ALCOHOL USE AND ASSOCIATED HEALTH BEHAVIOURS OF WOMEN WHO HAVE BEEN TREATED FOR BREAST CANCER

Sarah Balaam BHthSc(Nut&Diet)(Hons1), APD

Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy

School of Nursing

Faculty of Health

Institute of Health and Biomedical Innovation (IHBI)

Queensland University of Technology

2019

Alcohol use and associated health behaviours of women who have been treated for breast cancer i Keywords

Alcohol consumption, breast cancer, health behaviours, health promotion, precede-proceed model, predisposing, reinforcing, enabling, survivorship.

ii Alcohol use and associated health behaviours of women who have been treated for breast cancer Abstract

Background

Breast cancer is the most commonly diagnosed cancer in Australian women (Australian Institute of Health and Welfare & Australasian Association of Cancer Registries, 2017). Fortunately, advances in screening, early detection, and diagnosis, together with more effective treatments, are associated with recently-improved survival rates for these women, despite increased incidence (Potter, Collins, Brown, & Hure, 2014; Rock et al., 2012; World Cancer Research Fund/American Institute for Cancer Research, 2007). Better survival is clearly a positive outcome for this growing population; however, survival is also associated with negative outcomes, such as an increased risk of secondary primary cancers (Pollard, Eakin, Vardy, & Hawkes, 2009), co-morbidities (Eakin et al., 2007; Eakin et al., 2006; Pollard et al., 2009; Rock, Byers, et al., 2013), and the debilitating long-term effects of cancer treatment.

Alcohol is a known carcinogen, with direct links to breast cancer development (Brooks, 2011; World Cancer Research Fund International/American Institute for Cancer Research, 2017a). Alcohol consumption after treatment for breast cancer amplifies the risk of secondary cancers (Rock, Byers, et al., 2013), adverse treatment-related effects (Gallicchio et al., 2015; Lydon et al., 2016; Mitchell & Woods, 2015; Smith, Gallicchio, Miller, Zacur, & Flaws, 2016; Tipples & Robinson, 2011), and comorbidities (Campbell et al., 2012; Rock, Byers, et al., 2013), while potentially increasing the risk of recurrence (Brooks, 2011; Kwan et al., 2013; Rock et al., 2012; Tan, Barber, & Shields, 2006; Winstanley et al., 2011). For example, the relative risk of breast cancer recurrence is increased by 20% for postmenopausal women who regularly consume more than three alcoholic drinks per week (Kwan et al., 2013). Unfortunately, national and international alcohol-related guidelines in this context are confusing, in that there is no consensus as to how many grams of alcohol constitute a standard drink (International Center for Alcohol Policies, 2007a) and there is little agreement regarding the precise intake that protects health (Winstanley et al., 2011). Adding to this ambiguity, the amount of alcohol consumed and the reasons for alcohol intake among the growing Australian population of women who

Alcohol use and associated health behaviours of women who have been treated for breast cancer iii have been treated for breast cancer are largely unknown. However, the evidence indicates, that alcohol consumption is a chronic disease risk factor that can and should be modified to enhance the health of this cohort.

Aims

The aim of this PhD study was to determine the predisposing, enabling, and reinforcing factors associated with alcohol consumption in women previously treated for breast cancer. The objectives were to: 1) quantify alcohol consumption in women who have been treated for breast cancer compared to guidelines and Australian norms, 2) investigate the decision-making and psychosocial processes associated with alcohol consumption in this cohort and to highlight any physical or psychosocial outcomes associated with alcohol use, and 3) determine whether a tailored e-health lifestyle intervention changed alcohol-related health behaviours.

Methods

An independent component of the Women’s Wellness after Cancer Program (WWACP) (D. J. Anderson, McGuire, & Porter-Steele, 2014; D. J. Anderson et al., 2017), this PhD project utilised mixed methods. Study 1 of the PhD project combined secondary analysis of quantitative alcohol-related and socio-demographic data from the randomised controlled WWACP trial (N = 269). Study 2 comprised the collection and analysis of qualitative alcohol-related data from a sub-set of seventeen WWACP intervention and control participants.

Results

Quantitative analysis of 269 female breast cancer participants (N = 269, n = 138 intervention, n = 131 control) provided the following results. The baseline alcohol intake pattern (frequency, quantity, type, and place) of participants reflected that of women in the general Australian population and was similar to other breast cancer cohorts. Binary logistic regression modelling identified significant differences between non-drinkers and drinkers. Participants in this cohort who consumed alcohol at baseline were highly educated (p = .017), current or past smokers (p = .008), and reported better quality of life scores in the social and family wellbeing domain (p = .009). Significant findings that related to the odds of moving to a higher level of alcohol intake, assessed using ordinal logistic regression, were associated with being a current or past smoker (p = .004), greater physical activity levels (p = .049), and

iv Alcohol use and associated health behaviours of women who have been treated for breast cancer better quality of life overall (p = .045). There was no quantifiable change to alcohol intake following participation in the WWACP intervention.

Qualitative findings from the 17 sub-study participants indicated that, overall, participants viewed alcohol consumption favourably. Australian social norms and a family history of alcohol use appeared to influence pre-cancer behaviours and beliefs, and predisposed alcohol-related behaviours after diagnosis. Other predisposing factors identified in this phase of the study included age, tobacco use, health-related quality of life, physical activity, baseline knowledge, exposure to alcohol education, and socially-mediated beliefs about alcohol. WWACP-related behaviour change enablers identified in this cohort related to the WWACP intervention content, timing, and delivery method. The factors that reinforced change included the physical consequences of alcohol consumption and partner support. The qualitative data also suggested that alcohol intake changed during diagnosis, treatment, and into the period of survivorship, and was influenced by participation in the WWACP intervention. The data also suggested that health professionals provided inconsistent education about alcohol, and participants had varying levels of receptivity to education, depending on their position on the cancer trajectory. This resulted in some critical knowledge deficiencies and little knowledge among participants about the risk factors attendant on alcohol consumption before and after cancer treatment.

Conclusions

This PhD study makes a significant contribution to the currently sparse evidence base regarding alcohol consumption in Australia’s growing population of women who have received treatment for breast cancer. The study provides insights into the predisposing, enabling, and reinforcing factors that shape alcohol consumption. It also provides grounds for further research into interventions that target these behaviours to reduce the potential harms from alcohol consumption after breast cancer treatment.

Alcohol use and associated health behaviours of women who have been treated for breast cancer v vi Alcohol use and associated health behaviours of women who have been treated for breast cancer Table of Contents

Keywords ...... ii Abstract ...... iii Table of Contents ...... vii List of Figures ...... x List of Tables ...... xi List of Abbreviations ...... xii Statement of Original Authorship ...... xiv PhD Supervisors ...... xv Acknowledgements ...... xvi Chapter 1: Introduction ...... 1 1.1 Context of the research project ...... 1 1.2 Background ...... 3 1.3 Study aim and research questions ...... 5 1.4 Design and methods overview ...... 5 1.5 Significance and scope of the research project ...... 6 1.6 Structure of the thesis ...... 6 Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment ...... 9 2.1 Incidence and prevalence of breast cancer ...... 9 2.2 Alcohol-related mortality and risk of breast cancer recurrence ...... 12 2.3 The role of alcohol and breast cancer ...... 17 2.4 Guidelines and recommendations ...... 27 2.5 Measurement of alcohol intake ...... 37 2.6 Gaps in the literature ...... 39 2.7 Conclusion ...... 40 Chapter 3: Theoretical Framework ...... 41 3.1 The Precede-Proceed model ...... 41 3.2 History of the Precede-Proceed model ...... 42 3.3 Components of the Precede-Proceed model ...... 43 3.3.1 Phase 1: Social assessment ...... 44 3.3.2 Phase 2: Epidemiological assessment ...... 46 3.3.3 Phase 3: Educational and ecological Assessment...... 50 3.3.4 Phase 4: Administrative and policy assessment and intervention alignment...... 52 3.3.5 Phases 5-8: Implementation and evaluation ...... 52 3.4 Principal features of the model ...... 53

Alcohol use and associated health behaviours of women who have been treated for breast cancer vii 3.5 Critical review of Precede-Proceed ...... 55 Chapter 4: Methodology and Study Design ...... 61 4.1 Introduction ...... 61 4.2 Research design ...... 61 4.2.1 Mixed method approach ...... 62 4.2.2 Research questions ...... 67 4.3 Study 1 – Secondary analysis of WWACP data ...... 68 4.3.1 Population ...... 68 4.3.2 Sample ...... 68 4.3.3 Recruitment strategy for the parent study ...... 69 4.3.4 Data collection procedures ...... 69 4.3.5 Randomisation ...... 71 4.3.6 Research assistant consultations ...... 71 4.3.7 Nurse consultations ...... 72 4.3.8 Intervention arm ...... 72 4.3.9 Usual care arm ...... 73 4.3.10 Study 1 data collection instruments ...... 73 4.4 Study 1 - Quantitative data analysis ...... 79 4.4.1 Data preparation ...... 79 4.4.2 Descriptive statistics ...... 83 4.4.3 Lost to follow up (LTFU) – missing data ...... 84 4.4.4 Binary logistic regression ...... 84 4.4.5 Normative data comparison ...... 85 4.4.6 Ordinal logistic regression - bivariate analysis ...... 86 4.4.7 Ordinal logistic regression - multivariable analysis ...... 87 4.4.8 Change over time – generalised estimating equations ...... 88 4.5 Study 2 – Qualitative alcohol study ...... 90 4.5.1 Research questions ...... 90 4.5.2 Sample and sampling technique ...... 91 4.5.3 Recruitment strategy ...... 91 4.5.4 Data collection ...... 92 4.6 Study 2 – Qualitative Data analysis ...... 94 4.6.1 Ensuring rigour ...... 94 4.7 Ethical considerations ...... 95 Chapter 5: Quantitative Results ...... 97 5.1 Introduction ...... 97 5.2 Study 1 - Quantitative results ...... 97 5.2.1 Description of the sample population ...... 97 5.2.2 Alcohol-specific analysis ...... 116 5.3 Chapter summary ...... 141 Chapter 6: Qualitative Results ...... 143 6.1 Description of qualitative sample ...... 143 6.2 Quality of Life ...... 147 6.3 Alcohol Behaviours ...... 149 6.4 Alcohol Environment ...... 153 6.5 The Role of the WWACP Intervention in Alcohol Behaviours ...... 157

viii Alcohol use and associated health behaviours of women who have been treated for breast cancer 6.6 Conclusion ...... 162 Chapter 7: Interpretation ...... 163 7.1 Predisposing factor: Baseline patterns of alcohol use ...... 164 7.1.1 Research question 1 ...... 164 7.1.2 Research question 2 ...... 169 7.2 Factors that enabled and reinforced changes in alcohol consumption ...... 176 7.2.1 Research question 3 ...... 176 7.3 Conclusion ...... 188 Chapter 8: Conclusions ...... 191 8.1 Background and aims ...... 191 8.2 Summary of major findings ...... 192 8.3 Implications of the study and recommendations ...... 195 8.3.1 Implications for education across clinical and community settings ...... 195 8.3.2 Implications for health promotion practice, policy and industry ...... 196 8.3.3 Implications for future research ...... 198 8.4 Study limitations ...... 199 8.5 Study strengths ...... 200 8.6 Conclusion ...... 201 Reference List ...... 203 Appendices ...... 235

Alcohol use and associated health behaviours of women who have been treated for breast cancer ix List of Figures

Figure 1.1. Summarised study design ...... 2 Figure 2.1. Influence of alcohol on the human body regarding breast cancer and chronic disease risk ...... 18 Figure 3.1. Generic representation of the precede-proceed model (Green & Kreuter, 2005) ...... 44 Figure 4.1. Research design overview ...... 62 Figure 5.1. WWACP study CONSORT diagram adjusted for PhD study of breast cancer only participants ...... 99 Figure 5.2. Alcohol intake (grams per day) separated according to group with outliers shown (N = 269) ...... 116 Figure 5.3. Alcohol intake categorised and separated according to group (N = 269) ...... 117 Figure 5.4. Alcohol intake (drinks per day) over the previous seven days ...... 118 Figure 5.5. Alcohol intake (grams per day) excluding non-drinkers, separated according to group with outliers shown (n = 222) ...... 119 Figure 5.6. Baseline red wine intake separated according to group ...... 120 Figure 5.7. Baseline white wine intake separated according to group ...... 120 Figure 5.8. Side-by-side boxplots of alcohol intake in grams per day (continuous variable) across three time-points...... 136 Figure 8.1. Precede (Phases 1-3) with study specific indicators ...... 194

x Alcohol use and associated health behaviours of women who have been treated for breast cancer List of Tables

Table 2.1 International Drinking Guidelines for General Populations (IARD, 2018; ICAP, 2007a) ...... 28 Table 4.1 Study 1 Data Collection Instruments, Items, Time-points and Mode of Administration ...... 73 Table 4.2 Overview of Statistical Analysis Performed ...... 82 Table 5.1 Descriptive Statistics for Continuous Variables for the Sample of WWACP Women With a History of Breast Cancer (N = 269) ...... 103 Table 5.2 Descriptive Statistics for Categorical Variables for the Sample of WWACP Women With a History of Breast Cancer (N = 269) ...... 104 Table 5.3 Comparison of Retained Participants Versus Lost to Follow Up (LTFU) at Time-point Two ...... 110 Table 5.4 Comparison of Retained Participants Versus Lost to Follow Up (LTFU) at Time-point Three ...... 113 Table 5.5 Baseline Description (Continuous Variables) of the Pattern of Alcohol Intake for Study Participants (N = 269) ...... 122 Table 5.6 Baseline Description (Categorical Variables) of the Pattern of Alcohol Intake for Study Participants (N = 269) ...... 123 Table 5.7 Binary Logistic Regression: Model One Socio-Demographic Predictors for Drinking Status (Drinker vs. Non-Drinker) at Baseline ..... 128 Table 5.8 Binary Logistic Regression: Model Two General Health Predictors for Drinking Status (Drinker vs. Non-Drinker) at Baseline ...... 128 Table 5.9 Comparison of Alcohol-Related Risk Between Australian Women and Study Participants (N = 267, Missing N = 2) ...... 130 Table 5.10 Odd Ratios from Ordinal Logistic Regression Showing Bivariate and Multivariable Relationships Among Alcohol Intake and Variables of Interest...... 133 Table 5.11 Alcohol Change Over Time (Continuous Variable) ...... 139 Table 5.12 Alcohol Change over Time (Ordinal Categorical Variable) Crude Percentages ...... 139 Table 5.13 Ordinal Logistic Generalised Estimating Equation Regression for Alcohol Intake ...... 140 Table 6.1 Demographic Characteristics of Sub-study Participants (n = 17) and Parent Study Participants (N = 269 less n17 = n252) at Baseline ...... 144

Alcohol use and associated health behaviours of women who have been treated for breast cancer xi List of Abbreviations

ABS Australian Bureau of Statistics AICR American Institute for Cancer Research AIHW Australian Institute of Health and Welfare ATSSSI Aboriginal, Torres Strait or South Sea Islander BMI Body mass index BLR Binary logistic regression CCA Cancer Council Australia CCV Cancer Council Victoria CHD Coronary heart disease COB Country of birth DQES v2 Dietary Questionnaire for Epidemiological Studies Version 2 DMII Diabetes mellitus type 2 FACT-G Functional Assessment of Cancer Therapy – General FFQ Food Frequency Questionnaire GEE Generalised estimating equation HBM Health Belief Model HR Hazard ratio IARD International Alliance for Responsible Drinking ICAP International Centre for Alcohol Policies IPAQ International Physical Activity Questionnaire LTFU Lost to follow up MCAR Missing completely at random NHMRC National Health and Medical Research Council PRECEDE Predisposing, reinforcing, and enabling constructs in educational diagnosis and evaluation PROCEED Policy, regulatory, and organisational constructs in education and environmental development QoL Quality of life QUT Queensland University of Technology RA Research assistant

xii Alcohol use and associated health behaviours of women who have been treated for breast cancer SCT Social Cognitive Theory SPSS IBM Statistical Package for Social Sciences

T1 Time 1 (week 0, baseline, pre-intervention)

T2 Time 2 (week 12, post-intervention)

T3 Time 3 (week 24, follow-up post-intervention) TTM Trans-Theoretical Model WC Waist circumference WCRF World Cancer Research Fund WFR Weighted food record WHO World Health Organisation WHR Waist-hip-ratio WWACP Women’s Wellness after Cancer Program

Alcohol use and associated health behaviours of women who have been treated for breast cancer xiii Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made.

QUT Verified Signature

xiv Alcohol use and associated health behaviours of women who have been treated for breast cancer PhD Supervisors

Principal Supervisor

Professor Alexandra McCarthy

RN, BN, MN (with Distinction), PhD.

Head, School of Nursing

University of Auckland, New Zealand

Associate Supervisor

Professor Helen Edwards, OAM

Assistant Dean (International and Engagement)

Faculty of Health

Queensland University of Technology, Australia

External Supervisor

Professor Elisabeth Isenring

PhD, AdvAPD, BHSc(Nut&Diet)(Hons1), GradCertHighEd

Head of Program, Master of Nutrition and Dietetics Practice

Associate Dean of Research

Faculty of Health Sciences and Medicine

Bond University, Australia

Alcohol use and associated health behaviours of women who have been treated for breast cancer xv Acknowledgements

First and foremost, I would like to thank my husband Brendan for his unwavering support and encouragement, without which I could not have completed this PhD. I am forever grateful for his love, loyalty, and for putting his own dreams on hold to allow me to chase mine. To Ethan, our effervescent toddler who brightened my long days in front of the computer, I thank you for being a good sport when getting shuffled around between Daddy, Nanna (my mother), and our ever so helpful neighbours so I could complete this thesis. I am also grateful for the encouragement, support, and understanding I received from my mother, father, brother, and other family and friends throughout this time.

I would sincerely like to thank my principal supervisor, Professor Alexandra (Sandie) McCarthy, who, after relocating to New Zealand, used her personal time to support this PhD through to completion. I am deeply indebted to Sandie for her superior guidance and constant support, which has allowed me to develop invaluable skills moving forward, and for introducing me to the Women’s Wellness team. My appreciation and thanks also to Professor Liz Isenring, Professor Helen Edwards, and past supervisors, Dr Ekta Agarwal and Dr Amanda McGuire, for their guidance and support.

Thank you also to Professor Debra Anderson (Women’s Wellness after Cancer Study Chief Investigator), for welcoming me into the team, providing me with many opportunities, including an APA scholarship to fund my studies, and supporting my contributions to the study. I feel very privileged to have been part of a larger project for my PhD research. Special thanks also to colleagues and friends, Dr Janine Porter- Steele and Nicole McDonald, as well as to fellow HDR students, Claire Nelson and Kristina Richardson, for your support along the way.

I would also like to thank and acknowledge the QUT Health Research Services team, the School of Nursing, Faculty of Health, IHBI and Lee Jones (IHBI Biostatistician) for the supportive environment created and offered to HDR students.

My thanks also to professional editor, Kylie Morris, who provided copyediting and proofreading services, according to university-endorsed guidelines and the Australian Standards for editing research theses.

xvi Alcohol use and associated health behaviours of women who have been treated for breast cancer

Finally, I wish to express my sincere thanks to the women who willingly participated in this research. Neither my research nor this thesis would have been possible without your time and effort, which was freely given.

Alcohol use and associated health behaviours of women who have been treated for breast cancer xvii Chapter 1: Introduction

This chapter outlines the context (Section 1.1), background (Section 1.2), and purpose (Section 1.3) of the research undertaken for this thesis. It also provides an overview of the study design and methods (Section 1.4). Section 1.5 describes the significance and scope of this research. Finally, Section 1.6 includes an outline of the remaining chapters of the thesis.

1.1 CONTEXT OF THE RESEARCH PROJECT

This PhD thesis comprises an alcohol-specific sub-study attached to a parent study. The parent study was a randomised controlled trial of an e-enabled healthy lifestyle intervention, namely the Women’s Wellness after Cancer Program (WWACP) (D. J. Anderson, McGuire, & Porter-Steele, 2014; D. J. Anderson et al., 2017). The program was designed for women who had been treated for breast, gynaecological, and blood cancers. The aim of the 12-week program was to improve health-related quality of life in women previously treated for these cancers through structured health promotion that incorporated strategies to optimise diet, exercise, and sleep; reduce smoking and alcohol intake; and manage menopausal symptoms and psychosocial stressors. The intervention was delivered virtually, using either Skype or FaceTime. The WWACP was funded through a National Health Medical Research Council (NHMRC) Partnership grant (#APP1056856). It was approved by the Human Research Ethics Committee of Queensland University of Technology (QUT) (ethics approval number 1300000335) in accordance with the NHMRC’s guidelines.

Study 1 of this PhD thesis drew on data from the parent study to explore the use of alcohol and any associated health behaviours and outcomes in the breast cancer cohort. Study 2 was a PhD sub-study that comprised interviews with a sample of WWACP participants in Study 1. This thesis was not grounded in the parent study intervention, rather in the problem of alcohol consumption following treatment of breast cancer. Hence, the parent study data, with its focus on health following cancer treatment, was opportunistically available to analyse in relation to the research questions. The PhD sub-study formed an important but independent component of

Chapter 1: Introduction 1 the parent study that also strengthened the parent study findings. Figure 1.1 provides a summarised version of the overall PhD study design. A detailed overview of the full PhD study design that encompasses the WWACP and the sub-study is available in Appendix A.

Baseline (T1) 6 weeks 12 weeks (T2) 24 weeks (T3)

Intervention (WWACP n = 138 ) Outcome Sustained outcome

Qualitative Randomisation ALCOHOL Metropolitan, rural and remote women Interviews N = 269 SUB-STUDY N = 17

Standard care (n = 131 ) Outcome Sustained outcome

Study One (secondary data analysis of WWACP) Study Two

Figure 1.1. Summarised study design

To address the alcohol-specific questions driving this thesis, the precede- proceed model (Green & Kreuter, 2005), which draws upon social cognitive theory (SCT) in addition to insights from several other relevant theories, underpins this PhD study.

My personal contribution to the parent program, in addition to assisting with dietary-related advice, was my role as the Research Assistant Support Manager on the WWACP team. In this role, I developed the research assistant processes and all related documentation, including the results template sheets and coding manuals for onsite data recording; co-authored the research assistant training manual; and contributed to the consultation nurse training manual. In addition, I facilitated face- to-face training and provided ongoing support for site-specific research assistants based in Brisbane, Sydney, Melbourne, and Perth. I designed and collated all documentation for participant information packs and was responsible for the assembly and nationwide dissemination of the packs to participants. I also facilitated all virtual research assistant appointments with pilot participants and acted as a research assistant for the purpose of data collection (via virtual appointments) and

2 Chapter 1: Introduction

data entry. In relation to my PhD research, I was responsible for all secondary analysis of data from the parent program that related to my PhD study. Furthermore, I was responsible for the design, obtaining ethical clearance, and collection of all interview data required for the sub-study, in addition to performing all analysis and manuscript writing.

1.2 BACKGROUND

Breast cancer is the most commonly diagnosed cancer in Australian women (Australian Institute of Health and Welfare & Australasian Association of Cancer Registries [AIHW & AACR], 2017; Australian Institute of Health and Welfare & Cancer Australia [AIHW & CA], 2012). Fortunately, advancements in screening, early detection, and diagnosis, coupled with more effective treatments are associated with recent improved survival rates for women with breast cancer despite increased prevalence (Potter et al., 2014; Rock et al., 2012; World Cancer Research Fund International/American Institute for Cancer Research [WCRFI/AICR], 2007). Improved survival is clearly a positive outcome for this growing population; however, outcomes such as greater risk of secondary primary cancers (Pollard et al., 2009); co-morbidities (Eakin et al., 2007; Eakin et al., 2006; Pollard et al., 2009;

Rock, Byers, et al., 2013); and the debilitating long-term effects of cancer treatment, including chronic fatigue (Rock, Byers, et al., 2013) are associated negative outcomes. The long-term risk of chronic disease in this population signifies a major public health concern (Pollard et al., 2009).

Consumption of alcohol is a well-known risk factor for the development of breast cancer in both pre- and postmenopausal women (Kushi et al., 2012; Weaver et al., 2013; WCRFI/AICR, 2017a). It is also an important risk factor in the context of breast cancer recurrence and treatment-related chronic disease (Kwan et al., 2013). For example, the World Health Organisation/International Agency for Research on Cancer classifies alcoholic beverages as Group One carcinogens, the highest ranking for an agent considered carcinogenic to humans, declaring alcohol a probable cause of cancer (Brooks, 2011). Breast cancer is a hormone-driven cancer and factors affecting hormonal status can increase breast cancer risk (AIHW & CA, 2012; White, DeRoo, Weinberg, & Sandler, 2017), recurrence (Rock, Byers, et al., 2013) and the development of secondary primary cancers, as well as increase the risk of chronic diseases (Rock, Byers, et al., 2013). Alcohol consumption can adversely

Chapter 1: Introduction 3 influence the hormonal milieu directly by affecting oestrogen metabolism (Kushi et al., 2012), and indirectly by contributing to surplus energy intake and potential weight gain (Cancer Council Australia, 2015; Kushi et al., 2012; Tramm, McCarthy, & Yates, 2011). Body fatness with excessive adipose stores negatively influences hormonal change (Rock, Byers, et al., 2013). Finally, evidence suggests that excessive alcohol intake when combined with other lifestyle choices, such as smoking (Eliott & Miller, 2014; Kushi et al., 2012; Ligibel, 2012; Nagata et al., 2007; Winstanley et al., 2011); or dietary factors, such as consumption of high fat diets (Hereld & Guo, 2011; Ligibel, 2012) and certain nutrient deficiencies (including low intake of dietary folate) (Duffy et al., 2009; Hereld & Guo, 2011; Winstanley et al., 2011), can further influence risk factors associated with the development and redevelopment of breast cancers.

National and international alcohol guidelines offer conflicting advice, particularly in their definitions of how many grams of alcohol constitute a standard drink (International Center for Alcohol Policies [ICAP], 2007a). Furthermore, current alcohol consumption thresholds to reduce the risk of harm are ambiguous and largely unknown in relation to cancer risk (Winstanley et al., 2011; WCRFI/AICR, 2017a). Hence, there is confusion around which set of guidelines people who have been treated for cancer should follow. Adding to this ambiguity, alcohol consumption and the reasons for their alcohol intake are largely unknown among the growing Australian population of women who have been treated for cancer. However, women within this population are believed to have similar dietary and alcohol patterns to healthy women in the general population (Milliron, Vitolins, & Tooze, 2013), which means that their alcohol consumption could constitute a cancer and chronic disease risk after cancer treatment. In summary, there is evidence to support increased breast cancer risk (Kushi et al., 2012; Weaver et al., 2013) and recurrence with increased alcohol intake (Kwan et al., 2013; Simapivapan, Boltong, & Hodge, 2016); however, there is limited knowledge regarding:

• The prevalence of alcohol consumption in at-risk Australian cohorts, such as women who have been treated for breast cancer.

• The factors that influence alcohol intake in this cohort (e.g., psychosocial influences, social norms, motivators).

4 Chapter 1: Introduction • The potential effects of alcohol consumption on dietary intake and overall wellness in this cohort.

• Whether alcohol intake can be influenced by a tailored lifestyle intervention.

This alcohol-related PhD study made a unique contribution to the parent study through the quantitative and qualitative lens it applied to alcohol consumption in Australian women with breast cancer.

1.3 STUDY AIM AND RESEARCH QUESTIONS

The overall aim of this PhD was to determine the predisposing, enabling, and reinforcing factors associated with alcohol consumption in women previously treated for breast cancer.

The objectives involved exploration of the following research questions, framed by the Precede-Proceed model:

Research Questions

1. What is the pattern (frequency, quantity, type, and place) of alcohol use amongst Australian women previously treated for breast cancer?

2. What are the demographic, psychosocial, and health-related factors associated with alcohol use?

3. Is a tailored lifestyle intervention associated with changes in alcohol- related health behaviours in this cohort?

1.4 DESIGN AND METHODS OVERVIEW

This mixed method PhD study comprised two studies. A brief outline of each study is provided below, with additional detail provided in Chapter 4.

Study 1 Study 1 of the PhD study involved secondary analysis of alcohol-related data collected in the WWACP study. This was a single-blinded randomised controlled trial with two arms (intervention and control) and with three measurement time- points (baseline, 12 weeks, and 24 weeks). A broad range of data were collected by various means during the course of the study, including Key Survey, an online survey tool; virtual research assistant appointments; and virtual nurse consultations.

Chapter 1: Introduction 5 WWACP sample size calculations indicated that a total of 250 participants, split evenly across both arms (n = 125 each), was required to obtain meaningful differences in clinical and subjective indicators. Study participants (female only) were recruited via the National Breast Cancer Foundation via Register 4 and through major cancer hospitals in Queensland, Victoria, Western Australia, and New South Wales. The WWACP study commenced in January 2015, with staggered recruitment across all sites. Recruitment closed in March 2016. A subset of participants from both arms who had completed the WWACP in its entirety were invited to participate in Study 2.

Study 2 Study 2 utilised qualitative interviews to gain a greater understanding of the quantitative data obtained in Study 1. This phase was unique to the PhD study, contributing significantly to the parent study due to the in-depth, alcohol-specific findings it generated. Seventeen consenting participants took part in virtual interviews regarding their alcohol behaviours.

1.5 SIGNIFICANCE AND SCOPE OF THE RESEARCH PROJECT

This study is the first to assess, via quantitative and qualitative means, the impact of alcohol-related content embedded in a holistic e-enabled healthy lifestyle intervention specifically for women treated for breast cancer. The study scoped the prevalence of alcohol consumption, elicited the process of decision-making behind the adoption or rejection of healthy lifestyle behaviours that related to alcohol, and determined whether a healthy lifestyle intervention modified alcohol-related risks.

1.6 STRUCTURE OF THE THESIS

This introductory chapter has provided a broad summary of the PhD thesis and its association with the much larger parent project. The next chapters of this thesis comprise a literature review (Chapter 2) that explores the existing body of knowledge on this topic and highlights gaps that require addressing, thus providing the rationale for the PhD Study. Chapter 3 describes and justifies the theoretical framework that underpins the study. A detailed description of the methodology and study design follows, including details of ethical considerations and analysis processes for both quantitative and qualitative datasets (Chapter 4). The results chapters present the quantitative findings (Chapter 5) and qualitative findings

6 Chapter 1: Introduction (Chapter 6), which are then thoroughly explored in the interpretation chapter (Chapter 7). The interpretation chapter is driven by theoretical insights from the literature and illuminated by the qualitative data to explain the parent study findings. The conclusion provides a summary of the PhD study’s major findings, limitations, and strengths, and recommendations for future research (Chapter 8).

Chapter 1: Introduction 7 8 Chapter 1: Introduction Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment

The purpose of this review is to scope the literature in relation to alcohol consumption, the effects of alcohol on the human body that relate to breast cancer, and any associated health behaviours displayed by women who have been treated for breast cancer. This literature review has seven sections. First, the incidence and prevalence of breast cancer are discussed, with a focus on the Australian population and the characteristics of the target group (Section 2.1). Second, alcohol-related breast cancer mortality and the risk of recurrence are explored (Section 2.2). Third, the role that alcohol potentially plays in breast cancer development and recurrence is discussed (Section 2.3). Fourth, the evolution of alcohol-related guidelines, their inconsistencies, and their intended audience are scoped (Section 2.4). This is followed by a discussion of the importance of selecting the most appropriate tool to measure alcohol consumption, which is likely to be underreported (Section 2.5). Section 2.6 summarises current gaps identified in the literature before the chapter concludes (Section 2.7).

2.1 INCIDENCE AND PREVALENCE OF BREAST CANCER

Breast cancer is the most commonly diagnosed cancer in women worldwide (International Agency for Research on Cancer, 2012). The most recent complete data indicate that approximately 1.7 million new cases were diagnosed globally in 2012, accounting for 25% of all cancers (IARC, 2012). According to international incidence data, developed countries (for example, the United Kingdom and Australia) have up to a four-fold greater incidence rate of breast cancer compared to less developed countries, such as Pakistan and India (Ghiasvand, Adami, Harirchi, Akrami, & Zendehdel, 2014; IARC, 2012).

Breast cancer is also the most frequently diagnosed cancer in Australian women (AIHW & AACR, 2019). According to the Australian Institute of Health and Welfare (AIHW) new breast cancer cases accounted for 28% of all female cancers

Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment 9 recorded in 2008 (AIHW & CA, 2012) and in 2010 (Cancer Australia, 2014), and were estimated to account for 28.4% of all new cancers in 2017 (Cancer Australia, 2018). Estimations for 2017 predicted that one in eight women were at risk of diagnosis before the age of 85 (AIHW & AACR, 2017). Australian incidence rates have more than doubled since the establishment of national records began in 1982 with 5,310 cases, to 13,567 new cases in 2008 (AIHW & CA, 2012). They continued to rise with 14,181 new cases recorded in 2010 (Cancer Australia, 2014), 15,902 new cases in 2013 (AIHW & AACR, 2017) and a new high of 26,953 cases in the 2016- 17 period (AIHW & AACR, 2019).

Increased incidence could be due to advancements in screening and diagnosis (AIHW & AACR, 2017; AIHW & CA, 2012) and differences in screening and diagnosis between developed and developing countries (Shield, Soerjomataram, & Rehm, 2016); however, there is also evidence to suggest that increased incidence is also attributable to the Western diet, including alcohol consumption (Cottet et al., 2009).

In terms of prevalence, GLOBOCAN 2012 five-year prevalence estimations for selected World Health Organization (WHO) regions reflect 1.96 million, 1.618 million, and 1.276 million cases, respectively, for Europe, the Americas, and the Western Pacific region, which includes Australia (IARC, 2012). The AIHW and Cancer Australia have noted that the prevalence of breast cancer in Australian women tends to increase with age (AIHW & AACR, 2017; AIHW & CA, 2012), and is more common in higher socioeconomic groups compared to lower socioeconomic groups (AIHW & AACR, 2017). A recent international comparison of age- standardised breast cancer incidence rates by Ghiasvand et al. (2014) reported that a considerably higher proportion of women residing in developed countries are postmenopausal at diagnosis and, on average, 10 years older when compared to women diagnosed with breast cancer who reside in less developed countries (307.6 per 100,000 vs. 65.4 per 100,000 respectively). Ghiasvand et al. (2014) classified the menopausal status of women by age; thus, women aged 50 years and over were determined postmenopausal. Profile data derived from various cohort studies (ranging from N = 90 to N = 7,443 participants) indicate a large proportion of women living with a diagnosis of breast cancer are middle-aged or older (D. J. Anderson et al., 2015; Kim et al., 2017; LeMasters, Madhavan, Sambamoorthi, &

10 Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment Kurian, 2014; Potter et al., 2014; Rock, Byers, et al., 2013; Scott et al., 2013; Zhao et al., 2013), well-educated (D. J. Anderson et al., 2015; Kim et al., 2017; LeMasters et al., 2014; Zhao et al., 2013), postmenopausal (D. J. Anderson et al., 2015; Rock, Byers, et al., 2013; Templeton et al., 2013), and non-Hispanic white or Caucasian (D. J. Anderson et al., 2015; LeMasters et al., 2014; Rock, Byers, et al., 2013; Rogers et al., 2015; Scott et al., 2013; Zhao et al., 2013).

Progress in the early detection and treatment of breast cancer has resulted in better survival rates (Potter et al., 2014; Rock et al., 2012; WCRFI/AICR, 2007). Hence, breast cancer mortality rates decreased by almost 30% between 1994 and 2011 (Cancer Australia, 2014) with recent trends in age-standardised mortality rates showing a decrease from 30 to 20 per 100,000 females between 1982 and 2017 (AIHW & AACR, 2017). Nonetheless, estimations of the risk of cancer-related death for females in 2017 was 1 in 41, which was second to the risk of death from lung cancer before the age of 85 (1 in 29) (AIHW & AACR, 2017). This estimation has since been revised to 1 in 43 in 2019 (AIHW & AACR, 2019). The AIHW reported one-year and five-year relative survival rates at diagnosis as 97.9% and 90.2%, respectively according to the 2017 data (AIHW & AACR, 2017). The most recent data indicate the five-year relative survival rate for breast cancer has improved to 90.8% (AIHW & AACR, 2019). Five-year conditional relative survival for those who had already survived 10 and 15 years after diagnosis are reported as 94.6% and 95.5%, respectively, with lower relative survival rates noted for persons diagnosed before the age of 35 years or over 70 years (AIHW & AACR, 2017). The 10-year relative survival rate has increased significantly over time (from 64% in the 1988-93 period to 83% in the 2006-10 period), with the largest survivorship increases evident in those aged between 50-69 years (AIHW & CA, 2012).

Younger women tend to be diagnosed with more aggressive cancers that can be more difficult to treat; hence, they have poorer survival rates (AIHW & CA, 2012; Roder et al., 2012). The survival rate of women aged over 70 years is influenced by a number of factors; for example, the presence of co-morbidities and the adoption of less aggressive treatments (AIHW & CA, 2012). Improved survival is clearly a positive outcome for this growing population; however, outcomes such as an increased risk of second primary cancers (Pollard et al., 2009); co-morbidities (Eakin et al., 2007; Eakin et al., 2006; Pollard et al., 2009; Rock, Byers, et al., 2013); and

Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment 11 specific long-term effects of cancer treatment, including chronic fatigue (Bower, 2008; National Breast and Ovarian Cancer Centre, 2012; Rock, Byers, et al., 2013), treatment-induced menopause (National Breast and Ovarian Cancer Centre, 2012), compromised bone health (Tipples & Robinson, 2011), lymphoedema (National Breast and Ovarian Cancer Centre, 2012), and insomnia (Bower, 2008; National Breast and Ovarian Cancer Centre, 2012) are associated negatives.

In summary, breast cancer appears to be a disease of more developed countries. This is attributed to more advanced screening procedures and infrastructure, and because the majority of published literature on breast cancer has evolved in more developed countries (Potter et al., 2014; Rock et al., 2012; WCRFI/AICR, 2007). Fortunately, decreased mortality rates and increased relative survival rates have produced more positive outcomes for this population of primarily well-educated Caucasian women who tend to be postmenopausal and more advanced in age. However, extended survival is likely to be offset by chronic disease risks, including the recurrence of cancer. Many of these risks are mediated by alcohol, which will be discussed in Section 2.3. Prior to this, breast-cancer specific mortality, overall mortality, and recurrence as related to alcohol consumption is discussed.

2.2 ALCOHOL-RELATED MORTALITY AND RISK OF BREAST CANCER RECURRENCE

Many papers discuss the association between alcohol consumption and mortality (in terms of overall survival and breast cancer-specific survival) in women with breast cancer; however, the findings of these studies are inconsistent. For example, many studies suggest that alcohol intake is neither associated with overall survival (Dal Maso et al., 2008; Hellmann, Thygesen, Tolstrup, & Gronbaek, 2010; Holmes et al., 1999; Kwan et al., 2010) or breast cancer-specific survival (Borugian et al., 2004; Dal Maso et al., 2008; Gou et al., 2013; Harris, Bergkvist, & Wolk, 2012; M. Holm et al., 2013; Rohan, Hiller, & McMichael, 1993), while some suggest a cardio-protective effect thatrelates to overall survival (Barnett et al., 2008; Flatt et al., 2010; Harris et al., 2012; Newcomb et al., 2013; Reding et al., 2008; Vrieling et al., 2012). Conversely, others suggest an increased risk of breast cancer-specific mortality (Allemani et al., 2011; Green McDonald, Williams, Dawkins, & Adams- Campbell, 2002; Hebert, Hurley, & Ma, 1998; M. Holm et al., 2013; Kwan et al., 2010; Vrieling et al., 2012). One meta-analysis identified a dose-response

12 Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment relationship between alcohol consumption of >20g/day and increased risk of breast cancer specific mortality; however, this trend was not evident for recurrence risk (Gou et al., 2013). Fewer studies (Flatt et al., 2010; Hebert et al., 1998; M. Holm et al., 2013; Kwan et al., 2010; Kwan et al., 2013; Vrieling et al., 2012) have focussed on alcohol intake and risk of recurrence; however, of those that have, three reported no significant findings (Flatt et al., 2010; L.-E. Holm et al., 1993; Saxe, Rock, Wicha, & Schottenfeld, 1999), while Holm et al. (2013) and Kwan et al. (2010) reported increased risks of recurrence that related to pre-diagnosis and post-diagnosis alcohol consumption, respectively (M. Holm et al., 2013; Kwan et al., 2010). One study limited its focus to beer intake only, reporting that a high intake of beer was significantly associated with increased recurrence risk (Hebert et al., 1998).

A recent review aimed to identify the extent to which alcohol consumption was associated with breast cancer recurrence or second primary breast cancer (Simapivapan, Boltong, & Hodge, 2016). This paper, which synthesised sixteen separate studies representing data from 35,690 participants (women diagnosed with breast cancer; ≥ 18 years of age), highlights the inconsistencies in the knowledge and the methodological issues in this field that preclude firm evidence. For example, all studies were observational (14 cohort and 2 case-control studies) with five related to risk of second primary breast cancer and eleven related to breast cancer recurrence. Of the five studies reviewed that considered the association between the development of second primary breast cancer and alcohol consumption, three reported no significant findings (Bernstein, Thompson, Risch, & Holford, 1992; Li, Malone, Porter, & Daling, 2003; Trentham-Dietz, Newcomb, Nichols, & Hampton, 2007). A further two reported modest associations that related to both pre- and post-diagnosis consumption, with one suggesting that commencing drinking at a later age increased risk (Knight et al. 2009), while the other reported that seven or more drinks per week increased risk of second primary breast cancer development (Li, Daling, Porter, Tang, & Malone, 2009).

