6 years) from 27 schools in Cork, Ireland participated in the study. Seventy two percent of the sample participated in organized sport with 28% reported not being involved in organized sport. This study uses the same data as presented in Chapter 5, in conjunction with this data a further group of 525 high level male sport participants (14.4 ±0.7 years) from the

235

Sport participants v’s non participants provincial region (Munster) participated and were used as a comparative group. Correlates of physical activity were assessed by questionnaire, and levels of physical activity measured using accelerometers with a subsample of participants (n =303).

Results: Adolescents participating in organized sport had higher mean daily accelerometer counts per minute ( P < . 01) than non-participants. Multiple analysis of variance and logistic regression revealed that participants involved in organized sport had a more positive physical activity correlate profile than non-sport participants. In particular, organized sport participants were found to have a higher physical activity stage of change, perceived competence and social support compared to those not involved in organized sport, these trends were stronger among high level sport participants.

Conclusion: The current study identifies key factors associated with organized sport participation. Factors identified in this study which account for participation in sport and participation should be emphasized within schools (PE & School Sports) and community based activities designed to promote sport participation in adolescents.

Keywords: Adolescents; Physical Activity; Exercise behaviour

6.2 Introduction

Due to the various health benefits associated with regular physical activity (Strong et al. , 2005) and the likelihood that active adolescents become active adults (Telama et al. , 1997) efforts encouraging adolescents to adopt a physically active lifestyle, or to remain physically active, are of significant importance. Organized sport has been found to contribute a substantial portion of adolescent’s physical activity (Katzmarzyk & Malina, 1998; Booth et al. , 2004). Organized sport has accounted for between 55-65% of adolescents daily moderate and vigorous physical activity (MVPA) in self report studies (Katzmarzyk & Malina, 1998; Booth et al. , 2004) and 23% of daily MVPA in a study using accelerometers with 6-12 year old males (Wickel et al. , 2007). Adolescent’s who participate in organized sport have been found to have higher levels of overall physical

236

Sport participants v’s non participants activity and to engage in more moderate and vigorous activity compared to those not involved in sport (Pate et al. , 2000; Booth et al. , 2004; Santos et al. , 2004). Furthermore, youths involved in organized sport were found to have higher levels of activity on days when they engaged in organized sport compared to days they were not engaged in organized sport (Wickel et al. , 2007). Therefore, participation in organized sport may be seen as an effective way to increase total levels of physical activity and to assist in achieving recommended levels of moderate and vigorous physical activity.

Participation in organized sport has been associated with numerous benefits for adolescents. Adolescents involved in organized sport report higher perceptions of general health and well being (Fisher et al. , 1996; Steptoe & Butler, 1996; Ferron et al. , 1999; Pate et al. , 2000; Steiner, 2000; Harrison & Narayan, 2003; Michaud et al. , 2006) and lower levels of health threatening behaviour (Ferron et al. , 1999; Pate et al. , 2000; Steiner, 2000; Michaud et al. , 2006). Organized sport participation has also been correlated with higher physical fitness levels among adolescents (Hoffman et al. , 2005) and higher perceived self image (Ferron et al. , 1999; Harrison & Narayan, 2003; Michaud et al. , 2006). Sports programmes may promote positive health behaviours and deter negative health behaviours by placing a premium on health and fitness as prerequisites to optimal sports performance (Pate et al., 2000). It may also be possible that participation in organized sport promotes health by placing youth in pro-social environments during time periods that they would be available for participating in problem behaviours(Pate et al. , 2000).

Measured variables that promote or inhibit attitudes towards and engagement in physical activity are classified as ‘correlates of physical activity’ (Bauman et al. , 2002; Michaud et al. , 2006). An extensive body of research which has investigated the correlates of physical activity among adolescents reveals that physical activity is influenced by various personal/biological, psychological, social and environmental correlates (Sallis et al. , 2000; Biddle et al. , 2005)

237

Sport participants v’s non participants evident in older adolescents (Pate et al. , 2000; Santos et al. , 2004; Michaud et al. , 2006; Johnston et al. , 2007; Toftegaard-Støckel et al. , 2010), this trend has been found to be most pronounced among females (Fisher et al. , 1996; Steptoe & Butler, 1996; Katzmarzyk & Malina, 1998; Pate et al. , 2000; Sallis et al. , 2000; Michaud et al. , 2006; Toftegaard- Støckel et al. , 2010). Higher socio-economic status (SES) has also found to be associated with participation in organized sports among adolescents (Steptoe & Butler, 1996; Ferron et al. , 1999; Booth et al. , 2004; Santos et al. , 2004; Michaud et al. , 2006; Johnston et al. , 2007; Toftegaard-Støckel et al. , 2010), it has been hypothesized that there may be a more positive attitude towards sport in families from higher SES (Ferron et al. , 1999; Dollman & Lewis, 2010). Other correlates which are related to organized sport participation include parental interest in and level of sport and activity (Toftegaard-Støckel et al. , 2010) parental education (Ferron et al. , 1999; Santos et al. , 2004; Michaud et al. , 2006) and being from intact families (Ferron et al. , 1999; Michaud et al., 2006).

Few studies have examined the correlates of organized sport participation (Toftegaard-Støckel et al. , 2010), furthermore to the authors knowledge no study has examined correlate profile of adolescent high level sport performers. Knowledge of these correlates would assist in the development of targeted interventions to increase sport and physical activity levels. Therefore the purpose of this study is to (1) describe levels of physical activity among adolescent Irish sport participants and non-sport participants, and, (2) analyze the personal, psycho-social and environmental correlate profile of non-sport participants, sport participants and high level sport performers.

6.3 Methodology

6.3.1 Study Sample Participants in the current study were adolescents attending second level schools in Cork, a county in southern Ireland. A list of schools in the Cork region from the Department of Education and Science website (2008) were stratified by school type (secondary, community, vocational, comprehensive); school location (urban/ rural); and by gender

238

Sport participants v’s non participants

(single sex/ co-educational. A cross sectional sample of the schools in the Cork region were then randomly chosen and invited to participate in the study. In total 27 schools participated in the study with a junior class (year 1-3) and a senior class (year 4-5) in each school participating. Four schools from the initial sample declined the invitation to participate and were replaced by alternative schools from the same strata. From this sampling framework 1,114 (47% males; 53% females) adolescents with a mean age of 15.0 ±1.6 participated. For the purpose of this study this sample will be referred to as the mainstream group. The mainstream group data used in the current study was the same as the data used in Chapter 5.

In addition a further cohort (n = 525) of high level male sport performers from provincial Gaelic Games Development squads participated in the study. Gaelic games (hurling and Gaelic football) are the national games in Ireland; they are field based games with 15 players on each team. Gaelic games are the most popular competitive games played in Ireland with 23% of Irish adolescent’s being members of competitive teams outside of school (Woods et al. , 2010). Ireland is divided into 4 provinces and each province is further divided into counties. Participants from the province of Munster who were selected on their county development under 14 squads in 2009 and in 2010 were invited to participate in the project. In conjunction, adolescents on the Cork County under 15, 16 and 17 development hurling and Gaelic football squads in 2010 were also invited to participate in the project. A total of 525 high level sports participants with a mean age of 14.4 ± 0.7 participated in the project. No structured Gaelic Games development squads existed for females when the data was collected; therefore analysis of high level sport participants was confined to males. For the purpose of this study this group will be referred to as the high level sport participants.

The University of Limerick research ethics committee provided approval for the study. Each participant’s parent/ guardian completed a written informed consent and each participant completed a written informed consent before data collection. In total 1,639 adolescents with a mean age of 14.8 ± 1.4 years participated in the study.

239

Sport participants v’s non participants

6.3.2 Correlates of Physical Activity Questionnaire The constructs measured in the correlates of physical activity questionnaire were created based on the Socio-Ecological Model which posits that multiple domains of variables influence physical activity (Elder et al. , 2007). The correlate questionnaire was influenced by the Trial of Activity for Adolescent Girls Study(TAAG, 2004) , Sallis et al ., (2002) and focus group research carried out by the primary author (Burns et al. , 2011). Correlates of physical activity were organised in 4 blocks: personal/ biological, psychological, social and environmental. Details of the psychological, social and environmental correlates are provided in Table 6.1. Variables in the personal/ biological block included: age (years, months), ethnicity, residence (urban, rural), family structure (living with one parent; both parents), screen time inactivity (hours) and socio economic status. The Irish CSO measure of parental occupation was used to classify individuals based on socio economic status (CSO). The classification adhered to the international occupation classification ISCO Com (CSO, 2008). The code to which a person’s occupation is classified is determined by the kind of work he or she performs in earning a living irrespective of the place in which, or the purpose for which, it is performed (CSO, 2008). Participant’s parents were classified to one of ten specific socio economic groups. In addition a residual group “All others classified gainfully employees and unknown” was used where sufficient details were not provided. The classification aims to bring together persons with similar social and economic statuses. Challenges associated with measurement of socioeconomic status are presented in section 2.8.3. Behavioural correlates in included were current sport participation and engagement in screen time inactivity. In general, sport is a competitive activity undertaken in the context of rules defined by a regulator agency (Bouchard et al. , 2007). Sport participation was the main dependent variable used in the current study. This definition of sport was simplified to ‘organised structured activity that is played or done according to rules and involves winning or losing’ for the purpose of the current research. Sport participation was measured using a single item with participants responding yes/no to the statement ‘I currently play sport competitively’. A limitation of the measure of sport participation in the current study is that it assessed only ‘current’ participation in sport. Previous participation in sport participation which has been identified as an important correlate of activity was not

240

Sport participants v’s non participants included in the current research (Van der Horst et al. , 2007). Screen time inactivity was assessed using a simple tool developed by the researcher. Participants were asked to identify time spent (i) watching TV and (ii) engaged in computer related activity on weekdays and on weekends. No reliability or validity information exists for this tool.

The questionnaire was administered in one 40 minute class period by the primary researcher and trained data collectors. Initial instruction was provided and the questionnaire was completed in a systematic fashion i.e. section by section. The correlates used were found to have acceptable consistency with Cronbach alpha’s from the current study ranging from 0.6 -0.93 (Table 6.1).

241

Sport participants v’s non participants

Table 6.1: Description of ‘Correlates of Physical Activity Questionnaire’ Variable Name Description Items Cronbach References [range] alpha Psychological Variables Stage of change Physical activity stage of change; 1= Currently physically active, 4[1 ,2] n/a Marcus &Rossi (1992) 4=Regularly active for past 6 months Change strategies Cognitive and behavioural change strategies; 1= never, 5 =very often 9 [1,5] 0.76 TAAG (2004); Dishman & Motl (2005) Self –efficacy Self-efficacy; 1= Disagree a lot, 5= Agree a lot 8 [1,5] 0.84 Saunders et al. (1997); Motl et al. (2000); TAAG (2004) Perceived competence Perceived competence at games and sport; Positive and negative 7 [1,4] 0.86 Harter (1982) statements – really true for me; sort of true for me Body Image Body image – Positive and negative statements – relay true for me sort of 6[1,4] 0.92 Harter (1985) true for me Enjoyment of activtiy Enjoyment of physical activity; 1= Disagree a lot, 5= Agree a lot: reverse 16 [1,5] 0.91 Heesch et al. (2006); Motl et al. (2000) scoring Kendzeirski & De Carlo (1991) Perceived barriers Perceived Barriers to Physical Activity, 1=Never, 4=Very Often 22 [1,4] 0.92 Heesch et al. (2006); Sallis et al. (2002) Enjoyment of PE Enjoyment of PE; 1= disagree a lot; 5= agree a lot 1 [1,5] n/a TAAG (2004); Motl et al. (2001) Intention to be active Intention to be active on most days; 1=sure I will not be active, 5= sure I 1 [1,5] n/a Godin & Shephard (1986) will be active Perceived benefits Perceived benefits about being active; 1= Disagree a lot, 5 = Agree a lot 9[1,5] 0.88 Sallis et al. (2002); Motl et al. (2000); TAAG (2004) Outcome expectancy value Importance on outcome of being active; 1= Disagree a lot, 5 = Agree a 9[1,5] 0.93 Sallis et al. (2002); Motl et al. (2000); lot TAAG (2004) Social Variables Active groups of friends Active with different groups of friends; 1=Yes; 2 = No 3[1,2] 0.6 n/a Encourage peers to be active Encouragement of peers; 1= never; 5 = every day 1[1,5] n/a TAAG (2004) Social support peers Social Support from peers; 1= none, 5= everyday 3 [1,5] 0.83 Sallis et al. (1999); TAAG (2004) Social support family Social support from family; 1= none, 5 = everyday 5 [1,5] 0.85 Sallis et al. (1999); TAAG (2004) Environmental Variables Transport Transportation to and from physical activities; 1= Not at all difficult, 4= 3 [1,4] 0.78 Evenson et al. (2006; TAAG (2004) impossible Access to facilities Ease of access to physical activity facilities 14 [1,3] 0.74 Evenson et al. (2006); TAAG (2004) Perceived environment Perceived environmental suitability for physical activity; 1=disagree a 10 [1,5] 0.86 Evenson et al. (2006); TAAG (2004) lot, 5= agree a lot

242

Sport participants v’s non participants

6.3.3 Physical Activity Protocol

6.3.4 Instrument Quality and Dependability

6.3.5 Promotion of Compliance

243

Sport participants v’s non participants lower on weekends, (Masse et al. , 2005; Treuth et al. , 2007; Sirard et al. , 2008) therefore a reminder text message was sent each morning and afternoon on weekend days.

6.3.6 Inclusion Criteria

244

Sport participants v’s non participants

Accelerometers may malfunction and therefore it is very important to be able to identify spurious data (Masse et al. , 2005). The current study identified spurious data as 10 minute counts of continuous identical variables that were not zero (Masse et al. , 2005; Metzger et al. , 2008) and a 1 minute count of greater than 20,000 counts per minute (Masse et al. , 2005). Furthermore, mean daily counts were analysed and questionable data (N = 1) were removed from the analysis.

6.3.7 Measured Aspects of Physical Activity

Counts per minute were analysed in the current study as it is the only accelerometer variable that has been rigorously validated during free living conditions using doubly

245

Sport participants v’s non participants labelled water (Ekelund et al. , 2001; Riddoch et al. , 2004; Sirard et al. , 2008), furthermore mean counts per minute evaluates raw data scores without the use of any external criteria. Mean counts per minute were calculated by dividing the sum of activity counts for a valid day by the number of minutes of wear time in that day. A weekly mean count per minute was computed based on each valid day, similar to Troiano et al. (2008).

Time spent in physical activity of different intensities is based on the application of count thresholds derived from calibration studies that classify accelerometer output to measured activity energy expenditure. Accelerometer counts during wear time were classified as sedentary, low, moderate or vigorous intensity. Sedentary behaviour was calculated based on minutes accumulated below 100 counts per minute during periods when the monitor was worn expressed as mean minutes per day. This threshold was developed in a calibration study carried out among adolescent girls (Treuth et al. , 2004 a) and has been widely used (Healy et al. , 2007; Healy et al. , 2008; Matthews et al. , 2008).

Moderate and vigorous activity count thresholds were calculated using the age specific equations developed by the Freedson group as published by Trost et al, (2002).

METs=2.757 + (0.0015 x counts min -1) – (0.08957 x age [yr]) – (0.000038 x counts min -1 x age [yr]).

246

Sport participants v’s non participants activity, which would not be a correct representation of the activity pattern . Therefore, for a 14 year old participant moderate intensity activity was classified as between 215 – 472 counts per 5 second epoch. Time spent in activity of a defined intensity was determined by summing 5 second epochs where counts met criteria for that intensity.

Increasing number of studies are reporting the time spend in bouts of activity that are typically 5, 10, 15 or 20 minutes long (Janz et al. , 2006). Ward et al. (2005) recommends that the number and lengths of MVPA bouts as well as total duration of MVPA should be reported. Therefore, the current study calculated the mean daily time in modified moderate and vigorous bouts across all valid days. Modified moderate and vigorous bouts were established when 7 minutes out of a 10 minute period had activity levels greater than or equal to the moderate threshold. A bout was terminated by 3 consecutive minutes below the threshold (Troiano et al. , 2008). This is in agreement with Masse et al. (2005) who recommends that bout data reduction methods should to take account of normal breaks in physical activity. Bouts of physical activity were computed using 1 minute epochs as youths activity is classified by short bouts of intermittent activities of varying intensities (Rowlands & Eston, 2007), therefore, mean scores across each minute would provide a more accurate picture of whether a person is involved in bouts of physical activity. An example of this would be if children were playing a game of soccer and they were vigorously active for 40 seconds and they in low levels of activity for 20 seconds waiting for the ball to be retrieved they would not have achieved 70% of time in moderate and vigorous activity. The cumulative mean score across 1 minute epochs allows a more lenient threshold which would cater for such sporadic short bursts of activity with rest periods. In conjunction with this, all other large cross sectional studies use 1 minute epochs to categorise bouts of moderate and vigorous intensity physical activity (Riddoch et al. , 2007; Troiano et al. , 2008).

Adherence to recommended physical activity guidelines were analysed by estimating the proportion of adolescents achieving 420 mins of moderate and vigorous activity during the week (Dept. of Health and Children, 2009). The percentage of participants reaching 60, 120, 180, 240, 300, 360 and 420 minutes of MVPA per week was

247

Sport participants v’s non participants calculated both by including all moderate and vigorous 5 second epochs and by restricting inclusion to bouts.

6.3.8 Accelerometer Data Analysis

A series of data reduction macros in Visual Basic for Applications was used to analyse the data (Appendix B). This was adapted based on the NHANES accelerometer analysis (Troiano et al. , 2008). To provide validity to the macros a number of days wear time were analysed manually and fictional data was developed and analysed.

6.3.9 Stature and Body Mass Stature (mm) and body mass (kg) (Seca Leicester Height Meter and Seca Digital Floor Scales, Bodycare, UK) were recorded barefoot and with excess bulky clothing removed. Children were categorised as normal weight, overweight or obese using the International Obesity Task Force definitions (Cole et al. , 2000).An evaluation and review of BMI as a measurement of overweight and obesity is provided in section 2.8.5.

6.3.10 Statistical Analysis

Descriptive statistics were used to examine personal characteristics and physical activity levels of the sample. Independent sample t tests were used to analyse differences in levels of activity between sport participants and non-participants. Chi square analysis compared the numbers of sport participants and non-participants achieving recommended levels of physical activity and differences in personal/ biological characteristics.

Analysis of variance and multinomial logistic regression were used to investigate the correlates of physical activity among non-sport participants, sport participants and high level sport participants. Appropriate use of analysis of variance and logistic regression

248

Sport participants v’s non participants presupposes that certain distributional characteristics of the variables involved, are at least approximately achieved. Therefore, preliminary analysis was conducted to examine violation of the assumptions of normality, univariate and multivariate outliers, linearity and multicollinearity. Extreme outliers were identified in the data and removed from the analysis. Multicollinearity was found to exist with between the self-efficacy correlate scale and a number of other correlate scales (enjoyment, stage of change, intention to be active, social support scales) and was thus removed from the regression analysis.

Differences in correlate scores among sub groups were analysed using multivariate analysis of variance (MANOVA). When significant differences were found univariate analysis of variance (ANOVA) and Tukey post hoc analysis were used to investigate the nature of these differences. To lower the probability of a Type 1 error due to multiple comparisons a conservative alpha of P < . 01 was used in the analysis. Scales that were not normal were transformed (Tabachnik & Fidell, 1998) and scales that were not normal and were not sensitive to transformation were removed from the initial analysis and analysed non-parametrically. No difference was found in analyses using transformed variables and original non – transformed variables, therefore it was decided to report results based on the original non transformed data.

Multinomial logistic regression was used to assess the strength of relationship between correlates of physical activity and organized sport participation. Bivariate pearson correlation analysis was initially carried out to assess the association between the correlates of physical activity and male and female organized sport participation. All variables significantly related to organized sport participation were entered into the multinomial logistic regression analysis. This procedure has been used in similar research studies (Ferron et al. , 1999; Michaud et al. , 2006). To enable meaningful analysis correlate scales were reduced to a 2 point scale indicating high and low levels of the correlate of interest e.g. high perceived competence and low perceived competence. Gender specific median values in the mainstream group were used to categorize high and low levels of each correlate. Multinomial logistic regression was conducted with non-participants as the reference group and correlate scores as independent variables. The results are expressed as adjusted odds ratios (OR) with 95% confidence intervals (95% CI).

249

Sport participants v’s non participants

All statistical analyses were computed using Predictive Analytics Software (PASW) PASW statistical software package, Version 18.0.

6.4 Results

Table 6.2 highlights the personal/ biological characteristics and sport participation levels among male and female participants. More than 80% of male participants and 63% of female participants in the mainstream group reported being involved in organized sport. A Chi square test for independence (with Yates Continuity Correction) indicated significantly higher levels of sport participation among males compared to females in the mainstream group, χ 2 (1, 1091) =39.14, P < .001, phi =.19. Age was found to be inversely related to organized sport participation among females with higher levels of participation evident in the younger age category (70.3% of 12-14 year olds participated in organized sport v 55.6% of 15-17 year olds participated in organized sports) χ 2 (1, N = 569)= 12.7, P < .001, phi =-.15). High level sport participants were found to have higher socioeconomic status, to be of Irish ethnicity, to live in rural setting and to have lower levels of overweight and obesity compared to those in the mainstream group.

Levels of accelerometer measured activity among sport participants and non- participants are shown in Table 6.3. Mean counts per minute were higher among male sport participants, t (106) =3.5, P = .001; and female sport participants, t(121) = 2.9, P = .004; compared to non-participants. Male sport participants were found to engage in significantly more moderate and vigorous activity, t (105) = 3.9, P < .001 than male non-sport participants, while female sport participants were found to participate in significantly more vigorous activity t (121)=4.3, P < .001, than non-sport participants. A significantly higher proportion of male sport participants (42%) achieved the recommended weekly 420 mins of MVPA (60 mins MVPA x 7 days) compared to male non-sport participants (11%), χ 2 (1, 108) =7.2, P = .007, phi=-.28. This trend was not evident among female sport participants (8.8%) and non-participants (7%). However when less stringent criteria are applied a clear difference is evident among female sport participants and non participants. When the

250

Sport participants v’s non participants criteria are reduced to 360 mins MVPA per week (equivalent to 60 mins MVPA on 6 days) 24% of female sport participants as opposed to 9% of female non sport participants ( P < 0.05) were found to achieve this threshold. Over 90% of all participants were found to achieve more than 120 mins of MVPA per week. Only 11.1% of male sport participants and 2.5% of female sport participants achieved the recommended weekly 420 minutes of MVPA in bouts non-sport participants did not engage in sufficient bouts of activity to reach this threshold.

251

Sport participants v’s non participants

Table 6.2: Personal/ biological characteristics and sport participation levels among male and female participants

Variable Elite Sport Sport Participants - Non -Sport Participants - Sport Participants - Non -Sport Participants - Male Male Male Female Participants - Female N = N = 524 N = 419 N = 102 N = 374 N = 212 Mean Age 14.4±0.7 15.0±1.5 15.3±1.5 14.7±1.5 15.3±1.6 Ethnicity % Irish 98 92.9 86.6 91.6 91.5 Other 2 7.1 13.4 8.4 8.5 Residence % Rural 66.8 57.9 41.8 58.9 45.3 Urban 34.2 42.1 58.2 41.1 54.7 Family situation% Living with both parents 94.7 87.7 84.3 87.3 81.5 Living with one parent 5.3 12.3 15.7 12.7 18.5 Socioeconomic Status a High SES 54.6 36.8 37.8 36.8 37.5 Medium SES 33.6 45.3 44.9 46.2 42.5 Low SES 11.8 18.0 17.3 17.0 20.0 Age (years)% 12-14 n/a b 83.7 16.3 70.3 29.7 15-17 77.6 22.4 55.6 44.4 Overall 80.4 19.6 62.9 37.1 Body Mass Index% Normal Weight 91.0 79.2 73.9 76.4 78.2 Overweight 8.0 18.5 13.1 21.0 18.5 Obese 1.0 2.4 13 2.6 3.2 a Socio economic status groups classified as high, medium, low bNo age profile included for elite participants as they were from age defined development squad groups e.g. Under 14, 15 etc.

252

Sport participants v’s non participants

Table 6.3: Levels of physical activity among sport participants and non-participants Males Females

Sport Sport non- Sport Sport non- Participants participants Participants participants (N = 80) (N = 27) (N = 80) (N = 43)

Mean daily activity Mean (SEM) daily counts per minute 453.8 (16.1) 347.3 (21.9)** 348.7 (11.2) 294.6 (10.7)** Mean (SEM) daily mins of VPA 10.1 (0.9) 7.4 (1.8) 6.0 (0.5) 2.6 (0.4)** Mean (SEM) daily mins of MVPA 57.0 (2.2) 40.3 (3.4)** 39.6 (1.8) 34.3 (2.2) Mean (SEM) daily mins of low PA 277.7 (7.2) 244.3 (12.6)* 268.7 (6.0) 253.8 (7.0) Mean (SEM) daily mins sedentary 511.2 (8.4) 541.6(16.3) 567.5 (9.0) 612.6 (13.3)** Mean (SEM) daily mins of MVPA accumulated in bouts 32.1 (2.1) 17.4 (2.9)** 16.6 (1.7) 11.7 (1.7)* Mean (SEM) daily number of MVPA bouts 1.9 (.12) 1.2 (.2)** 1.0 (.09) 0.7 (0.1)* Achieve recommended thresholds of MVPA Achieve mean 7 day MVPA >420 mins/ wk (%) 42 11.1** 8.8 7 Achieve mean 7 day MVPA >360 mins/ wk (%) 61.7 29.6** 23.8 9.3* Achieve mean 7 day MVPA >300 mins/ wk (%) 72.8 48.1** 41.5 20.9* Achieve mean 7 day MVPA >240 mins/ wk (%) 88.9 63** 56.3 37.2 Achieve mean 7 day MVPA >180 mins/ wk (%) 96.3 74.1** 81.3 69.8 Achieve mean 7 day MVPA >120 mins/ wk (%) 97.5 92.6 98.8 97.3 Achieve mean 7 MVPA >60 mins/ wk (%) 98.8 100 100 100 Achieve recommended thresholds of MVPA in bouts Achieve mean 7 day bouts of MVPA >420 mins/ wk (%) 11.1 0 2.5 0 Achieve mean 7 day bouts of MVPA >360 mins/ wk (%) 19.8 3.7* 3.8 2.3 Achieve mean 7 day bouts of MVPA >300 mins/ wk (%) 29.6 7.4* 5.0 2.3 Achieve mean 7 day bouts of MVPA >240 mins/ wk (%) 39.5 11.1* 12.5 4.7 Achieve mean 7 day bouts of MVPA >180 mins/ wk (%) 58.0 22.2** 18.8 7.0 Achieve mean 7 day bouts of MVPA >120 mins/ wk (%) 77.8 48.1** 42.5 20.9* Achieve mean 7 day bouts of MVPA >60 mins/ wk (%) 88.9 63** 65.0 53.5 **P<.01; *P<.05 significant difference among sport participants and non-participants

253

Sport participants v’s non participants

Mean scores for the correlates of physical activity are presented in Table 6.4. A multiple analysis of variance was performed to investigate differences in the correlates of sport participation between sub groups of participants. There was a significant difference between the groups on the combined dependent variables, F (84, 3833) = 11.32, P < . 001; Wilks Lambda =0.42; η2=0.2. Univariate analysis indicated that 19 of the 22 physical activity correlates were found to reach significance using a conservative alpha of P < .01 (Table 6.4). When the guidelines suggested by Cohen (1977) are applied, the effect sizes were found to vary from small to large with partial eta squared ranging from .01-.27.

Post hoc Tukey analysis demonstrated a clear trend with male and female sport participants displaying a more positive physical activity correlate profile compared to those not involved in organized sport (Table 6.4). In all psycho-social correlates with the exception of body image male and female sport participants reported significantly more positive perceptions of physical activity compared to those not involved in organized sport. High level sport participants were found to have significantly higher socioeconomic status, be in higher physical activity stages of change, engage in less screen time, and to receive more social support from family than all other sub groups (Table 6.4).

Correlates that were not found to be significantly related to male and female sport participation in the bivariate analysis were not included in the multinomial logistic regression. Table 6.5 highlights the odds ratios and confidence intervals of the correlates of physical activity with male and female non-sport participants as the reference groups. Living in a rural setting, higher physical activity stages of change and higher perceived competence were associated with male and female sport participation. Irish ethnicity, intention to be active and social support from peers was significantly associated with male sport participation while enjoyment of PE and social support from family was associated female sport participation.

254

Sport participants v’s non participants

Table 6.4: Group means, standard deviations, F values, effect size and univariate analysis for sport participation Variable Elite Sport Sport Non -Sport Sport Non -Sport F η2 Pairwise Comparison Participants Participants Participants Participants Participants Male Male Male Female Female N = 524 N = 419 N = 102 N = 357 N = 212 Group= 1 2 3 4 5 Personal characteristics Socioeconomic status a 2.4 ± 1.9 3.4 ± 2.5 3.5 ± 3.0 3.4 ± 2.6 3.4 ± 2.7 4.4* .01 1<2,3,4,5 Age 14.4 ± 0.7 15.0 ± 1.5 15.3 ± 1.5 14.7 ± 1.5 15.3 ± 1.6 6.2** .02 1<2,3,4,5; 4<2,3,5 Body mass index 21.7 ± 2.2 20.9 ± 3.2 21.7 ± 4.4 21.5 ± 3.8 21.5 ± 3.6 1.7 NS Screen Time 11.6 ± 3.9 13.7 ± 4.2 15.6 ± 4.6 13.7 ± 4.2 14.0 ± 4.2 6.2** .02 1<2,3,4,5; 2,4,5<3 Psychological Variables Stage of change 4.8 ± 0.6 4.4 ± 1.0 3.2 ± 1.4 4.0 ± 1.1 2.9 ± 1.3 79.9** .24 1>2,3,4,5; 2>3,4,5; 4>3,5 Change strategies 29.4 ± 5.9 28.0 ± 6.2 22.7 ± 7.8 27.4 ± 6.0 23.6 ± 6.4 32.4** .12 1>3,4,5; 2,4>3,5 Self-efficacy 31.5 ± 6.1 30.6 ± 5.7 25.0 ± 7.7 30.1 ± 5.6 25.3 ± 7.1 50.1** .17 1>3,4,5; 2,4>3,5 Perceived Competence 21.5 ± 3.2 21.2 ± 3.7 16.7 ± 4.1 20.2 ± 3.6 15.0 ± 3.8 116.8** .3 1,2>3,4,5; 4>3,5; 3>5 Body Image 18.9 ± 3.6 19.1 ± 3.9 18.9 ± 4.5 17.1 ± 4.7 16.0 ± 4.9 22.9** .08 1,2,3<4,5 Enjoyment of activity 71.0 ± 8.6 71.0 ± 7.8 61.2 ± 13.3 69.9 ± 8.3 60.5 ± 12.7 67.9** .22 1,2,4,>3,5 Perceived barriers b 48.2 ± 15.3 45.6 ± 12.5 56.3 ± 14.1 51.0 ± 12.3 63.0 ± 12.8 76.5** .24 1,2,4<3,5; 2<4 Intention to be active 4.0 ± 1.0 4.0 ± 0.9 3.0 ± 1.0 3.7 ± 0.8 2.9 ± 0.9 60.0** .2 1,2>3,4,5; 4>3,5 Enjoyment of PE 4.6 ± 0.8 4.5 ± 1.0 3.9 ± 1.4 4.2 ± 1.1 3.4 ± 1.4 34.3** .12 1,2,4>3,5; 4>3,5 Perceived benefits 36.5 ± 7.4 38.1 ± 5.8 34.6 ± 7.5 38.3 ± 5.4 36.2 ± 5.8 13.9** .05 1<2,4; 2,4>3,5 Outcome expectancy value 36.4 ± 7.3 37.5 ± 8.1 36.8 ± 7.6 38.8 ± 6.6 37.7 ± 6.4 2.1 NS Social Variables Active groups of friends b 3.5 ± 0.7 3.6 ± 0.8 4.4 ± 1.2 3.9 ± 0.9 4.5 ± 1.1 49.2** .17 1,2<3,4,5; 4<3,5 Encourage peers to be active 3.4 ± 1.1 3.2 ± 1.1 2.3 ± 1.2 2.9 ± 0.9 2.0 ± 1.0 56.5** .19 1,2>3,4,5; 4>3,5 Social support peers 10.7 ± 2.7 10.2 ± .2.5 7.2 ± 3.1 9.4 ± 2.4 6.7 ± 2.7 92.7** .27 1,2>3,4,5; 4>3,5 Social support family 18.9 ± 3.9 17.3 ± 4.3 12.2 ± .5.0 17.1 ± 3.9 12.5 ± 4.6 77.2** .24 1>2,3,4,5; 2,4>3,5 Environmental Variables Transport b 4.5 ± 1.6 4.8 ± 1.7 5.4 ± 1.9 4.9 ± 1.6 5.4 ± 1.9 10.1** .03 1<3,4,5; 2<3,5; 4<5 Access to facilities 20.1 ± 3.0 20.4 ± 3.1 21.7 ± 3.4 20.0 ± 3.0 20.9 ± 3.2 5.1** .02 1,2,4<3; 4<5 Perceived environment 34.2 ± 7.4 34.4 ± 7.3 34.1 ± 7.3 34.0 ± 7.5 34.1 ± 7.7 0.4 NS

**P<.01; *P<.05 significant difference among sport participants and non-participants a Higher scores equate to lower socioeconomic status. b Scales which are negatively marked – higher scores equate to less favorable attitude to physical activity.

255

Sport participants v’s non participants

Table 6.5: Odds ratio and confidence intervals for correlates of physical activity by level of male and female sport participation Males Females

Variable Elite Sport Sport Participants Non-Sport Sport Participants Non-Sport Participants Participants Participants OR (95%CI) OR (95%CI) OR (95%CI) OR (95%CI) OR (95%CI) Personal characteristics Irish Ethnicity 14.1 (1.5, 130.4)* 1.8 (0.7, 4.8) 1 - Rural Residence 3.9 (1.8, 8.3)** 2.2 (1.1, 4.3)* 1 2.2 (1.3, 3.6)** 1 Living with both parents 1.1 (0.3, 3.5) 0.8 (0.4, 2.0) 1 - Socioeconomic status a 0.5 (0.2, 1.2) 1.2 (0.6, 2.4) 1 - Age n/a - 0.9 (0.5, 1.6) 1 Body mass index - - - Screen Time 0.5 (0.2, 1.1)** 0.9 (0.5, 1.9) 1 - Psychological Variables Stage of change 7.1(2.9, 16.9)** 2.2 (1.1, 4.4)* 1 2.1 (1.3, 3.5)** 1 Change strategies 1.4 (0.6, 3.2) 0.7 (0.3, 1.5) 1 0.9 (0.5, 1.6) 1 Self-efficacy - - - Perceived Competence 4.2(1.7, 9.9)** 4.4 (2.0, 9.5)** 1 5.4 (3.2, 9.2)** 1 Body Image - - 0.7 (0.4, 1.1) 1 Enjoyment of activity 1.9 (0.8, 4.4) 1.5 (0.8, 2.8) 1 0.9 (0.5, 1.5) 1 Perceived barriers a 1.6 (0.7, 3.8) 0.9 (0.4, 2.0) 1 0.7 (0.3, 1.2) 1 Intention to be active 4.2 (1.9, 9.8)** 4.2 (2.2, 8.2)** 1 0.9 (0.5, 1.6) 1 Enjoyment of PE 1.5 (0.7, 3.3) 1.5 (0.8, 2.8) 1 1.7 (1.0, 2.8)* 1 Perceived benefits - 0.8 (0.5, 1.4) 1 Outcome expectancy value - 0.8 (0.5, 1.3) 1 Social Variables Active groups of friends a 0.7(0.3, 1.7) 0.7 (0.4, 2.8) 1 1.1 (0.7, 2.0) 1 Encourage peers to be active 1.7 (0.6, 4.9) 0.8 (0.4, 1.6) 1 1.8 (0.9, 3.2) 1 Social support peers 2.5 (1.0, 6.1)* 2.2 (1.0, 4.7)* 1 1.7 (0.9, 3.0) 1 Social support family 2.3(0.9, 5.6) 1.7 (0.8, 3.7) 1 2.5 (1.4, 4.4)** 1 Environmental Variables Transport a 0.3 (0.2, 0.7)** 0.7 (0.3, 1.1) 1 1.0 (0.6, 1.7) 1 Access to facilities 1.8 (0.8, 4.0) 1.5(0.8, 2.9) 1 0.8 (0.4, 1.3) 1 Perceived environment -

**P<.01; *P<.05 significant difference among sport participants and non-participants

256

Sport participants v’s non participants

6.5 Discussion

Levels of sport participation among Irish adolescents were found to be relatively high when compared to previous international studies (Michaud et al. , 2006; Johnston et al. , 2007; Dollman & Lewis, 2010; Toftegaard-Støckel et al. , 2010). A greater proportion of males compared to females were found to engage in organized sport (Fisher et al. , 1996; Steptoe & Butler, 1996; Pate et al. , 2000; Michaud et al. , 2006; Toftegaard-Støckel et al. , 2010), and levels of organized sport were also found to be negatively associated with age; this trend was most pronounced among females (Michaud et al. , 2006; Johnston et al. , 2007; Toftegaard-Støckel et al. , 2010).

Participants who are involved in organized sport were found to have higher overall accelerometer counts per minute, therefore, it can be concluded that sport participants have higher levels of overall habitual activity compared to non-sport participants (Pate et al. , 2000; Booth et al. , 2004; Santos et al. , 2004). Previous research has identified the importance of organized sport in assisting adolescents to achieve the recommended levels of physical activity (Booth et al. , 2004; Michaud et al. , 2006; Wickel et al. , 2007). In the current study a higher proportion of male sport participants (40%) were found to achieve the recommended weekly 420 mins of MVPA compared to male non-sport participants (11%). Among females this trend was not evident when the criterion was set at 420 mins per week, it appears that few female sport participants or non-participants achieve the recommended levels of activity every day. When the criteria is reduced to 360 mins (equivalent to 60 mins MVPA x 6 days) or 300 mins (60 mins x 5 days) however a clear trend emerges with more female sport participants achieving this threshold compared to non-participants. Participants who were involved in organized sport were also found to be more likely to be in the action and maintenance stages of physical activity change. Therefore, it can be concluded that organized sport participants are more active than non-sport participants and that targeting sport participation may be one method of increasing overall levels of physical activity.

Few sport participants and no non-sport participants were found to accumulate a mean weekly 420 mins of MVPA in bouts; this is not altogether surprising given the sporadic nature of children and youth physical activity (Bailey et al. , 1995; Ward et al. ,

257

Sport participants v’s non participants

2005). Future research should investigate if adolescents accumulate extra health benefits from MVPA accumulated in activity bouts.

Over 90% of Irish adolescents were found to engage in over 120 mins of MVPA per week, furthermore the mean scores from sport participants and non-participants indicated that most Irish adolescents were in the preparation, action and maintenance stages of physical activity stage of change. Interventions to promote physical activity among Irish adolescents, therefore, should aim to increase physical activity among a relatively active population who are ready to increase physical activity.

A number of personal/ biological characteristics have been found to be associated with organized sport participation. Previous research has found that adolescent males and females from higher socio-economic backgrounds were more likely to participate in organized sport (Ferron et al. , 1999; Dollman & Lewis, 2010) while other studies have found this trend among females only (Toftegaard-Støckel et al. , 2010). In the current study socio economic status was not found to be an important predictor of organized sport participation among the mainstream group. It should be noted however that those in the high level sport participant group were found to be from significantly higher socio economic backgrounds than sport participants and non-sport participants in the mainstream group. A recent national study of Irish adolescents sport participation and physical activity study Woods et al, (2010) also found that social class had no influence on Irish adolescent’s extracurricular sport participation, it should be noted that this study did not have a distinct elite group.

Higher levels of organized sport participation among Irish adolescents were also found among participants from rural backgrounds. This has not been found in previous studies however it may be hypothesized that organized sport provides those living in a rural setting with an outlet to meet with and spend time with friends, this may not be as important in urban areas due to proximity of living nearer to friends.

Personal/ biological differences were found between the mainstream group and the high level sport participant group. A greater proportion of participants in the high level sport group were found to be of Irish ethnicity. An obvious explanation of this is that the high level sport participant group consisted of players of Gaelic Games which are Irish national games and immigrants may not have the same level of exposure to

258

Sport participants v’s non participants these games as Irish children and adolescents. Furthermore, the high level sport participants were found to have lower levels of overweight and obesity compared to those in all other subgroups.

A large body of research has investigated the influence of sport participation on health behaviours and perceived well being (Steptoe & Butler, 1996; Ferron et al. , 1999; Pate et al. , 2000; Harrison & Narayan, 2003; Hoffman et al. , 2005; Michaud et al. , 2006). To the authors knowledge no previous study has comprehensively investigated the psychological, social and environmental correlates of physical activity among sport participants and non-participants. In the current study, a clear trend emerges with sport participants displaying a more positive physical activity correlate profile than non-sport participants. Sport participation/ non participation was found to be a stronger predictor of correlate scores than gender, with male and female sport participants reporting similar correlate scores and male and female non-participants reporting similar correlate scores. An exception to this trend was body image with males reporting higher perceived body image scores irrespective of category of sport participation. It appears that males have a more positive perception of body image than females; sport participation does not appear to influence this correlate.

Regression and analysis of variance identified key differences between sport participants and non-participants. Sport participants were in the higher physical activity stages of change, had higher perceived competence and received more social support from peers and family. In an Australian study Dollman and Lewis (2010) also found that participation in adolescent sport was associated with parental support. This support was found to be in the form of instrumentation (buying equipment, transportation) and emotional (permission, encouragement and playing with the child) support (Dollman & Lewis, 2010). Parental involvement in physical activity may also promote children and adolescent sport participation, in a Danish study Toftegard et al. (2010) reports that adolescents who reported parents as active were almost twice as likely to participate in sport compared to those who did not perceive their parents as active. Therefore, targeting parental support may be an effective means of indirectly promoting adolescent sport participation.

To the authors knowledge no other study has examined the association between peer social support, perceived competence and organized sport participation. Research

259

Sport participants v’s non participants which has focused on physical activity however has identified the importance of peer social support (Sallis et al. , 2002; Hohepa et al. , 2006; King et al. , 2008) and perceived competence (Stein et al. , 2007; Kahn et al. , 2008)to activity. Attempts to increase organized sport participation therefore should take cognizance of the importance of these factors and should attempt to promote joining and attending sports clubs with peers. Sport clubs and organizations should also attempt to create an environment where adolescents feel comfortable and competent to participate.

Self-efficacy has previously been found to be strongly associated with levels of and changes in physical activity (Harrison et al. , 2006; Barr-Anderson et al. , 2007). In the current study physical activity self-efficacy was higher among organized sport participants compared to non-participants in descriptive analysis and in analysis of variance. Self-efficacy was not included in the regression due to the strong correlation between self-efficacy and change strategies (r=0.6), perceived competence (r=0.49), enjoyment (r=0.57), social support peers (=0.53) and social support from family (r=0.57). It appears however that confidence in ability to be active is an important correlate of sport participation. Other scales such as intention to be active and physical activity stage of change which measure readiness for and intention to be active have been found to be significant. The relationship between self efficacy and various personal and environmental variables provides support for the Social Cognitive Theory. This theory identifies self efficacy as a central tenet in the person – behavior interaction, i.e. prior to change occurring a person must believe in his/her capacity to change behavior. The strong relationship between self efficacy and various personal and environmental (social) variables highlights the possible mediating effect self efficacy can have on these variables and physical activity; i.e. increases in self efficacy may result in increases in various other correlate scores and on levels of physical activity.

The high level sport participant group was found to display a more positive correlate profile than all other groups of participants. Key correlates that differed between this group and the mainstream groups were being from higher socio economic background, higher physical activity stages of change, less screen time inactivity and more social support from family. Future research should further examine the levels of physical activity to assess if high level sport participants have higher levels of activity.

260

Sport participants v’s non participants

Limitations of the current study were that the high level sport participant group consisted solely of male participants. The inclusion of a high level sports group of female athletes would permit a detailed comparison between mainstream and elite Irish adolescents. In conjunction with this, the cross sectional design of the study makes it difficult to assess whether it is participation in organized sport which develops a positive correlate profile or it those with a positive correlate profile gravitate towards sport. A further limitation of the current study was the measure of sport participation used. This measure assessed only ‘current’ participation in sport. Previous participation in sport participation which has been identified as an important correlate of activity was not included in the current research (Van der Horst et al. , 2007). Strengths of the current study were that a comprehensive valid set of correlates with construct validity were used. The current study also uses short 5 second epochs to measure MVPA which may increase the accuracy of measurement. Previous studies have used longer epochs which may have led to an underestimation of habitual activity especially as youth activity tends to be short bursts of intermittent activity.

6.6 Conclusion

Organized sport participation is associated with higher levels of physical activity and a more positive physical activity correlate profile. Specifically sport participants were found to be in the higher stages of physical activity change, to have higher levels of perceived competence and to receive more social support from peers and family, these trends were stronger among high level sport participants. Efforts to promote sport participation and reduce drop out from sport among adolescents should take cognizance of these key elements.

261

Sport participants v’s non participants

262

Chapter 7

Development and Validation of the Male and Female Adolescent Physical Activity Correlates (APAC) Questionnaires

7.1 Abstract

Background: Physical activity is a complex behaviour influenced by personal, psychological, social and environmental factors. An in-depth understanding and a valid means of measuring these factors is important in the development and evaluation of effective interventions. The purpose of the current study was to describe the factor structure of the correlates of physical activity and to develop comprehensive gender specific correlate questionnaires.

Methods: A total of 1,114 (48% males, 15.1 ± 1.5 years; 52% females, 14.9 ± 1.6 years) participants from 27 schools in Cork, a county in southern Ireland completed a supervised questionnaire measuring potential correlates of physical activity. Physical

263

Principal Component Analysis activity was measured on 7 days using Actigraph accelerometers in a subsample of the population (N = 303). Principal components analysis (PCA) was used to explore the factor structure of the questionnaire to aid further analyses with these data. The internal consistency of the factors identified was assessed using Cronbach’s alpha, reproducibility of the correlates has been previously established. Pearson correlations and hierarchical linear regressions were administered to examine the validity of the questionnaires using objectively measured physical activity (moderate and vigorous) as the dependent variable.

Results: Gender specific ‘Adolescent Physical Activity Correlate (APAC)’ questionnaires were developed for male and female adolescents. Principal component analysis revealed an interpretable 13 factors solution for males and 14 factor solution for female. No new factors were created; the factors identified were reduced versions of the original correlate scales. Internal consistency estimates of all scales were acceptable and ranged from 0.70- 0.94. Correlations between the questionnaires and physical activity showed evidence of convergent validity. Correlate scales were found to explain between 37- 42% of the variance in MVPA and VPA among males and females in the regression analysis.

Conclusion: Evidence is provided which supports the convergent validity and internal consistency reliability of the ‘APAC’ questionnaires among adolescents.