Four of the eleven studies that related to recurrence and alcohol consumption reported a modest but significant association between these two factors (Hebert et al., 1998; M. Holm et al., 2013; Kwan et al., 2010; Kwan et al., 2013). The studies assessed pre- or post-diagnosis alcohol intake, with those assessing post-diagnosis intake reporting more consistent associations. Two studies (McLaughlin, Trentham-

Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment 13 Dietz, Hampton, Newcomb, & Sprague, 2014; Nechuta et al., 2015) described a broader definition (any second breast cancer event), which was evaluated together with recurrence by the review authors. Of these, one reported a significant linear relationship between increased risk and increasing post-diagnosis intake (McLaughlin et al., 2014). The other suggested a small positive association, again with post-diagnosis intake of at least one drink per day, quantified as 12 grams per day (Nechuta et al., 2015). The remaining five studies reporting on recurrence revealed no significant associations (Brewster et al., 2007; Flatt et al., 2010; Muscat et al., 2003; Saxe et al., 1999; Vrieling et al., 2012).

There were a number of noteworthy limitations to this systematic review. Primarily, these related to the large amount of heterogeneity (clinical and methodological) inherent in the studies reviewed. For example, quantification of alcohol consumption varied greatly, with a range of validated and non-validated tools utilised across the studies. Moreover, many reported secondary analyses: in the majority of studies alcohol consumption was not the primary study focus, resulting in inadequate descriptions of methodological processes. In addition, the time frames for data collection ranged from 5 years to 10 years post-diagnosis, which is noteworthy given that time since diagnosis can influence alcohol consumption behaviours and that the development of second primary breast cancer tend to present later still. Finally, the definitions of recurrence and second primary cancers varied between studies, which could have resulted in potential overlap of definitions. Despite these limitations and in the absence of a meta-analysis because of them, the authors suggested these findings could provide much needed guidance to alcohol-related guidelines for breast cancer survivor populations (Simapivapan et al., 2016). Recurrence risk is discussed further in Section 2.3.

Differences in study design and variables, such as menopausal status, type and stage of breast cancer, frequency of alcohol consumption, and provision of other critical information, including the time-point at which alcohol is consumed (i.e. pre- or post-breast cancer diagnosis) contribute to difficulties when deciphering mortality and recurrence risk. To determine more explicit estimates of mortality risk after a diagnosis of breast cancer and to thoroughly explore how alcohol consumption might influence overall survival and all-cause mortality, Ali et al. (2014) undertook a meta- analysis of studies that explored alcohol consumption and the prognosis of breast

14 Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment cancer. The meta-analysis considered data from published papers and individual participant data from 11 large breast cancer cohorts. Large case cohort data were derived from the Studies of Epidemiology and Risk Factors in Cancer Heredity (SEARCH) (Barnett et al., 2008) breast cancer cohort and the European Prospective Investigation into Cancer and Nutrition (EPIC) (Riboli et al., 2002), as well as from nine Breast Cancer Association Consortium (BCAC) studies (The Breast Cancer Association Consortium, 2006).

Ali et al. (2014) identified a total of 22 published studies, 10 of which reported findings on alcohol consumption before diagnosis and 12 that reported on intake after diagnosis. Eleven studies reported on breast cancer-specific mortality, with six reporting no association and four reporting an increased risk of breast cancer-specific mortality with increased alcohol intake. However due to heterogeneous data sets, with different referent groups and variable forms (e.g., continuous or categorical), as well as lack of sufficient data on the exposure variable, meta-analysis of data from the majority of studies focusing on breast cancer specific survival was not possible. Elimination of studies for various reasons left 11 studies in total included in the meta-analysis. Six reported on pre-diagnosis alcohol intake and overall survival, and five reported on post-diagnosis alcohol intake. The meta-analysis compared non- drinkers against moderate drinkers, which were considered women who consumed less than two units of alcohol per day (<14U per week). The estimated equivalent of ethanol for one unit of alcohol was reported as 8g and one drink was reported to contain 12.5g of ethanol. Meta-analysis hazard ratios (HR) indicated pre-diagnosis moderate alcohol intake was associated with improved survival (HR, 0.80; 95% CI 0.73, 0.88), while post-diagnosis alcohol intake was not associated with all-cause mortality (HR, 0.95; 95% CI 0.85, 1.05). Meta-analysis of large cohort data highlighted that there was minimal evidence to suggest a link with breast cancer- specific mortality in women with ER-positive disease, regardless of their pre- or post-diagnosis alcohol intake. Furthermore, a small reduction in breast cancer- specific mortality was noted for women with ER-negative disease who consumed alcohol post-diagnosis. No association was found for pre-diagnosis alcohol consumption for women in the EPIC and BCAC studies.

Overall, Ali et al. (2014) reported that moderate alcohol consumption after a diagnosis of breast cancer did not adversely affect the survival of women in this

Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment 15 population. However, the studies reviewed contained data from as early as 1991 to 2013, during which time many screening and treatment advances have been made, all of which could potentially confound the results reported (Ali et al., 2014). There are also limited Australian cohorts featured in the meta-analysis by Ali et al. (2014); these include a 1993 study by Rohan et al. (1993) and the Australian Breast Cancer Family Study (Dite et al., 2003) data, which were analysed alongside other Breast Cancer Association Consortium (BCAC) case-control studies. Furthermore, the majority of evidence was derived from epidemiological studies that relied heavily on self–reported data. The studies also have marked differences in design and treat variables differently, thus making this a particularly difficult matter to quantify and compare. In response to such methodological issues, Ali et al. (2014) suggested future research should embed ongoing dietary and lifestyle assessments within randomised clinical trials. Presumably, the authors anticipated that this would ensure consistency of measurement tools that allow for easier comparison of alcohol intake across studies.

Despite inconsistent mortality-related study findings, enough quality evidence exists to establish alcohol as a causal factor in breast cancer development. When considering mortality-related evidence from the World Cancer Research Fund International/American Institute for Cancer Research (WCRFI/AICR), the 2017 WCRFI/AICR report on breast cancer prevention states there is “probable” and “convincing” evidence that the consumption of alcoholic drinks increase the risk of developing breast cancer in both pre- and postmenopausal women, respectively (WCRFI/AICR, 2017a). In 2012, global alcohol-attributable breast cancers were estimated to account for 8.6% of all breast cancer incidence and 7.3% of breast cancer-specific mortality (Shield et al., 2016). Shield et al. (2016) reported their findings from a recent review of meta-analyses that, in part, restricted focus to the effects of light drinking (<21g/day of alcohol) on breast cancer mortality, among other outcomes. In 2012, alcohol-attributable breast cancer deaths for light drinkers were estimated to account for 17.5% of all alcohol-attributed breast cancer mortality globally (Shield et al., 2016). In Australia, 5,785 deaths were attributed to alcohol in 2015; 36% (n = 2,106) of these deaths were cancer-related, with 7% (n = 397) exclusively due to breast cancer (Lensvelt, Gilmore, Liang, Sherk, & Chikritzhs,

16 Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment

2018). Breast cancer was the leading cause of alcohol-attributed deaths in Australian women in 2015 (Lensvelt et al., 2018).

In relation to alcohol-attributable breast cancer mortality risk after treatment, evidence is limited. A more recent WCRFI Continuous Update report on breast cancer survivors, released October 2014, confirmed there was inadequate evidence on pre-diagnosis and post-diagnosis exposure to alcohol to conclude whether alcohol consumption either decreases or increases breast cancer survival risk (WCRFI/AICR, 2014). More importantly, the WCRFI/AICR noted alcohol as one exposure amongst many potentially contributing factors to breast cancer development. Moreover, there might never be strong evidence available from randomised controlled trials to truly elucidate the risk (WCRFI/AICR, 2014).

In summary, the potential protective effects of alcohol intake appear limited to reducing the risk of cardiovascular disease and all-cause mortality. The data do not support commencement of alcohol consumption for cardiovascular benefits for individuals who do not consume alcohol. Despite some reassurance of a reduction in the relationship between alcohol consumption and adverse effects after a diagnosis of breast cancer compared to earlier reports, there are many other ways in which alcohol intake can influence health outcomes, and these should be considered to ensure optimal health after treatment for cancer. Furthermore, a focus on mortality is not concerned with the reasons behind alcohol consumption or the many other effects that alcohol can have on quality of life for women who have been treated for breast cancer.

2.3 THE ROLE OF ALCOHOL AND BREAST CANCER

Consumption of alcohol is considered a risk factor for the development of breast cancer in both pre- and postmenopausal women (Kushi et al., 2012; Weaver et al., 2013) and a potentially important factor in the context of breast cancer recurrence (Kwan et al., 2013; Simapivapan, Boltong, & Hodge, 2016), despite inconsistencies in mortality-related findings. There are a number of direct and indirect ways in which alcohol affects the human body. Figure 2.1 was developed following a review of the literature to summarise the effects that are significant to this research. Primarily, these relate to breast cancer development and/or recurrence risk, and the impact of alcohol consumption on existing treatment-related symptoms.

Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment 17 ↑ risk breast cancer ↑ risk breast cancer recurrence

Associated Hormonal Change lifestyle factors ↑ Energy Weight gain (kJ) intake Smoking High fat diet Oestrogen metabolism Low dietary folate

• Fatigue + • Potential Early exhaustion • protective ↓ mobility Alcohol intake effect for CHD • Sleep disturbance • & DMII ↓ desire to be physically active

Physical activity Nutritionally poor ↑ risk Hot flush chronic severity Energy dense disease Low sleep ↓ Bone health quality

Figure 2.1. Influence of alcohol on the human body regarding breast cancer and chronic disease risk

The ethanol content of alcohol is identified as an active carcinogen (Brooks, 2011). In 1988, the World Health Organisation/International Agency for Research on Cancer classified alcoholic beverages as Group One carcinogens, the highest level ranking for an agent considered carcinogenic to humans, declaring alcohol a probable cause of cancer (Brooks, 2011). According to the Australian Cancer Council, the fact that alcohol is a recognised carcinogen is relatively unknown by many Australians (Eliott & Miller, 2014).

Factors affecting hormonal status can increase breast cancer risk (AIHW & CA, 2012), recurrence (Rock, Byers, et al., 2013), and the development of secondary primary cancers, as well as increase the risk of chronic disease (Rock, Byers, et al., 2013). For example, alcohol intake can influence hormonal shifts by stimulating oestrogen receptor signalling (Y. Li et al., 2009), thereby increasing oestrogen metabolism and exposure to endogenous oestrogen and androgen (Kushi et al., 2012). These theoretically influence the risk of recurrence and poor prognosis

18 Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment

(Brooks, 2011; Kwan et al., 2013; Rock et al., 2012; Tan et al., 2006; Winstanley et al., 2011). A re-analysis of studies focusing on circulating sex hormones and breast cancer risk factors in postmenopausal women reported that the consumption of >20g of alcohol per day increased the levels of all circulating sex hormones (Key et al., 2011). Additionally, carcinogenesis influenced by alcohol-induced changes to gene expression have been linked to moderate intakes of alcohol (10+ g/day), which primarily influence oestrogen receptor positive (ER+) tumour development and growth (J. Wang et al., 2017). Thus, hormone-sensitive breast cancers (i.e., oestrogen receptor-positive tumours) appear more strongly related to alcohol use than hormone-insensitive breast cancers (Baglia, Malone, Tang, & Li, 2017; C. I. Li et al., 2010; Shield et al., 2016; J. Wang et al., 2017; White et al., 2017). While the association between alcohol intake and tumour type is potentially important, data collection for this variable in the parent study relied on participant recall, as medical charts were not accessible. As a result, data for this variable were not considered accurate enough for inclusion in this PhD study and testing for any association between alcohol intake and tumour type was considered to be outside the scope of this thesis.

Although the biological mechanisms that link alcohol intake to cancer are not completely understood (Kushi et al., 2012), it has been identified that postmenopausal women with high oestrogen levels (e.g., those on hormone replacement) are at greater risk than those with low levels of circulating oestrogen (AIHW & CA, 2012). Furthermore, research suggests that the study of lifetime alcohol consumption could be more appropriate given that the latent effects of alcohol consumption, which relate to biological pathways of carcinogenesis, are hypothesised to influence the development of breast cancer up to 35 years post- consumption (Shield et al., 2016).

Associations between alcohol intake and the effects on body weight and BMI are inconsistent (Dumesnil et al., 2013). Findings appear primarily related to patterns of alcohol intake (quantity and frequency) and concurrent intake of other foods. Early research revealed that associations between total alcohol intake and high BMI or increased waist circumference (≥88cm) were limited to women who reported heavy alcohol intakes (≥28 drinks/week) only and that more frequent drinkers had lower odds ratios for being obese (Tolstrup et al., 2005). Furthermore, increased

Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment 19 frequency of alcohol consumption (i.e., daily drinking compared to <1 drink per week) has been inversely associated with the development of abdominal obesity in women (Tolstrup et al., 2008). However, drinking frequency in the same study was not associated with major weight loss (Tolstrup et al., 2008). Similar inverse associations have been noted in male populations (Dumesnil et al., 2013).

A recent meta-analysis (N = 7028 men and women) considered the relationship between alcohol intake and changes to adiposity measures (body weight [BW], waist circumference [WC] and WC adjusted for BMI [WCBMI]) in relation to potential genetic predisposition to adiposity (Rohde et al., 2017). Although the findings in relation to genetic predisposition were inconclusive, the authors reported the same inverse association, as discussed above, between alcohol intake and changes to both BW and WC, for both genders. Alcohol intake was significantly associated with a higher change in WCBMI in women, with a 0.5mm/year increase [95% CI 0.2, 0.9, p = .002] per drink/day noted (Rohde et al., 2017). Studies suggest that the association between alcohol and weight gain differs according to intake. For example, light to moderate intakes appear to be linked to lower body weight, whereas abstinence and very low intakes and heavy intakes appear to be linked to overweight and obesity (Worsley, Wang, & Hunter, 2012).

Alcohol is one of four macronutrients that, together with fat, protein, and carbohydrates, contribute to an individual’s total energy intake. Although nutritionally poor (NHMRC, 2006), alcohol is energy-dense (Rohde et al., 2017; Winstanley et al., 2011) and is often consumed in conjunction with other energy- dense drinks or mixers and energy-dense accompaniments, such as chips or nuts (Suter, 2005; Tramm et al., 2011). Whether consumed with energy-dense additions or not, excessive alcohol consumption that leads to surplus energy intake can contribute to undesirable weight gain (McCarthy, Yates, & Shaban, 2013) and has the ability to disinhibit appetite (Kushi et al., 2012; Tramm et al., 2011; Winstanley et al., 2011). Excessive weight gain can lead to fatigue (Cavuoto & Nussbaum, 2014), early exhaustion (Cavuoto & Nussbaum, 2014), reduced mobility (Cavuoto & Nussbaum, 2014), sleep disturbance (Larsson & Mattsson, 2001), and less desire to participate in physical activity (Larsson & Mattsson, 2001), in addition to increasing the risks associated with the development of other chronic diseases (Kushi et al., 2012).

20 Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment Studies indicate that being overweight or obese is associated with both recurrence and morbidity in women who have been diagnosed with cancer, regardless of their menopausal status (Protani, Coory, & Martin, 2010; Rock, Pande, et al., 2013). However, the WCRF reports that body fatness probably provides some protective effect against breast cancer development for premenopausal women, whilst there is convincing evidence to suggest that excessive body fat is a clear risk factor for the development of breast cancer for postmenopausal women (WCRFI/AICR 2007). Additionally, abdominal fatness and adult weight gain are noted as probable causes of breast cancer in postmenopausal women (WCRFI/AICR, 2007). Physiologically, body fatness with excessive adipose stores produces increased levels of oestrogen, leptin, and insulin, among other hormones, which again influence hormonal change and increase the risk of recurrence and cancer progression (Rock, Byers, et al., 2013). While overweight and obese women treated for breast cancer are at greater risk of developing chronic diseases, such as type two diabetes, cardiovascular disease, and hypertension (Campbell et al., 2012; Rock, Byers, et al., 2013), weight loss in this group of women is associated with positive effects on hormone and biological factors (Rock, Pande, et al., 2013). These include facilitating beneficial changes in insulin and inflammatory factors that affect oestradiol levels, and therefore reduce the risk of recurrence (Rock, Byers, et al., 2013; Rock, Pande, et al., 2013).

Excessive alcohol consumption can adversely affect bone health, including bone loss (Tipples & Robinson, 2011), as can natural menopause (Lustberg, Reinbolt, & Shapiro, 2012), ageing (Lustberg et al., 2012), and the type of breast cancer treatment received (Tipples & Robinson, 2011). Alcohol ingestion disrupts calcium metabolism, which is vital for the rebuilding of bone (Twiss, Gross, Waltman, Ott, & Lindsey, 2006), and thus good bone health. Therefore, avoidance of excessive alcohol consumption is an important factor in the prevention of osteoporosis (Twiss et al., 2006) and subsequent fractures (Tipples & Robinson, 2011). Hormone therapy can also influence bone loss. Tamoxifen, a common breast cancer treatment in Australia (Cancer Australia, 2011), has little effect on bone health; however, different forms of aromatase inhibitors that are often used to treat postmenopausal women with breast cancer are associated with negative effects on bone health (Tipples & Robinson, 2011). The concurrent effect of these factors can

Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment 21 substantially and adversely influence bone health, which is important for women who commonly experience treatment-induced menopause after breast cancer treatment.

Furthermore, there are synergistic relationships between alcohol intake and other lifestyle factors. Evidence suggests that excessive alcohol intake when combined with other lifestyle choices, such as smoking (Eliott & Miller, 2014; Kushi et al., 2012; Ligibel, 2012; Nagata et al., 2007; Winstanley et al., 2011); or dietary factors, such as consumption of high fat diets (Hereld & Guo, 2011; Ligibel, 2012); and certain nutrient deficiencies, including low intake of dietary folate (Duffy et al., 2009; Hereld & Guo, 2011; Shield et al., 2016; Winstanley et al., 2011), can further influence the risk factors (i.e., induce hormonal change) associated with the development of breast and other cancers. Conversely, elevated folate levels might actually mitigate the risk of breast cancer that results from high alcohol consumption (WCRFI/AICR, 2007).

Research also suggests some health benefits associated with alcohol consumption (Leasure, Neighbors, Henderson, & Young, 2015; Smyth et al., 2015). Moderate alcohol consumption has long been associated with a reduced risk of coronary heart disease (CHD), and more recently, diabetes mellitus type two (DMII). The cardioprotective effect of light to moderate alcohol intake was recently confirmed in a large USA population-based prospective cohort followed for over 10 years (N = 58,827, adults aged ≥ 20 years) (Gémes et al., 2016). Gémes et al. (2016) reported that light to moderate intakes were inversely and linearly associated with the risk of acute myocardial infarction and more specifically, the frequency of alcohol intake was strongly associated with lower risk of acute myocardial infarction. They reported that the hazard ratio (HR) for a one-drink increment in daily consumption was 0.72 (95% CI 0.62, 0.86) (Gémes et al., 2016). Similarly, data from 12 countries participating in the Prospective Urban Rural Epidemiological study (N = 155,875, adults aged 35-70 years) indicated that current drinking was associated with reduced myocardial infarction (HR 0.76 [95% CI 0.63, 0.93]), as well as an increased risk of alcohol-related cancers (HR 1.51 [95% CI 1.22, 1.89]) (Smyth et al., 2015). Some researchers have suggested the relationship between alcohol intake and lower risk of CHD could in part be explained by the inverse associations that exist between alcohol-drinking patterns (frequency and total alcohol intake) and obesity (Tolstrup

22 Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment et al., 2005), as well as physical activity (Leasure et al., 2015), which will be discussed next. In relation to DMII, protective effects appear mostly linked to the antioxidants contained in red wine (primarily resveratrol) and with persons reporting well-controlled DMII (Gepner, Golan, Harman-Boehm, & et al., 2015).

The relationship between physical activity and alcohol consumption is also of interest in this context. However, the strength and direction of this relationship have long been debated (French, Popovici, & Maclean, 2009) and studies with breast cancer cohorts appear non-existent. French et al. (2009) utilised data from the Behavioural Risk Factor Surveillance System, to assess the relationship between alcohol intake and physical activity (French et al., 2009). The cross-sectional analysis of Behavioural Risk Factor Surveillance System 2005 data (N = 230,000 adults) found strong evidence to suggest that alcohol intake (categorised into abstainers, light, moderate, and heavy drinkers) and physical activity (measured in minutes per week) were positively correlated (French et al., 2009). The results indicated that compared to abstainers, women’s alcohol intake (categorised as light, moderate, and heavy) increased with their weekly exercise, reported as 5.7, 10.1, and 19.9 more minutes per week, respectively (French et al., 2009). The probability of exercising vigorously increased in line with increases in alcohol intake (French et al., 2009). A more recent study reported similar findings with an elderly population (González-Rubio et al., 2016).

Finally, alcohol consumption is thought to influence the severity of hot flushes in women (Gallicchio et al., 2015; Mitchell & Woods, 2015; Smith et al., 2016). It is also associated with poor sleep quality (Lydon et al., 2016). These factors were particularly relevant to women in the WWACP study cohort who often experienced treatment-induced hot flushes and sleep disturbances (D. J. Anderson et al., 2015; Crandall, Petersen, Ganz, & Greendale, 2004; Mar Fan et al., 2010).

The most recent breast cancer-specific Continuous Update Project Report, developed by the World Cancer Research Fund International and the American Institute for Cancer Research was released in 2017 (WCRFI/AICR, 2017a). The report provided relative risk estimates for the development of breast cancer from alcohol on a global scale (WCRFI/AICR, 2017a). The results from the dose-response meta-analysis for premenopausal breast cancer revealed a statistically significant 5% increased risk per 10 grams of ethanol per day (RR 1.05 [95% CI 1.02, 1.08]). The

Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment 23 relative risk for postmenopausal breast cancer was reported as 9% per 10 grams of ethanol per day (RR 1.09 [95% CI 1.07, 1.12]) (WCRFI/AICR, 2017a). When considered in isolation, the linear relationship between alcohol consumption and the development of breast cancer is irrefutable; however, the likelihood of co-morbidities in long-term cancer survivorship is high and the influence of alcohol on these is complex ( Rock et al., 2012). Hence, the relationship between alcohol consumption in women diagnosed with breast cancer and the synergy of these variables with overall lifestyle warrants exploration.

To thoroughly explore the effects of alcohol in this population it is important to identify the rates of alcohol consumption. As discussed in detail later (Section 2.4), Australia has implemented detailed guidelines for alcohol intake. For women, these currently recommend no more than two standard drinks per day and no more than four drinks on any single occasion (NHMRC, 2009). A review of Australian trends in per capita consumption of alcohol by Chikritzhs, Allsop, Moodie, and Hall (2010), utilising data from the Australian Bureau of Statistics and the World Advertising Research Centre, suggested historical underestimation; and hence, the false impression of stabilised alcohol consumption since the early 1990s. Chikritzhs et al. (2010) suggested a significant increase in consumption since 1990, thought to be related to the increased ethanol content of common alcoholic beverages. In contrast, in relation to excessive intake by both men and women, Australian Health Survey 2011-12 data suggested a small decrease in average daily consumption above the recommended two standard drinks per day, from 20.9% in 2008 to 19.5% in 2012 (Australian Bureau of Statistics [ABS], 2012b). Nonetheless, this means that almost one-fifth of the Australian population consumed more than the recommended amount during that time. Consistently, in a subset of Australians from the 2004 to 2012 South Australian Health Omnibus Survey, 21.6% of men and women aged 18 years and over consumed alcohol above guideline recommendations (Bowden, Delfabbro, Room, Miller, & Wilson, 2014). Data from the 2014-15 Health Survey indicated a decline, with 17.5% of the Australian population exceeding the guideline recommendation; however, this decline appeared to be linked to a decrease in the number of men exceeding the recommendation. The latest results indicate 9.3% of women exceeded this recommendation in the 2017-18 period (ABS, 2018), which remains largely unchanged from the 2014-15 results and was similar to the 2011-12

24 Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment

finding of 10.1% (ABS, 2015). Of those surveyed as part of the 2014-15 National Health Survey, over 75% of women reported consumption of alcohol within the previous 12 months (ABS, 2015). This figure decreased to 73.3% according to the 2017-18 survey data (ABS, 2018). International alcohol intakes for women appear similar to Australian figures, with US population-based estimates suggesting 65% of US women consume alcohol (Newcomb et al., 2013), while UK figures indicate that just over 51% of British women consumed alcohol in the week prior to being interviewed for the 2016 Opinions and Lifestyle Survey (Office for National Statistics, 2017).

Bowden et al. (2014) suggested predictors of excessive alcohol intake in Australian women include higher household income and limited awareness that alcohol consumption is a risk factor for cancer (Bowden et al., 2014). Similarly, secondary analysis of data from the Victorian Lifestyle and Neighbourhood Environmental Study (VicLANES) by Giskes, Turrell, Bentley, and Kavanagh (2011), which focused on socioeconomic status and harmful alcohol consumption behaviours, reported that socioeconomically-advantaged women were more likely to consume alcohol at or above the recommended level. Compared to women who were lower income earners, higher income earners were at greater risk of long-term harm as a result of alcohol consumption (Giskes et al., 2011). González-Rubio et al. (2016) recently reported on alcohol-related health behaviours in an elderly Spanish population (aged 55-85 years). They reported that moderate drinkers (standard drink equivalent to 10g pure alcohol; females < 25g/day; males 40g/day) compared to abstainers (included occasional drinkers with < 4 alcoholic drinks per month) had significantly higher socioeconomic status (p = .024) (González-Rubio et al., 2016). Furthermore, the WCRFI/AICR state that alcohol consumption, as well as tobacco smoking, is the most commonly-used carcinogenic agent in higher income countries (WCRFI/AICR, 2007).

Preventability estimates developed by the WCRFI/AICR with evidence deemed “convincing” and “probable”, suggest that between 11% and 22% of breast cancers in the USA and United Kingdom, respectively could be prevented if consumption of alcohol was avoided (WCRFI/AICR, n.d.). Unfortunately, no preventability estimates are available for Australia, although alcohol consumption by women appears similar across Australia, the USA, and the UK (Institute of Alcohol

Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment 25 Studies, 2013; Newcomb et al., 2013). For example, the population-based Queensland Cancer Risk Study conducted in 2004 (N = 9,419) found that 66.4% (n = 6,252) of participants, both men and women, regularly consumed alcohol (DiSipio et al., 2006). Furthermore the daily intake increased with age. Almost two-thirds of participants considered that “regular drinkers” (defined as drinking at least once per month) consumed alcohol in excess of the guidelines, which during the study period recommended no more than two standard drinks for men and one standard drink for women per day (DiSipio et al., 2006). More recently, Australian Health Survey results for 2011-12 have suggested that women who consumed excessive alcohol are likely to be aged between 55 and 64 years (ABS, 2012b).

While the psychosocial determinants of alcohol use in this cohort are not well understood, patterns of alcohol consumption in women who have been diagnosed with breast cancer have been likened to that of the general population (Eakin et al., 2007; Potter et al., 2014; Rock et al., 2012). Likewise, an Australian study on women’s diet quality (N = 4,350), which included alcohol consumption, found no differences between Australian women diagnosed with breast cancer (n = 281) and healthy female controls (n = 4,069) (Potter et al., 2014). Furthermore, a 2013 USA study by Newcomb et al. (2013) investigating alcohol consumption in women both pre- and post-breast cancer diagnoses (n = 22,890 and n = 4,881 respectively), reported little change in women’s pre-diagnosis alcohol consumption compared to their consumption post-diagnosis. However, Simonsson et al.’s (2014) analysis of 934 Swedish breast cancer patients found that while approximately 25% of participants had decreased their alcohol intake post-operatively, the majority maintained their previous intake level and 13.6% increased their alcohol consumption.

Meanwhile, results from a large international pooled analysis of women diagnosed with breast cancer (N = 9329) indicated that the relative risk of recurrence increased by 20% in postmenopausal survivors who regularly consumed more than three drinks per week (where a standard drink was defined as 12-14g of pure alcohol) (Kwan et al., 2013). Compared to persons who abstain from alcohol, the risk of breast cancer occurrence is 10% to 12% greater with each drink consumed per day (Kushi et al., 2012). There appears to be a dose-response relationship, although contentious at times, between regular alcohol consumption and increased risk of

26 Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment breast cancer (Allen et al., 2009; Collaborative Group on Hormonal Factors in Breast Cancer, 2002; Tan et al., 2006). A dose-response meta-analysis of alcohol consumption and site-specific cancer risk included 118 studies (published online before 2012) of female breast cancer (Bagnardi et al., 2014). The following relative risk (RR) figures were presented for women reporting light (≤ 12.5 g/day), moderate (≤ 50g/day), and heavy (> 50g/day) alcohol intakes. Compared to non-drinkers and occasional drinkers, the RR of breast cancer was 1.04 (95% CI 1.01, 1.07) for light drinkers, 1.23 (95% CI 1.19, 1.28) for moderate drinkers, and 1.61 (95% CI 1.33, 1.94) for heavy drinkers (Bagnardi et al., 2014). A more recent prospective study confirmed the dose-response relationship, while highlighting the importance of tumour subtype and the age of women when they started drinking (i.e., pre- or post- first full-time pregnancy) (Romieu et al., 2015). The relationship between breast cancer risk and alcohol intake was strongest for women who starting drinking pre- first full-time pregnancy (Romieu et al., 2015). There is currently no evidence to support any safe alcohol consumption threshold for cancer avoidance (Brooks, 2011; Cancer Council Australia, 2015; WCRFI/AICR, 2017a).

In summary, alcohol is a known human carcinogen that can adversely affect hormonal change, influence weight gain, bone health, hot flush severity, sleep quality, and has its own adverse effects compounded by other factors. Light to moderate alcohol intakes might actually offer some protective effects that relate to CHD and DMII, but not to cancer. Internationally, the patterns of alcohol consumption of healthy females appear similar to the consumption patterns of Australian women, while a diagnosis of breast cancer does not appear to influence changes in alcohol intake behaviours. Higher socioeconomic groups appear to be more regular drinkers, who are generally older in age. In women who choose to drink after a diagnosis of breast cancer, the risks of recurrence and poor prognosis are probably increased, yet the relationship of recurrence and prognosis with alcohol has not been established with certainty. As outlined in the next section, the guidelines and recommendations for alcohol intake have also not been clearly explained.

2.4 GUIDELINES AND RECOMMENDATIONS

Worldwide, there are numerous guidelines to prevent cancer and chronic disease. Population-based guidelines are primarily produced for healthy populations, and as such, often form the basis for new policies. General health guidelines tend to

Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment 27 incorporate or refer to alcohol-specific recommendations; however, uniformity is limited. Over time, evidence supporting the link between alcohol consumption and the development of cancer has strengthened, resulting in more tailored alcohol- related cancer preventative recommendations. This section discusses and compares both international and national alcohol-related guidelines in terms of origin, the evidence that has shaped them, what defines a standard drink, and the significance of guidelines for women diagnosed with breast cancer. Guideline knowledge levels within the general Australian population, as well as among women treated for breast cancer are also described.

The International Centre for Alcohol Policies (ICAP) (2007a) and the International Alliance for Responsible Drinking (IARD) (2018) provided a set of collated international drinking guidelines for healthy populations, including those developed by the National Health and Medical Research Council Australia (2009). Tabulated guidelines by country of origin show variations in what constitutes a “standard” drink in terms of ethanol content and the recommended maximum daily consumption (or upper limits) for women. Due to inconsistencies, it is more appropriate to compare grams of pure alcohol when quantitatively reviewing intake recommendations for women. When the quantities recommended to reduce the risk of injury arising from a single occasion are excluded, it is evident that recommended intakes vary substantially from 14g to just over 27g of pure alcohol per day (IARD, 2018; ICAP, 2007a). A modified table of this information for countries considered similar to Australia in terms of population demographics and genetic make-up is provided below (Table 2.1) (IARD, 2018; ICAP, 2007a).

Table 2.1 International Drinking Guidelines for General Populations (IARD, 2018; ICAP, 2007a)

Country Source Women Standard Drink Australia National Health and No more than two standard 10g Medical Research drinks (20g/day) on any day (NHMRC) reduces the lifetime risk of harm

Australian Guidelines to No more than four standard Reduce Health Risks from drinks (40g/day) on a single Drinking Alcohol (NHMRC, occasion reduces the risk of

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2009) alcohol-related injury arising from that occasion Note: currently under review 2016-2018 with proposed release date set for late 2019 (NHMRC, n.d) New Health Promotion Agency No more than two standard 10g

Zealand drinks (20g) per day and no Low-risk alcohol drinking more than 10 standard drinks advice (Health Promotion (100g) per week AND at least Agency) two alcohol-free days every week reduces your long-term health risks No more than four standard drinks (40g) on any single occasion reduces your risk of injury on a single occasion of drinking, United Department of Health Should not regularly drink more 8g Kingdom UK Chief Medical Officers’ than 14 units a week Low Risk Drinking (112g/week), Guidelines (2016) (UK Department of Health, Welsh Government, & Scottish Government, 2016) United Department of Agriculture One drink per day (14g/day) 14g States and Department of Health not to exceed three drinks per & day (42g/day), not to exceed Human Services - Dietary 7units/week (98g/week), Guidelines for Americans 2015-2020 Over 60 years old: up to 12 g/day or 84g/week, never more National Institute of than 24g at once, Alcohol Abuse and Alcoholism Note: amounts represent upper (NIAAA) limits Rethinking Drinking webpage NIAAA Older Adults

Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment 29 webpage

Substance Abuse and Mental Health Services Administration and Administration on Aging, Older Americans behavioral health - Issue brief 2: Alcohol misuse and abuse prevention 2012 Canada Canadian Centre on 10 drinks a week, with no more 13.5g Substance Abuse, than two drinks a day most days Low Risk Alcohol Drinking - equates to approx. 138 g/week Guidelines or 27.6 g/day,

(Butt, Beirness, Gliksman, No more than three drinks on Paradis, & Stockwell, 2011) any single occasion

Note: amounts represent upper limits

Subsequent to the ICAP’s (2007a) summary of guidelines, a study by Furtwængler and de Visser (2013) highlighted the lack of global consensus about a “standard” drink, in addition to what is considered low-risk drinking according to published guidelines. Alcohol-related guidelines and recommendations from over 57 different countries were identified and direct comparisons made between 27 countries in which the ethanol content of a “standard” drink was converted to grams (Furtwængler & de Visser, 2013). Maximum daily consumption of recommended intake for women ranged from 10 grams to 42 grams of ethanol per day (Furtwængler & de Visser, 2013). The authors remarked that the setting of a daily limit is largely influenced by the outcome measure being considered; for example, the risk of chronic disease versus injury on a single occasion, or morbidity versus mortality (Furtwængler & de Visser, 2013). A study by Kerr and Stockwell (2012) investigated the ability of consumers to adequately track their alcohol consumption, noting the difficulties that arise due to guideline inconsistencies and general over- pouring of alcoholic drinks that often lead to underestimating intake. Kerr and

30 Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment Stockwell (2012) also commented that standard drink definitions often contain less ethanol than actual drinks.

Alcohol-related guidelines developed by the NHMRC provide health advice relating to upper limits of alcohol intake (Bowden et al., 2014). These guidelines were first adopted in Australia in 1987 and revised subsequent to new evidence (Bowden et al., 2014). New evidence meant the intake for men was halved from four standard drinks per day to two, whilst the limit for women remained at two per day (Bowden et al., 2014; NHMRC, 2009). Evidence pertaining to the risks associated with alcohol use in healthy men and women, including lifetime risk of harm from alcohol-related disease and injury, formed the basis of these guidelines (Bowden et al., 2014). The latest revision, the NHMRC Australian Guidelines to Reduce Health Risks from Drinking Alcohol, was published in 2009. Unlike other NHMRC guidelines, these guidelines are not ascribed “levels of evidence” ratings, largely as a result of the analytical approach used in their development (NHMRC, 2009). However they are currently under review, with the proposed release date set for late 2019-early 2020 (NHMRC, n.d). For healthy women, the extant guidelines recommend no more than two standard drinks on any given day to reduce alcohol- related harm over a lifetime (NHMRC, 2009). One standard drink is defined as containing no more than 10g of ethanol; that is, 100ml of red wine (NHMRC, 2009). The NHMRC alcohol guidelines are not exclusively intended for persons diagnosed with cancer. Although the risks associated with the consumption of alcohol and alcohol-related disease, such as breast cancer, are outlined within the guidelines, statistical explanations in relation to cancer risk are complex for the general public to understand. Hence, tailored, cancer-specific recommendations have been developed by organisations such as Cancer Council Australia (Cancer Council Australia, 2015).

Cancer Council Australia’s (CCA) position statement (Cancer Council Australia, 2015) on alcohol and cancer risk is periodically updated and lists key messages and recommendations collated from a number of sources, including those discussed above, which relate to alcohol consumption and cancer risk. The dose- response relationship is highlighted, in addition to a recommendation to limit (or better still completely avoid) alcohol consumption (Winstanley et al., 2011; Cancer Council Australia, 2015). The CCA statement further recommends to drink within

Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment 31 the NHMRC guidelines, which portray much broader population-based health messages (NHMRC, 2009; Cancer Council Australia, 2015).

Canada has also produced a number of cancer-specific nutrition and alcohol- related guidelines, which offer advice either directly to the consumer (Canadian Centre on Substance Abuse, 2014a, 2014b) or the health professional (Dietitians of Canada, 2011; Dietitians of Canada & Dietitians Association of Australia, 2008, 2011). Their recommendation is the same; that is, for women who wish to reduce their risk of cancer and cancer recurrence, either abstain from alcohol consumption or limit consumption to one drink per day (Canadian Centre on Substance Abuse, 2014a, 2014b; Dietitians of Canada, 2011; Dietitians of Canada & Dietitians Association of Australia, 2008, 2011). One standard drink is considered 150ml of table wine, 350ml of beer, or 45ml of liquor (Dietitians of Canada & Dietitians Association of Australia, 2008), and when converted into grams of ethanol as per the guidelines outlined above, one standard drink contains approximately 13.5 grams of ethanol, which is larger than one Australian standard drink (Furtwængler & de Visser, 2013). Furthermore, these resources advise persons to refrain from drinking for “health reasons” and to never commence drinking in response to perceived health benefits. They also suggest that regular physical activity, a healthy diet, and other lifestyle changes, such as not smoking, are far more beneficial (Dietitians of Canada & Dietitians Association of Australia, 2008). The American Cancer Society’s Guidelines for Nutrition and Physical Activity for Cancer Prevention further mirrors the above advice, recommending limiting alcohol intake to no more than one drink per day (equivalent to 14g/day pure alcohol) for women for cancer prevention (American Cancer Society Medical and Editorial Content Team, 2012; Kushi et al., 2012). These guidelines were current at time of PhD submission.

The World Cancer Research Fund International together with the American Institute for Cancer Research published the Second Expert Report for Food, Nutrition, Physical Activity and the Prevention of Cancer: a Global Prospective in 2007. This report provided an in-depth systematic analysis of global evidence across the food, nutrition, and physical activity domains that relate to the development and recurrence risk of specific cancer types. Report findings form the basis for cancer prevention recommendations and consider evidence relevant to persons living with a diagnosis of cancer, as well as those previously treated for cancer with no evidence

32 Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment of relapse. Given that alcohol intake can increase the risk of numerous cancer types, and in light of the potential benefits that moderate alcohol consumption has in relation to coronary heart disease (CHD), the WCRF/AICR (2007) recommended limiting intake of alcoholic drinks. However, for women who do decide to drink alcohol, the WCRFI/AICR (2007) recommended consuming no more than one drink per day (where a standard drink contains approximately 10-15g of ethanol). The latest breast cancer specific WCRF/AICR Continuous Update Project report released in early 2017 recommended “for cancer prevention, don’t drink alcohol” and that if you do drink, “limit alcoholic drinks and follow national guidelines” (p. 118). This report also stated that existing cancer prevention recommendations would be reviewed in 2017 in light of conclusions from other cancer reports (WCRFI/AICR, 2017a). The most recent WCRF/AICR Diet and Cancer report, released in May 2018, confirmed that current evidence does not identify any “safe” threshold for alcohol consumption in relation to breast cancer risk (p. 59). The updated cancer prevention recommendation states, “for cancer prevention, it’s best not to drink alcohol” (WCRF/AICR, 2018, p. 84).

Most health guidelines are shaped by the potential benefit that low to moderate alcohol consumption has demonstrated in healthy populations (Kwan et al., 2013; Newcomb et al., 2013; Rock et al., 2012; WCRF/AICR, 2007). However, evidence supporting any possible protective effect for cardiovascular disease, including CHD in women diagnosed with breast cancer is inconclusive (Flatt et al., 2010; Kwan et al., 2013; Newcomb et al., 2013). If the WCRF/AICR (2007) recommendation was quantified solely on breast cancer risk, the recommendation would be to abstain from alcohol entirely. This is a direct result of there being no evidence to support any safe threshold of alcohol consumption for breast cancer avoidance (WCRF/AICR, 2007). Similar findings have shaped Australia’s national guidelines for alcohol consumption.

Unfortunately, the NHMRC guideline recommendations for alcohol consumption do not appear to be widely understood within the general Australian population. In 2010, less than one year after the release of the new NHMRC alcohol guidelines, data from Australia’s National Drug Strategy Household Survey (N = 26,648) indicated that less than 5% of respondents could correctly identify the number of drinks recommended for low-risk drinking, including long-term and

Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment 33 episodic drinking (Livingston, 2012). More recently, between 2011 and 2012, Bowden et al. (2014) reviewed awareness levels of the same NHMRC guidelines within a sample of 5,770 South Australian survey respondents, finding that just over half (53.5%; n = 3,084) could correctly identify the recommended intake threshold for women. Furthermore, 39.2% (n = 2,262) of the South Australian community responded that they did not know the recommended number of standard drinks for women (Bowden et al., 2014). Likewise, the proportion of women who responded “don’t know” to the National Drug Strategy Household Survey low-risk drinking level question significantly increased between survey rounds, from 42.9% (n = 4,306) in 2007 to 45.9% (n = 6,060) in 2010 (Livingston, 2012). Most commonly, it was younger respondents and those who consumed higher amounts of alcohol who tended to respond with higher estimates of what low-risk drinking levels actually comprise (Livingston, 2012). In terms of cancer risk, Bowden et al. (2014) reported that women were more likely than men to perceive alcohol as an important risk factor; however, the majority of the population did not recognise this important link. The perception that cigarette smoking and environmental pollutants were factors in cancer development far surpassed that of alcohol as a cancer-related factor (Bowden et al., 2014). Knowledge of low-risk drinking thresholds does not appear to be strong within the Australian population (Bowden et al., 2014; Livingston, 2012; Wolfaardt, Brownbill, Mahmood, & Bowden, 2018), nor is there adequate awareness about alcohol’s link to cancer (Bowden et al., 2014). The extent of alcohol-related knowledge among women treated for breast cancer is unknown, and yet, according to a study of trends in modifiable lifestyle-related risk factors after breast cancer diagnosis, overall compliance with health guidelines over the course of survivorship is probably low (Zhao et al., 2013). It is likely that the consumption of alcohol in this group reflects that of the general population (Milliron et al., 2013).

A recent study examined the adherence of female cancer survivors to the American Cancer Society (ACS) Guidelines on Nutrition and Physical Activity, in the Yale Fitness Intervention Trial (FIT) cohort (Park, Knobf, Kerstetter, & Jeon, 2019). Secondary analysis grouped data into four categories (BMI, physical activity, diet and alcohol), and calculated scores in accordance with adapted ACS Guideline adherence scoring protocols (McCullough et al., 2011; Park et al., 2019; Thomson et al., 2014). Scoring, which ranged from 0 (no adherence) to 8 (highest adherence),

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revealed moderate adherence across all three time-points (baseline, 4.2 score; 6 months, 4.9 score; 12 months, 4.8 score), across both intervention and control groups. Unfortunately, initial education regarding the guidelines at baseline did not result in greater numbers of participants who were highly adherent to weight (BMI), diet or alcohol guidelines beyond baseline (6 or 12 months). Notably, it appeared adherence to the alcohol guideline (responses scored as > 1 drink/day = 0; 1 drink/day = 1; non-drinkers = 2) contributed most to the final overall scores. The authors noted that additional research was required to expand understanding of patients’ individual awareness of the guidelines and the environmental characteristics (such as education by primary care providers) that appeared influence their uptake of the guidelines. In-depth qualitative interviews were suggested as a useful avenue for providing insight into this issue (Park et al., 2019).

Research also suggests that strategies for increasing awareness levels, such as the inclusion of effective warning labels on alcoholic beverages, is warranted (Bowden et al., 2014), although these are often difficult to implement when policies (for harm reduction and retail) are managed by different government sectors (Wettlaufer, Cukier, Giesbrecht, & Greenfield, 2012) and potentially undermined by the alcohol industry (P. Anderson & Rutherford, 2002). The Australian government recently delayed the implementation of alcohol labelling laws that enforce mandatory health warning labels on all alcohol products; instead voluntary labelling introduced by the alcohol industry is in place (Mathews, Thorn, & Giorgi, 2013). Voluntary warnings most commonly advise against drinking while pregnant (Mathews et al., 2013), with little emphasis placed on cancer risk. Mathews et al. (2013) suggested that interference from the alcohol industry, utilising strategies similar to those adopted by the cigarette industry to delay health warning labels, and more recently plain packaging, are the primary cause of this delay. In November 2017, after the second evaluation of the voluntary industry alcohol labelling initiative, the focus remained solely on the harms of drinking while pregnant (Mathews et al., 2013). Reports indicated some improvement from industry for the uptake of voluntary labelling; however, some product categories were noted as low (Mathews et al., 2013). Despite this, the voluntary agreement was again extended (Food Regulation, 2017). There was no evidence of implementation of cancer-specific warning labels for bottles of alcohol.

Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment 35 Irrespective of the labelling debate, alcohol consumption is problematic due to its firm integration in social life. As such, the provision of information on the risks of drinking might not necessarily increase compliance with guidelines if the drinker is not motivated to moderate their intake (Furtwængler & de Visser, 2013). The provision of readily accessible cancer-related alcohol recommendations offers the opportunity for women treated for breast cancer to make informed lifestyle choices. However, Australia’s national alcohol guidelines, which place little emphasis on cancer prevention, are often a point of reference for not only their intended healthy population but also for women who have been treated for breast cancer. As such these women potentially encounter confusing messages.

It is also important to understand how the beverage alcohol industry can influence policy, regulations, and individuals to reframe their perception of alcohol- related harms, and that these influences are not always obvious. A critical review of the ICAP work (which was accessed for a factual compilation of current alcohol- related guidelines in this thesis) conducted by the Institute of Alcohol Studies, questioned the underlying motives of an organisation that is funded by the international beverage industry (P. Anderson & Rutherford, 2002). The reviewers concluded that essentially “ICAP's mission seems to be to reframe alcohol policy away from policy that minimises harm towards policy that promotes the positive aspects of alcohol consumption” (para 42). Clearly the alcohol industry is making “strenuous efforts to influence public opinion and government policy on alcohol issues” in an effort to “avoid the fate of the tobacco industry” (P. Anderson & Rutherford, 2002, para 1). This critical review sheds light on the scope of problems that are encountered when attempting to change alcohol-related behaviours that reduce harm. Additional research for this thesis also identified that the IARD is essentially the ICAP rebranded.

In summary, alcohol-related guidelines are designed for healthy populations. Globally, substantial variance in relation to what defines a standard drink, as well as recommended alcohol thresholds, is evident. Alcohol-related cancer-specific guidelines focus closely on cancer prevention, while some confusion is evident around the suitability of any alcohol consumption for women treated for breast cancer. Guideline recommendations have changed in light of new evidence; however, they do not appear to be well known or adhered to by the wider Australian

36 Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment female population. Furthermore, there is no assurance that this population is adequately informed about the potential consequences of their drinking behaviour in relation to their risk of cancer recurrence, secondary primary cancers, and the development of other chronic diseases. It is unlikely that a recommendation based on evidence for one group of cancer survivors would be transferrable, and indeed suitable, for all cancer survivors. However, given the many ways in which alcohol consumption can influence cancer risk in women who have been diagnosed with breast cancer, it is important that a greater sense of awareness is created around this topic and within this population.

2.5 MEASUREMENT OF ALCOHOL INTAKE

The collection of accurate data on alcohol consumption for research is challenging. The various forms of measurement used to assess alcohol consumption and the accuracy and validity of the data obtained are considered in this section.

The way in which alcohol-related questions are administered significantly influences the rigour and validity of data obtained; and thus, the ability to answer study questions. Alcohol consumption tends to be measured from two main viewpoints, depending on the nature of the research and the target population: as a drug, and thereby an addictive substance often linked to substance abuse, or as a source of energy that is associated with dietary intake (King, 1994). When considering women who have been treated for breast cancer and their alcohol consumption as it relates to health behaviours across the period of survivorship, the latter viewpoint is more useful for a number of reasons. First, identification of the participant’s dietary intake incidentally elicits alcohol intake and pattern. Second, due to known alcohol-related health risks, the topic of alcohol consumption can be very sensitive, especially for women who have experienced a traumatic life experience, such as a breast cancer diagnosis and treatment. Thus, the focus on alcohol is moderated by assessing alcohol consumption together with everyday foods (Clemens & Matthews, 2008).

There are many methods used to assess dietary intake, including food frequency questionnaires (FFQs), 24-hour dietary recall, dietary record, and diet history (Shim, Oh, & Kim, 2014). Additionally, there are more novel approaches, such as photographic diary records (Rollo, 2012) and web-based 24-hour dietary

Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment 37 recalls (Schatzkin et al., 2009). If solely considering alcohol intake, quantity- frequency and graduated quantity-frequency methods are commonly used for large- scale population studies due to their concise format (Clemens & Matthews, 2008). Each method is susceptible to measurement error and has strengths and limitations that determine their suitability for use in certain study designs (Shim et al., 2014). Furthermore, depending on study outcomes, some methods assess actual intake within a specific and short period, while others focus more broadly on usual intake over a longer time.

For example, a repeated diet history can provide good insight into dietary patterns by measuring usual intake over an extended period; however, this requires a dietitian or trained interviewer to administer a number of open- and closed-ended questions (Shim et al., 2014) to obtain valid data. This can be time-consuming and expensive, which might not be suitable for large-scale multi-modal studies with repeated measures. An alternative method is to utilise a FFQ that also measures usual intake and portion size, but that can be self-administered and is less burdensome in relation to time and cost. FFQs are widely used in epidemiological studies; however, they do rely on self-reported intake, and have therefore been questioned as to accuracy (Schatzkin et al., 2009). When considering data that have originated from self-reported intake measures, it is important to note that the risk of bias for underestimation is present and likely high (Nagata et al., 2007). Thomson et al. (2003) recommended a multimodal approach to measure dietary interventions; for example, the use of FFQs for usual intake plus dietary recalls. In addition, the use of qualitative measures that elicit more detail about patterns of and reasons behind alcohol consumption should also be considered, as these could clarify the robustness of the quantitative intakes obtained (Swift & Tischler, 2010).

Considering alcohol intake only, Clemens and Matthews (2008) reviewed the Dietary Questionnaire for Epidemiological Studies Version 2 (DQES v2) compared to the quantity-frequency approach in a cohort of women aged 25 to 30 years from the Australian Longitudinal Study of Women’s Health. The DQSEv2 is widely validated in the Australian setting and in women diagnosed with breast cancer (Cancer Council Victoria, March 2014; Giles & Ireland, 1996; Hodge, Patterson, Brown, Ireland, & Giles, 2000; Xinying, 2004). Clemens and Mathews (2008) reported that according to their sensitivity and specificity testing, the DQES v2 has

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the potential to overestimate risky to high alcohol consumers compared to the quantity-frequency approach. This could relate to the specific threshold for each measurement method and the proportion of participants categorised to these groups (Clemens & Matthews, 2008). Nevertheless, the DQES v2 demonstrates high reliability when compared to data collected concurrently using quantity-frequency methods (Clemens & Matthews, 2008). Moreover, the design of the overall FFQ, with its specific focus on diet and not alcohol, could to some extent neutralise any negative perceptions that are associated with reporting alcohol consumption on its own (Clemens & Matthews, 2008).

In summary, alcohol intake is associated with negative connotations and can therefore be difficult to accurately assess for the purposes of research. The context in which alcohol-related questions are asked can have a bearing on the responses obtained. Likewise, the chosen measurement tool must be appropriate for the study design and its known limitations should be addressed, when and if possible, to reduce potential error and possible bias. There appears to be no “gold standard” for dietary assessment when undertaking large-scale repeated measures studies where participant burden and budget restrictions are also necessary considerations.

2.6 GAPS IN THE LITERATURE

This literature review identified a number of gaps regarding alcohol use and the related health behaviours of women who have been treated for breast cancer. In Australian women who have survived breast cancer, the evidence remains unclear regarding:

• What is appropriate to drink in terms of alcohol and what women in this population are currently drinking in relation to quantity, pattern of consumption, and the type and strength of beverage.

• How much women know about the risks of alcohol consumption and breast cancer.

• The reasons why women in this population choose to consume alcohol.

• Whether alcohol-related behaviours can be modified to benefit women during survivorship, and to sustain those changes.

Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment 39 The recent meta-analysis of studies of alcohol consumption and prognosis of breast cancer by Ali et al. (2014) suggested further investigation is required into the drinking habits over time of this group. To overcome methodological issues encountered when studying the intake of alcohol, it was noted that embedding such research in randomised controlled intervention trials is the most appropriate way to approach this topic and resolve such issues.

2.7 CONCLUSION

The purpose of this PhD is to address the gaps highlighted by this literature review. The proportion of women living with breast cancer in Australia is growing. There is a large body of literature on the known health risks these women are likely to encounter over the course of survivorship and these risks warrant action to ensure that quality of life and health is maintained beyond the treatment phase. Alcohol consumption is a modifiable health risk; however, a holistic approach to address the multi-faceted problems that these women experience appears the best solution. This PhD project was linked to an e-enabled multi-modal randomised controlled lifestyle intervention, which embodied this holistic approach by focusing on women’s wellness after cancer.

40 Chapter 2: Literature Review - Alcohol Consumption After Breast Cancer Treatment

Chapter 3: Theoretical Framework

The model that guided this thesis is outlined and justified in this chapter. The chapter commences with an introduction to the precede-proceed model (Section 3.1) and a brief history (Section 3.2). This is followed by an explanation of each of the model’s phases and their theoretical underpinnings (Section 3.3). The hallmarks of the model are then discussed (Section 3.4), before the chapter concludes with a critical evaluation of the precede-proceed model, including its strengths and limitations, which provide the rationale for its use in this sub-study (Section 3.5).

3.1 THE PRECEDE-PROCEED MODEL

The parent study of which this thesis forms a part was informed by Bandura’s social cognitive theory (SCT), which is a well-respected theory in terms of explaining and predicting health behaviour (Glanz, Rimer, & Viswanath, 2008). However, while SCT is relevant to some aspects of the alcohol-specific sub-study, issues other than those explained by SCT influence alcohol consumption. For this reason, a more comprehensive explanatory framework to shape the research questions, and to collect, analyse, and interpret the data was sought.

To address the alcohol-specific questions driving this thesis, the precede- proceed model (Green & Kreuter, 2005), which draws upon SCT, in addition to insights from several other relevant theories, underpins the sub-study. The model’s eclectic theoretical underpinnings enable users to draw upon multiple theoretical perspectives while firmly contextualising the problem at hand (Porter, 2016). Thus, it can usefully guide research that aims to encourage beneficial changes in health behaviour that are often difficult to enact.

Precede-proceed (referred to hereafter as “Precede”, with the acronym capitalised only in the first letter in line with academic convention in this field at the time) demonstrated repeated success in research-based population health planning (Green & Kreuter, 2005). The model provides a logical structure with which to examine the many individual and socially-situated behaviours and other factors that can directly and indirectly influence health outcomes and health-related quality of life in a given situation (Green & Kreuter, 2005). In brief, the model comprises two

Chapter 3: Theoretical Framework 41 stages: 1) Precede, wherein the population and health concern of interest is thoroughly assessed; and 2) Proceed, which uses Precede data to develop and evaluate interventions. For the purpose of this exploratory thesis, only the first stage of the model was utilised.

3.2 HISTORY OF THE PRECEDE-PROCEED MODEL

The original Precede model was developed by Green and colleagues in the 1970s. It was developed from a range of theoretical perspectives, including self- efficacy, to help plan health education programs (Green & Kreuter, 2005). Development was based on the notion that an educational diagnosis or assessment should be carried out prior to intervention planning to fully understand the problem at hand, and thus meet the demonstrated needs (Glanz et al., 2008; Green & Kreuter, 2005). Much in the same way that a medical diagnosis precedes a treatment plan, so too should an educational diagnosis precede any intervention plan (Glanz et al., 2008). The acronym “Precede” represents three primary constructs: predisposing, reinforcing, and enabling constructs in educational/environmental diagnosis and evaluation (Green & Kreuter, 2005).

The model was expanded in the early 1990s to acknowledge the importance of environmental factors that also act as determinants for health and health behaviours (Glanz et al., 2008, p. 409). For example, alcohol-related behaviours are heavily influenced by factors outside the individual, such as social inequalities, industry, media, and politics (Glanz et al., 2008). The model acknowledges the existence of such powerful external forces (Glanz et al., 2008). The acronym “Proceed” was subsequently added to the model in 1991, thus incorporating the three additional constructs of policy, regulatory, and organisational constructs in educational and environmental development (Green & Kreuter, 2005). This second component guides the implementation of the actions identified during the Precede phase (Green & Kreuter, 2005). Further revisions to the model occurred in 2005 to accommodate growing interest in the field of genetics and to incorporate ecological and participatory approaches (Glanz et al., 2008; Green & Kreuter, 2005). These approaches are considered essential elements, not only for health behaviour change programs, but also for much broader-reaching public health programs (Glanz et al., 2008; Green & Kreuter, 2005).

42 Chapter 3: Theoretical Framework Precede-proceed is well represented in the literature, starting with Green’s original 1974 paper that introduced Precede (Glanz et al., 2008; Green & Kreuter, 2005). From there, four texts explaining adaptations and enhancements to the model were published between 1980 and 2005. More than 1,000 papers that describe the use of the model within varied disciplines have been published to-date (Glanz et al., 2008; Green & Kreuter, 2005; Porter, 2016). The fourth and most recent edition saw the authors abandon capitalisation of the acronym for the preferred “precede- proceed”; the model is therefore referred to as such for the remainder of the thesis.

3.3 COMPONENTS OF THE PRECEDE-PROCEED MODEL

The parent study design was influenced by Bandura’s social cognitive theory, drawing particularly upon Bandura’s SCT notion of self-efficacy and its role in the uptake of health behaviours. While self-efficacy does play a role in alcohol consumption, alcohol use is mediated by more than individual perceptions of control. The nature of the problem addressed in this PhD study required a structured and systematic “logic model” that applied multiple lenses to help understand how alcohol consumption is reciprocally influenced by the other lifestyle choices and pressures on participants. For example, as highlighted in the literature review (see Error! Reference source not found.), alcohol consumption can affect the human body in many ways and often has synergistic relationships with other lifestyle choices, such as smoking. Similarly, in this PhD study there was a particular need to understand the issue of health-related quality of life (HRQoL) and how it affects and sustains lifestyle choices. From this understanding, potential reasons for the success or failure of the lifestyle intervention in relation to alcohol consumption could be determined. The Precede component of the model, which systematically works through such issues, guided all aspects of this PhD study, including the research questions, data collection methods, and interpretive approach. As described by Glanz et al. (2008), the model was used as a kind of road map to the desired location with all possible avenues available to explore, while the behaviour change theories incorporated along the way acted as specific directions to the destination. The alcohol-related discussion points noted throughout of each of the following sections (Sections 3.3.1, 3.3.2, and 3.3.3) demonstrate how this PhD study utilised this model to gain insight into the complexities that surround alcohol use and related behaviours in this context.

Chapter 3: Theoretical Framework 43 Precede-proceed has four distinct planning phases, one implementation phase and three separate evaluation phases (see Figure 3.1) (Glanz et al., 2008). Each phase is underpinned by discrete concepts or theories, which are systematically outlined in this section.

PRECEDE evaluation tasks: Specifying measureable objectives and baseline

PHASE 4 Administrative and policy PHASE 3 assessment and Educational and PHASE 1 intervention ecological PHASE 2 Social alignment assessment Epidemiological assessment assessment

HEALTH Predisposing Genetics PROGRAM

Educational Reinforcing Behaviour Strategies Health Quality of Life Policy regulation Enabling Environment organisation

PHASE 5 PHASE 6 PHASE 7 PHASE 8 Implementation Process evaluation Impact evaluation Outcome evaluation

PROCEED evaluation tasks: Monitoring and continuous quality improvement

Figure 3.1. Generic representation of the precede-proceed model (Green & Kreuter, 2005)

3.3.1 Phase 1: Social assessment The social assessment phase provides a starting point to engage the target community and its individuals. In relation to the problem at hand, this phase identifies community members’ perceptions of the identified health behaviour, their capacity to problem solve, their current resources, and their strengths and limitations (Glanz et al., 2008; Green & Kreuter, 2005). Furthermore, it identifies their readiness to change, and encourages them to define their social conditions and quality of life concerns in relation to the behaviour (Glanz et al., 2008; Green & Kreuter, 2005). Green and Kreuter (2005) described this process as a “social diagnosis”, in which broad community participation is sought to access multiple sources of subjective and objective information, as the first step in health program planning. Depending on the needs of the proposed program, the variables collected during this phase might

44 Chapter 3: Theoretical Framework include population-level social and economic indicators, such as living arrangements, rurality, and education (Green & Kreuter, 2005). Additionally, subjective quality of life indicators at the individual level are collected, including perceptions of stress and specific long-term treatment-related health concerns (Green & Kreuter, 2005). Green and Kreuter (2005) listed a number of social indicators that potentially affect quality of life. These, which include achievement, aesthetics, alienation, comfort, discrimination, happiness, hostility, performance, self-esteem, unemployment and welfare (Green & Kreuter, 2005) could be relevant to the population of interest in relation to alcohol consumption. The rationale for the social assessment phase of precede-proceed considers the iterative relationship between ecological and educational perspectives, which are discussed later (see Section 3.3.3), as necessary considerations in the development and maintenance of effective health programs (Green & Kreuter, 2005).

Glanz et al. (2008) noted that there are three main concepts that influence the social assessment phase in this model. These concepts work from the community level, with a focus on community-driven change. They include participation and relevance, community organisation, and community mobilisation. Glanz and colleges (2008) defined participation and relevance as “community organising that should start where the people are and engage community members as equals” (p. 294). Community organisation relates to the process in which community groups are assisted to identify common concerns, goals, and available resources for mobilisation, and to develop strategies to achieve their collectively-desired goals (Glanz et al., 2008). Community mobilisation is a strongly participant-driven process. During this process, preventative needs are elicited from members of the community via their involvement in various activities, such as needs assessments or assisting with problem identification and program design (Glanz et al., 2008). For example, the intervention tested in this study was developed after intensive scoping work with women with breast cancer. This helped to determine what their health promotion concerns and needs were, and their preferences for the content and format of the WWACP intervention (D. J. Anderson et al., 2011; McCarthy, Tramm, Shaban, & Yates, 2013). In terms of this PhD study, a crucial step in the sub-study development was to ensure that a breast cancer advocate with extended knowledge of research processes reviewed the proposed stem questions and interview procedure. A

Chapter 3: Theoretical Framework 45 highly experienced community-based breast cancer care nurse also reviewed this information.

3.3.2 Phase 2: Epidemiological assessment Green and Kreuter (2005) referred to Phase 2 as the Epidemiological Diagnosis, during which the “problem-solving principles of epidemiology” provide a reliable and credible foundation that effectively guide the planner (p. 79). The planner undertakes health, behavioural, environmental, and genetic assessments by asking the “when”, “where”, and “what” happened questions (Tramm, 2010). This assessment enables the researchers to identify the main health concerns to be addressed or explored during the study. It also uncovers the most influential behavioural and environmental factors that potentially affect the target health outcomes (Green & Kreuter, 2005). Most importantly, it is during this phase that priority concerns or factors are translated into measurable objectives (Green & Kreuter, 2005).

In particular, factors likely to influence alcohol consumption are elicited from this assessment phase, be they health, behavioural, or environmental in nature. This can be achieved using a variety of methods, including reviewing the literature and readily available datasets. For example, secondary analysis of relevant epidemiological datasets (e.g., Australian Bureau of Statistics, Australian Health Survey, Australian Institute of Health and Wellness) has established that older women tend to consume alcohol more frequently and more regularly than younger women, who are more likely to binge drink (ABS, 2013a). This epidemiological knowledge allows the planner to strategize how they might approach the issue of alcohol consumption in a population of older women.

Vital indicators of health, such as disability, discomfort, fertility, fitness, morbidity, mortality, and physiological risk factors are also elicited from this assessment phase (Green & Kreuter, 2005). This provides planners with the opportunity to rank those indicators that are most pertinent to the target population in relation to the health concern according to pre-defined dimensions stated by the authors (i.e., distribution, duration, functional level, incidence, intensity, longevity, and prevalence) (Green & Kreuter, 2005). Thus, this phase also provides a rationale as to which indicators warrant the use of sometimes scarce program resources (Green & Kreuter, 2005). For example, qualitative content analysis might uncover that

46 Chapter 3: Theoretical Framework alcohol consumption negatively contributes to the severity of hot flushes (Mitchell & Woods, 2015), which causes unnecessary discomfort for women in the target population.

Specific behavioural indicators also considered in Phase 2 include self-care, compliance, consumption patterns, preventative actions, and coping (Green & Kreuter, 2005), which are often influenced by a person’s marital status, household income, and mental health status. These influences often become the variables of interest for a study. For example, dealing with depression (coping, self-care) might influence the amount of alcohol consumed (i.e., compliance with a healthy diet or alcohol-related guidelines, consumption patterns) (Hack & Degner, 2004; Hasking, Lyvers, & Carlopio, 2011). Moreover, behavioural indicators often have a reciprocal relationship with the environment in which the target population live. Environmental indicators are noted as economic, physical, service-related, and social factors, which are all subsequently dependant on dimensions of accessibility, affordability, and equity (Green & Kreuter, 2005). Hence, the actions of decision makers outside of the individual can also affect the social or physical environment, which could influence the behaviours of those at risk (Glanz et al., 2008). For example, laws that prohibit drink driving effectively reduce the amount that an individual can drink while out in public if they are the designated driver (Babor & Winstanley, 2008). Likewise, living in a remote region of Australia could mean that the individual is more likely to encounter additional social pressure to drink, as well as have reduced access to health services (Allan, Clifford, Ball, Alston, & Meister, 2012). In these scenarios, social factors and services are considered environmental indicators that are beyond the participants’ control, but very likely to influence their health outcomes (Glanz et al., 2008; Green & Kreuter, 2005).

The influence of genetics on the health outcome is also considered in Phase 2. As understanding of the human genome expands, the influence of genetics on externalising behaviour is growing, especially across areas of public health practice that relate to diet and obesity (Green & Kreuter, 2005). Emerging research in relation to alcohol-specific genetic factors and externalising behaviour-related genetic influences suggests that the relationship between genetics, behaviour, and alcohol could differ according to age and sex (Meyers et al., 2014). Much of this research is focused on adolescent and young adult age groups, in which specific genetic

Chapter 3: Theoretical Framework 47 influences appear more influential and closely associated with problematic alcohol use (Meyers et al., 2014). However, the influence of genetics on alcohol intake is an important consideration regardless of age; specific stem questions aimed at eliciting information on any family history of alcohol abuse or alcoholism were therefore included in the sub-study.

This phase is complex and can result in a diverse range of factors to be considered. Many of these factors influence the participant’s capacity to change from within and across the community, interpersonal, and individual levels. When considering theoretical models to explain behaviour, it is therefore necessary to understand that no single theory can adequately help the planner to predict or explain the extent of change possible. Precede-proceed acknowledges this, suggesting that it is necessary to draw on concepts from multiple theories to effectively work through Phase 2 (Glanz et al., 2008). The theories deemed most relevant to consider for this phase in relation to this PhD’s subject matter include Bandura’s social cognitive theory: self-efficacy, the transtheoretical model: stages of change, and the health belief model. Each theory is discussed briefly below.

Social cognitive theory is particularly useful for predicting and explaining behavioural change at the interpersonal level, as it considers not only determinants of behaviour, but also the process of behaviour change (Bartholomew, Parcel, Kok, Gottlieb, & Fernandez, 2010). That is, it focuses on the constant reciprocal triadic interplay between behavioural, environmental, and personal factors that affect an individual’s actions (Bandura, 1997; Bartholomew et al., 2010; McAlister, Perry, & Parcel, 2008). This is particularly relevant to Phase 2 of Precede as it can assist with identification of specific environmental factors and behaviours that could contribute to alcohol consumption. Furthermore, this theory emphasises the importance of perceived self-efficacy as the key determinant that influences all others (outcome expectations, goals, and sociocultural factors including various facilitators and impediments) in health behaviour (Bandura, 2004). Bandura (2004) defined self- efficacy as “people’s judgements of their capabilities to organize and execute courses of action required to attain designated types of performances” (p. 391). Thus, to improve the quality of life for its participants, the parent program focused on improving the self-efficacy of the individual across multiple health areas. Appendix

48 Chapter 3: Theoretical Framework

B provides examples of self-efficacy information sources embedded in the parent study that potentially influenced alcohol-related behaviours.

When using the Precede model to guide study design and assist with analysis, as was required for the sub-study, components of the health belief model and the transtheoretical model’s stages of change are particularly useful. These theories provide a deeper understanding of why and how change might occur at the individual level.

Similar to Precede, the health belief model (HBM) has developed over time and has long been used as a guiding framework for developing health behaviour interventions (Glanz et al., 2008). However, the HBM does not consider potential influences on the health concern from an epidemiological perspective. The HBM authors suggested that an individual’s readiness to act could be defined in relation to their perception of their susceptibility to and seriousness of the problem, although they do not necessarily need to be consciously or continually aware of these (Rosenstock, 1966). Hence, the HBM incorporates four psychological variables: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers (Bartholomew et al., 2010; Rosenstock, 1966). All of these focus on the individual’s perception of a problem in relation to the perceived benefits and barriers of taking action for the expected outcome (Glanz et al., 2008, p. 42; Sharifirad, Entezari, Kamran, & Azadbakht, 2009). Similar to SCT, a particular focus of the modern HBM is self-efficacy; that is, the perceived control that participants have to change their health behaviours. For example, after consideration of the four key HBM variables, how confident is the individual to consciously reduce their daily alcohol consumption and still feel they can impose that self-discipline when attending social events? Additional alcohol-related examples for the four psychological variables of the HBM are provided in Appendix B.

The transtheoretical model (TTM) was developed by Prochaska and DiClemente in the early 1980s when considering the quitting behaviours of smokers (quit-on-own versus professional help) (as cited in, Rimer, Glanz, & National Cancer Institute [U.S.], 2005). The model was quickly expanded for application to a wide range of health behaviours, including alcohol and substance abuse, breast cancer screening, and the consumption of high-fat diets (Glanz et al., 2008). This theory recognises that achieving the desired behaviour relies on the starting point of the

Chapter 3: Theoretical Framework 49 person in relation to their readiness to change (Rimer et al., 2005). For change to occur, the individual moves through five core stages: pre-contemplation, contemplation, preparation, action, and maintenance (Rimer et al., 2005), with the potential for relapse to occur at any stage along the way. This theory is particularly relevant to Phase 2 of Precede, as it provides an understanding of what might underpin addiction-related actions or non-actions. This can provide insight into whether an individual will progress or regress with the said behaviour change (see Appendix B for a description of each stage and an alcohol-related example). This brief overview only highlights the main stages of change; however, it should be noted that the TTM includes many other constructs that further explore the processes of change, decisional balance and self-efficacy (Glanz et al., 2008).

As with Precede, the TTM recognises that no single theory can account for all aspects of behaviour change; therefore, a host of theories need to be drawn on. Thus, drawing on specific concepts from the three theories discussed will account for the complexities of behavioural change. These complexities are considered further in Phase 3 of the Precede model, when educational and ecological assessments focus on the predisposing, enabling, and reinforcing factors that influence the behaviours and environments of the target group.

3.3.3 Phase 3: Educational and ecological Assessment. Reaching this stage of Precede (i.e., having progressed through Phases 1 and 2) has potentially identified hundreds of influencing factors that can affect the alcohol- related behaviours of this population. This phase not only focuses on initiating and sustaining the change process but also assists with the management of these potential causal factors. This is achieved by grouping the identified factors into logical categories according to the educational and ecological approaches often adopted by population health programs (Green & Kreuter, 2005). For this to occur, the researcher needs to identify the most pertinent antecedent and reinforcing factors, categorised as either predisposing, reinforcing, or enabling, that will influence the possibility that the behavioural or environmental change will occur (Glanz et al., 2008). Theoretically, Phase 3 is similar to Phase 2, in that it is underpinned by many change theories and principles that span all three levels of change including individual, interpersonal, and community (Glanz et al., 2008).

50 Chapter 3: Theoretical Framework

Predisposing factors for people in the at-risk group or target population might include the individuals’ “knowledge, attitudes, beliefs, personal preferences, existing skills, and self-efficacy beliefs,” all of which are noted as antecedents to behaviour (Glanz et al., 2008, p. 415; Green & Kreuter, 2005). That is, these factors provide the rationale for the behaviour (Green & Kreuter, 2005). For example, a person might value their health and believe that their diet (food and drink consumption) can influence health; consequently, they might be mindful not to over-indulge and only consume alcohol on special occasions. Predisposing factors are best explored using theories that work at the individual level, such as the health belief model (Glanz et al., 2008).

Reinforcing factors are those within and outside of at-risk individuals control (Simons-Morton, McLeroy, & Wendel, 2012). These are factors that likely support or provide momentum for the behaviour to continue, described as “factors following a behaviour that provide continuing reward or incentive for the persistence or repetition of the behaviour” (Glanz et al., 2008, p. 415). For example, a short-term sense of relief (the incentive) might be felt when alcohol consumption is used as a mechanism to deal with stress and or depression. Reinforcing factors are best understood with the use of interpersonal level theories and principles, such as social cognitive theory and adult learning (Glanz et al., 2008).

Enabling factors include both individual skills and environmental factors (Simons-Morton et al., 2012), which are also considered antecedents to behavioural and or environmental change, as they provide the motivation or environmental policy to be realised (Green & Kreuter, 2005). These factors can affect behaviour, either directly or indirectly, via an environmental mediator (Glanz et al., 2008). This might include programs and services that are necessary for a behaviour or environmental outcome to be realised (Glanz et al., 2008, p. 415). For example, to reduce the prevalence of lung cancer, quit smoking programs with free quit hotline services are available and reinforced by current laws that prohibit smoking in many public spaces. Enabling factors are best understood using theories and principles that focus at the community level such as: participation and relevance, community organisation, and community mobilisation (Glanz et al., 2008, p. 415).

In relation to alcohol consumption, it is important to consider all three factors: predisposing, reinforcing, and enabling, as they can positively and negatively

Chapter 3: Theoretical Framework 51 influence the individuals’ outcome behaviour. Evidently, participant information assessed in Phase 3 cannot be obtained nor interpreted robustly without first working through Phases 1 and 2. The findings of this study are interpreted in light of the identified predisposing, reinforcing, and enabling factors that influence participant alcohol-related behaviours.

3.3.4 Phase 4: Administrative and policy assessment and intervention alignment. The data from Phases 1-3 enable the researcher to identify and organise the predisposing, enabling, and reinforcing factors that are likely to influence the desired behaviour change. Phase 4 marks the start of the Proceed component of the model and subsequently requires the alignment of key intervention components, with the most important determinants of change already highlighted (Glanz et al., 2008).

3.3.5 Phases 5-8: Implementation and evaluation Phases 5 to 8 make up the remaining stages of the Proceed portion of the model. These phases were not required for the purpose of this thesis; however, to illustrate the full capacity of the model, each remaining phase is briefly described below. Phase 5 requires the health intervention to be ready for implementation (Glanz et al., 2008). Care needs to be taken to ensure that quality implementation occurs. For example, staff need to be hired and trained appropriately, materials need to be developed, and recruitment and advertising should commence (Simons-Morton et al., 2012). The final three phases focus on evaluating different aspects of the implemented program. One of the hallmarks of the model is that evaluation processes have been systematically built into the planning process as a whole (Green & Kreuter, 2005).

Phase 6 involves a process evaluation of the program according to established protocols, while Phases 7 and 8 consider the respective impact and outcome evaluations of change in relation to the objectives outlined during the assessment process (Green & Kreuter, 2005). For example, a program designed to curb antisocial behaviour at a local sporting venue might focus one aspect of the program on reducing binge drinking by limiting the number of drinks sold at any one time. The impact evaluation phase could assess the immediate effect of the program on the target behaviour in relation to the environmental change. However, the outcome evaluation relies on health status and quality of life indicators that were shaped early

52 Chapter 3: Theoretical Framework

in the planning process. These could therefore take longer to evaluate, as the data required can take longer to become available, especially if the outcome of interest is chronic disease-related (Green & Kreuter, 2005). Depending on the needs of the program, and given that Phase 7 and 8 can require considerable resources to conduct evaluations of impact and outcome, compared to Phase 6 processes, not all evaluation phases are necessarily conducted (McKenzie, Neiger, & Thackeray, 2013).

3.4 PRINCIPAL FEATURES OF THE MODEL

Decades of use have provided the model’s authors (Green & Kreuter, 2005) with feedback that has enabled them to strengthen and develop the model into its current form. During this time, the model’s strengths have been built upon. These are noted by the authors as flexibility and scalability, evidence-based processes and evaluability, participation, and provision of a platform for the application of evidence-based “best practice” (Green & Kreuter, 2005, p. 18). Another of the model’s hallmarks is its eclectic use of theories, as discussed above. This section briefly discusses each of the six hallmarks of flexibility and scalability, evidence- based processes, evaluability, principle of participation, provision of a platform for applying evidence-based “best practice”, and extensive use of theories.

Testament to the flexibility and scalability of the model are the more than 1,000 published applications, extensions, descriptions, and reviews of the model or components of the model in use across diverse populations, contexts, and for varied health concerns (Green & Kreuter, 2005). From large-scale public health campaigns supported by governments (Glanz et al., 2008), to workplace health promotion programs (Post, Daniel, Misan, & Haren, 2015), and small scale individual research works (Johnson, 2016; Tramm, 2010), including this PhD thesis, the model is easily adaptable for working within limited resources.

Another hallmark of the model is its use of evidence-based processes and evaluability. Application of the model provides the planner with a systematic approach to information gathering that is evidence-based and iteratively evaluable. Precede-proceed steps the user through each phase by asking appropriate questions to identify the ultimate concern and desired goal of the proposed program before developing these into measurable objectives, and then developing assessment tasks to

Chapter 3: Theoretical Framework 53 identify relevant factors or conditions that might influence the outcome and ranking these findings, in measurable terms, into order of relevance or importance given the health concern. The authors note that by developing measurable objectives throughout the planning process, the model provides the user with a ready-made evaluation system that provides ongoing feedback (Green & Kreuter, 2005).

The current model also maintains its fundamental principle of participation. This concept implies that to achieve successful change there must be active participation from the intended audience throughout the planning and evaluation phases (Glanz et al., 2008; Green & Kreuter, 2005). That is, the research program must involve participants (e.g., individuals directly affected by the health concern, stakeholders with vested interests) who can define their problems and goals of greatest concern in relation to the program (Glanz et al., 2008; Green & Kreuter, 2005; Porter, 2016). This means that participants can help develop and implement context-based solutions for concerns that address their community’s health and quality of life issues (Glanz et al., 2008; Green & Kreuter, 2005; Porter, 2016).

The authors (Green & Kreuter, 2005) note that by carefully applying precede- proceed’s evidence-based planning processes, strategies, and methods can be appropriately tailored to fit the unique circumstances of the target population. Hence, irrespective of the context, the model can provide practitioners with a sound platform for applying evidence-based “best practice”, another of the models notable hallmarks (Green & Kreuter, 2005).

The final hallmark for discussion is the model’s extensive use of theories. These can be either preordained, such as social cognitive theory and the health belief model, or if the existing theory or concept in the model does not answer the question, any other theory that suits the problem at hand can be utilised. Tramm, McCarthy, and Yates (2012) demonstrated the latter in a qualitative study that explained the health behaviours of women after breast cancer treatment. The authors noted that to enhance data interpretation and thus produce contextually relevant recommendations for health practice, the use of additional theoretical lenses other than those suggested by the model was necessary (Tramm, 2010; Tramm, McCarthy, & Yates, 2012).

54 Chapter 3: Theoretical Framework

3.5 CRITICAL REVIEW OF PRECEDE-PROCEED

This section critically evaluates the application of the model, including its strengths and limitations, and thereby provides the rationale for its use in this PhD study. Undoubtedly, precede-proceed has been thoroughly tested across different areas of health research and program planning (Gielen & Green, 2015; Glanz et al., 2008; Green & Kreuter, 2005). As noted above, over 1,000 published papers [available at http://www.lgreen.net] highlight the scope of precede-proceed use. For example, the model has been used for qualitative studies of heart health promotion within community church settings (Banerjee et al., 2015), and investigations into the health behaviours of younger women with cancer treatment-induced menopause (McCarthy et al., 2013). Furthermore, it has guided the training of student health professionals in oral cancer prevention and detection skills (Cannick et al., 2007), as well as clinical randomised control trials in rehabilitation centres that focus on patient sleep quality post-surgery (Ranjbaran, Dehdari, Sadeghniiat-Haghighi, & Majdabadi, 2015).

In the late 1990s, an interactive computer version of precede-proceed, known as EMPOWER (Enabling Methods of Planning and Organising Within Everyone's Reach), was designed specifically for cancer studies (Glanz et al., 2008; Green & Kreuter, 2016). EMPOWER provides a logical format for program development, one that allows for replication and critique of programs and components of established programs, regardless of whether they are original or integrated as programs mature (Glanz et al., 2008).

A 2012 review of tools available to public health practice identified precede- proceed as one of two key program-planning frameworks available for implementing an evidence-based approach to improve population health (Jacobs, Jones, Gabella, Spring, & Brownson, 2012). That is, an approach that integrates concepts of behaviour change theories into the health program planning, implementation, and evaluation phases to provide logical structure and organisation for planners (Jacobs et al., 2012). Social marketing is regarded as the other key program-planning framework available in this space (Jacobs et al., 2012). More importantly, precede- proceed has guided some of the most successful outcomes of the 20th century for public health achievements in relation to increasing motor vehicle safety and reduced rates of smoking-related cancers (Gielen & Green, 2015). Gielen and Green (2015)

Chapter 3: Theoretical Framework 55 explored the successes of such programs, reporting that successful outcomes were the result of change to the reciprocal relationship between normative behaviour and environments. Such change was achieved over time by means of policy restraints and or penalties, economic barriers to the unhealthy behaviours in question, and the provision of viable alternative behaviours (Gielen & Green, 2015).