Keywords : Physical activity; Measurement; Youth; Factor analysis

7.2 Introduction

The promotion of regular physical activity among children and adolescents has been identified as being an important public health issue (Department of Health and Children, 2009) due to its positive impact on body fat composition, cardio vascular and muscular fitness, bone strength and mental health (Physical Activity Guidelines Advisory Committee, 2008). Recent evidence suggests that Irish adolescents are not attaining the recommended levels of physical activity (Woods et al. , 2010; Burns et al. , 2011); in

264

Principal Component Analysis

conjunction with this physical activity levels tend to decline in adolescents (Brodersen et al. , 2007) with inactive adolescents tending to become inactive adults (Telama et al. , 1997). Therefore, there is a need to develop effective physical activity promotional strategies having both a cognitive and behavioural basis for this target group.

Measured variables that are associated with physical activity are classified as ‘correlates of physical activity’(Bauman et al. , 2002). The current study used the Social Cognitive Theory and Ecological Theory to provide a theoretical underpinning (Woods et al. , 2009). This approach posits that physical activity is influenced by various personal, psychological, social and environmental correlates; this theory has been used extensively in physical activity research (Barr-Anderson et al. , 2007; Elder et al. , 2007; Kelly et al. , 2010). To effectively promote physical activity there is a need to have a clear understanding of the factors promoting or inhibiting participation in physical activity (Baranowski et al. , 1998; Kelly et al. , 2010; Salmon, 2010). Failure of many physical activity promotional efforts may be due to insufficient emphasis on the modifications of the key correlates of physical activity resulting in the development of inappropriate content and strategies (Saunders et al. , 1997).

Previous research has identified that factors such as perceived competence, peer support, self-efficacy and physical activity stage of change are important correlates of adolescent physical activity (Sallis et al. , 2000; Sallis et al. , 2002; Ommundsen et al. , 2006; Kelly et al. , 2010; Burns et al. , 2011; Burns et al. , 2011a). Correlates of physical activity have been found to differ across gender (Sallis et al. , 2002; Schmitz et al. , 2002; Tergerson & King, 2002; Wu et al. , 2003; Burns et al. , 2011), indicating a need for separate correlate questionnaires for males and females.

Value of scientific data depends on the precision with which the variables under consideration are observed and measured (Dunn et al. , 1999). Using valid and reliable measures of correlates of physical activity reduces measurement error and strengthens the conclusions that can be drawn (McMinn et al. , 2009). To date, work done on the development and validation of scales measuring correlates of physical activity has focused on small groups of scales or scales confined to one category of influence e.g. psychological

265

Principal Component Analysis scales (Saunders et al. , 1997; Motl et al. , 2001; Motl et al. , 2003; Sherwood et al. , 2004; Ommundsen et al. , 2006; Pirasteh et al. , 2008). To the author’s knowledge, no study has attempted to develop, assess the reliability and validity of a broad comprehensive questionnaire which investigates potential correlates from personal/behavioural, psychological, social, environmental and behavioural domains.

Validity of a scale refers to the degree to which a tool, a test, a questionnaire or a survey actually measures what it intends to measure (Streiner & Norman, 2003). The validation of a scale involves the collection of empirical evidence which provides support for its use (Pallant, 2005). Different authors have categorised the types of validity in different ways. The Streiner & Norman (2003) classification is presented in this research. The main types of validity in this classification are; content validity, criterion validity and construct validity (Streiner & Norman, 2003). Content validity considers whether a measurement tool assesses the breadth and depth of the construct it intends to measure. e.g. how effectively a measure of self efficacy for activity includes all the aspects associated with this construct (Streiner & Norman, 2003). Often the process of establishing content validity may include the use of a panel of experts, reviewing relevant literature and conducting focus groups (Streiner & Norman, 2003). Criterion validity concerns the relationship between scale scores and the best measure of that construct. There are two types of criterion related validity, firstly concurrent validity when a newly developed tool is compared to another test, which is considered to be the ‘gold standard’ to measure the construct in question. Secondly, predictive validity refers to the degree to which a test can predict a behaviour that will occur in the future (Streiner & Norman, 2003). Due to limited ‘gold standard’ measures this validity is often difficult to assess. Construct validity relates to the extent to which the variables used in research adequately assess the conceptual variables they were designed to measure (Stangor, 2006). Various procedures can be used to assess the construct validity of a tool. Convergent construct validity compares a new tool to a well established tool that measures a related construct, e.g. comparing correlates of physical activity questionnaire with levels of physical activity should be highly correlated as these measures should be related to each other. This differs from criterion concurrent validity as neither of these tests are ‘gold 266

Principal Component Analysis

standards’ (Streiner & Norman, 2003). An intra-test examination determines to what degree the items within a scale are related to each other and also provides evidence of construct validity. Discriminate construct validity is based on the premise that a tool is more closely related to a separate tool that measures a similar construct that to a tool that measures a less similar construct. Validity of an instrument to measure the correlates of physical activity is problematic as there is no established gold standard with which to compare the new measure (McMinn et al. , 2009). Therefore, it has been recommended that that using a combination of methods is likely to provide the best estimates of validity (McMinn et al. , 2009). A number of techniques have been used in the literature to provide evidence of validity among scales. Firstly, some validation studies have used factor analysis to validate the content and structure (construct validity) of measurement tools (Saunders et al. , 1997; Motl et al. , 2003; Pirasteh et al. , 2008; MacNamara & Collins, in press). It should be noted however, that while factor analysis can be used to reduce a set of questions on a particular construct or to guide grouping questions together into coherent factors (Steiger, 1994; Goodwin, 1999), this does not verify if a questionnaire is measuring what it is supposed to (content validity) (McMinn et al. , 2009). Secondly, the use of inter item correlations between items within a scale has been identified as a method of assessing construct validity of items within a specific scale (MacNamara & Collins, in press). Thirdly, regression and correlation analysis using objective measures of physical activity have also been used to assess how effective factor analysed questionnaires are at explaining the phenomenon of interest i.e. physical activity. This provides further information relating to the convergent construct validity of the scale (Sherwood et al. , 2004; Ommundsen et al. , 2006). A weakness of all these approaches is that they provide limited support for the content validity of a scale. It should be noted however that the correlates included in the ‘APAC questionnaire’ were modified from previously developed tools. These individual tools have been found to be valid and reliable e.g. developed using a panel of experts thus providing some content validity to the scales. Section 2.14 provides the reliability and validity information pertinent to each individual scale. Further support for the content validity of the overall ‘APAC questionnaire’ is provided in the extensive review of literature which was used to identify the key correlates

267

Principal Component Analysis associated with physical activity (Chapter 2) and the series of focus group interviews which searched for ‘other’ correlates not identified in the literature (Chapter 4).

Therefore the purpose of the current study was to develop and refine comprehensive gender specific ‘Adolescent Physical Activity Correlate (APAC)’ questionnaires and to assess the associated reliability and validity of this questionnaire.

7.3 Methods

7.3.1 Sampling Procedure

Participants in the current study were adolescents attending second level schools in Cork, a county in southern Ireland. A list of schools in the Cork region from the Department of Education and Science website (2008) were stratified by school type (secondary, community, vocational, comprehensive); school location (urban/ rural); and by gender (single sex/ co-educational. A cross sectional sample of the schools in the Cork region were then randomly chosen and invited to participate in the study. In total 27 schools participated in the study with a junior class (year 1-3) and a senior class (year 4-5) in each school participating. Four schools from the initial sample declined the invitation to participate and were replaced by alternative schools from the same strata.

The targeted sample was 80% of the selected group of approximately 40 students at each of the 27 schools (i.e. 40 adolescents X 27 schools = 1,080). The sample size exceeded the recruitment goal and a total of 1,114 adolescents (48% males; 52% females) participated in the study. The year group with the smallest representation in the sample was year 5 which accounted for 15% of the sample while the largest representation was year 1 with 26% of overall participants. Participants in year 6 in Irish secondary schools were not invited to participate in the study due to the exam commitments associated with this year

268

Principal Component Analysis

7.3.2 Adolescent Physical Activity Correlates (APAC) Questionnaire The content of the ‘APAC’ questionnaire was primarily influenced by the Trial of Activity for Adolescent Girls Study (TAAG, 2004) , Sallis et al, (2002) and focus group research carried out by the primary author (Burns et al. , 2011). Correlates of physical activity were organised in 4 blocks: personal/ biological, psychological, social and environmental. Details of the psychological, social and environmental correlates included in the original questionnaire are provided in Table 7.1, three of the scales included were relevant for females only and related to perceived male influence, social norms and teachers views of female physical activity. Variables in the personal/ biological block included: age (years, months), ethnicity, residence (urban, rural), family structure (living with one parent; both parents), screen time inactivity (hours) and socio economic status. The Irish CSO measure of parental occupation was used to classify individuals based on socio economic status (CSO). The classification adhered to the international occupation classification ISCO Com (CSO, 2008). The code to which a person’s occupation is classified is determined by the kind of work he or she performs in earning a living irrespective of the place in which, or the purpose for which, it is performed (CSO, 2008). Participant’s parents were classified to one of ten specific socio economic groups. In addition a residual group “All others classified gainfully employees and unknown” was used where sufficient details were not provided. The classification aims to bring together persons with similar social and economic statuses. Challenges associated with measurement of socioeconomic status are presented in section 2.8.3. Behavioural correlates in included were current sport participation and engagement in screen time inactivity. In general, sport is a competitive activity undertaken in the context of rules defined by a regulator agency (Bouchard et al. , 2007). Sport participation was defined as ‘organised structured activity that is played or done according to rules and involves winning or losing’. Sport participation was measured using a single item with participants responding yes/no to the statement ‘I currently play sport competitively’. A limitation of the measure of sport participation in the current study is that it assessed only ‘current’ participation in sport. Previous participation in sport participation which has been

269

Principal Component Analysis identified as an important correlate of activity was not included in the current research (Van der Horst et al. , 2007). Screen time inactivity was assessed using a simple tool developed by the researcher. Participants were asked to identify time spent (i) watching TV and (ii) engaged in computer related activity on weekdays and on weekends. Based on the findings from focus group interviews (Chapter 3) a number of modifications were made to the initial ‘APAC questionnaire’. Three additions to the ‘APAC questionnaire’ were completely new researcher generated questions or tools. The reliability, validity and factor structure of these tools has not previously been assessed. Firstly, a measure was developed by the researcher and included in the APAC questionnaire which assessed participant’s engagement in activity with different groups of friends (school friends, neighbourhood friends, other friends). Secondly, a screen time inactivity measure was included in the APAC questionnaire. Participants were asked to identify time spent (i) watching TV and (ii) engaged in computer related activity on weekdays and on weekends. Finally, an item was included in the perceived barriers scale to assess if they felt ‘physical actively being played too seriously’ affected their participation in physical activity. This was assessed on a four point likert scale (Never – Very often). A detailed explanation and justification of the modifications to the ‘APAC questionnaire’ including the addition of other validated tools based on the focus group interviews is provided in section 4.5.1.

The ‘APAC’ questionnaire was administered in one 40 minute class period by the primary researcher and trained data collectors. Initial instruction was provided and the questionnaire was completed in a systematic fashion i.e. section by section. Internal consistency for the questionnaire measured with Cronbach alpha’s in the current sample was found to range from 0.6 to 0.93 (Table 7.1). Two scales in the original questionnaire did not meet the acceptable internal consistency criteria of 0.7 (Tabachnik & Fidell, 2007); however they were deemed sufficiently important to include in the analyses.

Reliability of the questionnaire was analysed using one week test re-test reliability with a sub-sample of 62 adolescent girls (mean age 14.63 ± 1.18 years) in a previous study (Chapter 4 of this PhD). Intraclass correlation coefficients (ICC) ranged from .71 - .94 (Burns et al ., 2011a).

270

Principal Component Analysis

Table 7.1: Description of ‘Correlates of Physical Activity Questionnaire’ Variable Name Description Items Cronbach References [range] alpha Psychological Variables Stage of change Physical activity stage of change; 1= Currently physically active, 4[1 ,2] n/a Marcus &Rossi (1992) 4=Regularly active for past 6 months Change strategies Cognitive and behavioural change strategies; 1= never, 5 =very often 9 [1,5] 0.84 TAAG (2004); Dishman & Motl (2005) Self –efficacy Self-efficacy; 1= Disagree a lot, 5= Agree a lot 8 [1,5] 0.83 Saunders et al. (1997); Motl et al. (2000); TAAG (2004) Perceived competence Perceived competence at games and sport; Positive and negative 7 [1,4] 0.80 Harter (1982) statements – really true for me; sort of true for me Body Image Body image – Positive and negative statements – relay true for me sort of 6[1,4] 0.92 Harter (1985) true for me Enjoyment of activtiy Enjoyment of physical activity; 1= Disagree a lot, 5= Agree a lot: reverse 16 [1,5] 0.91 Heesch et al. (2006); Motl et al. (2000) scoring Kendzeirski & De Carlo (1991) Perceived barriers Perceived Barriers to Physical Activity, 1=Never, 4=Very Often 22 [1,4] 0.91 Heesch et al. (2006); Sallis et al. (2002) Enjoyment of PE Enjoyment of PE; 1= disagree a lot; 5= agree a lot 1 [1,5] n/a TAAG (2004); Motl et al. (2001) Intention to be active Intention to be active on most days; 1=sure I will not be active, 5= sure I 1 [1,5] n/a Godin & Shephard (1986) will be active Perceived benefits Perceived benefits about being active; 1= Disagree a lot, 5 = Agree a lot 9[1,5] 0.86 Sallis et al. (2002); Motl et al. (2000); TAAG (2004) Outcome expectancy value Importance on outcome of being active; 1= Disagree a lot, 5 = Agree a 9[1,5] 0.93 Sallis et al. (2002); Motl et al. (2000); lot TAAG (2004) School climate: teachers^ Perceptions of teacher support; 1= Disagree a lot, 5 = Agree a lot 2[1,5] 0.84 TAAG (2004) Girls norms^ Perceptions of girls physical activity; 1= Disagree a lot, 5 = Agree a lot 1[1,5] n/a TAAG (2004) Boys influence on girls^ Perceived influence of boys on girls activity;1= Disagree a lot,5 =Agree a 3[1,5] 0.61 TAAG (2004) lot Social Variables Active groups of friends Active with different groups of friends; 1=Yes; 2 = No 3[1,2] 0.6 Burns et al. (2010) Encourage peers to be active Encouragement of peers; 1= never; 5 = every day 1[1,5] n/a TAAG (2004) Social support peers Social Support from peers; 1= none, 5= everyday 3 [1,5] 0.75 Sallis et al. (1999); TAAG (2004) Social support family Social support from family; 1= none, 5 = everyday 5 [1,5] 0.85 Sallis et al. (1999); TAAG (2004) Environmental Variables Transport Transportation to and from physical activities; 1= Not at all difficult, 4= 3 [1,4] 0.78 Evenson et al. (2006; TAAG (2004) impossible Access to facilities Ease of access to physical activity facilities 14 [1,3] 0.73 Evenson et al. (2006); TAAG (2004) Perceived environment Perceived environmental suitability for physical activity; 1=disagree a 10 [1,5] 0.71 Evenson et al. (2006); TAAG (2004) lot, 5= agree a lot ^ Girls only scales

271

Principal Component Analysis

7.3.3 Physical Activity

A subsample (N = 303) of the participants (47% male, 15.32 ± 1.6 years; 53% female, 15.06 ± 1.6 years) were asked to wear an Actigraph accelerometer (MTI model 7164, Fort Walton Beach, FL) over their right hip for a period of 7 days. Participants were asked to wear the accelerometer during waking hours and to remove it when swimming or bathing. To be included in the analysis participants were required to wear the accelerometer for a minimum of 600 minutes on 4 separate days. A total of 233 participants (47% male; 53% female) had sufficient wear time to be included in the analysis.

Details of the accelerometer protocol and procedure have been documented in chapter 6 of this PhD (Burns et al. , 2011). Measures used for the validation procedures were average Actigraph counts during wear time, minutes of moderate and vigorous activity (MVPA), minutes of vigorous activity (VPA), moderate and vigorous activity accumulated in bouts and time sedentary.

7.3.4 Stature and Body Mass Stature (mm) and body mass (kg) (Seca Leicester Height Meter and Seca Digital Floor Scales, Bodycare, UK) were recorded barefoot and with excess bulky clothing removed. Children were categorised as normal weight, overweight or obese using the International Obesity Task Force definitions (Cole et al. , 2000).An evaluation and review of BMI as a measurement of overweight and obesity is provided in section 2.8.4.

7.3.5 Statistical Analysis

Analyses were conducted using the Statistical Package for the Social Sciences (SPSS) version 18. Principal component analyses (PCA) was used in preference to exploratory factor analysis as the purpose of the analysis was to give an empirical summary of the data set, for which principal component analysis is more suitable (Nie et al. , 1975; Tabachnik & Fidell, 2007). Data was rotated to achieve a simpler and more meaningful factor pattern. In the initial analyses both orthogonal (varimax) and oblique

272

Principal Component Analysis

(direct oblimin) rotations were run. Both sets of analyses provided similar results, which is to be expected when correlations were found to exist between items (Tabachnik & Fidell, 2007). Orthogonal rotations are presented in the analyses as they are simpler to handle (Nie et al. , 1975), and are more widely used in factor analysis and principal component analysis literature. A number of othogonal rotations exist, the current study used varimax rotations which are the most common orthogonal rotation as the variance of loading within factors is maximised, thus the interpretation of each factor is made easier as the variables that correlate with that factor are more clearly identified (Munro, 2005).

The sample size and the strength of relationships between items were assessed to ensure the data was suitable for factor analysis. It should be noted that there is little consensus on the definitive sample size or strength of the relationship among variables; however the current study fulfills the majority of criteria outlined by experts in the field. Some researchers have identified the ratio of subjects to items which is of importance in determining the suitability of the data, with the recommendations varying from 3 -6 subjects per item in the questionnaire with a minimum of 250 subjects (Cattell, 1978), to 10 subjects per item (Munro, 2005). Other criteria of sampling adequacy that were applied include the Kaiser- Meyer- Olkin measure of sampling adequacy (KMO) which assesses patterns of items. Higher values above the recommended .05 level and closer to 1 signify a compact pattern of correlates where factor analysis would be deemed suitable (Field, 2005; Tabachnik & Fidell, 2007). Bartletts test of Sphericity is used to test for an adequate level of correlation between items, with P < . 05 recommended for factor analysis (Field, 2005; Tabachnik & Fidell, 2007). Item total correlations were also examined to assess the suitability of the data for factor analysis. It is recommended that items should correlate between 0.3 and 0.9; i.e. have some relationship yet avoiding issues associated with multicollinearity (Field, 2005).

Separate principal component analyses were carried out for psychological, social and environmental correlates of physical activity in the current study. The current study is based on the socio-ecological model of health which posits that physical activity is based on separate personal/ biological, psychological, social and environmental correlates. It would appear logical therefore, to examine each of these domains of influences separately and to identify the important factors within each domain of

273

Principal Component Analysis influence. In conjunction with this, as previously outlined, for data to be considered suitable for factor analysis a degree of correlation is recommended between items (Field, 2005). Higher item total correlations were observed in items within a homogenous category e.g. social support from peers items were found to correlate with social support from family items, these items did not correlate as favorably with items from items from psychological and environmental domains. A further justification for separate analysis was to assist in managing and interpreting the data. An initial principal component analysis was carried out with all psychological, social and environmental variables included. Due to the large quantity of items difficulties arose in identifying the correct number of factors to extract and in interpreting the data. Furthermore, most other factor analysis research has used individual scales or a small number of scales from a specific category of influence (Saunders et al. , 1997; Motl et al. , 2001; Sherwood et al. , 2004). In conjunction, as outlined in the previous section, some researchers have recommended a high ratio of questionnaire items to participants to assess the suitability of the data for principal component analysis (Cattell, 1978; Munro, 2005). Separate psychological, social and environmental analyses increase the item participant ratio in the current study further supporting the suitability of the data for component analysis.

No analyses were carried out on the personal/ biological correlates of physical activity due to the unsuitability of this data for principal component analysis. Much of the data gained in the personal/ biological category were diverse factual pieces of information which did not correlate together e.g. age of student, ethnicity etc. Furthermore most items consisted of single items which did not cluster with other personal/ biological items in a factor analysis. Due to the non modifiable nature of much of this data e.g. gender, age and the importance of this information in providing context and demographic details of the participants in future studies questions relating to personal/ biological information from the original questionnaire were retained in the final ‘APAC’ questionnaires.

A number of statistical techniques such as Cattells scree test and Horns parallel analysis were used to decide the number of factors to retain from the analyses (Tabachnik & Fidell, 2007). Data from Kaiser’s criterion also were examined; however these data were examined with caution as previous researchers have been critical of this technique overestimating the number of factors to extract in principal component

274

Principal Component Analysis

analysis (Zwick & Velicier, 1986; Velicier & Jackson, 1990). In all analyses the factor extraction techniques were used as a guide on the possible number of factors to extract. Multiple analyses were run with the number of factors to retain being set manually e.g. if the criteria recommended a 3 factor solution, analyses were run extracting 2, 3 and 4 factors to identify the cleanest factor structure (Costello & Osbourne, 2005). The cleanest factor structure was determined using item loadings above 0.3, with no or fewest complex loadings (Field, 2005) and which was conceptually meaningful and interpretable.

After extraction of factors, efforts were made to reduce the number of items in the questionnaire. Currently, there is a lack of criteria and guidelines relating to questionnaire data reduction techniques. The following protocol was developed based on the procedure used by MacNamara & Collins (in press). Items that loaded simply (i.e. loading on one factor) but with low loading less than .40 were removed from the analyses (Comrey & Lee, 1992; MacNamara & Collins, in press). Items with a loading of greater than .40 were examined and those that were not conceptually meaningful i.e. they did not measure the same construct as the cluster of items that formed the factor, were not considered in the analyses.

Strong data has been identified as having no or few complex loaded items (Field, 2005) therefore; more stringent criteria were applied to items with complex loading (i.e. items with loading of > 30 on more than one factor). Items which loaded on more than one factor and did not have a loading of greater than .50 on either factor were removed from the analyses. Complex loaded items with a loading of greater than .50 were examined to see if they fitted logically into the factor structure based on the wording of the item and the label given to the factor, where this was not the case these items were removed.

Conceptually meaningful items with simple loadings of greater than .40 and complex loaded items with a factor loading of greater than .50 were further examined. A number of trial factor analyses were run with lower loaded items removed in an attempt to find the factor structure of best fit; i.e. reduced number of items which explained most of the variance.

275

Principal Component Analysis

Where pairs of items within the same factor were found to be repetitive, the item with the lower loading was removed. Factors which did not have acceptable levels of internal consistency, (Cronbach alpha > .7) (Tabachnik & Fidell, 2007) were also not considered in the interpretation. After data reduction techniques were applied to the data, a second set of principal components analyses were used to confirm the integrity of factor structure (Henson et al. , 2004; MacNamara & Collins, in press).

For each factor that was identified Cronbach’s alpha was calculated to assess the internal reliability of the factor (Field, 2005). Cronbach’s alpha of > 0.7 was used to indicate acceptable internal consistency (Tabachnik & Fidell, 2007). Item total correlations were calculated for the items within each factor to indicate the degree of correlation between each item and the total factor score, with > 0.3 used to indicate acceptable correlations (Nunnally & Bernstein, 1994), this was used to further support the internal consistency of each factor. .

Factor scores i.e. composite variables which provide information on an individual’s placement on the factor were computed (DiStefano et al. , 2009). The convergent validity of the newly developed ‘APAC’ questionnaires were assessed using Pearson correlations to examine the relationship between the factor scores and levels of physical activity measured using Actigraph accelerometers.

Hierarchical linear regression analyses were used to investigate how much variance in moderate and vigorous physical activity and vigorous physical activity can be explained by the newly developed ‘APAC’ questionnaires. Vigorous physical activity data was skewed therefore data for this variable was log transformed for the analyses. Hierarchical linear regressions were conducted using data from the subsample of participants who had sufficient accelerometer wear time (N = 233) and the factor scores from the ‘APAC’ questionnaires. In the regression analyses moderate and vigorous physical activity (MVPA) and log transformed vigorous physical activity (VPA) were the dependent variables and personal variables and correlate factor scores were the independent variables. Blocks of variables were created (personal, psychological, social and environmental) and entered into the regression analyses in a standard order. Personal/ demographic factors and correlate scales that were bivariately correlated (P < . 1) with moderate and vigorous physical activity and log transformed vigorous activity were included in the first block of the regression analyses. Due to the

276

Principal Component Analysis

small number of participants reporting non Irish ethnicity (males, N = 6; females N = 8) this variable was also excluded from the regression analysis due to the difficulties drawing definitive conclusions. Personal variables were entered in the first block to assess the influence of these correlates and to assess whether other variables explained extra unique variance than explained by personal variables. Psychological factor scores were entered second followed by social factor scores and finally environmental factor scores. This procedure allowed evaluation of the explained variance when each additional block was added to the analyses.

7.4 Results

The data in the current study was found to be suitable for factor analysis. The large sample size in the current study (N = 1,114; 48% male, 52% female) is considered ‘very good’ based on the recommendations of Comrey & Lee (1992), and exceeds the recommended 300 cases recommended by Tabachnik and Fidell (2007) thus supporting the use of factor analysis. The ratio of subjects to items in the current study was found to reach the recommended levels 3-6 subjects per item with a minimum of 250 subjects (Cattell, 1978). Both social and environmental analyses reached the recommended 10 subjects per item (Munro, 2005), however due to the extensive number of psychological correlates, these were not found to achieve this criteria. The result of the Bartlett’s Tests of Sphericity was highly significant for psychological, social and environmental correlate scales for males and females ( P < . 001 for all) suggesting there was adequate correlation between the variables and that the data was suitable for factor analysis. The KMO Measure of Sampling adequacy also revealed significant results further supporting the suitability of the data for factor analysis (KMO >.8; P < . 001 for all) (Field, 2005). The item correlation matrix was also examined and no evidence of multicollinearity was evident between the variables, further supporting the suitability of the data for factor analysis.

Psychological correlates were found to load on 8 factors for males with eigenvalues ranging from 20.763 to 1.789. This factor solution explained 47.6% of the variance in male psychological correlates (Appendix C.2). A nine factor structure

277

Principal Component Analysis emerged for females with eigenvalues ranging from 21.449 to 1.868, this factor structure explained 47.2% of the variance (Appendix C.3). These factor solutions were supported by Parallel Analysis, this analysis has been identified as the most accurate to assess the true number of factors to retain (Zwick & Velicier, 1986).

A three factor structure was retained to best explain male and female social influences on physical activity. Among males eigenvalues ranged from 5.122 to 1.097, with 62.3% of the variance explained by the three factor structure. Eigenvalues ranged from 5.101 to 1.065 among females with 62.2% of the variance explained (Appendix C.4 and C.5).

The criteria for factor extraction identified a three factor solution for male and female environmental influences. Eigenvalues ranged from 4.766 -1.1872 among males, accounting for 32.3% of the total explained variance. Female eigenvalues ranged from 5.028 – 1.881, accounting for 33% of the variance. These factor solutions were found to represent the most conceptually sound and easily interpretable explanation of the items measured (Appendix C.6 and C.7).

7.4.1 Interpretation and Naming of Factors

In line with previous literature the interpretation and naming of factors was influenced by the pivotal terms i.e. items that load most heavily within any factor (Tabachnik & Fidell, 2007) and / or a conceptually meaningful term which encapsulated the items in the factor e.g. self-efficacy.

Data reduction criteria have been outlined in the statistical analysis section, among male psychological correlates; this resulted in 22 items with low loadings and 14 items with complex loadings being removed. Two items in the original barriers scale were found to load on the enjoyment factor; however similar items which were almost identical existed in the enjoyment scale; therefore these items were deemed to be repetitive and were also removed. The final psychological factor structure consisted of 57 items for males The retained items within each of these factors were found to have acceptable corrected item total correlations within their factor (range 0.44 - 0.81) (Nunnally & Bernstein, 1994). Among females, 18 items with low loadings, 17 items

278

Principal Component Analysis

with complex loadings, two items that did not conceptually fit the factor structure and one item which was repetitive were removed from the analysis. The reduced female scale consisted of 63 items. The retained items in each of these factors were found to have acceptable corrected item total correlations (range 0.39 - 0.83) (Nunnally & Bernstein, 1994).

In the analysis of male and female social influences three complex loaded items were removed from the analysis. A social influences scale, investigating physical activity with different groups of friends was found to have low internal consistency among males and females with Cronbach alpha’s of 0.549 and 0.602 respectively. This scale was removed from the analysis resulting in six items being retained in a two factor structure for males and females. The removal of this scale was found to increase overall explained variance of social influences among male and female participants. The six items which were retained were found to have acceptable corrected item-total correlations for males (range 0.50 - 0.77) and for females (range 0.54 - 0.71).

Ten items with low loadings and two items with complex loading were removed from male environmental correlates of physical activity. Eight items with low loadings and four items with complex loading were removed from the female analysis. The 16 items which were retained were found to have acceptable corrected item-total correlations for males (range 0.33 - 0.68) and females (range 0.34 - 0.72).

A second principal component analysis was conducted on the reduced psychological; social; and environmental categories for males and females (Henson et al. , 2004; MacNamara & Collins, in press) . This analysis confirmed the factor structures and can be seen in Tables 7.2 – 7.7. The reduced psychological correlate scales were found to explain 56.6% of the variance among male correlates and 56.1% of the variance among female correlates. The reduced social correlate scales were found to explain 72.6% of the variance among males and 71.6% of the variance among females; while the environmental scales explained 47.0% of male variance and 48.8% of female variance.

The factors and items in the ‘APAC’ questionnaires for males and females are presented in Appendix C.8 and C.9. Two items which did not load strongly in the factor analysis (physical activity stage of change; intention to be active), and one item from a

279

Principal Component Analysis scale which were removed due to poor internal consistency (active with friends in neighborhood) were found to significantly correlate with male and female MVPA and log transformed VPA and were also included in the final ‘APAC’ questionnaires for males and females. Pearson Correlations were found to range from .29 to .40 for males and .21 to .35 for females ( P < . 01 for all). The components of the male and female ‘APAC questionnaires’ are presented in Table 7.8 and 7.9. The original and final complete ‘APAC’ questionnaires for male and female adolescents are presented in Appendices C.10- C.12.

Table 7.2: Factor loadings of the data reduced psychological correlates for males F1 F2 F3 F4 F5 F6 F7 F8 Q92 .885 Q88 .837 Q90 .833 Q87 .830 Q89 .790 Q94 .776 Q95 .766 Q91 .741 Q93 .686 Q36 .741 Q44 .707 Q34 .683 Q38 .678 Q33 .659 Q47 .606 Q32 .606 Q35 .556 Q43 .542 .302 Q41 .413 Q29 .877 Q30 .824 Q31 .819 Q28 .808 Q26 .804 Q27 .734 Q72 .652 Q70 .646 Q69 .630 Q73 .623 Q71 .605 Q65 .601 Q55 .596 Q68 .585 Q58 .557 Q59 .508 Q80 .749 Q86 .705 Q84 .695

280

Principal Component Analysis

Q79 .695 Q81 .655 Q82 .629 Q85 .549 Q16 .696 Q11 .685 Q14 .304 .646 Q18 .606 Q17 .553 Q15 .548 Q4 .743 Q3 .706 Q5 .699 Q9 .669 Q6 .627 Q24 .661 Q25 .658 Q22 .644 Q19 .318 .624 Variance 22.0 10.2 6.0 5.5 4.3 3.4 2.7 2.5

Table 7.3: Factor loadings of the data reduced psychological correlates for females F1 F2 F3 F4 F5 F6 F7 F8 Q32 .711 Q36 .707 Q46 .677 Q34 .671 Q40 .654 Q43 .642 Q42 .637 Q44 .627 Q47 .615 .306 Q67 .613 Q35 .597 Q38 .583 Q41 .568 Q95 .846 Q92 .820 Q88 .816 Q90 .796 Q89 .763 Q93 .727 Q87 .692 Q94 .641 Q91 .616 Q29 .867 Q31 .861 Q26 .854 Q28 .853 Q30 .791 Q27 .774 Q13 .693

281

Principal Component Analysis

Q16 .642 Q11 .365 .606 Q14 .337 .605 Q18 .551 Q17 .546 Q15 .533 Q12 .530 Q3 .715 Q4 .723 Q5 .703 Q6 .632 Q9 .590 Q10 .515 Q1 .515 Q19 .341 .681 Q25 .646 Q22 .638 Q24 . .604 Q20 .584 Q21 .578 Q70 .660 Q72 .645 Q56 .628 Q58 .599 Q73 .592 Q65 .537 Q79 .655 Q80 .645 Q85 .629 Q86 .610 Q82 .606 Q78 .476 Q96 Q97 Variance 21.9 8.7 6.9 4.4 3.5 3.1 2.7 2.6

282

Principal Component Analysis

Table 7.4: Factor loadings of the data reduced social correlates for males F1 F2 Q11 .884 Q12 .851 Q10 .758 Q9 .655 Q5 .918 Q4 .868 % Variance 54.0 18.6

Table 7.5: Factor loadings of the data reduced social correlates for females F1 F2 Q11 .876 Q12 .794 .310 Q10 .781 Q9 .705 Q5 .901 Q4 .321 .817 % Variance 54.9 16.7

Table 7.6: Factor loadings of the data reduced environmental correlates among males F1 F2 F3 Q2 .797 Q10 .738 Q8 .716 Q5 .587 Q3 .582 Q1 .572 Q14 .665 Q22 .608 Q25 .608 Q19 .585 Q16 .567 Q24 .540 Q15 .523 Q27 .877 Q26 .856 Q28 .695 % Variance 24.4 11.7 10.9

283

Principal Component Analysis

Table 7.7: Factor loadings of the data reduced environmental correlates among females F1 F2 F3 Q2 .836 Q10 .803 Q8 .713 Q5 .669 Q3 .615 Q14 .663 Q25 .635 Q22 .602 Q24 .589 Q11 .567 Q19 .550 Q16 .502 Q15 .495 Q28 .876 Q27 .875 Q26 .665 % Variance 25.8 11.9 11.1

284

Principal Component Analysis

Table 7.8: Description and internal consistency of the ‘Male Adolescent Physical Activity Correlates Questionnaire’ Variable Name Description Items Cronbach References [range] alpha Psychological Variables Stage of change Physical activity stage of change; 1= Currently physically active, 4[1 ,2] n/a Marcus &Rossi (1992) 4=Regularly active for past 6 months Change strategies Cognitive and behavioural change strategies; 1= never, 5 =very often 5 [1,5] 0.82 TAAG (2004); Dishman & Motl (2005) Self –efficacy Self efficacy; 1= Disagree a lot, 5= Agree a lot 6 [1,5] 0.80 Saunders et al. (1997); Motl et al. (2000); TAAG (2004) Perceived competence Perceived competence at games and sport; Positive and negative 4[1,4] 0.72 Harter (1982) statements – really true for me; sort of true for me Body Image Body image – Positive and negative statements – relay true for me sort of 6[1,4] 0.91 Harter (1985) true for me Enjoyment of activity Enjoyment of physical activity; 1= Disagree a lot, 5= Agree a lot: reverse 10 [1,5] 0.88 Heesch et al. (2006); Motl et al. (2000) scoring Kendzeirski & De Carlo (1991) Perceived barriers Perceived Barriers to Physical Activity, 1=Never, 4=Very Often 10 [1,4] 0.83 Heesch et al. (2006); Sallis et al. (2002) Intention to be active Intention to be active on most days; 1=sure I will not be active, 5= sure I 1 [1,5] n/a Godin & Shephard (1986) will be active Perceived benefits Perceived benefits about being active; 1= Disagree a lot, 5 = Agree a lot 9[1,5] 0.86 Sallis et al. (2002); Motl et al. (2000); TAAG (2004) Outcome expectancy value Importance on outcome of being active; 1= Disagree a lot, 5 = Agree a 9[1,5] 0.94 Sallis et al. (2002); Motl et al. (2000); lot TAAG (2004) Social Variables Active groups of friends Active with different groups of friends; 1=Yes; 2 = No 1[1,2] n/a Burns et al. (2010) Social support peers Social Support from peers; 1= none, 5= everyday 2 [1,5] 0.70 Sallis et al. (1999); TAAG (2004) Social support family Social support from family; 1= none, 5 = everyday 4 [1,5] 0.83 Sallis et al. (1999); TAAG (2004) Environmental Variables Transport Transportation to and from physical activities; 1= Not at all difficult, 4= 3 [1,4] 0.79 Evenson et al. (2006; TAAG (2004) impossible Access to facilities Ease of access to physical activity facilities 7 [1,3] 0.70 Evenson et al. (2006); TAAG (2004) Perceived environment Perceived environmental suitability for physical activity; 1=disagree a 6 [1,5] 0.77 Evenson et al. (2006); TAAG (2004) lot, 5= agree a lot

285

Principal Component Analysis

Table 7.9: Description and internal consistency of the ‘Female Adolescent Physical Activity Correlates Questionnaire’ Variable Name Description Items Cronbach References [range] alpha Psychological Variables Stage of change Physical activity stage of change; 1= Currently physically active, 4[1 ,2] n/a Marcus &Rossi (1992) 4=Regularly active for past 6 months Change strategies Cognitive and behavioural change strategies; 1= never, 5 =very often 7 [1,5] 0.82 TAAG (2004); Dishman & Motl (2005) Self –efficacy Self efficacy; 1= Disagree a lot, 5= Agree a lot 8 [1,5] 0.83 Saunders et al. (1997); Motl et al. (2000); TAAG (2004) Perceived competence Perceived competence at games and sport; Positive and negative 6 [1,4] 0.82 Harter (1982) statements – really true for me; sort of true for me Body Image Body image – Positive and negative statements – relay true for me sort 6[1,4] 0.93 Harter (1985) of true for me Enjoyment of activtiy Enjoyment of physical activity; 1= Disagree a lot, 5= Agree a lot: 13 [1,5] 0.91 Heesch et al. (2006); Motl et al. (2000) reverse scoring Kendzeirski & De Carlo (1991) Perceived barriers Perceived Barriers to Physical Activity, 1=Never, 4=Very Often 6 [1,4] 0.73 Heesch et al. (2006); Sallis et al. (2002) Intention to be active Intention to be active on most days; 1=sure I will not be active, 5= sure I 1 [1,5] n/a Godin & Shephard (1986) will be active Perceived benefits Perceived benefits about being active; 1= Disagree a lot, 5 = Agree a lot 9[1,5] 0.77 Sallis et al. (2002); Motl et al. (2000); TAAG (2004) Outcome expectancy value Importance on outcome of being active; 1= Disagree a lot, 5 = Agree a 9[1,5] 0.91 Sallis et al. (2002); Motl et al. (2000); lot TAAG (2004) School climate: teachers Perceptions of teacher support; 1= Disagree a lot, 5 = Agree a lot 2[1,5] 0.82 TAAG (2004) Social Variables Active groups of friends Active with different groups of friends; 1=Yes; 2 = No 1[1,2] 0n/a Burns et al. (2010) Social support peers Social Support from peers; 1= none, 5= everyday 3 [1,5] 0.71 Sallis et al. (1999); TAAG (2004) Social support family Social support from family; 1= none, 5 = everyday 5 [1,5] 0.84 Sallis et al. (1999); TAAG (2004) Environmental Variables Transport Transportation to and from physical activities; 1= Not at all difficult, 4= 3 [1,4] 0.70 Evenson et al. (2006; TAAG (2004) impossible Access to facilities Ease of access to physical activity facilities 8 [1,3] 0.72 Evenson et al. (2006); TAAG (2004) Perceived environment Perceived environmental suitability for physical activity; 1=disagree a 5 [1,5] 0.82 Evenson et al. (2006); TAAG (2004) lot, 5= agree a lot

286

Principal Component Analysis

7.4.2 Internal Consistency Reliability

Cronbach alpha’s coefficients were calculated to assess the internal consistency of the data reduced male and female ‘APAC’ questionnaires. The internal consistencies of the questionnaires were excellent with a Cronbach Alpha of 0.934 for males and 0.936 for females (Tabachnik & Fidell, 2007). The internal consistency of individual male and female scores all exceeded the acceptable Cronbach alpha of 0.7 (Table 7.10) (Tabachnik & Fidell, 2007).

Table 7.10: Internal consistency of the ‘APAC’ questionnaires Male Female Psychological Cronbach Psychological Cronbach Factors alpha Factors alpha Factor 1 0.939 Factor 1 0.913 Value Enjoyment Factor 2 0.877 Factor 2 0.913 Enjoyment Value Factor 3 0.906 Factor 3 0.925 Body image Body image Factor 4 0.827 Factor 4 0.829 Barriers Self-efficacy Factor 5 0.860 Factor 5 0.820 Benefits Change strategies Factor 6 0.798 Factor 6 0.817 Self-efficacy Perceived competence Factor 7 0.820 Factor 7 0.730 Change strategies Barriers Factor 8 0.723 Factor 8 0.766 Perceived competence Benefits N/A Factor 9 0.817 Teacher influence Social Social Factors Factors Factor 1 0.826 Factor 1 0.836 Family support Family support Factor 2 0.703 Factor 2 0.713 Peer support Peer support Environmental Environmental Factors Factors Factor 1 0.772 Factor 1 0.820 Perceived environment Perceived environment Factor 2 0.700 Factor 2 0.724 Access to facilities Access to facilities Factor 3 0.785 Factor 3 0.704 Transport barriers Transport barriers

287

Principal Component Analysis

7.4.3 Validity

Tables 7.11 and 7.12 show Pearson correlations between factors in the reduced ‘APAC’ questionnaires, accelerometer measured physical activity and BMI. Significant correlations between accelerometer measured activity and individual factor scores were found to be small to medium. Among both males and females significant relationships were evident between physical activity and factors measuring efficacy, perceived competence, physical activity stage of change, intention to be active, active with friends in neighborhood, and transport barriers ( P < .05). Among males factors measuring change strategies, family support and perceived environment were also related to physical activity. For female’s factors measuring body image, barriers, benefits and peer support were related to physical activity.

Hierarchical multiple regressions were used to assess the ability of the APAQ questionnaire to predict male and female MVPA and VPA. Personal correlate scales which were found to be related to moderate and vigorous physical activity or vigorous activity were included in the regression analysis (P < . 1). Total variance in MVPA explained by the correlates was 42%, F (18, 74) = 3.56, P < . 01 for males and 38%, (20, 97) = 2.98, P < . 01 for females. For males’ sport participation, intentions to be active and perceived environment were found to be significant predictors of MVPA (Table 7.13). Among females, age, perceived body image, self-efficacy and peer support were significantly related to MVPA (Table 7.14).

Explained variance in log transformed VPA among males was 37%, F (17, 84) = 2.94, P < . 01 and among females was 41% (19, 98) =43.6, P < . 01. Stage of change and perceived environment was found to be significantly related to VPA among males (Table 7.13). Among females sport participation and age were significantly related to vigorous activity (Table 7.14).

288

Principal Component Analysis

Table 7.11: Pearson correlations between the APAQ questionnaire, activity levels and BMI for males Counts Sedentary MVPA Log Vigorous BMI MVPA per minute in bouts Psychological Factors Factor 1 .150 -.166 .107 .174 -.032 -.018 Value Factor 2 .051 -.129 -.025 .077 -.035 -.011 Enjoyment Factor 3 -.041 -.086 -.097 -.053 -.158* -.053 Body Image Factor 4 -.069 -.004 -.106 .001 .089 -.056 Barriers* Factor 5 -.053 -.062 .020 .015 .063 -.011 Benefits Factor 6 .228* -.173 .276** .285** -.005 .190 Self-efficacy Factor 7 .185 -.063 .225* .200* .072 .240* Change strategies Factor 8 .154 -.164 .220* .165 -.091 .220 Perceived competence Factor 9 .29** -.22* .36** .40** -.04 .24* Stage of change Factor 10 .33** -.16 .38** .37** -.08 .30 Intention to be Active Social Factors Factor 1 .221* -.232* .253** .277** -.065 .192* Family support Factor 2 .081 -.113 .110 .052 .027 .110 Peer support Factor 3 -.24* .27** -.29** -.17 .03 -.22* Active with friends in neighbourhood# Environmental Factors Factor 1 .269** -.078 .362** .258** -.096 .384** Perceived environment Factor 2 .064 -.033 -.090 -.103 .021 -.143 Access to facilities# Factor 3 -.091 .199* -.055 -.068 .016 .038 Transport# #Negatively marked scales *P < .05; ** P<.01

289

Principal Component Analysis

Table 7.12: Pearson correlations between the ‘APAC’ questionnaire, activity levels and BMI for females Counts Sedentary MVPA Log BMI MVPA in per minute Vigorous bouts Psychological Factors Factor 1 .137 .081 .046 .105 -.014 .141 Enjoyment Factor 2 -.104 .072 -.019 -.060 -.046 .000 Value Factor 3 -.183* -.266** -.181* .101 -.368 -.124 Body image Factor 4 .269** -.214* .328** .169 .035 .150 Self-efficacy Factor 5 -.054 .030 -.063 -.021 .075 -.010 Change strategy Factor 6 .083 -.094 .050 .228* -.130* -.039 Perceived competence Factor 7 -.274** .192* -.168 -.181* -.044 -.049 Barriers# Factor 8 .117 -.248** .041 .103 .143* .027 Benefits Factor 9 .060 .002 .098 -.054 -.161** .090 Teacher influence# Factor 10 .3** -.29** .24** .35** -.001 .17 Stage of change Factor 11 .33** -.23** .21* .24** .02 .16 Intention to be Active Social Factors Factor 1 .049 -.339** .003 .346** -.007 -.040 Family support Factor 2 .285** -.140 .310** .199* .099 .210* Peer support Factor 3 -.24** .29** -.21* -.25** -.01 -.07 Active with friends in neighbourhood# Environmental Factors Factor 1 -.007 -.125 .103 -.130 .025 .006 Perceived Environment Factor 2 .154 -.063 .072 .039 -.70 .117 Access to facilities# Factor 3 -.255** .358** -.117 -.200* -.004 -.021 Transport# #Negatively marked scales *P < .05; ** P<.01

290

Principal Component Analysis

Table 7.13: Results of Regression Analysis Explaining MVPA and log transformed VPA for males Variable MVPA VPA

2 2 ß P R R ß P 2 2 R R change change Personal Variables .13 .13 .10 .10 Play sport competitively .26 .02 .19 .09 Age - - BMI - - Socio-economic status - - Screen time - - Psychological Variables .29 .16 .28 .18 Value .14 .10 .20 .03 Enjoyment -.15 .12 -.02 .81 Body image -.12 .19 -.09 .33 Perceived barriers .01 .16 .14 .15 Perceived benefits -.01 .88 -.003 .97 Self-efficacy -.06 .58 -.07 .56 Change strategies .07 .44 .05 .62 Perceived competence -.01 .91 -.04 .68 Stage of change .20 .06 .29 .009 Intention to be active .22 .04 .22 .05 Social Influences .31 .03 .30 .02 Family support -.01 .94 .05 .66 Peer support -.11 .27 -.13 .21 Neighbourhood co-participation -.01 .31 .05 .64 Environmental Influences .42 .11 .37 .08 Perceived environment .35 .000 .29 .002 Access to facilities -.04 .95 -.05 .58 Transport barriers -.005 .95 -.03 .77 - = variable not entered: P >.1 from Pearson correlation

291

Principal Component Analysis

Table 7.14: Results of Regression Analysis Explaining MVPA and log transformed VPA for females Variable MVPA VPA

2 2 ß P R R ß P 2 2 R R change change Personal Variables .09 .09 .30 .32 Play sport competitively .08 .42 .25 .01 Age .28 .003 -.29 .001 BMI - - Socio-economic status .12 .15 - Screen time - - Psychological Variables .28 .19 .37 .05 Enjoyment -.08 .40 -.06 .53 Value -.04 .63 -.06 .45 Body image -.17 .05 .006 .94 Self-efficacy .22 .02 .05 .58 Change strategies -.15 .10 -.04 .58 Perceived competence -.04 .70 -.001 .99 Perceived barriers -.08 .39 -.006 .95 Perceived benefits .05 .55 .03 .72 Teacher influence^ .10 .22 -.09 .24 Stage of change .17 .09 .18 .06 Intention to be active .03 .81 -.07 .54 Social Influences .34 .06 .38 .01 Family support -.03 .81 .08 .46 Peer support .21 .04 .10 .30 Neighbourhood co-participation -.14 .10 -.11 .19 Environmental Influences .38 .04 .41 .03 Perceived environment .05 .57 -.15 .07 Access to facilities .13 .15 .09 .28 Transport barriers -.07 .44 -.07 .42 ß= beta - = variable not entered: P >.1 from Pearson correlation ^Correlate scale relevant for females only

292

Principal Component Analysis

7.5 Discussion

Valid and reliable measures of the correlates of physical activity in adolescence are lacking but are needed to effectively promote physical activity. Previous research has attempted to assess the psychometric properties of a small number of scales (Saunders et al. , 1997; Motl et al. , 2000; Motl et al. , 2001; Dishman et al. , 2002; Motl et al. , 2003; Dishman et al. , 2005; Pirasteh et al. , 2008) however no previous research study has attempted to assess the factor structure, validity and reliability of a comprehensive range of psychological, social and environmental correlate scales. This study provides evidence for the reliability and validity of the ‘APAC’ questionnaires among Irish adolescents.

Separate ‘APAC’ questionnaires were developed for males and females (Appendix C.8 and C9) as factors promoting or inhibiting participation in physical activity may differ among males and females (Tergerson & King, 2002; Wu et al. , 2003; Burns et al. , 2011; Burns et al. , 2011). In the overall factor structure factors which were salient for males were also found to be salient for females. Gender differences existed within factors with specific items found to be more important for males or females, for example females identified bad weather as a major barrier to activity; this was not evident among males. The initial ‘APAC’ questionnaires were data reduced using previously developed criteria (Comrey & Lee, 1992; MacNamara & Collins, in press). This resulted in the removal of 56 of items from both male and female questionnaires resulting in more succinct instruments, with weak items being removed.

The psychological category resulted in eight distinct factors for males explaining 56.6% of the variance and nine distinct factors for females explaining 56.1% of the variance. Similar to previous factor analysis research the perceived enjoyment (Motl et al. , 2001), perceived benefits (Motl et al. , 2000), outcome expectancy values (Dishman et al. , 2005), perceived barriers (Dishman et al. , 2005) and perceived competence scales (Sherwood et al. , 2004) were found to be best described by single factor among adolescent males and females. The school climate teacher’s scale which was a female only scale was also found to be described by a single factor (female only scale) (Birnbaum et al. , 2005). Self-efficacy has previously been described as a

293

Principal Component Analysis multidimensional construct, (Saunders et al. , 1997), however the results from the current study indicate a single factor structure for self-efficacy and are in agreement with previous research (Motl et al. , 2000; Wu et al. , 2002; Dishman et al. , 2005). The change strategies scale was factor analyzed by Dishman et al. (2005) and found to consist of 2 separate factors. In the current ‘APAC’ questionnaire the items were found to load on one factor, which is in agreement with a study of Iranian adolescent girls (Pirasteh et al. , 2008). To the authors knowledge no previous study has factor analyzed body image scale, in the current study this was found to be a uni- dimensional construct.

Social influences were found to consist of two distinct factors namely peer and family support. The total variance explained from these factors was 72.6% for males and 71.6% for females. The social influences scale was found to load on a single factor in a previous study with preadolescent youth and to explain 37% of the variance (Saunders et al. , 1997). Due to the higher explained variance of a two factor solution and that in adolescence peer influence may become a more salient influence (Sallis et al. , 2002; De Roiste & Dinneen, 2005) a two factor solution may be more appropriate for this group. The two factor structure is also in agreement with a previous study (Pirasteh et al. , 2008). The item relating to being active with friends in the neighborhood was found to be significantly related to physical activity and was also included in the final questionnaire.

Environmental influences consisted of three distinct factors for males and females explaining 47.0% and 48.8% of the variance respectively. Recent environmental scales which have been developed for use in the TAAG study (Evenson et al. , 2006) have not been previously factor analyzed. The current study found that each of the three scales (perceived transport, access to facilities, perceived environment) were uni-dimensional constructs.

The overall questionnaires were found to have excellent levels of internal consistency with each of the factors displaying acceptable levels of consistency (Tabachnik & Fidell, 2007). Within each scale inter item total correlations were also found to exceed the recommended 0.3 level (Nunnally & Bernstein, 1994). The clean factor structure (i.e. items loading above .4, few cross loading items) (Field, 2005) also provides support for the construct validity of the questionnaires. Furthermore, the explained variance of the ‘APAC’ questionnaire varied from 47.0% in male

294

Principal Component Analysis

environmental factors to 72.6% in male social influences, when compared to previous factor analysis research this is relatively high. A review of factor analysis studies in educational research found that almost 30% of research studies accounted for less than 30% of the matrix variance (Henson et al. , 2004). In conjunction, the correlate and regression analysis highlight relationships between factors in the questionnaire and objective measures of physical activity providing some evidence of the convergent validity of the ‘APAC’ questionnaires. Therefore, this preliminary research provides reliability and validity support for the ‘APAC’ questionnaires among adolescents.

The multiple regression analysis found that between 38 – 42% of MVPA and 37- 41% of VPA can be explained by the newly developed questionnaires. Differences were found between correlates influencing male and female physical activity. Factors related to male physical activity included sport participation, intention to be active, perceived environment and stage of change. Among females sport participation, body image, self-efficacy peer support and age were related to physical activity. A similar TAAG study has explained between 30 – 35% of the variance for MVPA and 26 – 30% for VPA among different ethnicities of US adolescent females (Kelly et al. , 2010). No clear correlate trend was evident across all ethnicities in this study, however barriers to physical activity, social support from friends, sport participation and BMI were found to be significantly related to MVPA with at least one ethnic group (Kelly et al ., 2010) Other studies have reported between 8% and 37% of variance of objectively measured physical activity (Trost et al. , 1999; Pfeiffer et al. , 2009). Self report studies have been found to have higher explained variance than objective measures (Sallis et al. , 1999; Sallis et al. , 2002; Loucaides et al. , 2007). A possible explanation for this stronger association may be attributable to shared methods variance, i.e. self reported thoughts and feelings about physical activity may correspond more closely to self reported physical activity than objectively measures physical activity (Trost et al. , 1999; Kelly et al. , 2010) . These findings provide further support for the use of the ‘APAC’ questionnaires in future research studies.

Two items which were not found to load strongly on the current factor structure and one item which was part of a scale which was removed from the analysis due to lack of internal consistency were found to correlate with measures of physical activity. It might be that these items (intention to be active, physical activity stage of change and

295

Principal Component Analysis being active with friends in the neighborhood) should stand alone in the analysis, but it is also possible that the inclusion of other similar items might show these items as part of other factors not addressed by the current questionnaire e.g. intention to be active factor. Future research should aim to develop valid, reliable scales based on these single items.

Strengths of the current study included the use of a comprehensive questionnaire which was developed through an extensive review of literature and a series of focus groups (Burns et al. , 2011). A further strength of the study was the use of an objective measure of physical activity to assess the convergent validity of the questionnaire. A limitation of the current study was that the participants were all from schools in Cork a region in southern Ireland, however previous Irish studies have reported similar trends in adolescent correlates of activity (De Roiste & Dinneen, 2005; Burns et al., 2011a) and levels of activity (Dowd et al., 2010; Harrington, 2010; McCrorie et al., 2010; Woods et al., 2010). Future research should attempt to assess the reliability and validity with national and international samples.

7.6 Conclusion

Male and female ‘APAQ questionnaires’ were developed for Irish adolescents based on previously validated tools and focus group interviews in the current study. The development of the questionnaire involved (i) extensive review of literature to identify key correlates; (ii) the identification of previously developed measures to best represent these correlates; (iii) the use of focus groups to support the inclusion of identified correlates and to assist in identification of ‘new’ correlates which warranted inclusion; (iv) the use of factor analysis to explore the internal consistency, factor structure and redundant nature of scales or items within specific scales; (v) to use inter item correlations to assess the construct validity within each scale; (vi) to assess the convergent construct validity of the questionnaire using objective measures of physical activity and (vii) to describe a questionnaire which has appropriate construct validity. It is recommended that future research confirms the structure of the questionnaire with a broader sample of adolescents.

296

Chapter 8

Discussion

8.1 Introduction

The primary purpose of this research was to develop and assess the validity of a questionnaire to measure the correlates of activity among Irish adolescents. This research also provides important information on correlates of activity and objectively measured levels of activity among different sub groups of Irish adolescents. While the results of each chapter have been discussed individually, this chapter discusses the important overall findings of the research.

8.2 Physical Activity

8.2.1 Methodological Difficulties with Accelerometer Research

Due to the nature of physical activity in youth, i.e. short bursts of activity of mixed intensity (Ward et al., 2005) and issues relating to recall bias associated with self report methods (Metzger et al., 2008) the use of objective measures of physical activity have been recommended (Ekelund et al., 2004). Accelerometers have been found to be a feasible valid objective measure of physical activity in youth (Troiano et al., 2008) . It

297

Discussion should be noted however that no universally accepted methodological model exists for accelerometer data collection and analysis (Pate et al., 2006). Direct comparisons between studies are difficult due to methodological differences e.g. epoch length, cut off points used to classify MVPA, classification of non-wear time (Ruiz et al., 2011). Physical activity measurement would benefit greatly from a universal standard accelerometer data collection and analysis protocol; this would enable meaningful comparisons to be made between different research studies. This is of particular importance due to the increased availability and use of accelerometers in research studies.

The current study has developed a rigorous approach in data collection and data analysis which could form the methodological basis for future research studies. The use of initiatives to promote compliance was found to be effective with 77% of participants found to achieve the required levels of wear time (600 minutes on four days) this was higher than previous studies, e.g. Troiano et al. (2008) found that 67% of participants had sufficient wear time to be included in the analysis. The current study used an extensive range of procedures to promote compliance as recommended by Trost et al. (2005) and Ward et al. (2005), these procedures are outlined in chapter five. In particular the use of reminder text messages to participant’s phones to remind them to put on the accelerometer each morning was found to be effective in the promotion of wear time among participants. Data analysis procedures were adapted from the procedures developed for use in the National Health and Nutrition Examination Survey (NHANES) (Troiano et al., 2008); however the current study used a shorter five second epoch to measure MVPA compared to the one minute epoch used in the NHANES study. This shorter epoch resulted in data collection every five seconds and due to the nature of youth activity, i.e. short bursts of intermittent activity (Bailey et al., 1995; Ward et al., 2005), provides a more sensitive measure of activity among adolescents (Riddoch et al., 2007). A wide range of outcome measures were assessed in the current study e.g. minutes spent in sedentary, low, moderate and vigorous activity, number and duration of moderate and vigorous bouts, and the percentage of participants who reached the recommended 420 minutes of moderate and vigorous activity per week. Few published research studies have used such an extensive range of outcome measures. Therefore, this methodological procedure could provide the basis of future studies to measure activity levels of children and adolescents.

298

Discussion

8.2.2 Activity Counts among Irish Adolescents

This research is of significant importance as currently limited data exists which examines objectively measured physical activity among Irish adolescents. Mean counts per minute evaluates a raw data score without the use of any external criteria. It should be noted however that differences exist in the classification of wear time and non-wear time between studies. These differences can influence mean counts per minute.

The current study which is based on the methodological approach of the NHANES study (Troiano et al ., 2008), defined non wear time as 60 minutes of consectutive zero’s with an allowance of 1-2 minutes with counts between 0 -100. Mean counts per minute in the current study were found to be 427.2 ± 144.9 for males and 328.9 ± 97.7 for females. These findings were found to be similar to older adolescents in the US, NHANES study. In this study, mean counts per minute among 12 -15 year olds were 521 (24.1) and 381 (13.7) for males and females respectively. Among 16-19 year olds mean counts per minute were 428.9 (12.6) and 327.2 (6.9) for males and females (Troiano et al. , 2008).

A study of European adolescents’ from Norway, Denmark, Portugal and Estonia used a different protocol to define non-wear time (Riddoch et al., 2004). In this study ten consectuive minutes of zero’s were classified as non- wear time. Mean counts per minute among 15 year old males males were 615 ± 228, while among mean counts per minute among females were 491 ± 163. To facilitate a comparison with European adolescents, mean counts per minute from the current study were also analysed based on this protocol. Using this protocol, mean counts per minute among Irish adolescent males were 525.7 ± 155.5, and females were 455.6 ± 130.7. It should be noted that data from the current study was based on adolescents aged 12-18 years whereas Riddoch et al. (2004) data was based on 15 year olds. It appears however, that levels of activity among Irish adolescents are at the lower end of the spectrum when compared to European adolescents.

299

Discussion

8.2.3 Time Spent in Activity of Different Intensities

As previously outlined, comparison of time spent in activities of various intensities between studies is limited due to various methodological differences. In the current study, Irish adolescent males were found to accumulate 52.8 ± 20.5 MVPA minutes per day, while females accumulated 37.8 ± 15.4 MVPA minutes per day. The US, NHANES study used the same cut points to classify MVPA. This study reports that 12-15 year old males were found to accumulate 45.3 (3.4) minutes of MVPA per day and 16-19 year old males engaged in MVPA for 32.7 (2.2) minutes per day. Females aged 12-15 engaged in 24.6 (1.8) minutes of MVPA per day, with females aged 16-19 accumulated 19.6 (2.4) MVPA minutes per day. It should be noted however, that the shorter 5 second epochs used in the current study, results in a more sensitive measure of time in moderate and vigorous activity compared to the longer one minute epoch used in the NHANES study. It may be argued that the use of longer one minute, or 30 second epochs results in underestimation of time in MVPA due to the smoothing of the data (Nilsson et al. , 2002; Riddoch et al. , 2007) and the use of shorter provides a more sensitive and accurate measure of habitual activity.

Data from the recent European HELENA study (Ruiz et al., 2011), collected accelerometer data from 2,200 adolescents from ten countries. This study used 15 second epochs to measure activity levels and classified MVPA as > 2,000 (3 METs) counts per minute. This is is lower than that used in the current study, thus limiting comparisons that can be made. Male moderate and vigorous activity in this study was found to be 64 (48 – 81) minutes per day, while females engaged in 49 (37 – 62) minutes per day. This study also reports differences in levels of MVPA across European countries. Higher levels of activity were recorded in Central and Northern Europe compared to Southern Europe. The magnitude of these differences was substantial with males reporting from Southern Europe reporting 7% lower MVPA, while females reported 23% lower MVPA ( P < 0.001).

This finding is supported by Riddoch and colleagues in the ‘European Youth Heart Study’ (Riddoch et al., 2004). This study investigated activity levels in four European countries (Norway, Denmark, Portugal, Estonia). Low cut points of > 1,500 counts per minute were used to classify MVPA in this research. This resulted in higher levels of MVPA compared to other studies and limits its comparability. An interesting

300

Discussion

trend which emerged from this study however, was that MVPA was found to vary between countries. Levels of MVPA was found to vary from 77 minutes per day among 15 year old males in Portugal to 110 minutes among 15 year old Estonian males. Among 15 year old females minutes in MVPA varied from 60 minutes per day in Portugal to 82 minutes per day on Norway.

Central and Northern European countries which have been found to have higher levels of activity have a ‘culture of activity’ (Fox et al., 2003). For example in Denmark, cycling is a prevalent form of transport with 60% to 70% of children riding their bikes to school each day (Fox, 2003). These countries have also been found to have lower levels of overweight and obesity (Fox, 2003; Lobstein & Frelut, 2003). It may be that in Ireland, adolescents lack a culture of, and extrinsic need for, habitual activity. For example levels of active transportation in Ireland have been found to be low, with 38 – 43% of Irish adolescents actively commuting to school (Woods et al., 2010). This ‘activity toxic’ and ‘obesogenic’ environment (IOTF, 2003), may negatively influence Irish adolescents habitual activity.

Irish adolescent males were found to engage in 28.4 ± 19.1 minutes of MVPA accumulated in bouts per day, while females engaged in 14.9 ± 13.8 MVPA minutes accumulated in bouts. The NHANES study reports that US males aged 12-15 engage in 18.6 (2.3) minutes per day in bouts, while females engage in 7.1 (0.9) minutes per day. Among older US adolescents aged 16-19 years, males engaged in MVPA bouts for 10.9 (1.1) minutes per day, while females engaged in 5.5 (1.3) minutes per day (Troiano et al., 2008). It should be noted that in the current study a bout was deemed to have started when seven minutes of a ten minute period was above the moderate threshold. Troiano et al., (2008) set this criterion as eight minutes of a ten minute period; therefore, the results of these findings are not directly comparable. It appears however, that Irish adolescents engage in more bouts of moderate and vigorous activity than their US counterparts.

Similar to previous research males were found to be more active than females (Troiano et al. , 2008; Woods et al. , 2010) and overall levels of physical activity were found to be negatively associated with age in adolescence (Troiano et al. , 2008; Woods et al. , 2010). Sport participants were found to participate in more physical activity than non-sport participants (Pate et al. , 2000; Booth et al. , 2004; Santos et al. , 2004), 301

Discussion therefore sport participation should be further promoted through school and community efforts.

An interesting finding in the current study was that female MVPA was not found to be lower among older adolescents. It should be noted however that the level of activity among adolescent females is very low with mean daily minutes of MVPA being 37.4 ± 15.4 minutes per day of which 14.9 ± 13.8 were accumulated in bouts. Nonetheless, the fact that no difference was found between young and older female adolescents is an interesting finding; however, it should be interpreted with caution due to the small sample size. Some support for this finding can be found in the recent HELENA study. In this study data was gathered from 1,184 adolescent girls aged between 12 – 17 years from ten European countries between 2006 and 2008. In girls, both average physical activity counts per minute (12 year olds 396 counts per minute v 17 year olds 364 counts per minute; P = 0.7) and daily MVPA (12 year olds 51 mins per day ; 17 year olds 50 mins per day; P = 0.06) were similar across different age groups (Ruiz et al. , 2011). Methodological differences between the current study and Ruiz et al . (2011) study make it impossible for direct comparisons to be made on levels of activity. It may be that due to the low levels of activity among young adolescent females; there is limited potential for activity to decrease with age in adolescence. Future large scale studies should further investigate this trend in female MVPA.

8.2.4 Irish Adolescents Achieving the Recommended Levels of Activity

When the recommended levels of physical activity are used as the criterion relatively few Irish adolescents achieve this threshold. Thirty four per cent of Irish males and eight percent of Irish females achieved 420 minutes of MVPA per week. When this criterion is changed to 300 minutes per week, 66.7% of males and 33.9% of females were found to be sufficiently active to meet threshold. As previously outlined, the shorter 5 second epochs used in the current study, results in a more sensitive measure of time in moderate and vigorous activity compared to studies using longer epochs. This makes comparisons between studies difficult. Furthermore, the current study is based on a weekly accumulation of time in moderate and vigorous activity whereas other research has applied reaching a threshold on separate days. For example,

302

Discussion

in the NHANES study, Troiano et al . (2008) used the recommendation of 60 minutes of MVPA on five separate days of the week. Among 12 -15 year old adolescents, only 11.9% of males and 3.4% of females achieved the threshold of 60 minutes of MVPA on five days of the week. Among 16-19 year old adolescents, 10% of males and 5.4% of females aged achieved this recommendation for activity (Troiano et al ., 2008).

A recent Irish self report study has found that 15% of Irish adolescent males and 9% of Irish adolescent females were found to achieve the recommended 60 minutes of MVPA per day (Woods et al ., 2010). This is somewhat lower than levels of adherence among males in the current study; it may be that due to the sensitive nature of data collection in the current study short episodes of moderate and vigorous activity e.g. walking up the stairs are recorded; these would be difficult to record using self report measures.

European researchers which have used lower cut points to define MVPA have reported considerably higher levels of adherence to the recommended levels of MVPA. Riddoch and colleagues report 82% European of adolescent males achieve the guidelines, with 62% of females reaching the threshold (MVPA cut points >1,500 counts per minute) (Riddoch et al., 2004). The HELENA study reports that 56.8% of males and 27.5% of females achieved this threshold (MVPA cut points >2,000 counts per minute) (Ruiz et al ., 2011) . In English children aged 11 years, Riddoch et al., (2007) reports that only 5.1% of males and 0.4% of females achieved the guideleines. This study however, used relatively higher cut off points to calculate MVPA (3,600 counts per minute).

Difficulties also exist in comparing the bouts of activities from relevant research. The current study reports that 8.3% of males and 1.6% of females achieved the recommended 420 minutes of MVPA per week in bouts. The NHANES set the recommendation criteria as achieving 30 minutes MVPA in bouts on five separate days. In this study only 7.1% of 16-19 year old males and 4.1% of 16-19 year old females achieved this threshold (Troiano et al., 2008). The number of younger adolescents achieving this threshold is not presented by Troiano and colleagues, (2008). Due to the methodological differences outlined these results are not directly comparable, and identify the importance of the development of a universally accepted protocol for accelerometer analysis. 303

Discussion

A positive finding from the current study is that most Irish adolescents are at least moderately active, with 91% of males and 77% of females accumulating 180 minutes of moderate and vigorous activity per week. While not directly comparable, this is higher than reports by Woods et al. (2010) who found that 65% of adolescents achieve the 60 minutes moderate and vigorous criteria on three seperate days. In conjunction with this, less than 5% of participants reported being in the pre contemplation stage of change (Chapter 3, Chapter 5) , therefore, it appears that most Irish adolescents engage in some level of activity and have a positive outlook relating to activity which should lead to a receptive audience for effective interventions.

8.2.5 Sedentary Inactivity and Health Implications

Irish adolescents were found to have similar levels of sedentary inactivity compared to European adolescents. In the current study Irish adolescent males were found to accumulate 8.6 ± 1.3 hours per day in sedentary inactivity. Irish females were found to be sedentary for 9.7 ± 1.4 hours per day. The HELENA study reports that European adolescent males engaged in 9.0 (8.0 – 9.5) hours per day, while adolescent females accumulated 9.1 (8.4 – 9.8) hours per day. Sedentary time was also found to differ across European countries in this study, with Southern countries reporting higher levels of sedentary inactivity (Ruiz et al ., 2011).

The findings from the current study are in agreement with Woods and colleagues (2010), who found that in addition to the 6 hours spent sitting in school Irish adolescents report a large amount of time in sedentary leisure time activities (females 4.5 ± 2.7; males 3.9 ± 2.6). Furthermore, 99% of the Irish children and adolescents in this study were found to exceed the recommendation for screen time inactivity (Woods et al., 2010). Participants in the current study were found to spend between 60 – 70% of their day in sedentary pursuits. Due to the multitude of adverse health outcomes associated with sedentary behaviours and due to the amount of time spent in sedentary pursuits, future public health recommendations aimed at decreasing sedentary behaviour is needed.

304

Discussion

Levels of sedentary inactivity among Irish and other European adolescents are slightly higher than those observed in the US. The NHANES study reports that US adolescent males engaged in between 7.37 – 7.91 hours per day, whereas females engaged in 7.7-8.13 hours per day (Mathews et al., 2008). Steel et al., (2010) reports that in the UK, males aged 9-10 accumulated 7.5 ± 0.9 hours while females engaged in 7.7 ± 0.9 hours.

These findings suggest that Irish adolescents participate in low levels of activity and have high levels of sedentary inactivity compared European and US counterparts. It should be noted that data for the current study was collected during the winter months in Ireland (October – May), a time with reduced number of daylight hours and in which adolescents may engage in lower levels of activity. Future large scale studies should be carried out among Irish adolescents during summer and winter months to support the findings of this research.

Participation in regular activity has been associated with economic benefits, in the UK it was estimated that physical inactivity directly cost the health service £1.06 billion (Allender et al. , 2007) and was responsible for 3.1% of morbidity and mortality. Physical activity has also been associated with various health benefits and reduction of morbidity (Strong et al. , 2005). Evidence from the current study indicates that the Irish adolescents have low levels of activity and high levels of sedentary behaviour. Therefore policy makers should view investment in effective promotion of physical activity as a wise decision, as initial success from such a low base would be achievable and result in positive long term impact on health and wealth of the nation.

8.3 Relationship between Correlate Scales and Physical Activity

Correlate scores were found to be negatively associated with age in adolescence (Gyurcsik et al. , 2006; Schaben et al. , 2006), males were found to have a more positive correlate profile than females (Wu et al. , 2003), sport participants were found to have a more positive correlate profile than non-sport participants (Toftegaard-Støckel et al. , 2010), those in higher stages of physical activity change were found to have a more positive correlate profile than those in lower stages of change (Kim, 2004; Prapavessis

305

Discussion et al. , 2004) and those who participate in higher levels of activity were found to have more positive correlate profile than those with lower levels of activity. These findings provide some construct validity for the correlate scales, therefore the effective targeting of key correlates should positively influence activity levels (Dishman et al. , 2004).

Physical activity has been found to be influenced by personal/ biological, psychological, social and environmental correlates in the current study. The overall explained variance in MVPA and VPA in the final ‘APAC’ questionnaires was between 37 - 42%. This is similar to Kelly et al. (2010) who reported an explained variance of 30 - 35%. Higher levels of explained variance has been reported by studies using self report methods, however this may be due to shared methods variance in these studies i.e. self report thoughts and feelings relating to physical activity may correspond more closely with self report measures of physical activity than objective measures of activity (Kelly et al. , 2010).

It is difficult to hypothesize what other factors account for the 60 – 65% of the variance which is currently unexplained. It may be that other important correlates exist which were not included in the current ‘APAC questionnaire’ or that correlates have as yet been uncovered in the literature. Furthermore, it may be that inaccuracies in measuring physical activity and correlates of activity may result in low levels of explained variance. An alternative hypothesis would be that factors influencing physical activity are a very individual phenomenon and that the inter relationship and influences on these factors vary from person to person. It may be necessary to use more in depth qualitative methods to assess the importance of specific correlates and interrelationships between correlates. For example despite having competence for, enjoyment in and opportunities to be active an adolescent may not be active as his/ her peers do not provide support for participation in activity, this adolescent may place importance in peer support and this may inhibit his/ her participation in activity. Other adolescents may not perceive peer support as being as important and it may not influence their activity levels. Therefore, a one size fits all approach used in the regression analysis may only be useful to identify key correlates in large scale studies. A more individual qualitative approach may be needed to increase explained variance, for example, a study which has used a qualitative approach in regression analysis has reported up to 59% explained variance in physical activity (Sallis et al. , 1999). It appears therefore, that

306

Discussion

research has been more effective at identifying key specific correlates associated with physical activity than explaining activity at the population level.

8.4 Key Correlates of Activity

8.4.1 Personal/ Biological Correlates

A number of personal and biological correlates were found to be important predictors of physical activity in this research. A one way between groups multiple analysis of variance revealed significant difference between males and females across combined correlate scales. Follow up analyses revealed that scores on 13 of the 22 potential correlates were found to differ across gender. Males were found to display a more positive correlate profile across all correlates (Chapter 5). The relationship between levels of activity gender is in agreement with previous research (Wu et al., 2003). Wu et al. (2003) also reported lower self-efficacy, lower perceived benefits and higher perceived barriers among adolescent females. Similar correlates were found to be associated with physical activity among males and females; however some differences were evident in the strength of the associations. Among both males and females the physical activity stage of change, self-efficacy and sport participation were identified as being important correlates. Among males perceived environment was identified as being an important correlate, while among females elements of peer support were identified as being key correlates (Chapter 5, Chapter 7).

Sport participants were found to have a more positive correlate profile than non- sport participants (Chapter 6) and were found to have higher levels of overall physical activity (Chapter 5), these findings are in agreement with previous research (Booth et al. , 2004; Michaud et al. , 2006; Wickel et al. , 2007). Sport participation however was found to be negatively associated with age in adolescence; this trend was most pronounced among females (Chapter 6). This provides support for the sporting hill developed by Lunn & Layte (2008) which highlights the dramatic decrease in female team sport during adolescence. Reasons for the decrease in sport participation were investigated in chapter 4. In particular, females made reference to sport becoming too

307

Discussion serious with age, it was felt that the parents, coaches and other players all became very competitive leading to adolescents not feeling comfortable participating (Grieser et al. , 2006; Eime et al. , 2010). The promotion of social recreational sport as an alternative to competitive sport may have some merit, as older adolescent’s desire decreased competition and more non competitive less structured activity (Humbert et al. , 2008). It would also be logical to promote individual health related activities among adolescents especially among those who do not participate in team sport as these activities have been found to have higher adherence rates than team based sport among adolescent girls.

No relationship was found in the current study between BMI and levels of MVPA. It should be noted that the relatively small sample size for which activity data was collected makes it difficult to generalize, however similar trends are reported in the literature. Sallis et al. (2000) reported that BMI was found to be related to activity levels in six of 21 studies (29%); similarly Biddle et al . (2005) reports a relationship between activity and BMI in two of eight studies, these effects were found to be small. A number of hypotheses could be provided to explain this indeterminate relationship. It may be hypothesized that BMI is not a sufficiently accurate measure of overweight and obesity and that other measures such as skin fold measurements should be also be used which would illuminate a stronger relationship. An alternative hypothesis is that physical activity is just one factor in an ‘obesogenic environment’ impacting on adolescents’ lives and that other factors such as screen time inactivity, genetic factors and diet should also be analyzed.

8.4.2 Psychological Correlates

Perceived competence was found to strongly influence physical activity, in particular participation in skill related activities. In Chapter 3 perceived competence levels was found to be related to physical activity stage of change, with those in the action and maintenance stages reporting higher perceived competence than those in the earlier stages of change (Stein et al. , 2007; Kahn et al. , 2008). Perceived competence was also found to be related to sport participation in logistic regression and analysis of variance (Chapter 6) and to be correlated with levels of activity (Chapter 5). The

308

Discussion

importance of perceived competence for participation in skilled related activity was also highlighted in the qualitative research in Chapter 4. This research found that in particular females perceived themselves as having poor competence levels which manifested in feeling uncomfortable and self conscious performing skill related activities in front of peers. This often resulted in females gravitating away from skill related activities to more individual activities performed for extrinsic health benefits, which is in agreement with Harter’s Theory of Competence (Harter, 1982) and previous research (Hohepa et al. , 2006; Humbert et al. , 2008). Therefore, promotional strategies should provide opportunities for adolescents to engage in activities they feel comfortable and competent participating in.

Self-efficacy and intention to be active which is rooted in efficacy as it assesses confidence in ability to be active, were found to be important predictors of physical activity. Scores on the self-efficacy scale were found to be higher among those in the later stages of physical activity change, while intention to be active was found to significantly predict stage of change in the regression analysis (Chapter 3). Previous research has also found self-efficacy to effectively differentiate individuals at different physical activity stages of change (Kim, 2004; Berry et al. , 2005). In Chapter 6 multiple analyses of variance found that sport participants had higher self-efficacy than non-sport participants and intention to be active was found to be significantly higher among sport participants compared to non-participants in the regression analysis. Chapter 5 found that self-efficacy was found to be correlated with males and females physical activity levels, while intention to be active was a predictor of male MVPA and VPA in the regression analysis. This is in agreement with previous research which has also identified self-efficacy as an important correlate of physical activity (Wu et al. , 2003; Lytle et al. , 2009). Dishman et al. (2004) found that targeting self-efficacy in an intervention can positively influences efficacy and levels of activity, these results encourage the use of self –efficacy as a targeted correlate in future interventions designed to increase physical activity among adolescents.

Enjoyment of physical activity was found to be correlated with levels of enjoyment. Enjoyment scores were found to discriminate across stage of change with those in lower physical activity stages of change displaying lower levels of enjoyment (Chapter 3). Fun and enjoyment were identified as an important reason for participation

309

Discussion in physical activity in the focus group interviews (Chapter 4). In Chapter 5, physical activity enjoyment was found to correlate with objective measures of male MVPA and VPA and female VPA. Similarly, in chapter 6 sport participants were found to report significantly higher levels of exercise enjoyment than non sport participants.

Enjoyment of physical education was not found to be directly correlated with levels of activity; however the current structure of physical education was identified by participants in the qualitative study as negatively influencing attitudes towards physical activity. In particular females identified feeling self conscious performing skilled activities in front of peers and made specific references to males dominating PE class as negatively influencing their participation. Previous research has identified the possible benefits of single sex, non competitive PE classes (Allender et al. , 2006; Eime et al. , 2010), findings from the current research are in agreement with this. In conjunction with this, a number of participants expressed a desire to have an input into the activities participating in during PE class. As adolescence is a time when independence is sought (De Roiste & Dinneen, 2005) empowering adolescents to have an input may have a positive impact on participation levels and attitudes towards physical activity and PE (Rees et al. , 2006).

8.4.3 Social Correlates

Peer support was found to be an important influence on participation in physical activity in the current research; this is in agreement with previous research which has also identified peer support as being a salient influence among adolescents (Hohepa et al. , 2006; King et al. , 2008). In the TAAG study peer support was found to be only variable which was consistently related to objectively measured physical activity across different ethnic groups of adolescent females (Kelly et al. , 2010). Developmental theory believes that peer influence increases during adolescence as parental influence decreases (De Roiste & Dinneen, 2005), however the current study found peer influence to be an important influence on physical activity throughout adolescence (Chapter 3). Peer support was also found to be associated with physical activity stage of change (Chapter 3) and sport participation (Chapter 6), with participants in higher stages of change and sport participants reporting higher levels of peer support. Furthermore, in chapter 5,

310

Discussion

MANOVA and Tukey post hoc analysis revealed that participants in the lowest group of MVPA were found to have lower perceived peer support than those in the most active groups. Peer support was found to manifest through co-participation, verbal encouragement, logistic support (Chapter 4) which is in agreement with previous research (Coleman et al. , 2008; Eime et al. , 2010). Due to the influence exerted by peers on participation in physical activity future interventions should target adolescents to join new activities and to be active with peers.

Parental support was also identified as being an important correlate of adolescent physical activity; this was particularly evident in support for organized sport. Male and female sport participants were found to report higher levels of parental support than non-participants in the analysis of variance (Chapter 6). These findings are in agreement with previous research (Dollman & Lewis, 2010). An interesting difference emerged in the type of parental support provided to male and females; males identified receiving advice on skill acquisition and development and received support through transportation and attending events. Females were more likely to receive support through co-participation e.g. walking with parents and verbal encouragement to be active (Chapter 4).

8.4.4 Environmental Correlates

The relationship between environmental correlates and physical activity is not as clear as other categories of correlates. This may be partly explained by the difficulties associated with measuring these correlates of activity as outlined in section 2.6.5. Those involved in sport participation were found to perceive a more positive environmental correlate profile than non-sport participants, however this was not found to be as important as other categories of correlates (Chapter 6). Among males perceived environment was found to be a significant predictor of activity in the regression analyses (Chapter 5 and Chapter 7).

Environmental correlates were not found to be as important as inter personal and intrapersonal influences on physical activity (Chapter 3 and 4) this is in agreement with other research (Sallis et al. , 1999; Schaben et al. , 2006). It should be noted however,

311

Discussion that the environmental correlates measured access to, opportunities for and suitability of the local environment for activity. These constructs are primarily factual in nature therefore, it is not unexpected that differences were not found across gender and age. Further support for this hypothesis is provided in the factor analysis, environmental correlates were found to have the lower explained variance than social and psychological categories of correlates (Chapter 7) and environmental correlates were also found to have lower correlations with physical activity than other correlates. It may be hypothesized therefore, that interpersonal and intra personal factors develop a desire for activity, environmental influences can assist or act as barrier to this activity. If the desire to be active does not exist, perceived environment and access to factilities are unimportant (Chapter 4). Future research should further explore the environmental influence and attempt to develop scales which investigate the interaction between the individual and the environment.

The limited association between environmental correlates and levels of activity may be due to methodological limitations of the correlates measured. Firstly, there is a dearth of literature which has studied the environmental correlates of activity (Sallis et al. , 2000; Biddle et al. , 2005; Davison & Lawson, 2006; Van der Horst et al. , 2007). This resulted in fewer environmental correlates being identified in the literature. The recently developed ‘Neighbourhood Environment Walkability Scale for Youth’ (NEWS – Y) has been found to be reliable with adolescents. This scale has been developed for adolescents after the initial ‘APAC questionnaire’ had been developed. This provides a comprehensive measure of environmental correlates assessing perceived land use mix – diversity, recreation facility availability, pedestrian / automobile traffic safety, crime safety, aesthetics, walking cycling facilities, street connectivity, land use mix-access and residential density (Rosenberg et al. , 2009). Some evidence also exists which have found this scale to be a valid measure of adolescent environmental influences (Adams et al. , 2009). Secondly, the ability to measure characteristics of the physical environment is greatly facilitated by the use of Geographic information Systems (GIS). Davison & Lawson (2006) believe that participants perception of their environment should also be measured in conjunction with GIS measurements, as people’s perceptions may in fact motivate their behaviour more than the true nature of the situation (Davison & Lawson, 2006). A further limitation of the current research is the lack of an objective measure of

312

Discussion

the structural environmental influences on physical activity. The researcher did not have access to this technology in the current study, however it is hoped that a future study will gather this data for areas included in the current research. Thirdly, the current study does not include a measure of active transportation. Evidence exists which identifies those who actively commute to school as being more habitually physically active (Cooper et al. , 2003; Lee et al. , 2008). The Irish CSPPA study reports that 40% of Irish adolescents actively commute to school, journey durations in this study were found to be on average 15 minutes (Woods et al. , 2010). Therefore, adolescent who commute to and from school may accumulate a significant portion of the recommended levels of physical activity. Future research should include measures of active transportation.

8.5 ‘APAC’ Questionnaire

Correlates of physical activity have been found to differ among male and female adolescents (Tergerson & King, 2002; Wu et al. , 2003). The current study also reports differences in male and female correlate profiles (Chapter 4 & 5), therefore separate male and female ‘APAC’ questionnaires were developed.

The final ‘APAC’ questionnaire was found to have acceptable levels of reliability with Cronbach alpha of each factor exceeding the recommended .7 level (Tabachnik & Fidell, 2007). Further support for the internal consistency is provided as inter item total correlations for each item exceeded the recommended .3 level (Nunnally & Bernstein, 1994). Test retest reliability was assessed in chapter 3 and intra class correlations were found to be acceptable (range 0.71 – 0.94).

The ‘APAC’ questionnaire was also found to have evidence of validity. The clean factor structure (i.e. items loading above .4, few cross loading items) (Field, 2005) provides support for the construct validity of the questionnaires. In conjunction, the explained variance in the factor analysis was found to range from 47% in male environmental factors to 72.6% in male social influences. This is found to be relatively high compared to a review of factor analysis studies in educational research which found that almost 30% of research studies explain less than 30% of the variance

313

Discussion

(Henson et al. , 2004). Correlations between objectively measured physical activity and factor scores provide support for the convergent validity of the questionnaires. Furthermore, hierarchical regression analysis using factor scores and objective measures of activity explains more of the variance than previous research (Kelly et al. , 2010), further supporting the validity of the questionnaire.

Therefore, the ‘APAC’ questionnaires are valid measures and should be used in future studies investigating the correlates of activity among Irish adolescents.

8.6 Limitations

• Despite the development of a rigorous data collection and data analysis criterion for use with accelerometers, the relatively small sample size (n = 303) makes it difficult to make definite conclusions. • Despite collecting physical activity data using triaxial accelerometers, the data was analyzed uniaxially as no algorithm currently exists to facilitate triaxial analysis. It is envisaged that if and when this algorithm is developed the data will be analyzed triaxially. Previous research has identified little difference in data analyzed unixially and triaxially (Vanhelst et al. , 2011), however it is still identified as a limitation in the current study. • Chapter 6 investigated the correlates of activity between sport participants and non-sport participants. An elite sport s participants groups was used as a reference group, this elite sports group were GAA elite development squads. At the time of data collection no female equivalent group existed. The inclusion of an elite female group would permit a detailed comparison of correlates between mainstream and elite sports adolescents. • All participants (excluding elite reference sample) in chapters 3 – 7 were from Cork a region in southern Ireland. It should be noted however, that similar results have been reported in correlates of activity (De Roiste & Dinneen, 2005) and levels of activity in other Irish studies with greater geographical spread (Dowd et al. , 2010; Woods et al. , 2010). • Limitations existed in the assessment of the environmental correlates in the ‘APAC questionnaire’. Firstly, no objective measure of the built environment was included in the current study. Secondly, limited environmental correlates

314

Discussion

were included in the current research. The recently developed ‘Neighbourhood Environment Walkablility Scale’ (NEWS-Y) (Rosenberg et al. , 2009) would provide a more comprehensive measure of potential correlates influencing adolescent activity. A third limitation of the environmental correlates was the lack inclusion of a measure of active transportation.

315

Discussion

316

Chapter 9

Conclusions and Recommendations for Future Research

9.1 Conclusion

The male and female ‘APAC’ questionnaires developed in the current study were found to be reliable, to have factorial validity, and to have convergent validity using objective measures of physical activity. These questionnaires should be used in future studies investigating correlates of physical activity among adolescents, as they have evolved from a comprehensive range of correlates.

Knowledge of the key correlates of activity is important to stem age related decline in adolescent physical activity and to promote levels of activity. Key correlates identified among males and females in the current research include, physical activity stage of change, self-efficacy, family support and sport participation. Perceived environment was an important correlate among males, elements of peer support were important for females. The effective targeting of these factors in an environment which promotes activity may be effective strategy in the promoting physical activity. The final ‘APAC’ questionnaires were found to explain between 37 – 42% of the variance in MVPA and VPA among males and females in the regression analysis.

Levels of activity among Irish adolescents appear relatively low compared to international research, with low levels of Irish adolescents achieving the recommended

317

Conclusions and Recommendations levels of activity. Due to the long term economic and health benefits associated with physical activity the development and implementation of physical activity promotional interventions among Irish adolescents is required.

9.2 Recommendations for Future Research

• Researchers in Ireland should collaborate together to develop an accepted protocol for accelerometer data collection and analysis. This protocol should be used in the studies used to measure physical activity among Irish adolescents. • Researchers in Ireland should work together to gather national accelerometer physical activity data. This data should be collected periodically to develop a national database of activity levels among children and adolescents. Physical activity data from regional studies such as this study should be pooled with other regional studies in Ireland and re analysed to provide large scale data on physical activity levels. • The ‘APAC’ questionnaires should be used in conjunction with objective measures of activity to monitor of levels and correlates of activity among Irish adolescents. This would establish a baseline measure of the correlate profiles of Irish adolescents, changes to these profiles would then be evident in subsequent studies. • Intervention studies targeting correlates of activity should use the ‘APAC’ questionnaires to assess pre and post correlate profiles among adolescents. • The reliability and the validity of the ‘APAC’ questionnaires should be investigated with an international group of adolescents to further support the psychometric properties of these tools. • The influence of overweight and obesity on physical activity appears unclear, future research should use skinfold measurement in conjunction with BMI to further explore this relationship. • Physical activity and sedentary pursuits are related yet distinct behaviours, future research should investigate influences on sedentary behaviour. Reduction in time spent in sedentary pursuits may be an effective way to increase activity among adolescents.

318

Conclusions and Recommendations

• The physical activity stage of change measure provides a brief cost effective method of identifying readiness to change among adolescents. PE teachers should monitor the physical activity stage of change scores in students and should attempt to provide appropriate stage matched strategies to promote activity among students. • The efficacy of single sex non competitive PE classes in which participants have an input into the activities that are covered should be investigated in future research. • A school and community based physical activity promotion intervention targeting the key correlates identified in this research should be developed, implemented and evaluated. • Sporting organizations should aim to develop social and recreational sport emphasizing participation as opposed to competition. • Future interventions should promote joining and maintaining activity with peers due to the major influence peer support was found to exert. • Measures of environmental correlates of activity should be further developed using objective Geographic Information Systems (GIS) and more comprehensive subjective measures such as the recently developed ‘Neighbourhood Environment Walkability Scale for Youth’ (Rosenberg et al. , 2009).

319

References

Abbott, R. D., Rodriguez, B. L., Burchfiel, C. M. & Curb, J. D. (1994). Physical activity in older middle-aged men and reduced risk of stroke: the Honolulu Heart Program. Am J Epidemiol, 139(9), 881-893.

Adams, M. A., Ryan, S., Kerr, J., Sallis, J., Patrick, K., Frank, L. & Norman, G. J. (2009). Validation of the Neighborhood Environment Walkablity Scale (NEWS) items using geographic information systems. J Phys Act Health, 6(Suppl 1), 113-123.

Australian Dept. of Health and Ageing, Get out and get active: Australia's Physical Activity Recommendations for 12 - 18 year Olds (2004).

Ainsworth, B., E. (2000). Challenges in measuring physical activity in women. Exerc Sport Sci Rev, 28, 93-96.

Ajzen, I. (1991). The Theory of Planned Behavior. Org Beh Hum Dec, 50 179-211.

Ajzen, I. & Fishbein, M. (1980). Understanding attitudes and predicting social behavior . Englewood Cliifs, New Jersey: Prentice Hall.

Allender, S., Cowburn, G. & Foster, C. (2006). Understanding participation in sport and physical activity among children and adults: a review of qualitative studies. Health Educ Res, 21(6), 826 - 835.

321

References

Allender, S., Foster, C., Scarborought, P. & Rayner, M. (2007). The burden of physical activity - related ill health in the UK. J Epidemiol Comm Health, 61(4), 344- 348.

American Heart Association (2004), Exercise (Physical Activity) and Children - AHA Scientific Position, Retrieved June 14th 2004, from http://www.americanheart.org/presenter.jhtml?identifier=4596 .

Anderson, C. B., Hagstromer, M. & Whitt, M. C. e. a. (2005). Validation of the PDPAR as an adolescent diary: effect of accelerometer cut points. Med Sci Sport Exer, 37, 1224-1230.

Armitage, C. J. & Conner, M. (2001). Efficacy of the Theory of Planned Behaviour: A meta analytic review. Br J Soc Psychol, 40, 471-499.

Armstrong, N. & Bray, S. (1991). Physical activity patterns defined by continuous heart rate monitoring. Arch Dis Child, 66, 245-247.

Armstrong, N. & Simons-Morton, B. G. (1994). Physical Activity and blood lipids in adolescents Pediatr Exerc Sci, 6, 381-405.

Bailey, R. C., Olsen, S. L., Pepper, J., Porszasz, J., Bartow, T. J. & Cooper, D. M. (1995). The level and tempo of children’s physical activities: an observational study. Med Sci Sports Exerc, 27, 1033-1041.

Bailey, R. C., Olson, J. & Pepper, S. L. (1995). The level and tempo of children's physical activities: an observational study. Med Sci Sport Exer, 27, 1033-1041.

322

References

Bandura, A. (1977). Social Learning Theory . Englewood Cliffs: New Jersey: Prentice Hall.

Bandura, A. (1986). Social Foundations of Thought and Action. A Social Cognitive Theory . Englewood Cliffs, NJ: Prentice Hall.

Bandura, A. (1994). Self efficacy. In Ramachaudran, V. S., Encyclopedia of human behaviour . New York: Academic Press. 4 , 71-81.