Precede-proceed is often used within the cancer arena, particularly for improving the efficiency of cancer screening programs (Buranaruangrote, Sindhu, Mayer, Ratinthorn, & Khuhaprema, 2014; Hayes Constant, Winkler, Bishop, & Taboada Palomino, 2014; Senore, Inadomi, Segnan, Bellisario, & Hassan, 2015; Studts, Tarasenko, & Schoenberg, 2013). A recent Peruvian study aimed at increasing breast cancer screening rates used precede-proceed to guide conversations between health providers and women in the target population (Hayes Constant et al., 2014). Precede-proceed was chosen in an effort to expand design from the long- emphasised focus on individual cognitive processes to incorporate potential influences from broader social and cultural contexts, thus embracing the ecological approach (Hayes Constant et al., 2014). This qualitative pilot study then utilised grounded theory to identify the central themes of interest (Hayes Constant et al., 2014); however, in common with many other studies, this study did not utilise all of the phases of precede-proceed. Interestingly, within this space, the assessment phases of Precede are utilised most and often coupled with the desired principles and or theories. For example, principles of community organisation and Precede were applied to consider the extent of individual physician behaviour for the promotion of breast cancer screening practices (Taylor, Taplin, Urban, Mahloch, & Majer, 1994; Taylor et al., 1996). Meanwhile, Precede’s ecological approach of defining predisposing, reinforcing, and enabling factors provided an appropriate structure for a successful study in Thai women that elicited factors likely to influence the stage of breast cancer at time of diagnoses (Buranaruangrote et al., 2014). On the other hand, the model’s focus on both individual and system level environmental factors was considered by Arnsberger et al. (2006) as more appropriate than other models for explaining behavioural influences that affected time to diagnosis. This study focused on understanding the variances in follow-up times among multicultural women who presented with abnormal mammogram results (Arnsberger et al., 2006).

56 Chapter 3: Theoretical Framework

The malleability of precede-proceed means it is also used to guide evaluative reviews of cancer screening programs. Senore et al. (2015) reviewed 44 existing colorectal cancer-screening programs with the aim of optimising screening acceptance and screening rates. The authors emphasised the importance of explaining behavioural change via recognition of the constant interplay between predisposing, enabling and reinforcing factors (Senore et al., 2015). The findings suggested the use of multifactorial organised programs that targeted factors across multiple levels and considered factors outside of the clinical space as the most effective (Senore et al., 2015). Similarly, Schoueri-Mychasiw, Campbell, and Mai (2012) used both precede- proceed and the HBM to understand the antecedents of health behaviours and to guide a critical review of breast screening program use among immigrant and minority women. In total, eight studies from across Canada, the United Kingdom, and Australia were identified for review (Schoueri-Mychasiw et al., 2013). The findings highlighted how successful programs were more likely to have developed and implemented strategies that were unique to the target population; for example, targeting barriers to knowledge and language via translated information and reminder letters (Schoueri-Mychasiw et al., 2013). Again, the reviewers noted the importance of developing tailored screening recruitment interventions within the immigrant groups themselves, as not all target populations were homogeneous (Schoueri- Mychasiw et al., 2013).

In Australia, McCarthy et al. (2013) conducted a small (N = 85) mixed- methods descriptive study guided by precede-proceed that focused on the health behaviours of young women with cancer treatment-induced menopause. Exploration of the qualitative data subset (n = 22) revealed predisposing, enabling, and reinforcing factors such as entrenched pre-cancer diagnosis health behaviours and the variable nature of social supports (McCarthy et al., 2013). It was noted that the aforementioned potentially influenced the women’s ability to engage with health promotion behaviours (McCarthy et al., 2013). Identification of such factors not only provided a deeper insight to the quantitative data obtained, it highlighted the real need for flexibility and the tailoring abilities planners need when designing health promotion initiatives specific to this cohort (McCarthy et al., 2013). These findings informed the intervention developed in the WWACP, evaluation of which is currently underway in the parent study.

Chapter 3: Theoretical Framework 57 Hence, it appears that components rather than the whole of precede-proceed are often used. Users tend to focus on a particular component or strength of the model most relevant to the context, such as the ecological approach taken or the user-driven ability to select specific principles or theories that best fit the health concern. Additionally, users appreciate how the model considers the influence of environmental factors at both the individual and system levels that can affect behaviours, and its evaluative properties. Clearly, precede-proceed is highly adaptable to situational requirements and is used across diverse facets of health promotion research. Despite the many strengths of this framework, there are also limitations to consider.

The key criticism relates to the complexity of the framework. By far the biggest deterrent for adopting this model is the associated costs of thoroughly and appropriately working through each phase. This can require high levels of financial and human resources, as many phases are heavily data-driven and can require technical skill (Glanz et al., 2008). This could explain why, as discussed above, components of the model are often used in isolation. Another limitation noted by Glanz et al. (2008), is the sometimes-lengthy process of working through each phase and resultant lack of immediate action. This can lead to frustration when utilised in community settings (Glanz et al., 2008). Furthermore, it has been noted that for qualitative data to be analysed in a contextually-relevant way, it is important that researchers remain open to theoretical flexibility when using the precede-proceed model (Tramm et al., 2012). Thus, the researcher must sometimes consider perspectives outside of those detailed in the model if robust analysis is to be achieved (Tramm et al., 2012). Finally, although the precede-proceed planning process emphasises thorough assessments to help with the development of program goals and objectives (Simons-Morton et al., 2012), it does not necessarily provide specifics around intervention development and methods (Glanz et al., 2008). This final point can be considered both a limitation and strength of the framework, depending on the situation. Unlike some models, which can be too prescriptive and lead to difficulties if the model is not appropriate for the situation at hand and that can lead to developmental problems later, precede-proceed is highly adaptable.

Many of these limitations were not a concern for the development of this PhD study, as the parent study and its pilot studies were fully funded to collect all of the

58 Chapter 3: Theoretical Framework

data required for this PhD. Moreover, not all phases of the framework need to be actioned in the one study. If desired, precede-proceed can guide the process of evaluation and also critique of the study design. Therefore, despite the above limitations, precede-proceed provides a well-rounded, logical pathway that was chosen to guide the development and evaluation of this PhD study. Precede-proceed guided this thesis two-fold. Firstly, Phase 1 and 2 of the model were used to develop, implement, and evaluate stem interview questions and appropriate processes prior to roll-out, as well as to highlight variables of interest, and therefore the tools required to collect them. Secondly, Phases 1-3 provided much needed structure for analysis and interpretation of the collected data that can be seen in Chapter 4:, Chapter 6: and Chapter 7:.

In conclusion, precede-proceed is an appropriate and powerful model that can effectively drive the planning, development, and evaluation of health behaviour interventions. The model thoroughly considers the complexities of many public health problems and is widely-used. It can be utilised as a whole or in part to strengthen most interventions. It does not limit the understanding of behaviour change to a single theoretical model, and guides the planner to consider the environmental and personal influences, whether they be internal or external, that affect health. With guidance from appropriate theories for each phase, precede- proceed also provides a basis of suitable constructs for the analysis of qualitative data.

Chapter 3: Theoretical Framework 59

60 Chapter 3: Theoretical Framework

Chapter 4: Methodology and Study Design

4.1 INTRODUCTION

This chapter presents the design of this PhD project. This mixed method study is described in relation to the methodological approach taken, research questions, population, sampling, recruitment strategies, data collection procedures and instruments, and data analysis procedures for each phase. The chapter concludes with a discussion of the ethical considerations.

4.2 RESEARCH DESIGN

This PhD comprised two distinct studies, one quantitative and one qualitative, both of which explored alcohol-specific research questions to address the overall study aim. This PhD was attached to a much larger parent study, namely the Women’s Wellness after Cancer Program (WWACP). The aim of the parent program was to enhance the health-related quality of life (HRQoL) of women treated for breast, gynaecological and blood cancers, via the implementation of a whole-of- lifestyle intervention (the WWACP).

Study 1 of the PhD involved a secondary analysis of all alcohol-related quantitative data from the WWACP study, in addition to the relevant demographic, psychosocial, and health-related data that were collected.

Study 2 of the PhD involved a qualitative investigation of alcohol-related factors and behaviours with a purposive sample of participants in the WWACP study (see Figure 1.1 and Figure 4.1). Study 2, which explored how and why the behaviours in Study 1 might have occurred, constituted my unique contribution to the parent study. A detailed overview of the study design and how it fits within the broader WWACP study is available in Appendix A.

Chapter 4: Methodology and Study Design 61 WWACP Secondary Alcohol Data Analysis Sub-Study N = 269 N = 17

Study One Study Two (Quantitative Data) (Qualitative Data)

Figure 4.1. Research design overview

4.2.1 Mixed method approach This is a mixed method study. A mixed methods approach is useful in situations where, as in the situation established in the literature review, there is limited evidence about a particular topic, or if one research method is insufficient to explore the issue in the necessary detail to achieve the desired outcome (Creswell & Plano Clark, 2011). Hence the mixed method (interchangeably known as multimethod) design of this study is a pragmatic response to the nature of research problem. Polit and Beck (2008) note that researchers who choose to work exclusively in either the qualitative or quantitative paradigm often maintain that the two approaches are incompatible and should not be used in the one study. Others argue that in certain lines of inquiry, rigidly applied philosophical lenses resulting in pre-determined research methods cannot reconcile divergent findings or adequately illuminate commonalities in the data (Malina, Hanne, Nørreklit & Selto, 2011). Proponents of this latter idea propose that when they are partnered with care, the qualitative and quantitative approaches to research are in fact compatible and complementary (Cresswell & Plano, 2011; Malina et al., 2011; Polit & Beck, 2008). The characteristics of mixed method research, plus its advantages and disadvantages, are explored in this section.

Researchers have used multiple methods in the one study for many years, but the terms ‘mixed method’ and ‘multimethod’ only gained purchase from the late

62 Chapter 4: Methodology and Study Design 1980s (Brewer & Hunter, 1989). Cresswell and Plano Clark (2011) explain that there are two broad types of mixed method research. The first is the methodological category, which is more complex. The methodological category of mixed method research entails combining two or more of the following aspects of inquiry in the one study:

1. Different methodologies (i.e. the overarching philosophical frameworks driving the research question e.g. feminism, positivism).

2. Different study designs (different plans of action that link the philosophical assumptions to the methods, such as discourse analysis or randomised controlled trials).

3. Different methods (specific techniques for data collection and analysis, e.g. surveys, blood sampling, interviews).

4. Different data types (e.g. numerical, words, observations) (Cresswell & Plano, 2011).

In the second category of mixed method research, the defining feature is the emphasis on the nature of the data collected (‘close-ended’ or ‘open-ended’) rather than the source of the data (the underpinning worldview) as in methodologically- driven mixed method research (Cresswell & Plano, 2011). Within this definition, close-ended data, gathered with instruments such as numerical attitude rating scales, census records and behavioural checklists, require statistical analysis through largely pre-ordained mathematical procedures (Cresswell & Plano, 2011). Open-ended data, collected through means such as interviews, observations, written texts, audio or visual images, and cultural artefacts, are typically analysed by aggregating these source materials inductively or deductively into categories of information, rather than mathematically. Privileging the nature of the data over the source of the data in this way is considered paradigmatically neutral. It neither perpetuates the traditionally adversarial relationship between qualitative or quantitative data that has complicated arguments in this field, nor does it restrict researchers to the modes of data collection and analysis prescribed by these different paradigms (Cresswell & Plano, 2011; Polit & Beck, 2008). It is also pragmatic, recognising the recent trend in some traditionally qualitative approaches (such as ethnography and narrative methods) to employ non-traditional quantitative methods of data collection such as

Chapter 4: Methodology and Study Design 63 surveys and event history modelling to answer complex questions (Cresswell & Plano, 2011). This PhD is an example of this second category of mixed method research.

The ‘mixing’ of data, with the intention of forming a more complete picture of the problem than if either dataset was used in isolation, is a defining feature of mixed methods research (Cresswell & Plano, 2011). Data can be combined and analysed in three ways (Cresswell & Plano, 2011; Driscoll, Appiah-Yeboah, Salib, & Rupert, 2007):

1. Merging the data by bringing the datasets together after they have been separately collected (concurrent design).

2. Connecting the two datasets by collecting one dataset and using the information to both shape the nature of the data collected in the other dataset and to inform subsequent data interpretation (sequential design). This approach is used in this PhD.

3. Embedding one dataset into another, so that it provides a supportive role for the other from the outset of the study (e.g. the constant comparison technique used in Grounded Theory).

Polit and Beck (2008) summarise the four main advantages of data mixing as complementarity, incrementality, enhanced validity and pragmatism.

The term ‘complementarity’ reconciles the two basic modes of human communication in research. Qualitative approaches employ words and deeds, while quantitative approaches employ numbers. Both modes of communication are fallible, yet both have inherent value in the right situation. For example, Malina et al. (2011) argue that numbers in isolation do not necessarily tell the whole story. They note that in a mixed method study, the numerical outliers that in statistical parlance ‘distort’ the findings, can in fact become extremely useful and interesting when explained in context by the qualitative data (Malina et al., 2011). Complementarity recognises that in combination each approach can play to its strengths while compensating for the limitations of the other (Cresswell & Plano, 2011; Malina et al., 2011; Polit & Beck, 2008). In the process, a deeper, broader, more holistic perspective on the problem is attained.

64 Chapter 4: Methodology and Study Design

‘Incrementality’ refers to the deeper and broader understanding that is possible when investigating a research problem in different ways. The numerical data generated from quantitative hypotheses can address the ‘how often’ and ‘how many’ questions well, but don’t necessarily supply all the information needed to comprehensively answer a research question (Malina et al., 2011). Quantitative data can benefit from clarification with the ‘how does it happen’ and ‘why’ questions’ typical of qualitative inquiry, and vice versa (Polit & Beck, 2008).

The term ‘enhanced validity’ builds on the positivist notion of validity, which refers to the extent to which the instrument used in the study truly measures what it is intended to measure, and whether the conclusions made in a study are accurate and truly grounded in the data (Polit & Beck, 2008).. Validity is enhanced where a hypothesis is supported by different types of data; where the triangulation of methods allows alternative interpretations of the same data to be tested; and where researchers can determine, through qualitative methods such as interview or observation, the ways that the research context might influence the numerical findings. In essence, enhanced validity enables researchers to be more confident about the accuracy and relevance of the results (Polit & Beck, 2008). Malina et al. (2011) make an important point about the particular value of mixed methods in ‘failed’ trials; that is, in cases where the hypothesis is not actually supported by the quantitative data. Statistical confirmation of hypotheses is usually considered vital and it is often difficult to publish the results of quantitative research when hypotheses are not supported by the data and/or the underlying theory is found to be inadequate or incomplete (Malina et al., 2011). An advantage of mixing method is that when the numerical data don’t confirm a hypothesis, analysis of the related qualitative data to illuminate why this might have occurred allows the researcher to legitimately shift from hypothesis confirmation to hypothesis refinement or refutation (Malina et al., 2011).

Finally, as Polit and Beck (2008) argue, some research questions can only be answered through more than one method of data collection. The health service researcher is often driven by pragmatism, needing to choose the right tool or set of tools to answer a discrete question that is framed by pressing service constraints. In this context, practical outcomes rather than dogmatic epistemological considerations are foremost. Johnson and Onwuegbuzie (2004) note that mixed method research

Chapter 4: Methodology and Study Design 65 rightfully concentrates on method, which thereby attempts to reconcile the insights provided by qualitative and quantitative research into workable solutions to real world problems. Citing the examples of classical pragmatists such as Dewey, Peirce and James, they argue for the empirical and practical benefits of mixed method, which can in fact help to inform philosophical thinking (Johnson & Onwuegbuzie, 2004).

Irrespective of approach, all research has weaknesses. Common criticisms of mixed method designs centre on paradigmatic and logistical issues. Mixed method research is a relatively new approach and how best to reconcile different paradigms in the one study has not been fully resolved to the satisfaction of the academic community. Researchers who work in mixed method have written about the difficulties often encountered when methodological purists review their work, and who, for a variety of often valid theoretical, philosophical and technical reasons cannot reconcile research approaches that do not accord with their own worldview (Johnson & Onwuegbuzie, 2004).

Accommodating other worldviews as required by mixed method research can be problematic. Investigators often only understand one approach at the expense of another so a team with different knowledge and skills is usually required (Cresswell & Plano, 2011), with team members willing and able to learn the research language and worldview of the others (Johnson & Onwuegbuzie, 2004). Moreover, the rationale for adopting mixed methods is sometimes not clearly articulated by the researchers, which can result in a less-than-robust design (Bazely, 2004). Bazely (2004) argues that some studies do not really use mixed method at all, because they don’t truly recognise the full contribution of each method to the overall study. Bazely suggests that to limit the tensions often encountered in mixing method, the purpose of the research must be made clear and the design should be presented through a theory-driven, logical chain of evidence clearly related to that purpose (Bazely, 2004).

Other criticisms focus on the limited evidence to guide the transformation of different data types into a cohesive whole, particularly the difficulties involved in resolving different types of data and the weighting given to each (Malina et al., 2011). For example, some mixed method approaches attempt to ‘quantitize’ qualitative data, which demonstrates a limited understanding of the multidimensional

66 Chapter 4: Methodology and Study Design

nature of qualitative data and reduces its valuable flexibility and depth (Bazely, 2004; Driscoll et al., 2007). This leads to the logistical criticisms of mixed method approaches, which focus on the time, money and skills necessary to avoid such pitfalls5. Undeniably, significant investments in time and human resources are often required to design and implement a rigorous mixed method study (Cresswell & Plano, 2011; Johnson & Onwuegbuzie, 2004) although the same could be said for all research. Even when the study is successfully completed, there are often difficulties in publishing mixed method papers. By their nature such papers are long and complex, hence mixed method researchers have voiced their frustration in finding peer reviewers not conversant in the approach (Malina et al., 2011).

On balance, in the context of the problem driving this study, the advantages of mixed method design outweigh the disadvantages. The political nature of the research problem, the need to deliver a pragmatic solution, the many dissonances in the problem that must be understood through different lenses and then reconciled, and the depth and breadth of data able to be collected to investigate the problem mean that mixed method is an appropriate method to drive this study.

In summary, this study comprised a sequential mixed methods design, wherein the close-ended survey data collected in Study 1 informed the type and number of open-ended interview questions developed for Study 2.

4.2.2 Research questions The overall aim of this PhD was to determine the predisposing, enabling and reinforcing factors associated with alcohol consumption in women previously treated for breast cancer. To achieve this aim, the following research questions framed by Precede were asked:

1. What is the pattern (frequency, quantity, type, and place) of alcohol use amongst Australian women treated for breast cancer who consume alcohol?

2. What are the demographic, psychosocial, and health-related factors associated with alcohol use?

3. Is a tailored lifestyle intervention associated with change in alcohol-related health behaviours in this population?

Chapter 4: Methodology and Study Design 67 4.3 STUDY 1 – SECONDARY ANALYSIS OF WWACP DATA

4.3.1 Population All women enrolled in the parent study met the following eligibility criteria: Inclusion Criteria:

• female;

• 18 years of age or older;

• diagnosed with breast, blood or gynaecological cancer;

• recently received treatment other than surgery for breast, blood, or gynaecological cancer (i.e., chemotherapy and/or radiotherapy), in the last 24 months;

• in remission, with no metastatic cancer;

• could speak, read, and understand grade 10 English;

• had access to a computer or iPad tablet device;

• possessed adequate computer skills to use the internet and email with ease;

• had reliable internet access.

Exclusion Criteria (option to discuss with consultation nurse):

• were not well enough to participate for study duration;

• had ongoing complications from treatment;

• had further surgery scheduled.

The parent study sample (including breast, blood, and gynaecological cancers) was 351. Complete baseline data for secondary analysis was available for the 269 breast cancer participants of interest to this PhD (see the study CONSORT diagram available in Figure 5.1).

4.3.2 Sample Sample size calculations for the parent study were based on detecting change in health-related quality of life using the Functional Assessment of Cancer Therapy – General (FACT-G) (Brucker, Yost, Cashy, Webster, & Cella, 2005). Unavoidable delays in ethical clearance and slower than anticipated recruitment meant the a priori power analysis pre-intervention commencement was revised. A posteriori power

68 Chapter 4: Methodology and Study Design

analysis was then undertaken, which revealed that 125 participants per intervention and control groups (total 250 participants) was adequate to detect change in the primary outcome (FACT-G total, standardised mean detectable difference of 0.6 or greater, 80% power, type I error of 5% (two-tailed), and accounting for 10% lost to follow-up and 15% non-response) (D. J. Anderson et al., 2017).

Power and sample size calculation software (Dupont & Plummer, 2014) was utilised to identify the detectable effect size for alcohol consumption in a given number of participants (secondary analysis) for the PhD study Phase 1, which comprised breast cancer-only participants. The calculation was based on achieving 80% power with 269 participants across two groups and a type I error of 5% (two- tailed). As there is little information regarding clinically meaningful difference when it comes to alcohol intake (WCRFI/AICR, 2017a) the Pink Pilot Study (D. J. Anderson & Lang, 2011) standard deviation of 9.7g/day was used. Even with the clinical difference not well known, this enabled the detection of at least a 2.4 g/day difference between the groups. This is less than one quarter of a standard drink.

4.3.3 Recruitment strategy for the parent study Participants were recruited from major public and private cancer units throughout Australia. These included the Kim Walters Choices Centre at the Wesley Hospital, the Princess Alexandra Hospital, and the Royal Brisbane and Women’s Hospital in Queensland; the Peter MacCallum Cancer Institute in Victoria; and St John of God Murdoch Hospital in Western Australia. Site-specific research assistants performed onsite recruitment. In addition, participants were also recruited via consumer groups using newsletters, emails, and online via advertisements on the Breast Cancer Network Australia website, the National Breast Cancer Foundation’s Register4, and CanSpeak Queensland. Details of all potential participants from online registrations were forwarded to a central email address ([email protected]), which was monitored by research assistants based at the QUT, where all eligibility and consent forms were managed, confirmed, and securely stored. Copies of the patient information and consent form are available in Appendix C.

4.3.4 Data collection procedures

Prior to randomisation, all consenting participants completed the time-one (T1) questionnaire administered via Key Survey, a specialist online survey creation and

Chapter 4: Methodology and Study Design 69 management system designed by QUT (2011). All participants also completed three virtual appointments: baseline (T1), Week 12 (T2), and Week 24 (T3) with a trained research assistant, during which biophysical measures and a number of questionnaires were completed. Participants in the intervention arm (12-week WWACP) also received three virtual consultations (Week 0, Week 6, and Week 12) with a specialist cancer care nurse who delivered the WWACP. The study closed to recruitment in August 2016.

Although the use of technology for virtual data collection is relatively new, current literature suggests this method of data collection is becoming more common and more efficient. A number of studies (Bonn, Trolle Lagerros, & Bälter, 2013; Lassale et al., 2013; Powell-Young, 2012; Pursey, Burrows, Stanwell, & Collins, 2014; WCRFI/AICR, 2017a) have tested the validity of self-reported web-based measures (i.e., height, weight, body mass index, waist and hip circumferences) compared to face-to-face data collection methods with a trained research assistant. Generally speaking, these studies have concluded that there is good agreement between the two methods of measurement and across different populations, including young adults (Pursey et al., 2014) and adults (Bonn et al., 2013; Lassale et al., 2013). However self-reported measures might not be as valid among certain adolescent ethnic and female specific sub-populations (Powell-Young, 2012). Earlier studies have reported satisfactory agreement for self-reported measures of BMI and waist circumference in a population of middle-aged and overweight workers (Dekkers, van Wier, Hendriksen, Twisk, & van Mechelen, 2008), while another study suggested older persons could require additional assistance to obtain more accurate recordings of height and weight (Kuczmarski, Kuczmarski, & Najjar, 2001)

The WWACP study incorporated a number of steps to improve the accuracy of all biophysical measures taken. First, although web-based, self-reported data for height and weight were collected via Key Survey questionnaires at T1, T2, and T3, and these measures were also taken with guidance provided by the research assistant in a virtual capacity. The literature suggests the accuracy of such measures is improved if participants are provided with written and pictorial instructions about how to take the required measures (i.e., height, waist and hip circumference). For this reason, all participants were sent an information pack with detailed instructions prior

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to their first research assistant appointment. The potential benefits and limitations of using virtual appointments for data collection are outlined below.

Benefits associated with adopting a virtual method for data collection:

• It widened the study’s recruitment scope (i.e., increased potential for the recruitment of rural and remote participants).

• It increased the appointment time flexibility for both participant and researcher.

• Appointments were performed in the privacy of the participant’s home or desired location.

• It reduced the study costs (i.e., no need to reimburse travel expenses for parking, public transport and taxi fares).

• It reduced the costs for participants (e.g., reduced the need for child care and time off work).

The limitations to consider included:

• Possible bias remains as data were still self-reported.

• The participant had to own weight scales (and a smartphone or digital camera to show the research assistant the scale reading).

• The participant required a measuring tape (this was mailed out from the WWACP prior to the first virtual assessment).

• The participant had to be prepared for the appointment.

• A stable internet connection was required (this was incorporated in the study inclusion criteria).

4.3.5 Randomisation Blocked random number sequencing allocated participants to either the intervention or usual care (control) arm. The research assistants were blinded to the allocation.

4.3.6 Research assistant consultations All research assistants received face-to-face training supported by a project- specific training manual (WWACP Training Manual and Policies and Procedures for

Chapter 4: Methodology and Study Design 71 Research Assistants) prior to commencement of the trial. Each virtual consultation took no more than one hour to complete.

4.3.7 Nurse consultations Only participants in the intervention arm received consultations with a specialist cancer nurse. Each consultation was used to work through the WWACP program; initiate discussions on healthy eating and physical activity; deliver tailored health education, including the alcohol-related content outlined within the iBook; discuss specific medical concerns and relapse prevention; and to plan tailored goals with strategies to achieve them. Alcohol consumption was discussed in detail during these consultations. To ensure this occurred, the participant record form used by the consultation nurses was designed to prompt discussion on the topic of alcohol among other topics; for example, exercise. To ensure the consultation nurse was equipped with the latest evidence regarding alcohol consumption and risk of recurrence, each WWACP nurse was provided with a project-specific training manual (WWACP Training Manual and Policies and Procedures for Consultation Nurses) and standardised training prior to commencement of the trial. As part of the training manual, all nurses were also directed to the Cancer Council Australia Position Statement on Alcohol and Cancer (Cancer Council Australia, 2015). Strict protocols ensured that all participants were referred to the appropriate health professional if required.

4.3.8 Intervention arm Interactive iBook and website – overview and alcohol-related content The WWACP interactive iBook delivered a 12-week program that aimed to improve overall wellbeing for women who had recently completed cancer treatment. The iBook covered many aspects of health management; however, for the purpose of this thesis, only the alcohol-related content is discussed. The iBook was downloadable from the WWACP website [available at http://wwacp.com.au] as an interactive iBook and also as a PDF. Additionally, the website provided a discussion board to encourage the formation of peer support groups. Appropriate etiquette for the use of the discussion board was provided online and content was regularly monitored by a WWACP health professional.

72 Chapter 4: Methodology and Study Design

The topic of alcohol consumption after the diagnosis and treatment of cancer was flagged throughout the iBook. Alcohol-related content was outlined within the Healthy Eating after Cancer section, with the clear recommendation to limit the intake of alcoholic drinks after cancer as per the WCRFI/AICR (2007). An alcohol- specific fact sheet was also provided. The fact sheet detailed the potential harms of alcohol intake, including the risk of cancer recurrence, provided examples of standard drinks, and helpful tips to reduce the amount of alcohol consumed. The fact sheet was carefully worded to minimise any sense of blame, shame, or guilt that women could experience in relation to their past or present alcohol consumption and their diagnosis of cancer. Current NHMRC guidelines (2009) for alcohol consumption were also provided, together with recommendations from Cancer Council Australia (2015). The adverse effects of alcohol were mentioned throughout the iBook.

4.3.9 Usual care arm Usual care was considered any standard care, including general information about the importance of diet, exercise, restriction of alcohol consumption, and tobacco use, or any information about additional support services that the individual might have received during the course of their usual clinic visits. The participants’ surgeon, general practitioner, cancer care nurses, or any other allied health professional could provide this information. A hard copy of the intervention book was provided to all control group participants on study completion.

4.3.10 Study 1 data collection instruments The WWACP was a large study, and as such, had many measures of health and health-related behaviours. Table 4.1 outlines the instruments deemed relevant to my PhD project, with further detail provided in Appendix D.

Table 4.1 Study 1 Data Collection Instruments, Items, Time-points and Mode of Administration

Instrument / Tool Number Time-point of items administered

Administered via Key Survey (Online)

Chapter 4: Methodology and Study Design 73 Socio-demographic questions 10 T1

Dietary intake 6 T1, T2, T3

Height and weight 4 T1, T2, T3

FACT G (Functional Assessment of Cancer Therapy 27 T1, T2, T3 – General (Cella et al., 1993)

Sleep quality and habits (Pittsburgh Sleep Quality 10 T1, T2, T3 Index (Buysse, Reynolds, Monk, Berman, & Kupfer, 1989)

Health behaviour (smoking) 3 T1, T2, T3

CES-D Depression Scale (Radloff, 1977) 20 T1, T2, T3

Greene Climacteric Scale – vasomotor subscale 2 T1, T2, T3 (Greene, 1998)

Administered by Research Assistant (Virtually) T1 (all) General health and medical history questions 6 T2, T3 (part only) (changes to medication - recorded at T2 and T3)

Biophysical measures: height, weight, waist and hip 4 T1, T2, T3 circumferences (BMI and waist-hip ratio calculated) Height (T1 only)

Food frequency questionnaire (FFQ): Dietary 74 T1, T2, T3 Questionnaires for Epidemiology Studies Version 2 (DQES v2) (Cancer Council Victoria, March 2014; Giles & Ireland, 1996)

Additional alcohol questions (ABS, 2012a; 2 T1, T2, T3 Australian Government Department of Health, 2013)

International Physical Activity Questionnaire – 7 T1, T2, T3

74 Chapter 4: Methodology and Study Design

Short Form (IPAQ) (Craig et al., 2003)

The details of the instruments specific to this PhD study, including reliability and validity, are discussed below.

Biophysical measures Accrual of adipose tissue around particular regions of the body is associated with metabolic abnormalities, including coronary heart disease and diabetes (Hammond & Litchford, 2012; Stewart, 2012). Predicting an individual’s risk of such comorbidities is commonly measured using three techniques simultaneously. These are: waist to hip ratio (WHR), body mass index (BMI), and waist circumference (WC) (Stewart, 2012). To ensure accuracy, participants were provided with a standardised tape measure and detailed pictorial instructions, as well as verbal and visual guidance from the research assistant to record each measure.

WHR is calculated using an individual’s waist measurement divided by their hip measurement [WHR = WC/HC]. Waist and hip measurements are taken three times each and averaged at each time point. The research assistant averaged the measures; however, all other calculations were performed using SPSS software (IBM Corp, Released 2013). The adult WHR cut-off point for women is > 0.8, this defines increased risk of cardiovascular disease and all-cause mortality (Stewart, 2012). WHR is considered a more accurate predictor of obesity-related disease than mortality when compared to BMI and WC (Stewart, 2012).

BMI is an approximate measure of body composition based on an individual’s relative weight and height. While not without its limitations, such as consideration of ethnicity (Stewart, 2012), when using population norms, BMI can help to identify whether a person’s weight is appropriate for their height, thus indicating potential over-nutrition or under-nutrition (Hammond & Litchford, 2012). Women with BMIs of ≥ 25.0 kg/m2 and ≥ 30.0 kg/m2 are classified as overweight and obese, respectively. Both classifications carry an increased risk of cancer and co-morbidities (Stewart, 2012). Participants were asked to measure their height prior to the virtual research assistant appointment, as additional assistance was required for this task. During the virtual appointment, research assistants sighted the participant’s measured weight immediately after they stepped off the scales.

Chapter 4: Methodology and Study Design 75 WC by itself can be used as an independent predictor of risk to support BMI (Stewart, 2012). It is a valid measure of fat distribution around the abdominal area for risk assessment; however, it is not valid in pregnancy or if the participant has a BMI greater than 35 kg/m2 (Stewart, 2012).

Assessing WHR, BMI, and WC at multiple time-points enabled monitoring of any changes in these risk factors. It took an average of approximately 10 minutes to obtain these measures. Appendix E details measurement instructions for the research assistants, which is a modification of what participants received.

Dietary intake measures Food frequency questionnaires have been used extensively since the 1990s in epidemiological studies (Shim et al., 2014). Since that time, numerous changes have enhanced the accuracy of this assessment method (Shim et al., 2014). Patterson et al. (1999) described the measurement characteristics of the USA-based Women’s Health Initiative Food Frequency Questionnaire (WHI-FFQ). This was used to elicit the dietary intake of 113 participants over a three-month period against data obtained from concurrent short-term dietary recall methods, including a four-day food record and four 24-hour dietary recalls (Patterson et al., 1999). The study investigated the agreement of 30 nutrient estimates obtained via the WHI-FFQ compared to nutrient estimates obtained via the eight days of dietary intake noted above. Of the 30 nutrient estimates in question, 21 nutrient estimates from food records and 22 nutrient estimates from dietary recalls were within 10% of the WHI-FFQ nutrient estimates (Patterson et al., 1999). Furthermore, test and retest reliability of repeat FFQs was found to be high when compared to a much larger WHI-FFQ sample (N = 16,747). In relation to alcohol intake, correlation coefficients were found to be consistently high (approximately 0.90) between all three measures (FFQ, food records, and dietary recall) (Patterson et al., 1999).

As discussed in Section 2.5 of this document, the most commonly-used Australian FFQ is the Dietary Questionnaire for Epidemiological Studies Version 2 (DQES v2), which was primarily designed to capture usual dietary intake over the preceding 12-month period (Giles & Ireland, 1996). The original DQES was validated in the Melbourne Collaborative Cohort Study with men and women aged 40-69 at recruitment from 1990 to 1994 (Cancer Council Victoria, March 2014; Giles & Ireland, 1996). It has demonstrated reliability when assessed for agreement

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using comparable data from weighted food diaries/records (WFR), when administered to 159 participants in an Australian clinical intervention trial, over a considerably shorter time-frame (one-month) (Xinying, 2004). Mean energy and nutrient intakes were reported to be within ± 20% difference of each other when comparing 10 selected nutrients, with correlation coefficients for individual nutrients ranging from 0.22 to 0.78 with a median of 0.41 (Xinying, 2004). Version 2 of the DQES was subsequently validated in Australian women aged 16-48 years using WFRs to test reliability. It reportedly demonstrated good correlation coefficients for nutrient intakes when compared with data from other studies using different FFQs (Hodge et al., 2000). This updated version has been used extensively with women from the Breast Cancer Family Registry, in the Australian Longitudinal Study of Women’s Health (Alhazmi, Stojanovski, McEvoy, & Garg, 2014; Cancer Council Victoria, March 2014) and in the Pink Women’s Wellness Program Study (D. J. Anderson & Lang, 2011), which preceded the parent program discussed here. More recently, the DQES v2 was assessed for relative validity against data from five one- day WFRs and for reproducibility over a one-month period using repeated measures in an Australian population of young adults aged 18 to 34 years (Hebden, Kostan, O'Leary, Hodge, & Allman-Farinelli, 2013). The results indicate that the DQES v2 demonstrated good reproducibility, with significant positive intra-class correlation coefficients (p < .01), which in women ranged from 0.69 for vegetable servings to 0.91 for protein intake (Hebden et al., 2013). The DQES v2 FFQ was noted as over- estimating fruit servings compared to the WRF, likely a result of the fruit juices being combined with whole fruit serves, and alcohol intake among the younger female population was notably higher when assessed with the DQES v2 FFQ (Hebden et al., 2013). This might have been a direct result of “usual” intake being measured by the DQES v2 FFQ versus the WFR intake omitting specific days in which alcohol was most likely consumed; for example, a weekend day (Hebden et al., 2013). Nevertheless, with the exception of protein intake for males, the DQES v2 FFQ is considered a valid measure for fruit and vegetable servings, as well as for the nutrients and alcohol studied in this population (p < .05) (Hebden et al., 2013).

The studies cited above describe how the DQES has been validated across varied time-frames. To reduce potential confounding that could arise from overlapping time-periods (i.e., administering the DQES v2 with the 12-month recall

Chapter 4: Methodology and Study Design 77 time-frame, three times during a six-month period), during the WWACP study the DQES v2 captured data over a one-month period. The wording was modified to read: “Over the last 1 month, on average, how often did you eat the following foods?”(Appendix D Part C).

The DQES v2 is a straightforward tool for the collection of usual dietary intake that was efficiently administered from multiple sites within Australia. The tool covered 74 food items grouped into four main categories, including: 1) cereal foods, sweets, and snacks; 2) dairy products, meats, and fish; 3) fruits; and 4) vegetables, in addition to intake of alcoholic beverages and scales for portion size (Cancer Council Victoria, March 2014). Three alcohol-specific questions obtained data on the frequency of intake (i.e., Over the last 1 month, how often did you drink beer, wine and/or spirits?); the quantity of intake (i.e., … on days when you were drinking, how many glasses of beer, wine and/or spirits altogether did you drink?), and the maximum number of drinks consumed in a 24 hour period (i.e., … what was the maximum number of glasses of beer, wine and/or spirits that you drank in 24 hours?). To assist with the quantification of alcohol intake, the DQES v2 provided a description of what constituted one “glass” of alcohol. The DQES v2 was cost- effective to administer and took approximately 15 minutes to complete. The data were submitted online and analysis performed externally by Cancer Council Victoria using Australian nutrient composition data from NUTTAB95 and other relevant sources, as per accepted protocol (Cancer Council Victoria, March 2014).

In short, the measurement of diet is complex (G. S. Martin, 2004), and as with any dietary intake method, there are limitations to consider. First, the time-frame in which the DQES v2 was administered required modification to ensure the quality of data collected for WWACP was not compromised. Second, it has been noted that the collection of data for fruit and vegetable serves, as well as soft drink and caffeine intake was not adequate. To address this, additional questions derived from the Australian Health Survey 2011-12 (ABS, 2012a) were utilised to quantify intake of fruit and vegetable serves, soft drink, and caffeine. These additional questions, which were validated and normed within the Australian population (ABS, 2012), were administered via the Key Survey platform.

To identify alcohol-related behaviours, two additional alcohol-related questions were administered directly after the completion of the FFQ. These

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established “when” (i.e., “On which days in the last 7 days did you have drinks that contained alcohol?”) and “where” (i.e., “Where do you usually drink alcohol?”) the participant consumed alcohol. The questions were previously validated within the Australian population (ABS, 2012; Australian Government Department of Health, 2013; AIHW, 2013;), having originated from the Australian Health Survey (ABS, 2012a) and the National Drug Strategy Household Survey (Australian Government Department of Health, 2013) respectively, with the “when” question modified slightly to include the final option “None, I did not drink this week”. These questions were trialled in the WWACP pilot study.

4.4 STUDY 1 - QUANTITATIVE DATA ANALYSIS

4.4.1 Data preparation This project utilised statistical software, namely the Statistical Package for the Social Sciences (SPSS) Version 22 (IBM Corp, Released 2013), to conduct secondary analysis of quantitative WWACP data for breast cancer participants only. The data for the variables of interest were requested from the WWACP data manager on completion of the parent study. Data for each time-point were then merged into a single working dataset, cleaned, corrected as required, and reviewed. New variables were created where necessary (i.e., income re-categorisations, number of comorbidities present, alcohol intake categorisation) and calculations (i.e., body mass index [BMI], waist-hip-ratio [WHR], time since diagnosis) were performed.

Age in years was analysed as a continuous variable, as well as categorised according to the Australian Bureau of Statistics (ABS) standard categorisations to allow for comparisons (ABS, 2012b). Income data were collapsed from 11 categories into low (≤ $40,000), middle (>$40,000 to $100,000), and high (> $100,000) income groups, with cut-points based on the Victorian Population Health Survey 2015 (Department of Health and Human Services, 2017) categorisations and the ABS Household Income and Income Distribution 2011 documentation (ABS, 2013b). After much consideration, all responses to the “don’t know” income option were treated as missing data. Justification for this decision included: 1) there was only a small number of participants in this category and the majority of analyses required could not be run with small cell counts, and 2) no discerning pattern was evident to allow reallocation into another income group. For example, the participants were not

Chapter 4: Methodology and Study Design 79 all elderly and therefore potentially on pensions, which could justify re-allocation into the low-income group.

Time since diagnosis (in months) was calculated using the date of the participants’ most recent breast cancer diagnosis (identified at the T1 research assistant interview) together with the T1 online Key Survey completion date. The time since diagnosis continuous variable was then categorised into ≤ 6 months, > 6 to 12 months, > 12 to 24 months, > 24 to 36 months, > 36 to 48 months, and > 48 months for more useful interpretation.