Bandura, A. (1997). Self Efficacy: The exercise of control . New York: Freeman.

Baquet, G., Stratton, G., Van Praagh, E. & Berthoin, S. (2007). Improving physical activity assessment in prepubertal children with high-frequency accelerometry monitoring: A methodological issue. Prev Med, 44, 143-147.

Bar Or, O. & Baranowski, T. (1994). Physical activity, adiposity, and obesity among adolescents. Pediatr Exerc Sci, 6, 348-360.

Bar Or, O. & Baranowski, T. (1996). Physical activity adiposity and obesity among adolescents. Pediatr Exerc Sci, 6, 348-360.

Baranowski, T., Anderson, C. & Carmack, C. (1998). Mediating variable framework in physical activity interventions. How are we doing? How might we do better? Am J Prev Med, 15, 266-297.

Baranowski, T., Cullen, K. & Baranowski, J. (1999). Psychosocial correlates of dietary intake. Am Rev Nutr, 19, 17-40.

323

References

Baranowski, T., Cullen, K., Nicklas, T., Thompson, D. & Baranowski, J. (2003). Are Currrent Health Behavioural Change Models Helpful in Guiding Prevention of Weight Gain Efforts? Obes Res, 11, 23S-43S.

Baranowski, T., Lin, L. & Wetter, D. (1997). Theory as mediating variables: Why aren't community intervention trials working as desired? Ann Epidemiol, S7, S89-95.

Barr-Anderson, D. J., Young, D. R., Sallis, J. F., Neumark-Sztainer, D. R., Gittelsohn, J., Webber, L., Saunders, R., Cohen, S. & Jobe, J. B. (2007). Structured physical activity and psychosocial correlates in middle-school girls. Prev Med, 44(5), 404-409.

Bauman, A. E. & Bull, F. C. (2007). Environmental Correlates of Physical Activity and Walking in Adults and Children: A Review of Reviews, London: Review undertaken for National Institute of Health and Clinical Excellence.

Bauman, A. E., Sallis, J. F., Dzewaltowski, D. A. & Owen, N. (2002). Toward a better understanding of the influences on physical activity: The role of determinants, correlates, causal variables, mediators, moderators, and confounders. Am. J. Prev. Med., 23(2S), 5-14.

Bellizzi, M. C. & Dietz, W. H. (1999). Workshop on childhood onesity: summary of the discussion. ASCN, 70, 173-5.

Berry, T., Naylor, P. J. & Wharf-Higgins, J. (2005). Stages of change in adolescents: an examination of self efficacy, decisional balance adn reasons for relapse. J Adolesc Health, 37, 452-459.

324

References

Beunen, G. P., Malina, R. M., Renson, R., Simons, J., Ostyn, M. & Lefevre, F. (1992). Physical activity and growth maturation and performance. A longitudinal study. Med Sci Sport Exer, 24, 576-585.

Biddle, S. & Nigg, C. R. (2000). Theories of exercise behavior. Int J Sport Psychol, 31(2), 290-304.

Biddle, S., Treasure, D. C. & Wang, J. (2008). Motivational Characteristics. In Smith, A. L. &Biddle, S., Youth Physical Activity and Sedentary Behaviour: Challenges and Solutions . Champaign, IL: Human Kinetcs.

Biddle, S. J. H., Gorely, T., Marshall, S. J., Murdley, I. & Cameron, N. (2004). Phyiscal activity and sedentary behaviours in youth: issues and controversies. . J R Soc Promot Health, 124(1), 29-33.

Biddle, S. J. H., Gorely, T. & Stensel, D. J. (2004). Health enhancing physical activity and sedentary behaviour in children and adolescents. J Sport Sci, 22, 679-701.

Biddle, S. J. H., Whitehead, S. H., O'Donovan, T. M. & Nevill, M. E. (2005). Correlates of Participation in Physical Activity for Adolescent Girls: A Systematic Review of Recent Literature. J Phys Act Health, 2(4), 423.

Birnbaum, A. S., Evenson, K. R., Motl, R. W., Voorhees, C. C., Sallis, J. F., Elder, J. P. & Dowda, M. (2005). Scale development for perceived school climate for girls' physical activity. Am J Health Behav, 29(3), 250-7.

Blair, S. N. (1989). Exercise and fitness in childhood: implications for a lifetime of health’. In Blair, S. N., Clark, D. G., Cureton, K. J. &Powell, K. E., Perspectives in Exercise Science and Sport Medicine . New York: : McGraw – Hill.

325

References

Blair, S. N., Clark, D. G. & Cureton, K. J. (1989). Perspectives in Exercise Science and Sport Medicine . New York: McGraw – Hill. .

Bock, B. C., Marcus, B. H., Pinto, B. & Forsyth, L. H. (2001). Maintenance of physical actiivty following an individualised motivationally tailored intervention. Ann Behav Med, 23, 79-87.

Boivin, M., Vitaro, F. & Gagnon, C. (1992). A Reassessment of the Self - Perception Profile for Children: Factor Structure, Reliability, and Convergent Validity of a French Version among Second through Sixth Grade Children. Int J Behav Dev, 15(2), 273-290.

Booth, M. L., Okely, A. D., Chey, T. & Bauman, A. E. (2004). Patterns of Activity Energy Expenditure Among Australian Adolescents. J. Phys. Act. Health., 1(3), 246-258.

Booth, M. L., Okely, A. D., Chey, T. & Bauman, A. E. (2004). Patterns of Activity Energy Expenditure Among Australian Adolescents. J Phys Act Health, 1(3), 246-258.

Bouchard, C., Blair, S. & Haskell, W. (2007). Physical Activity and Health . Champaign, IL: Human Kinetics.

Bouten, C. V., Sauren, A. A., Verduin, M. & Janssen, J. D. (1997). Effects of placement and orientation of body fixed accelerometers on the assessment of energy expenditure during walking. Med. Biol. Eng. Comput., 35, 50-56.

326

References

Bratteby, L. E., Sandhagen, B., Enghardt, H. & Samuelson, G. (1998). Total energy expenditure and physical activity as assessed by the doubly labeled water method in Swedish adolescents in whom energy intake was underestimated by 7-d diet records. Am J Clin Nutr, 67, 906-911.

Braveman, P. A., Cubbin, C., Egerter, S., Chideya, S., Marchi, K. S., Metzler, M. & Posner, S. (2005). Socioeconomic Status in Health Research: One size does not fit all. JAMA, 294, 2879-2888.

Bricker, S. K., Kanny, D. & Mellinger Birdsong, A. (2002). School Transportation Modes ------Georgia 2000. MMWR Weekly, 351(32), 704-705.

Brodersen, N. H., Steptoe, A. S., Boniface, D. R. & Wardle, J. (2007). Trends in physical activity and sedentary behaviour in adolescence: ethnic and socioeconomic differences. Br J Sports Med, 41, 140-144.

Brunton, G., Harden, A., Rees, R., Kavanagh, J., Oliver, S. & Oakley, A. (2003). Children and Physical Activity: A systematic review of barriers and facilitators – Executive Summary , London: London: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London.

Brustad, R. J. (1993). Who will go out and play? Parental and psychological influences on children's attraction to physical activity. Pediatr Exerc Sci, 55, 210-223.

Burns, C. (2004), Influence of a School - Based Intervention on Physical Activity, Sedentary Behaviour and Weight Gain in 9-11 Year Old Children, Unpublished theses, MA, Waterford, Waterford Insititute of Technology.

327

References

Burns, C., McNamara, A. & MacDonncha, C. (2011), Perceptions of adolescent males and females on barriers and facilitators to physical activity, Unpublished theses, Limerick, University of Limerick.

Burns, C., Murphy, J. & MacDonncha, C. (2011a), Correlates of Physical Activity among Adoelscent Females: Influence of Year in School and Physical Activity Stage of Change, Unpublished theses, Limerick, University of Limerick.

Burns, C., Murphy, J., Vaughan, J. & MacDonncha, C. (2011), Correlates of physical activity among Irish adolecsents, Unpublished theses, Limerick, University of Limerick.

Burrows, C., Eves, F. & Cooper, D. M. (1999). Children's perceptions of exercise - are children mini - adults? Health Education, 99(2), 61-69.

Butcher, K., Sallis, J., Mayer, J. A. & Woodruff, S. (2008). Correlates of Physical Activity Guideline Compliance for Adolescents in 100 U.S. Cities. J Adolesc Health, 42(4), 360-368.

Calfas, K. J., Sallis, J. F., Lovato, C. Y. & Campbell, J. (1994). Physical activity and its determinants before and after college graduation. Med Exerc Nutr Health, 3, 323-334.

Campbell, K., Waters, E., O'Meara, S., Kelly, S. & Summerbell, S. (2002). Interventions for preventing obesity in children. Cochrane Database Syst Rev., 2, CD001871. doi:10.1002/14651858.CD001871.

328

References

Catellier, D. J., Hannan, P. J., Murray, D. M., Addy, C. L., Conway, T. L., Yang, S. & Rice, J. C. (2005). Imputation of Missing Data When Measuring Physical Activity by Accelerometry. Med Sci Sport Exer, 37(11 Suppl), S555-S562.

Cattell, R. B. (1966). The scree test for the number of factors. Multivar Behav Res, 1, 245-276.

Cattell, R. B. (1978). The Scientific Use of Factor Analysis . New York: Plenum.

Cavadinin, C., Siega Riz, A. M. & Popkin, B. (2000). US adolescent food intake trends from 1965-1996. Arch Dis Child, 83, 18-24.

Cavill, N., Biddle, S. & Sallis, J. (2001). Health enhancing physical activity for young people: Statement of the United Kingdom Expert Consensus Conference. Pediatr Exerc Sci, 13, 12-25.

CDC, Centre for Disease Control. (2000). Youth risk behaviour surveillance- United States (YRBSS) 1999: Morbidity and Mortality Weekly Report. 49 : 1-94.

Central Statistics Office (2008), Surveys and Methodologies : Classifications online Retrieved August 15, 2008, from http://www.cso.ie/surveysandmethodologies/classifications_faq.htm .

Chen, K. Y. & Bassett, D. R. J. (2005). The Technology of Accelerometry-Based Activity Monitors: Current and Future. Med Sci Sport Exer, 37(11), S490-S500.

Coakley, J. & White, A. (1992). Making decisions: gender and sport participation among British adolescents. Sociol. Sport J, 9(1), 20-35.

329

References

Cohen, D. A., Scribner, R. A. & Farley, T. A. (2000). A Structural Model of Health Behavior: A Pragmatic Approach to Explain and Influence Health Behaviors at the Population Level. Preventive Medicine, 30(2), 146-154.

Cohen, S. (1977). Statistical power analysis for the behavioural sciences . New York: New York Academic.

Cole, T. J., Bellizzi, M. C., Flegal, K. M. & Dietz, W. H. (2000). Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ., 320(7244), 1240.

Coleman, L., Cox, L. & Roker, D. (2008). Girls and young women's participation in physical activity: psychological and social influences. Health Educ Res, 23(4), 633-647.

Comrey, A. L. & Lee, H. B. (1992). A first course in factor analysis (2nd ed.) . Hillsdale, NJ: Lawrence Erlbaum Associates.

Connor, S. (2003). The sporting leisure and lifestyle patterns of Irish adolescents . Dublin: The Liffey Press.

Cooper, A., C., Page, A., S. & Foster, L., J. (2003). Commuting to school: Are children who walk more physically active? Am J Prev Med, 25(4), 273-276.

Costello, A. B. & Osbourne, J. W. (2005). Best Practices in Exploratory Factor Analysis: Four Recommendations for Getting the Most From Your Analysis. Practical Assessment, Research & Evaluation, 10(7), 1-9.

330

References

Côté, J., Salmela, J. H. & Baria, A. (1993). Organising and interpreting unstructured qualitative data. The Sport Psychologist, 7, 127-137.

Craggs, C., Corder, K., van Sluijs, E. M. F. & Griffin, S. J. (2011). Determinants of Change in Physical Activity in Children and Adolescents: A Systematic Review. Am J Prev Med, 40(6), 645-658.

Craig, S., Bandini, L., Lichtenstein, A., Schafer, E. & Dietz, W. H. (1996). The impact of physical activity on lipids, lipoproteins, adn blood pressure in preadolescent girls. Pediatrics, 98, 389 - 395.

Crawford, D. W., Jackson, E. L. & Godbey, G. (1991). A hierarchical model of leisure constraints. Leisure Sci, 13(4), 309-320.

Crespo, C. J., Troiano, R., S.J., B., C.A., M. & R.E., A. (2001). Television watching, energy intake and obesity in US children. Arch Pediatr Adolesc Med, 155, 360- 365.

Crespo, C. J., Troiano, R. P., S.J., B., C.A., M. & R.E., A. (2001). Television watching, energy intake and obesity in US children. Arch Pediatr Adolesc Med, 155, 360- 365.

Central Statistics Office Ireland: Classifications (2008), Surveys and Methodologies : Classifications., Online Retrieved August 15, 2008, from http://www.cso.ie/surveysandmethodologies/classifications_faq.htm .

Dahlgren, G. & Whitehead, M. (1991). Policies and strategies to promote social equity in health , Stockholm: Institute for Future Studies

331

References

Davies, P., S.,W., Gregory, J. & White, A. (1995). Physical activity and body fatness in preschool children. Int J Obes, 276, 241-246.

Davison, K. K. & Lawson, C. T. (2006). Do attributes in the physical environment influence children's physical activity? Int J Behav Nutr Phys Act, 3(19), 1-17.

Davison, K. K., Li, K., Baskin, M. L., Cox, T. & Affuso, O. (2004). Measuring parental support for children's physical activity in white and African American parents: The Activity Support Scale for Multiple Groups (ACTS-MG). Prev Med, 52(1), 39-43.

De Bourdeaudhuij, I., Philippaerts, R., Crombez, G., Matton, L., Wijndaele, K., Balduck, A.-L. & Lefevre, J. (2005). Stages of change for physical activity in a community sample of adolescents. Health Education Research, 20(3), 357-366.

De Roiste, A. & Dinneen, J. (2005). Young People's Views about Opportunities Barriers and Supports to Recreation and Leisure. Research Commissioned by the National Children's Office Dublin: Dept of Health and Children.

Department of Health and Children, Health Service Executive (2009), The National Guidelines on Physical Activity for Ireland. ISBN 978-1-906218-27-0., online. Retrieved 5 July 2010, from http://www.getirelandactive.ie/pdfs/GIA_GUIDE.pdf .

Department of Education and Skills (2008), Search for a School, online. Retrieved 15 August, 2008, from http://www.education.ie/home/home.jsp?pcategory=10917&ecategory=12016&l anguage=EN .

332

References

DiLorenzo, T. M., Stucky-Ropp, R. C., Vander Wal, J. S. & Gotham, H. J. (1998). Determinants of exercise among children II: Alongitudinal analysis. Prev Med, 27, 470-477.

Dishman, R. K. & Buckworth, J. (1996). Increasing physical activity: A quantitative synthesis. Med Sci Sport Exer, 28, 706-719.

Dishman, R. K., Motl, R. W., Sallis, J. F., Dunn, A. L., Birnbaum, A. S., Welk, G. J., Bedimo-Rung, A. L., Voorhees, C. C. & Jobe, J. B. (2005). Self-Management Strategies Mediate Self-Efficacy and Physical Activity. Am. J. Prev. Med., 29(1), 10-18.

Dishman, R. K., Motl, R. W., Saunders, R., Felton, G., Ward, D. S., Dowda, M. & Pate, R. R. (2004). Self-efficacy partially mediates the effect of a school-based physical-activity intervention among adolescent girls. Prev. Med., 38(5), 628- 636.

Dishman, R. K., Motl, R. W., Saunders, R. P., Dowda, M., Felton, G., Ward, D. S. & Pate, R. R. (2002). Factorial Invariance and Latent Mean Structure of Questionnaires Measuring Social-Cognitive Determinants of Physical Activity among Black and White Adolescent Girls. Prev Med, 34(1), 100-108.

Dishman, R. K. & Sallis, J. (1994). Determinants and interventions for physical activity and exercise. In Bouchard, C., Shephard, R. J. &Thomas, J., Physical Activity, fitness, and health: International proceedings and consensus statement . Champaign, IL, England: Human Kinetics , 214 - 238.

Dishman, R. K., Washburn, R. A. & Heath, G. (2004). Physical Actiivty Epidemiology . Champaign, IL: Human Kinetics.

333

References

DiStefano, C., Zhu, M. & Mindrila, D. (2009). Understanding and Using Factor Scores: Considerations for the Applied Researcher Practical Assessment, Research & Evaluation, 14(20), 1-11.

Dollman, J. & Lewis, N. R. (2010). The impact of socioeconomic position on sport participation among South Australian youth. J Sci Med Sport, 13(3), 318-322.

Dovey, S. & Chalmers, D. J. (1998). Continuity and change in sporting leisure time physical activities during adolescence. BJ Sports Med, 32(1), 53-57.

Dowd, K., Macken, P. & Donnelly, A. (2010). The Physical Activity Research Programme for Adolescent Females , Limerick: Report by University of Limerick and County Limerick Sports Partnership.

Dowda, M., Ainsworth, B. E., Addy, C. L., Saunders, R. & Riner, W. (2001). Physical Activity and Weight Status in 8 to 16 Year Olds. Arch Pediatr Adolesc Med, 155, 711-717.

Dowda, M., McKenzie, T. L., Cohen, D. A., Scott, M. M., Evenson, K. R., Bedimo- Rung, A. L., Voorhees, C. C. & Almeida, M. J. (2007). Commercial venues as supports for physical activity in adolescent girls. Prev Med, 45(2), 163-168.

Duda, J. L. (1989). The relationship between task and ego orientation and the perceived purpose of sport among male and female high school athletes. J Sport and Ex Psych, 11, 318-335.

Duke, J., Huhman, M. & Heitzler, C. (2003). Physical Activity Levels Among Children Aged 13 years—United States, 2002. MMWR Weekly, 52(33), 785-788.

334

References

Dumith, S. C., Domingues, M. R., Gigante, D. P., Hallal, P. C., Menezes, A. M. B. & Kohl, H. W. (2010). Prevalence and correlates of physical activity among adolescents from Southern Brazil. Rev Saude Publica, 44, 457-467.

Dunn, J. G. H., Bouffard, M. & Rogers, W. T. (1999). Assessing item content relevance in sport psychology scale construction research: Issues and recommendations. Meas Phys Educ Exerc Sci, 3, 15-36.

Dunton, G. F., Jamner, M. S. & Cooper, D. M. (2003). Assessing the perceived environment among minimally active girls; validity and relations to physical activity outcomes. Am J Health Promot, 18, 70-73.

Ebbeling, C., B., Pawlak, D. B. & Ludwig, D. S. (2002). Childhood Obesity: public- health crisis, common sense cure. Lancet, 360, 473-482.

Eiberg, S., Hasselstrom, V. & Gronfeldt, V. e. a. (2005). Maximum oxygen uptake and objectively measured physical activity in Danish children 6-7 years of age: the Copenhagen school children intervention study. Br J Sports Med, 39, 725-730.

Eime, R. M., Payne, W. R., Casey, M. M. & Harvey, J. T. (2010). Transition in participation in sport and unstructured physical activity for rural living adolescent girls. Health Educ Res, 25(2), 282-293.

Eisenmann, E. E. (2004). Physical activity and cardiovascular disease risk factors in children and adolescents: an overview. Canadian Journal of Cardiology, 20, 295-301.

335

References

Eisenmann, J. C., Katzmarzyk, P. T. & Tremblay, M. S. (2004). Leisure- Time Physical Activity Levels Among Canadian Adolescents 1981-1998. J Phys Act Health, 1, 154-162.

Ekelund, U., Aman, J. & Westerterp, K. R. (2003). Is the ARTEACC index a valid indicator of free living physical activity in adolescents? Obes Res, 11, 793-801.

Ekelund, U., Aman, J., Yhgve, A., Renman, C., Westertop, K. & Sjostrom, M. (2002). Physical activity but not energy expenditure is reduced in obese adolescents.A case control study. Am J Clin Nutr, 76, 935-941.

Ekelund, U., Sardinha, L., Anderssen, S., Harro, M., Franks, P., Brage, S., Cooper, A., Andersen, L., Riddoch, C. & Froberg, K. (2004). Associations between objectively assessed physical activity and indicators of body fatness in 9- to 10- y-old European children: A population-based study from 4 distinct regions in Europe (the European youth heart study). Am J Clin Nutr, 80(3), 584-590.

Ekelund, U., Sjostrom, M., Yngve, A., Poortvliet, E., Nilsson, A., Froberg, K., Wedderkopp, N. & Westerterp, K. (2001). Physical activity assessed by activity monitor and doubly labeled water in children. Med Sci Sport Exer, 33(2), 275- 281.

Elder, J. P., Lytle, L., Sallis, J. F., Young, D. R., Steckler, A., Simons-Morton, D., Stone, E., Jobe, J. B., Stevens, J., Lohman, T., Webber, L., Pate, R., Saksvig, B. I. & Ribisl, K. (2007). A description of the social-ecological framework used in the trial of activity for adolescent girls (TAAG). Health. Educ. Res., 22(2), 155- 165.

Epstein, L. H. & Roemmich, J. N. (2001). Reducing sedentary behaviour: role in modifying physical activity. Exerc Sport Sci Rev, 29(3), 103-108.

336

References

Eston, R. G., Rowlands, A. V. & Ingledew, D. K. (1998). Validity of heart rate, pedometry, and accelerometry for predicting the energy cost of children's activities. J Appl Physiol, 84(1), 362-371.

Evenson, K., Birnbaum, A., Bedimo-Rung, A., Sallis, J., Voorhees, C., Ring, K. & Elder, J. (2006). Girls' perception of physical environmental factors and transportation: reliability and association with physical activity and active transport to school. Int. J. Behav. Nutr. Phys. Act., 3(1), 28.

Everitt, B. S. (1975). Multivariate analysis: the need for data and other problems. Br J Psychiatry, 126, 237-240.

Fahey, T., Delaney, L. & Gannon, B. (2005). School Children and Sport in Ireland . Dublin: ESRI.

Ferron, C., Narring, F., Cauderay, M. & Michaud, P. A. (1999). Sport activity in adolescence: associations with health perceptions and experimental behaviours. Health Educ Res, 14(2), 225-233.

Field, A. (2005). Discovering statistics using SPSS . London, UK: Sage Publications.

Fisher, M., Juszczak, L. & Friedman, S. B. (1996). Sports participation in an urban high school: Academic and psychologic correlates. J Adolesc Health, 18(5), 329-334.

Flegal, K. M., Ogden, C. L., Wei, R., Kuczmarski, R. J. & Johnson, C. L. (2001). Prevalence of overweight in US children: comparison of US growth charts from the Centers for Disease Control and Prevention with other reference values for body mass index. Am J Clin Nutr, 2001(73,).

337

References

Fogelholm, M. & Kukkonen-Harjula, K. (2000). Does physical activity prevent weight gain - a systematice review. Obes Res, 1(2), 95-111.

Foster, C. & Hillsdon, M. (2004). Changing the environment to promote health- enhancing physical activity. J Sports Sci, 22(8), 755-769.

Fox, K. R. (2003). Childhood obesity and the role of physical activity. J R Soc Promot Health, 124(1), 34-39.

Fox, K. R. & Corbin, C. (1989). The physical self perception profile: Development and preliminary validation. J Sport and Ex Psych, 11, 408-430.

Fredriks, A. M., van Buuren, S., Wit, J. M. & Verloove-Vanhorick, S. P. (2000). Body index measurements in 1996-7 compared with 1980. Arch Dis Child, 82, 107- 112.

Freedson, P., Pober, D. & Janz, K. F. (2005). Calibration of Accelerometer Output for Children. Med Sci Sport Exer, 37(11 Suppl), S523-S530.

Friedenreich, C. M. & Thune, I. (2001). A review of physical activity and prostate cancer risk. Cancer Cause Control, 12, 461-475.

Garcia, A. W., Norton Broda, M. A., Frenn, M., Coviak, C., Pender, N. J. & Ronis, D. L. (1995). Gender and Developmental Differences in Exerise Beliefs Among Youth and Prediction of Their Exercise Behavior. J Sch Health, 65(6), 213-219.

338

References

Garcia, A. W., Pender, N. J., Antonakos, C. L. & Ronis, D. L. (1998). Changes in Physical Activity Beliefs and Behaviors of Boys and Girls Across the Transition to Junior High School. J Adolesc Health, 22, 394-402.

Gasevic, D., Vukmirovich, I., Yusuf, S., Teo, K., Chow, C., Dagenais, G. & Lear, S. A. (2011). A direct assssment of "Obesogenic" built environments: challenges and recommendations. J Environ Public Health, doi:10.1155/2011/161574.

Giovanucci, E., Ascherio, A., Rimm, E. B., Colditz, G. A., Stampfer, M. J. & Willett, W. C. (1995). Physical activity, obesity, and risk for colon cancer and adenoma in men. Ann Intern Med, 122, 327-334.

Gittelsohn, J., Steckler, A., Johnson, C. C., Pratt, C., Grieser, M., Pickrel, J., Stone, E., Conway, T. L., Coombs, D. & Staten, L. (2006). Formative research in school and community based health programs and studies: "State of the Art" and the TAAG approach. Health Educ Behav, Feb 33(1), 25-39.

Godin, G. (1994). Theories of reasoned action and planned behavior: usefulness for exercise promotion. Med Sci Sport Exer, 26, 1391-1394.

Godin, G. & Shephard, R. (1986). Psychosocial Factors Influencing Intentions to Exercise of Young Students from Grades 7 to 9. Res. Q. Exerc. Sport., 47 41-52.

Godin, G., Valois, P. & LePage, L. (1993). The pattern of influence of perceived behavioral control upon exercising behavior. J Behav Med, 16, 81-102.

Goetz, J. P. & LeCompte, M. D. (1981). Problems of reliability and validity in ethnographic research. Rev Educ Res, 52, 31-60.

339

References

Going, S. B., Levin, S., Harrell, J. S., Stewart, D., Kushi, L., Cornell, C. E., Hunsberger, S., Corbin, C. & Sallis, J. (1999). Physical activity assessment in American Indian schoolchildren in the Pathways study’. Am J Clin Nutr, 69(suppl), 788- 795.

Goldthorpe, J. H. (2000). On Sociology: Numbers, Narratives and the Integration of Research and Theory . Oxford: Oxford University Press.

Goodwin, L. D. (1999). The Role of Factor Analysis in the Estimation of Construct Validity. Meas Phys Educ Exerc Sci, 3(2), 85-100.

Gordon-Larsen, P., Adair, L. S. & Popkin, B. M. (2002). Ethnic differences in physical activity and inactivity patterns and overweight status. Obes Res, 10(3), 141-149.

Gordon-Larsen, P., McMurray, R. G. & Popkin, B. M. (2000). Determinants of adolescent physical activity and inactivity patterns. Pediatrics, 105(6), E83.

Gorely, T. (2005). The Determinants of Physical Activity and Inactivity in Young People. In Cale, L. &Harris, J., Exercise and Young People: issues, implcations and initiatives . New York: Palrave MacMillan.

Graneheim, U. H. & Lundman, B. (2004). Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness. Nurse Educ Today, 24, 105-112.

Green, L. W., Richard, L. & Potvin, L. (1996). Ecological foundations of health promotion. Am J Health Promot, 10(4), 270-81.

340

References

Gregory, J. (2000). National diet and nutrition survey young people aged 4-18 years Vol. 1 Report of the diet and nutrition survey . London: TSO (The Stationery Office).

Grieser, M., Vu, M., Bedimo-Rung, A., Neumark - Sztainer, D., Moody, J., Young, D. R. & Moe, S. (2006). Physical activity attitudes, preferences and practices in African American, Hispanic and Caucasian girls. Health Educ Behav, 33(1), 40- 51.

Guba, E. G. (1981). Criteria for assessing the trustowrthiness of naturalistic inquiries. Educ Comm Tech J, 29, 75-91.

Guillaume, M., Lapidus, L., Bjorntorp, P. & Lambert, A. (1997). Physical Activity, Obesity and Cardiovascular Risk Factors in Children. The Belgian Luxembourg Child Study II. Obes Res, 5(6), 549-556.

Gyurcsik, N. C., Spink, K. S., Bray, S. R., Chad, K. & Kwan, M. (2006). An ecologically based examination of barriers to physical activity in students from grade seven through first-year university. J Adolesc Health, 38(6), 704-711.

Haas, S. & Nigg, C. R. (2009). Construct validation of the stages of change with strenuous, moderate, and mild physical activity and sedentary behaviour among children. J Sci Med Sport, 12(5), 586-591.

Hagger, M. S., Chataisarantis, N. L. D. & Biddle, S. J. H. (2002). A Meta-Analytic Review of the Theories of Reasoned Action and Planned Behavior in Physical Activity: Predictive Validity and the Contribution of Additional Variables. J Sport and Ex Psych, 24, 3-32.

341

References

Hagger, M. S. & Chatziarantis, N. L. D. (2008). Youth attitudes. In Smith, A. L. &Biddle, S., Youth Phyiscal Activity and Sedentary Behaviour . Champaign, IL: Human Kinetics.

Hardman, A. E. & Stensel, D. J. (2005). Physical Activity and Health: The Evidence Explained . Oxon: Routledge.

Hardmann, K. & Marshall, J. J. (2000). Worldwide survey of the state and status of school physical education. Final report , Manchester: University of Manchester.

Harrell, J. S., McMurray, R.G., Baggett, C.D., Pennell, M.L., Pearce, P.F., Bangdiwala, S.I. (2005). Energy costs of physical activity in children amd adolescents. Med Sci Sport Exer, 37, 329-336.

Harrington, D. (2010), Objective Assessment and Modification of Physical Activity and Health Indices in Adolescent Females, Unpublished theses PhD, Limerick, University of Limerick.

Harrington, J., Perry, I., Lutomski, J., Morgan, K., McGee, H., Shelley, E., Watson, D. & Barry, M. (2008). SLAN 2007: Survey of Lifestyle, Attitudes and Nutrition in Ireland. Dietary Habits of the Irish Population. , Dublin: Department of Health and Children.

Harrison, M., Burns, C. F., McGuinness, M., Heslin, J. & Murphy, N. M. (2006). Influence of a health education intervention on physical activity and screen time in primary school children: 'Switch Off-Get Active. J Sci Med Sport, 9(5), 388- 394.

342

References

Harrison, P. A. & Narayan, G. (2003). Differences in behaviour, psychological factors, and environmental factors associated with participation in school sports and other activities in adolescence. J Sch Health, 73(3), 113-120.

Harro, M. & Riddoch, C. (2000). Physical Activity. In Paediatric Exercise Science and Medicine . Oxford: Oxford University Press.

Harter, S. (1982). The Perceived Competence Scale for Children. Child. Dev., 53(1), 87-97.

Harter, S. (1985). Manual for the self perception profile for children. Denver: University of Denver.

Healy, G. N., Dunstan, D. W., Salmon, J., Cerin, E., Shaw, J. E., Zimmet, P. Z. & Owen, N. (2007). Objectively measured light-intensity physical activity is independently assosciated with 2-h plasma glucose. Diabetes Care, 30(6), 1384- 1389.

Healy, G. N., Wijndaele, K., Dunstan, D. W., Shaw, J. E., Salmon, J., Zimmet, P. Z. & Owen, N. (2008). Objectively measured sedentary time, physical activity, and metabolic risk: the Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Diabetes Care, 31(2), 369-371.

Heesch, K. C., Masse, L. C. & Dunn, A. L. (2006). Using Rasch modeling to re- evaluate three scales related to physical activity: enjoyment, perceived benefits and perceived barriers. Health. Educ. Res., 21(suppl_1), i58-72.

Heini, A. F. & Weisnier, R. L. (1997). Divergent trends in obesity and fat intake patterns: The American paradox. Am J Med, 102, 259-264.

343

References

Heinonen, A., Oja, P., Kannus, P., Sievanen, H., Haapasola, H., Manttari, A. & Vouri (1995). Bone mineral density in female athletes representing sports with different loading characteristics of the skeleton’. Bone, 17, 197-203.

Heinzelmann, F. & Bagley, R. W. (1970). Response to physical activity programs and their effects on health behavior. Health Rep, 85, 905-911.

Heitzler, C. D., Martin, S. L., Duke, J. & Huhman, M. (2006). Correlates of physical activity in a national sample of children aged 9-13 years. Prev Med, 42(4), 254- 260.

Henson, R., Capraro, R. M. & Capraro, M. M. (2004). Reporting practice and use of exploratory factor analysis in educational research journals. Research in the Schools, 11, 61-72.

Hernandez, B., Gortmaker, S. L., Colditz, G. A., Peterson, K. E., Laird, N. M. & Parra- Cabrera, S. (1999). Association of obesity with physical activity, television programs and other forms of video viewing among children in Mexico City. Int J Obes, 23, 845-854.

Hiilloskorpi, H., Fogelholm, M., Laukkanen, R., Pasanen, M., Oja, P., Mänttäri, A. & Natri, A. (1999). Factors affecting the relation between heart rate and energy expenditure during exercise. Int J Sports Med, 20, 438-443.

Himes, J. H. (2009). Challenges of Accurately Measuring and Using BMI and Other Indicators of Ocesity in Children. Pediatrics, 124(1), S3-S22.

344

References

Hoefer, W., McKenzie, T. L., Sallis, J., Marshall, J. J. & Conway, T. (2001). Parental provision of transportation for adolescent physical activity. Am J Prev Med, 21, 887-892.

Hoffman, J. R., Jie, K., Faigenbaum, A. D. & Ratamess, N. A. (2005). Recreational Sports Participation is Associated with Enhanced Physical Fitness in Children. Res Sports Med, 13(2), 149-161.

Hohepa, M., Schofield, G. & Kolt, G. S. (2006). Physical activity: what do high school students think? J. Adolesc. Health., 39 328 - 336.

Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Pschometrika, 30, 179-185.

Hotelling, H. (1933). Analysis of a complex of statisical variables into principal components J Educ Psychol, 24, 27-35.

Humbert, M. L., Chad, K. E., Bruner, M. W., Spink, K. S., Muhajarine, N., Anderson, K. D., Girolami, T. M., Odnokon, P. & Gryba, C. R. (2008). Using a Naturalistic Ecological Approach to Examine the Factors Influencing Youth Physical Activity Across Grades 7 to 12. Health Educ Behav, 35(2), 158-173.

Hussey, J., Gormley, J. & Bell, C. (2001). Physical activity in Dublin children aged 7-9 years. Br J Sports Med, 35, 268-273.

IOTF (2003). IOTF Internation Obesity Task Foce - Position Paper, Waiting for a green light for health - Europe at the crossroads for diet and disease: http://www,iotf.org/media/euobesity2.pdf

345

References

Jackson, A., Morrow, J. J., Bowles, H., FitzGerald, S. & Blair, S. (2007). Construct validity evidence for single-response items to estimate physical activity levels in large sample studies. Res. Q. Exerc. Sport. , 78(2), 24-31.

Jackson, C., Smith, R. A. & Conner, M. (2003). Applying an extended version of the Theory of PLanned Behaviour to physical activity J Sport Sci 21, 119-133.

Jago, R. & Baranowski, T. (2004). Non-curricular approaches for increasing physical activity in youth: a review. Prev Med, 39, 157 - 163.

Janis, I. L. & Mann, L. (1977). Decision making: A psychological analysis of conflict, choice and commitment New York: Free Press.

Janssen, I., Craig, W. & Boyce, W. (2004). Association between overweight and obesity with bullying behaviors in school-aged children. Pediatrics, 113(5), 1187-1194.

Janssen, I., Katzmarzyk, P. T., Boyce, W. F., Vereecken, C., Mulvihill, C., Roberts, C., Currie, C. & Pickett, W. (2005). Comparison of overweight and obesity prevalence in school-aged youth from 34 countries and their relationships with physical activity and dietary patterns. Obes Rev, 6(2), 123-132.

Janz, K. F., Gilmore, J. M. & Burns, T. L. (2006). Physical Activity augments bone accrual in young children: the Iowa Bone Development Study. J Pediatr, 148, 793-799.

Janz, N. & Becker, M. (1984). The health belief model: a decade later. Health Educ Q, 11, 1-47.

346

References

Johnson, M. S., Figueroa Colon, R., Hers, S. L., Fields, D. A., Sun, M. & Hunter, G. R. (2000). Aerobic fitness, not energy expenditure influences subsequent increase in adiposity in black and white children. Pediatrics, 106(4), 50.

Johnston, L. D., Delva, J. & O'Malley, P. M. (2007). Sports Participation and Physical Education in American Secondary Schools: Current Levels and Racial/Ethnic and Socioeconomic Disparities. Am J Prev Med, 33(4, Supplement 1), S195- S208.

Jones, J. W. (1989). Personality and epistemiology: Cognitive social learning theory as a philosophy of science. Zygon, 24(1), 23-38.

Jose, K. A., Blizzard, L., Dwyer, T., McKercher, C. & Venn, A. J. (2011). Childhood and adolescent predictors of leisure time physical activity during the transition from adolescence to adulthood: a population based cohort study. Int J Behav Nutr Phys Act, 8, 54.

Kahle, E., Zipf, W., Lamb, D., Horswill, C. & Ward, K. (1996). Associaton between mild, routine exercise and improved insulin dynamics and glucose control in obese adolescents. In J Sports Med, 17, 1-6.

Kahn, J. A., Huang, B., Gillman, M. W., Field, A. E., Austin, S. B., Colditz, G. A. & Frazier, A. L. (2008). Patterns and Determinants of Physical Activity in U.S. Adolescents. J Adolesc Health, 42(4), 369-377.

Kaiser, H. F. (1974). An index of factorial simplicity. Pschometrika, 39, 31-36.

Karterliotis, K. (2008). Validation of the Physical Self-Perception Profile among College Students. J Educ Hum Dev, 2(1), 1-10.

347

References

Katzmarzyk, P. T. & Malina, R. M. (1998). Contribution of organized sports participation to estimated daily energy expenditure in youth. Pediatr Exerc Sci, 10(4), 378-386.

Kelleher, C., Nic Gabhainn, S., Friel, S., Corrigan, H., Nolan, G., Sixsmith, J., Walsh, O. & Cooke, M. (2003). National health and lifestyles survey: Survey of lifestyles, attitudes and nutrition (Slan 2002) and the Irish health behaviour in school-aged children survey (HBSC) Centre for Health Promotion Studies, NUI Galway & The Department of Public Health Medicine and Epidemiology, UCD.

Kelly, E. B., Parrs-Medina, D., Pfeiffer, K. A., Dowda, M., Conway, T. L., Webber, L. S., Jobe, J. B., Going, S. & Pate, R. R. (2010). Correlates of Physical Activity in Black, Hispanic, and White Middle School Girls. J. Phys. Act. Health., 7(2), 184-193.

Kendzierski, D. & DeCarlo, K. J. (1991). Physical Activity Enjoyment Scale: two validation studies. J Sports Psychol, 13(1), 50-64.

Kilpatrick, M., Bartholomew, J. & Riemer, H. (2003). The Measuremement of Goal Orientations in Exercise. J Sp Behav, 26, 121-136.

Kim, Y.-h. (2004). Korean adolescents' exercise behavior and its relationship with psychological variables based on stages of change model. J Adolesc Health, 34(6), 523-530.

Kimm, S. Y., Glynn, N. W., Kriska, A. M., Kronsberg, S. S., Daniels, S. K., P.B., C. & Sabry, Z. I. (2002). Decline in physical activity in black girls and white girls during adolescence. N Engl J Med, 347(10), 709-715.

348

References

Kimm, S. Y., Glynn, N. W., McMahon, R. P., Voorhees, C. C., Striegel-Moore, R. H. & Daniels, S. R. (2006). Self-perceived barriers to activity participation among sedentary adolescent girls. Med Sci Sports Exerc, 38(3), 534-540.

Kimm, S. Y. S., Glynn, N. W., Kriska, A. M., Fitzgerald, S. L., Aaron, D. J., Similo, S. L., McMahon, R. P. & Barton, B. A. (2000). Longitudinal changes in physical activity in a biracial cohort during adolescence. Med Sci Sport Exer, 32(8), 1445-1454.

King, K. A., Tergerson, J. L. & Wilson, B. R. (2008). Effect of Social Support on Adolescents' Perceptions of and Engagement in Physical Activity. J Phys Act Health, 5(3), 374-384.

Kip, K. E., McCreath, H. E., Roseman, J. M., Hulley, S. B. & Schreiner, P. J. (2002). Absence of risk factor change in young adults after family heart attack or stroke. Am J Prev Med, 22 258-66.

Kohl, H., Fulton, J. & Casperson, C. (2000). Assessment of physical activity among children and adolescents; a review and synthesis. Prev Med, 3(2), S54-S76.

Kothari, A., Edwards, N., Yanicki, S., Hansen-Ketchum, P. & Kennedy, M. A. (2007). Socioecological Models: Strengthening Intervention Research in Tobacco Control. Drogues, sante et societe, 6(1), 1-24.

Kraut, A., Melamed, S., Gofer, D. & Froom, P. (2003). Effect of School Age Sports on Leisure Time Physical Activity in Adults: The CORDIS Study. Med Sci Sport Exer, 35(12), 2038-2042.

349

References

Krebs, N., Himes, J. J., Jacobson, d., Niclas, T. A., Guilday, P. & Styne, D. (2007). Assessment of Child and Adolescent Overweight and Obesity. Pediatrics, 120(4), S193-S228.

Krizek, K. J., Birnbaum, A. S. & Levinson, D. M. (2004). A schematic for focusing on youth in investigations o community design and physical activity. Am J Health Promot, 19(1), 33-38.

Krueger, R. A. & Casey, M. A. (2000). Focus Groups, 3rd Edition. A Practical Guide for Applied Research . Thousand Oaks, California: Sage Publications.

Kuczmarski, R. J. & Flegal, K. M. (2000). Crteria for definition of overweight in transition: background and recommendations for the United States. Am J Clin Nutr, 72(5), 1051-1074.

Lakka, T. A., Venalainen, J. M., Rauramaa, R., Salonen, R., Toumilehto, J. & Salonen, J. T. (1994). Relation of Leisure Time Physical Activity and Cardiorespiratory Fitness to the Risk of Acute Myocardial Infarction in Men’. New Eng J Med, 330, 1549-1554.

Lalor, K. & Baird, K. (2006). Our views anybody listening?; Researching the views and needs of young people in Co. Kildare, Research commissioned by Youth Work Ireland in conjunction with Centre for Social and Educational Research , Dublin: Dublin Institute of Technology.

Lance, C. E., Butts, M. M. & Michels, L. C. (2006). The sources of four commonly reported cut off criteria: What did they really say? Organizational Research Methods (ORM), 9, 202 - 220.

350

References

Lee, C. C. & Blair, S. N. (2002). Cardiorespiratory fitness and stroke mortality in men. Med Sci Sport Exer, 34, 592-595.

Lee, E., Nigg, C. R., DiClemente, C. & Courneya, K. (2001). Validating motivational readiness for exercise behavior with adolescents. Res Q Exerc Sport, 72(4), 401- 410.

Lee, M. C., Orenstein, M. R. & Richardson, M. J. (2008). Systematic review of active commuting to school and children's physical activity and weight. J Phys Act Health, Nov 5(6), 930-949.

Leslie, E., Coffee, N., Frank, L., Owen, N., Bauman, A. E. & Hugo, G. (2007). Walkability of local communities: Using geographic information systems to objectivel assess relevant environmental attributes. Health & Place, 13, 111- 122.

Lincoln, Y. S. & Guba, E. G. (1984). Naturalistic inquiry . London: Sage.

Livingstone, M. B. E., Robson, P. J., Wallace, J. M. W. & McKinley, M. C. (2003). How active are we? Levels of routine physical activity in children and adults. . Proc Nutr Soc, 62, 681-701.

Lobstein, T. & Frelut, M. L. (2003). Prevalence of overweight among children in Europe. Int J Obes, 27, 1136-1138.

Loucaides, C. A., Plotnikoff, R. C. & Bercovitz, K. (2007). Differences in the Correlates of Physical Activity Between Urban and Rural Canadian Youth. J Sch Health, 77(4), 164-170.

351

References

Lubans, D. R., Okely, A. D., Morgan, P. J., Cotton, W., Puglisi, L. & Miller, J. (2011). Description and evaluation of a social cognitive model of physical activity behaviour tailored for adolescent girls. Health Educ Res, [Epub ahead of print],.

Lunn, P. (2007). Fair Play? Sport and Social Disadvantage in Ireland. Dublin: ESRI.

Lunn, P. & Layte, R. (2008). Sporting Lives: An Analysis of a Lifetime of Irish Sport . Dublin: ESRI.

Lytle, L., Murray, D., Evenson, K., Moody, J., Pratt, C., Metcalfe, L. & Parra-Medina, D. (2009). Mediators Affecting Girls Levels of Physical Activity Outside of School: Findings from the Trial of Activity in Adolescent Girls. Ann Behav Med, 38(2), 124-136.

MacCullum, R. C., Widaman, K. F., Zhang, S. & Hong, S. (1999). Sample size in factor analysis. Psychol Methods, 4, 84-99.

MacNamara, A. & Collins, D. (in press). The development and initial validation of the Psychological Characteristics of Developing Excellence Questionnaire. J Sports Sci .

MacPhail, A. & Halbert, J. (2005). The implementation of a revised physical education syllabus in Ireland: circumstances, rewards and costs. Eur Phys Ed Rev, 11(3), 287-308.

Maffeis, C., Talamini, G. & Tato, L. (1998). Influence of diet, physical activity and parents’ obesity on children’s adiposity: a four – year long longitudinal study. Int J Obes, 22, 758-764.

352

References

Maiman, L. A. & Becker, M. H. (1974). The Health Belief Model: Origins and correlates in psychological theory. Health Educ Monogr, 2, 336-353.

Mairs, H. (1999), Attitudes to Physical Education: A Case Study, Unpublished masters thesis, Belfast, University of Ulster.

Malina, R. M. (2001). Physical activity and fitness : pathways from childhood to adulthood. Am J Hum Biol, 13, 162-172.

Marcus, B. H., Emmons, K. M., Simkin-Silverman, L. R., Linnan, L. A., Taylor, E. r., Bock, B. C., Roberts, M. B., Rossi, J. S. & Abrama, D. B. (1998). Evaluation of motivationally tailored vs. standard self-help physical activity interventions at the workplace. Am J Health Promot, 12, 246-253.

Marcus, B. H. & Forsyth, L. H. (2003). Motivating People to Be Physically Active . Champaign IL: Human Kinetics.

Marcus, B. H., Rossi, J. S., Selby, V. C., Niaura, R. S. & Abrams, D. B. (1992). The stages and processes of exercise adoption and maintenance in a worksite sample. Health. Psychol., 11(6), 386-395.

Marcus, B. H. & Simkin, L. R. (1993). The stages of exercise behavior. J Sport Med Phys Fit, 33, 83-88.

Marmot, M. G., Rose, G., Shipley, M. & Hamilton, P. J. (1978). Employment grade and coronary heart disease in British civil servants. J Epidemiol Comm Health, 32, 244-249.

353

References

Marshall, S. J., Gorely, T. & Biddle, S. J. H. (2006). A descriptive epidemiology of screen based media use in youth: A review and critique. J Adolesc 29, 333-349.

Masse, L. C., Fuemmeler, B. F., Anderson, C. B., Matthews, C. E., Trost, S. G., Catellier, D. J. & Treuth, M. (2005). Accelerometer Data Reduction: A Comparison of Four Reduction Algorithms on Select Outcome Variables. Med Sci Sport Exer, 37(11 Suppl), S544-S554.

Matthews, C., Chen, K., Freedson, P., Buchowski, M., Beech, B., Pate, R. & Troiano, R. (2008). Amount of time spent in sedentary behaviours in the United States, 2003-2004. Am J Epidemiol, 167(7), 875-881.

Matthews, C. E. (2005). Calibration of Accelerometer Output for Adults. Med Sci Sport Exer, 37(11 Suppl), S512-S522.

Mattocks, C., Ness, A., Leary, S., Tilling, K., Blair, S. N., Shield, J., Deere, K., Saunders, J., Kirkby, J., Smith, G. D., Wells, J., Wareham, N., Reilly, J. & Riddoch, C. (2008). Use of accelerometers in a large field-based study of children: Protocols, design issues, and effects on precision. . Journal of Physical Activity & Health, 5(1), S98-S111.

McCarthy, D. H., Ellis, S. M. & Cole, T. J. (2003). Central overweight and obesity in British youth agesd 11-16 years: cross sectional surveys of waist circumference. BMJ, 326, 1-4.

McClain, J., Abraham, T., Brusseau, T. & Tudor-Locke, C. (2008). Epoch length and accelerometer output in children: Comparison to direct observation. Med. Sci. Sport. Exerc., 40(12), 2080-2087.

354

References

McCormack, G. R., Corti, B. G. & Bulsara, M. (2007). Correlates of Using Neighborhood Recreational Destinations in Physically Active Respondents. J Phys Act Health, 4, 39-53.

McCrorie, T. A., Rennie, K. L., Wallace, J. M. W. & Livingstone, M. B. E. (2010). Are Northern Irish adolescents meeting physical activity recommendations? Proceedings of the Nutrition Society, 69(OCE5), E358.

McEvoy, O. (2008). Teenagers' views on solutions to alcohol misuse: Report on a national consultation , Dublin: Dept. of Health and Children.

McKenzie, T. L., Nader, P., Strikmiller, P., Yang, M., Stone, E., Perry, C., Taylor, W. C., Epping, J., Felgman, H., Luepker, R. & Kelder, S. (1996). School physical education: The effect of the Child and Adolescent Trial for Cardiovascular Health (CATCH). Prev Med, 25, 423-431.

McLaren, L. & Hawe, P. (2005). Ecological perspectives in health research J Epidemiol Comm Health, 59, 6-14.

McLeroy, K. R., Bibeau, D., Steckler, A. & Glanz, K. (1988). An ecological perspective on health promotion programs. Health Educ Q, 15, 351-78.

McMinn, A. M., Van Sluijs, E. M., Harvey, N. C., Cooper, C., Inskip, H. M., Godfrey, K. M. & Griffin, S. J. (2009). Validation of a maternal questionnaire on correlates of physical activity in preschool children. Int J Behav Nutr Phys Act, 6, 81-93.

355

References

McMinn, D., Rowe, D. A., Stark, M. & Nicol, L. (2010). Validity of the New Lifestyles NL-1000 Accerlometer for Measuring Time Spent in Moderate - to - Vigorous Physical Activity in School Settings. Meas Phys Educ Exerc Sci, 14, 67-78.

Melanson, E. & Freedson, P. (1995). Validity of the Computer Science and Applications Inc. (CSA) activity monitor. Med Sci Sport Exer, 27(6), 1-7.

Metzger, J. S., Catellier, D. J., Evenson, K. R., Treuth, M. S., Rosamond, W. D. & Siega-Riz, A. M. (2008). Patterns of objectively measured physical activity in the United States. Med Sci Sport Exer, 40(4), 630-638.

Meyers, L. S., Gamst, G. & Guarino, A. J. (2006). Applied multivariate research: Design and interpretation . Thousand Oaks, CA: Sage Publications.

Michaud, P. A., Jeannin, A. & Suris, J. C. (2006). Correlates of extracurricular sport participation among Swiss adolescents. Eur J Pediatr, 165(8), 546-555.

Miles, M. B. & Huberman, A. M. (1984). Qualitative data analysis . Beverley Hills, CA: Sage.

Morris, J. N., Everitt, M. G., Pollard, R., Chave, S. P. W. & Semmence, A. M. (1980). Vigorous exercise in leisure time: protection against coronary heart disease. The Lancet, 316(8206), 1207-1210.

Morris, J. N., Heady, J. A., Raffle, P. A. P., Parks, J. W. & Roberts, G. G. (1953). Coronary Heart Disease and physical activity of work. The Lancet, 1053(7), 1111-120.

356

References

Mota, J., Delgado, N., Almeida, M., Ribeiro, J. C. & Santos, M. P. (2006). Physical Activity, Overweight, and Perceptions of Neighborhood Environments Among Portuguese Girls. J Phys Act Health, 3, 314-322.

Motl, R. W., Dishman, R. K., Felton, G. & Pate, R. R. (2003). Self motivation and physical activity among black and white adolescent girls Med Sci Sport Exer, 35, 128-136.

Motl, R. W., Dishman, R. K., Saunders, R., Dowda, M., Felton, G. & Pate, R. R. (2001). Measuring enjoyment of physical activity in adolescent girls. Am J Prev Med, 21(2), 110-117.

Motl, R. W., Dishman, R. K., Trost, S. G., Saunders, R. P., Dowda, M., Felton, G., Ward, D. S. & Pate, R. R. (2000). Factorial Validity and Invariance of Questionnaires Measuring Social-Cognitive Determinants of Physical Activity among Adolescent Girls. Prev Med, 31(5), 584-594.

Motl, R. W., Dishman, R. K., Ward, D. S., Saunders, R. P., Dowda, M., Felton, G. & Pate, R. (2005). Perceived physical environment and physical activity across one year among adolescent girls : self efficacy as a possible mediator? J Adolesc Health, 37, 403-408.

Mulvihill, K., Rivers, K. & Adggelton, P. (2000). Physical Activity 'At Our Time' , London: Health Education Authority.

Munro, B. H. (2005). Statistical Methods for Health Care Research (5th Ed.) . Boston MA: Lippincott Williams and Wilkins.

357

References

Muris, P., Meesters, C. & Fijen, P. (2003). The Self Perception Profile for Children: further evidence for its factor structure, reliability, and validity. Pers Indiv Differ, 35(8), 1791-1802.

Murphy, N. M., Riddoch, C. J., Cran, G. W. & Boreham, C. (1994). Physical Activity and Physical Fitness in Northern Irish Schoolchildren- Are they Related? In Duffy, P. &Dugdale, L., HPER- Moving Toward The 21st Century . Champaign, IL: Human Kinetics.

Mutz, D. C., Roberts, D. F. & Vuuren, D. P. (1993). Reconsidering the displacement hypothesis : Television’s influence on children’s time use. Comm Res, 20, 51- 75.

Naidoo, J. & Wills, J. (2001). Health promotion: Foundations for Practice, Second Edition . London: Bailliere Tindall.

National Heart Alliance. (2006). Physical Activity, Young People and the Physical Environment: Summary of Evidence, Dublin: Irish Heart Foundation.

Neumark - Sztainer, D., Story, M., Hannan, P. J., Tharp, T. & Rex, J. (2003). Factors Associated with changes in physical activity: A cohort study of inactive adolescent girls. Arch Pediatr Adolesc Med, 157(8), 803-810.

Nie, N. H., Hull, C. H., Jenkins, J. G., Steinbrenner, K. & Bent, D. H. (1975). SPSS: Statistical Package for the Social Sciences (2nd Ed.) . New York: McGraw Hill.

Nigg, C. R. & Courneya, K. S. (1998). Transtheoretical Model: Examining Adoelscent Exercise Behavior. J Adolesc Health, 22, 214-224.

358

References

Nigg, C. R. & Paxton, R. J. (2008). Conceptual Perspectives. In Smith, A. L. &Biddle, S., Youth Physical Activity and Sedentary Behaviour: Challenges and Solutions . Champaign, IL: Human Kinetics.

Nilsson, A., Anderson, L.B., Ommundsen, Y., Froberg, K., Sardinha, L.B., Piel-Aulin, K, Ekelund, U. (2009). Corelates of objetively assessed physical activity and sedentary time in children: a cross sectional study (The European Youth Heart Study). BMC Public Health, 9, 322.

Nilsson, A., Ekelund, U., Yngve, A. & Sjostrom, M. (2002). Assessing physical activity among children with accelerometers using different time sampling intervals and placements. Pediatr Exerc Sci, 14(1), 87-96.

Norman, G. J., Nutter, S. K., Ryan, S., Sallis, J. F., Calfas, K. J. & Patrick, K. (2006). Community Design and Access to Recreational Facilities as Correlates of Adolescent Physical Activity and Body-Mass Index. J Phys Act Health, 3(1), S118-S128.

Nunnally, J. C. (1978). Psychometric theory (2nd ed.) . New York: McGraw Hill.

Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric theory (3rd ed.) . New York: McGraw-Hill.

Nutbeam, D. & Harris, E. (2004). Theory in a nutshell: A Practical Guide to Health Promotion Theories . Sydney: McGraw Hill.

O' Sullivan, S. (1997), Socialization of the primary school child into a physically active lifestyle: A public health perspective, Unpublished thesis, Limerick, University of Limerick.

359

References

O' Sullivan, S. (2002). The Physical Activity of Children: A Study of 1,602 Irish Schoolchildren Aged 11-12 years. Ir Med J, 95, 78-81.

O'Dea, J. A. (2003). Why do kids eat healthful food? Perceived benefits of and barriers to healthful eating and physical activity among children and adolescents. J Am Diet Assoc, 103(4), 497-501.

O Sullivan, S. (1997), Socialization of the primary school child into a physically active lifestyle: A public health perspective, Unpublished thesis, Limerick, University of Limerick.

O Sullivan, S. (2002). The Physical Activity of Children: A Study of 1,602 Irish Schoolchildren Aged 11-12 years. Ir Med J, 95, 78-81.

Ogilvie, D., Mitchell, R., Mutrie, N., Petticrew, M., Platt, S. (2008). Perceived characteristics of the environment associated with active travel: development and testing of a new scale. Int J Behav Nutr Phys Act, 5(32), 1-10.

Ommundsen, Y., Klasson-Heggebo, L. & Anderssen, S. A. (2006). Psycho-social and environmental correlates of location-specific physical activity among 9- and 15- year-old Norwegian boys and girls: the European Youth Heart Study. Int J Behav Nutr Phys Act, 3, 32.

Owen, N., Leslie, E., Salmon, J. & Fotheringham, M. J. (2000). Environmental Determinants of Physical Activity and Sedentary Behavior. Exerc Sport Sci Rev, 28(4), 153-158.

360

References

Paffenbarger, R. S. & Hale, W. E. (1975). Work activity and coronary heart disease. J Phys Act Health, 2, 412-422.

Pallant, J. (2005). SPSS : Survival Manual (3rd ed) . Berkshire (UK): Open University Press.

Pan, S., Cameron, C., DesMeules, M., Morrison, H., Craig, C. & Jiang, X. (2009). Individual, social, environmental, and physical environmental correlates with physical activity among Canadians: a cross-sectional study. BMC Public Health, 9(1), 21.

Pate, P., Pratt, M., Blair, S., Haskell, W., Macera, C., Bouchard, C., Buchner, D., Ettinger, W., Heath, G., King, A., Kriska, A., Leon, A., Marcus, B., Morris, J., Paffenbarger, R., Patrick, K., Pollock, M., Rippe, J., Sallis, J. & Wilmore, J. (1995). Physical activity and public health: A recommendation from the centers for disease control and prevention and the American college of sports medicine. . Journal of the American Medical Association, 273(5), 402-407.

Pate, R. R., Dowda, M., O'Neill, J. R. & Ward, D. S. (2007). Change in Physical Activity Participation Among Adolescent Girls from 8th to 12th Grade. Journal of Physical Activity & Health, 4(1), 3-16.

Pate, R. R., Stevens, J., Pratt, C., Sallis, J. F., Schmitz, K. H., Webber, L. S., Welk, G. & Young, D. R. (2006). Objectively Measured Physical Activity in Sixth-Grade Girls. Arch Pediatr Adolesc Med, 160(12), 1262-1268.

Pate, R. R., Trost, S., Felton, G., Ward, D., Dowda, M. & Saunders, R. (1997). Correlates of physical activity behaviour in rural youth. Res Q Exerc Sport, 68(3), 241-248.

361

References

Pate, R. R., Trost, S. G., Levin, S. & Dowda, M. (2000). Sports Participation and Health-Related Behaviors Among US Youth. Arch Pediatr Adolesc Med, 154(9), 904-911.

Patton, M. (1980). Qualitative Evaluation Methods . Beverley Hills, CA: Sage.

Patton, M. (2002). Qualitative Research and Evaluation Methods . Thousand Oaks, CA: Sage.

Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 6(2), 559-572.

Penpraze, V., Reilly, J. J., MacLean, C. M., Montgomery, C., Kelly, L. A., Paton, J. Y., Altchison, T. & Grant, S. (2006). Monitoring of Physical Activity in Young Children: How Much Is Enough? Pediatr Exerc Sci, 18(4), 483-491.

Pfeiffer, K. A., Dowda, M., McIver, K. L. & Pate, R. R. (2009). Factors Related to Objectively Measured Physical Activity in Pre School Children. Pediatr. Exerc. Sci., May 21(2), 196-208.

Physical Activity Guidelines Advisory Committee (2008), Physical Activtiy Guidelines advisory Committee Report, 2008. Washington, DC: U.S. Department of Health and Human Servieces, 2008, online. Retrieved 15 July 2011, from http://www.health.gov/paguidelines/committeereport.aspx .

Pietrobelli, A., Faith, M. S., Allison, D. B., Gallagher, D., Chiumello, G. & Heymsfield, S. (1998). Body mass index as a measure of adiposity among children and adolescents: A validation study. J Pediatr, 132(2), 204-210.

362

References

Pikora, T., Giles-Corti, B., Bull, F. C., Jamzorik, K. & Donovan, R. (2003). Developing a Framework for Assessment of the Environmental Determinants of Walking and Cycling. Soc Sci Med, 56, 1693-1703.

Pirasteh, A., Hidarnia, A., Asghari, A., Faghihzadeh, S. & Ghofranipour, F. (2008). Development and validation of psychosocial determinants measures of physical activtiy among Iranian adolescent girls. BMC Public Health, 8, 150-160.

Portney, L. G. & Watkins, M. P. (2000). Foundations of Clinical Research: Applications to Practice . New York: Prentice Hall.

Poulsen, A. A. & Ziviani, J. M. (2004). Health enhancing physical activity: Factors influencing engagement patterns in children. Aust Occup Therap J, 51(2), 69-79.

Prapavessis, H., Maddison, R. & Brading, F. (2004). Understanding exercise behavior among New Zealand adolescents: A test of the Transtheoretical Model. J. Adolesc. Health., 35(4), 346.e17-346.e27.

Prochaska, J. & DiClemente, C. (1992). Stages of change in the modification of problem behaviors. Prog Behav Modif. , 28 183-218.

Prochaska, J. J., Rodgers, M. & Sallis, J. (2002). Association of parent and peer support with adolescent physical activity. Res Q Exerc Sport, 73(2), 206-210.

Puyau, M. R., Adolph, A. L., Vohra, F. A. & Butte, N. F. (2002). Validation and Calibration of Physical Activity Monitors in Children. Obesity, 10(3), 150-157.

363

References

Raitakari, O. T., Porka, K., Taimela, S., Temal, R., Rasanen, L. & Vikari, J. S. A. (1994). Effects of persistent physical activity and inactivity on coronary risk factors in children and young adults. Am J Epidemiol, 140, 195-205.

Raustorp, A., Stahle, A., Gudasic, H., Kinnunen, A. & Mattsson, E. (2005). Physical activity and self- perception in school children assessed with the Children and Youth - Physical Self Perception Profile. Scand J Med Sci Sports, 15, 126-134.

Redding, C. A., Rossi, J. S., Rossi, S. R., Velicier, W. F. & Prochaska, J. J. (2000). Health Belief Models. IEJHE, 3, 180-193.

Rees, R., Kavanagh, J., Harden, A., Shepherd, J., Brunton, G., Oliver, S. & Oakley, A. (2006). Young people and physical activity: a systematic review matching their views to effective interventions. Health Educ Res, 21, 806 - 825.

Reynolds, K. D., Killen, J. D., Bryson, S. W., Maron, D. J., Taylor, C. B., Maccoby, N. & Farquhar, J. W. (1990). Psychosocial predictors of physical activity in adolescents. Prev Med, 19(5), 541-551.

Riddoch, C., Andersen, L. B., Wedderkopp, N., Harro, M., Klasson-Heggebo, L., Sardinha, L. B., Cooper, A. & Ekelund, U. (2004). Physical activity levels and patterns of 9- and 15 year-yr-old European Children. Med Sci Sport Exer, 36, 88 - 92.

Riddoch, C., Murphy, N. M., Cran, G. W. & Mahoney, C. (1990), The Northern Ireland Health and Fitness Survey, Unpublished theses, Belfast, Queens University.

364

References

Riddoch, C. J., Andersen, L. B., Wedderkopp, N., Harro, M. & Klasson-Heggebø (2004). Physical activity levels and patterns of 9 and 15-year-old European children. Med Sci Sport Exer, 36(1), 86-92.

Riddoch, C. J. & Boreham, C. A. G. (1995). The health related physical activity of children. Res Q Exerc Sport, 65, 84-88.

Riddoch, C. J., Mattocks, C., Deere, K., Saunders, J., Kirkby, J., Tilling, K., Leary, S., Blair, S. & Ness, A. (2007). Objective measurement of levels and patterns of physcial activity. Arch Dis Child, 92 963-969.

Robbins, L. B., Pender, N. J. & Kazanis, A. S. (2003). Barriers to physical activity perceived by adolescent girls. J Midwifery Womens Health, 48(3), 206-212.

Roberts, S. B. & Dallal, G. E. (2001). The New Childhood Growth Charts. Nutrition Reviews, 59(2), 31.

Robinson, T. N. & Killen, J. D. (1995). Ethnic and gender differences in the relationship between television viewing and obesity, physical activity and dietary fat intake. J Health Educ, 26, S91-98.

Rosenberg, D., Ding, D., Sallis, J., Kerr, J., Norman, G. J., Durant, N., Harris, S. K. & Saelens, B. E. (2009). Neighborhood Environment Walkability Scale for Youth (NEWS-Y): Reliability and relationship with physical activity. Prev Med, 49, 213-218.

Rosenstock, I. M., Strecher, V. J. & Becker, M. H. (1988). Social learning theory and the health belief model. Health Educ Quart, 15, 175-183.

365

References

Rovniak, L. S., Anderson, E. S., Winett, R. A. & Stephens, R. S. (2002). Social cognitive determinants of physical activity in young adults: A prospective structural equation analysis. Ann Behav Med, 24(2), 149-156.

Rowland, T. (1998). The biological basis of physical activity. Med Sci Sports Exerc, 30, 392 - 399.

Rowlands, A. V. & Eston, R. G. (2007). The measurement and interpretation of children's physical activity. J Sport Sci Med (JSSM), 6(3), 270-276.

Rowlands, A. V., Powell, S. M., Humphries, R. & Eston, R. G. (2006). The effect of accelerometer epoch on physical activity output measures. J. Exerc. Sci. Fitness., 4, 51-57.

Ruiz, J. R., Ortega, F. B., Martanez-Gamez, D., Labayen, I., Moreno, L. A., De Bourdeaudhuij, I., Manios, Y., Gonzalez-Gross, M., Mauro, B., Molnar, D., Widhalm, K., Marcos, A. n., Beghin, L. & Castillo, M. J. (2011). Objectively Measured Physical Activity and Sedentary Time in European Adolescents. American Journal of Epidemiology, 174(2), 173-184.

Ryan, D., Mannix McNamara, P. & Deasy, C. (2006). Health Promotion in Ireland: Priciples, Practice and Research . Dublin: Gill and MacMillan.

Ryan, R. M., Frederick, C.M., Lepes, D., Rubio, N., Sheldon, K.M. (1997). Intrinsic Motivation and Exercise Adherence. Int J Sport Psychol, 28, 335-354.

Saelens, B. E., Gehrman, C. A., Sallis, J. F., Calfas, K. J., Sarkin, J. A. & Caparosa, S. (2000). Use of self-management strategies in a 2-year cognitive-behavioral intervention to promote physical activity. Behavior Therapy, 31(2), 365-379.

366

References

Saelens, B. E., Sallis, J., Black, J. & Chen, D. (2003). Neighborhood- based differences in physical activity: an environmental scale evaluation. Am J Pub Health, 93, 1552- 1558.

Saksvig, B. I., Catellier, D. J., Scmitz, K. H., Conway, T., Going, S., Ward, D., Strikmiller, P. & Treuth, M. S. (2007). Travel by Walking Before and After School and Physical Activity Among Adolescent Girls’. Arch Pediatr Adolesc Med, 161, 153-158.

Salbe, A. D., Weyer, C., Harper, I., Lindsay, R. S., Ravussin, E. & Tataranni, P. A. (2002). Assessing risk factors for obesity between childhood and adolescence: II Energy metabolism and physical activity’. Pediatrics, 110, 307-314.

Sallis, J., Cervero, R. B., Ascher, W., Hendrerson, W., Henderson, K. A., Kraft, M. K. & Kerr, J. (2006). An Ecological Approach to Creating Active Living Communities. Annu Rev Public Health, 27, 297-322.

Sallis, J., Johnson, M., Calfas, K., Caparosa, S. & Nichols, J. (1997). Assessing perceived physical environmental variables that may influence physical activity. Res Q Exerc Sport, 68, 345-351.

Sallis, J. & McKenzie, T. L. (1991). Physical education's role in public health. Res Q Exerc Sport, 64, 25-31.

Sallis, J. & Patrick, K. (1994). Overview of the international consensus conference on physical activity guidelines for adolescents. Pediatr Exerc Sci, 6, 299-301.

367

References

Sallis, J. F., Buono, M. J., Roby, J. J., Micale, F. G. & Nelson , J. A. (1993). Seven-day recall and other physical activity self reports in children and adolescents. Med Sci Sport Exer, 25, 95-108.

Sallis, J. F., Calfas, K. J., Alcaraz, J. E., Gehrman, C. & Johnson, M. F. (1999). Potential mediators of change in a physical activity promotion course for university students: Project grad. Ann Behav Med, 21(2), 149-158.

Sallis, J. F., Conway, T. L., Prochaska, J. J., McKenzie, T. L., Marshall, S. J. & Brown, M. (2001). The association of school environments with youth physical activity. Am J Pub Health, 91, 618 - 620.

Sallis, J. F., Hovell, M. F., Richard Hofstetter, C., Faucher, P., Elder, J. P., Blanchard, J., Caspersen, C. J., Powell, K. E. & Christenson, G. M. (1989). A multivariate study of determinants of vigorous exercise in a community sample. Prev Med, 18(1), 20-34.

Sallis, J. F. & Kerr, J. (2006). Physical Activity and the Built Environment. Research Digest, 7(4), 1-8.

Sallis, J. F. & Owen, N. (2002). Ecological models of health behaviour. In Glanz, K., Rimer, B. K. &Lewis, F. M., Health Behaviour and Health Education: Theory, Research and Practice, third edition . San Francisco: Jossey-Bass , 462-484.

Sallis, J. F., Prochaska, J. J. & Taylor, W. C. (2000). A review of correlates of physical activity of children and adolescents. Med Sci Sport Exer, 32(5), 963.

Sallis, J. F., Prochaska, J. J. & Taylor, W. C. (2000). A review of correlates of physical activity of children and adolescents. Med Sci Sport Exer, 32(5), 963-75.

368

References

Sallis, J. F., Prochaska, J. J., Taylor, W. C., Hill, J. O. & Geraci, J. C. (1999). Correlates of physical activity in a national sample of girls and boys in Grades 4 through 12. Health Psychol, 18(4), 410-415.

Sallis, J. F., Simons-Morton, B. G., Stone, E. J., Corbin, C. B., Epstein, L. H., Faucette, N., Iannotti, R. J., Killen, J. D., Klesges, R. C., Petray, C. K., Rowland, T. W. & Taylor, W. C. (1992). Determinants of physical activity and interventions in youth. Med Sci Sport Exer, 24(6), 248-257.

Sallis, J. F., Taylor, W. C., Dowda, M., Freedson, P. S. & Pate, R. R. (2002). Correlates of vigorous physical activity for children in grades 1 through 12: comparing parent-reported and objectively measured physical activity. Pediatr. Exerc. Sci., 14(1), 30-44.

Salmon, J. (2010). Factors in Youth Physical Activity Participation: From Psychological Aspects to Environmental Correlates. Res Sports Med, 18(1), 26- 36.

Santos, M. P., Esculcas, C. & Mota, J. (2004). The Relationship Between Socioeconomic Status and Adolescents' Organized and Nonorganized Physical Activities. Pediatr Exerc Sci, 16(3), 210-218.

Saris, W. H. M., Blair, S., van Baak, M. A., Eaton, S. B., Davies, P. S. W., Di Pietro, L., Fogelholm, M., Rissanen, A., Schoeller, D. A., Swinburn, B., Tremblay, M., S., Westerterp, K. & Wyatt, H. (2003). How much physical activity is enough to prevent unhealthy weight gain? Outcome of the IASO 1st Stock Conference and consensus statement. Obes Res, 4, 101-114.

369

References

Saunders, R. P. (1986), Beliefs, values and normative changes related to continuation and noncontinuation in an exercise program, Unpublished thesis (PhD), Columbia, University of South Carolina.

Saunders, R. P. & Moody, J. (2006). Community agency survey formative research results from the TAAG study. Health Educ Behav, 33, 12-24.

Saunders, R. P., Pate, R. R., Felton, G., Dowda, M., Weinrich, M. C., Ward, D. S., Parsons, M. A. & Baranowski, T. (1997). Development of Questionnaires to Measure Psychosocial Influences on Children's Physical Activity. Prev Med, 26(2), 241-247.

Scanlon, T. K., Stein, G. L. & Ravizza, K. (1989). An in - depth study of former elite figure skaters: II. Sources of enjoyment. JSEP, 11, 65-83.

Schaben, J. A., Welk, G. J., Joens-Matre, R. & Hensley, L. (2006). The Predictive Utility of the Children's Physical Activity Correlates (CPAC) Scale Across Multiple Grade Levels. J Phys Act Health, 3(1), 59-69.

Schmitz, K. H., Lytle, L. A., Phillips, G. A., Murray, D. M., Birnbaum, A. S. & Kubik, M. Y. (2002). Psychosocial Correlates of Physical Activity and Sedentary Leisure Habits in Young Adolescents: The Teens Eating for Energy and Nutrition at School Study. Prev Med, 34(2), 266-278.

Schmitz, K. H., Treuth, M., Hannan, P., McMurray, R., Ring, K. B., Catellier, D. & Pate, R. (2005). Predicting Energy Expenditure from Accelerometry Counts in Adolescent Girls. Med Sci Sport Exer, 37(1), 155-161.

370

References

Schneider, M. & Cooper, D. M. (2011). Enjoyment of exercise moderates the impact of a school based physical activity intervention. Int J Behav Nutr Phys Act, 8(64), doi: 10.1186/1479-5868-8-64.

Schumann, B. B., Striegel - Moore, R. H., McMahon, R. P., Waclawiw, M. A., Morrison, J. A. & Screiber, G. B. (1999). Psychometric Properties of the Self - Perception Profile for Children in a Biracial Cohort of Adoelscent Girls: The NHLBI Growth and Health Study. J Pers Assess, 73(2), 260-275.

Scott, M. M., Evenson, K. R., Cohen, D. A. & Cox, C. E. (2007). Comparing perceived and objectively measured access to recreational facilities as predictors of physical activity in adolescent girls. J. Urban Health, 84(3), 346-359.

Shavelson, R. J., Hubner, J. J. & Stanton, G. C. (1976). Self concept: Validation of construct interpretations. Rev Educ Res, 46, 407-441.

Shenton, A. K. (2004). Strategies for ensuring trustworthinessin qualitative research projects. Education for Information, 22, 63-75.

Sherwood, N. E., Beech, B. M., Klesges, L. M., Story, M., Killen, J., McDonald, T., Robinson, T. N., Pratt, C., Zhou, A., Cullen, K. & Baranowski, J. (2004). Measurement characteristics of weight concern and dieting measures in 8-10- year-old African-American girls from GEMS pilot studies. Prev Med, 38(Supplement 1), 50-59.

Simons-Mortan, B. G., Taylor, W. C. & Huang, W. I. (1994). Validity of the physical activity interview and Caltrac with preadolescent children. Res Q Exerc Sport, 65, 84-88.

371

References

Sirard, J., Kubik, M., Fulkerson, J. & Arcan, C. (2008). Objectively measured physical activity in urban alternative high school students. Med Sci Sport Exer, 40(12), 2088-2095.

Sirard, J. R., Pfeiffer, K. A. & Pate, R. R. (2006). Motivational factors associated with sports program participation in middle school students. J Adolesc Health, 38(6), 696-703.

Snook, S. C. & Gorusch, R. L. (1989). Component analysis versus common factor analysis: A Monte Carlo study. Psychol Bull, 106, 148-154.

Sohun, R. C. (2001), The impact of Physical Education provision in post-primary schools on physical activity, physical fitness and psychosocial parameters in a cohort of Irish adolescents, Unpublished thesis, Limerick, University of Limerick.

Sparkes, A. (1998). Validity in qualitative inquiry and problems of criteria: Implications for sport psychology. Sport Psychol, 12, 363-386.

Steele, R. M., Van Sluijs, E. M., Sharp, S. J., Landsbaugh, J. R., Ekelund, U. & Griffin, S. J. (2010). An invesitgation of patterns of childrens sedentary and vigorous physical activity throughout the week. Int J Behav Nutr Phys Act, 7(88), doi:10.1186/1479-5868-7-88.

Steele, R. M., van Sluijs, E. M. F., Cassidy, A., Griffin, S. J. & Ekelund, U. (2009). Targeting sedentary time or moderate- and vigorous-intensity activity: Independent relations with adiposity in a population-based sample of 10-y-old British children. Am J Clin Nutr, 90(5), 1185-1192.

372

References

Steiger, J.H. (1994), Factor Analysis in the 1980's and the 1990's: Some Old Debates and Some New Developments, online. Retrieved 15 March 2011, from http://www.statpower.net/Content/312/Handout/Steiger94.PDF .

Stein, C., Fisher, L., Berkey, C. & Colditz, G. (2007). Adolescent Physical Activity and Perceived Competence: Does Change in Activity Level Impact Self-Perception? J Adolesc Health, 40(5), 462.e1-462.e8.

Steiner, H. R. W. M., Renee Pavelski, Traci Pitts, and Helena Kraemer (2000). Adolescents and Sports: Risk or Benefit? . Clin Pediatr, 39(3), 161-166.

Steptoe, A. S. & Butler, N. (1996). Sports participation and emotional wellbeing in adolescents. The Lancet, 347(9018), 1789-1792.

Stevens, J., Murray, D. M., Baggett, C. D., Elder, J. P., Lohman, T. G., Lytle, L. A., Pate, R. R., Pratt, C. A., Treuth, M. S., Webber, L. S. & Young, D. R. (2007). Objectively Assessed Associations between Physical Activity and Body Composition in Middle-School Girls: The Trial of Activity for Adolescent Girls. Am. J. Epidemiol., 166(11), 1298-1305.

Stevens, J., Murray, D. M., Catellier, D. J., Hannan, P. J., Lytle, L. A., Elder, J. P., Young, D. R., Simons- Morten, D. G. & Webber, L. S. (2005). Design of the Trial of Activity in Adolescent Girls (TAAG). Contemp Clin Trials, 26(2), 223- 233.

Stokols, D. (1992). Establishing and maintaining healthy environments: Toward a social ecology of health promotion. American Psychologist, 47(1), 6-22.

373

References

Stokols, D. (1996). Translating Social Ecological Theory into Guidelines for Community Health Promotion. Am J Health Promot, 10(4), 282-98.

Strong, W. B., Malina, R. M., Blimkie, C. J., Daniels, S. R., Dishman, R. K., Gutin, B., Hergenroeder, A. C., Must, A., Nixon, P. A., Pivarnik, J. M., Rowland, T., Trost, S. & Trudeau, F. (2005). Evidence based physical activity for school age youth. J. Pediatr., 146(6), 732-737.

Strong, W. B., Malina, R. M., Blimkie, C. J. R., Daniels, S. R., Dishman, R. K., Gutin, B., Hergenroeder, A. C., Must, A., Nixon, P. A. & Pivarnik, J. M. (2005). Evidence based physical activity for school-age youth. J Pediatr, 146, 732 - 737.

Trial of Activity for Adolescent Girls, Study Protocol (2004), Trial of Activity for Adolescent Girls, Study Protocol, online. Retrieved 21 March 2009, from http://www.cscc.unc.edu/taag/descpub.php .

Tabachnik, B. G. & Fidell, L. S. (1998). Using Multivariate Statistics. 3rd ed . New York: NY:Harper Collins College Publishers.

Tabachnik, B. G. & Fidell, L. S. (2007). Using multivariate statistics (5th Ed.) . Boston: Pearson Education.

Telama, R., Yang, X., Laakso & Viikari, J. (1997). Physical activity in childhood and adolescence as predictor of physical activity in young adulthood. Am J Prev Med, 13(4), 317-323.

Tergerson, J. L. & King, K. A. (2002). Do perceived cues, benefits, and barriers to physical activity differ between male and female adolescents? J Sch Health, 72(9), 374-380.

374

References

Terry, D. J. & O'Leary, J. E. (1995). The Theory of Planned Behavior: the effects of behavioral control and self-efficacy. Br J Soc Psychol, 34, 199-220.

Tesch, R. (1990). Qualitative research analysis types and software tools . New York: Falmer Press.

Thomas, J., Nelson, J. & Silverman, S. (2005). Research Methods in Physical Activity - 5th Edition . Champaign, Illinois: Human Kinetics.

Thurstone, L. L. (1931). Multiple factor analysis. Psychol Rev, 38, 406-427.

Titze, S., Willibald, J., Janschitz, S. & Oja, P. (2007). Environmental, Social and Personal Correlates of Cycling for Transportation in a Student Population. J Phys Act Health, 4, 66-79.

Toftegaard-Støckel, J., Nielsen, G. A., Ibsen, B. & Andersen, L. B. (2010). Parental, socio and cultural factors associated with adolescents' sports participation in four Danish municipalities. Scand J Med Sci Sports, 21, doi:10.1111/1600- 0838.2010.01093.x.

Trafimow, D., Sheeran, P., Conner, M. & Finlay, K. A. (2002). Evidence that perceived control is a multidimensional construct: perceived control and perceived difficulty. Br J Soc Psychol, 41, 101-122.

Tremblay, M., S. (2003). Is the Canadian childhood obesity epidemic related to physical activity? Int J Obes, 27, 1100-1105.

375

References

Treuth, Schmitz, M. S., Catellier, K. & J, D. (2004 a). Defining Accelerometer Thresholds for Activity Intensities in Adolescent Girls. Med Sci Sport Exer, 36(7), 1259-1266.

Treuth, M. S., Adolph, A. L. & Butte, N. F. (1998). Energy expenditure in children predicted from heart rate and activity calibrated against respiration calorimetry. Am J Physiol, 275, 12-18.

Treuth, M. S., Catellier, D. J., Schmitz, K. H., Pate, R. R., Elder, J. P., McMurray, R. G., Blew, R. M., Yang, S. & Webber, L. (2007). Weekend and Weekday Patterns of Physical Activity in Overweight and Normal-weight Adolescent Girls. Obesity, 15(7), 1782-1788.

Treuth, M. S., Sherwood, N. E., Baranowski, T., Butte, N. F., Jacobs, D. R., McClanahan, B., Gao, S., Rochon, J., Zhou, A., Robinson, T. N., Pruitt, L., Haskell, W. & Obarzanek, E. (2004). Physical activity self-report and accelerometry measures from the Girls health Enrichment Multi-site Studies. Prev Med, 38(Supplement 1), 43-49.

Troiano, Berrigan, R. P., Dodd, D. & W, K. (2008). Physical Activity in the United States Measured by Accelerometer. Med. Sci. Sport. Exer., 40(1), 181-188.

Troiano, M. S., Macera, C. A. & Ballard Barbash, R. (2001). Be physically active each day. How can we know? J Nutr, 131, 1085-1091.

Troiano, R. P. (2005). A Timely Meeting: Objective Measurement of Physical Activity.

Trost, S. (2001). Objective measurement of physical activity in youth: Current issues, future directions. Exerc Sport Sci Rev, 29(1), 32-36.

376

References

Trost, S. G. (2007). Measurement of physical activity in children and adolescents. Am J Lifestyle Med, 1(4), 299-314.

Trost, S. G., McIver, K. L. & Pate, R. R. (2005). Conducting Accelerometer-Based Activity Assessments in Field-Based Research. Med Sci Sport Exer, 37(11, Suppl), S531-543.

Trost, S. G., Pate, R. G., Saunders, R., Ward, D. S., Dowda, M. & Felton, G. (2001). A prospective study of the determinants of physical activity in rural fifth grade children. Prev Med, 26(2), 257-263.

Trost, S. G., Pate, R. R., Freedson, P. S., Sallis, J. F. & Taylor, W. C. (2000). Using objective physical activity measures with youth: How many days of monitoring are needed? . Med Sci Sport Exer, 32(2), 426 - 431.

Trost, S. G., Pate, R. R., Sallis, J. F., Freedson, P. S., Taylor, W. C., Dowda, M. & Sirard, J. (2002). Age and gender differences in objectively measured physical activity in youth. Med. Sci. Sport. Exer., 34(2), 350-355.

Trost, S. G., Pate, R. R., Ward, D. S., Saunders, R. & Riner, W. (1999). Correlates of objectively measured physical activity in preadolescent youth. Am. J. Prev. Med., 17(2), 120-126.

Trost, S. G., Ward, D. S., Moorehead, S. M., Watson, P. D., Riner, W. & Burke, J. R. (1998). Validity of the Computer Science and Applications (CSA) activity monitor in children. Med Sci Sport Exer, 30(4), 629-633.

377

References

Tuomilehto, J., Lindstrom, J., Erikson, J. G. & Valle, T. T. (2001). Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med, 344, 1343-50.

Twisk, J. W., Kemper, H. C. G. & Van Mechelen, W. (2000). Tracking of activity and fitness and the relationship with cardiovascular disease risk factors. Med Sci Sport Exer, (32), 8.

Twisk, J. W. R., Kemper, K. C. G., van Mechelen, W., Post, G. B. & van Lenthe, F. J. (1998). Body fatness : Longitudinal relationship of body mass index and the sum of skinfolds with other risk factors for coronary heart disease. Int J Obes, 22, 915-922.

UNESCO, United Nations Educational Scientific and Cultural Organisation (1978), ‘International Charter of Physical Education and Sport : Adopted by the General Conference at its twentieth session, from http://www.unesco.org/education/nfsunesco/pdf/SPORT_E.PDF [accessed 10 April 2008]. .

US Dept of Health and Human Services (1996). Physical activity and health: a report of the Surgeon General, Atlanta: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

Van der Horst, K., Chin A Paw, M. J., Twisk, J. W. & Van Mechelen, W. (2007). A brief review on correlates of physical activity and sedentariness in youth. Med Sci Sport Exer, 39(8), 1241-1250.

378

References

Van Dongen-Melman, J. E. W. M., Koot, K. M. & Verhulst, F. C. (1993). Cross- Cultural validation of Harter's Self- Perception Profile for Children in a Dutch Sample. Educ Psychol Meas, 53(3), 739-753.

Van Dyck, D., Cardon, G., Deforche, B., Giles-Corti, B., Sallis, J., Owen, N. & De Bourdeaudhuij, I. (2010). Environmental and Psychosocial Correlates of Acceleroemter Assessed and Self Reported Physical Activity in Belgian Adults. Int J Behav Med, Oct 31 [Epub ahead of print].

Van Mechelen, W. & Kemper, H. C. G. (1995). Habitual physical activity a longitudinal perspective. In Kemper, H. C. G., In: The Amsterdam Growth and Fitness Study: A Longitudinal Analysis of Health, Fitness and Lifestyle . Champaign, IL: Human Kinetics.

Van Reusal, B., Renson, R., Beunen, G., Claessens, A. L., Lefevre, F., Lysens, R. & Vanden Eynde, B. (1997). A longitudinal study of youth sport participation and adherence ot sport in adulthood. Int Rev Soc Sport, 32(4), 373-387.

Vanhelst, J., Beghin, L., Bergman, P., Sjostrom, M. & Gottrand, F. (2011). Comparison of Physical Activity Assessed by Uniaxial and Triaxial Acceleroemtry in Adolescents under Free Living Conditions: The HELENA study. Presented at ICAMPAM, Glasgow .

Velicier, W. F. & Jackson, D. N. (1990). Component Analysis Versus Common Factor Analysis - Some Further Observations. Multivar Behav Res, 25(1), 97-114.

Viira, R. & Raudsepp, L. (2000). Achievement goal orientations, beliefs about sport success and sport emotions as related to moderate to vigorous physical activity of adolescents. Psychology & Health, 15(5), 625-633.

379

References

Vu, M. B., Murrie, D., Gonzalez, V. & Jobe, J. B. (2006). Listening to Girls and Boys Talk About Girls’ Physical Activity Behaviors. Health Educ Behav, 33(1), 81- 96.

Wallhead, T. L. & Buckworth, J. (2004). The role of physical education in the promotion of youth physical activity. Quest (00336297), 56(3), 285-301.

Wallston, K. A., Wallston, B. S. & DeVellis, R. F. (1978). Locus of control of health: a review of the literature. Health Education Monographs, 6, 107-117.

Ward, D. S., Evenson, K. R., Vaughn, A., Rodgers, A. B. & Troiano, R. P. (2005). Accelerometer Use in Physical Activity: Best Practices and Research Recommendations. Med. Sci. Sport. Exerc., 37(11 Suppl), S582–S588.

Warren, J. M., Henry, C. J. K., Lightowler, H. J., Bradshaw, S. M. & Perwaiz, S. (2003). Evaluation of a pilot school programme aimed at the prevention of obesity in children. Health Promot Int, 18(4), 287-296.

Weber, R. P. (1990). Basic Content Analysis . Newbury Park, CA: Sage Publications.

Weiss, M. R., Barber, H., Sisley, B.L., & Ebbeck, V. (1991). Developing competence and confidence in novice female coaches: Perceptions of ability and affective experiences following a season long coaching internship JSEP, 13, 336-363.

Welk, G., Blair, S., Wood, K., Jones, S. & Thompson, R. (2000). A comparative evaluation of three accelerometry- based activity monitors. Med Sci Sport Exer, 32(9), S489-S497.

380

References

Welk, G. J. (2005). Principles of Design and Analyses for the Calibration of Accelerometry-Based Activity Monitors. Med Sci Sport Exer, 37(11 Suppl), S501-S511.

Welk, G. J., Wood, K. & Morss, G. (2003). Parental influences on physical activity in children: An Exploration of Potential Mechanisms. Pediatr Exerc Sci, 15, 19-33.

Westertop, K., R. (1999). Assessment of physical activity level in relation to obesity: current evidence and research issues’. Med Sci Sport Exer, 31, S522-525.

Weston, A. T., Petosa, R. & Pate, R. R. (1997). Validation of an instrument for measurement of physical activity in youth. American College of Sports Medicine, 29(1), 138-143.

Whitt-Glover, M. C., Taylor, W. C., Floyd, M. F., Yore, M. M., Yancey, A. K. & Matthews, C. E. (2009). Disparities in Physical Activity and Sedentary Behaviors Among US Children and Adolescents: Prevalence, Correlates, and Intervention Implications. J Public Health Pol, 30, S309-S334.

Wichstraum, L. (1995). Harter's Self-Perception Profile for Adolescents: Reliability, Validity, and Evaluation of the Question Format. J Pers Assess, 65(1), 100.

381

References

Wickel, Eisenmann, E. E. & C, J. (2007). Contribution of Youth Sport to Total Daily Physical Activity among 6- to 12-yr-old Boys. Med Sci Sport Exer, 39(9), 1493- 1500.

Wilson, D. K., Williams, J., Evans, A., Mixon, G. & Rheaume, C. (2005). Brief Report: A Qualitative Study of Gender Preferences and Motivational Factors for Physical Activity in Underserved Adolescents. J Pediatr Psychol, 30(3), 293- 297.

Wong, B. Y., Faulkner, G. & Buliung, R. (2011). GIS measured environmental correlates of active school transport: a systematic review of 14 studies. In J Behav Nutr Phys Act, 6(8), 39.

Woods, C., Foley, E., O’Gorman, D., Kearney, J. & Moyna, N. (2004). The Take PART Study: Physical Activity Research for Teenagers , Dublin: Dublin City University.

Woods, C., Tannehill, D., Quinlan, A., Moyna, N. & Walsh, J. (2010). The Chidren's Sport Participation and Physical Activity Study (CSPPA). Research Report No. 1. , Dublin, Ireland: Dublin City University and The Irish Sports Council.

Woods, C. B., Nelson, N. M., O' Gorman, D. J., Foley, E. & Moyna, N. M. (2009). The Take PART Study (Physical Activity Research for Teenagers): Rationale and Methods. J Phys Act Health, 6(2), 170-177.

WHO (1986), The Ottawa Charter for Health Promotion, World Health Organisation, Geneva: Switzerland, Retrieved 7 April, 2008, from http://www.who.int/hpr/hprdocuments/ottawa.html .

382

References

Wu, T.-Y., Ronis, D. L., Pender, N. & Jwo, J.-l. (2002). Development of Questionnaires to Measure Physical Activity Cognitions among Taiwanese Adolescents. Prev Med, 35(1), 54-64.

Wu, T., Y., Pender, N. & Noureddine, S. (2003). Gender Differences in the Psychosocial and Cognitive Correlates of Physical Activity Among Taiwanese Adolescents: A Structural Equation Modeling Approach. Int. J. Behav. Med., 10(2), 93-105.

Zwick, W. R. & Velicier, W. F. (1986). Comparison of five rules for determining the number of components to retain. Psychol Bull, 99, 432-42.

383

References

384

Appendix A

Information Sheets and Consent Forms

A.1 Introduction

This appendix presents consent forms information sheets used throughout the research.