Physical activity levels for the previous seven days were assessed via completion of the IPAQ short form (Craig et al., 2003), which was administered by the research assistants. Results were calculated according to the IPAQ short form instructions with low, moderate, and high final categories presented. As these categories are associated with health benefits rather than simply meeting the general public health recommendation of 30 minutes of moderate-intensity physical activity on most days, the cut-points are higher (IPAQ Group, 2005). In brief, high physical activity levels were taken as at least 1,500 MET-minutes/week derived from vigorous-intensity activity or at least 3,000 MET-minutes/week derived from any combination of walking, moderate- or vigorous-intensity activities. Moderate physical activity levels were considered as at least 20 minutes/day of vigorous- intensity activity on ≥ 3 days/week; or at least 30 minutes/day of moderate-intensity activity on ≥ 5 days/week; or any combination of activity totalling at least 600 MET- minutes/week. Participants who did not meet the criteria for either the high or moderate activity levels were considered to have a low physical activity level (IPAQ Group, 2005). All IPAQ calculations were performed by the WWACP Research Assistant Manager and independently reviewed by another WWACP team member.

Quality of life was assessed using the FACT-G (Cella et al., 1993), providing an overall score and sub-domain scores. Higher scores represent better health. I undertook all calculations except the quality of life scoring for the FACT-G tool (performed by the WWACP Data Manager). BMI for each time-point was calculated using the RA-collected data (not self-reported data) and categorised according to WHO (2011, 2017) guidelines. WHR and WC were split into categories as per guideline risk thresholds (WHO, 2011), and the number of co-morbidities was

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assessed using medical history data obtained by the research assistants at T1 interview.

Alcohol intake data, presented in grams per day of pure ethanol consumption, were not normally distributed. This was due to the large number of participants who either had not consumed alcohol during the previous month or consumed small amounts. Hence, an ordinal categorised alcohol intake variable for each time-point was created. The cut-points for each category reflected a 10-gram increment, which reflects one standard drink. This categorisation split is commonly seen in the literature (Ali et al., 2014; WCRFI/AICR, 2017b). The categories were nil intake, > 0 to ≤ 10 grams per day, > 10 to ≤ 20 grams per day, > 20 to ≤ 30 grams per day, > 30 to ≤ 40 grams per day, and > 40 grams per day. Categorisation of the alcohol intake variable accommodated issues commonly seen with alcohol consumption data. The categorical variable “drinking status” was also created to identify participants who did not consume alcohol (non-drinker) and those who did (drinkers), at baseline.

A considerable number of outliers was also identified for the outcome variable (alcohol intake); therefore, all potential outliers were investigated prior to running descriptive statistics. Outliers were identified by ID number using side-by-side clustered boxplots for alcohol intake (continuous form) at each time-point. Further investigation involved a number of steps, such as ensuring the value of each outlier was reasonably consistent with its corresponding time-point, the maximum number of drinks was deemed either likely or unlikely when considering all other alcohol- related responses, days of the week intake reasonably explained intake value in question, review of the interview date identified whether consumption had occurred over a festive period, and the food frequency questionnaire hardcopy correctly reflected the dataset value. Following thorough investigation, all outliers were deemed true and correct; thus, all remained in the analysis.

An overview of the statistical analysis used to answer each research question is provided in Table 4.2.

Chapter 4: Methodology and Study Design 81 Table 4.2 Overview of Statistical Analysis Performed

Research Question Variables Statistical Analysis

1. What is the pattern • Alcohol intake Univariate descriptive (frequency, quantity, type (continuous and statistics: and place) of alcohol use ordinal categorical) • Frequencies and amongst Australian women • Number of glasses per descriptives treated for breast cancer? day • Proportions and • Max. number of counts

glasses in 24 hr • Bar charts period • Box plots • Type and frequency of alcohol consumed • Days of the week Binary logistic regression alcohol consumed • Place of consumption • Drinking status (non- drinker/drinker)

2. What are the DV: Alcohol intake Ordinal logistic demographic, psychosocial (ordinal categorical) regression modelling: and health-related factors IV: Smoking status; • Bivariate associated with alcohol use? physical activity level; • Multivariable no. of comorbidities; quality of life; income; education attainment.

3. Is a tailored lifestyle DV: Alcohol intake Descriptive statistics intervention associated with (ordinal categorical) Generalised estimating change in alcohol-related IV: Time (time-point and equation for logistic health behaviours in this group) regression population?

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4.4.2 Descriptive statistics Frequencies and summary descriptive statistics were used to analyse participants’ demographic and general health data to describe the general characteristics of the population. Means and standard deviations reported continuous outcome variables; for example, age. Where data were not normally distributed, the appropriate non-parametric test was utilised, with the median and interquartile range reported. Proportions and counts reported categorical outcome variables; for example, ethnicity and menopausal status. Descriptive variables included:

• age; • smoking status; • ethnicity (country of birth, • menopausal status; ancestry, ATSSSI, language • quality of life; spoken at home); • BMI; • education; • WHR and WC; • employment status; • comorbidities; and • household income; • physical activity level. • postcode;

Descriptive statistics also identified patterns of alcohol intake at baseline. As discussed previously, patterns of alcohol intake were assessed using a number of tools. The primary tool for assessing intake was the Dietary Questionnaire for Epidemiological Studies Version 2 (Giles & Ireland, 1996), which is a food frequency questionnaire (FFQ) that was administered by research assistants at each time-point. The FFQ is produced by Cancer Council Victoria (CCV) and was purchased by the parent study to assess usual dietary intake over the previous one month. FFQ analysis was undertaken by the CCV and provided to the parent study. Alcohol-related variables using descriptive statistics included:

• alcohol intake (g/day, continuous and categorical); • drinking status (drinker/non-drinker, dichotomous); • quantity (no. of glasses per day and maximum no. in a 24-hour period); • type of alcohol and consumption frequency; • days of the week alcohol consumed; and • place of consumption.

Chapter 4: Methodology and Study Design 83 Side-by-side boxplots compared the baseline alcohol intake of the intervention and control groups using the continuous alcohol variable. As commonly seen in the literature, alcohol intake data were highly skewed and consequently analysis of the data was often performed using the categorical form. Bar graphs enabled a comparison of intake for the intervention and control groups using the categorised alcohol variable. To provide a more accurate reflection of alcohol intake for women who chose to drink, all non-drinkers (those with zero intake reported for the previous month) were excluded from the analysis (using the continuous alcohol intake variable) and the median daily alcohol intakes were presented for each group.

Independent t-tests, Pearson’s Chi-squared tests, Fisher-Freeman-Halton tests and/or Fisher’s exact tests, were applied to identify any imbalances between the intervention and control groups at baseline.

4.4.3 Lost to follow up (LTFU) – missing data A lost to follow up (LTFU) analysis was performed to ensure data were missing completely at random (MCAR) and not for any other reason. This satisfied an important test assumption for the change over time analysis (see Section 4.4.8). Many of the variables (and an associated number of categories in each) presented for LTFU analysis required recoding prior to testing. This was to avoid cell counts of zero disrupting analysis and to reduce the number of small cell counts (< 5). Additionally, some continuous variables were recoded into categorical variables, as the data were not normally distributed. Continuous variables were compared using t- tests with two-tailed significance. Comparison of categorical variables was assessed using Pearson Chi-squares with two-sided significance, when cell counts were greater than five. For cell counts of less than five, Fischer’s exact tests were used for tables of 2 x 2 and Fisher-Freeman-Halton tests for tables larger than 2 x 2 with small cell counts. Missing data points were not included in the analysis.

4.4.4 Binary logistic regression To identify any differing characteristics between women who abstained from alcohol consumption (non-drinkers) within the month prior to baseline interview and those who consumed alcohol (drinkers), binary logistic regression (BLR) was applied. BLR allowed for the adjustment of multiple variables in the same model.

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Several assumptions must be met when performing this type of analysis, including: 1) the dependent variable must be dichotomous (drinking status: non- drinker vs. drinker), 2) one or more independent variables are either continuous or categorical, 3) all variables must be mutually exclusive and exhaustive, 4) there must be a minimum of 15 cases per independent variable, 5) there must be no linear relationship between the continuous independent variables and the logit transformation of the dependent variable, 6) there must be no outliers, and 7) there must be no multicollinearity among the independent variables (Laerd Statistics, 2013a; Statistics Solutions, 2017).

To prepare the data for analysis, categories were collapsed where necessary to avoid small cell counts, which is undesirable for BLR. To test assumptions prior to analysis, the linearity of the continuous variables with respect to the logit of the dependent variable (assumption 5) was assessed via the Box-Tidwell procedure (Box & Tidwell, 1962). Outliers were removed as necessary and the models re-run if standardised residuals were > 3 or < -3 (assumption 6) (PennState Eberly College of Science, 2017). Multicollinearity checks led to the removal of certain independent variables (i.e., FACT-G total score and employment) and the separation of variables into two separate models. For example, income and employment could not be placed in the same model, as unemployed people are generally on a low income.

The BLR models utilised backward step likelihood ratios to analyse socio- demographic characteristics (Model One) and general health characteristics (Model Two) of the cohort in relation to drinking status. Independent predictor variables in Model One included group, age, country of birth (COB), income, education, and marital status. Independent predictor variables included in Model Two were the FACT-G wellbeing subdomains (physical, social, emotional, and functional), BMI, waist circumference risk, smoking status, physical activity level, number of co- morbidities, and time since diagnosis.

4.4.5 Normative data comparison To compare the drinking behaviours of women in the study population to women in the general Australian population, normative data from the National Health Survey: 2014-15 (ABS, 2015) were drawn upon. Two patterns of alcohol consumption were considered for comparison based on the 2009 National Health and Medical Research Council (NHMRC) guidelines to reduce health risks associated

Chapter 4: Methodology and Study Design 85 with alcohol consumption for healthy men and women. These were: 1) single occasion risk and 2) lifetime risk for adults. The latter was considered particularly important for this cohort. Single occasion risk refers to individuals who consume a number of drinks without their blood alcohol concentration returning to zero in between drinks. The NHMRC (2009) guideline states that for healthy men and women “drinking no more than four standard drinks on a single occasion reduces the risk of alcohol-related injury arising from that occasion” (p. 5). Lifetime risk considers the consumption of two or more standard drinks per day on average (ABS, 2015). The NHMRC (2009) guideline states that for healthy men and women “drinking no more than two standard drinks on any day reduces the lifetime risk of harm from alcohol-related disease or injury” (p. 2). Study variables that identified participants who consumed alcohol “every day” and two or more drinks on days when drinking, were combined to identify participants who met or exceeded the lifetime risk guideline.

There were some differences in data collection methods between datasets. The data collection periods differed, and normative data were concerned with the previous 12 months’ intake, while the study data assessed intake only over the previous month. Furthermore, normative data alcohol intake was assessed using “standard drinks” while study data used the food frequency questionnaire (FFQ), which assessed intake using “glasses” of alcohol consumed. For example, when considering wine consumption, on average, a standard drink of red wine is considered 100ml (or 10 grams of ethanol), whereas the FFQ provides the example that “1 bottle wine (750ml) = 6 glasses” to guide the participants’ estimation of their intake. This could result in underestimation of intake in the study data compared to the normative data, which considers one bottle of wine to be approximately 7.5 standard drinks. Despite these differences in data collection methods, this comparison provided a useful evaluation of how this sample of women previously treated for breast cancer consumed alcohol compared to those in the general population.

4.4.6 Ordinal logistic regression - bivariate analysis For bivariate analyses, given that the data were not normally distributed, associations were analysed using the categorical variable for alcohol intake. The previously categorised alcohol intake variable, which was ordinal in nature, was

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collapsed from six into four categories to ensure small cell counts did not interfere with running the tests. The categories were nil intake, > 0 to ≤ 10 grams per day, > 10 to ≤ 20 grams per day, and > 20 grams per day. To retain the ordinal nature of the dependent variable and in preparation for modelling that explicitly defined a dependent variable, bivariate analysis was undertaken using logistic regression as opposed to Chi-square. Most of the independent variables of interest; for example, smoking status, employment status, BMI, and number of comorbidities present, were also collapsed after reviewing cross tabulations to identify small cell counts.

4.4.7 Ordinal logistic regression - multivariable analysis For multivariable analysis, a multivariable modelling approach utilising ordinal logistic regression was selected to take into consideration a range of variables as identified in the literature that potentially relate to alcohol intake and to adjust for possible confounding. Ordinal logistic regression requires four assumptions to be met. First, the dependent variable must be ordinal. Second, the independent or predictor variables being tested can be either continuous or categorical. Third, there should be no multicollinearity between independent variables, meaning the assumption of multicollinearity has been tested for each independent variable. To test for multicollinearity, dummy variables for each independent variable included in the initial model were created and linear regression statistics (collinearity diagnostics) were run to obtain tolerance values. A tolerance value of less than 0.1 might indicate a potential collinearity issue between independent variables. Finally, the proportional odds assumption, which ensures each independent variable has the same effect at each cut-point of the ordinal dependent variable, must not be violated (Laerd Statistics, 2013b). All assumptions were met either prior to testing or during testing.

Independent variables considered for the analysis (as informed by the literature review and the underpinning theoretical model) were first reviewed using cross- tabulation to identify empty or small cell counts. The variables were then collapsed to ensure small cell counts would not interfere with running the model before bivariate analysis using the alcohol dependent variable (categorical: ordinal) and the independent variables (continuous and categorical) of choice were performed. Independent variables of interest considered in this analysis were age; marital status; time since most recent cancer diagnosis (in months, categorised); education attainment; employment status at diagnosis; current gross household income

Chapter 4: Methodology and Study Design 87 categorised into low, middle, and high income; smoking status categorised as a simple binary variable; number of co-morbidities present represented as five categories; FACT-G quality of life scores including sub-domain scores for physical, social, emotional, and functional wellbeing, each split into quartiles (lower scores indicate poorer quality of life) (Cella et al., 1993); Pittsburgh Sleep Quality Index score (Buysse et al., 1989), Greene Climacteric Scale subscale score for vasomotor concerns (Greene, 1998); body mass index (BMI) categorised as a binary variable; waist circumference risk and waist-to-hip ratio risk according to WHO (2011) guideline classifications and physical activity levels as assessed by the IPAQ Short form (Craig et al., 2003) for the previous seven days and presented as low, moderate, and high. Following bivariate analysis, a strategy of manual backward stepwise elimination was adopted using all independent variables that returned a p-value of < 0.2. The results of the bivariate analysis and the final model are presented in the next chapter.

Ordinal logistic regression results were expressed as odds ratios, with their associated 95% confidence intervals. Statistical significance, as a guide to the reliability of the findings, was reported with results declared “significant” at the conventional level of 5% or less (two-tailed hypothesis tests). Change in the Nagelkerke’s pseudo-R2 statistic at each step of the backward elimination was used to compare relative contributions of the variables in each model.

4.4.8 Change over time – generalised estimating equations Change over time for the repeated measure of alcohol intake was assessed in several ways. This was due to the nature of the dependent data, which limited the scope of testing that could be used to assess change in alcohol intake over time. For example, the dependent variable data were highly skewed due to the large number of non-drinkers in the cohort. This made it difficult to identify whether intake changed as a result of the intervention for women who did consume alcohol. The alcohol intake data also contained outliers (some extreme) that were deemed real and therefore needed to remain in the analysis. Hence, the ordinal categorical variable was chosen over the continuous variable for the primary analysis because the result would provide a more detailed and informative picture of the association between change in alcohol intake over the different time-points. In contrast, the Friedman Test, which is the non-parametric alternative to the repeated measures ANOVA test,

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is more appropriate for application with skewed continuous data. Unlike the ordinal logistic generalised estimating equation (GEE) (Liang & Zeger, 1986) that was used, the Friedman test cannot account for a study design with two groups (intervention and control) and it is not appropriate to test each group separately (Bland & Altman, 1995). Furthermore, the ordinal logistic GEE was adopted over the multinomial logistic regression, which is also used for categorical variables with more than two categories; however, multinomial logistic regression assumes that the categories are not ordered in any meaningful way.

GEE produces an advanced statistical model that can capture the change in the ordinal categorical outcome variable (alcohol intake) through specifying the single, time-related indicator (time [repeated-measures intervals, i.e., T1, 0 weeks; T2, 12 weeks; T3, 24 weeks]). GEE is an extension of the generalised linear model designed to accommodate categorical variables (i.e., logistic regression) for repeated measures (Heck, Thomas, & Tabata, 2012). The underlying mathematical models and estimation methods that are required to analyse categorical outcomes are very different to those required for continuous outcomes, and findings are reported as odds ratios. Categorical-outcome models can require more compromises during the investigation process than is typically seen with continuous-outcome models (Heck et al., 2012). For example, it is difficult to provide odds ratios for each possible comparison within an interaction; that is, alcohol intake of each group at each time- point. When testing the interaction in the GEE, exploring all possible comparisons leads to multiple comparison problems. To avoid this problem, one group can be set as the referent group to compare all other groups. Hence, to ensure the most useful output was produced for interpretation, the T1 intervention group was set as the referent group.

To prepare the data for GEE analysis, time observations (T1, T2, T3) were organised vertically (i.e., in long form), which means that each subject had multiple lines (one for each time-point) (Heck et al., 2012). This differed to the data form used for all previous analysis (which were organised horizontally or in wide form), for which there was only one line for each subject. Fortunately, SPSS provides options for data restructuring. Thus, prior to running GEE analyses, a new long form dataset was created that included all necessary variables and the newly-created “time” variable. Several steps were taken to define the model before conducting the

Chapter 4: Methodology and Study Design 89 analysis; for example, identifying the type of outcome, defining the regression model as ordinal logistic, and selecting the correlation structure as autoregressive or AR(1) (Heck et al., 2012).

As with any statistical test, there are assumptions that must be met when using GEE. First, cases are assumed dependent (repeated measures) within subjects and independent between subjects (IBM Knowledge Center, n.d.). Second, any missing data (i.e., LTFU) must be deemed missing completely at random (MCAR) (Liang &

Zeger, 1986). To test this assumption, the LTFU T2, and T3 p-values were considered and determined non-significant, indicating that the data were MCAR. All assumptions were met prior to running analysis.

In brief, the change in alcohol intake over time was assessed in several ways using descriptive statistics, as well as advanced modelling techniques to provide the most informative picture. An analysis of the continuous alcohol variable compared at each time-point was presented using side-by-side boxplots that highlighted the outliers. In addition, crude percentages of the categorical (ordinal) alcohol variable at each time-point were provided, and finally, an ordinal logistic generalised estimating equation regression model was applied. The GEE was adopted over all other tests for its ability to retain the meaningful order of the categorical dependant variable, while also accounting for the intervention and control groups.

4.5 STUDY 2 – QUALITATIVE ALCOHOL STUDY

4.5.1 Research questions Study 1, with its quantitative analysis of alcohol use, raised some interesting questions in relation to the breast cancer cohort. Study 2 of this PhD study provided the opportunity to ask questions that delved deeper into the quantitative findings, whilst providing an environment in which the participant felt comfortable to share. Study 2 utilised qualitative research techniques to answer the PhD research questions, framed by Precede, for the purpose of meeting the overarching study aim outlined in Section 4.2. Like Study 1, Study 2 asked the following questions:

Among Australian women who have been treated for breast cancer: 1. What is the pattern (frequency, quantity, type, and place) of alcohol use amongst Australian women treated for breast cancer who consume alcohol?

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2. What are the demographic, psychosocial, and health-related factors associated with alcohol use?

3. Is a tailored lifestyle intervention associated with change in alcohol-related health behaviours in this population?

4.5.2 Sample and sampling technique Study 2 participants had completed the WWACP study in its entirety. Where participants had been randomised to the control group, upon completion they had been posted a hard copy of the WWACP handbook and all associated factsheets. Hence, at the time of interview, all Study 2 participants had received the WWACP program in some form (i.e., electronic and hardcopy, fully supported by a consultation nurse for women in the intervention group or an unsupported hardcopy of the WWACP program for women in the control group).

Purposive, maximum variation sampling ensured that all perspectives were covered (Palys, n.d.), and that a well-balanced sample of women from both arms of the study who consumed more than the recommended amount of alcohol, moderate amounts, and those who abstained from alcohol consumption, were represented. This allowed for the complexity and breadth of alcohol-related issues in this population to be thoroughly explored.

Previous research in similar cohorts that asked similar questions (D. J. Anderson et al., 2011; Camp, 2008; Lim, Gonzalez, Wang-Letzkus, Baik, & Ashing- Giwa, 2013; McCarthy et al., 2013; Tracy, 2012) reported that a sample of between 10 and 26 participants would be adequate for Study 2. In this context, the quality of the data is often considered more informative than the number of participants who provide data. That is, the final sample size is often determined by the richness of data gathered and analysed along the way (Tracy, 2012).

4.5.3 Recruitment strategy All participants who had progressed through the 24-week randomised control trial (intervention and control arm, post-Week 24, T3) were eligible to participate in the alcohol study. After a minimum one-month grace period, potential participants were approached for Study 2. Potential participants were obtained from one of two lists. The first list was of those participants who had been assigned to myself as their research assistant for all parent study data collection. The reason for this was to take

Chapter 4: Methodology and Study Design 91 advantage of previously-built rapport with the participant. The second list comprised potential participants in the WWACP with whom I’d had no previous interactions. This was obtained from a fellow WWACP QUT research assistant. Participants on both lists were invited (via email) to participate in the alcohol-specific qualitative interviews. Details of the study, ethical clearance, and consent forms were provided to potential participants. Copies of these are available in Appendix F, Appendix G, and Appendix H. Given the short two and a half month recruitment period dictated by PhD enrolment and associated timelines, care was taken to ensure representative and knowledgeable participants were recruited, whilst ensuring a sufficient number of participants was recruited to obtain meaningful data (Tracy, 2012).

4.5.4 Data collection The semi-structured interviews took place at a time mutually agreed upon by the participant and interviewer (myself). In keeping with the parent study, virtual interviews were chosen as the primary contact method as this reduced costs and more importantly, allowed the scope of Study 2 to be widened beyond the greater Brisbane region. As with the virtual consultation methods used in the parent study for the collection of quantitative data, the literature suggests that virtual methods for conducting research-based qualitative interviews are valid and effective (Turney, 2008).

The interviews drew on the rapport established during my previous interactions with the participants in my role as research assistant and were sensitive to the recent health issues that these women had faced. Semi-structured interviews, together with the recursive interview technique, were used to stimulate discussion rather than dictate it (Tracy, 2012). This technique is conversational in style, and thus involved much open questioning, which allowed the participant to influence the flow, and to some extent, the content of the interview. This form of guided conversation required good skills of social interaction from the interviewer, so as to avoid making the participant feel they were being interrogated (Draper & Swift, 2011). Although a set line of questions was on hand, it was my role as interviewer to welcome any new deviations that arose during the interviews, thereby treating participants and their individual situations as unique (Gilgun & Sussman, 2014). All interviews were audio recorded and transcribed with consent from the participants.

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Stem questions were consistent with Green and Kreuter’s (2005) Precede model, which draws upon social cognitive theory (which supported the parent program), in addition to insights from several other relevant theories. The model was used to help interrogate alcohol-related behaviours through the mining of potential predisposing, enabling, and reinforcing factors that ultimately might have influenced alcohol-related choices and behaviour in this population. The following are examples of qualitative questions that were asked (see Appendix I for the interview protocol and Appendix J for a detailed list of the qualitative questions).

1. Describe for me how you view alcohol? … and is this view the same as what you had before your breast cancer diagnosis?

2. Since completing treatment, would you say that your alcohol consumption patterns have returned to what they were before treatment? ... or before diagnosis? If no, how have they differed?

3. Do you find there are certain times of the day that you are more likely to have a drink? … What is your main desire for having a drink at this time?

4. Are there any situations in which you are more likely to drink?

5. Do you have any traditions that you do when you have a drink? That is, sit down and put your feet up/recap the day with your partner/consume some cheese and crackers.

6. How frequently would you purchase alcohol, either for home or when outside of the home?

7. Have you ever been provided information about alcohol consumption and breast cancer? If so, can you identify where or from who you received information?

After completion of all interviews, audio files were securely sent for transcription to Rev (https://www.rev.com/transcription), a company specialising in verbatim audio transcribing services. Approximately 17 hours of audio required transcription.

To ensure data quality and trustworthiness were maintained during the collection process, a number of steps were adhered to (Saumure & Given, 2008). For example, whilst undertaking fieldwork, overt observations were also mindfully

Chapter 4: Methodology and Study Design 93 recorded as field notes. This helped me to make sense of the information arising from audiotaped interview data, thus adding richness to the findings (Tracy, 2012). From participant observations to headnotes, these notes were transformed from raw records to formal, typed field notes (Tracy, 2012).

4.6 STUDY 2 – QUALITATIVE DATA ANALYSIS

Once all of the transcribed manuscripts were returned and saved, the following analysis process was undertaken to thematically analyse the data. All manuscripts were de-identified with an alias name assigned, reviewed against the original audio, and corrected where necessary. The transcriptions were then imported into NVivo for Mac to assist with ordering of the data for thematic analysis. All analysis was undertaken using NVivo for Mac version 11.4.0 (2015). Primary-cycle coding (Tracy, 2012), which embodied initial themes consistent with the alcohol study interview questions, was then undertaken as a first pass through and referred to as nodes in NVivo. Approximately 50 codes were initially created, and then adjusted before additional codes were added at the conclusion of the first pass. These early themes were discussed intensively with the PhD supervisors before the initial codes and nodes were condensed (removed or collapsed) before a second pass. Additional umbrella codes/nodes were also created, based on Precede theoretical themes, such as QoL, health, genetics, behaviour, and environment. The second pass involved an intensive review of the first pass node content and consideration of whether and how the content aligned with Precede using the model’s theme indicators for guidance. The third pass was undertaken to determine whether coded content/behaviour identified was predisposing, reinforcing, and/or enabling in nature. A full list of the codes utilised for thematic analysis is available in Appendix K.

4.6.1 Ensuring rigour The concept that describes the worth or “goodness” of qualitative research, is “rigour” (Emden & Sandelowski, 1998). Rigour is the qualitative research equivalent of robustness (Emden & Sandelowski, 1998), although the concepts of reliability and validity are anti-ethical to the philosophies underpinning much quantitative research. Hence, rigour is considered more a matter of transparency and trustworthiness, dependability and confirmability (Emden & Sandelowski, 1998; Tramm, 2010), than “measurable” generalisability. While a precise definition has long been debated

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(Emden & Sandelowski, 1998; Krefting, 1991), the criterion for dependability examines whether findings drawn from the data are plausible and consistent (Krefting, 1991; Liamputtong & Ezzy, 2009; Tramm, 2010). To satisfy this criterion, repeat listening to the audio recordings, re-reading of transcripts, adherence to code- recode procedures, and consistent consultation with supervisory team was undertaken.

Confirmability refers to the ability of the researcher to provide a clear link between the conduct of the study, its findings and the researcher’s explanations (Tramm, 2010). For example, if the study is reviewed by a person external to the study, that person should be able to follow the natural progression of events and understand the reasoning behind decisions (Krefting, 1991). Exposing the decision trail enhances the dependability and confirmability of a qualitative research project and appropriate practices were undertaken to ensure this (Krefting, 1991). For example, a codebook that provided the rationale for, and full descriptions of, each code was developed, periodically updated, and discussed regularly with the principal supervisor; this formed a critical part of the decision trail (Fade & Swift, 2011). Moreover, all aspects of the method, results, findings and interpretation were discussed with the thesis supervisors, as well as with extended members of the WWACP team.

Lastly, I often reflected upon my own role in the interview process and how this might have influenced the data obtained and the analysis process (Draper & Swift, 2011; Tramm, 2010). This reflexive aspect of the analysis was also discussed regularly with my supervisory team.

4.7 ETHICAL CONSIDERATIONS

The Women’s Wellness after Cancer Study was granted ethics approval by the Human Research Ethics Committee of Queensland University of Technology (QUT ethics approval number 1300000335) in accordance with the NHMRC’s guidelines. The project was also granted ethics approval from all participating sites. The WWACP research project was deemed low risk as it posed minimal risk of harm to participants. As such, low risk physical activity-related harm, such as muscle soreness might be experienced by the participant. To address this potential risk, suggested exercise regimens with varied intensity levels were gradually introduced

Chapter 4: Methodology and Study Design 95 throughout the course of the program for those participants allocated to the intervention group. Regular stretching was also encouraged and participants were provided with contact details of research staff should there be any additional concerns.

On recruitment, all participants received information about the WWACP research project, including details of the study design, expected benefits, and potential risks. The consent form confirmed that all study information was read and clearly understood, including that participation in the study was voluntary and participants were free to leave at any time. Participants were informed that all data collected remained confidential, de-identified, and securely stored for the duration of the project and beyond to protect the participant’s privacy.

As this PhD sub-study (Study 2) was attached to the parent program and only minor variations to the data collection processes were anticipated, the sub-study only required a variation (amendment) to the original ethics form (Ethics Clearance Number 1300000335). Participants for the sub-study were sourced from the existing WWACP database hosted by Queensland University of Technology. The variation request was therefore submitted for ethics approval from the Human Research Ethics Committee of Queensland University of Technology and the variation was granted. Variation submission number 1300000335 was granted 20th October 2015 (see Appendix F).

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Chapter 5: Quantitative Results

5.1 INTRODUCTION

This chapter presents the quantitative results for this PhD study for the purpose of answering the research questions outlined below.

Research Questions

1. What is the pattern (frequency, quantity, type, and place) of alcohol use amongst Australian women treated for breast cancer who consume alcohol?

2. What are the demographic, psychosocial, and health-related factors associated with alcohol use?

3. Is a tailored lifestyle intervention associated with change in alcohol-related health behaviours in this population?

Section 5.2 of this chapter outlines the results for Study 1 of this PhD project. Section 5.2.1 provides a description of the WWACP breast cancer participants. This includes the CONSORT diagram outlining the WWACP attrition rates, a description of the socio-demographic attributes, and the general health of the participants at baseline. This section also presents the results of statistical analysis for participants considered lost to follow up (LTFU) at Time-points 2 and 3 compared to retained participants. Section 5.2.2 provides the results pertaining to the alcohol-specific research questions, and also provides a brief comparison of the sample’s alcohol intake versus women in the general Australian population, using normative data. The chapter concludes with a summary of the findings (Section 5.3).

5.2 STUDY 1 - QUANTITATIVE RESULTS

5.2.1 Description of the sample population Attrition rates A total sample of 351 (N = 351) women consented to participate and completed the T1 survey before randomisation into intervention (n = 175) or control (n = 176) groups. Sixty-seven (n = 67, 19%) women withdrew before completing the

Chapter 5: Quantitative Results 97 T1 RA interview during which data on cancer type, among other important information, were collected. For the purpose of this PhD, which only considered women with a history of breast cancer, an additional 15 (4%) women were removed from the analysis (eight with gynaecological cancers and seven with a blood cancer). This left a total of n = 269 (77%, intervention n = 138, control n = 131) participants with a history of breast cancer remaining at T1 with complete data.

Twenty-three participants (n = 23; intervention n = 14; control n = 9) withdrew before completion of T2, leaving a cohort of 246 (intervention n = 124 and control n = 122). This was an attrition rate of 8.6% since baseline. A further 12 (intervention n

= 3; control n = 9) participants withdrew prior to study completion at T3, leaving a total of n = 234 (intervention n = 121 and control n = 113) completing the study in its entirety. For breast cancer participants only, this equates to an attrition rate of 4.9% from T2 and 13.0% since baseline.

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Key T1 Time-point 1; baseline; 0 weeks I Intervention T2 Time-point 2; intervention end; 12 weeks SC Standard care T3 Time-point 3; follow-up; 24 weeks KS Key survey RA Research assistant appointment

Registered interest n = 798

Not eligible n = 13 Not consented n = 373 Withdrawn before consent n = 31 Consent to participate n = 381

Consented but not Declined participation completed T1 survey n = 23 after consent n = 7 Randomised to Intervention or Standard care n = 351 T1 Withdrawn (post KS) n = 67 I = 30 SC = 37 Intervention n = 175 Standard care n = 176 Non BCa n = 15 I = 7 SC = 8 T1 Total removed or KS, RA T1 Complete n = 138 KS, RA T1 Complete n = 131 withdrawn n = 82

T1 Total n = 269 T2 Withdrawn n = 23 I = 14 KS, RA T2 Complete n = 124 KS, RA T2 Complete n = 122 SC = 9

T2 Total n = 246 T3 Withdrawn n = 12 I = 3 KS, RA T3 Complete n = 121 KS, RA T3 Complete n = 113 SC = 9

T3 Total n = 234

Figure 5.1. WWACP study CONSORT diagram adjusted for PhD study of breast cancer only participants

Chapter 5: Quantitative Results 99 Description of socio-demographic attributes and general health at baseline Table 5.1 and Table 5.2 present a description of socio-demographic attributes and some general health indicators for participants at baseline. Descriptive statistics are presented for the cohort as a whole, as well as split by group, to identify any imbalance between intervention and control groups at baseline. Relevant statistical tests were applied to identify any between group differences.

The mean age of the participants was 53.5 years (SD 8.3) and over 80% identified as post-menopausal at baseline (80.7%, n = 217). A larger proportion of control participants were aged between 45 and 54 years, 47.7% (n = 62) compared to the intervention group 34.3% (n = 47); however, this was not statistically different (p = .121). The majority (76.6%, n = 206) of participants were either married or in a de facto relationship. The remainder identified as either separated or divorced (10.4%), single (9.3%) or widowed (2.2%).

The majority of participants were born in Australia (66.5%) and spoke English as their first language (91.1%). Only one participant identified as an Indigenous Australian. Over 50% of the cohort identified as having north-west European ancestry; however, a further 19.3% selected “other” and primarily stated countries that are considered part of north-western Europe, including Ireland, the United Kingdom, the Netherlands, and Germany. Participants resided throughout Australia; however, most were located in New South Wales (26.8%, n = 72), Queensland (27.5%, n = 74), and Victoria (17.1%, n = 46). No participants resided in the Northern Territory.

The cohort was well educated. Almost 60% had attained tertiary qualifications (n = 158), 34.9% of whom reported undergraduate or college degrees and 23.8% of whom reported postgraduate studies. The educational balance between the groups appeared uneven; however, this was not reflected statistically (p = .246). The intervention group compared to the control group had more participants with trade certificates or diplomas (25.5% vs. 18.6%) and less participants who had attained postgraduate qualifications (19.7% vs. 28.7%). Employment status prior to cancer diagnosis revealed that the majority of participants were employed on a full-time (43.5%, n = 117) or part-time (36.4%, n = 98) basis. Gross annual household income data were categorised into low, middle, and high income according to the Victorian Population Health Survey classifications (Department of Health and Human

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Services, 2017). More than half of the cohort (53.5%) were classified as high income.

Indicators of general health that were deemed relevant to this PhD study according to the literature included time since diagnosis, medical history, smoking status, quality of life scores, physical activity level, and biophysical measures.

At study commencement, the majority of participants had been diagnosed for between six to 24 months (6-12 months, 27.5%, n = 74; 12-24 months, 36.8%, n = 99). The intervention group compared to the control group had more participants with fewer months since diagnosis (≤ 6 months, 17.0% vs. 6.9%) and less participants in the >6 to 12 months category (23.7% vs. 32.3%). This imbalance was not statistically significant (p = .065).

There was a fairly even split of participants with either no co-morbidities, only one, two, or three at 17.5%, 17.8%, 20.8%, and 16.7%, respectively. A large proportion of the participants had either never smoked (67.7%, n = 182) or had smoked in the past (27.9%, n = 75), compared to only a small number of current smokers (3.7%, n = 10). This pattern was evident across both the intervention and control groups.

The full cohort mean FACT-G total score was 79.9 (SD 13.9) and both groups appeared well balanced (p = .689). Ranges indicated that the control group had slightly lower scores for all sub-domains compared to the intervention group; however, this was not reflected in the mean scores. In relation to how physically active the participants were, the intervention group had less participants reporting low and high physically activity levels compared to the control group, with 5.1% compared to 9.2% and 19.9% compared to 23.8%, respectively. This imbalance was not statistically significant (p = .276). Overall, the highest proportion of participants identified as having moderate physical active levels (70.3%).

BMI indicated that most participants were above the healthy normal weight range and were classified as either overweight (34.9%, n = 94) or obese Class I, II, or III (collectively 26.3%, n = 71). The intervention and control groups were reasonably well balanced in terms of BMI (p = .783); however, the intervention group had slightly more participants classified as underweight (2.2%, n = 3 compared to 0.8%, n = 1) and obese class III (5.1%, n = 7 compared to 2.4%, n = 3). Almost three-

Chapter 5: Quantitative Results 101 quarters of the sample were identified as being at risk of adverse health conditions due to their current waist circumference, with close to 50% (49.8%, n = 134) identified as being at substantially increased risk of developing health issues. Less than one quarter of the sample (24.5%, n = 66) had a waist measure that was not associated with adverse health risk. Just over half of the sample (51.7%, n = 139) had a WHR of less than 0.85, which is not associated with any adverse health risks.

Overall, both the intervention and control groups were well-balanced at baseline.

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Table 5.1 Descriptive Statistics for Continuous Variables for the Sample of WWACP Women With a History of Breast Cancer (N = 269)

Intervention (n = 138) Control (n = 131) p- All (N = 269) Variable N M (SD) Range N M (SD) Range value N M (SD) Range Age (years) 137 53.5 (8.8) 34-72 130 53.5 (7.7) 39-74 .950 267 53.5 (8.3) 34-74 Missing 1 1 2

Quality of life (FACT-G) Physical wellbeing (PWB) 136 22.2 (3.8) 10.0-28.0 130 21.4 (4.4) 5.0-28.0 .087 266 21.8 (4.1) 5.0-28.0 Social/family wellbeing (SWB) 136 20.0 (5 .5) 5.0-28.0 129 20.7 (6.5) 3.5-28.0 .390 265 20.4 (6.0) 3.5-28.0 Emotional wellbeing (EWB) 135 18.3 (3.4) 7.0-24.0 130 18.6 (4.0) 1.0-24.0 .458 265 18.5 (3.7) 1.0-24.0 Functional wellbeing (FWB) 136 19.8 (4.6) 8.0-28.0 129 19.2 (5.7) 2.3-28.0 .326 265 19.5 (5.2) 2.3-28.0 FACT-G Total Score 135 80.2 (12.8) 47.0-107.0 130 79.5 (15.0) 35.3-103.0 .689 265 79.9 (13.9) 35.3-107.0

Note. Test statistic: Independent t-test

Chapter 5: Quantitative Results 103 Table 5.2 Descriptive Statistics for Categorical Variables for the Sample of WWACP Women With a History of Breast Cancer (N = 269)

Intervention Control All

(n = 138) (n = 131) p-value (N = 269) Variable N % N % N % Age (years) grouped 25 – 34 years 2 1.5 0 0.0 .121 b 2 0.7 35 – 44 years 25 18.2 15 11.5 40 14.9 45 – 54 years 47 34.3 62 47.7 109 40.5 55 – 64 years 49 35.8 41 31.5 90 33.5 65 years and over 14 10.2 12 9.2 26 9.7 Missing 1 1 2 0.7 Country of birth Australia 91 66.4 88 67.7 .825 a 179 66.5 Elsewhere 46 33.6 42 32.2 88 32.7 Missing 1 1 2 0.7 Aboriginal, Torres Strait or South Sea Islander Yes 0 0.0 1 0.8 .487 c 1 0.4 No 136 100.0 128 99.2 264 98.1 Missing 2 2 4 1.5 Language other than English Yes 8 5.9 12 9.2 .309 a 20 7.4 No 127 94.1 118 90.8 245 91.1 Missing 3 1 4 1.5 Ancestry Oceanian 16 11.9 15 11.6 .815 b 31 11.5 North-West European 68 50.7 68 52.7 136 50.6 Southern and Eastern European 13 9.7 18 14.0 31 11.5 North African and Middle Eastern 1 0.7 0 0.0 1 0.4

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Intervention Control All

(n = 138) (n = 131) p-value (N = 269) Variable N % N % N % Ancestry (continued) South-East Asian 4 3.0 4 3.1 8 3.0 Southern and Central Asian 1 0.7 1 0.8 2 0.7 People of the Americas 2 1.5 0 0.0 2 0.7 Other 29 21.6 23 17.8 52 19.3 Missing 4 2 6 2.2 Marital status Married or De facto 105 77.2 101 78.3 .675 b 206 76.6 Separated or divorced 14 10.3 14 10.9 28 10.4 Widowed 2 1.5 4 3.1 6 2.2 Single 15 11.0 10 7.8 25 9.3 Missing 2 2 4 1.5 Highest education level obtained Primary to year 10 completed 10 7.3 12 9.3 .246 a 22 8.2 Year 12 completed 12 8.8 15 11.6 27 10.0 Trade, certificate or diploma 35 25.5 24 18.6 59 21.9 University or college degree 53 38.7 41 31.8 94 34.9 Postgraduate degree 27 19.7 37 28.7 64 23.8 Missing 1 2 3 1.1 Employment status (prior to diagnosis) Full-time employment 59 43.1 58 44.6 .812 b 117 43.5 Part-time employment 51 37.2 47 36.2 98 36.4 Home duties 11 8.0 10 7.7 21 7.8 Retired 14 10.2 11 8.5 25 9.3 Unemployed or unable to work 2 1.5 4 3.1 6 2.2 Missing 1 1 2 0.7

Chapter 5: Quantitative Results 105 Intervention Control All

(n = 138) (n = 131) p-value (N = 269) Variable N % N % N % Gross annual household income Low income (≤$40k) 12 9.2 7 5.6 .525 a 19 7.1 Middle income ($40k to $100k) 48 36.6 46 36.5 94 34.9 High income (>$100k) 71 54.2 73 57.9 144 53.5 Missing 7 5 12 4.5 Postcode New South Wales 43 31.2 29 22.1 .477 b 72 26.8 Victoria 19 13.8 27 20.6 46 17.1 Queensland 39 28.3 35 26.7 74 27.5 South Australia 15 10.9 12 9.2 27 10.0 Western Australia 13 9.4 18 13.7 31 11.5 Tasmania 7 5.1 7 5.3 14 5.2 Australian Capital Territory 2 1.4 3 2.3 5 1.9 Missing 0 0 0 0.0 Time since diagnosis (months) ≤ 6 months 23 17.0 9 6.9 .065 b 32 11.9 > 6 to 12 months 32 23.7 42 32.3 74 27.5 > 12 to 24 months 48 35.6 51 39.2 99 36.8 > 24 to 36 months 22 16.3 23 17.7 45 16.7 > 36 to 48 months 5 3.7 1 0.8 6 2.2 > 48 months 5 3.7 4 3.1 9 3.3 Missing 3 1 4 1.5 Smoking status Never smoked 91 66.4 91 70.0 .753 b 182 67.7 Smoked in the past 42 30.7 33 25.4 75 27.9 Regular smoker 3 2.2 5 3.8 8 3.0 Casual smoker 1 0.7 1 0.8 2 0.7 Missing 1 1 2 0.7

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Intervention Control All

(n = 138) (n = 131) p-value (N = 269) Variable N % N % N % Menopausal status Pre-menopausal 7 5.1 7 5.4 .860 a 14 5.2 Peri-menopausal 20 14.6 16 12.3 36 13.4 Post-menopausal 110 80.3 107 82.3 217 80.7 Missing 1 1 2 0.7 Co-morbidities (no. present) NIL present 26 18.8 21 16.0 .994 a 47 17.5 1 co-morbidity 24 17.4 24 18.3 48 17.8 2 co-morbidities 30 21.7 26 19.8 56 20.8 3 co-morbidities 22 15.9 23 17.6 45 16.7 4 co-morbidities 13 9.4 13 9.9 26 9.7 5 co-morbidities 11 8.0 12 9.2 23 8.6 6 or more co-morbidities 12 8.7 12 9.2 24 8.9 Missing 0 0 0 0.0 BMI Underweight (< 18.5 kg/m2) 3 2.2 1 0.8 .783 b 4 1.5 Normal weight (≥ 18.5 to 24.9 kg/m2) 46 33.8 47 37.3 93 34.6 Overweight (≥ 25 to 29.9 kg/m2) 50 36.8 44 34.9 94 34.9 Obese class I (≥ 30 to 34.9 kg/m2) 20 14.7 21 16.7 41 15.2 Obese class II (≥ 35 to 39.9 kg/m2) 10 7.4 10 7.9 20 7.4 Obese class III (≥ 40 kg/m2) 7 5.1 3 2.4 10 3.7 Missing 2 5 7 2.6 Waist circumference risk ≤ 80 cm no associated risk 32 23.4 34 26.2 .851 a 66 24.5 > 80 cm to 88 cm increased risk 36 26.3 31 23.8 67 24.9 > 88 cm substantially increased risk 69 50.4 65 50.0 134 49.8 Missing 1 1 2 0.7

Chapter 5: Quantitative Results 107 Intervention Control All

(n = 138) (n = 131) p-value (N = 269) Variable N % N % N % Waist-to-hip ratio risk < 0.85 no associated risk 73 53.3 66 50.8 .386 a 139 51.7 ≥ 0.85 substantially increased risk 64 46.7 64 49.2 128 47.6 Missing 1 1 2 0.7 Physical activity level Low 7 5.1 12 9.2 .276 a 19 7.1 Moderate 102 75.0 87 66.9 189 70.3 High 27 19.9 31 23.8 58 21.6 Missing 2 1 3 1.1 Notes. a Pearson’s Chi-square test of homogeneity; b Fisher-Freeman-Halton Test; c Fisher’s exact test.