A.2 Principal’s letters and consent forms

A.2.1 Principals letters

Date Principal’s Letter Questionnaire

Dear Principal, My name is Con Burns, I am a lecturer on the Recreation and Leisure Course in Cork Institute of Technology. I am currently pursuing my PhD with Dr. Ciaran MacDonncha

A-1

Appendix A from the PE and Sport Science Dept in the University of Limerick. I am investigating the factors that promote or inhibit attitudes towards and engagement in physical activity among Irish adolescents. I am writing to you inviting your school to participate in the project. I have developed a comprehensive questionnaire which I hope to administer to young people in schools in Cork city and county starting in September 2009. The questionnaire will take approximately 40 minutes to complete. The project has been approved by the Research Ethics Committee in the University of Limerick. I have attached the relevant documentation. Please find enclosed: • Parent information sheet • Young person information sheet • Parent consent form • Young person consent form. I will make phone contact in approximately one week to see if your school is interested in participating in the project. If you are willing to participate in the project I would hope with your permission to make contact with a PE teacher in the school and to liaise with him/her. Thanking you in advance, ______Con Burns, Lecturer Department of Social and General Studies, Cork Institute of Technology, [email protected] 086-3755616

A-2

Appendix A

Principal’s Letter Focus Group Date

Dear Principal, My name is Con Burns, I am a lecturer on the Recreation and Leisure Course in Cork Institute of Technology. I am currently pursuing my PhD through the Department of Physical Education and Sport Science at the University of Limerick. The area I am researching is into the ‘Factors that Promote or Inhibit Attitudes Towards and Engagement in Physical Activity among Irish Adolescents’. I am writing to you to invite your school to participate in phase 1 of the research study. I am hoping with your consent to meet with the Physical Education Teacher(s) in your school to organize a number of focus groups with students in your school. Each focus group will consist of approximately 9 students and will last approximately 1 hour. These focus groups will discuss factors that affect individual’s participation in physical activity. Ethical approval for the current project has been granted by the University of Limerick Ethics Committee. Please find enclosed a principal consent form, for your signature should you agree to your schools participation. I have also enclosed a parental consent form which each must be completed for each participant prior to partaking in the study. If you have any queries please don’t hesitate to contact me. I will be in touch with you shortly to enquire whether you are interested in your schools involvement in this study.

Yours sincerely,

______Mr. Con Burns, Lecturer in the Department of Social and General Studies, Cork Institute of Technology E –mail: [email protected] Mobile: 086-3755616

A-3

Appendix A

Date Principal’s Letter - Accelerometer

Dear Principal, My name is Con Burns, I am a lecturer on the Recreation and Leisure Course in Cork Institute of Technology. I am currently pursuing my PhD with Dr. Ciaran MacDonncha from the PE and Sport Science Dept in the University of Limerick. I am investigating the factors that promote or inhibit attitudes towards and engagement in physical activity among Irish adolescents. I am writing to you inviting your school to participate in the project. The project would involve two classes from your school wearing an accelerometer (a small device which is worn on the hip and which records physical activity) for a period of one week. Participants would also be asked to complete a questionnaire relating to factors influencing physical activity. The questionnaire will take approximately 40 minutes to complete. The project has been approved by the Research Ethics Committee in the University of Limerick. I have attached the relevant documentation. Please find enclosed: • Parent information sheet and consent form • Young person information sheet and consent form I will make phone contact in approximately one week to see if your school is interested in participating in the project. If you are willing to participate in the project I would hope with your permission to make contact with a PE teacher in the school and to liaise with him/her.

Thanking you in advance, ______Con Burns, Lecturer Department of Social and General Studies, Cork Institute of Technology, E-mail: [email protected] Mobile: 086-3755616

A-4

Appendix A

A.2.2 Principal Consent Form

Principal Consent Form

Should you agree to your schools participation in this study please sign the consent form below.

Title of Project: An investigation into factors promoting or inhibiting attitudes towards and engagement in physical activity among Irish adolescents.

I consent to the involvement in this research project.

Name: (please print): ______(Principal)

Name (please print)______(School)

Signed:______

A-5

Appendix A

A.3 Information Sheets and Consent Forms

A.3.1 Information Sheets and Consent Forms - Questionnaire

1/09/09

Dear Parent/Guardian, Cork Institute of Technology and the University of Limerick are working together on a research project which is investigating factors affecting physical activity among Irish adolescents. We are requesting your child’s participation in the research project. The project involves completion of a questionnaire which will be administered in your child’s school. Please read the enclosed “Parent/ Carer Information Sheet ” and provide your child with the “Young Person Information Sheet ”. If you and your child agree to your child’s participation please complete the “Parent Consent Form” and the “ Young Person Consent Form ”. Your co-operation in the prompt return of the completed forms will be greatly appreciated. Forms can be returned to the PE teacher in your child’s school. The participation in the study promises to be enjoyable for each individual young person.

Thanking you in advance,

______Mr. Con Burns Dr. Ciaran MacDonncha Dept of Social and General Studies, PESS Department Cork Institute of Technology University of Limerick Cork . Limerick. E-Mail:[email protected] EMail:[email protected] Phone: 021-4326836 Phone: 061 – 213162

A-6

Appendix A

Parent/Carer Information Sheet

Title of Project: Determinants of Physical Activity among Irish Adolescents

Background : Regular long term physical activity has been found to result in improved health and well being, however, national and international research has found that physical activity decreases dramatically in adolescence. The reasons for this decline remain largely unclear. This project aims to examine the main factors that positively and negatively affect physical activity among Irish adolescents.

Methods : Questionnaire: Your child will be asked to complete a questionnaire investigating the factors that promote or inhibit attitude towards and engagement in physical activity. The questionnaire will take approximately 40 minutes to complete and will be carried out during school time.

Participation is entirely voluntary and based on your written consent and that of your child. You may withdraw your consent and discontinue the participation of your child at any time. Should you have any questions concerning the project groups or your child’s participation in it, pleases contact Mr Con Burns, Dept of Social and General Studies, Cork Institute of Technology; Tel: 021-4326836 or Dr. Ciaran MacDonncha, PESS Department, University of Limerick; Tel: 061-213162 This study has been approved by the Ethics Committee of the Physical Education and Sport Sciences Department. The Chairman of the University of Limerick Research Ethics Committee C / O Vice President Academic and Registrar's Office University of Limerick, Limerick. Tel: (061) 202022.

A-7

Appendix A

Young Person Information Sheet (Questionnaire)

Title of Project: Determinants of Physical Activity among Irish adolescents

What is the project about? We are interested to find out the reasons why some young people are involved in lots of sport and physical activity and why others are not so active. Your school, the University of Limerick and Cork Institute of Technology are working together on the project.

What will you have to do? You will be asked to complete a questionnaire on factors influencing physical activity. The questionnaire will take approximately 40 minutes to complete.

What if I do not want to take part? Participation in the research is voluntary; you do not have to complete the questionnaire if you so wish.

Who else is taking part? Some of the other classes in your school as well as young people from other schools

What if I have more questions or do not understand something . Any further questions you might have will be answered by the research team or your PE teacher.

A-8

Appendix A

Written Informed Consent Form – Parent/ Guardian

There are no foreseeable risks or discomforts to my child if he/she agrees to participate in the study. I understand that any questions I have concerning the research study or my child’s participation in it, before or after my consent, will be answered by Dr. Ciaran MacDonncha, PESS Department, University of Limerick; Tel: 061-213162, or Mr. Con Burns, Department of Social and General Studies, Cork Institute of Technology; Tel: 021-4326836.

I have read the above information. The nature, demands, risks and benefits of the project to my child have been explained to me. I understand that I may withdraw my consent and discontinue the participation of my child at any time. In signing this consent form, I am not waiving any legal claims, rights or remedies.

Childs Name : ______

Parent /Guardian

Print:______Sign: ______Date ______

Investigator Print:______Sign:______Date ______

A-9

Appendix A

Young Person Consent Form

I understand that my parent(s)/guardian(s) have given permission for me to participate in a study concerning ‘ Determinants of Physical Activity among Irish Adolescents’ . My involvement is voluntary, and I have been told that I may withdraw from participation at any time.

Participant

Print:______Sign:______

Date:______

Parent/ Carer Witnessing Signature

Print:______Sign:______

Date:______

A-10

Appendix A

A.3.2 Information Sheets and Consent Forms – Focus groups

Date Dear Parent/Guardian,

The Department of Physical Education and Sport Sciences at the University of Limerick in conjunction with Cork Institute of Technology are working together on a research project which is investigating factors affecting physical activity among Irish adolescents. Please read the enclosed information sheet and provide your child with the young person information sheet. If you and your child are agreeable to your child’s participation please complete the parent and young person consent forms. Your co-operation in the prompt return of the completed forms will be greatly appreciated, as the focus group interviews will not start until your consent has been given. Forms can be returned to the PE teacher within your school. Please return the forms whether you agree or disagree to participate. The participation in the study promises to be enjoyable for each individual young person and involves minimal risk.

Thanking you in advance,

A-11

Appendix A

Parent/Carer Information Sheet – Focus Group

Title of Project: Determinants of Physical Activity among Irish Adolescents Background : Regular long term physical activity has been found to result in improved health and well being. In conjunction with this national and international research has found that physical activity and sport participation decrease dramatically in adolescence. The reasons for this dramatic decrease in participation levels among adolescents remain largely unclear. To maximise participation in physical activity and sport among adolescent’s the factors that positively and negatively affect physical activity need to be well understood.

Methods : Discussion (Focus) Groups: The discussion groups will consist of 6-10 young people and 2 researchers. The researchers will ask a series of questions relating to physical activity which will be discussed by the group.

Results: The results of the research study may be published but no reference to your child or the school will be made. The data collected will be stored on file; the research team are the only personnel who will have access to the information. The principal of your child’s school will be provided with the findings. Participation is entirely voluntary and based on your written consent and that of your child. You may withdraw your consent and discontinue the participation of your child at any time. Should you have any questions concerning the focus groups or your child’s participation in it, pleases contact Mr Con Burns, Dept of Social and General Studes, Cork Institute of Technology; Tel: 021-4326836 or Dr. Ciaran MacDonncha, PESS Department, University of Limerick; Tel: 061-213162. This study has been approved by the Ethics Committee of the Physical Education and Sport Sciences Department. If you have concerns about this study and wish to contact someone independent, you may contact: The Chairman of the University of Limerick Research Ethics Committee C / O Vice President Academic and Registrar's Office University of Limerick, Limerick. Tel: (061) 202022.

A-12

Appendix A

Young Person Information Sheet

Title of Project: Determinants of Physical Activity among Irish adolescents

What will you have to do? You are being asked to take part in a focus group (small group discussion) with two researchers and a number of your classmates. In the focus group the researcher will ask a number of questions relating to why young people are/ are not physically active. These questions will be discussed in the group.

What if I do not want to take part? Participation in the focus group is voluntary; you do not have to take part in the focus group if you so wish.

Who else is taking part? Some of the other classes in your school as well as young people from other schools

What if I have more questions or do not understand something . The researcher and your teacher will be able to answer any questions you have relating to the project.

A-13

Appendix A

Written Informed Consent Form – Parent/ Guardian

To gather information on the factors that affect physical activity among Irish adolescents. This information can be used in programmes designed to increase physical activity.Participation in the project will be an enjoyable and educational experience; Gathering of information on levels of physical activity among Irish adolescent. Currently, there is limited information on activity levels among Irish young people,

Childs Name : ______

Parent /Guardian

Print:______Sign: ______Date ______

Investigator Print:______Sign:______Date ______A-14

Appendix A

Young Person Consent Form

I……………………………………….. , understand that my parent(s)/guardian(s) have given permission for me to participate in a study concerning ‘ Determinants of Physical Activity among Irish Adolescents’ . My involvement is voluntary, and I have been told that I may withdraw from participation at any time.

Participant

Print:______Sign:______

Date:______

Parent/ Carer Witnessing Signature

Print:______Sign:______

Date:______

A-15

Appendix A

A.3.3 Information Sheets and Consent Forms – Accelerometer

Date

Dear Parent/Guardian, Cork Institute of Technology and the University of Limerick are working together on a research project which is investigating factors affecting physical activity among Irish adolescents. We are requesting your child’s participation in the research project. The project involves your child wearing an accelerometer (device to measure physical activity) for a period of 1 week and completing a physical activity questionnaire in school. Please read the enclosed “Parent/ Carer Information Sheet ” and provide your child with the “Young Person Information Sheet ”. If you and your child agree to your child’s participation please complete the “Parent Consent Form” and the “ Young Person Consent Form ”. Your co-operation in the prompt return of the completed forms will be greatly appreciated. Forms can be returned to the PE teacher in your child’s school. The participation in the study promises to be enjoyable for each individual young person.

Thanking you in advance, Mr. Con Burns Dr. Ciaran MacDonncha Dept of Social and General Studies, PESS Department Cork Institute of Technology University of Limerick E-mail:[email protected] E- mail:[email protected] Phone: 021-4326836 Phone: 061 – 213162

A-16

Appendix A

Parent/Carer Information Sheet – Accelerometer Title of Project: Determinants of Physical Activity among Irish Adolescents Aims: The aims of the current project are to: • Collect data on physical activity levels of Irish adolescents • Examine the factors that positively and negatively affect physical activity among Irish adolescents.

Methods : 1. Your child will be asked to wear an accelerometer for a period of 1 week. An accelerometer is a small device which is worn on the hip and which records physical activity. The participant should take off the accelerometer when they are in water, if they are participating in contact sports and when they are in bed. The researchers will text the participants or the participants’ parent/ guardian to remind them to put on the accelerometer each morning. 2. Your child will be asked to complete a questionnaire investigating the factors that promote or inhibit participation in physical activity. The questionnaire will take approximately 40 minutes to complete and will be carried out during school time. A number of prizes will be raffled among those who participate in the project.

Results: The data collected will be stored on file; the professionals on the research team will be the only personnel who will have access to the information. No reference to your child or the school will be made. Participation is entirely voluntary and based on your written consent and that of your child. You may withdraw your consent and discontinue the participation of your child at any time.

Should you have any questions concerning the project or your child’s participation in it, pleases contact Mr Con Burns, Dept of Social and General Studes, Cork Institute of Technology; Tel: 021-4326836 or Dr. Ciaran MacDonncha, PESS Department, University of Limerick; Tel: 061-213162. This study has been approved by the Ethics Committee of the Physical Education and Sport Sciences Department. The Chairman of the University of Limerick Research Ethics Committee C / O Vice President Academic and Registrar's Office, University of Limerick, Tel: (061) 202022.

A-17

Appendix A

Young Person Information Sheet - Accelerometer

Title of Project: Determinants of Physical Activity among Irish Adolescents

What will you have to do? You will be asked to wear an accelerometer for a period of 1 week. An accelerometer is a small device which is worn on the hip and which measures physical activity. You will not wear the accelerometer in water, playing contact sports and when you are in bed. You will receive a text each morning to remind you to put on the accelerometer.

You will be asked to complete a questionnaire on factors influencing physical activity. The questionnaire will take approximately 40 minutes to complete. A number of prizes will be raffled among those who participate in the project.

What if I do not want to take part? Participation in the research is voluntary; you do not have to take part if you so wish.

Who else is taking part? Some of the other classes in your school as well as young people from other schools

What if I have more questions or do not understand something . Any further questions you might have will be answered by the research team or your PE teacher.

A-18

Appendix A

Parent/ Guardian Consent Form

Dr. Ciaran Mac Donncha from the University of Limerick and Mr. Con Burns from Cork Institute of Technology have requested the participation of my child in a research study into the “Determinants of Physical Activity among Irish Adolescents”. There are no foreseeable risks or discomforts to your child if he/she agrees to participate in the study. I understand that any questions I have concerning the research study or my child’s participation in it, before or after my consent, will be answered by Dr. Ciaran MacDonncha, PESS Department, University of Limerick; Tel: 061-213162, or Mr. Con Burns, Department of Social and General Studies, Cork Institute of Technology; Tel: 021-4326836. I have read the above information. The nature, demands, risks and benefits of the project to my child have been explained to me. I understand that I may withdraw my consent and discontinue the participation of my child at any time. In signing this consent form, I am not waiving any legal claims, rights or remedies.

Childs Name : ______

Please tick the elements of the project you are happy for your child to participate in: 1. Questionnaire

2. Wearing the accelerometer for 1 week

3. Providing a text number to receive texts to remind him/her to wear the accelerometer

Parent /Guardian

Print:______Sign: ______Date ______

A-19

Appendix A

Young Person Consent Form

I understand that my parent(s)/guardian(s) have given permission for me to participate in a study examining ‘Determinants of Physical Activity among Irish Adolescents’. My involvement is voluntary, and I have been told that I may withdraw from participation at any time.

Please tick the elements of the project you are willing to participate in 1. Questionnaire

2. Wearing the accelerometer for 1 week

3. Providing a text number to receive texts to remind you to wear the accelerometer

Please provide text number if you are happy to receive texts to remind you to wear the accelerometer. Text Number for Reminder Texts: ______

Participant

Print:______Sign:______

Date:______

Parent/ Carer Witnessing Signature

Print:______Sign:______

Date:______A-20

Appendix A

A.3.4 Physical Activity Accelerometer information handout

Slide 1

Physical Activity Study

Accelerometers

Slide 2 What is an accelerometer

• An instrument that measures:

- How much physical activity you do

- How intense your physical activity is

- How many steps you take each day

Slide 3

A-21

Appendix A

Slide 4 Where to place accelerometer?

• Put belt on around waist

• Make sure the belt is comfortable – loosen or tighten to make sure it is comfortable

• Turn the belt so the accelerometer is over the right hip

• You can wear the accelerometer over or under clothing.

Slide 5 When do I wear the accelerometer? • The accelerometer must not be worn while: 1. Swimming 2. Taking a bath 3. Having a shower 4. In bed

• The accelerometer must be worn at all other times including - While playing sports - During school

Slide 6 How long do I wear the accelerometer for? • You will wear the accelerometer for 1 week

• After 1 week the researcher/ PE teacher will collect the accelerometer from you

• You will get a text every morning to remind you to wear the accelerometer

A-22

Appendix A

Slide 7 Prizes

• At the end of the week an i-pod shuffle will be raffled among those who successfully wore the accelerometer for the week.

• The data will show up those who wore the accelerometer and those who wore the accelerometer for most/ all of the week will be entered into the raffle

• If you need to take the accelerometer off at any stage, you must fill in the diary sheet.

Slide 8 What happens if I lose the accelerometer? • If you lose the accelerometer you must contact me as soon as possible

• Contact details: Con Burns 086-3755616

• It is very important that you mind the accelerometer and take good care of it

9.3A.4 Data Cleaning Procedure

The data cleaning and screening process involved the following steps; (i) checking for errors and (ii) finding and correcting the errors in the data file (Pallant, 2005). For categorical variables the frequency values of the inputted data were generated. The maximum and minimum values were provided in this output; and were assessed to ensure the data inputted was in the range of possible scores for that variable. Assessment of the number of valid and missing cases was also carried out. For continuous variables, mean, standard deviation, maximum and minimum values were screened. When out of range responses were found cases were sorted either ascending or descending. The case ID of the error was then identified the questionnaire was rechecked and the score was replaced with the ‘correct’ value.

A-23

Appendix A

Descriptive scores identified the percentage of variables missing. When participants were missing data an 80% rule was used. This meant that when 80% of the items in a scale were answered the mean value of the other items in the scale was used to replace the missing value. If less than 80% of the items in a scale were not answered participants scores in this scale were not included in the analysis.

A-24

Appendix B

Accelerometer protocol

B.1 Introduction

This Appendix contains information relating to the accelerometer protocol and analysis, from Chapters five and six. A sample activity summary sheet which was provided to each individual after wearing the accelerometer is included. The data reduction macro’s which were developed to analyse the accelerometer data are also included.

B-1

Appendix B

B.2 Summary Output Sheet for Participants

Name of Student: The Recommended Daily Physical Activity is 60minutes Moderate to Vigorous activity per day. Moderate to Vigorous activity includes, jogging/ brisk walking, playing a match. Any form of exercise in which you break a sweat would be considered moderate to vigorous activity.

Daily Activity Summary Very Sedentary Light Moderate Vigorous Vigorous Date (mins) (mins) (mins) (mins) (mins) Steps KCALS

------

19/01/2010 638 127 13 2 0 3132 48.86

20/01/2010 1106 278 56 0 0 12024 165.45

21/01/2010 1247 189 4 0 0 3043 8.91

22/01/2010 1254 154 32 0 0 6682 90.72

23/01/2010 1316 116 8 0 0 3252 21.85

24/01/2010 1421 19 0 0 0 219 0

25/01/2010 1355 85 0 0 0 999 0

26/01/2010 801 95 4 0 0 1966 9.05

B-2

Appendix B

B.3 Analysis of Non-wear Time

This section provides details of macro used to differentiate non wear time from wear time in the analysis.

B.3.1 Sorting of Non-wear Time

Non Wear Sorting - Creates DATAxxx.xlsx file of non wear data - also counts the total activity count Sub non_wear() nwperiod = 60 'Length of the Non Wear Period Application.ScreenUpdating = False ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Summary").Select Range("B4").Select While ActiveCell.Value <> "" TheFile = Dir(ThePath & "Data" & (ActiveCell.Value + 100) & "*.csv") outputfile = "Data" & (ActiveCell.Value + 100) & ".xlsx" Workbooks.Add ActiveWorkbook.SaveAs Filename:=ThePath & outputfile, FileFormat:=xlOpenXMLWorkbook, CreateBackup:=False Sheets("Sheet1").Select Sheets("Sheet1").Name = "Non-Wear" Columns("D:E").Select Selection.NumberFormat = "dd-mmm-yyyy hh:mm:ss" Range("A1").Select ActiveCell.Value = "DAY" ActiveCell.Offset(0, 1).Value = "Non-Wear Number" ActiveCell.Offset(0, 2).Value = "Non-Wear Duration" ActiveCell.Offset(0, 3).Value = "Start Time" ActiveCell.Offset(0, 4).Value = "End Time" ActiveCell.Offset(1, 0).Select Workbooks.Open (ThePath & TheFile) Columns("A:A").Select Selection.TextToColumns Destination:=Range("A1"), DataType:=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=False, Tab:=False, _ Semicolon:=False, Comma:=False, Space:=False, Other:=False, FieldInfo _ :=Array(1, 4), TrailingMinusNumbers:=True Selection.NumberFormat = "[$-F800]dddd, mmmm dd, yyyy" '"dd/mm/yyyy" Columns("B:B").Select Selection.TextToColumns Destination:=Range("b1"), DataType:=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=False, Tab:=False, _ Semicolon:=False, Comma:=False, Space:=False, Other:=False, FieldInfo _ :=Array(1, 4), TrailingMinusNumbers:=True Selection.NumberFormat = "hh:mm:ss;@" Range("A12").Select Dim paxn As Date 'minute*/ Dim dur_nw As Integer Dim end_nw As Date Dim strt_nw As Date Dim dur_nw_time As Date

Dim start As Boolean Dim first_day As Boolean Dim last_day As Boolean first_day = True Dim stopped As Boolean stopped = True Dim reset As Boolean reset = True

While ActiveCell.Value <> ""

B-3

Appendix B

last_day = False paxinten = 0 paxn = FormatDateTime(ActiveCell.Value + ActiveCell.Offset(0, 1).Value, vbGeneralDate)

stp_min = Minute(ActiveCell.Offset(1, 1).Value) cur_min = Minute(ActiveCell.Offset(0, 1).Value) While cur_min = stp_min And ActiveCell.Offset(1, 1).Value <> "" stp_min = Minute(ActiveCell.Offset(1, 1).Value) cur_min = Minute(ActiveCell.Offset(0, 1).Value) paxinten = paxinten + ActiveCell.Offset(0, 2).Value ActiveCell.Offset(1, 0).Select Wend activeday = Weekday(ActiveCell.Offset(-1, 0).Value) 'Activates the last day flag If activeday <> Weekday(ActiveCell.Value) Then last_day = True If ActiveCell.Offset(1, 1).Value = "" Then ActiveCell.Offset(1, 0).Select If first_day Then nw_num = 0 If first_day Or stopped Or reset Then strt_nw = 0 'starting minute for the non-wear period*/ end_nw = 0 'ending minute for the non-wear period*/ start = 0 'indicator for starting to count the non-wear period*/ dur_nw = 0 'duration for the non-wear period*/ reset = False 'indicator for resetting and starting over*/ stopped = False 'indicator for stopping the non-wear period*/ cnt_non_zero = 0 'counter for the number of minutes with intensity between 1 and 100*/ End If

'The non-wear period starts with a zero count*/ If paxinten = 0 And start = 0 Then strt_nw = paxn 'assign the starting minute of non-wear*/ start = 1 End If

'accumulate the number of the non-wear minutes*/ If start And paxinten = 0 Then end_nw = paxn 'keep track of the ending minute for the non-wear period*/ dur_nw = dur_nw + 1 End If

'keep track of the number of minutes with intensity between 1-100 If paxinten > 0 And paxinten <= 100 Then cnt_non_zero = cnt_non_zero + 1

'before reaching the 3 consecutive minutes of 1-100 intensity, if encounter one minute with zero intensity, reset the counter*/ If paxinten = 0 Then cnt_non_zero = 0

'A non-wear period ends with 3 consecutive minutes of 1-100 intensity, one missing count, or one minute with >100 intensity*/ If cnt_non_zero = 3 Or paxinten > 100 Then If dur_nw < nwperiod Then reset = 1 'reset if less than &nwperiod minutes of non-wear*/ Else: stopped = True End If End If

'last minute of the day*/ If last_day And dur_nw >= nwperiod Then stopped = True 'reset if its the end of a day and hasn't reached the non wear threshold 'If last_day And dur_nw < nwperiod Then reset = True If last_day Then reset = True

'output one record for each non-wear period*/ If stopped Then nw_num = nw_num + 1 Windows(outputfile).Activate Sheets("Non-Wear").Select ActiveCell.Value = WeekdayName(activeday) ActiveCell.Offset(0, 1).Value = nw_num ActiveCell.Offset(0, 2).Value = dur_nw ActiveCell.Offset(0, 3).Value = strt_nw ActiveCell.Offset(0, 4).Value = end_nw ActiveCell.Offset(1, 0).Select Windows(TheFile).Activate End If first_day = False Wend

B-4

Appendix B

Windows(TheFile).Activate ActiveWorkbook.Close saveChanges:=False Windows(outputfile).Activate ActiveWorkbook.Close saveChanges:=True Windows("Activity_Summary.xlsm").Activate ActiveCell.Offset(1, 0).Select Wend Application.ScreenUpdating = True End Sub

B.3.2 Generation of Non-wear Minutes Summary

Non Wear Summary-Reads the DATAxxx.xlsx file and summarises into Active Summary file Sub non_wear_summary() Application.ScreenUpdating = False ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Summary").Select Range("B4").Select Dim day_flg As Integer While ActiveCell.Value <> "" Dim nonwearsummary(6) As Double 'sum of non-wear data For i = 0 To 6 'zero the non-wear summary matrix nonwearsummary(i) = 0 Next i outputfile = "Data" & (ActiveCell.Value + 100) & ".xlsx" Workbooks.Open (ThePath & outputfile) Sheets("Non-Wear").Select Range("a2").Select

While ActiveCell.Value <> "" If ActiveCell.Value = "Monday" Then day_flg = 0 If ActiveCell.Value = "Tuesday" Then day_flg = 1 If ActiveCell.Value = "Wednesday" Then day_flg = 2 If ActiveCell.Value = "Thursday" Then day_flg = 3 If ActiveCell.Value = "Friday" Then day_flg = 4 If ActiveCell.Value = "Saturday" Then day_flg = 5 If ActiveCell.Value = "Sunday" Then day_flg = 6 nonwearsummary(day_flg) = nonwearsummary(day_flg) + ActiveCell.Offset(0, 2).Value ActiveCell.Offset(1, 0).Select Wend Windows(outputfile).Activate ActiveWorkbook.Close saveChanges:=False Windows("Activity_Summary.xlsm").Activate For i = 0 To 6 'zero the non-wear summary matrix ActiveCell.Offset(0, (2 + i)).Value = nonwearsummary(i) Next i ActiveCell.Offset(1, 0).Select Wend Application.ScreenUpdating = True End Sub

B-5

Appendix B

B.4 Analysis of Total Counts

This section provides details of the analysis of raw count data.

B.4.1 Total Counts for Qualifying Days

Total Count – Uses the Non Wear data in the Active Summary file and counts the total activity in the DATAxxx.xlsx file for qualifying days only (ie <840min of non- wear). Sub total_count() Application.ScreenUpdating = False ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Summary").Select Range("B4").Select While ActiveCell.Value <> "" totalcount = 0 TheFile = Dir(ThePath & "Data" & (ActiveCell.Value + 100) & "*.csv") Workbooks.Open (ThePath & TheFile) Columns("A:A").Select Selection.TextToColumns Destination:=Range("A1"), DataType:=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=False, Tab:=False, _ Semicolon:=False, Comma:=False, Space:=False, Other:=False, FieldInfo _ :=Array(1, 4), TrailingMinusNumbers:=True Selection.NumberFormat = "[$-F800]dddd, mmmm dd, yyyy" '"dd/mm/yyyy" Columns("B:B").Select Selection.TextToColumns Destination:=Range("b1"), DataType:=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=False, Tab:=False, _ Semicolon:=False, Comma:=False, Space:=False, Other:=False, FieldInfo _ :=Array(1, 4), TrailingMinusNumbers:=True Selection.NumberFormat = "hh:mm:ss;@"

Windows("Activity_Summary.xlsm").Activate Sheets("Summary").Select

For i = 1 To 7 If ActiveCell.Offset(0, (1 + i)).Value < 840 And ActiveCell.Offset(0, (1 + i)).Value <> 0 Then Windows(TheFile).Activate Range("a12").Select

While ActiveCell.Value <> "" If Weekday(ActiveCell.Value) = i + 1 Then totalcount = totalcount + ActiveCell.Offset(0, 2).Value If i = 7 Then If Weekday(ActiveCell.Value) = 1 Then totalcount = totalcount + ActiveCell.Offset(0, 2).Value End If ActiveCell.Offset(1, 0).Select Wend Windows("Finished_Activity_Summary2.xlsm").Activate Sheets("Summary").Select End If Next i ActiveCell.Offset(0, 9).Value = totalcount Windows(TheFile).Activate ActiveWorkbook.Close saveChanges:=False Windows("Activity_Summary.xlsm").Activate Sheets("Summary").Select ActiveCell.Offset(1, 0).Select Wend End Sub

B-6

Appendix B

B.4.2 Counts Number of Qualifying Days

Qualify Days – This uses the Non Wear data in the Active Summary file and counts the qualifying days only (ie <840min of non-wear) Sub qualify_days() ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Summary").Select Range("a4").Select While ActiveCell.Value <> "" tt = 0 For i = 1 To 7 If ActiveCell.Offset(0, (2 + i)).Value < 840 And ActiveCell.Offset(0, (2 + i)).Value <> 0 Then tt = tt + 1 End If Next i ActiveCell.Offset(0, 12).Value = tt ActiveCell.Offset(1, 0).Select Wend End Sub

B.5 Analysis of Activity Data

This section provides details of teh protocol used to analyse moderate and vigorous data.

B.5.1 Filter out Non-qualify Days

Qualify - Takes data from tab Summary and moves to Qualify Summary tab of subjects that pass 4 days activity criteria Sub qualify() ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Summary").Select Range("a4").Select While ActiveCell.Value <> "" tt = 0 For i = 1 To 7 If ActiveCell.Offset(0, (2 + i)).Value < 840 And ActiveCell.Offset(0, (2 + i)).Value <> 0 Then tt = tt + 1 End If

Next i If tt >= 4 Then ActiveCell.EntireRow.Select Application.CutCopyMode = False Selection.Copy Sheets("Qualify-Summary").Select Selection.Insert Shift:=xlDown ActiveCell.Offset(1, 0).Select Sheets("Summary").Select End If ActiveCell.Offset(1, 0).Select Wend End Sub

B-7

Appendix B

B.5.2 Creation of Moderate, Vigorous and Moderate/ Vigorous Tabs

Activity Level - Opens DATAxxx.xlsx file and adds Moderate,Vigorous and Mod/Vig Data Sub activity_level() boutperiod = 1 'sets the bout period to 1 minute Application.ScreenUpdating = False ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Qualify-Summary").Select Range("B4").Select While ActiveCell.Value <> "" ridageyr = ActiveCell.Offset(0, 1).Value

'Low threshold*/ lowthresh = 9

'moderate threshold*/ If ridageyr = 6 Then modthresh = 1400 / 12 If ridageyr = 7 Then modthresh = 1515 / 12 If ridageyr = 8 Then modthresh = 1638 / 12 If ridageyr = 9 Then modthresh = 1770 / 12 If ridageyr = 10 Then modthresh = 1910 / 12 If ridageyr = 11 Then modthresh = 2059 / 12 If ridageyr = 12 Then modthresh = 2220 / 12 If ridageyr = 13 Then modthresh = 2393 / 12 If ridageyr = 14 Then modthresh = 2580 / 12 If ridageyr = 15 Then modthresh = 2781 / 12

If ridageyr = 16 Then modthresh = 3000 / 12 If ridageyr = 17 Then modthresh = 3239 / 12 If ridageyr >= 18 Then modthresh = 2020 / 12

'vigorous threshold*/ If ridageyr = 6 Then vigthresh = 3758 / 12 If ridageyr = 7 Then vigthresh = 3947 / 12 If ridageyr = 8 Then vigthresh = 4147 / 12 If ridageyr = 9 Then vigthresh = 4360 / 12 If ridageyr = 10 Then vigthresh = 4588 / 12 If ridageyr = 11 Then vigthresh = 4832 / 12 If ridageyr = 12 Then vigthresh = 5094 / 12 If ridageyr = 13 Then vigthresh = 5375 / 12 If ridageyr = 14 Then vigthresh = 5679 / 12 If ridageyr = 15 Then vigthresh = 6007 / 12 If ridageyr = 16 Then vigthresh = 6363 / 12 If ridageyr = 17 Then vigthresh = 6751 / 12 If ridageyr >= 18 Then vigthresh = 5999 / 12 TheFile = Dir(ThePath & "Data" & (ActiveCell.Value + 100) & "*.csv") outputfile = "Data" & (ActiveCell.Value + 100) & ".xlsx" 'Creates the file name Workbooks.Open Filename:=ThePath & outputfile 'Opens the file which was created by the Non_Wear Macro Sheets.Add After:=Sheets(Sheets.Count) Sheets("Sheet1").Select Sheets("Sheet1").Name = "Moderate" Columns("D:E").Select Selection.NumberFormat = "dd-mmm-yyyy hh:mm:ss" Range("A1").Select ActiveCell.Value = "DAY" ActiveCell.Offset(0, 1).Value = "Moderate Activity Bout Number" ActiveCell.Offset(0, 2).Value = "Moderate Activity Bout Duration" ActiveCell.Offset(0, 3).Value = "Start Time" ActiveCell.Offset(0, 4).Value = "End Time" ActiveCell.Offset(1, 0).Select

Sheets("Sheet2").Select Sheets("Sheet2").Name = "Vigerous" Columns("D:E").Select Selection.NumberFormat = "dd-mmm-yyyy hh:mm:ss" Range("A1").Select ActiveCell.Value = "DAY" ActiveCell.Offset(0, 1).Value = "Vigerous Activity Bout Number" ActiveCell.Offset(0, 2).Value = "Vigerous Activity Bout Duration" ActiveCell.Offset(0, 3).Value = "Start Time" ActiveCell.Offset(0, 4).Value = "End Time"

B-8

Appendix B

ActiveCell.Offset(1, 0).Select Sheets("Sheet3").Select Sheets("Sheet3").Name = "Moderate.Vigerous" Columns("D:E").Select Selection.NumberFormat = "dd-mmm-yyyy hh:mm:ss" Range("A1").Select ActiveCell.Value = "DAY" ActiveCell.Offset(0, 1).Value = "Moderate/Vigerous Activity Bout Number" ActiveCell.Offset(0, 2).Value = "Moderate/Vigerous Activity Bout Duration" ActiveCell.Offset(0, 3).Value = "Start Time" ActiveCell.Offset(0, 4).Value = "End Time" ActiveCell.Offset(1, 0).Select

Workbooks.Open (ThePath & TheFile) Columns("A:A").Select Selection.TextToColumns Destination:=Range("A1"), DataType:=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=False, Tab:=False, _ Semicolon:=False, Comma:=False, Space:=False, Other:=False, FieldInfo _ :=Array(1, 4), TrailingMinusNumbers:=True Selection.NumberFormat = "[$-F800]dddd, mmmm dd, yyyy" '"dd/mm/yyyy" Columns("B:B").Select Selection.TextToColumns Destination:=Range("b1"), DataType:=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=False, Tab:=False, _ Semicolon:=False, Comma:=False, Space:=False, Other:=False, FieldInfo _ :=Array(1, 4), TrailingMinusNumbers:=True Selection.NumberFormat = "hh:mm:ss;@" Range("A12").Select Dim paxn As Date 'minute*/ Dim strt_mv As Date Dim end_mv As Date Dim strt_m As Date Dim end_m As Date Dim strt_v As Date Dim end_v As Date

Dim start_mv As Boolean Dim start_v As Boolean Dim start_m As Boolean Dim first_day As Boolean first_day = True Dim stopped_mv As Boolean Dim stopped_v As Boolean Dim stopped_m As Boolean stopped_mv = True stopped_v = True stopped_m = True Dim reset_mv As Boolean Dim reset_v As Boolean Dim reset_m As Boolean reset_mv = True reset_v = True reset_m = True

While ActiveCell.Value <> "" last_day = False paxinten = ActiveCell.Offset(0, 2).Value paxn = FormatDateTime(ActiveCell.Value + ActiveCell.Offset(0, 1).Value, vbGeneralDate) If first_day Then activeday = Weekday(ActiveCell.Value) If Not first_day Then activeday = Weekday(ActiveCell.Offset(-1, 0).Value) 'Activates the last day flag If activeday <> Weekday(ActiveCell.Value) Then last_day = True If ActiveCell.Offset(1, 1).Value = "" Then ActiveCell.Offset(1, 0).Select End If 'moderate or vigorous activity*/ If paxinten >= modthresh Then mv = 1 Else: mv = 0 End If 'vigorous activity*/ If paxinten >= vigthresh Then v = 1 Else: v = 0 End If

B-9

Appendix B

'moderate activity*/ If paxinten >= modthresh And paxinten < vigthresh Then m = 1 Else: m = 0 End If

If first_day Then m_num = 0 v_num = 0 mv_num = 0 End If

'moderate or vigerous activity*/ If first_day Or stopped_mv Or reset_mv Then strt_mv = 0 'starting minute for the activity bout*/ end_mv = 0 'ending minute for the activity bout*/ start_mv = False 'indicator for starting the activity bout*/ dur_mv = 0 'duration for the Moderate Vigerous Activity period*/ reset_mv = False 'indicator for resetting and starting over*/ mv_cnt = 0 'number of minutes for the activity bout*/ stopped_mv = False 'indicator for stopping the activity bout*/ End If

'vigerous activity*/ If first_day Or stopped_v Or reset_v Then strt_v = 0 'starting minute for the activity bout*/ end_v = 0 'ending minute for the activity bout*/ start_v = False 'indicator for starting the activity bout*/ dur_v = 0 'duration for the Vigerous Activity period*/ reset_v = False 'indicator for resetting and starting over*/ v_cnt = 0 'number of minutes for the activity bout*/ stopped_v = False 'indicator for stopping the activity bout*/ End If

'moderate activity*/ If first_day Or stopped_m Or reset_m Then strt_m = 0 'starting minute for the activity bout*/ end_m = 0 'ending minute for the activity bout*/ start_m = False 'indicator for starting the activity bout*/ dur_m = 0 'duration for the Moderate Activity period*/ reset_m = False 'indicator for resetting and starting over*/ m_cnt = 0 'number of minutes for the activity bout*/ stopped_m = False 'indicator for stopping the activity bout*/ End If

'start the bout when a count with intensity >= the threshold is encountered*/ If mv = 1 And start_mv = False Then strt_mv = paxn 'assign the starting minute of the bout*/ start_mv = True End If

If v = 1 And start_v = False Then strt_v = paxn 'assign the starting minute of the bout*/ start_v = True End If

If m = 1 And start_m = False Then strt_m = paxn 'assign the starting minute of the bout*/ start_m = True End If

'accumulate minutes with intensity counts >= the threshold*/ If start_mv = True And mv = 1 Then mv_cnt = mv_cnt + 1 end_mv = paxn 'keep track of the ending minute for the bout*/ dur_mv = dur_mv + 0.0833

End If

If start_v = True And v = 1 Then v_cnt = v_cnt + 1 end_v = paxn 'keep track of the ending minute for the bout*/ dur_v = dur_v + 0.0833 End If

B-10

Appendix B

If start_m = True And m = 1 Then m_cnt = m_cnt + 1 end_m = paxn 'keep track of the ending minute for the bout*/ dur_m = dur_m + 0.0833 End If

'stop when encounter a minute with intensity < threshold or missing*/ If mv = 0 Then If mv_cnt < boutperiod Then reset_mv = 1 'reset if less than the bout length(1min in this case)*/ Else: stopped_mv = True End If End If

If v = 0 Then If v_cnt < boutperiod Then reset_v = 1 'reset if less than the bout length(1min in this case)*/ Else: stopped_v = True End If End If

If m = 0 Then If m_cnt < boutperiod Then reset_m = 1 'reset if less than the bout length(1min in this case)*/ Else: stopped_m = True End If End If

'last minute of the day*/ If last_day And mv_cnt >= boutperiod Then stopped_mv = 1 If last_day And v_cnt >= boutperiod Then stopped_v = 1 If last_day And m_cnt >= boutperiod Then stopped_m = 1

If last_day Then reset_mv = True reset_v = True reset_m = True End If

'output one record for each activity bout*/ If stopped_mv = True Then mv_num = mv_num + 1 Windows(outputfile).Activate Sheets("Moderate.Vigerous").Select ActiveCell.Value = WeekdayName(activeday) ActiveCell.Offset(0, 1).Value = mv_num ActiveCell.Offset(0, 2).Value = dur_mv ActiveCell.Offset(0, 3).Value = strt_mv ActiveCell.Offset(0, 4).Value = end_mv ActiveCell.Offset(1, 0).Select Windows(TheFile).Activate End If

If stopped_v = True Then v_num = v_num + 1 Windows(outputfile).Activate Sheets("Vigerous").Select ActiveCell.Value = WeekdayName(activeday) ActiveCell.Offset(0, 1).Value = v_num ActiveCell.Offset(0, 2).Value = dur_v ActiveCell.Offset(0, 3).Value = strt_v ActiveCell.Offset(0, 4).Value = end_v ActiveCell.Offset(1, 0).Select Windows(TheFile).Activate End If

If stopped_m = True Then m_num = m_num + 1 Windows(outputfile).Activate Sheets("Moderate").Select ActiveCell.Value = WeekdayName(activeday) ActiveCell.Offset(0, 1).Value = m_num ActiveCell.Offset(0, 2).Value = dur_m ActiveCell.Offset(0, 3).Value = strt_m

B-11

Appendix B

ActiveCell.Offset(0, 4).Value = end_m ActiveCell.Offset(1, 0).Select Windows(TheFile).Activate End If

ActiveCell.Offset(1, 0).Select first_day = False Wend Windows(TheFile).Activate ActiveWorkbook.Close saveChanges:=False Windows(outputfile).Activate ActiveWorkbook.Close saveChanges:=True Windows("Activity_Summary.xlsm").Activate ActiveCell.Offset(1, 0).Select Wend Application.ScreenUpdating = True End Sub

B.5.3 Summary of Moderate and Vigorous Analysis

Activity Level Summary - Reads the DATAxxx.xlsx file and summarises into Active Summary file Sub Activity_Level_summary() Application.ScreenUpdating = False ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Qualify-Summary").Select Range("B4").Select Dim day_flg As Integer While ActiveCell.Value <> "" outputfile = "Data" & (ActiveCell.Value + 100) & ".xlsx" Workbooks.Open (ThePath & outputfile) For t = 1 To 3 Dim activitysummary(6) As Double 'sum of non-wear data For i = 0 To 6 'zero the non-wear summary matrix activitysummary(i) = 0 Next i Windows(outputfile).Activate

If t = 1 Then Sheets("Moderate").Select If t = 2 Then Sheets("Vigerous").Select If t = 3 Then Sheets("Moderate.Vigerous").Select

Range("a2").Select While ActiveCell.Value <> "" If ActiveCell.Value = "Monday" Then day_flg = 0 If ActiveCell.Value = "Tuesday" Then day_flg = 1 If ActiveCell.Value = "Wednesday" Then day_flg = 2 If ActiveCell.Value = "Thursday" Then day_flg = 3 If ActiveCell.Value = "Friday" Then day_flg = 4 If ActiveCell.Value = "Saturday" Then day_flg = 5 If ActiveCell.Value = "Sunday" Then day_flg = 6

activitysummary(day_flg) = activitysummary(day_flg) + ActiveCell.Offset(0, 2).Value ActiveCell.Offset(1, 0).Select Wend Windows("Activity_Summary.xlsm").Activate For i = 0 To 6 'zero the non-wear summary matrix ActiveCell.Offset(0, ((7 * t) + 5 + i)).Value = activitysummary(i) Next i Next t Windows(outputfile).Activate ActiveWorkbook.Close saveChanges:=False Windows("Activity_Summary.xlsm").Activate ActiveCell.Offset(1, 0).Select Wend Application.ScreenUpdating = True End Sub

B-12

Appendix B

B.6 Analysis of Bout Data

This section provides details fo the analysis used for bout data.