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Comparison of retained versus lost to follow-up

Table 5.3 and Table 5.4 provide descriptive statistics for T2 and T3 participants who were lost to follow up (LTFU), compared to the retained cohort, at each time- point.

Time-point 2 lost to follow up

At T2 (Week 12, WWACP intervention completion), a small percentage (8%, n = 22) of the target population were LTFU. Table 5.3 compares the demographics and variables of interest for the retained sample (n = 247) and those participants LTFU (n = 22). There were no statistically significant differences (p > .05) between the retained sample at T2 and those LTFU, indicating that any missing data were missing completely at random. Over-interpretation of percentages should be avoided, as two people moving in either direction make up a 10% difference in the comparison. In general, results indicate that a higher proportion of Australian-born participants were LTFU (p = .057). A higher proportion of participants with lower education levels were LTFU (p = .089), as were participants with quality of life scores in the lowest quartile LTFU (p = .090).

Time-point 3 lost to follow up

At T3 (Week 24, follow-up), 13% (n = 35) of the target population were LTFU. Table 5.4 compares the demographics and variables of interest for the retained sample (n = 234) and those participants LTFU (n = 35). Again, there were no statistically significant differences (p > .05) between the retained sample at T3 and those LTFU, and over interpretation of percentages of small cell counts should be avoided. In general, the results indicate that a higher proportion of participants with lower education levels were LTFU (p = .203). A higher proportion of participants diagnosed between six to 12 months, and a smaller proportion diagnosed between 12 to 24 months prior to study commencement, were LTFU (p = .147). Finally, a higher proportion of non-drinkers were LTFU (p = .212).

In the absence of any identifiable patterns of missing data (non-significant results), all missing data were deemed missing completely at random (MCAR) for both time-points. This result ensures the MCAR test assumption for the change over time test is not violated.

Chapter 5: Quantitative Results 109 Table 5.3 Comparison of Retained Participants Versus Lost to Follow Up (LTFU) at Time-point Two

T2 retained sample T2 Participants p-value (n = 247) LTFU (n = 22) Variable N % N % Group Intervention 125 50.6 13 59.1 .446 a Control 122 49.4 9 40.9 Mean age ± SD 53.7 ± 8.2 52.1 ± 9.2 .387 b Country of birth Australia 160 65.3 19 86.4 .057 c Elsewhere 85 34.7 3 13.6 Missing 2 0 Marital status Married or de facto 190 77.9 16 76.2 .859 a Separated/divorced/widowed/single 54) 22.1 5 23.8 Missing 3 1 Highest education level obtained Year 12 completed or less 41 16.8 8 36.4 .089 d Trade, certificate or diploma 56 23 3 13.6 College/university/postgrad degree 147 60.2 11 50.0 Missing 3 0 Employment status (prior to diagnosis) Full-time employment 106 43.3 11 50.0 .879 d Part-time employment 91 37.1 7 31.8 Home duties/retired/unemployed/unable to work 48 19.6 4 18.2 Missing 2 0

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T2 retained sample T2 Participants p-value (n = 247) LTFU (n = 22) Variable N % N % Gross annual household income Low income (≤ $40,000) 16 6.8 3 15.0 .325 d Middle income (> $40,000 – $100,000) 88 37.1 6 30.0 High income (> $100,000) 133 56.1 11 55.0 Missing 10 2 2 Time since diagnosis (categorised) ≤ 6 months 28 11.5 4 18.2 .161 d > 6 to 12 months 65 26.7 9 40.9 > 12 to 24 months 95 39.1 4 18.2 > 24 months 55 22.6 5 22.7 Missing 4 0 Smoking status Current smoker/past smoker 78 31.8 7 31.8 .999 c Never smoker 167 68.2 15 68.2 Missing 2 0 Physical activity level - baseline High physical activity level 55 22.5 3 13.6 .687 d Moderate physical activity level 171 70.1 18 81.8 Low physical activity level 18 7.4 1 4.5 Missing 3 0 0 No. of co-morbidities present (categorised) Nil co-morbidities 45 18.2 2 9.1 .319 d 1 to 2 co-morbidities 96 38.9 8 36.4 3 to 4 co-morbidities 66 26.7 5 22.7 ≥ 5 co-morbidities 40 16.2 7 31.8 Missing 0 0

Chapter 5: Quantitative Results 111 T2 retained sample T2 Participants p-value (n = 247) LTFU (n = 22) Variable N % N % Quality of life: FACT-G Total score - baseline < 25th (scores < 72) 54 22.2 10 45.5 .090 d 25th to < 50th (scores ≥ 72 to 81) 64 26.3 4 18.2 50th to < 75th (scores ≥ 82 to 89) 52 21.4 5 22.7 ≥ 75th (scores ≥ 90) 73 30.0 3 13.6 Missing 4 0 Alcohol intake categorised - baseline NIL g/day 40 16.2 7 31.8 .339 d > 0 to ≤ 10 g/day 144 58.3 10 45.5 > 10 to ≤ 20 g/day 33 13.4 3 13.6 > 20 g/day 30 12.1 2 9.1 Missing 0 0 Note. a Pearson Chi-Square test; b t test; c Fisher’s Exact Test; d Fisher-Freeman-Halton Test

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Table 5.4 Comparison of Retained Participants Versus Lost to Follow Up (LTFU) at Time-point Three

T3 Retained Sample T3 Participants p-value (n = 234) LTFU (n = 35) Variable N % N % Group Intervention 121 51.7 17 48.6 .729 a Control 113 48.3 18 51.4 Mean age ± SD 53.6 ± 8.3 52.9 ± 8.5 .649 b Country of birth Australia 153 65.9 26 74.3 .328 a Elsewhere 79 34.1 9 25.7 Missing 2 0 Marital status Married or de facto 178 77.1 28 82.4 .488 a Separated/divorced/widowed/single 53 22.9 6 17.6 Missing 3 1 Highest education level obtained Year 12 completed or less 39 16.8 10 29.4 .203 a Trade, certificate or diploma 53 22.8 6 17.6 College/university/postgrad degree 140 60.3 18 52.9 Missing 2 1 Employment status (prior to diagnosis) Full-time employment 99 42.7 18 51.4 .532 a Part-time employment 88 37.9 10 28.6 Home duties/retired/unemployed/unable to work 45 19.4 7 20.0 Missing 2 0

Chapter 5: Quantitative Results 113 T3 Retained Sample T3 Participants p-value (n = 234) LTFU (n = 35) Variable N % N % Gross annual household income Low income (≤ $40,000) 16 7.1 3 9.4 .731 d Middle income (> $40,000 – $100,000) 84 37.3 10 31.3 High income (> $100,000) 125 55.6 19 59.4 Missing 9 3 Time since diagnosis (categorised) ≤ 6 months 26 11.3 6 17.1 .147 a > 6 to 12 months 60 26.1 14 40.0 > 12 to 24 months 91 39.6 8 22.9 > 24 months 53 23.0 7 20.0 Missing 4 0 Smoking status Current smoker/past smoker 73 31.5 12 34.3 .738 a Never smoker 159 68.5 23 65.7 Missing 2 0 Physical activity level - baseline High physical activity level 53 22.9 5 14.3 .480 d Moderate physical activity level 162 70.1 27 77.1 Low physical activity level 16 6.9 3 8.6 Missing 3 0 No. of co-morbidities present (categorised) Nil co-morbidities 44 18.8 3 8.6 .357 d 1 to 2 co-morbidities 88 37.6 16 45.7 3 to 4 co-morbidities 63 26.9 8 22.9 ≥ 5 co-morbidities 39 16.7 8 22.9 Missing 0 0

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T3 Retained Sample T3 Participants p-value (n=234) LTFU (n=35) Variable N % N % Quality of life: FACT-G Total score - baseline < 25th (scores < 72) 53 23.0 11 31.4 .336 a 25th to < 50th (scores ≥ 72 to 81) 61 26.5 7 20.0 50th to < 75th (scores ≥ 82 to 89) 47 20.4 10 28.6 ≥ 75th (scores ≥ 90) 69 30.0 7 20.0 Missing 4 0 Alcohol intake categorised - baseline NIL g/day 37 15.8 10 28.6 .212 d > 0 to ≤ 10 g/day 138 59.0 16 45.7 > 10 to ≤ 20 g/day 30 12.8 6 17.1 > 20 g/day 29 12.4 3 8.6 Missing 0 0 Note. a Pearson Chi-Square test; b t test; c Fisher’s Exact Test; d Fisher-Freeman-Halton Test

Chapter 5: Quantitative Results 115 5.2.2 Alcohol-specific analysis Description of alcohol intake patterns at baseline Research Question 1: What is the pattern (frequency, quantity, type, and place) of alcohol use amongst Australian women treated for breast cancer?

Baseline distributions of alcohol intake for both groups were fairly similar when assessed visually using side-by-side boxplots for the continuous variable and a bar chart for the categorised variable (see Figure 5.2 and Figure 5.3). Table 5.5 shows the median daily alcohol intake for the cohort as a whole was 3.9 g/day (range 0.0-76.9 g/day). Relevant statistical tests were applied to identify any between group differences.

o outliers; * extreme outliers Figure 5.2. Alcohol intake (grams per day) separated according to group with outliers shown (N = 269)

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Figure 5.3. Alcohol intake categorised and separated according to group (N = 269)

Participants who reported drinking the same beverage “every day” over the previous month accounted for 5.9% of the cohort. Please note, this did not take into account those participants who drank every day, but not necessarily the same beverage; hence, this finding could be underestimated. In considering the proportion of participants who reported drinking on each day out of the previous seven days irrespective of the type of beverage, 6.7% of the cohort were identified as daily drinkers, 6.3% were noted as drinking 5-6 days per week, and 30.9% abstained from alcohol consumption (refer to Figure 5.4).

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Figure 5.4. Alcohol intake (drinks per day) over the previous seven days

To assess the baseline alcohol intake in grams per day for participants who did choose to drink (intervention n = 110, control n = 112), all non-drinkers (17.5%, intervention n = 28, control n = 19) were excluded from the analysis. The median daily alcohol intake for drinkers was 5.2 g/day (range 0.26–52.3 g/day) for participants in the intervention and 6.1 g/day (range 0.26-76.9 g/day) for participants in the control group. Figure 5.5 highlights that the control group had more outliers.

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o outliers; * extreme outliers

Figure 5.5. Alcohol intake (grams per day) excluding non-drinkers, separated according to group with outliers shown (n = 222)

Almost two-thirds of the participants who reported alcohol consumption consumed between one and two glasses of alcohol per day (34.6% and 30.9%, respectively). Almost 50% of these participants reported drinking a maximum of 1-2 glasses over a 24-hour period during the preceding month. The intervention group had fewer participants who consumed a maximum of 3-4 glasses with 18.8% (n = 26), compared to the control group with 29% (n = 38). At baseline, the majority of participants reported drinking wine; therefore, the frequency of red and white wine consumption, including sparkling wines, is outlined in Table 5.5, Figure 5.6, and Figure 5.7. Bar charts for all of the alcohol types, grouped by intervention and control, are available in Appendix L.

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Figure 5.6. Baseline red wine intake separated according to group

Figure 5.7. Baseline white wine intake separated according to group

120 Chapter 5: Quantitative Results In the seven days preceding the interview, alcohol was most often consumed on a Friday, Saturday, and/or Sunday, with 39.4%, 45.7%, and 31.6% of participants, respectively, answering “yes” to consuming alcohol on these days. There was a significant difference in the number of intervention participants compared to control group participants who reported consuming alcohol on a Monday (n = 18, 13% vs. n = 30, 22.9%, respectively; p = .035). Most alcohol consumption occurred at home (66.5%), in restaurants (57.2%), and/or at a friend’s place of residence (42.8%). Overall, the patterns of alcohol intake were reasonably well-balanced between the intervention and control groups at baseline.

Chapter 5: Quantitative Results 121 Table 5.5 Baseline Description (Continuous Variables) of the Pattern of Alcohol Intake for Study Participants (N = 269)

Intervention (n = 138) Control (n = 131) p-value All (N = 269) Variable N Median Range N Median Range N Median Range Alcohol intake (g/day) 138 3.5 0.0-52.3 131 4.5 0.0-76.9 .301 a 269 3.9 0.0-76.9 Alcohol intake – drinkers 110 5.2 0.26-52.3 112 6.1 0.26-76.9 .502 a 222 5.3 0.26-76.9 only (g/day) Notes. a Non-parametric equivalent t-test

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Table 5.6 Baseline Description (Categorical Variables) of the Pattern of Alcohol Intake for Study Participants (N = 269) Intervention Control All p-value (n = 138) (n = 131) (N = 269) Variable N % N % N % Alcohol intake categorised NIL g/day 28 20.3 19 14.5 .813 b 47 17.5 > 0 to ≤ 10 g/day 78 56.5 76 58.0 154 57.2 > 10 to ≤ 20 g/day 16 11.6 20 15.3 36 13.4 > 20 to ≤ 30 g/day 8 5.8 7 5.3 15 5.6 > 30 to ≤ 40 g/day 5 3.6 5 3.8 10 3.7 > 40 g/day 3 2.2 4 3.1 7 2.6 Missing 0 0.0 Quantity - Total number of glasses per day, on days of alcohol consumption (previous month) None 28 20.3 19 14.5 .567 b 47 17.5 1 glass per day 42 30.4 51 38.9 93 34.6 2 glasses per day 47 34.1 36 27.5 83 30.9 3 glasses per day 12 8.7 15 11.5 27 10.0 4 glasses per day 6 4.3 7 5.3 13 4.8 5 glasses per day 1 0.7 1 0.8 2 0.7 6 glasses per day 2 1.4 1 0.8 3 1.1 7 glasses per day 0 0.0 0 0.0 0 0.0 > 8 glasses per day 0 0.0 1 0.8 1 0.4 Missing 0 0.0 Maximum number of glasses per 24 hours (previous month) None 28 20.3 19 14.5 .363 b 47 17.5 1-2 glasses 67 48.6 64 48.9 131 48.7 3-4 glasses 26 18.8 38 29.0 64 23.8 5-6 glasses 10 7.2 6 4.6 16 5.9

Chapter 5: Quantitative Results 123 Intervention Control All p-value (n = 138) (n = 131) (N = 269) Variable N % N % N % Maximum number of glasses per 24 hours (previous month) (continued) 7-8 glasses 4 2.9 3 2.3 7 2.6 9-10 glasses 2 1.4 1 0.8 3 1.1 > 11 glasses 1 0.7 0 0.0 1 0.4 Missing 0 0.0 Consumption frequency – white wine including sparkling wines Never 44 31.9 41 31.3 .969 b 85 31.6 < once a month 16 11.6 19 14.5 35 13.0 1-3 days per month 32 23.2 26 19.8 58 21.4 1 day per week 11 8.0 12 9.2 23 8.6 2 day per week 12 8.7 15 11.5 27 10.0 3 day per week 10 7.2 6 4.6 16 5.9 4 day per week 6 4.3 4 3.1 10 3.7 5 day per week 1 0.7 2 1.5 3 1.1 6 day per week 1 0.7 1 0.8 2 0.7 Everyday 5 3.6 5 3.8 10 3.7 Missing 0 0.0 Consumption frequency – red wine Never 70 50.7 54 41.2 .894 b 124 46.1 < once a month 17 12.3 17 13.0 34 12.6 1-3 days per month 20 14.5 21 16.0 41 15.2 1 day per week 13 9.4 16 12.2 29 10.8 2 day per week 6 4.3 5 3.8 11 4.1 3 day per week 4 2.9 4 3.1 8 3.0 4 day per week 3 2.2 4 3.1 7 2.6 5 day per week 3 2.2 5 3.8 8 3.0

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Intervention Control All p-value (n = 138) (n = 131) (N = 269) Variable N % N % N % Consumption frequency – red wine (continued) 6 day per week 0 0.0 1 0.8 .894 b 1 0.4 Everyday 2 1.4 4 3.1 6 2.2 Missing 0 0.0 Days of week consumed (last 7 days) – only “yes” answers represented Monday 18 13.0 30 22.9 .035 a* 48 17.8 Tuesday 26 18.8 32 24.4 .265 a 58 21.6 Wednesday 31 22.5 25 19.1 .495 a 56 20.8 Thursday 22 15.9 23 17.6 .723 a 45 16.7 Friday 48 34.8 58 44.3 .111 a 106 39.4 Saturday 62 44.9 61 46.6 .788 a 123 45.7 Sunday 39 28.3 46 35.1 .227 a 85 31.6 Place of consumption (general intake) –only “yes” answers represented Home 91 65.9 88 67.2 .830 a 179 66.5 Friends 56 40.6 59 45.0 .460 a 115 42.8 Party 35 25.4 32 24.4 .859 a 67 24.9 Rave 2 1.4 1 0.8 1.000 c 3 1.1 Restaurant 78 56.5 76 58.0 .805 a 154 57.2 Licence premises 38 27.5 36 27.5 .992 a 74 27.5 School 0 0.0 2 1.5 .236 c 2 0.7 Workplace 1 0.7 4 3.1 .204 c 5 1.9 Public place 8 5.8 11 8.4 .479 a 19 7.1 Car 0 0.0 0 0.0 NA 0 0.0 Elsewhere 8 5.8 6 4.6 .653 a 14 5.2 Notes. * p < .05; a Pearson’s Chi-square test of homogeneity; b Fisher-Freeman-Halton Test; c Fisher’s exact test; NA Not applicable.

Chapter 5: Quantitative Results 125 Predictors of drinking status (drinker vs. non-drinker) at baseline There were 222 drinkers and 47 non-drinkers at baseline. Hence, it was important to identify and understand potential predictors of drinking status (drinker vs. non-drinker) in this population. To do this, binary logistic regression (BLR) was performed to ascertain the effects of several socio-demographic factors and general health factors regarding the likelihood that participants were drinkers compared to non-drinkers.

Two BLR models with the dichotomous dependent variable “drinking status” were run with all test assumptions met prior to or during analysis. Backward step likelihood ratio elimination was applied to each model to arrive at the final models shown (see Section 4.4.4 for more detail). Model One considered the socio- demographic factors and included group, age, country of birth (COB), household income, education attainment, and marital status as independent variables in the initial model. The final model contained two independent variables (COB and education) and was statistically significant (χ2 = 8.143, 2 df, p = .017); however, it could only explain 5.3% (Nagelkerke R-squared) of the variance in whether or not a participant had consumed alcohol (see Table 5.7). Participants who had attained university/college or postgraduate qualifications had 2.65 times (1/0.377) the odds of being a drinker compared to those who had completed year 12 or less (95% CI 0.169, 0.84; p = .017). However, compared to attaining university level qualifications, having attained a trade, technical certificate, or diploma showed no effect on drinking status (p = .797). Although COB remained in the final model, the result did not reach statistical significance.

126 Chapter 5: Quantitative Results Model Two considered general health factors. It included FACT-G wellbeing subdomains (physical, social/family, emotional, and functional), Centre for Epidemiologic Studies Depression Scale (CES-D) scores, BMI, waist circumference risk, smoking status, physical activity level, number of co-morbidities, and time since diagnosis as independent variables in the initial model. Again, the final model contained two independent variables (FACT-G social/family wellbeing and smoking status) and was statistically significant (χ2 = 13.452, 2 df, p = .001). Model Two explained 8.7% (Nagelkerke R-squared) of the variance in whether or not a participant had consumed alcohol (see Table 5.8). In relation to quality of life, for each one-unit increase in FACT-G social/family wellbeing score, the odds of being a drinker increased by a factor of 1.073 (95% CI 1.018, 1.131; p = .009). Moreover, the odds of being a drinker were 3.3 times (95% CI 1.370, 7.949; p = .008) greater for participants who were current smokers or past smokers compared to never smokers.

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Table 5.7 Binary Logistic Regression: Model One Socio-Demographic Predictors for Drinking Status (Drinker vs. Non-Drinker) at Baseline

Final Model B SE Wald df p Odds Ratio (95% CI) Country of Birth -0.649 0.351 3.414 1 .065 0.523 (0.263, 1.040) Education Completed Year 12 or less -0.976 0.410 5.677 1 .017* 0.377 (0.169, 0.841) Trade, technical certificate or diploma -0.112 0.438 0.066 1 .797 0.894 (0.379, 2.107) University/college & postgraduate degrees 5.911 2 .052 1.00^ Constant 2.078 0.282 54.429 1 .000 7.992

Note: Country of birth is for elsewhere compared to Australia; ^ Education has university/college and postgraduate degrees set as the referent; * p < .05

Table 5.8 Binary Logistic Regression: Model Two General Health Predictors for Drinking Status (Drinker vs. Non-Drinker) at Baseline

Final Model B SE Wald df p Odds Ratio (95% CI) Quality of life: FACT-G Social/Family Wellbeing 0.070 0.027 6.769 1 .009* 1.073 (1.018, 1.131) Smoking status 1.194 0.449 7.082 1 .008* 3.300 (1.370, 7.949) Constant -0.138 0.558 0.061 1 .804 0.871 Note: Smoking status is for current or past smokers compared to never smokers; * p < .05

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Comparison of study population alcohol intake to normative data

Normative data were drawn on from the National Health Survey: 2014-15 (ABS, 2015) to compare the alcohol intake of the WWACP population to women in the general Australian population (see Section 4.4.5 for more detailed information about how these data were prepared for this comparison).

Table 5.9 illustrates the comparison of alcohol-related single occasion risk and lifetime risk between Australian women and study participants according to age group. The proportion of women exceeding the guideline for alcohol-related single occasion risk was greatest in the youngest age group and appeared to reduce as age increased. This trend was evident across both cohorts. Alcohol-related lifetime risk increased as age increased for women in both groups; however, the proportions were smaller in the study participants. A higher proportion of study participants consumed alcohol than those in the general population; however, the pattern of consumption did not exceed more than two drinks per day on average. The proportion of non- drinkers tended to increase with age in the general population. This trend appears similar in the study population excluding women in the 65-74 age group, which reflect a smaller proportion of non-drinkers.

Chapter 5: Quantitative Results 129 Table 5.9 Comparison of Alcohol-Related Risk Between Australian Women and Study Participants (N = 267, Missing N = 2)

Australian Women %a Study participants % (n)

Age group (years) 25-34 35-44 45-54 55-64 65-74 25-34 35-44 45-54 55-64 65-74

Short-term/Single occasion riskb • Did not exceed guideline 35.1 41.1 46.6 55.3 58.0 50.0 (1) 72.5 (29) 73.4 (80) 68.9 (62) 80.8 (21) • Exceeded guidelines 43.5 36.1 31.3 21.1 9.4 50.0 (1) 15.0 (6) 10.1 (11) 8.9 (8) 3.8 (1) • Never consumed alcohol^ 21.6 22.3 22.1 22.4 31.8 - 12.5 (5) 16.5 (18) 22.2 (20) 15.4 (4)

Longer term/Lifetime riskc • Did not exceed guideline 39.9 40.7 41.9 42.7 36.6 100 (2) 87.5 (35) 81.7 (89) 71.1 (64) 73.1 (19) • Exceeded guidelines 7.0 9.7 10.9 10.2 11.2 - - 1.8 (2) 6.7 (6) 11.5 (3) • Never consumed alcohol^ 21.6 22.3 22.1 22.4 31.8 - 12.5 (5) 16.5 (18) 22.2 (20) 15.4 (4)

Note. a Data from National Health Survey 2014-15 (ABS, 2015). b 2009 NHMRC Single occasion risk guideline 'drinking no more than four standard drinks on a single occasion reduces the risk of alcohol-related injury arising from that occasion’(NHMRC, 2009). c 2009 NHMRC Lifetime risk guideline 'drinking no more than two standard drinks on any day reduces the lifetime risk of harm from alcohol-related disease or injury’ (NHMRC, 2009). ^ Never consumed or not during previous 12 months for normative data women and not consumed during previous one month for study participants.

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Modelling: Socio-demographic and health predictors of alcohol intake at baseline Research Question 2: What are the socio-demographic factors associated with these patterns of alcohol use?

This section presents modelling with baseline data to answer the second research question. Methods for ordinal logistic regression modelling used in this section were described in detail in the Methods Chapter (Sections 4.4.6 and 4.4.7), including test assumptions and variable recoding and formatting required prior to analysis. Bivariate analysis was performed as a screening process for independent variable selection prior to performing multivariable analysis.

The bivariate results showed no significant relationship between alcohol intake and smoking status, although a trend was evident (p = .053) (see Table 5.10). Further bivariate results showed there was no statistically significant relationship between alcohol consumption level and the other variables of interest such as age; marital status; time (in months) since most recent cancer diagnosis; education attainment; employment status at diagnosis; gross household income; number of co-morbidities present; FACT-G quality of life scores including sub-domain scores for physical, social/family, emotional, and functional wellbeing; sleep quality scores; Centre for Epidemiologic Studies Depression Scale (CES-D) scores; GCS vasomotor subscale scores; BMI; waist circumference risk; and waist-to-hip ratio risk according to WHO guideline classifications (2011) and physical activity levels.

The initial multivariable model showed that only smoking status was significant when all variables with a p-value < .2 were included. Table 5.10 presents the crude odds from bivariate analysis for independent variables, including smoking status, physical activity level, number of co-morbidities present, quality of life, gross household income, and education attainment that had a p-value < .2. Ordinal logistic regression using backward stepwise elimination was then performed to ascertain the effects of the aforementioned variables (those with a p-value of < .2) on alcohol consumption.

The final model considered the effects of smoking status; physical activity level categorised as high, moderate and low; number of co-morbidities present; and quality of life FACT-G scores presented as quartiles. The final model was

Chapter 5: Quantitative Results 131 statistically significant (χ2 = 26.676, 10 df, p = .003), the assumption of proportional odds could not be rejected (χ2 = 19.731, 20 df, p = .475), and there was no evidence of multicollinearity between the independent variables. As discussed in the methods chapter, all ordinal logistic assumptions were met either prior to testing or during testing. The effect of smoking was significant (p = .004). The odds of moving to a higher level of alcohol intake was 2.2 (95% CI 1.274, 3.647) times greater for participants who currently smoked or reported smoking in the past compared to never smokers (p = .004). The effect of physical activity was also significant (p = .049). The odds of moving to a higher level of alcohol intake was 3.1 (95% CI 1.201, 8.232) times greater for participants who reported moderate physical activity levels compared to participants who reported low physical activity levels (p = .020). Furthermore, the odds of moving to a higher level of alcohol intake was 3.6 (95% CI 1.251, 10.580) times greater for participants who reported high physical activity levels compared to participants who reported low physical activity levels (p = .018). The effect of co-morbidities was also significant (p = .035); however, this was only demonstrated in one category. The odds of moving to a higher level of alcohol intake was 2.3 (95% CI 1.142, 4.600) times greater for participants with two co-morbidities compared to participants with four or more co-morbidities (p = .020). Furthermore, the effect of quality of life in the final model was also significant (p = .045); however, only the lowest quality of life quartile demonstrated significance, and this was in the reverse direction. For participants who reported quality of life scores that fell into the lowest quartile (scores < 72) the odds of moving to a higher level of alcohol intake was 52% less (1 - 0.478 = 0.522, 95% CI 0.241, 0.946) than the odds for participants with scores in the highest quartile (scores ≥ 90, p = .034).

In summary, to answer the second research question, bivariate ordinal logistic regression models (unadjusted) were run on baseline data to test potential associations between alcohol consumption level and the independent variables of interest. Independent variables that showed a potential association were then added to an adjusted model and a backward stepwise elimination was performed to establish the final model, which was statistically significant. The final model conveyed smoking status, physical activity level, number of comorbidities, and quality of life as having a relationship with the amount of alcohol consumed.

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Table 5.10 Odd Ratios from Ordinal Logistic Regression Showing Bivariate and Multivariable Relationships Among Alcohol Intake and Variables of Interest.

Crude Odds (95% N Final Model Odds (95% CI) CI) Smoking status Current smoker or past smoker 85 1.629 (0.989, 2.683) 2.155 (1.274, 3.647) Never smoker 182 1.00 ^ # 1.00 **

Physical activity level High physical activity level 58 3.111 (1.131, 8.568) 3.636 (1.251, 10.580) Moderate physical activity level 189 2.499 (0.995, 6.278) 3.142 (1.201, 8.232) Low physical activity level 19 1.00 ^ 1.00 *

No. of co-morbidities Nil co-morbidities 47 0.979 (0.479, 2.000) 0.921 (0.433, 1.960) present 1 co-morbidity 48 0.808 (0.397, 1.645) 0.715 (0.339, 1.510) 2 co-morbidities 56 1.891 (0.965, 3.710) 2.293 (1.142, 4.600) 3 co-morbidities 45 1.053 (0.511, 2.171) 1.002 (0.480, 2.092) ≥ 4 co-morbidities 73 1.00 ^ 1.00 *

Quality of life: < 25th (scores < 72) 64 0.507 (0.264, 0.974) 0.478 (0.241, 0.946) FACT-G 25th to <50th (scores ≥ 72 to 81) 68 1.104 (0.587, 2.075) 1.225 (0.620, 2.418) 50th to <75th (scores ≥ 82 to 89) 57 0.917 (0.471, 1.782) 0.916 (0.461, 1.818) ≥ 75th (scores ≥ 90) 76 1.00 ^ 1.00 *

Quality of life: < 25th (scores < 20) 63 0.598 (0.311, 1.151) FACT-G sub-domain for 25th to < 50th (scores ≥ 20 to 22) 61 1.108 (0.577, 2.130) Physical Wellbeing 50th to < 75th (scores ≥ 23 to 24) 67 1.186 (0.628, 2.241) ≥ 75th (scores ≥ 25) 75 1.00 ^

Chapter 5: Quantitative Results 133 Crude Odds (95% N Final Model Odds (95% CI) CI) Income Low income (≤ $40,000) 19 0.641 (0.253, 1.626) Middle income (> $40,000 to $100,000) 94 0.647 (0.389, 1.074) High income (> $100,000) 144 1.00 ^

Education Completed junior high school (year 10) or less 22 0.847 (0.329, 2.179) Completed senior high school (year 12) 27 1.770 (0.738, 4.246) Trade, technical certificate or diploma 59 1.811 (0.908, 3.618) University or college degrees 94 1.906 (1.021, 3.554) Postgraduate degree 64 1.00 ^ Note:** p < .01, * p < .05, ^ p < .2, # (p = .053).

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Modelling: Comparing the effect of the intervention within and between the three time-points Research Question 3: Is a tailored lifestyle intervention associated with change in alcohol-related health behaviours in this population?

To answer the final research question, change in alcohol intake across the three study time-points was considered in several ways using descriptive statistics and advanced modelling techniques to provide the most informative picture (see Section 4.4.8 for more detail). The results for the dependent variable of alcohol intake (both the continuous and ordinal categorical variable forms) over time are presented in this section.

Descriptive statistics using medians and ranges for the continuous alcohol variable were compared across time-points. Table 5.11 indicates there was little variation between intervention and control groups (approximately 1 gram of alcohol per day at T1 and T2 and no variation at T3) and little change within groups across time-points. Figure 5.8 presents side-by-side boxplots of alcohol intake for each group across the three time-points and identifies the outliers.

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* extreme outliers; O outliers

Figure 5.8. Side-by-side boxplots of alcohol intake in grams per day (continuous variable) across three time-points.

Table 5.12 presents the frequencies and percentages for the ordinal categorical alcohol variable at each time-point for the intervention and control groups. The frequencies were reviewed to help explain any underlying patterns in the data. The proportion of participants in each alcohol category changed marginally from one time-point to the next. The intervention group reported a mostly consistent pattern of intake at each time-point, with the highest proportion of participants drinking between > 0 to ≤ 10 g/day, followed by non-drinkers, then those consuming between > 10 to ≤ 20 g/day, and finally, > 20 g/day. This pattern changed for the control group, with the highest proportion of participants drinking between > 0 to ≤ 10 g/day, followed by those consuming between > 10 to ≤ 20 g/day, then non-drinkers, and finally, > 20 g/day. The aforementioned pattern is only consistent for T1 and T2 in the control group, as T3 intake pattern mirrors that of the intervention group.

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Changes in the intervention group indicate that at T2 compared to T1, there were fewer participants in both the lowest and highest intake categories (non- drinkers and > 20 g/day) and more participants in the > 0 to ≤ 10 g/day and > 10 to ≤ 20 g/day categories. The largest proportional changes in the intervention group were evident between T2 and T3 in the > 0 to ≤ 10 g/day category, which saw a decrease from 59.7% (n = 71) to 54.4% (n = 62), respectively, and a subsequent increase in the > 10 to ≤ 20 g/day category, from 13.4% (n = 16) to 16.7% (n = 19) at T3. This suggests that at follow-up (T3) participants in the intervention group were consuming more alcohol than when the intervention ended (T2). Possible reasons for this are explored in the next chapter. The largest proportional changes in the control group were evident between T1 and T2 in the > 0 to ≤ 10 g/day category, which saw a decrease from 58.0% (n = 76) to 51.3% (n = 59), respectively, and a subsequent increase in the > 10 to ≤ 20 g/day category, from 15.3% (n = 20) to 19.1% (n = 22) at

T2. This suggests that participants in the control group had increased their alcohol intake by halfway through the study period (T2). Percentages indicated the largest between group change in proportions was evident in the T1 intervention and control non-drinkers, with 20.3% (n = 28) compared to 14.5% (n = 19), respectively. According to the percentages, some variation appeared within and between the groups; however, it was not clear whether any change in intake patterns was statistically significant. The final test only identified whether any significant changes were apparent.

An ordinal logistic GEE regression model was applied to assess the repeated measure of alcohol intake (using the ordinal categorical alcohol variable). All GEE assumptions were met prior to testing. For example, all cases were dependent (repeated measures) within subjects and independent between subjects, and missing data were deemed missing completely at random (MCAR) (see Sections 4.4.3 and 4.4.8 for more detail). Table 5.13 presents the results for the ordinal logistic GEE regression for alcohol intake. As discussed, ordinal logistic regression yields an odds ratio (OR) for a move from one category of alcohol consumption to the next higher category, with an OR greater than 1.00 indicating increased alcohol consumption. The main effect investigated was between groups (intervention vs. control) and time- points (T1, T2, and T3), plus all of the two-way interactions between them. Thus,

Chapter 5: Quantitative Results 137 baseline (T1) alcohol intake for the intervention group was set as the referent group against which all other interactions in the model were compared.

Overall, the ordinal logistic GEE regression model was not significant (p = .387), meaning no change in alcohol intake across time-points was detected.

Although the results cannot detail any specific intake change between T2 and T3 for the intervention group, or between T1 and T2 or T2 and T3 for the control group, the overall GEE model p-value indicates there was no detectable change across any of the time-points.

In summary, alcohol consumption remained relatively stable for both the intervention and control groups across the three time-points. Descriptive statistics suggest that a small variance of approximately 1 gram of alcohol less per day was reported in the intervention group compared to the control group from baseline. However, this is minute, as 1 gram of alcohol is equivalent to 1/10 of a standard drink only. Some variances, not identified as statistically significant, were noted when reviewing frequencies and percentages. For example, in the intervention group the trend for intake to increase over time was apparent, but not significant when tested with a robust modelling technique. GEE modelling showed a slight decrease in odds at T2 for the intervention group and a decrease for the control group at T3; however, these results were not significant overall.

The results for change over-time were not significant, nor noticeably different (presented with medians [ranges], crude percentages, and ordinal logistic GEE regression modelling). This suggests that the intervention had no discernible effect on reducing alcohol intake; thus, no further testing was deemed necessary to answer the final research question.

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Table 5.11 Alcohol Change Over Time (Continuous Variable)

Intervention Control All Variable N Median Range N Median Range N Median Range

T1 Alcohol intake (g/day) 138 3.5 0.0-52.3 131 4.4 0.0-76.9 269 3.9 0.0 - 76.9 T2 Alcohol intake (g/day) 119 3.2 0.0-46.7 115 4.5 0.0-50.4 234 3.7 0.0-50.4 T3 Alcohol intake (g/day) 114 3.7 0.0-57.8 107 3.7 0.0-37.8 221 3.7 0.0-57.8

Table 5.12 Alcohol Change over Time (Ordinal Categorical Variable) Crude Percentages

Intervention Control % (n) % (n) Alcohol intake categories NIL >0.0 to ≤10 >10 to ≤20 >20 Total NIL >0.0 to ≤10 >10 to ≤20 >20 Total T1 Alcohol intake (g/day) 20.3 (28) 56.5 (78) 11.6 (16) 11.6 (16) 100 (138) 14.5 (19) 58.0 (76) 15.3 (20) 12.2 (16) 100 (131) T2 Alcohol intake (g/day) 17.6 (21) 59.7 (71) 13.4 (16) 9.2 (11) 100 (119) 17.4 (20) 51.3 (59) 19.1 (22) 12.2 (14) 100 (115) T3 Alcohol intake (g/day) 18.4 (21) 54.4 (62) 16.7 (19) 10.5 (12) 100 (114) 19.6 (21) 50.5 (54) 18.7 (20) 11.2 (12) 100 (107)

Chapter 5: Quantitative Results 139 Table 5.13 Ordinal Logistic Generalised Estimating Equation Regression for Alcohol Intake

Intervention Control Time-point Odds ratio (95% CI) p-value Odds ratio (95% CI) p-value

1 1.000*^ 1.291 (0.820, 2.035) .270 2 0.920 (0.680, 1.244) .587 1.302 (0.808, 2.098) .279 3 1.070 (0.822, 1.394) .614 1.159 (0.714, 1.883) .550

*T1 intervention is the referent; ^ overall model p = .387

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5.3 CHAPTER SUMMARY

The results of the quantitative analyses of the data were presented in this chapter. Section 5.2.1 provided a description of the parent study sample at baseline, including an assessment of the lost to follow up data. Section 5.2.2 presented the results that address Research Questions 1 to 3, including ordinal logistic regression modelling.

The next chapter provides a brief overview of the Study 2 cohort to demonstrate that the interview participants were largely representative of the Study 1 cohort from which they were drawn. This is followed by an analysis of the qualitative data obtained in Study 2.

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Chapter 6: Qualitative Results

In this chapter, the qualitative data are presented as they relate to Phases 1 and 2 of Precede, the theoretical model that underpins this PhD. Phase 1 of Precede (or the social assessment) focuses upon subjectively-reported quality of life while Phase 2 (or the epidemiological assessment) considers the ways that participants describe their health and their health behaviour. These phases of Precede also consider the ways that quality of life and health behaviours are influenced by, and interact with, the participants’ environment. In essence, in this chapter the research questions are answered through an alternative lens to the ‘objective’ data presented in Chapter 5. That is, the chapter offers participants’ narrative descriptions of their alcohol-related quality of life, their alcohol behaviours, the social environment in which their alcohol behaviours manifest, and finally the impact of the WWACP intervention on these behaviours. This subjective lens can help explain the quantitative data.

6.1 DESCRIPTION OF QUALITATIVE SAMPLE

The recruitment period for Study 2 ran from 17th December 2015 to 2nd March 2016. In that window of time, there was opportunity to invite twenty-seven WWACP participants to participate (via email) and of these, 20 women responded (via email, post or phone) to the invitation to interview, and 17 consented. Of the 17 consenting interview participants, 10 were from the WWACP intervention group and seven were from the WWACP control group. Of the WWACP intervention participants approached, 10 completed interviews, whereas seven of the 15 control participants approached completed interviews.

Table 6.1 demonstrates that the qualitative sub-sample are representative of the sample as a whole. The age of the women in the qualitative sample ranged from 40 years to 71 years. Their alcohol intake reflected that of the WWACP study population at baseline with the majority consuming ≤ 10 grams of alcohol per day. The majority of the interview participants were either married or in a de facto relationship. All of the women had some form of post-Year 12 (high school) qualification, predominantly in the tertiary sector. Time since diagnosis ranged from

Chapter 6: Qualitative Results 143 9 months to just over 6 years. The qualitative cohort did not include any current or casual smokers. They reported a similar quality of life to the participants in Study 1.

Table 6.1 Demographic Characteristics of Sub-study Participants (n = 17) and Parent Study Participants (N = 269 less n17 = n252) at Baseline Parent Study Sub-Study (N = 252) (n = 17) Variable % (n) % (n) Group Intervention 50.8 (128) 58.8 (10) Control 49.2 (124) 41.2 (7) Mean age (years) ± SD 53.5 (8.2) 53.9 ± 9.5 Age (years) grouped 25 – 34 years 0.8 (2) - 35 – 44 years 14.7 (37) 17.6 (3) 45 – 54 years 40.5 (102) 41.2 (7) 55 – 64 years 34.1 (86) 23.5 (4) 65 years and over 9.1 (23) 17.6 (3) Missing 2 (0.8) - Country of birth Australia 66.7 (168) 64.7 (11) Elsewhere 32.5 (82) 35.3 (6) Missing 2 (0.8) - Aboriginal, Torres Strait or South Sea Islander Yes 0 (0) 5.9 (1) No 98.4 (248) 94.1 (16) Missing 1.6 (4) - Language other than English Yes 7.5 (19) 5.9 (1) No 90.0 (229) 94.1 (16) Missing 1.6 (4) - Ancestry Oceanian 12.3 (31) - North-West European 50.0 (126) 58.8 (10) Southern and Eastern European 11.9 (30) 5.9 (1) North African and Middle Eastern 0.4 (1) - South-East Asian 3.2 (8) - Southern and Central Asian 0.4 (1) 5.9 (1) People of the Americas 0.8 (2) - Other 19.0 (48) 23.5 (4) Missing 2.0 (5) 5.9 (1) Marital status Married or de facto 75.4 (190) 94.1 (16) Separated or devoiced 10.7 (27) 5.9 (1) Widowed 2.4 (6) - Single 9.9 (25) - Missing 1.6 (4) -

144 Chapter 6: Qualitative Results Parent Study Sub-Study (N = 252) (n = 17) Variable % (n) % (n) Highest education level obtained Primary to year 10 completed 8.7 (22) - Year 12 completed 10.7 (27) - Trade, certificate or diploma 20.6 (52) 41.2 (7) University undergraduate degree 34.9 (88) 35.3 (6) Postgraduate degree 23.8 (60) 23.5 (4) Missing 1.2 (3) - Employment status (prior to diagnosis) Full-time employment 43.3 (109) 47.1 (8) Part-time employment 36.5 (92) 35.3 (6) Home duties 8.3 (21) 0 (0) Retired 8.7 (22) 17.6 (3) Unemployed or unable to work 2.4 (6) - Missing 0.8 (2) - Gross annual household income Low income (≤ $40k) 7.1 (18) 5.9 (1) Middle income ($40k to $100k) 36.1 (91) 17.6 (3) High income (> $100k) 52.4 (132) 70.6 (12) Missing 4.4 (11) 5.9 (1) Postcode New South Wales 25.8 (65) 41.2 (7) Victoria 17.5 (44) 11.8 (2) Queensland 26.2 (66) 47.1 (8) South Australia 10.7 (27) - Western Australia 12.3 (31) - Tasmania 5.6 (14) - Australian Capital Territory 2.0 (5) - Missing 0 (0) - Time since diagnosis (months) ≤ 6 months 12.7 (32) - > 6 to 12 months 26.6 (67) 41.2 (7) > 12 to 24 months 36.9 (93) 35.3 (6) > 24 to 36 months 17.1 (43) 11.8 (2) > 36 to 48 months 2.4 (6) - > 48 months 2.8 (7) 11.8 (2) Missing 1.6 (4) - Quality of life (FACT-G) (mean scores ± SD) Physical wellbeing (PWB) 21.9 ± 4.1 20.9 ± 4.5 Social/family wellbeing (SWB) 20.4 ± 5.9 20.0 ± 7.9 Emotional wellbeing (EWB) 18.5 ± 3.7 17.5 ± 4.2 Functional wellbeing (FWB) 19.5 ± 5.2 19.8 ± 5.2 FACT-G total score 80.0 ± 13.9 78.2 ± 15.0 Smoking status Never smoked 67.1 (169) 76.5 (13) Smoked in the past 28.2 (71) 23.5 (4) Regular smoker 3.2 (8) - Casual smoker 0.8 (2) -

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Parent Study Sub-Study (N = 252) (n = 17) Variable % (n) % (n) Smoking status (continued) Missing 0.8 (2) - Menopausal status Pre-menopausal 5.6 (14) - Peri-menopausal 13.1 (33) 17.6 (3) Post-menopausal 80.6 (203) 82.4 (14) Missing 0.8 (2) - Co-morbidities (no. present) NIL present 17.5 (44) 17.6 (3) 1 co-morbidity 18.3 (46) 11.8 (2) 2 co-morbidities 20.2 (51) 29.4 (5) 3 co-morbidities 16.3 (41) 23.5 (4) 4 co-morbidities 10.3 (26) - 5 co-morbidities 8.7 (22) 5.9 (1) 6 or more co-morbidities 8.7 (22) 11.8 (2) Missing 0 (0) - BMI Underweight 1.6 (4) - Normal weight 34.9 (88) 29.4 (5) Overweight 35.7 (90) 23.5 (4) Obese class I 14.3 (36) 29.4 (5) Obese class II 7.1 (18) 11.8 (2) Obese class III 4.0 (10) - Missing 2.4 (6) 1 (5.9) Waist circumference risk ≤ 80 cm no associated risk 25.0 (63) 17.6 (3) > 80 cm to 88 cm increased risk 24.6 (62) 29.4 (5) > 88 cm substantially incr. risk 49.6 (125) 52.9 (9) Missing 0.8 (2) - Waist-to-hip ratio risk < 0.85 cm no associated risk 52.0 (131) 47.1 (8) ≥ 0.85 cm substantially incr. risk 47.2 (119) 52.9 (9) Missing 0.8 (2) - Physical activity level Low 7.5 (19) - Moderate 69.8 (176) 47.1 (8) High 21.4 (54) 23.5 (4) Missing 1.2 (3) - T1 Alcohol intake categorised Zero g/day 17.9 (45) 11.8 (2) > 0 to ≤ 10 g/day 57.1 (144) 58.8 (10) > 10 to ≤ 20 g/day 13.9 (35) 5.9 (1) > 20 to ≤ 30 g/day 5.2 (13) 11.8 (2) > 30 to ≤ 40 g/day 3.6 (9) 5.9 (1) > 40 g/day 2.4 (6) 5.9 (1) Missing 0 (0) -

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6.2 QUALITY OF LIFE

For the purpose of this study, quality of life (QoL) was defined as multidimensional concept that encompasses an individual’s subjective perception of their physical, social, emotional and functional well-being (Cella & Tulsky, 1993). The data were mined for subjective QoL indicators at the individual level, including specific long-term treatment-related health concerns (Green & Kreuter, 2005). These findings are presented in relation to how participants described their QoL both before and after treatment, including some specific areas of concern and how alcohol helped or hindered their QoL.

For all participants QoL was different before their cancer diagnosis and treatment, although it could be worse or better than previously. For example, Mae commented on feeling “indestructible” before she knew she had cancer, a perception that markedly changed after treatment. In contrast, Sharon’s pre-diagnosis QoL was not good before or after diagnosis, but was qualitatively changed after cancer. Her stressful professional life had led to major health events that put an end to her high- ranking career and these continued to adversely affect her QoL once she also developed cancer:

Sharon: Because I'd had a very stressful job and I used to drink then yes, I suffered a stroke at 46 … and that’s what came from my professional career. I came back for five years, but I could only go back part-time … as a clerical instead of management … after that five years, so I had a heart attack, so then that was the time I resigned.

For some, quality of life after diagnosis was different but still good. Helen described her anger at being treated like a “victim” of breast cancer whose quality of life is inevitably impaired:

Helen: I'm doing really well, I'm almost dangerous now. I'm back to doing everything … I still get people saying Oh how are you? You know and I'm thinking, would that be rude [if] I feel like saying fuck off. Because had I broken my leg, they wouldn't be asking me that question now. There's an assumption that if women have got breast cancer, I believe, it's the worst thing in the world. And it's not. It's something that can be managed.

Chapter 6: Qualitative Results 147 A few years on from diagnosis, Nellie reflected on the positivity of finally getting her life back on track and finishing jobs that she was not capable of completing earlier, such as her house renovations “so getting it done now, it's like the final thing that breast cancer got in the way of … so it's very positive”. But some participants expressed frustration because their breast cancer continued to affect the quality of their daily life, even when they felt well. They described constant reminders, whether it was physical limitations or the responsibility of ongoing care, that meant their history of cancer could not be forgotten:

Helen: I want to go and see the physio next week, I've still got some problems with my arm, I've got to go and see the oncologist, like, you think you’re over it and it's behind you and then you get a letter saying you have to see the oncologist … It's still almost a message that you're not really well. And I'm thinking, well, I am. So there's obviously a lot of effort … to keeping me well.

As Helen found, cancer treatment inevitably has side effects that must be managed and integrated into daily life. Hence participants described how they continued to suffer long-term treatment-related health concerns such as insomnia, hot flushes, problems with weight control, taste changes and reduced energy levels, and how these adversely affected their QoL. For example, Sharon’s weight gain as well as breathing difficulties impeded a return to her previous physical activity level, and Marge was also concerned about gaining abdominal fat:

Sharon: [I was] very, very physical until I put on weight after I had the cancer, even during my chemotherapy treatment I was walking 10 Ks a day. But now, whether it's medication or I've just been so much over weight, I couldn't come to do that walking … all [of my] chest and my tongue was affected it because I had radiation on the lungs.

Marge: I started tamoxifen when I finished chemo, I know that it’s [tamoxifen] not kind to weight gain either, and I drank a lot more … and I put on weight, and I really feel that that the alcohol was a big part of the weight gain.

Participants did not describe alcohol as a mediator of their QoL before cancer, but it was certainly something Rose thought about after cancer. Rose commented on her change in values about drinking after treatment, when alcohol consumption clearly and adversely affected her wellbeing the next day. She learnt to “put more

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value on getting up the next day and going for a walk in the morning.” Other participants also noted that aspects of their physical and or functional wellbeing changed following treatment, and consequently so did their relationship with alcohol. For example, Tracey described how the act of celebrating with a drink was no longer enjoyable due to treatment-related taste changes:

Tracey: Because of chemo it altered my tastes so much that now I can't even drink alcohol without it tasting funny. I can have maybe a little bit, but I don't have it as much as I used to. Which is, I don't know, I miss it. Like tonight I was going to stop at the bottle shop and buy a bottle … because I was celebrating by writing my first blog.

Trisha and Lara also commented on taste changes, and that alcohol no longer tasted pleasant. However abstaining from alcohol as a response did not bother Trisha and Lara reported her physical well-being improved with abstinence:

Trisha: I don't get any satisfaction out of drinking alcohol any more. I don't know why, but it's not the same anymore … I'm not missing it … it does taste different, different to what it used to taste.

Lara: I definitely view alcohol differently. But, now I find I cannot drink alcohol. It disagrees with my sleep, it makes my neck get tense, it gives me a headache and, I don't like the taste of it.

When alcohol was favourably described this was always associated with social events and in some cases appeared to improve certain aspects of QoL. This relationship is considered in more detail towards the end of this chapter in relation to the alcohol environment.

6.3 ALCOHOL BEHAVIOURS

Qualitative content indicated a shift in drinking behaviours following cancer diagnosis and treatment. This section demonstrates how participants’ alcohol behaviours changed in relation to the frequency, quantity, type and pattern of alcohol intake.

Frequency and quantity

The interviews highlighted that the frequency and quantity of alcohol intake changed throughout the course of the week. For example, participants who reported

Chapter 6: Qualitative Results 149 currently drinking almost daily often described a routine to their day, which included structured timing of drinking. Most participants reported routinely drinking at night either while preparing dinner or with dinner, after a busy day at work. The following comments reflect these daily drinking rituals:

Nellie: The ritual is that it is pretty much as soon as I get home the … bottle gets cracked, yeah. So it's before six o'clock the bottle gets opened … that's having a glass of wine and usually we have a bit of a chat and the first glass is while kinda [sic] preparing dinner … and the second glass is during [dinner], and then you know that little bit that's left of wine, is again, a final top up is, is kind of after our dinner.

Rose: It's that coming home … Particularly coming home from work. Unwinding. It's not as soon as I walk in the door. It's generally, quite often when I'm cooking or preparing a meal. Or, you know, getting sort of settled for the evening. Um, actually sort of get everything started with the cooking and then I think, oh actually I might have a glass of wine.

Bianca now refrains from daily intake following treatment, but described similar drinking behaviours prior to her diagnosis:

Bianca: Most nights [my husband] and I would have half a glass of wine with dinner. Almost every night. Before diagnosis.

Often daily drinking behaviours were self-monitored in recognition of the adverse effects of alcohol. Lara commented that she didn’t drink on a Sunday now because she had to go to work on Monday and the alcohol affected her motivation and alertness. Participants who did drink were more inclined to do so between Friday night and Sunday, and were more lenient with heavier drinking during these times when drinking wouldn’t affect other roles. For example, Bianca commented that if there was a night-time occasion, a weekend night, “and I'm not driving, I'd be very likely to have a drink.” Similarly, Sharon described holiday periods as a typical time when personal limitations were loosened:

Sharon: If I'm on holiday, it's a bit more lenient, but not very much. I might have a couple of beers then.

150 Chapter 6: Qualitative Results For those who did drink, the maximum number of drinks consumed over a 24- hour period, which likely represented a single drinking session, was most often between 1-2 glasses or 3-4 glasses.

Type and place

The type of alcoholic beverage consumed and place of consumption was largely influenced by the daily versus weekend intake patterns. Qualitative comments indicate that champagne or sparkling wines were often consumed at weekend-related social and celebratory events; for example, lunches with girlfriends or dinners at friends’ homes and restaurants. Mae commented that for her, drinking red wine was more often associated with settling down for an evening of drinking (not necessarily a daily occurrence), whereas “bubbles is more a quick drink after work, so it's more socially acceptable I suppose.” Regular daily intake tended to be confined to white and red wines, which were consumed at home. There were often reasons for this. For example, Tracey believed that wine intake was less harmful regardless of frequency. Spirits were viewed as hard liquor and were more likely to be associated by her with problematic drinking:

Tracey: I think because I was drinking wine or beer, I never saw that as being, like I never saw that in the same as people who have spirits and get themselves drunk and all that … but then, you know you got to wake up to yourself and go, wait a minute, it's exactly the same thing.

Patterns of intake

Patterns of intake clearly changed before and after diagnosis for some participants. Some comments on pre-diagnosis intake identified a number of participants who now rarely drink but who did regularly drink at least once per week to small amounts each night. Others reported moderately reduced intakes, while some reported heavily reducing their intake following their cancer diagnosis but not necessarily immediately following treatment:

Ruby: I will tend to have one glass of wine instead of three … and tend to just drink on the weekends. Whereas before, most nights I'd have one or two glasses of wine.

Where participants did not drink much prior to diagnosis, their intake remained the same post-diagnosis and treatment.

Chapter 6: Qualitative Results 151 When asked about alcohol consumption throughout the treatment period (regardless of the treatment type) some participants indicated that they did not drink at all while the majority of participants rarely drank during treatment and some reported drinking too much. Some reasons stated by participants who did not drink included feeling too unwell, experiencing chemotherapy-related taste changes, or they did not think it was a good idea while on treatment. For rare drinkers, intake appeared to occur between chemotherapy cycles when they were feeling better or on special occasions. Contrary to the conventional clinical wisdom that chemotherapy exacerbates reactions to alcohol, two participants who reported heavy intakes during the treatment period felt little to no ill effects. Lara found she suffered terrible consequences of drinking alcohol after treatment concluded, but consumed more alcohol during treatment because it had little effect:

Lara: It's interesting in comparison, because sort of after the first three months of chemo, then I actually drank a fair bit … Like I could drink half a bottle of wine and I would not feel drunk, and I'd not get a headache … And I was not drunk.

Coincidentally, it was after Mae’s third session of chemo that she decided to have a few drinks and noted no effect:

Mae: This is really weird. I'm not getting the same reaction that I've ever had in my life to drinking alcohol. Normally I have a reaction. I feel a bit relaxed, you know. A bit tired.

Mae also posted about her experience on a breast cancer support group social media page, where other group members informed her that "actually that's quite common … alcohol may have no effect whatsoever on you."

Finally, the patterns of intake described by most participants who consumed alcohol suggest some participants could be considered moderate lifetime drinkers who also engaged in episodic binge drinking even after diagnosis. The following comments describe weekend binge drinking patterns:

Marge: We really don’t drink midweek now at all … like, Friday, Saturday … are our drinking days, so we probably … have two to three bottles … over a weekend, yeah.

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Nellie: I wouldn't say it is common binge drinking on a Friday night (laughs) if I had four or five drinks.

6.4 ALCOHOL ENVIRONMENT

This section focuses on the environments in which participants either encountered or consumed alcohol, including the people that participants generally consumed alcohol with and how often they did so. Comments described various locations, social interactions and the environments in which alcohol-related information was encountered, which shaped the participants relationship with alcohol.

Participants described wide variation in the alcohol environments that they were exposed to pre- and post-diagnosis. Subsequently, their scope of experiences were framed either negatively or positively. For example, Sharon who did drink, reflected on social events with work colleagues prior to her cancer diagnosis that instilled an unhealthy drinking culture:

Sharon: Before I got sick, if you didn't drink, they'd sort of look down at you as a wimp, and what are you not drinking for, you’re not putting in and they'd really just … and they wouldn't take no for an answer. If you said no, they would buy you one.

Conversely, participants described feeling uncomfortable in situations where alcohol was served if they were infrequent or non-drinkers. Following treatment Ida, who was never a drinker, found that in social settings her cancer diagnosis became a legitimate excuse for not drinking and she was comfortable with that:

Ida: Since treatment people are all sympathetic. If you don't drink alcohol, they just understand. I don't have to justify why I don’t want any … they go, "Oh, well she's had cancer, she doesn’t want alcohol” but that’s fine … whereas in previous times, I used to feel a little bit embarrassed, not drinking alcohol because everybody would be.

Mary commented that it was easier to only interact with people who respect her choice to abstain, otherwise “there's almost an expectation that you have a drink.”

The data highlighted that alcohol intake was often socially-driven and was perceived positively, even by non-drinkers. For example, Sally lived rurally and was

Chapter 6: Qualitative Results 153 not a big drinker (“you’re definitely in the minority”); however, she described how she would leave for a party with “good intentions” to drink but tended not to once she arrived:

Sally: Perhaps coming from a rural area. I'm not sure, but basically there's never a function unless there's alcohol. If you had a function with no alcohol there, you wouldn't get anyone to turn up.

Qualitative comments indicated that strength of character was particularly important for rurally-based participants who chose not to drink. For example Trisha noted that:

Trisha: I'm kind of headstrong I suppose, now [post-cancer]. Like I don't care what people think any more … [if] they want to think that I'm you know, a tight ass or you know, loser for not drinking well, that's their problem.

Rurality was a strong theme in these data, with alcohol-related rural social norms negatively affecting participants who lived rurally. It is possible these women face a less supportive community environment should they consider abstaining from alcohol for health reasons. However, metropolitan-based participants voiced similar views. Irrespective of their place of residence, the majority of participants viewed alcohol favourably, associating alcohol consumption with relaxation, socialising, celebrations, enjoyment and as a treat. Even non-drinkers who were more likely to see alcohol as insignificant, irrelevant or unnecessary in their own lives rarely expressed negative perceptions of it. Diana commented on her “emotional relationship” with alcohol and how this had coloured how she perceived it, remarking that alcohol was “quite insidious in the community but we [socially] accept it”. Yet this belief did not deter Diana from drinking “I enjoy having a drink socially”. Rose commented that alcohol was no less desirable to her after treatment; however, she felt uncomfortable about heavy intakes given her history of breast cancer:

Rose: I don't think I am comfortable drinking to excess anymore … I think it just sits hand in hand with being more conscious of trying to generally be healthier.

Although she no longer did so, Lucy reported a family history of alcoholism and being a very heavy drinker herself when younger. She described a “definite

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tendency, in our family, to get addicted to alcohol”. While a family history and potential detrimental home environment could lead to a higher intake, it could also have the opposite effect, as illuminated by these comments:

Sally: I could never think of my young kids seeing their mother drunk … I just never went there.

Mae: So my children grew up understanding, you know, how many drinks was too much. I never got blazingly drunk in front of them.

Education did not necessarily influence participants’ alcohol practices. The interviews suggested that while participants were generally well-educated, some did not acknowledge the link between breast cancer and alcohol consumption:

Rose: Maybe I haven't wanted to research it [alcohol] any further because I wanted to just continue to enjoy drinking and not feel that it's endangering me.

Tracey: I never really thought about the effects of it [alcohol]. Because I suppose you don't think, you don't, want to acknowledge the fact that you might be drinking a bit too much.

Lara expressed her concern about the lack appropriate warning labels in place to advise people of the link between alcohol consumption and cancer and felt that this was an important educative moment:

Lara: I think that perhaps they [other women] actually should see what this link is to alcohol. [We should] put a label on alcohol bottles that [demonstrate it] can link to cancer.

While alcohol is an important mediator of breast cancer development and recurrence, participants noted varying degrees of exposure to alcohol-specific education after diagnosis in terms of whether they received any information at all, who delivered it, the quality of the information provided or if it was accepted or not. Helen highlighted how education about alcohol after treatment was not necessarily a priority for her:

Helen: But even if there'd been information about alcohol, I wouldn't have bothered to read it because, I'd been more interested in what foods would make me well.

Chapter 6: Qualitative Results 155 While some participants could not recollect receiving any alcohol-related information during treatment and some clearly remembered not receiving anything, those who did receive information were often too overwhelmed to read further brochures or recall being inundated with too much information about too many things:

Marge: I don't know if I’m abnormal, but when I was diagnosed, I kind of shut down. I didn't want to read [the brochures] they gave me. I think at that stage, I don't know if I would have read it, in all honesty … but to have been told about it, I think I probably would have listened to that … it’s almost being told by somebody you trust.

The interview data did indicate, however, that the diagnosis of breast cancer could have been a spur to existing knowledge, as reflected in these comments:

Mae: I'm actually quite grateful for the cancer because it gave me a wake-up call to change my lifestyle. I felt I was slightly indestructible beforehand. I don't have that feeling now and I don't need alcohol. I don't need it as, as a crutch … I have other coping mechanisms now. I don't need it, don't want it, and if I could drink I probably wouldn't.

Lara: I think that when you face death you reflect a lot on your behaviour. And you're reflecting a lot on life to make each day your very best. So saying that became, you know my perspective, for not using alcohol as a coping mechanism to sort of blank out.

Sharon: I used to drink. But since I got sick [breast cancer], then I realised that there is a definite link. I have cut it right down.

The interviews generally indicated that participants were starting to feel physically and emotionally stronger after cancer treatment and that at the time of WWACP enrolment their quality of life was improving. Consequently, there was a shift in the alcohol environment as their lives were becoming qualitatively “normal” and some participants associated alcohol with “normal”:

Rose: It's funny because it's a measured property of feeling like I'm back to normal that my alcohol consumption is correct.

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Alcohol is also particularly associated with celebrating the return to normality. For example, Mae discussed the circumstances of her first drink after completion of her chemotherapy regimen:

Mae: Hey, let's all go out for dinner. I'm alive, I'm cured sort of thing … that's when I had a glass of red wine.

Marge: When I was feeling better, getting stronger, I started drinking more because I thought “Ah, I’ve done it. I’ve beaten it. This is absolutely fantastic. I’m gonna [sic] have a glass of champagne, a glass of wine” and I was actually drinking, I don't know, maybe five days a week. And probably close to three, maybe four standard drinks.

Even when drinking alcohol was not normal practice for some participants, celebrating achievements and milestones with alcohol is. For example, Diane was not a regular drinker. She did however appreciate the actions of a friend who brought a bottle of French champagne to celebrate each treatment milestone:

Diane: Once I had finished chemo or I had finished radiotherapy … she'd bring me in a bottle of booze, a bottle of French champagne. And, so I was determined when I was finished that I would celebrate.

In turn, Diane continued the tradition by sharing a bottle of French champagne with another friend who had just finished active treatment. Diane’s scenario clearly demonstrates how social norms can showcase alcohol as purposeful and a ‘positive’ in supportive environments.

Thus far, the data have addressed Research Questions 1 and 2. They have described participants’ baseline quality of life, their baseline alcohol behaviours and the environment that might interact with these factors. The next section addresses Research Question 3, which explores how the WWACP intervention might have interacted with these factors and potentially mediated them to influence behaviour change.

6.5 THE ROLE OF THE WWACP INTERVENTION IN ALCOHOL BEHAVIOURS

The way in which the WWACP subject matter was presented (in flexible virtual format by a trained health care professional) enabled positive behaviour

Chapter 6: Qualitative Results 157 change for some intervention participants. Sharon noted that the content and delivery of the WWACP were highly accessible:

Sharon: I have found out more from the Women’s Wellness [book] than I had with any of those other websites, because it was made to be more interesting instead of so clinical … you absorb that, and you thought about it more. And you followed it more … instead of just having a little bit here and a little bit there, and that sort of thing.

Virtual delivery meant that the WWACP maintained privacy for participants who did not want to disclose their diagnosis to others in a traditional cancer support group. Ruby had isolated herself from local support groups because she lived in a small community and did not want to be the topic of other people’s conversations. However, she felt that perhaps she would have received more alcohol-related information at an appropriate time if she had attended a support group or had earlier access to the WWACP. Ruby further noted that although the clinical health professionals she encountered during treatment meant well, she felt that that they provided very little concrete guidance for necessary behaviour change throughout the diagnosis and treatment phases. Although Ruby believed that this was because they did not want to appear to “blame” the woman for the cancer diagnosis, she would nonetheless have appreciated direction as to what behaviours to change and how to do so to prevent recurrence:

Ruby: And that's why the Wellness Programme was really good. In lots of ways; not just the alcohol way.

Rose commented that the WWACP was the only source from which she received information on alcohol as a risk factor for recurrence of cancer. Information about quantities increased knowledge levels for some intervention participants. In her post-intervention interview, Marge commented that she drank liberally after treatment because she was feeling stronger and felt she had “beaten” her cancer. Marge noted that the WWACP enabled her to identify that her post-treatment alcohol consumption was excessive:

Marge: It was only when I went on the Wellness Program, I realised that [my current alcohol intake] was excessive. To me it didn't feel excessive, but I realised it was. I think probably since doing the Wellness Research Program,

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you actually realise what two standard drinks are … I don't know whether I didn't want to think about it, but I hadn’t thought about it, and after I’d spoken to [WWACP research assistant] and really looked at it, I thought “Right, okay. I’ve gotta [sic] change this”. So, it was a really good eye-opener.

Participation in the control group also enabled modification of alcohol-related behaviours in some participants, possibly because it sensitised them to recommended guidelines. Lara was a control group participant who modified her intake during the study:

Lara: I actually think that I would drink three quarters of a glass of wine … It wasn't half full … which I think is what makes a standard glass … I thought in all reality I would say that it's more like four glasses of wine [per session].

However, some participants felt the WWACP messages about alcohol content were not strong enough:

Lara: It just says limit. Now, limit can be, well if I'm drinking four glasses of wine [laughs] then I limit it to three, is that good? No.

The interviews identified that the individual focus of the WWACP provided further incentives. The WWACP breast cancer nurses worked one-on-one with intervention participants to develop alcohol-specific skills to reduce their alcohol intake. This comprised the identification of alcohol-related goals, the tailoring of personal strategies to reduce the quantity of alcohol consumed, and the instillation of positive rather than negative messages. For example, participants who enjoyed having a drink were not directed to immediately abstain from alcohol. Instead, it was suggested that if they would like to reduce their risk of alcohol-related harm they might choose to consume less at times that they considered socially important. Hence, Ruby commented that while she still had the “odd glass” when her husband had a beer, she was now just as “happy with a glass of water in a wine glass. I still feel like I'm joining in, you know?”. Other participants offered similar strategies, which were guided by the WWACP breast cancer nurses. These included reducing the quantity of alcohol (e.g., completely filling the glass with ice before adding wine, choosing a low-alcohol wine, or choosing a better quality wine to consume less but enhancing flavour and enjoyment); substituting alcohol replacements like lemon, lime, and bitters; and avoidance strategies, such as refraining from bringing alcohol

Chapter 6: Qualitative Results 159 into the house, breaking the habit by going for a walk rather than sitting down with a drink, and brushing teeth as soon as a meal was consumed.

The timing of the WWACP after diagnosis was important to participants, and their preferred timing was as individual as they were. For example, Marge was diagnosed less than 12 months prior to WWACP commencement. She suggested that a timeframe within six months of completing treatment would have been most helpful for her to engage in the WWACP. Marge described this as the crucial stage when she felt the need to be normal again:

Marge: You think “Oh, I’ve gotta [sic] return to some semblance of normal” and then you realise … that normal doesn't exist anymore, so there’s a lot emotionally going on.

Marge further suggested that perhaps earlier still would have stopped the excessive drinking that occurred when she believed she had “beaten cancer” and received the all clear. On the other hand, Mary was almost 24 months post-diagnosis at the time of interview. She believed that the WWACP was not very successful for her because she just wasn’t ready to make changes even two years after diagnosis, especially changes that required a lot of organisation:

Mary: I really like the program, but I don't think I was mentally ready to be able to do it properly … I just didn't have the energy to do it. I went back to work, and I was just chasing my tail the whole time.

Participants’ personal situations also mediated their responses to WWACP messages. Several women (intervention and control) noted that their husbands continued to drink even though the participant herself had reduced her intake. For some, this was not a problem:

Ida: He would dearly like me to drink a little bit with him, but I suppose he's got used to it now, it's okay.

Although they reported no pressure from their partners to continue drinking, other participants believed their partner would benefit from reducing their alcohol consumption in collaboration with the participant:

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Trisha: I'm not against anyone drinking alcohol. My husband … I think enjoys it too much … but that's how he relaxes so, he’s not getting off his face but he has a couple of beers and then a rum at night.

However, an unsupportive home environment where alcohol is readily available can clearly counter any good intentions. For example, after consciously reducing her intake, Lara highlighted this as a potential problem:

Lara: It's difficult too socially, because my husband likes to drink beer, and so, it's changed our sort of dynamic … now that I'm not drinking as much … I've told him, "Don't buy me bottles of wine, because if the wine's in the house, I'm gonna [sic] drink it".

Factors other than the intervention mediated participants’ approaches to alcohol during the study period. For example, the continued physical effects of alcohol consumption during the intervention period similarly ranged from enjoyable to extremely unpleasant. Most reactions were adverse and participants discussed how these tended to discourage their drinking anyway. They cited effects such as nausea, vomiting, sleep disturbance, and vasomotor symptoms. For example, Mary had a few alcoholic drinks at the start of treatment, then, on one occasion became quite sick:

Mary: I just couldn't [drink again]. It was just not worth it 'cause it was, like, toxic. It was like putting more poisons [reference to chemotherapy treatment] in my body.

In Mary’s case, her physical response to concurrent alcohol intake and chemotherapy treatment was a significant deterrent to alcohol consumption prior to study enrolment, and her physical aversion meant she continued to minimise her intake after treatment. She had cognitively appraised alcohol as a toxin to be avoided, irrespective of participation in the study.

Similarly, Tracey reported that she now experienced hot flushes from consuming even small amounts of alcohol, while Mary noted that hot flushes commenced when she began eating a meal and were exacerbated if she had an alcoholic drink at the same time. Mary then remarked that the frequency of her hot flushes had reduced in line with reductions to her alcohol intake during the study period:

Chapter 6: Qualitative Results 161 Mary: But if I'm drinking as well, I think they [the drinks] just magnify it … it's just more. So, if I have one drink, I can feel the heat … I hardly have any hot flushes anymore. I think that's pretty major.

Some participants also reported sleep disturbances as a result of alcohol intake. These included unsettled or broken sleep, in addition to difficulties getting to sleep or staying asleep. Other physical consequences mentioned related to localised pain (i.e., pins and needles, swelling, tension) experienced in arms, neck, and or shoulders; severe migraines and headaches, nausea, and general undesirable feelings that disrupted the following day’s activities, all of which reinforced alcohol abstinence independent of the intervention.

Conversely, some participants experienced no adverse effects from alcohol at all and the intervention did not necessarily moderate their alcohol intake. Indeed, some reported beneficial effects when drinking alcohol that appeared to reinforce their consumption even when they were aware of the health risks this entailed. Alcohol often served as a coping mechanism that ameliorated the ongoing stress of diagnosis for those participants who had no adverse effects from alcohol even during chemotherapy. This issue is discussed further in the next chapter.

6.6 CONCLUSION

The qualitative findings presented in this chapter illustrate narratively how participants described alcohol in relation to their quality of life, health and social behaviours. The data highlight the environments that influenced participants’ interactions with alcohol before and after diagnosis. The findings also illuminate alcohol behaviours that were (and were not) associated with the intervention, and why these might occur. These qualitative data help illuminate the quantitative findings, providing potential insights into the ‘how’ and the ‘why’ of the quantitative results.

In the next chapter, Phase 3 of Precede is undertaken. In this interpretive phase of the thesis, the qualitative and quantitative data are considered in light of evidence from the literature to articulate the factors that might predispose, enable and reinforce the alcohol behaviours described in Chapters 5 and 6.

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Chapter 7: Interpretation

In this chapter, the quantitative and qualitative data are interpreted in light of the literature, congruent with Phase 3 of the Precede model. The aim of this chapter is to address the over-arching study aim; that is, to determine the factors that might influence alcohol consumption, before and after the intervention, in women previously treated for breast cancer. From the Precede perspective these factors are categorised into “predisposing”, “enabling” and “reinforcing”.

Predisposing factors largely provide the rationale for the behaviour and might include the individuals’ “knowledge, attitudes, beliefs, personal preferences, existing skills, and self-efficacy beliefs,” (Glanz et al., 2008, p. 415; Green & Kreuter, 2005). Enabling factors often provide the motivation for change and include both individual skills and environmental factors (Green & Kreuter, 2005; Simons-Morton et al., 2012). These factors can affect behaviour, either directly or indirectly, via an environmental mediator (Glanz et al., 2008). Reinforcing factors likely support or provide momentum for the behaviour to continue. They are the factors that, after a given behaviour is performed, “provide continuing reward or incentive for the persistence or repetition of the behaviour” (Glanz et al., 2008, p. 415). These are not always within the individual’s control (Simons-Morton et al., 2012).

These categories equate to the research questions in the following way:

1. Predisposing factor: What is the pattern (frequency, quantity, type, and place) of alcohol use amongst Australian women treated for breast cancer?

2. Predisposing factor: What are the demographic, psychosocial, and health- related factors associated with alcohol use?

3. Enabling and reinforcing factors: Is a tailored lifestyle intervention associated with change in alcohol-related health behaviours in this population?

Chapter 7: Interpretation 163 7.1 PREDISPOSING FACTOR: BASELINE PATTERNS OF ALCOHOL USE

7.1.1 Research question 1 What is the pattern (frequency, quantity, type, and place) of alcohol use amongst Australian women treated for breast cancer?

The measurement of alcohol intake is problematic for several reasons (Ali et al., 2014; Weaver et al., 2013). For example, self-reported alcohol intakes are historically subject to underreporting (Ali et al., 2014; Nagata et al., 2007). As found in this study, the intake data can also be skewed if there is a large proportion of non- drinkers; thus, using a continuous variable for intake potentially masks the true quantities consumed. In addition, categorisation cut-points are often aligned to country-specific alcohol guidelines that differ in standard drink sizes; consequently, cross-study comparisons can be difficult. In light of this, alcohol intake was assessed through different lenses (two quantitative and one qualitative) in this study to ensure that the research question was thoroughly investigated and the data interpreted in this chapter are as complete and defensible as possible.

Frequency and quantity The Australian recommendations for drinking are no more than two standard drinks on any day (≤ 20g/day) and no more than four standard drinks (40g/day) on a single occasion (NHMRC, 2009). Participants who reported drinking daily over the previous seven days accounted for 6.7% of the cohort, while a further 6.3% consumed alcohol 5-6 days per week. This accords with Australian figures, which indicate that in 2015, 6% of the general Australian population of both sexes were drinking daily and 8% were drinking 5-6 days per week (The National Centre for Education and Training on Addiction & Flinders University, 2015). This level of intake is not confined to Australia. Similar frequency patterns were evident in a Swedish breast cancer cohort, with 5.7% of participants reporting intake of ≥ 4 times during the previous week (Simonsson et al., 2014); and in an American breast cancer cohort, with 6.8% of participants reporting excessive alcohol consumption (defined as > 1 drink/day or ≥ 1 episode of four or more drinks in past 30 days) (Zhao et al., 2013).

For those participants who did report consuming alcohol, the interviews highlighted that the frequency and quantity of alcohol intake clearly changed

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throughout the course of the week, which explains the relatively low median daily intake (5.2 g/day, or approximately half a standard drink), but the wide range of 0.26 g/day to 52.3 g/day. The WWACP median reflects that identified by Kwan et al. (2013), in the After Breast Cancer Pooling Project, who reported a median intake of 5.3 g/day (range 0.39-149.32 g/day). Likewise, the majority of alcohol-consuming participants in a pooled analysis of oestrogen-receptor-positive breast cancer survivors (N = 6,596) by Nechuta et al. (2016), consumed between 0.36 g/day to < 6 g/day, which was equivalent to 30.6% (n = 1,942) of the cohort. The qualitative data collected for this PhD help explain the patterns of drinking in the WWACP and other studies. They suggest that participants incorporated alcohol into their highly structured Monday to Friday routines as a means of ’unwinding’ or debriefing the day with their partner. For example participants described enjoying a drink when preparing and or eating their evening meal, after their busy workday. Qualitative comments suggest that intake at these times was limited to one or two drinks during the working week. However, the qualitative results also indicate a tendency toward higher alcohol intake over weekend (Friday night to Sunday) and holiday periods. The reasoning behind these decisions to limit drinking to non-working times was generally related to the adverse physical effects of too much alcohol and consequent impact on working roles. The notion that drinking behaviours vary from weekday to weekend and throughout holiday periods is also evident in the literature (Lau- Barraco, Braitman, Linden-Carmichael, & Stamates, 2016; Room et al., 2012). A recent study of heavy-drinking nonstudents in the United States revealed that an individuals’ alcohol expectancies (social or tension-reduction expectations) strongly predicted alcohol consumption over the weekday and weekend periods. Stronger tension-reduction beliefs were associated with increased weekday consumption, while social expectations strongly predicted higher weekend consumption (Lau- Barraco et al., 2016). WWACP participant comments regarding “unwinding” with a drink on weekday evenings, support this notion that different cognitive pathways are involved in the decision-making process (Lau-Barraco et al., 2016). Hence tension- reduction expectancies possibly influenced the participant’s weekday drinking behaviours. Similar to the WWACP participants, drinking after 5 pm on a weekday was commonplace for most women who reported monthly drinking in a study that examined variation to time-of-day and day-of-week drinking patterns across five continents (Room et al., 2012). Room et al. (2012) also reported that drinking before

Chapter 7: Interpretation 165 5 pm on weekdays significantly predicted alcohol-related drinking problems in English-speaking sites, which included the New Zealand, United Kingdom and Isle of Man. The participants’ regard for minimising disruption to their working roles can be related back to cultural norms. Many Western cultures portray the widespread norm that work takes precedence over drinking, hence individuals are reluctant to combine alcohol with work (Heath, 2000). More so, Heath (2000) proposed that an alcoholic beverage tends be a significant marker in time, one that represents the shift from work to non-working periods or leisure time (Heath, 2000), which appeared to be the case in the study context as well.