B.6.1 Generation of Moderate Bouts

Bout_Calc_Mod - Creates Bout_Modxxx.xlsx file of moderate bout data Sub bout_calc_mod() Application.ScreenUpdating = False ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Qualify-Summary").Select Range("B4").Select While ActiveCell.Value <> "" ridageyr = ActiveCell.Offset(0, 1).Value 'moderate threshold*/ If ridageyr = 6 Then modthresh = 1400 If ridageyr = 7 Then modthresh = 1515 If ridageyr = 8 Then modthresh = 1638 If ridageyr = 9 Then modthresh = 1770 If ridageyr = 10 Then modthresh = 1910 If ridageyr = 11 Then modthresh = 2059 If ridageyr = 12 Then modthresh = 2220 If ridageyr = 13 Then modthresh = 2393 If ridageyr = 14 Then modthresh = 2580 If ridageyr = 15 Then modthresh = 2781 If ridageyr = 16 Then modthresh = 3000 If ridageyr = 17 Then modthresh = 3239 If ridageyr >= 18 Then modthresh = 2020

'vigorous threshold*/ If ridageyr = 6 Then vigthresh = 3758 If ridageyr = 7 Then vigthresh = 3947 If ridageyr = 8 Then vigthresh = 4147 If ridageyr = 9 Then vigthresh = 4360 If ridageyr = 10 Then vigthresh = 4588 If ridageyr = 11 Then vigthresh = 4832 If ridageyr = 12 Then vigthresh = 5094 If ridageyr = 13 Then vigthresh = 5375 If ridageyr = 14 Then vigthresh = 5679 If ridageyr = 15 Then vigthresh = 6007 If ridageyr = 16 Then vigthresh = 6363 If ridageyr = 17 Then vigthresh = 6751 If ridageyr >= 18 Then vigthresh = 5999 TheFile = Dir(ThePath & "Data" & (ActiveCell.Value + 100) & "*.csv") outputfile = "Bout_Mod" & (ActiveCell.Value + 100) & ".xlsx" 'Creates the file name Workbooks.Add ActiveWorkbook.SaveAs Filename:=ThePath & outputfile, FileFormat:=xlOpenXMLWorkbook, CreateBackup:=False Sheets("Sheet1").Select Sheets("Sheet1").Name = "Bout_Summary" Columns("D:E").Select Selection.NumberFormat = "dd-mmm-yyyy hh:mm:ss" Range("A1").Select ActiveCell.Value = "DAY" ActiveCell.Offset(0, 1).Value = "Bout Number" ActiveCell.Offset(0, 2).Value = "Bout Duration" ActiveCell.Offset(0, 3).Value = "Bout Start Time" ActiveCell.Offset(0, 4).Value = "Bout End Time" ActiveCell.Offset(1, 0).Select Workbooks.Open (ThePath & TheFile) Columns("A:A").Select Selection.TextToColumns Destination:=Range("A1"), DataType:=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=False, Tab:=False, _ Semicolon:=False, Comma:=False, Space:=False, Other:=False, FieldInfo _ :=Array(1, 4), TrailingMinusNumbers:=True Selection.NumberFormat = "[$-F800]dddd, mmmm dd, yyyy" '"dd/mm/yyyy" Columns("B:B").Select Selection.TextToColumns Destination:=Range("b1"), DataType:=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=False, Tab:=False, _

B-13

Appendix B

Semicolon:=False, Comma:=False, Space:=False, Other:=False, FieldInfo _ :=Array(1, 4), TrailingMinusNumbers:=True Selection.NumberFormat = "hh:mm:ss;@"

Range("A12").Select Dim paxn As Date 'minute*/ Dim start As Boolean Dim first_day As Boolean first_day = True Dim stopped As Boolean stopped = True Dim reset As Boolean reset = True

While ActiveCell.Value <> "" last_day = False paxinten = 0 paxn = FormatDateTime(ActiveCell.Value + ActiveCell.Offset(0, 1).Value, vbGeneralDate) stp_min = Minute(ActiveCell.Offset(1, 1).Value) cur_min = Minute(ActiveCell.Offset(0, 1).Value) While cur_min = stp_min And ActiveCell.Offset(1, 1).Value <> "" stp_min = Minute(ActiveCell.Offset(1, 1).Value) cur_min = Minute(ActiveCell.Offset(0, 1).Value) paxinten = paxinten + ActiveCell.Offset(0, 2).Value ActiveCell.Offset(1, 0).Select Wend activeday = Weekday(ActiveCell.Offset(-1, 0).Value) 'Activates the last day flag If activeday <> Weekday(ActiveCell.Value) Then last_day = True If ActiveCell.Offset(1, 1).Value = "" Then ActiveCell.Offset(1, 0).Select

'moderate or vigorous activity*/ If paxinten >= modthresh Then mv = 1 Else: mv = 0 End If 'vigorous activity*/ If paxinten >= vigthresh Then v = 1 Else: v = 0 End If 'moderate activity*/ If paxinten >= modthresh And paxinten < vigthresh Then m = 1 Else: m = 0 End If

'set up a 10 minute window*/ Dim win_paxn(9) As Date 'minute*/ Dim win_int(9) As Double 'intensity*/ Dim win_flg(9) As Integer 'bout flag*/

If first_day Then m_num = 0

If first_day Or stopped Or reset Then strt_m = 0 'starting minute for the bout*/ end_m = 0 'ending minute for the bout*/ found = 0 'set to 1 if a bout has been established*/ reset = 0 'reset the counts and start over*/ stopped = 0 'indicator for stopping the bout*/ start = 0 'start set to 1 if one above the threshold count is encountered*/ m_cnt = 0 'number of minutes with counts >= the threshold*/ sum10 = 0 'the total intensity counts from the 10 minute window*/ cnt_below = 0 'counter for number of minutes with intensity below the threshold*/ For i = 0 To 9 'initialize the 10 minute window*/ win_paxn(i) = 0 win_int(i) = 0 win_flg(i) = 0 Next i End If

'if the intensity count is >= the threshold, start the bout*/ If m = 1 And start = False Then start = True

'accumulate the counts*/

B-14

Appendix B

If start = True Then m_cnt = m_cnt + 1

'set up a moving window of 10 minutes*/ If m_cnt >= 1 And m_cnt <= 10 And Not found Then win_paxn(m_cnt - 1) = paxn win_int(m_cnt - 1) = paxinten win_flg(m_cnt - 1) = m If paxinten = "" Then reset = 1 'if encounter a missing count before reaching the 10 minute count, reset and start again*/ End If

'when reach 10 minutes, count the total number of intensity counts that are >= threshold*/ If m_cnt = 10 And Not reset Then sum10 = 0 For Each i In win_flg sum10 = sum10 + i Next End If 'if 7 out of 10 minutes with intensity counts >= the threshold, a bout is established*/ If sum10 >= 7 Then found = True

'if less than 7 minutes with intensity counts>= the threshold, continue to search move the 10-minute window down, one minute at a time

If sum10 >= 0 And sum10 < 7 And m_cnt > 10 Then For i = 0 To 8 win_paxn(i) = win_paxn(i + 1) win_int(i) = win_int(i + 1) win_flg(i) = win_flg(i + 1) Next i 'read in minute 10*/ win_paxn(9) = paxn win_int(9) = paxinten win_flg(9) = m sum10 = 0 For Each i In win_flg sum10 = sum10 + i Next If sum10 = 0 Then reset = True 'skip the windows with no valid minutes*/ End If

'after the bout is established*/ If found Then 'assign the starting minute for the activity bout*/ If strt_m = 0 Then For i = 0 To 9 If win_flg(i) = 1 Then 'find the first minute with intensity count>=the threshold*/ strt_m = win_paxn(i) i = 11 End If Next i End If 'assign the ending minute for the activity bout*/ If end_m = 0 Then 'the last minute in the 10 minute window is below the threshold*/ If win_flg(7) = 0 And win_flg(8) = 0 And win_flg(9) = 0 Then end_m = win_paxn(6) cnt_below = 3

'the last 3 minutes in the 10 minute window are below the threshold*/ ElseIf win_flg(8) = 0 And win_flg(9) = 0 Then end_m = win_paxn(7) cnt_below = 2 'the last 3 minutes in the 10 minute window are below the threshold*/ ElseIf win_flg(9) = 0 Then end_m = win_paxn(8) cnt_below = 1 Else: end_m = win_paxn(9) End If End If If paxn > win_paxn(9) Then If m = 1 Then cnt_below = 0 end_m = paxn End If

B-15

Appendix B

If m = 0 Then cnt_below = cnt_below + 1 'keep track of the number of minutes with intensity counts below the threshold*/ End If 'bout terminates if 3 consecutive minutes below the threshold are encountered, or a missing count, or the last minute of the day*/ If cnt_below = 3 Or m = "" Or last_day Then stopped = True 'last.Day Or End If

If stopped = True Then Dim dur_m As Long dur_m = DateDiff("n", strt_m, end_m) + 1 m_num = m_num + 1 Windows(outputfile).Activate Sheets("Bout_Summary").Select ActiveCell.Value = WeekdayName(activeday) ActiveCell.Offset(0, 1).Value = m_num ActiveCell.Offset(0, 2).Value = dur_m ActiveCell.Offset(0, 3).Value = strt_m ActiveCell.Offset(0, 4).Value = end_m ActiveCell.Offset(1, 0).Select Windows(TheFile).Activate End If

first_day = False

Wend Windows(TheFile).Activate ActiveWorkbook.Close saveChanges:=False Windows(outputfile).Activate ActiveWorkbook.Close saveChanges:=True Windows("Activity_Summary.xlsm").Activate ActiveCell.Offset(1, 0).Select Wend End Sub

B.6.2 Generation of Vigorous Bouts

Bout_Calc_Vig - Creates Bout_Vigxxx.xlsx file of vigorous bout data Sub bout_calc_vig() Application.ScreenUpdating = False ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Qualify-Summary").Select Range("B4").Select While ActiveCell.Value <> "" ridageyr = ActiveCell.Offset(0, 1).Value 'moderate threshold*/ If ridageyr = 6 Then modthresh = 1400 If ridageyr = 7 Then modthresh = 1515 If ridageyr = 8 Then modthresh = 1638 If ridageyr = 9 Then modthresh = 1770 If ridageyr = 10 Then modthresh = 1910 If ridageyr = 11 Then modthresh = 2059 If ridageyr = 12 Then modthresh = 2220 If ridageyr = 13 Then modthresh = 2393 If ridageyr = 14 Then modthresh = 2580 If ridageyr = 15 Then modthresh = 2781 If ridageyr = 16 Then modthresh = 3000 If ridageyr = 17 Then modthresh = 3239 If ridageyr >= 18 Then modthresh = 2020

B-16

Appendix B

If ridageyr = 12 Then vigthresh = 5094 If ridageyr = 13 Then vigthresh = 5375 If ridageyr = 14 Then vigthresh = 5679 If ridageyr = 15 Then vigthresh = 6007 If ridageyr = 16 Then vigthresh = 6363 If ridageyr = 17 Then vigthresh = 6751 If ridageyr >= 18 Then vigthresh = 5999 TheFile = Dir(ThePath & "Data" & (ActiveCell.Value + 100) & "*.csv") outputfile = "Bout_Vig" & (ActiveCell.Value + 100) & ".xlsx" 'Creates the file name Workbooks.Add ActiveWorkbook.SaveAs Filename:=ThePath & outputfile, FileFormat:=xlOpenXMLWorkbook, CreateBackup:=False Sheets("Sheet1").Select Sheets("Sheet1").Name = "Bout_Summary" Columns("D:E").Select Selection.NumberFormat = "dd-mmm-yyyy hh:mm:ss" Range("A1").Select ActiveCell.Value = "DAY" ActiveCell.Offset(0, 1).Value = "Bout Number" ActiveCell.Offset(0, 2).Value = "Bout Duration" ActiveCell.Offset(0, 3).Value = "Bout Start Time" ActiveCell.Offset(0, 4).Value = "Bout End Time" ActiveCell.Offset(1, 0).Select Workbooks.Open (ThePath & TheFile) Columns("A:A").Select Selection.TextToColumns Destination:=Range("A1"), DataType:=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=False, Tab:=False, _ Semicolon:=False, Comma:=False, Space:=False, Other:=False, FieldInfo _ :=Array(1, 4), TrailingMinusNumbers:=True Selection.NumberFormat = "[$-F800]dddd, mmmm dd, yyyy" '"dd/mm/yyyy" Columns("B:B").Select Selection.TextToColumns Destination:=Range("b1"), DataType:=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=False, Tab:=False, _ Semicolon:=False, Comma:=False, Space:=False, Other:=False, FieldInfo _ :=Array(1, 4), TrailingMinusNumbers:=True Selection.NumberFormat = "hh:mm:ss;@" Range("A12").Select Dim paxn As Date 'minute*/ Dim start As Boolean Dim first_day As Boolean first_day = True Dim stopped As Boolean stopped = True Dim reset As Boolean reset = True While ActiveCell.Value <> "" last_day = False paxinten = 0 paxn = FormatDateTime(ActiveCell.Value + ActiveCell.Offset(0, 1).Value, vbGeneralDate) stp_min = Minute(ActiveCell.Offset(1, 1).Value) cur_min = Minute(ActiveCell.Offset(0, 1).Value) While cur_min = stp_min And ActiveCell.Offset(1, 1).Value <> "" stp_min = Minute(ActiveCell.Offset(1, 1).Value) cur_min = Minute(ActiveCell.Offset(0, 1).Value) paxinten = paxinten + ActiveCell.Offset(0, 2).Value ActiveCell.Offset(1, 0).Select Wend activeday = Weekday(ActiveCell.Offset(-1, 0).Value) 'Activates the last day flag If activeday <> Weekday(ActiveCell.Value) Then last_day = True If ActiveCell.Offset(1, 1).Value = "" Then ActiveCell.Offset(1, 0).Select

B-17

Appendix B

Else: m = 0 End If

'set up a 10 minute window*/ Dim win_paxn(9) As Date 'minute*/ Dim win_int(9) As Double 'intensity*/ Dim win_flg(9) As Integer 'bout flag*/ If first_day Then v_num = 0 If first_day Or stopped Or reset Then strt_v = 0 'starting minute for the bout*/ end_v = 0 'ending minute for the bout*/ found = 0 'set to 1 if a bout has been established*/ reset = 0 'reset the counts and start over*/ stopped = 0 'indicator for stopping the bout*/ start = 0 'start set to 1 if one above the threshold count is encountered*/ v_cnt = 0 'number of minutes with counts >= the threshold*/ sum10 = 0 'the total intensity counts from the 10 minute window*/ cnt_below = 0 'counter for number of minutes with intensity below the threshold*/ For i = 0 To 9 'initialize the 10 minute window*/ win_paxn(i) = 0 win_int(i) = 0 win_flg(i) = 0 Next i End If

'if the intensity count is >= the threshold, start the bout*/ If v = 1 And start = False Then start = True

'accumulate the counts*/ If start = True Then v_cnt = v_cnt + 1

'set up a moving window of 10 minutes*/ If v_cnt >= 1 And v_cnt <= 10 And Not found Then win_paxn(v_cnt - 1) = paxn win_int(v_cnt - 1) = paxinten win_flg(v_cnt - 1) = v If paxinten = "" Then reset = 1 'if encounter a missing count before reaching the 10 minute count, reset and start again*/ End If

'when reach 10 minutes, count the total number of intensity counts that are >= threshold*/ If v_cnt = 10 And Not reset Then sum10 = 0 For Each i In win_flg sum10 = sum10 + i Next End If 'if 7 out of 10 minutes with intensity counts >= the threshold, a bout is established*/ If sum10 >= 7 Then found = True

'if less than 7 minutes with intensity counts>= the threshold, continue to search move the 10-minute window down, one minute at a time If sum10 >= 0 And sum10 < 7 And v_cnt > 10 Then For i = 0 To 8 win_paxn(i) = win_paxn(i + 1) win_int(i) = win_int(i + 1) win_flg(i) = win_flg(i + 1) Next i 'read in minute 10*/ win_paxn(9) = paxn win_int(9) = paxinten win_flg(9) = v sum10 = 0 For Each i In win_flg sum10 = sum10 + i Next If sum10 = 0 Then reset = True 'skip the windows with no valid minutes*/ End If

'after the bout is established*/ If found Then 'assign the starting minute for the activity bout*/ If strt_v = 0 Then For i = 0 To 9 If win_flg(i) = 1 Then 'find the first minute with intensity count>=the threshold*/ strt_v = win_paxn(i)

B-18

Appendix B

i = 11 End If Next i End If 'assign the ending minute for the activity bout*/ If end_v = 0 Then 'the last minute in the 10 minute window is below the threshold*/ If win_flg(7) = 0 And win_flg(8) = 0 And win_flg(9) = 0 Then end_v = win_paxn(6) cnt_below = 3

'the last 3 minutes in the 10 minute window are below the threshold*/ ElseIf win_flg(8) = 0 And win_flg(9) = 0 Then end_v = win_paxn(7) cnt_below = 2 'the last 3 minutes in the 10 minute window are below the threshold*/ ElseIf win_flg(9) = 0 Then end_v = win_paxn(8) cnt_below = 1 Else: end_v = win_paxn(9) End If End If If paxn > win_paxn(9) Then If v = 1 Then cnt_below = 0 end_v = paxn End If If v = 0 Then cnt_below = cnt_below + 1 'keep track of the number of minutes with intensity counts below the threshold*/ End If 'bout terminates if 3 consecutive minutes below the threshold are encountered, or a missing count, or the last minute of the day*/ If cnt_below = 3 Or v = "" Or last_day Then stopped = True 'last.Day Or End If

If stopped = True Then Dim dur_v As Long dur_v = DateDiff("n", strt_v, end_v) + 1 v_num = v_num + 1 Windows(outputfile).Activate Sheets("Bout_Summary").Select ActiveCell.Value = WeekdayName(activeday) ActiveCell.Offset(0, 1).Value = v_num ActiveCell.Offset(0, 2).Value = dur_v ActiveCell.Offset(0, 3).Value = strt_v ActiveCell.Offset(0, 4).Value = end_v ActiveCell.Offset(1, 0).Select Windows(TheFile).Activate End If first_day = False Wend Windows(TheFile).Activate ActiveWorkbook.Close saveChanges:=False Windows(outputfile).Activate ActiveWorkbook.Close saveChanges:=True Windows("Activity_Summary.xlsm").Activate ActiveCell.Offset(1, 0).Select Wend End Sub

B.6.3 Generation of Moderate/ Vigorous Bouts

Bout_Calc - Creates Boutxxx.xlsx file of moderate/vigorous bout data Sub bout_calc()

Application.ScreenUpdating = False ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Qualify-Summary").Select

B-19

Appendix B

Range("B4").Select While ActiveCell.Value <> "" ridageyr = ActiveCell.Offset(0, 1).Value 'moderate threshold*/ If ridageyr = 6 Then modthresh = 1400 If ridageyr = 7 Then modthresh = 1515 If ridageyr = 8 Then modthresh = 1638 If ridageyr = 9 Then modthresh = 1770 If ridageyr = 10 Then modthresh = 1910 If ridageyr = 11 Then modthresh = 2059 If ridageyr = 12 Then modthresh = 2220 If ridageyr = 13 Then modthresh = 2393 If ridageyr = 14 Then modthresh = 2580 If ridageyr = 15 Then modthresh = 2781 If ridageyr = 16 Then modthresh = 3000 If ridageyr = 17 Then modthresh = 3239 If ridageyr >= 18 Then modthresh = 2020

'vigorous threshold*/ If ridageyr = 6 Then vigthresh = 3758 If ridageyr = 7 Then vigthresh = 3947 If ridageyr = 8 Then vigthresh = 4147 If ridageyr = 9 Then vigthresh = 4360 If ridageyr = 10 Then vigthresh = 4588 If ridageyr = 11 Then vigthresh = 4832 If ridageyr = 12 Then vigthresh = 5094 If ridageyr = 13 Then vigthresh = 5375 If ridageyr = 14 Then vigthresh = 5679 If ridageyr = 15 Then vigthresh = 6007 If ridageyr = 16 Then vigthresh = 6363 If ridageyr = 17 Then vigthresh = 6751 If ridageyr >= 18 Then vigthresh = 5999 TheFile = Dir(ThePath & "Data" & (ActiveCell.Value + 100) & "*.csv") outputfile = "Bout" & (ActiveCell.Value + 100) & ".xlsx" 'Creates the file name Workbooks.Add ActiveWorkbook.SaveAs Filename:=ThePath & outputfile, FileFormat:=xlOpenXMLWorkbook, CreateBackup:=False Sheets("Sheet1").Select Sheets("Sheet1").Name = "Bout_Summary" Columns("D:E").Select Selection.NumberFormat = "dd-mmm-yyyy hh:mm:ss" Range("A1").Select ActiveCell.Value = "DAY" ActiveCell.Offset(0, 1).Value = "Bout Number" ActiveCell.Offset(0, 2).Value = "Bout Duration" ActiveCell.Offset(0, 3).Value = "Bout Start Time" ActiveCell.Offset(0, 4).Value = "Bout End Time" ActiveCell.Offset(1, 0).Select Workbooks.Open (ThePath & TheFile) Columns("A:A").Select Selection.TextToColumns Destination:=Range("A1"), DataType:=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=False, Tab:=False, _ Semicolon:=False, Comma:=False, Space:=False, Other:=False, FieldInfo _ :=Array(1, 4), TrailingMinusNumbers:=True Selection.NumberFormat = "[$-F800]dddd, mmmm dd, yyyy" '"dd/mm/yyyy" Columns("B:B").Select Selection.TextToColumns Destination:=Range("b1"), DataType:=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=False, Tab:=False, _ Semicolon:=False, Comma:=False, Space:=False, Other:=False, FieldInfo _ :=Array(1, 4), TrailingMinusNumbers:=True Selection.NumberFormat = "hh:mm:ss;@" Range("A12").Select Dim paxn As Date 'minute*/ Dim start As Boolean Dim first_day As Boolean first_day = True Dim stopped As Boolean stopped = True Dim reset As Boolean reset = True

While ActiveCell.Value <> "" last_day = False paxinten = 0 paxn = FormatDateTime(ActiveCell.Value + ActiveCell.Offset(0, 1).Value, vbGeneralDate)

B-20

Appendix B

activeday = Weekday(ActiveCell.Offset(-1, 0).Value) 'Activates the last day flag If activeday <> Weekday(ActiveCell.Value) Then last_day = True If ActiveCell.Offset(1, 1).Value = "" Then ActiveCell.Offset(1, 0).Select 'moderate or vigorous activity*/ If paxinten >= modthresh Then mv = 1 Else: mv = 0 End If 'vigorous activity*/ If paxinten >= vigthresh Then v = 1 Else: v = 0 End If 'moderate activity*/ If paxinten >= modthresh And paxinten < vigthresh Then m = 1 Else: m = 0 End If

'set up a 10 minute window*/ Dim win_paxn(9) As Date 'minute*/ Dim win_int(9) As Double 'intensity*/ Dim win_flg(9) As Integer 'bout flag*/

If first_day Then mv_num = 0 If first_day Or stopped Or reset Then strt_mv = 0 'starting minute for the bout*/ end_mv = 0 'ending minute for the bout*/ found = 0 'set to 1 if a bout has been established*/ reset = 0 'reset the counts and start over*/ stopped = 0 'indicator for stopping the bout*/ start = 0 'start set to 1 if one above the threshold count is encountered*/ mv_cnt = 0 'number of minutes with counts >= the threshold*/ sum10 = 0 'the total intensity counts from the 10 minute window*/ cnt_below = 0 'counter for number of minutes with intensity below the threshold*/ For i = 0 To 9 'initialize the 10 minute window*/ win_paxn(i) = 0 win_int(i) = 0 win_flg(i) = 0 Next i End If

'if the intensity count is >= the threshold, start the bout*/ If mv = 1 And start = False Then start = True

'accumulate the counts*/ If start = True Then mv_cnt = mv_cnt + 1

'set up a moving window of 10 minutes*/ If mv_cnt >= 1 And mv_cnt <= 10 And Not found Then win_paxn(mv_cnt - 1) = paxn win_int(mv_cnt - 1) = paxinten win_flg(mv_cnt - 1) = mv If paxinten = "" Then reset = 1 'if encounter a missing count before reaching the 10 minute count, reset and start again*/ End If

'when reach 10 minutes, count the total number of intensity counts that are >= threshold*/ If mv_cnt = 10 And Not reset Then sum10 = 0 For Each i In win_flg sum10 = sum10 + i Next End If 'if 7 out of 10 minutes with intensity counts >= the threshold, a bout is established*/ If sum10 >= 7 Then found = True

B-21

Appendix B

'if less than 7 minutes with intensity counts>= the threshold, continue to search move the 10-minute window down, one minute at a time

If sum10 >= 0 And sum10 < 7 And mv_cnt > 10 Then For i = 0 To 8 win_paxn(i) = win_paxn(i + 1) win_int(i) = win_int(i + 1) win_flg(i) = win_flg(i + 1) Next i 'read in minute 10*/ win_paxn(9) = paxn win_int(9) = paxinten win_flg(9) = mv sum10 = 0 For Each i In win_flg sum10 = sum10 + i Next If sum10 = 0 Then reset = True 'skip the windows with no valid minutes*/ End If

'after the bout is established*/ If found Then 'assign the starting minute for the activity bout*/ If strt_mv = 0 Then For i = 0 To 9 If win_flg(i) = 1 Then 'find the first minute with intensity count>=the threshold*/ strt_mv = win_paxn(i) i = 11 End If Next i End If 'assign the ending minute for the activity bout*/ If end_mv = 0 Then 'the last minute in the 10 minute window is below the threshold*/ If win_flg(7) = 0 And win_flg(8) = 0 And win_flg(9) = 0 Then end_mv = win_paxn(6) cnt_below = 3

'the last 3 minutes in the 10 minute window are below the threshold*/ ElseIf win_flg(8) = 0 And win_flg(9) = 0 Then end_mv = win_paxn(7) cnt_below = 2 'the last 3 minutes in the 10 minute window are below the threshold*/ ElseIf win_flg(9) = 0 Then end_mv = win_paxn(8) cnt_below = 1 Else: end_mv = win_paxn(9) End If End If If paxn > win_paxn(9) Then If mv = 1 Then cnt_below = 0 end_mv = paxn End If If mv = 0 Then cnt_below = cnt_below + 1 'keep track of the number of minutes with intensity counts below the threshold*/ End If 'bout terminates if 3 consecutive minutes below the threshold are encountered, or a missing count, or the last minute of the day*/ If cnt_below = 3 Or mv = "" Or last_day Then stopped = True 'last.Day Or End If

If stopped = True Then Dim dur_mv As Long dur_mv = DateDiff("n", strt_mv, end_mv) + 1 mv_num = mv_num + 1 Windows(outputfile).Activate Sheets("Bout_Summary").Select ActiveCell.Value = WeekdayName(activeday) ActiveCell.Offset(0, 1).Value = mv_num ActiveCell.Offset(0, 2).Value = dur_mv ActiveCell.Offset(0, 3).Value = strt_mv ActiveCell.Offset(0, 4).Value = end_mv ActiveCell.Offset(1, 0).Select

B-22

Appendix B

Windows(TheFile).Activate End If first_day = False Wend Windows(TheFile).Activate ActiveWorkbook.Close saveChanges:=False Windows(outputfile).Activate ActiveWorkbook.Close saveChanges:=True Windows("Activity_Summary.xlsm").Activate ActiveCell.Offset(1, 0).Select Wend End Sub

B.6.4 Summary of Moderate Bouts

Bout_Calc_Mod_Summary- Reads Bout_Modxxx.xlsx file and summarises Moderate bout data into Activity Summary File Sub Bout_Calc_Summary_Mod() Application.ScreenUpdating = False ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Qualify-Summary").Select Range("B4").Select Dim day_flg As Integer

While ActiveCell.Value <> "" outputfile = "Bout_Mod" & (ActiveCell.Value + 100) & ".xlsx" Workbooks.Open (ThePath & outputfile) Dim boutsummary(6) As Double 'sum of bout data Dim qtybout(6) As Double 'number of bouts per day For i = 0 To 6 'zero the non-wear summary matrix boutsummary(i) = 0 qtybout(i) = 0 Next i Windows(outputfile).Activate Sheets("Bout_summary").Select Range("a2").Select While ActiveCell.Value <> "" If ActiveCell.Value = "Monday" Then day_flg = 0 qtybout(0) = qtybout(0) + 1 End If If ActiveCell.Value = "Tuesday" Then day_flg = 1 qtybout(1) = qtybout(1) + 1 End If If ActiveCell.Value = "Wednesday" Then day_flg = 2 qtybout(2) = qtybout(2) + 1 End If If ActiveCell.Value = "Thursday" Then day_flg = 3 qtybout(3) = qtybout(3) + 1 End If If ActiveCell.Value = "Friday" Then day_flg = 4 qtybout(4) = qtybout(4) + 1 End If If ActiveCell.Value = "Saturday" Then day_flg = 5 qtybout(5) = qtybout(5) + 1 End If If ActiveCell.Value = "Sunday" Then day_flg = 6 qtybout(6) = qtybout(6) + 1 End If boutsummary(day_flg) = boutsummary(day_flg) + ActiveCell.Offset(0, 2).Value ActiveCell.Offset(1, 0).Select Wend

B-23

Appendix B

Windows("Activity_Summary.xlsm").Activate For i = 0 To 6 'enter the summary data to Active Summary Page ActiveCell.Offset(0, (36 + (2 * i))).Value = qtybout(i) ActiveCell.Offset(0, (37 + (2 * i))).Value = boutsummary(i) Next i Windows(outputfile).Activate ActiveWorkbook.Close saveChanges:=False Windows("Activity_Summary.xlsm").Activate ActiveCell.Offset(1, 0).Select Wend Application.ScreenUpdating = True End Sub

B.6.5 Summary of Vigorous Bouts

Bout_Calc_Vig_Summary - Reads Bout_Vigxxx.xlsx file and summarises Vigorous bout data into Activity Summary File Sub Bout_Calc_Summary_Vig() Application.ScreenUpdating = False ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Qualify-Summary").Select Range("B4").Select Dim day_flg As Integer

While ActiveCell.Value <> "" outputfile = "Bout_Vig" & (ActiveCell.Value + 100) & ".xlsx" Workbooks.Open (ThePath & outputfile) Dim boutsummary(6) As Double 'sum of bout data Dim qtybout(6) As Double 'number of bouts per day For i = 0 To 6 'zero the non-wear summary matrix boutsummary(i) = 0 qtybout(i) = 0 Next i Windows(outputfile).Activate Sheets("Bout_summary").Select Range("a2").Select While ActiveCell.Value <> "" If ActiveCell.Value = "Monday" Then day_flg = 0 qtybout(0) = qtybout(0) + 1 End If If ActiveCell.Value = "Tuesday" Then day_flg = 1 qtybout(1) = qtybout(1) + 1 End If If ActiveCell.Value = "Wednesday" Then day_flg = 2 qtybout(2) = qtybout(2) + 1 End If If ActiveCell.Value = "Thursday" Then day_flg = 3 qtybout(3) = qtybout(3) + 1 End If If ActiveCell.Value = "Friday" Then day_flg = 4 qtybout(4) = qtybout(4) + 1 End If If ActiveCell.Value = "Saturday" Then day_flg = 5 qtybout(5) = qtybout(5) + 1 End If If ActiveCell.Value = "Sunday" Then day_flg = 6 qtybout(6) = qtybout(6) + 1 End If

boutsummary(day_flg) = boutsummary(day_flg) + ActiveCell.Offset(0, 2).Value ActiveCell.Offset(1, 0).Select

B-24

Appendix B

Wend Windows("Activity_Summary.xlsm").Activate For i = 0 To 6 'enter the summary data to Active Summary Page ActiveCell.Offset(0, (50 + (2 * i))).Value = qtybout(i) ActiveCell.Offset(0, (51 + (2 * i))).Value = boutsummary(i) Next i

Windows(outputfile).Activate ActiveWorkbook.Close saveChanges:=False Windows("Activity_Summary.xlsm").Activate ActiveCell.Offset(1, 0).Select Wend Application.ScreenUpdating = True End Sub

B.6.6 Summary of Moderate/ Vigorous Bouts

Bout_Calc_Summary - Reads Boutxxx.xlsx file and summarises Moderate/Vigorous bout data into Activity Summary File Sub Bout_Calc_Summary() Application.ScreenUpdating = False ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Qualify-Summary").Select Range("B4").Select Dim day_flg As Integer

While ActiveCell.Value <> "" outputfile = "Bout" & (ActiveCell.Value + 100) & ".xlsx" Workbooks.Open (ThePath & outputfile) Dim boutsummary(6) As Double 'sum of bout data Dim qtybout(6) As Double 'number of bouts per day For i = 0 To 6 'zero the non-wear summary matrix boutsummary(i) = 0 qtybout(i) = 0 Next i Windows(outputfile).Activate Sheets("Bout_summary").Select Range("a2").Select While ActiveCell.Value <> "" If ActiveCell.Value = "Monday" Then day_flg = 0 qtybout(0) = qtybout(0) + 1 End If If ActiveCell.Value = "Tuesday" Then day_flg = 1 qtybout(1) = qtybout(1) + 1 End If If ActiveCell.Value = "Wednesday" Then day_flg = 2 qtybout(2) = qtybout(2) + 1 End If If ActiveCell.Value = "Thursday" Then day_flg = 3 qtybout(3) = qtybout(3) + 1 End If If ActiveCell.Value = "Friday" Then day_flg = 4 qtybout(4) = qtybout(4) + 1 End If If ActiveCell.Value = "Saturday" Then day_flg = 5 qtybout(5) = qtybout(5) + 1 End If If ActiveCell.Value = "Sunday" Then day_flg = 6 qtybout(6) = qtybout(6) + 1 End If boutsummary(day_flg) = boutsummary(day_flg) + ActiveCell.Offset(0, 2).Value

B-25

Appendix B

ActiveCell.Offset(1, 0).Select Wend Windows("Activity_Summary.xlsm").Activate For i = 0 To 6 'enter the summary data to Active Summary Page ActiveCell.Offset(0, (64 + (2 * i))).Value = qtybout(i) ActiveCell.Offset(0, (65 + (2 * i))).Value = boutsummary(i) Next i Windows(outputfile).Activate ActiveWorkbook.Close saveChanges:=False Windows("Activity_Summary.xlsm").Activate ActiveCell.Offset(1, 0).Select Wend Application.ScreenUpdating = True End Sub

B.6.7 Mean data for Activity Bouts

average_activity - Averages data from Qualify Summary for Activity and Bout data Sub average_activity()

Windows("Activity_Summary.xlsm").Activate Sheets("Qualify-Summary").Select Range("B4").Select While ActiveCell.Value <> "" tt = 0 cumt_act_mod = 0 cumt_act_vig = 0 cumt_act = 0 cumt_bout_mod = 0 cumt_bout_mod_qty = 0 cumt_bout_vig = 0 cumt_bout_vig_qty = 0 cumt_bout = 0 cumt_bout_qty = 0 For i = 1 To 7 If ActiveCell.Offset(0, (1 + i)).Value < 840 And ActiveCell.Offset(0, (1 + i)).Value <> 0 Then tt = tt + 1 cumt_act_mod = cumt_act_mod + ActiveCell.Offset(0, (11 + i)).Value cumt_act_vig = cumt_act_vig + ActiveCell.Offset(0, (18 + i)).Value cumt_act = cumt_act + ActiveCell.Offset(0, (25 + i)).Value

cumt_bout_mod = cumt_bout_mod + ActiveCell.Offset(0, (37 + ((i - 1) * 2))).Value cumt_bout_vig = cumt_bout_vig + ActiveCell.Offset(0, (51 + ((i - 1) * 2))).Value cumt_bout = cumt_bout + ActiveCell.Offset(0, (65 + ((i - 1) * 2))).Value

cumt_bout_mod_qty = cumt_bout_mod_qty + ActiveCell.Offset(0, (36 + ((i - 1) * 2))).Value cumt_bout_vig_qty = cumt_bout_vig_qty + ActiveCell.Offset(0, (50 + ((i - 1) * 2))).Value cumt_bout_qty = cumt_bout_qty + ActiveCell.Offset(0, (64 + ((i - 1) * 2))).Value End If Next i ActiveCell.Offset(0, 79).Value = cumt_act_mod / tt ActiveCell.Offset(0, 80).Value = cumt_act_vig / tt ActiveCell.Offset(0, 81).Value = cumt_act / tt ActiveCell.Offset(0, 83).Value = cumt_bout_mod_qty / tt ActiveCell.Offset(0, 84).Value = cumt_bout_mod / tt ActiveCell.Offset(0, 85).Value = cumt_bout_vig_qty / tt ActiveCell.Offset(0, 86).Value = cumt_bout_vig / tt ActiveCell.Offset(0, 87).Value = cumt_bout_qty / tt ActiveCell.Offset(0, 88).Value = cumt_bout / tt ActiveCell.Offset(1, 0).Select Wend End Sub

B-26

Appendix B

B.7 Analysis of Low/ Sedentary Data

This section provides analysis of the low intensity activity and sedentary data.

B.7.1 Low / Sedentary Data

LowSednAct – Creates a file LowSednxxx.xlsx which contains data for Low and Sedentary Activity Sub LowSednAct() boutperiod = 1 'sets the bout period to 1 minute Application.ScreenUpdating = False ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Qualify-Summary").Select Range("B4").Select While ActiveCell.Value <> "" ridageyr = ActiveCell.Offset(0, 1).Value 'Low threshold*/ lowthresh = 100 'moderate threshold*/ If ridageyr = 6 Then modthresh = 1400 If ridageyr = 7 Then modthresh = 1515 If ridageyr = 8 Then modthresh = 1638 If ridageyr = 9 Then modthresh = 1770 If ridageyr = 10 Then modthresh = 1910 If ridageyr = 11 Then modthresh = 2059 If ridageyr = 12 Then modthresh = 2220 If ridageyr = 13 Then modthresh = 2393 If ridageyr = 14 Then modthresh = 2580 If ridageyr = 15 Then modthresh = 2781 If ridageyr = 16 Then modthresh = 3000 If ridageyr = 17 Then modthresh = 3239 If ridageyr >= 18 Then modthresh = 2020

TheFile = Dir(ThePath & "Data" & (ActiveCell.Value + 100) & "*.csv") outputfile = "LowSedn" & (ActiveCell.Value + 100) & ".xlsx" Workbooks.Add ActiveWorkbook.SaveAs Filename:=ThePath & outputfile, FileFormat:=xlOpenXMLWorkbook, CreateBackup:=False Sheets("Sheet1").Select Sheets("Sheet1").Name = "Sedentary" Columns("D:E").Select Selection.NumberFormat = "dd-mmm-yyyy hh:mm:ss" Range("A1").Select ActiveCell.Value = "DAY" ActiveCell.Offset(0, 1).Value = "Sedentary Bout Number" ActiveCell.Offset(0, 2).Value = "Sedentary Bout Duration" ActiveCell.Offset(0, 3).Value = "Start Time" ActiveCell.Offset(0, 4).Value = "End Time" ActiveCell.Offset(1, 0).Select Sheets("Sheet2").Select Sheets("Sheet2").Name = "Low" Columns("D:E").Select Selection.NumberFormat = "dd-mmm-yyyy hh:mm:ss" Range("A1").Select ActiveCell.Value = "DAY" ActiveCell.Offset(0, 1).Value = "Low Number" ActiveCell.Offset(0, 2).Value = "Low Duration" ActiveCell.Offset(0, 3).Value = "Start Time" ActiveCell.Offset(0, 4).Value = "End Time" ActiveCell.Offset(1, 0).Select 'Opens the Raw Data File Workbooks.Open (ThePath & TheFile) Columns("A:A").Select Selection.TextToColumns Destination:=Range("A1"), DataType:=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=False, Tab:=False, _ Semicolon:=False, Comma:=False, Space:=False, Other:=False, FieldInfo _ :=Array(1, 4), TrailingMinusNumbers:=True Selection.NumberFormat = "[$-F800]dddd, mmmm dd, yyyy"

B-27

Appendix B

'"dd/mm/yyyy" Columns("B:B").Select Selection.TextToColumns Destination:=Range("b1"), DataType:=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=False, Tab:=False, _ Semicolon:=False, Comma:=False, Space:=False, Other:=False, FieldInfo _ :=Array(1, 4), TrailingMinusNumbers:=True Selection.NumberFormat = "hh:mm:ss;@" Range("A12").Select

Dim paxn As Date 'minute*/

Dim strt_lw As Date Dim end_lw As Date Dim strt_sd As Date Dim end_sd As Date

Dim start_lw As Boolean Dim start_sd As Boolean

Dim first_day As Boolean Dim last_day As Boolean first_day = True

Dim stopped_lw As Boolean Dim stopped_sd As Boolean

stopped_lw = True stopped_sd = True

Dim reset_lw As Boolean Dim reset_sd As Boolean reset_lw = True reset_sd = True

While ActiveCell.Value <> "" last_day = False paxinten = 0 paxn = FormatDateTime(ActiveCell.Value + ActiveCell.Offset(0, 1).Value, vbGeneralDate) stp_min = Minute(ActiveCell.Offset(1, 1).Value) cur_min = Minute(ActiveCell.Offset(0, 1).Value) While cur_min = stp_min And ActiveCell.Offset(1, 1).Value <> "" stp_min = Minute(ActiveCell.Offset(1, 1).Value) cur_min = Minute(ActiveCell.Offset(0, 1).Value) paxinten = paxinten + ActiveCell.Offset(0, 2).Value ActiveCell.Offset(1, 0).Select Wend If first_day Then activeday = Weekday(ActiveCell.Value) If Not first_day Then activeday = Weekday(ActiveCell.Offset(-1, 0).Value) 'Activates the last day flag If activeday <> Weekday(ActiveCell.Value) Then last_day = True If ActiveCell.Offset(1, 1).Value = "" Then ActiveCell.Offset(1, 0).Select End If

If first_day Then lw_num = 0 sd_num = 0 End If 'low activity*/ If paxinten >= 0 And paxinten <= lowthresh Then sd = 1 Else: sd = 0 End If 'Sedentary activity*/ If paxinten > lowthresh And paxinten < modthresh Then lw = 1 Else: lw = 0 End If

'Sedentary activity*/ If first_day Or stopped_sd Or reset_sd Then strt_sd = 0 'starting minute for the activity bout*/ end_sd = 0 'ending minute for the activity bout*/ start_sd = False 'indicator for starting the activity bout*/ dur_sd = 0 'duration for the Sedentary Activity period*/

B-28

Appendix B

reset_sd = False 'indicator for resetting and starting over*/ sd_cnt = 0 'number of minutes for the activity bout*/ stopped_sd = False 'indicator for stopping the activity bout*/ End If

'low activity*/ If first_day Or stopped_lw Or reset_lw Then strt_lw = 0 'starting minute for the activity bout*/ end_lw = 0 'ending minute for the activity bout*/ start_lw = False 'indicator for starting the activity bout*/ dur_lw = 0 'duration for the Low Activity period*/ reset_lw = False 'indicator for resetting and starting over*/ lw_cnt = 0 'number of minutes for the activity bout*/ stopped_lw = False 'indicator for stopping the activity bout*/ End If

If sd = 1 And start_sd = False Then strt_sd = paxn 'assign the starting minute of the bout*/ start_sd = True End If

'start the bout when a count with intensity >= the threshold is encountered*/ If lw = 1 And start_lw = False Then strt_lw = paxn 'assign the starting minute of the bout*/ start_lw = True End If

'accumulate minutes with intensity counts >= the threshold*/ If start_sd = True And sd = 1 Then sd_cnt = sd_cnt + 1 end_sd = paxn 'keep track of the ending minute for the bout*/ dur_sd = DateDiff("n", strt_sd, end_sd) + 1 End If

If start_lw = True And lw = 1 Then lw_cnt = lw_cnt + 1 end_lw = paxn 'keep track of the ending minute for the bout*/ dur_lw = DateDiff("n", strt_lw, end_lw) + 1 End If

'stop when encounter a minute with intensity < threshold or missing*/ If lw = 0 Then If lw_cnt < boutperiod Then reset_lw = 1 'reset if less than the bout length(1min in this case)*/ Else: stopped_lw = True End If End If

If sd = 0 Then If sd_cnt < boutperiod Then reset_sd = 1 'reset if less than the bout length(1min in this case)*/ Else: stopped_sd = True End If End If

'last minute of the day*/ If last_day And lw_cnt >= boutperiod Then stopped_lw = True If last_day And sd_cnt >= boutperiod Then stopped_sd = True 'reset if its the end of a day and hasn't reached the non wear threshold 'If last_day And dur_nw < nwperiod Then reset = True If last_day Then reset_lw = True reset_sd = True End If

'output one record for each activity bout*/ If stopped_sd = True Then sd_num = sd_num + 1 Windows(outputfile).Activate Sheets("Sedentary").Select ActiveCell.Value = WeekdayName(activeday) ActiveCell.Offset(0, 1).Value = sd_num ActiveCell.Offset(0, 2).Value = dur_sd ActiveCell.Offset(0, 3).Value = strt_sd

B-29

Appendix B

ActiveCell.Offset(0, 4).Value = end_sd ActiveCell.Offset(1, 0).Select Windows(TheFile).Activate End If

If stopped_lw = True Then lw_num = lw_num + 1 Windows(outputfile).Activate Sheets("Low").Select ActiveCell.Value = WeekdayName(activeday) ActiveCell.Offset(0, 1).Value = lw_num ActiveCell.Offset(0, 2).Value = dur_lw ActiveCell.Offset(0, 3).Value = strt_lw ActiveCell.Offset(0, 4).Value = end_lw ActiveCell.Offset(1, 0).Select Windows(TheFile).Activate End If first_day = False Wend

B.7.2 Identification of Non-Wear in Sedentary Data

Sedn_NonWear – Opens file LowSednxxx.xlsx and the Non-Wear file Dataxxx.xlsx, the macro then modifies data which was counted as Sedentary to Non-Wear Sub Sedn_NonWear() Application.ScreenUpdating = False ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Qualify-Summary").Select Range("B4").Select While ActiveCell.Value <> "" 'Sets the file names Sednfile = "LowSedn" & (ActiveCell.Value + 100) & ".xlsx" NonWearfile = "Data" & (ActiveCell.Value + 100) & ".xlsx"

'Opens the Two Files Workbooks.Open (ThePath & Sednfile) Workbooks.Open (ThePath & NonWearfile) Sheets("Non-Wear").Select Range("A2").Select While ActiveCell.Value <> "" nw_day = ActiveCell.Value Dim nw_start As Date Dim nw_end As Date nw_start = ActiveCell.Offset(0, 3).Value nw_end = ActiveCell.Offset(0, 4).Value Windows(Sednfile).Activate Sheets("Sedentary").Select Range("A2").Select Dim sd_start As Date Dim sd_end As Date

While ActiveCell.Value <> "" If ActiveCell.Value = nw_day Then

B-30

Appendix B

sd_start = ActiveCell.Offset(0, 3).Value sd_end = ActiveCell.Offset(0, 4).Value If sd_start >= nw_start And sd_end <= nw_end Then ActiveCell.Offset(0, 5).Value = "Non Wear" End If

If sd_start >= nw_start And sd_start < nw_end And sd_end > nw_end Then ActiveCell.Offset(0, 5).Value = "Non Wear" ActiveCell.EntireRow.Insert Shift:=xlDown, CopyOrigin:=xlFormatFromLeftOrAbove

ActiveCell.Value = nw_day ActiveCell.Offset(0, 2).Value = DateDiff("n", nw_end, sd_end) ActiveCell.Offset(0, 3).Value = nw_end ActiveCell.Offset(0, 4).Value = sd_end ActiveCell.Offset(1, 0).Select End If

If sd_start < nw_start And sd_end > nw_start And sd_end <= nw_end Then ActiveCell.Offset(0, 5).Value = "Non Wear" ActiveCell.EntireRow.Insert Shift:=xlDown, CopyOrigin:=xlFormatFromLeftOrAbove

ActiveCell.Value = nw_day ActiveCell.Offset(0, 2).Value = DateDiff("n", sd_start, nw_start) ActiveCell.Offset(0, 3).Value = sd_start ActiveCell.Offset(0, 4).Value = nw_start ActiveCell.Offset(1, 0).Select

End If

End If ActiveCell.Offset(1, 0).Select Wend Windows(NonWearfile).Activate ActiveCell.Offset(1, 0).Select Wend Windows(NonWearfile).Activate ActiveWorkbook.Close saveChanges:=False Windows(Sednfile).Activate ActiveWorkbook.Close saveChanges:=True Windows("Activity_Summary.xlsm").Activate ActiveCell.Offset(1, 0).Select Wend End Sub

B.7.3 Summary of Sedentary Data

Sedn_Summary - Reads LowSednxxx.xlsx file and summarises Low & Sedentary Activity data into Activity Summary File Sub Sedn_summary() Application.ScreenUpdating = False ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Qualify-Summary").Select Range("B4").Select Dim day_flg As Integer While ActiveCell.Value <> "" outputfile = "LowSedn" & (ActiveCell.Value + 100) & ".xlsx" Workbooks.Open (ThePath & outputfile) For t = 1 To 2 Dim activitysummary(6) As Double 'sum of non-wear data For i = 0 To 6 'zero the non-wear summary matrix activitysummary(i) = 0 Next i Windows(outputfile).Activate

If t = 1 Then Sheets("Sedentary").Select If t = 2 Then Sheets("Low").Select

B-31

Appendix B

If ActiveCell.Offset(0, 5).Value = "" Then activitysummary(day_flg) = activitysummary(day_flg) + ActiveCell.Offset(0, 2).Value End If

ActiveCell.Offset(1, 0).Select Wend

Windows("Activity_Summary.xlsm").Activate Sheets("Qualify-Summary").Select For i = 0 To 6 'zero the non-wear summary matrix ActiveCell.Offset(0, ((7 * t) + 89 + i)).Value = activitysummary(i) Next i Next t Windows(outputfile).Activate ActiveWorkbook.Close saveChanges:=False ActiveCell.Offset(1, 0).Select Wend Application.ScreenUpdating = True End Sub

B.7.4 Mean Sedentary and Low Activity Data

Qualify Sedentary Low - Averages data from Sedentary and Low Activity Sub qulaify_Sedentary_low() ThePath = "C:\Accelerometer\Raw_Data\" Windows("Activity_Summary.xlsm").Activate Sheets("Qualify-Summary").Select Range("b4").Select While ActiveCell.Value <> "" tt = 0 cumt_act_sedn = 0 cumt_act_low = 0

For i = 1 To 7 If ActiveCell.Offset(0, (1 + i)).Value < 840 And ActiveCell.Offset(0, (1 + i)).Value <> 0 Then tt = tt + 1 cumt_act_sedn = cumt_act_sedn + ActiveCell.Offset(0, (95 + i)).Value cumt_act_low = cumt_act_low + ActiveCell.Offset(0, (102 + i)).Value End If Next i

ActiveCell.Offset(0, 112).Value = cumt_act_sedn / tt ActiveCell.Offset(0, 113).Value = cumt_act_low / tt ActiveCell.Offset(1, 0).Select Wend End Sub

B-32

Appendix C

Principal Component Analysis

C.1 Introduction

Appendix C contains principal component analyses output relating to Chapter 7, ‘Development adn Validation of the Male and Female Adolescent Physical Activity Correlates (APAC) questionnaire.

C-1

Appendix C

C.2 Factor Loadings of Complete Psychological Correlates for Males

F1 F2 F3 F4 F5 F6 F7 F8 Q36 .685 Q44 .655 Q38 .628 Q34 .607 Q32 .601 .322 Q33 .597 Q67 -.593 .325 Q47 .589 Q53 -.580 Q35 .570 Q43 .564 .351 Q46 .564 Q40 .491 Q41 .477 Q49 -.433 -.336 -.337 Q39 .420 Q45 .402 .372 Q42 .392 .362 .302 Q54 -.391 .363 Q76 .370 Q66 -.352 Q37 .311 -.319 Q70 .624 Q55 .611 Q69 .602 Q65 .601 Q72 .596 Q73 .583 Q58 .575 Q68 .562 Q59 .518 -.344 Q71 .512 Q51 .488 Q56 .483 Q52 .475 Q64 .458 Q50 .423 Q62 .409 Q75 -.358 .393 -.321 Q74 .392 Q63 -.343 .369 Q61 -.329 .345 Q92 .872 Q95 .834 Q88 .833 Q90 .827 Q87 .786 Q89 .779 Q94 .757 Q91 .733 Q93 .689

C-2

Appendix C

Q18 .661 Q11 .601 Q16 .542 Q14 .311 .534 Q15 .501 Q77 .498 Q1 .484 Q8 .476 .399 Q12 .443 Q17 .442 Q13 .396 Q80 .699 Q86 .692 Q79 .675 Q84 .657 Q81 .651 Q82 .631 Q83 .439 .574 Q85 .556 Q78 .382 .460 Q29 .856 Q31 .803 Q28 .800 Q30 .799 Q26 .791 Q27 .728 Q25 .661 Q24 .554 Q19 .353 .554 Q60 -.347 .403 -.534 Q57 .410 -.512 Q22 .327 .504 Q20 .483 Q23 .479 Q21 .320 .424 Q48 .315 -.331 Q3 .692 Q5 .688 Q4 .685 Q9 .309 .631 Q6 .616 Q10 .445 .496 Q2 .396 .401 Q7 .363 Variance 21.9 7.3 4.6 3.9 3.4 2.5 2.1 1.9

C-3

Appendix C

C.3 Factor Loadings of Complete Psychological Correlates for Females

F1 F2 F3 F4 F5 F6 F7 F8 Q32 .701 Q36 .675 Q34 .670 Q46 .657 Q67 -.627 Q40 .624 Q33 .618 Q43 .613 Q47 .610 Q42 .600 .359 Q53 -.592 -.346 Q44 .587 Q35 .582 Q38 .577 Q41 .545 .314 Q45 .508 .377 Q83 .473 .314 .432 Q49 -.471 .363 -.402 Q37 .443 Q64 -.441 .369 Q76 .396 Q77 .389 .324 -.347 Q61 -.384 Q54 -.367 Q63 -.330 Q1 .305 .303 Q95 .843 Q88 .830 Q92 .797 Q90 .780 Q89 .768 Q93 .725 Q87 .661 Q94 .654 Q91 .622 Q84 .449 .421 Q6 .685 Q9 .623 Q5 .623 Q3 .605 Q4 .588 Q10 .563 Q2 .521 Q12 .487 Q14 .481 Q16 .478 Q17 .474 Q8 .443 -.337 Q14 .356 .431 -.337 Q7 .403 Q11 .364 .397 -.356 -.356

C-4

Appendix C

Q18 .373 -.350 -.350 Q70 .620 Q73 .560 Q72 .549 Q65 .537 Q58 .529 Q56 .518 Q74 -.340 .472 Q51 .441 Q71 .433 Q52 .429 Q75 -.368 .424 -.321 Q69 .413 Q55 .412 Q50 .378 -.358 Q15 .304 -.369 Q59 .366 Q68 -.348 .364 Q28 .849 Q29 .848 Q31 .846 Q26 .840 Q27 .770 Q30 .767 Q66 -.449 Q48 -.350 Q25 .668 Q24 .644 Q19 .562 Q60 .560 Q57 .533 Q22 .519 Q20 -.471 Q23 .413 .467 Q21 .347 -.407 Q86 .511 Q85 .504 Q78 .503 Q80 .503 Q82 .448 Q79 .340 .443 Q81 .386 .437 Q98 -.331 Q96 .677 Q97 .671 Q99 .565 Q101 .495 Q100 .413 Variance 22.2 7.7 4.7 3.5 2.6 2.3 2.2 2.0

C-5

Appendix C

C.4 Factor Loadings of Complete Social Correlates for Males

F1 F2 F3 Q11 .849 Q12 .824 Q10 .702 Q9 .674 Q8 .597 .516 Q5 .880 Q4 .842 Q6 .303 .518 -.472 Q7 .376 .517 -.303 Q1 .772 Q3 .716 Q2 .554 % Variance 42.7 10.5 9.1

C.5 Factor Loadings of Complete Social Correlates for Females

F1 F2 F3 Q11 .827 Q12 .752 .321 Q9 .743 Q10 .717 Q8 .632 Q5 .859 Q4 .775 Q6 .303 .623 -.386 Q7 .384 .614 Q1 .754 Q3 .723 Q2 .658 % Variance 42.5 10.9 8.9

C-6

Appendix C

C.6 Factor loadings of Complete Environmental Correlates for Males

F1 F2 F3 Q2 .819 Q10 .745 Q8 .670 Q5 .561 Q3 .559 Q1 .539 Q17 .468 .369 Q4 .378 .363 Q14 .648 Q22 .602 Q25 .587 Q24 .547 Q19 .542 Q13 .523 Q16 .487 Q15 .468 Q11 .454 Q20 .364 Q12 Q23 Q9 Q28 .708 Q27 .704 Q26 .390 .571 Q7 .455 Q21 .426 Q18 Q6 % Variance 17.5 8.1 6.7

C-7

Appendix C

C.7 Factor loadings of Complete Environmental Correlates for Females

F1 F2 F3 Q2 .810 Q10 .775 Q8 .678 Q5 .673 Q3 .548 Q4 .536 .473 Q1 .434 Q25 .613 Q14 .597 Q19 .589 Q11 .558 Q22 .550 Q15 .472 Q16 .468 Q24 .468 .372 Q17 .342 .464 Q26 .375 .399 Q28 .349 Q27 .344 Q20 .532 Q21 .512 Q23 .486 Q9 .474 Q7 .441 Q13 .322 .410 Q12 .410 Q6 .376 Q18 % Variance 18.3 7.6 7.1

C-8

Appendix C

C.8 Male ‘APAC’ Questionnaire: Factors and Items

Factor Items Psychological Factors How important are these things? Factor 1 Having fun is...... Value associated with Feeling good about myself is...... physical activity Being in shape is...... (9 items) Being in a better mood is..... Spending more time with my friends is...... Controlling my weight is...... Making new friends is..... Being better in sports, dance or other activities is.... Looking good is...... When I am active Factor 2 It’s no fun at all Enjoyment It’s not at all interesting (10 items) It makes me depressed I dislike it I feel bored I enjoy it I feel as though I would rather be doing something else I find it pleasurable It’s very exciting My body feels good Factor 3 I am satisfied with my appearance or I am I am dissatisfied with my appearance Body Image I am satisfied with my hair and face or I am I am dissatisfied with my hair and (6 items) face I feel that I look well or I am I feel that I don’t look well I am satisfied with my body or I am I am dissatisfied with my body I am satisfied with how I look or I am I am dissatisfied with how I look I am satisfied with my height and weight or I am I am dissatisfied with my height and weight How often do the following keep you from being physically active? Factor 4 Lack of time Perceived Barriers Social demands (time away from your friends) (10 items) Lack of money Family demands (work at home) Lack of a convenient place to be active Lack of equipment Work demands (work outside of home) Minor aches and pains I am too tired to exercise I lack the knowledge on how to do physical activities If I was to be physically active during my free time on most days .. Factor 5 It would help me control my weight Perceived Benefits I would feel better about myself (7 items) It would help me get in shape It would make me look better It would put me in a better mood It would make me better in sports dance and other activities I would make new friends I can be physically active during my free time on most days even if I have to Factor 6 stay at home Self-efficacy I can be physically active during my free time on most days (6 items) I can be physically active during my free time on most days even if it is hot or cold outside I can be physically active during my free time on most days no matter how busy my day is I have the co-ordination I need to be physically active during my free time on

C-9

Appendix C

most days I can ask my best friend to be physically active with me during my free time on most days Factor 7 How often was each of these things true for you in the last month? Change strategies I try to think more about the benefits of physical activity and less about the ( 5 items) hassles of being active I think about the benefits I will get from being physically active I say positive things to myself about physical activity I set goals to do physical activity When I get off track with my physical activity plans, I tell myself I can start again and get right back on track Factor 8 I don’t do well at a new game or sport or I do well at new games or sports Perceived competence I am among the last to be chosen for games and sports or I am one of the first to (4 items) be chosen for games and sports I feel I am better than others my age at games and sport or I feel I am not as good as others my age at games and sport I do very well at all kinds of games and sport or I don’t feel that I am very good when it comes to games and sports Stage of change I am currently physically active; I intend to become more physically active in (1 item) the next 6 months; I currently engage in regular physical activity; I have been regularly active for the past 6 months; In the past, I have been regularly active for a period of at least 3 months Intention to be active During my free time on most days.... (1 item) I am sure I will not be active; I probably will not be active; I may or may not be active; I probably will be active; I am sure I will be active Social Factors Factor 1 During a typical week how often has a member of your household...... Family support Watched you participate in physical activity or sports? (4 items) Told you that you are doing well in physical activity or sports? Provided transportation to a place where you can do physical activities or sports? Done a physical activity or played sports with you? Factor 2 During a typical week how often..... Peer support Do your friends encourage you to do physical activities or play sports? (2 items) Do you encourage your friends to do physical activities or play sports? Neighbourhood co- In the last week I have been ..... participation (1 item) Physically active with friends in my neighbourhood Environmental Factors There are footpaths on most streets in my neighbourhood Factor 1 My neighbourhood streets are well lit at night Perceived I often see other boys and girls playing outdoors in my neighbourhood Environment Walkers and bikers on the streets in my neighbourhood can be easily seen by (6 items) people in their homes There are bicycle or walking tracks in my neighbourhood There are many places I like to go within walking distance of my home Factor 2 Is it easy to get to the following from home and school? Access to facilities Health/ leisure club (7 items) Swimming pool Somewhere that dance/ gymnastic classes take place Recreation/ community centre Playing fiend (soccer, GAA or rugby) Tennis court Hall for martial arts Factor 3 If you Transport barriers Stayed after school for an activity every day how difficult would it be for you to (3 items) get home afterwards Wanted to do an after school activity someplace else besides school every day, how difficult would it be for you to get there? Wanted to do an after school activity someplace else besides school every day, how difficult would it be for you to get home afterwards?

C-10

Appendix C

C.9 Female ‘APAC’ questionnaire: Factors and Items

Factor Items Psychological Factors When I am active Factor 1 I enjoy it Enjoyment It’s no fun at all (13 items) It feels good I dislike it It’s very pleasant It’s very exciting I get something out of it It’s not at all interesting I feel as though I would rather be doing something else I find it boring I find it pleasurable It makes me depressed My body feels good How important are these things Factor 2 Feeling good about myself is...... Value associated with Having fun is...... physical activity Being in shape is...... (9 items) Being in a better mood is..... Controlling my weight is...... Looking good is...... Spending more time with my friends is...... Making new friends is..... Being better in sports, dance or other activities is.... I am satisfied with my appearance or I am I am dissatisfied with my appearance Factor 3 I feel that I look well or I am I feel that I don’t look well Body Image I am satisfied with how I look or I am I am dissatisfied with how I look (6 items) I am satisfied with my body or I am I am dissatisfied with my body I am satisfied with my hair and face or I am I am dissatisfied with my hair and face I am satisfied with my height and weight or I am I am dissatisfied with my height and weight Factor 4 I can be physically active during my free time even if I could watch TV or play Self-efficacy (8 items) video games instead I can be physically active during my free time on most days even if I have to stay at home I can be physically active during my free time on most days I can be physically active during my free time on most days even if it is hot or cold outside I can be physically active during my free time on most days no matter how busy my day is I have the co-ordination I need to be physically active during my free time on Factor 5 most days Change strategies I can ask my best friend to be physically active with me during my free time on (7 items) most days I can ask my parent or other adult to do physically active things with me How often was each of these things true for you in the last month? I think about the benefits I will get from being physically active I try to think more about the benefits of physical activity and less about the hassles of being active I say positive things to myself about physical activity When I get off track with my physical activity plans, I tell myself I can start again and get right back on track I set goals to do physical activity I make back up plans to be sure I get my physical activity I do things to make physical activity more enjoyable

C-11

Appendix C

Factor 6 I do very well at all kinds of games and sports or I don’t feel that I am good Perceived competence when it comes to games and sports (6 items) I am among the last to be chosen for games and sports or I am one of the first to be chosen for games and sports I feel I am better than others my age at games and sport or I feel I am not as good as others my age at games and sport I don’t do well at a new game or sport or I do well at new games or sports I wish I could be better at games and sports or I feel that I am good enough at games and sport I think I could easily participate in a sport I have never done before or I would struggle to feel comfortable playing a game or sport I have never tried Factor 7 How often do the following keep you from being physically active? Perceived Barriers Lack of time (6 items) Social demands (time away from your friends) Weather is too bad I am too tired to exercise Family demands (work at home) Lack of a convenient place to be active Factor 8 If I was to be physically active during my free time on most days .. Perceived Benefits It would help me get in shape (6 items) It would help me control my weight I would make new friends I would feel better about myself It would make me better in sports dance and other activities It would help me spend more time with my friends Factor 9 Most teachers act like they think it is more important for boys to be physically Teacher influence active than girls (2 items) Most PE teachers act like they think it is more important for boys to be physically active than girls I am currently physically active; I intend to become more physically active in the Stage of change next 6 months; I currently engage in regular physical activity; I have been (1 item) regularly active for the past 6 months; In the past, I have been regularly active for a period of at least 3 months During my free time on most days.... Intention to be active I am sure I will not be active; I probably will not be active; I may or may not be (1 item) active; I probably will be active; I am sure I will be active Social Factors Factor 1 During a typical week how often has a member of your household...... Family support Watched you participate in physical activity or sports? (4 items) Told you that you are doing well in physical activity or sports? Provided transportation to a place where you can do physical activities or sports? Done a physical activity or played sports with you? Factor 2 During a typical week how often..... Peer support Do your friends encourage you to do physical activities or play sports? (2 items) Do you encourage your friends to do physical activities or play sports? Neighbourhood co- In the last week I have been ..... participation(1 item) Physically active with friends in my neighbourhood

Environmental Factors There are footpaths on most streets in my neighbourhood Factor 1 My neighbourhood streets are well lit at night Perceived I often see other boys and girls playing outdoors in my neighbourhood Environment Walkers and bikers on the streets in my neighbourhood can be easily seen by (5 items) people in their homes There are bicycle or walking tracks in my neighbourhood Factor 2 Is it easy to get to the following from home and school? Access to facilities Health/ leisure club (8 items) Swimming pool Somewhere that dance/ gymnastic classes take place Recreation/ community centre

C-12

Appendix C

Basketball court Playing field (soccer, GAA or rugby) Tennis court Hall for martial arts Factor 3 If you...... Transport barriers Stayed after school for an activity every day how difficult would it be for you to (3 items) get home afterwards Wanted to do an after school activity someplace else besides school every day, how difficult would it be for you to get there? Wanted to do an after school activity someplace else besides school every day, how difficult would it be for you to get home afterwards?

C-13

Appendix C

C.10 Original Questionnaire

University of Limerick O L L S C O I L L U I M NI G H

Lifestyle Survey for Young People

INSTRUCTIONS

Thank you for helping us with this survey. By answering these questions you will be helping us to find out more about the physical activity habits of Irish Adolescents and their attitudes to physical activity. Physical activity or exercise includes playing sports, jogging, cycling, walking briskly, digging in the garden, climbing stairs, heavy housecleaning or any other activity that gets you moving and breathing harder.

This survey is a collaboration between your school and the University of Limerick and is being administered in schools across County Cork.

Take your time to read each question carefully and answer it as best as you can. Please be as factual as possible. Remember we are only interested in your opinions. Your answers will be kept private. This is not a test and there is no right or wrong answers.

For each question there are a number of different answers you can mark. For some questions there will be a blank space for you to write your answer. For most questions there will be a set of boxes; you should select the one appropriate to you and mark with a tick.

If you are unclear about any question, please ask the test administrators.

Please write today’s date: Day Month Year

C-14

Appendix C

Section A (Please complete blanks or tick the box as appropriate)

1. Student Code : ______2. Age : _____ years _____ months

3. Nationality: ______4. Sex: Male Female

5. Number of Brothers: _____ Age of Brother(s): ______(youngest to oldest e.g.11, 13, 23)

6. Number of Sisters: ___ Age of Sister(s): ______(youngest to oldest e.g.11, 13, 23)

7. Class Year: 1st 2nd 3rd 4th 5th 6th

8. Residence: Town or city Village or in the countryside

9 (a) Does your father/ guardian work as an employee or are they self employed in their main job (Please tick the appropriate box) a. Employee b. Self employed with paid employees c. Self employed without paid employees d. None of the above

9 (b) Does your mother/ guardian work as an employee or are they self employed in their main job (Please tick the appropriate box) a. Employee b. Self employed with paid employees c. Self employed without paid employees d. None of the above

9 (c) What are your parents/ guardians occupation in their main jobs. In all cases describe occupation fully and precisely giving full job title.

Use precise terms such as Do NOT use general terms such as Retail Store Manager Manager Secondary Teacher Teacher Electrical Engineering Engineer

You can also write ‘has no paid job’ at the moment or unemployed. If you live with one parent/ guardian, just answer for that person and leave the other blank.

Father (guardian’s job) ______

Mothers (guardian’s job) ______

C-15

Appendix C

10. Do you mainly live with your father, mother or both parents? Please tick one box only.

Father Mother Both Parents

11. Have you any specific medical conditions that prevent you from participating in physical activity and sport?

Yes No If ‘Yes’, what is this condition

Section B

If you……. Not at all Somewhat Very Impossible difficult Difficult Difficult 1. ……stayed after school for an activity EVERY DAY, 1 2 3 4 how difficult would it be for you to get home afterwards 2. ……wanted to do an after-school activity someplace 1 2 3 4 else besides school EVERY DAY, how difficult would it be to get there ? 3…wanted to do an after-school activity someplace else 1 2 3 4 besides school EVERY DAY, how difficult would it be for you to get home afterwards?

Section C

1. For each question below please mark YES or No. Please be sure to follow the instructions carefully. Physical activity or exercise includes such activities as playing sports, jogging, walking briskly, digging in the garden, climbing stairs, heavy housecleaning or any other activity where exertion is similar to these activities.

For activity to be regular it must add up to a total of 30 or more minutes per day, and be done at least 5 days per week. For example, you could take a 30-minute walk or take a 10- minute walk, rake leaves for 10 minutes and climb up stairs for 10 minutes. Tick either the yes or no box for each question. No Yes

1. I am currently physically active.

2. I intend to become more physically active in the next 6 months.

3. I currently engage in regular physical activity.

4. I have been regularly physically active for the past 6 months.

5. In the past, I have been regularly physically active for a period of at least 3 months.

C-16

Appendix C

2. How often was each of these things true for you in the last month?

Never Rarely Some- Often Very times Often 1. I do things to make physical activity more enjoyable. 1 2 3 4 5

2. I think about the benefits I will get from being physically active. 1 2 3 4 5

3. I try to think more about the benefits of physical activity and less 1 2 3 4 5 about the hassles of being active. 4. I say positive things to myself about physical activity. 1 2 3 4 5

5. When I get off track with my physical activity plans, I tell myself I 1 2 3 4 5 can start again and get right back on track. 6. I have a friend of family member who encourages me to do 1 2 3 4 5 physical activity. 7. I try different kinds of physical activity so that I have more options 1 2 3 4 5 to choose from. 8. I set goals to do physical activity. 1 2 3 4 5

9. I make back-up plans to be sure I get my physical activity. 1 2 3 4 5

Section D

Disagree a Disagree a Neither agree Agree Agree lot little not disagree a little a lot 1. I can be physically active during my free time on 1 2 3 4 5 most days. 2. I can ask my parent or other adult to do physically 1 2 3 4 5 active things with me. 3. I can be physically active during my free time on 1 2 3 4 5 most days even if I could watch TV or play video games instead. 4. I can be physically active during my free time on 1 2 3 4 5 most days even if it is very hot or cold outside. 5. I can ask my best friend to be physically active with 1 2 3 4 5 me during my free time on most days. 6. I can be physically active during my free time on 1 2 3 4 5 most days even if I have to stay at home. 7. I have the co-ordination I need to be physically 1 2 3 4 5 active during my free time on most days. 8. I can be physically active during my free time on 1 2 3 4 5 most days no matter how busy my day is.

C-17

Appendix C

Section E

In this part of the questionnaire, many of the items mention games and sports. Games and sports are any physical activity where you move your body for exercise or play e.g. bicycling, badminton, rollerblading, swimming, and ball games such as basketball and soccer.

For each statement tick the most appropriate box for you. See example below. Example: Really Sort of Really Sort of true for true for true for true for me me me me I like sport OR I don’t like sport very much

Really Sort of Really Sort of true true true for true for me for me me for me I do very well at all kinds OR I don’t feel that I am very of games and sports good when it comes to games and sports I wish I could be a lot OR I feel that I am good enough better at games and sport at games and sport

I think I could easily OR I would struggle to feel participate in a sport I comfortable playing a game have never done before. or sport I have never tried. I feel I am better than OR I feel I am not as good as others my age at games others my age at games and and sports sports

I prefer to watch games or OR I prefer to play games or sports sports

I don’t do well straight OR I do well at new games and away in a new game or sports straight away sport I am among the last to be OR I am usually one of the first chosen for games and to be chosen for games and sports sports

I am very satisfied with OR I am dissatisfied with how I how I look look

I am satisfied with my OR I am dissatisfied with my height and weight height and weight

I am satisfied with my OR I am dissatisfied with my body. body

I am satisfied with my OR I am dissatisfied with my appearance appearance

I am satisfied with my hair OR I am dissatisfied with my hair and face and face

I feel that I look well OR I feel that I don’t look well

C-18

Appendix C

Section F

When I am active…… Disagree a Disagree a Neither agree Agree Agree lot little not disagree a little a lot 1. I enjoy it 1 2 3 4 5

2. I feel bored 1 2 3 4 5

3. I dislike it 1 2 3 4 5

4. I find it pleasurable 1 2 3 4 5

5. Its no fun at all 1 2 3 4 5

6. It gives me energy 1 2 3 4 5

7. It makes me depressed 1 2 3 4 5

8. It frustrates me 1 2 3 4 5

9. Its very pleasant 1 2 3 4 5

10. My body feels good 1 2 3 4 5

11. I get something out of it 1 2 3 4 5

12. Its very exciting 1 2 3 4 5

13. Its not at all interesting 1 2 3 4 5

14. It gives me strong feelings of success 1 2 3 4 5

15. It feels good 1 2 3 4 5

16. I feel as though I would rather be doing something 1 2 3 4 5 else.

C-19

Appendix C

Section G

How often do the following keep you from being physically active? Never Rarely Some- Very times Often 1. Self-conscious about my looks when I do activities 1 2 3 4

2. Lack of interest in physical activity 1 2 3 4

3. I lack self discipline (will power) 1 2 3 4

4. Lack of energy 1 2 3 4

5. I do not have anyone to do physical activities with me 1 2 3 4

6. I do not enjoy physical activities 1 2 3 4

7. I hate to fail so I do not try 1 2 3 4

8. lack of equipment 1 2 3 4

9. The weather is too bad 1 2 3 4

10. Lack of skills 1 2 3 4

11. I am too tired to exercise 1 2 3 4

12. I lack the knowledge on how to do physical activities 1 2 3 4

13. I’m chosen last for teams 1 2 3 4

14. I don’t like to sweat 1 2 3 4

15. Poor health 1 2 3 4

16. Fear of injury 1 2 3 4

17. Physical activity is hard work 1 2 3 4

18. Lack of a convenient place to do physical activity 1 2 3 4

19. Too overweight 1 2 3 4

20. Physical activity is boring 1 2 3 4

21. Minor aches and pains 1 2 3 4

22. Lack of money 1 2 3 4

Time Barriers 1 2 3 4

23. Lack of time 1 2 3 4

24. Work demands (work you get paid for) 1 2 3 4

25. Social demands (time away from my friends) 1 2 3 4

26. Family demands (work at home) 1 2 3 4

27. TV, computer games, internet 1 2 3 4

28. It is played too seriously 1 2 3 4

C-20

Appendix C

Section H

1. How do you feel about PE class? Disagree a Disagree a Neither agree Agree Agree lot little not disagree a little a lot a. I enjoy PE 1 2 3 4 5

2. Have you ever received formal instruction/coaching in sport or the physical activity that you participate in? (Exclude PE) Please tick one box only

a. No b. Yes, in school c. Yes by parent, guardian, family member or relative d. Yes, by coach outside school e. Yes both inside and outside school

3. During my free time on most days……. Please tick one box only

a. I am sure I will not be physically active b. I probably will not be physically active c. I may or may not be physically active d. I probably will be physically active e. I am sure I will be physically active

Section I

Disagree a Disagree a Neither agree Agree Agree lot little nor disagree a little a lot 1. There are many places I like to go within walking distance 1 2 3 4 5 of my home. 2. There are footpaths on most of the streets in my 1 2 3 4 5 neighbourhood. 3. There are bicycle or walking tracks in my neighbourhood. 1 2 3 4 5

4. It is safe to walk or jog in my neighbourhood. 1 2 3 4 5

5. Walkers and bikers on the streets in my neighbourhood can 1 2 3 4 5 be easily seen by people in their homes. 6. There is so much traffic that it makes it hard to walk in my 1 2 3 4 5 neighbourhood. 7. There is a lot of crime in my neighbourhood. 1 2 3 4 5

8. I often see other boys and girls playing outdoors in my 1 2 3 4 5 neighbourhood. 9. There are many interesting things to look at while walking 1 2 3 4 5 in my neighbourhood. 10. My neighbourhood streets are well lit at night. 1 2 3 4 5

C-21

Appendix C

Section J

Is it easy to get access to the following from home and school?

Yes No Yes No 1. Basketball court 9. Recreation/community centre 2. Beach or lake 10. Track

3. Golf course 11. Safe running route

4. Health/Leisure club 12. Swimming pool

5. Hall for martial arts classes 13. Walking, biking or hiking path or trail 6. Playing field (Soccer, 14. Tennis court GAA or Rugby) 7. Public Park 15.Somewhere that dance/gymnastic classes take place 8. Trampoline

Section K

Please tick yes/no for each statement

In the last week I have been …….. No Yes

1. Physically active with school friends

2. Physically active with friends in my neighbourhood.

3. Physically active with other friends

Please answer each question.

1. During a typical week, how often…. None Once Some- Almost Every- times every day day 1. …do you encourage your friends to do physical activities or play 1 2 3 4 5 sports? 2. …do your friends encourage you to do physical activities or play 1 2 3 4 5 sports? 3. …do your friends do physical activities or play sports with you? 1 2 3 4 5

4. …do your friends tell you that you are doing well at physical 1 2 3 4 5 activities or sports?

C-22

Appendix C

2. During a typical week how often has a member of your None Once Some- Almost Every- household …(for example, your father, mother, guardian, times every day day brother, sister, grandparent or other relative): 1. …encouraged you to do physical activities or play sports 1 2 3 4 5

2. …done a physical activity or played sports with you? 1 2 3 4 5

3. …provided transportation to a place where you can do physical 1 2 3 4 5 activities or sports? 4. …watched you participate in physical activities or sports? 1 2 3 4 5

5. …told you that you are doing well in physical activities or 1 2 3 4 5 sports?

Section L

1. On an average school day and weekend day, how many hours do you watch Weekday Weekend TV? day 1. I do not watch TV on an average day

2. Less than 1 hour per day.

3. About 1 hour per day

4. About 2 hours per day

5. About 3 hours per day

6. About 4 hours per day

7. About or more than 5 hours per day

2. On an average school day and weekend day, how many hours do you play Weekday Weekend video or computer games or use a computer for something that is not school day work? (Include activities such as Internet, Beebo, Play Station ) 1. I do not use computer on an average day

2. Less than 1 hour per day.

3. About 1 hour per day

4. About 2 hours per day

5. About 3 hours per day

6. About 4 hours per day

7. About or more than 5 hours per day

C-23

Appendix C

Section M

A. If I was to be physically active during B. How important are these things? my free time on most days…

a lot Disagree a little Disagree agree or disagree Neither little a Agree lot a Agree unimportant Very unimportant Somewhat important/unimportant Neither important Somewhat important Very

1. ... it would help me Spending more spend more time with time with my my friends friends is…. 2. … it would help get Being in shape me in shape is… 3. … it would help me Controlling my control my weight weight is… 4. … it would put me in Being in a better a better mood mood is… 5. … it would make me Being better in better in sports, dance, sports, dance, or or other activities other activities is.. 6. … it would be fun Having fun is…

7. … it would make me Looking good look better is… 8. … I would make Making new new friends is… friends 9. … I would feel better Feeling good about myself about myself is…

C-24

Appendix C

Section N

Please respond to each of the following statements. Disagree Disagree Neither Agree Agree a lot a little agree not a little a lot disagree 1…..PE teachers act like they think it is more important for boys 1 2 3 4 5 to be physically active than girls 2. ….most teachers act like they think it is more important for 1 2 3 4 5 boys to be physically active than girls 3….most girls think it is important to be physically active 1 2 3 4 5

4….boys make rude comments round girls who are being 1 2 3 4 5 physically active 5….being physically active around members of the opposite sex 1 2 3 4 5 makes me uncomfortable 6……boys stare too much at girls who are being physically active 1 2 3 4 5

Section O

1. For each statement below please tick YES or No. No Hurling/ Football Camogie 1. I am currently playing with a Gaelic Games team outside of school

2. I am currently playing with a Gaelic Games team in school.

3. I used to play with a Gaelic Games team but I don’t anymore

4. I never played Gaelic Games competitively

2. In 5 years time……. Please tick one box only

a. I am pretty sure I will not be playing Gaelic Games

b. I probably will not be playing Gaelic Games

c. I may or may not be playing Gaelic Games

d. I probably will be playing Gaelic Games

e. I am sure I will be playing Gaelic Games

C-25

Appendix C

3. I think the GAA ( Football, hurling, ladies Disagree a Disagree a Neither agree Agree Agree lot little not disagree a little a lot football, camogie ) …… 1. Is too serious 1 2 3 4 5

2. Takes too much time 1 2 3 4 5

3. Is something I am not interested in 1 2 3 4 5

4. Is boring 1 2 3 4 5

5. Is something I am good at 1 2 3 4 5

6. Is something my friends are interested in 1 2 3 4 5

7. Is something my family is interested in 1 2 3 4 5

8. Is too rough 1 2 3 4 5

9. Lacks discipline 1 2 3 4 5

10. Is well organised 1 2 3 4 5

11. Is fun/ enjoyable 1 2 3 4 5

12. Is an activity more suited to boys than girls 1 2 3 4 5

C-26

Appendix C

No Yes

4 (a) I play sport competitively

4 (b) If you answered yes to question 4 (a) proceed to question 4 (b). A number of statements that athletes have used to describe their feelings about sport are given below. For each statement tick how you are feeling right now. Almost Rarely Sometimes Frequently Almost never always 1. I’m accomplishing many worthwhile things in 1 2 3 4 5 sport 2. I feel so tired from my training that I have trouble 1 2 3 4 5 finding the energy to do other things 3. The effort I spend in sport would be better spent 1 2 3 4 5 doing other things 4. I feel overly tired from my sport participation 1 2 3 4 5

5. I am not achieving much in sport 1 2 3 4 5

6. I don’t care as much about my sport performance 1 2 3 4 5 as I used to 7. I am not performing up to my ability in sport 1 2 3 4 5

8. I feel ‘wiped out’ from sport 1 2 3 4 5

9. I’m not into sport like I used to be 1 2 3 4 5

10. I feel physically worn out from sport 1 2 3 4 5

11. I feel less concerned about being successful in 1 2 3 4 5 sport than I used to 12. I am exhausted by the mental and physical 1 2 3 4 5 demands of sport 13. It seems no matter what I do, I don’t perform as 1 2 3 4 5 well as I should 14. I feel successful at sport 1 2 3 4 5

15. I have negative feelings toward sport 1 2 3 4 5

Thank you for your support and cooperation.

C-27

Appendix C

C.11 Male Adolescent Physical Activity Correlate Questionnaire

University of Limerick O L L S C O I L L U I M NI G H

Male Adolescent Physical Activity Correlate (APAC) Questionnaire

INSTRUCTIONS

This survey is a collaboration between your school and the University of Limerick and is being administered in schools across County Cork.

If you are unclear about any question, please ask the test administrators.

Please write today’s date: Day Month Year

C-28

Appendix C

Section A (Please complete blanks or tick the box as appropriate)

1. Student Code : ______2. Age : _____ years _____ months

3. Nationality: ______4. Sex: Male Female

5. Number of Brothers: _____ Age of Brother(s): ______(youngest to oldest e.g.11, 13, 23)

6. Number of Sisters: ___ Age of Sister(s): ______(youngest to oldest e.g.11, 13, 23)

7. Class Year: 1st 2nd 3rd 4th 5th 6th

8. Residence: Town or city Village or in the countryside

9 (c) What are your parents/ guardians occupation in their main jobs. In all cases describe occupation fully and precisely giving full job title.

Use precise terms such as Do NOT use general terms such as Retail Store Manager Manager Secondary Teacher Teacher Electrical Engineering Engineer

You can also write ‘has no paid job’ at the moment or unemployed. If you live with one parent/ guardian, just answer for that person and leave the other blank.

Father (guardian’s job) ______

Mothers (guardian’s job)______

C-29

Appendix C

10. Do you mainly live with your father, mother or both parents? Please tick one box only.

Father Mother Both Parents

11. Have you any specific medical conditions that prevent you from participating in physical activity and sport?

Yes No If ‘Yes’, what is this condition ____

Section B

Section C

1. I am currently physically active.

2. I intend to become more physically active in the next 6 months.

3. I currently engage in regular physical activity.

4. I have been regularly physically active for the past 6 months.

5. In the past, I have been regularly physically active for a period of at least 3 months.

2. How often was each of these things true for you in the last month?

C-30

Appendix C

Never Rarely Some- Often Very times Often 1. I think about the benefits I will get from being physically active. 1 2 3 4 5

2. I try to think more about the benefits of physical activity and less 1 2 3 4 5 about the hassles of being active. 3. I say positive things to myself about physical activity. 1 2 3 4 5

4. When I get off track with my physical activity plans, I tell myself I 1 2 3 4 5 can start again and get right back on track. 5. I set goals to do physical activity. 1 2 3 4 5

Section D

Disagree a Disagree a Neither agree Agree Agree lot little not disagree a little a lot 1. I can be physically active during my free time on 1 2 3 4 5 most days. 2. I have the co-ordination I need to be physically 1 2 3 4 5 active during my free time on most days 3. I can be physically active during my free time on 1 2 3 4 5 most days even if it is very hot or cold outside. 4. I can ask my best friend to be physically active with 1 2 3 4 5 me during my free time on most days. 5. I can be physically active during my free time on 1 2 3 4 5 most days even if I have to stay at home. 6. I can be physically active during my free time on 1 2 3 4 5 most days no matter how busy my day is.

C-31

Appendix C

Section E

Really Sort of Really Sort of true true true for true for me for me me for me I do very well at all kinds OR I don’t feel that I am very of games and sports good when it comes to games and sports I feel I am better than OR I feel I am not as good as others my age at games others my age at games and and sports sports

I am very satisfied with OR I am dissatisfied with how I how I look look

I am satisfied with my OR I am dissatisfied with my height and weight height and weight

I am satisfied with my OR I am dissatisfied with my body. body

I am satisfied with my OR I am dissatisfied with my appearance appearance

I am satisfied with my hair OR I am dissatisfied with my and face hair and face

I feel that I look well OR I feel that I don’t look well

C-32

Appendix C

Section F

When I am active…… Disagree a Disagree a Neither agree Agree Agree lot little not disagree a little a lot 1. I enjoy it 1 2 3 4 5

2. I feel bored 1 2 3 4 5

3. I dislike it 1 2 3 4 5

4. I find it pleasurable 1 2 3 4 5

5. Its no fun at all 1 2 3 4 5

6. It makes me depressed 1 2 3 4 5

7. My body feels good 1 2 3 4 5

8. Its very exciting 1 2 3 4 5

9. Its not at all interesting 1 2 3 4 5

10. I feel as though I would rather be doing something 1 2 3 4 5 else.

Section G

How often do the following keep you from being physically active? Never Rarely Some- Very times Often 1. lack of equipment 1 2 3 4

2. I am too tired to exercise 1 2 3 4

3. I lack the knowledge on how to do physical activities 1 2 3 4

4. Lack of a convenient place to do physical activity 1 2 3 4

5. Minor aches and pains 1 2 3 4

6. Lack of money 1 2 3 4

7. Lack of time 1 2 3 4

8. Work demands (work you get paid for) 1 2 3 4

9. Social demands (time away from my friends) 1 2 3 4

10. Family demands (work at home) 1 2 3 4

C-33

Appendix C

Section H

During my free time on most days……. Please tick one box only

Section I

4. Walkers and bikers on the streets in my neighbourhood can 1 2 3 4 5 be easily seen by people in their homes.

5. I often see other boys and girls playing outdoors in my 1 2 3 4 5 neighbourhood. 6. My neighbourhood streets are well lit at night. 1 2 3 4 5

Section J

Is it easy to get access to the following from home and school?

Yes No Yes No 1. Hall for martial arts classes 5. Recreation/community centre 2. Health/Leisure club 6. Swimming pool

3. Playing field (Soccer, 7. Tennis court GAA or Rugby) 4.Somewhere that dance/gymnastic classes take place

C-34

Appendix C

Section K

Please tick yes/no for each statement

In the last week I have been …….. No Yes

1. Physically active with friends in my neighbourhood.

Please answer each question.

2. During a typical week how often has a member of your None Once Some- Almost Every- household …(for example, your father, mother, guardian, times every day day brother, sister, grandparent or other relative): 1. …done a physical activity or played sports with you? 1 2 3 4 5

2. …provided transportation to a place where you can do physical 1 2 3 4 5 activities or sports? 3. …watched you participate in physical activities or sports? 1 2 3 4 5

4. …told you that you are doing well in physical activities or 1 2 3 4 5 sports?

C-35

Appendix C

Section L

1. On an average school day and weekend day, how many hours do you watch Weekday Weekend TV? day 1. I do not watch TV on an average day

2. Less than 1 hour per day.

3. About 1 hour per day

4. About 2 hours per day

5. About 3 hours per day

6. About 4 hours per day

7. About or more than 5 hours per day

2. Less than 1 hour per day.

3. About 1 hour per day

4. About 2 hours per day

5. About 3 hours per day

6. About 4 hours per day

7. About or more than 5 hours per day

C-36

Appendix C

Section M

A. If I was to be physically active during B. How important are these things? my free time on most days…

a lot Disagree a little Disagree agree or disagree Neither little a Agree lot a Agree unimportant Very unimportant Somewhat important/unimportant Neither important Somewhat important Very

7. … it would make me Looking good look better is… 8. … I would make Making new new friends is… friends 9. … I would feel better Feeling good about myself about myself is…

Section N No Yes

I play sport competitively

Thank you for your support and cooperation.

C-37

Appendix C

C.12 Female Adolescent Physical Activity Correlate Questionnaire

University of Limerick O L L S C O I L L U I M NI G H

Female Adolescent Physical Activity Correlate (APAC) Questionnaire

INSTRUCTIONS

This survey is a collaboration between your school and the University of Limerick and is being administered in schools across County Cork.

If you are unclear about any question, please ask the test administrators.

Please write today’s date: Day Month Year C-38

Appendix C

Section A (Please complete blanks or tick the box as appropriate)

1. Student Code : ______2. Age : _____ years _____ months

3. Nationality: ______4. Sex: Male Female

5. Number of Brothers: _____ Age of Brother(s): ______(youngest to oldest e.g.11, 13, 23)

6. Number of Sisters: ___ Age of Sister(s): ______(youngest to oldest e.g.11, 13, 23)

7. Class Year: 1st 2nd 3rd 4th 5th 6th

8. Residence: Town or city Village or in the countryside

9 (c) What are your parents/ guardians occupation in their main jobs. In all cases describe occupation fully and precisely giving full job title.

Use precise terms such as Do NOT use general terms such as Retail Store Manager Manager Secondary Teacher Teacher Electrical Engineering Engineer

You can also write ‘has no paid job’ at the moment or unemployed. If you live with one parent/ guardian, just answer for that person and leave the other blank.

Father (guardian’s job) ______

Mothers (guardian’s job) ______

C-39

Appendix C

10. Do you mainly live with your father, mother or both parents? Please tick one box only.

Father Mother Both Parents

11. Have you any specific medical conditions that prevent you from participating in physical activity and sport?

Yes No If ‘Yes’, what is this condition ____

Section B

Section C

1. I am currently physically active.

2. I intend to become more physically active in the next 6 months.

3. I currently engage in regular physical activity.

4. I have been regularly physically active for the past 6 months.

5. In the past, I have been regularly physically active for a period of at least 3 months.

C-40

Appendix C

2. How often was each of these things true for you in the last month?

Never Rarely Some- Often Very times Often 1. I do things to make physical activity more enjoyable . 1 2 3 4 5

2. I think about the benefits I will get from being physically active. 1 2 3 4 5

3. I try to think more about the benefits of physical activity and less 1 2 3 4 5 about the hassles of being active. 4. I say positive things to myself about physical activity. 1 2 3 4 5

5. When I get off track with my physical activity plans, I tell myself I 1 2 3 4 5 can start again and get right back on track. 6. I set goals to do physical activity. 1 2 3 4 5

7. I make back-up plans to be sure I get my physical activity. 1 2 3 4 5

Section D

C-41

Appendix C

Section E

I am very satisfied with OR I am dissatisfied with how I how I look look

I am satisfied with my OR I am dissatisfied with my height and weight height and weight

I am satisfied with my OR I am dissatisfied with my body. body

I am satisfied with my OR I am dissatisfied with my appearance appearance

I am satisfied with my hair OR I am dissatisfied with my hair and face and face

I feel that I look well OR I feel that I don’t look well

C-42

Appendix C

Section F

When I am active…… Disagree a Disagree a Neither agree Agree Agree lot little not disagree a little a lot 1. I enjoy it 1 2 3 4 5

2. I find it boring 1 2 3 4 5

3. I dislike it 1 2 3 4 5

4. I find it pleasurable 1 2 3 4 5

5. Its no fun at all 1 2 3 4 5

6. It makes me depressed 1 2 3 4 5

7. Its very pleasant 1 2 3 4 5

8. My body feels good 1 2 3 4 5

9. I get something out of it 1 2 3 4 5

10. Its very exciting 1 2 3 4 5

11. Its not at all interesting 1 2 3 4 5

12. It feels good 1 2 3 4 5

13. I feel as though I would rather be doing something 1 2 3 4 5 else.

C-43

Appendix C

Section G

How often do the following keep you from being physically active? Never Rarely Some- Very times Often 1. I am too tired to exercise 1 2 3 4

2. Lack of a convenient place to do physical activity 1 2 3 4

3. Lack of time 1 2 3 4

4. The weather is too bad 1 2 3 4

5. Social demands (time away from my friends) 1 2 3 4

6. Family demands (work at home) 1 2 3 4

Section H

During my free time on most days……. Please tick one box only

Section I

Disagree a Disagree a Neither agree Agree Agree lot little nor disagree a little a lot 1. There are footpaths on most of the streets in my 1 2 3 4 5 neighbourhood. 2. There are bicycle or walking tracks in my neighbourhood. 1 2 3 4 5

3. Walkers and bikers on the streets in my neighbourhood can 1 2 3 4 5 be easily seen by people in their homes. 4. I often see other boys and girls playing outdoors in my 1 2 3 4 5 neighbourhood. 5. My neighbourhood streets are well lit at night. 1 2 3 4 5

C-44

Appendix C

Section J

Is it easy to get access to the following from home and school?

Yes No Yes No 1. Basketball court 5. Recreation/community centre 2. Health/Leisure club 6. Swimming pool

3. Hall for martial arts classes 7.Somewhere that dance/gymnastic classes take place 4. Playing field (Soccer, 8. Tennis court GAA or Rugby)

Section K

Please tick yes/no for each statement

In the last week I have been …….. No Yes

Physically active with friends in my neighbourhood.

Please answer each question.

2. During a typical week how often has a member of your None Once Some- Almost Every- household …(for example, your father, mother, guardian, times every day day brother, sister, grandparent or other relative): 2. …done a physical activity or played sports with you? 1 2 3 4 5

3. …provided transportation to a place where you can do physical 1 2 3 4 5 activities or sports? 4. …watched you participate in physical activities or sports? 1 2 3 4 5

5. …told you that you are doing well in physical activities or 1 2 3 4 5 sports?

C-45

Appendix C

Section L

1. On an average school day and weekend day, how many hours do you watch Weekday Weekend TV? day 1. I do not watch TV on an average day

2. Less than 1 hour per day.

3. About 1 hour per day

4. About 2 hours per day

5. About 3 hours per day

6. About 4 hours per day

7. About or more than 5 hours per day

2. Less than 1 hour per day.

3. About 1 hour per day

4. About 2 hours per day

5. About 3 hours per day

6. About 4 hours per day

7. About or more than 5 hours per day

C-46

Appendix C

Section M

A. If I was to be physically active during B. How important are these things? my free time on most days…

a lot Disagree a little Disagree agree or disagree Neither little a Agree lot a Agree unimportant Very unimportant Somewhat important/unimportant Neither important Somewhat important Very

7. … it would make me Looking good look better is… 8. … I would make Making new new friends is… friends 9. … I would feel better Feeling good about myself about myself is…

C-47

Appendix C

Section N

No Yes

4 (a) I play sport competitively

Thank you for your support and cooperation.

C-48