For WWACP participants who did drink, the maximum number of drinks consumed over a 24-hour period, which likely represented a single drinking session, was most often between 1-2 glasses or 3-4 glasses. Assuming these intakes were not occurring daily, they still remain within Australian recommendations for healthy populations. This finding demonstrates that the majority of participants adhered to the national guideline recommendations. Alcohol intake recommendations are discussed in more detail in Section 7.2.

In summary, the frequency and quantity of alcohol consumed by the WWACP cohort was influenced by factors that often changed throughout the week. Furthermore, the findings were reflective of other breast cancer cohorts and much the same as women in the general population. The latter finding has also been reported in earlier studies, both international and Australian-based (Bellizzi, Rowland, Jeffery, & McNeel, 2005; Eakin et al., 2007; Potter et al., 2014).

Type and place Participants described how their preferences for different types of alcoholic beverage and the place of consumption also varied according to their daily versus weekend intake patterns. Regular daily drinking often involved the consumption of red or white wines that were consumed at home, while champagne and sparkling wines were for socially-driven celebratory events at the weekend.

Other studies of breast cancer cohorts report a similar trend in that wine was the predominant drink of choice (Flatt et al., 2010; Vrieling et al., 2012; Weaver et al., 2013). Likewise, findings from the EPIC Study indicate that 91% of women classed as regular drinkers predominantly consume wine (Ferrari et al., 2014).

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Bottled wine is also the preferred beverage of 51% of women in the general Australian population (Australian Institute of Health and Welfare, 2017). There were often reasons for this. For example, one participant described how her perception of alcohol differed according to the type of beverage consumed. This participant was more likely to associate spirit consumption with problematic drinking than frequent intakes of beer or wine, which was viewed as less harmful. This perception was echoed in a qualitative study focussed on perceptions of breast cancer risk in a cohort of Australian women without breast cancer (Meyer et al., 2019).

Place of consumption in breast cancer-specific populations is not widely reported, yet 79% of the general population report mainly drinking in their own homes (AIHW, 2017). Additionally, the largest Australian independent research company reported that over a four-week period (October 2014 – September 2015; N = 7,621) 80.3% of Australian women preferred to drink wine at home (Roy Morgan Research, 2015). Drinking at home appears solitary, and therefore contrary to earlier research, which suggested men and women foster a sense of social connection via interactions that involve alcohol consumption (Drabble & Trocki, 2013; Manton, Pennay, & Savic, 2014). Given that the majority of participants in the study were married, from their comments it seems unlikely that these participants were drinking alone at home. The social complexities of alcohol consumption in this population are explored in more detail in Section 7.1.2, but it seems that the type and place of alcohol consumption for participants in the WWACP breast cancer cohort is much the same as for women in the general healthy population.

Patterns of intake Patterns of alcohol intake rarely stay the same over the course of a lifetime, as reflected in the normative data. Both the normative data and the study sample indicated that younger aged women across both groups tend to exceed the alcohol- related single occasion risk guideline (ABS, 2015; NHMRC, 2009). While the alcohol-related lifetime risk increases as age increases for women in both groups, other factors can influence change in alcohol consumption patterns over the course of a lifetime. For example, as women age their priorities and responsibilities, such as being pregnant and caring for a family, financial circumstances, or even career objectives change and directly influence how and whether they choose to drink (Muggli et al., 2016; Wilson et al., 2017). A diagnosis of breast cancer or any other

Chapter 7: Interpretation 167 traumatic life experience can do the same (Costanzo, Lutgendorf, & Roeder, 2011; Ko, Song, & Shin, 2017; Newcomb et al., 2013). Additionally, the time since diagnosis can largely influence intake behaviours (Ko et al., 2017). A 2013 study by Zhao et al. reported a quadratic trend for alcohol consumption over the course of survivorship (p < .001), thus indicating a U-shaped trend as opposed to linearity. In that cohort of mostly non-Hispanic white American breast cancer survivors, the number of participants reporting excessive alcohol consumption (> 1 drink/day or ≥ 1 episode of four or more drinks in past 30 days) initially increased as the time since diagnosis lapsed. Excessive intakes were low < 1 year after diagnosis, peaked at 15- 19 years post-diagnosis before declining to the lowest at ≥ 30 years (Zhao et al., 2013).

However Flatt and colleagues (2010) reported that intakes decreased post- diagnosis, and one year on were maintained after study enrolment (the longitudinal Women’s Healthy Eating and Living Study). Conversely, a recent Korean study of cancer survivors (mixed cancers) evaluated alcohol intake behaviours post-cancer diagnosis and in relation to extended time since diagnosis (Ko et al., 2017). The findings suggested that subjects deemed high-risk drinkers and quitters eventually converged with moderate drinkers as time (in years) since diagnosis lapsed (Ko et al., 2017).

While the quantitative results in the WWACP study also indicate a potential quadratic trend, qualitative comments described a wide variation of intake patterns pre- and post-cancer diagnosis as well as during the treatment phase. Participants who described themselves as non- or seldom drinkers prior to diagnosis tended to continue with this intake pattern post-diagnosis. While some comments on pre- diagnosis portrayed participants who frequently consumed moderate amounts of alcohol “one or two glasses” most nights, post-diagnosis they limited their consumption to one drink only at the weekend. Yet not all changes occurred immediately following diagnosis.

Some participants described continued alcohol consumption throughout the treatment period (regardless of the treatment type), while others reported rare or no alcohol consumption at all. In general, alcohol that was consumed occurred between treatment cycle periods. As highlighted in the qualitative results chapter, two participants described periodic binge drinking during the treatment phase, with little

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to no ill effect. This limited effect of alcohol during the treatment period lead to higher than usual intakes for one of the participants. Furthermore, participant comments described interactions with the wider breast cancer support community on this matter. Mae commented that feedback she received from the wider community via social media suggested that this was actually quite common and "alcohol may have no effect whatsoever on you".

Several studies (Hesketh, Aapro, Street, & Carides, 2010; Hilarius et al., 2012; Molassiotis, Stamataki, & Kontopantelis, 2013; Uomori, Horimoto, Mogushi, Matsuoka, & Saito, 2017; Warr, Street, & Carides, 2011) have also reported this, indicating that habitual alcohol intake is in fact associated with less chemotherapy- induced nausea and vomiting. Uomori et al. (2017) suggested the underlying physiological mechanism likely involves an acetaldehyde dehydrogenase isozyme, which interferes with the metabolism of emetic chemotherapy drugs. Perhaps potentially reduced incidence of chemotherapy-induced nausea and vomiting, together with absence of negative alcohol induced side effects, such as headaches, encouraged repetition of the behaviour and thus reinforced the drinking behaviour.

In summary, the drinking patterns discussed above share some similarities with other breast cancer cohorts and closely reflect those of the general Australian population. These patterns indicate limited awareness, understanding, and or compliance with recommendations for consumption of alcohol post-breast cancer diagnosis during the treatment period and beyond. To gain a better understanding of why women in this population are influenced to drink the way they do, potential predisposing factors that relate to demographic, psychosocial, and health-related factors identified in the cohort are discussed in the following section.

7.1.2 Research question 2 What are the demographic, psychosocial, and health-related factors associated with alcohol use?

In summary, the Study 1 cohort were middle-aged and predominately married, Australian-born Caucasians. They were mostly well-educated, employed at the time of diagnosis, and relatively affluent. Most had completed treatment six to 24 months prior to study enrolment and were non-smokers. At baseline, they reported reasonable health-related quality of life and were likely to have either no co-

Chapter 7: Interpretation 169 morbidities, or up to three co-morbidities. Participants reported moderate physical activity levels; however, the cohort was largely overweight or obese at baseline. These findings are reasonably generalisable to the Australian population (ABS, 2017) and for the most part, to other breast cancer populations (A. S. Anderson et al., 2014; D. J. Anderson et al., 2015; Ko et al., 2017; Kwan et al., 2013; Potter et al., 2014; Rock, Byers, et al., 2013; Rogers et al., 2015; Scott et al., 2013; Zhao et al., 2013). The significant predisposing factors identified in Study 1 include age, tobacco use, health-related quality of life, physical activity, baseline knowledge, exposure to alcohol education, and socially-mediated beliefs about alcohol.

The relatively mature age of the cohort and age-related and other social norms appeared to predispose the participants to specific patterns of alcohol use. It appears that as age increases so does the frequency of steady intake (Australian Institute of Health and Welfare, 2017; Plant, 2008). For example, the Australian Health Survey results for 2011-12 suggested that women who consumed excessive alcohol were likely aged between 55-64 years (ABS, 2012b). Similarly, women aged 65-74 years according to recent census data were more likely to steadily consume more alcohol (ABS, 2015). While statistics indicate that Australian women in their 50s are now more likely to be risky drinkers in terms of cumulative volume than those aged between 18 and 24 years (AIWH, 2017).

Younger women in this study also consumed alcohol, but, consistent with Australian norms for women, in a different pattern (AIHW, 2017; Plant, 2008). Younger women are more likely to binge drink, defined as > 4 standard drinks on a single occasion, rather than spread their drinking over time (AIHW, 2017). Participants’ qualitative comments acknowledged binge drinking while younger. Sally very rarely drank by the time of interview, but did note “I probably went out a little bit too hard and fast as a teenager”. Unfortunately, the proportion of Australian 50-59 year olds who also report drinking at very risky levels (≥ 11 standard drinks on a single occasion during the past 12 months) has also significantly increased since 2013 (AIHW, 2017).

Irrespective of the pattern of drinking, the cumulative total of alcohol consumed over the woman’s lifetime is critical. White et al. (2017) suggested that the risk of breast cancer increased for women who reported a heavy lifetime alcohol intake (≥ 230 drinks per year). The authors also identified an increased risk for

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moderate lifetime drinkers (60-229 drinks per year; equivalent to 1.2-4.4 drinks per week) who also binge drink (≥ 4 drinks at one sitting) (White et al., 2017). The latter finding is particularly relevant to the WWACP cohort, as participant comments described both pre- and post-cancer intake patterns that align with moderate lifetime drinkers who also periodically engaged in binge drinking.

The participants in this study were predominately Australian-born Caucasians, reflective of the Australian population (ABS, 2017). According to the literature, this fairly homogenous sample with similar ancestry is more likely to drink more frequently (A. S. Anderson et al., 2014; D. J. Anderson et al., 2015; Kwan et al., 2013; Potter et al., 2014; Rock, Byers, et al., 2013; Rogers et al., 2015; Scott et al., 2013). Study findings support the notion that participants’ ancestry influenced drinking behaviours. Comparison of the drinkers and non-drinkers in the WWACP indicated being Australian-born was likely to increase the odds of being a drinker. This is not unreasonable given the Australian drinking culture (Hildebrand et al., 2013; Jones & Magee, 2014; Miller, Coomber, Staiger, Zinkiewicz, & Toumbourou, 2010; Stafford, Allsop, & Daube, 2014) and that cultural norms are a critical determinant of alcohol consumption. Australia’s social norms with respect to alcohol consumption have a long history (Midford, 2005) and remain problematic today (Hildebrand et al., 2013). Alcohol is an accessible, mood altering, disinhibiting drug (Myers & Isralowitz, 2011). It can ease interactions between people and potentially enhance their enjoyment of social situations (Ettorre, 1997; Myers & Isralowitz, 2011). Cultural norms also influence patterns of drinking, which determine how (risky drinking, chosen type of beverage), when (frequency), and where a person chooses to drink (NHMRC, 2009). The associated drinking activities, behaviours, and personal characteristics of the individual and their drinking companions all contribute to the ready Australian acceptance of a drinking culture (NHMRC, 2009). This is further highlighted when rural Australian culture is considered.

For WWACP participants living outside metropolitan areas, the Australian rural culture clearly influenced alcohol-related behaviours and perceptions of alcohol. Qualitative findings highlighted how even non-drinkers and ‘seldom’ drinkers described alcohol consumption in a positive manner that effectively brought people together at social events. This finding is not unusual. A qualitative study of six rural Australian communities by Allan et al. (2012) (N = 46), identified five

Chapter 7: Interpretation 171 themes that related to the influence of alcohol in Australian rural culture. One theme, “learning to drink” was attributed to the influence of older persons. This theme highlighted how drinking practices, including learning how to “hold your alcohol”, were primarily learnt within families and passed on in a similar manner (Allan et al., 2012). It is often argued that within rural communities, alcohol consumption is associated with positive beliefs and values, including partaking in social activities and instilling a sense of belonging (Allan et al., 2012; Miller et al., 2010). For rural dwellers there appears to be strong normative pressure to drink and be a part of alcohol-related activities (Allan et al., 2012). Those who do not conform (non- drinkers) are described as outsiders who do not fit in (Allan et al., 2012).

In particular, rural living appears to encourage positive attitudes towards alcohol intake and potentially provides the accepting social environment that predisposes to drinking at a younger age (Miller et al., 2010). Unfortunately, this means that compared to persons living in urban communities, those living rurally experience higher rates of alcohol-related harm over the course of their lifetime (Allan et al., 2012). As discussed in more detail later in this section, rurality is further associated with the high-risk smoking behaviour that is intertwined with alcohol use. The Queensland Cancer Study suggested strongly that persons residing in rural or remote regions of Queensland were more likely to drink in excess and smoke daily, compared to those residing closer to cities (DiSipio et al., 2006). The 2016 National Drug Strategy findings further confirmed that “people living in Remote and Very Remote areas were more likely to smoke and drink at risky levels” (Australian Institute of Health and Welfare, 2017, p. 14).

Higher levels of education are believed to predispose the adoption of healthy behaviours, such as greater participation in cancer screening programs (Hsiou & Pylypchuk, 2012; Kunze & Böhm, 2009) and higher intake of fruit and vegetables (W. C. Wang & Worsley, 2014). Unfortunately, the opposite holds for alcohol consumption. Higher educational attainment is generally associated with greater alcohol intake (Bloomfield, Grittner, Kramer, & Gmel, 2006; Ferrari et al., 2014; Flatt et al., 2010; Kwan et al., 2013). The Study 1 results indicated that more highly- educated participants were significantly more likely to consume alcohol than those with a lesser degree of educational attainment (i.e., the odds of being a drinker compared to a non-drinker were greater); although educational attainment was not a

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significant predictor of greater intakes of alcohol in this study. Similarly, studies in other breast cancer-specific cohorts (Kwan et al., 2013) and non-cancer female cohorts (Bloomfield et al., 2006; Ferrari et al., 2014; Wilson et al., 2017) strongly support the association of higher education with alcohol consumption. The underlying reasons for this are not yet understood. Literature focusing on alcohol use among Australian and New Zealand tertiary students proposes that engagement in the culture of drinking that they often encounter in university could result in a more tolerant attitude towards alcohol intake that lasts the lifespan (Kypri, Langley, McGee, Saunders, & Williams, 2002; Muggli et al., 2016).

It might be, as Bowden et al. (2014) suggested, that irrespective of their level of education few Australian women are aware that alcohol is a risk factor for cancer. A recent qualitative study of women attending breast cancer screening in Scotland revealed a similar scenario (Conway, Wayke, Sugden, Mutrie & Anderson, 2016). The study aimed to establish views and attitudes toward lifestyle intervention approaches offered through national health screening centres. The authors noted participants’ surprise when informed that alcohol is a risk factor for breast cancer. Additionally, the authors described participants’ scepticism about the evidence-base of alcohol consumption and body weight as lifestyle-associated risk factors for cancer. In this study, discussions on alcohol were described as difficult for participants. They struggled to quantify their intake accurately and many appeared resistant to thinking about their personal relationship with alcohol (Conway et al., 2016). Resistance or defensive responses are not unreasonable given the personal risks implied when discussing alcohol behaviours in this context (Liberman & Chaiken, 1992).

Similarly, despite being mostly well-educated, the women who were interviewed in Study 2 revealed that this did not necessarily predispose them to link alcohol consumption and breast cancer. Participants who consumed alcohol described not wanting to consciously acknowledge this link prior to enrolling in the study because they enjoyed a drink and did not want to think that their actions were “endangering” their health or that they perhaps “drank too much”. In any case, knowledge by itself does not necessarily prompt behaviour change. Knowledge needs to be absorbed and appraised as valid before any action occurs (Green & Kreuter, 2005). This could explain the findings of a recent study from the United

Chapter 7: Interpretation 173 Kingdom that examined the effects of a television-led breast cancer mass media campaign designed to raise awareness of alcohol as a risk factor for cancer (N. Martin et al., 2018). N. Martin et al. (2018) reported that while knowledge of the risks of alcohol was raised after the campaign, it did not significantly change intentions to reduce alcohol consumption. Nonetheless, placing mandatory health messages on alcoholic beverages could provide a strong and necessary public health message that might increase awareness for many Australians. In light of these data, the WWACP alcohol-related content and educational strategies could benefit from review and redesign.

Cigarette smoking is often associated with alcohol consumption (Eakin et al., 2007; Flatt et al., 2010; Hamajima et al., 2002; Hausdorf et al., 2008; Zhao et al., 2013). While the proportion of current or past smokers in the WWACP was small, the quantitative results confirmed this association. Participants who were current or past smokers had greater odds of alcohol consumption compared to non-drinkers and had greater odds of consuming more alcohol, to the point that smoking status was a significant predictor of alcohol intake in this study. It is important to note that due to the low incidence of current or past smokers in the sample, this statistically significant result might not represent clinical significance outside the study context. However, this finding is not uncommon in breast cancer populations (Ko et al., 2017; Kwan et al., 2013; Zhao et al., 2013). For example, Flatt et al. (2010) reported of their breast cancer cohort that participants (N = 3,088) who had ever smoked were twice as likely to engage in moderate or heavy drinking than light drinkers. This association, which has also been reported in general populations (Ferrari et al., 2014; González-Rubio et al., 2016; Muggli et al., 2016), is possibly explained by the way high-risk health behaviours tend to be reciprocal (Eakin et al., 2007; Hausdorf et al., 2008; Zhao et al., 2013). For example, poor diet and inadequate physical activity coupled with tobacco and alcohol use form a complex of high-risk health behaviours recorded in women previously treated for cancer (Eakin et al., 2007; Zhao et al., 2013). All of these behaviours have real potential to undermine the health of this cohort. They are associated with a poorer prognosis and greater risk of cancer mortality and the development of treatment-induced chronic conditions (Zhao et al., 2013), particularly when they co-occur.

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Health-related of life (QoL) was the primary outcome of the WWACP and, consistent with the Precede model underpinning this sub-study, a variable of considerable interest in this PhD. As Figure 3.1 illustrates, Precede predicates QoL as both the desirable outcome of an intervention and a factor that predisposes participants to engage in health promotion activities such as alcohol minimisation. One driver of baseline abstinence was iatrogenic: many participants no longer enjoyed alcohol due to treatment-induced changes, such as dysgeusia (chemotherapy-related taste changes) and nausea. Hence, improving health-related quality of life after cancer treatment to counteract these symptoms was a likely catalyst for abstinence.

The WWACP cohort reported reasonable health-related QoL at baseline, although it was generally poorer than the QoL reported in studies of similar cohorts using the FACT-G (Cahir, Thomas, Dombrowski, Bennett, & Sharp, 2017; Hagstrom et al., 2016; Snyder et al., 2008). In WWACP participants, the findings suggest that the higher the overall FACT-G score, the greater the odds the participant would drink alcohol. This was particularly apparent in the social and family wellbeing domains, where higher scores in these domains meant the participant had greater odds of consuming alcohol. Similarly, González-Rubio et al. (2016) reported trends that suggested persons with higher scores in the psychosocial domain were more likely to drink moderate amounts than to abstain. The qualitative comments provided a possible explanation for this finding. Post-treatment participants were beginning to regain strength, both physically and emotionally, and as such their quality of life was improving. At the time of WWACP enrolment comments described lives that were returning to “normal”, and for many Australians, alcohol is associated with “normal” (Allan et al., 2012; Hildebrand et al., 2013; Jones & Magee, 2014; Miller et al., 2010). Descriptors of “normal” drinking behaviours were described in relation to the “correct” amount of alcohol now being consumed because the participant was feeling back to her normal self. Furthermore, the return to “normality” was cause for celebration with friends and family, which included alcohol consumption. Participants whom rarely drank also described the latter scenario.

Quality of life is also strongly associated with physical activity (Rogers, McAuley, Courneya, & Verhulst, 2008; Scott et al., 2013). This cohort was more physically active at baseline than the Australian norm for women. Just over 90% of

Chapter 7: Interpretation 175 the study cohort reported moderate or high physical activity levels in the preceding seven days. This was considerably more activity than adults aged 18-64 years in the general population, where 55.5% were deemed “sufficiently active” (ABS, 2015). Sufficiently active is “more than 150 minutes of moderate physical activity or more than 75 minutes of vigorous physical activity, or an equivalent combination of both, including walking” (ABS, 2015, p. 38). In the WWACP cohort, higher levels of physical activity were concurrently associated with higher alcohol intake and better quality of life. Given the implication of the parent study that the WWACP lifestyle intervention would in fact promote less consumption of alcohol while promoting better quality of life and physical activity, this result is counterintuitive. However, the literature indicates that it is not uncommon. Other studies of breast cancer cohorts (Flatt et al., 2010) and in general populations (Allen et al., 2009; Ferrari et al., 2014; French et al., 2009; González-Rubio et al., 2016) have reported greater alcohol consumption in the context of more physical activity. There could be a dose- dependent association between alcohol and physical activity, in which the strength of the association declines once alcohol intake increases from moderate to heavy (Smothers & Bertolucci, 2001).

A range of predisposing factors appeared to influence participants’ baseline patterns of alcohol consumption, including age, tobacco use, health-related quality of life, physical activity, baseline knowledge, exposure to alcohol education, and socially-mediated beliefs about alcohol. These are congruent with the indicators proposed in the Precede model (Green & Kreuter, 2005). The next chapter interprets any changes in these patterns after participants’ exposure to the control or intervention arms of the WWACP study.

7.2 FACTORS THAT ENABLED AND REINFORCED CHANGES IN ALCOHOL CONSUMPTION

7.2.1 Research question 3 Is a tailored lifestyle intervention associated with change in alcohol-related health behaviours in this population?

The previous section explained the factors that might have predisposed participants’ baseline alcohol intake. This section examines whether those patterns changed after participation in the WWACP and if any changes were sustained at 24

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weeks. The 12-week WWACP intervention encouraged, through tailored coaching, minimal to no alcohol consumption in the context of other health-promoting lifestyle changes or to consume alcohol within the recommended guidelines if choosing to drink. The focus in this section is on the enabling and reinforcing factors that potentially influenced any changes in alcohol consumption in WWACP participants.

In summary, there was little change in daily alcohol intake in the intervention and control groups across the three measurement points of baseline, Week 12 (completion of the intervention) and Week 24 (12-week follow-up). While the data at every time point were positively skewed due to the large number of non-drinkers and participants with low intakes (> 0 to ≤ 10g per day), the descriptive statistics did indicate some trends. For example, alcohol intake dipped at Week 12 in the intervention group, although this decrement was not maintained at Week 24. In contrast, alcohol intake in the control group increased at Week 12 and then reduced to almost match baseline intake by Week 24. However, intakes were relatively stable over time when the median intake for each group at each time point was considered. Descriptive statistics further indicated that participants in the control group were more likely to consume alcohol at baseline and that they consumed more daily alcohol at T1 and T2 than the intervention group. The overall trend for both groups throughout the study was that the majority of participants who consumed alcohol did so at the rate of one standard drink per day (> 0 to ≤ 10g per day). There were no statistically significant differences in intake between the groups and time-points.

Elements of these findings such as the daily intake amount, accord with those reported in some intervention studies in breast cancer cohorts by some authors (D. J. Anderson et al., 2015; Flatt et al., 2010; de Liz et al., 2018), but not others. For example, there were no within-group or between-group differences noted for alcohol consumption in the PINK Women’s Wellness Program Study, which was the pilot study for the WWACP (D. J. Anderson et al., 2015). In contrast, the Women’s Healthy Eating and Living study, which targeted vegetable, fat and fibre intake but not alcohol, nonetheless reported that alcohol intake did significantly decrease by 0.9 grams per month in the intervention and control groups at 1-year follow-up (p < .05) (Flatt et al., 2010). Similarly, de Liz et al. (2018) reported a decrease in alcohol consumption following an educational nutrition intervention that aligned with cancer prevention guidelines for women undergoing breast cancer treatment. This study

Chapter 7: Interpretation 177 considered the participants’ adherence to the 2007 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) guidelines for cancer prevention (WCRFI/AICR, 2007), which included the recommendation to limit alcohol to no more than one drink per day (quantified as 10-15g of ethanol/day). Intervention participants (n = 18) experienced biweekly telephone calls, personal meetings and monthly handouts that directly related to the WCRF/AICR recommendations for a period of 12 months. The control group received usual care (n = 68) during this period with provision of basic nutritional handouts. A significant decrease of 0.2 grams of ethanol per day (p = .02) was reported in the intervention group at 12 months and/or treatment end compared to baseline; however, no between-group differences were noted. Similar to the WWACP, the authors remarked on low baseline alcohol intakes, which revealed that all intervention participants’ and most control participants’ alcohol intakes were below the guideline recommendation prior to commencing the intervention (de Liz et al., 2018). Despite the smaller sample size of the aforementioned study, the WWACP cohorts’ mean alcohol intake also fell within the WCRF/AICR guideline recommendation.

Park, Knobf, Kerstetter and Jeon (2019), took a similar approach to de Liz et al. (2018) with their use of cancer-related guidelines to examine the health- promoting lifestyle behaviours associated with an exercise intervention. However Park et al. (2019), focused on adherence to the American Cancer Society guidelines on nutrition and physical activity for cancer survivors (Rock et al., 2012), which also referenced limiting alcohol consumption. This study assessed the effectiveness of an exercise intervention that included provision of nutritional education at baseline for female cancer survivors (83.1% breast cancer; 11.8% gynaecological cancer; n = 76 exercise intervention, n = 78 control group). The authors noted that adherence to the alcohol guideline contributed most to the overall participant scores at each time-point (alcohol responses scored as > 1 drink/day = 0; 1 drink/day = 1; non-drinkers = 2). Trends indicated excellent adherence to the alcohol guideline as the intervention group’s mean scores increased at each time-point (reflecting good adherence; drinking within or below recommended amount) and while the control group scores remained high, they were relatively stable (Park et al., 2019).

In contrast, a weight management intervention (weight loss and maintenance) for obese and rural breast cancer survivors indicated that a focus on energy balance

178 Chapter 7: Interpretation significantly deceased alcohol intake at intervention end (six months) (Fazzino, Fleming, & Befort, 2016). In this study, secondary analysis of data from a parent study assessed change in alcohol intake in participants who reported regular consumption at baseline (n = 37). The authors suggested that reductions were maintained through minimal mail-based contact through to the 18 month follow-up (Fazzino et al., 2016). The data indicated that during the weight loss intervention period, alcohol intake decreased from 19.6 grams per day at baseline to 2.3 grams per day at six months (p = .001). However, no significant changes were evident at the end of the maintenance phase (18 months) or between groups (Fazzino et al., 2016). This lack of intervention sustainability is not uncommon. For example, the results of an intensive lifestyle intervention with overweight or obese participants with type 2 diabetes mellitus, indicated that the intervention had no effect on reducing alcohol consumption at four year follow-up (Chao, Wadden, Tronieri & Berkowitz, 2019).

Additional lifestyle interventions that focus on a multitude of cancer related risk factors including alcohol intake are currently underway, including the Advancing Survivorship Cancer Outcomes Trial (ASCOT) (Beeken et al., 2016). Specifically designed for cancer survivors, this lifestyle intervention has a proposed completion date of 2020 (Beeken et al., 2016). However, a review of clinical trial registries (i.e. ClinicalTrials.gov [https://clinicaltrials.gov]; International Clinical Trials Registry Platform [https://ichgcp.net/clinical-trials-registry]; Cochrane Breast Cancer Specialised Register [https://ichgcp.net/clinical-trials-registry]) reveals the scarcity of alcohol-specific interventions in the breast cancer context. Alcohol reduction appears to be a particularly difficult lifestyle choice to address even in the face of a cancer diagnosis.

The literature indicates that a diagnosis of breast cancer could be as much a spur to lifestyle changes as a lifestyle intervention, although again the reports are inconsistent. For example, pre- and post-diagnosis alcohol intakes in a large sample of women with breast cancer in the United States (N = 4,881) indicated little mean change as a result of diagnosis (pre-cancer mean drinks per week 3.4 [SD 5.6] compared to post-cancer diagnosis mean drinks per week of 3.2 [SD 5.8]) (Newcomb et al., 2013). However, Newcomb et al, (2013) did note that 19% of pre-diagnosis regular drinkers in that study (defined as ≥ 3 drinks per week) became post-diagnosis non-drinkers, while 20% of pre-diagnosis non-drinkers shifted to post-diagnosis

Chapter 7: Interpretation 179 regular drinkers. Newcomb et al. (2013) did not propose an explanation for this finding.

Two issues emerge from the results of the study reported in this thesis. First, alcohol-related behaviour is complex. It might or might not be related to intervention or diagnosis, and can change over time for a variety of reasons. Second, underlying changes are not necessarily conveyed in quantitative results, because survey data in this field are notoriously subject to measurement and self-report bias (AIHW, 2017; Bowden et al., 2014; Stockwell et al., 2016). In this respect, qualitative data, which were obtained face-to-face from in-depth interviews during this study, provide an alternative lens for the drawing out and interpreting the findings. The remainder of this discussion applies this lens, with support from the literature, to determine potential enabling and reinforcing factors for alcohol-related behaviour change in the study.

Enabling factors for change The WWACP was designed to provide a supportive environment to enable positive changes in several health behaviours, including alcohol consumption. Green and Kreuter (2015) proposed that enabling factors are predominantly conditions of the person’s environment. The environment contains the social or structural resources and barriers that can facilitate or impede the target behaviour (Green & Kreuter, 2005). For example, participants’ access to a computer with a camera, email, and a private space for virtual interviews enabled their participation in the WWACP. Women interested in participation who did not possess these enablers were not eligible to enter the study. Hence, the relative affluence of the WWACP cohort appeared to be a major indicator, and enabler, for not only enhanced access to alcohol, but also the means to participate in the study. The main WWACP-related enablers to change in alcohol-related behaviours (both positive and negative) in this study were intervention delivery method, content and timing.

In terms of delivery method, the way in which the WWACP subject matter was presented (in flexible virtual format by a trained health care professional) enabled positive behaviour change for intervention participants. The interview data illustrated how virtual delivery meant that the WWACP maintained privacy for participants who did not want to disclose their diagnosis to others in a traditional cancer support group. Anonymity in addition to access, tailorability and convenience are highlighted

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as important factors in the development of effective eHealth interventions (Fleisher et al., 2015). Interventions that embody a mix of virtual delivery and printed materials appear to be particularly useful for treatment decision-making and survivorship issues amongst breast and prostrate cancer patients (Fleisher et al., 2015). The WWACP considered and incorporated all of these elements in the study design process. For example; the program book was available in hardcover and as an iBook; web-based resources were available via a participant portal; virtual nurse consultations were not only easily accessible, they were confidential and scheduled with participant convenience in mind. A recent systematic review of breast cancer- related eHealth interventions examined 24 studies originating from Europe, the United States of America and Asia (Triberti, Savioni, Sebri, & Pravettoni, 2019) and supports this multimodality approach. Similar to the WWACP, the most effective studies incorporated multi-component online services to address multiple aspects of quality of life (i.e. “social support, emotions and coping, and self-efficacy in health management”), in addition to offering support to the participant via contact with an expert mentor (Triberti et al., 2019, p. 11).

In terms of content, it was obvious from the interviews that the WWACP was the only source from which some participants received information on alcohol as a risk factor for recurrence of cancer. Evidence indicates this is contrary to patient’s expectations (Williams, Beeken, & Wardle, 2013) and particularly concerning given the growing body of evidence on this matter. A recent systematic review by Simapivapan et al. (2016) suggested stronger evidence should be offered to women regarding the relationship between moderate amounts of alcohol consumption and increased risk of recurrence. It appears the majority of cancer survivors and the people in their social networks believe it is “the doctor’s duty” to provide lifestyle advice, including alcohol advice, to cancer patients (Williams et al., 2013). This highlights the importance of appropriate and timely provision of alcohol-related education for the target population. However, the issue of health professionals not wanting to impute blame for the cancer diagnosis, therefore failing to provide “concrete guidance”, was also discussed in the qualitative data. This potentially explains why some patients were not exposed to alcohol-related information (Williams et al., 2013). The notion that health care professionals could appear insensitive or imply blame while providing lifestyle advice to cancer patients arose in

Chapter 7: Interpretation 181 a survey of 400 health professionals caring for cancer patients (Macmillan Cancer Support/ICM, 2011; Williams et al., 2013). Themes of blame were also evident in a qualitative study that focused on the perspectives of twenty-one health professionals who provided lifestyle advice to cancer survivors (Koutoukidis et al., 2018). In this study, health professionals described not wanting to generate feelings of guilt in the patient for previous lifestyle choices or future avenues for blame if recurrence transpired. Consequently, the advice provided was perhaps too subtle to inspire the patient to make useful lifestyle changes (Koutoukidis et al., 2018). It is critical that health care professionals work to overcome such barriers, as patients evidently welcome lifestyle advice and more importantly require guidance when interpreting complex public health recommendations at the individual level (Koutoukidis et al., 2018; Williams et al., 2013). Given that health professional practice does not currently match the expectations of patients, training clinicians to overcome practical barriers in health-education delivery has been suggested (Koutoukidis et al., 2018). This could be facilitated by tools and techniques to provide evidence in a supportive environment in a way that instils motivation instead of creating guilt (Koutoukidis et al., 2018). In this respect, promising outcomes were reported after the implementation of an e-learning resource specifically designed for cancer care health professionals (Murphy, Worswick, Pulman, Ford, & Jeffery, 2015). It is clear that future iterations of the WWACP would benefit from eliciting the participants’ previous interactions with health professions regarding health advice and provide adequate training for consultation nurses facilitating implementation of the program.

While information about quantities enhanced knowledge levels for some intervention participants, even more interesting was that participation in the control group also enabled modification of alcohol-related behaviours, possibly because it sensitised them to recommended guidelines.

The positive tone of the WWACP content was also enabling. For example, Ida appreciated the “do this ... instead of don’t do this” approach the WWACP took and noted “there's nothing restrictive. In fact, it's very encouraging because it's positive”. In this respect, the WWACP demonstrated what could be considered the “nudge” approach to instilling healthy behaviours. Coined by Thaler and Sunstein in 2008 a nudge is defined as “any aspect of the choice architecture that alters people’s behaviour in a predictable way without forbidding any options or significantly

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changing their economic incentives” (as cited in, Hansen & Jespersen, 2013, p. 7). This approach is now often embedded in public policy to motivate better consumer choices without expensive changes to legislation (Cabinet Office - Behavioural Insights Team, 2010) or heavy handedness on the part of the State (Earl, 2017). Originating from the behavioural sciences, the key to this approach is to frame options in cognitive terms (Earl, 2017) to stimulate beneficial health changes that align with peoples’ underlying motivations (Cabinet Office - Behavioural Insights Team, 2010), as was highlighted by Ida’s comment above. Hence, the developers of the WWACP were “choice architects”, prompting participants towards healthy options without restricting their freedom of choice (Thaler & Sunstein, 2008).

Alcohol nudges were particularly problematic to build in to the WWACP, because beneficial alcohol behaviours are difficult to define. WWACP recommendations adhered to Australian guidelines. Yet alcohol guidelines for people with a diagnosis of breast cancer (who are at greater risk) are confusing, in that they are much the same as those for the general Australian population. The international guidelines (Dietitians of Canada, 2011; Kushi et al., 2012; WCRFI/AICR, 2007) add to this complexity. While they ostensibly provide the same recommendation (limit consumption to no more than one drink per day for women), international measures of one “drink” of alcohol are not comparable. Depending on the international guideline one chooses to follow, a “drink” ranges between 10 and 15 grams of pure alcohol per standard drink. The Australian guideline of no more than two standard drinks on any day (≤ 20g per day), suggests that higher intakes are acceptable. The National Health and Medical Research Council alcohol guidelines (NHMRC, 2009) for the general Australian population are currently under review, with the proposed release date set for late 2019-early 2020 (NHMRC, n.d).

In terms of timing, the literature in cancer care also emphasises the “teachable moment”: the time after diagnosis when a woman is willing and able to hear messages about the need for behaviour change in relation to cancer treatment-related risk, and can absorb messages on how to achieve this (Bellizzi et al., 2005; Costanzo et al., 2011; Demark-Wahnefried, Aziz, Rowland, & Pinto, 2005). The problem is that the teachable moment is different for each individual.

The teachable moment for breast cancer (Costanzo et al., 2011) and other cancer cohorts (Bellizzi et al., 2005) is widely considered to occur immediately after

Chapter 7: Interpretation 183 cancer treatment has finished. Bellizzi et al. (2005) suggest that this is when individuals are more amenable to taking an active role in their post-treatment care, while Costanzo et al. (2011), attributed this moment to the uncertainty that follows the conclusion of treatment (Bellizzi et al., 2005). For example, when treatment concludes the patient’s “safety net” is effectively lost—they lose frequent contact with their care providers and no longer have their counsel and guidance, and fear of recurrence often increases—which motivates the individual to search for and adopt new coping strategies (Costanzo et al., 2011; Demark-Wahnefried et al., 2005). Hence, it has been suggested that debriefing oncologists should capitalise on their influential position, providing initial guidance at treatment. This nudge could be as simple as providing patients with the knowledge that their cancer diagnosis and treatment has increased their risk of developing long-term complications and that they might wish to do something about it when they feel able (Bellizzi et al., 2005), or as complex as capitalising fully on this opportune time to introduce a health promotion intervention (Costanzo et al., 2011). With this in mind, future iterations of the WWACP could make women aware of the program during their first post- treatment check-up, ready for uptake when they are ready or willing to do so.

Factors that reinforce change Reinforcing factors in Precede are the consequences of behaviour that determine whether the feedback received provides adequate incentive for sustaining that behaviour (Green & Kreuter, 2005). Precede is influenced in this respect by B. F. Skinner’s reinforcement theory, also known as operant learning or operant conditioning, which explains how behaviour is often a function of its consequences (as cited in, Richardson, 2013). Reinforcement theory emphasises how feedback is a critical part of the reinforcement process (Richardson, 2013). That is, feedback that contributes to pleasant experiences or outcomes will likely result in a repeat of the behaviour. Conversely, feedback that contributes to unpleasant experiences will most likely inhibit repetition of that behaviour (Richardson, 2013). For example, the unpleasant physical side effects of an alcohol-induced headache would be considered negative reinforcement. However, reinforcement theory only considers antecedents and consequences to actions and does not account for the many cognitive and social processes that can mediate, strengthen or sustain behaviour change (Richardson, 2013). Hence, Precede (Green & Kreuter, 2005) also draws on Bandura’s (1986)

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social cognitive theory (SCT). SCT provides insight into how one’s thoughts, feelings and environment, when combined with the body’s feedback to a stimuli (e.g., alcohol), can reinforce behaviours. Reinforcing factors identified in this study include the participants’ social support (including the WWACP) and their peer influences, which are also enabling factors and described in the preceding section 7.1.2. The most critical reinforcing factors elucidated in this study were the physical consequences of alcohol consumption and partner supports.

Intervention participants received tailored support from WWACP breast cancer nurses over the 12-week study period. This helped them to identify their goals and related strategies at baseline, with subsequent virtual meetings helping them to refine strategies and sustain alcohol minimisation. However, support for successful behaviour change often needs to come from more than one source (Glanz et al., 2008). Several participants identified how much the support of partners reinforced their desire to reduce alcohol intake while receiving the intervention. Conversely, lack of partner support can be a barrier. Several women (intervention and control) noted that their husbands continued to drink even though the participant herself had reduced her intake. For some, this was not a problem, but for others an unsupportive home environment where alcohol is readily available clearly countered any good intentions. The literature supports this finding. For example, in a sample of women with early stage breast cancer, Manne, Ostroff, Winkel, Grana, and Fox (2005), reported that unsupportive partner behaviours predicted greater levels of avoidant coping and distress in the patient. The authors further discussed the long-term detrimental effect of this on the women’s quality of life (Manne et al., 2005),

Optimal social supports and peer influences at the individual level that instil positive behaviour change will clearly differ from those required at the population level. Hence tackling the issue of alcohol consumption for whatever reason (e.g. cancer risk factor; prenatal drinking; risky drinking or alcohol dependence) requires interventions focused across downstream, midstream and upstream behavioural determinants of health. For example, downstream individual level interventions that address psychosocial factors and/or offer pharmacological treatments, if successful, are often widely taken up after rigorous testing (Babor, Aguirre-Molina, Marlatt, & Clayton, 1999). Alcoholics Anonymous is a well-known downstream program (https://aa.org.au). In the Australian context and although not a clinically-driven

Chapter 7: Interpretation 185 intervention, events such as Dry July, Sober October and FebFast appear to be gaining momentum as another approach to alcohol reduction (Pennay, Lubman & Frei, 2014). These annual charity events attempt to encourage a period of abstinence (Pennay et al., 2014). Midstream interventions include population-screening programs and brief interventions that deliver effective alcohol-related advice. In the United States, the latter have been incorporated into routine primary care and shown to reduce both quantity and frequency of alcohol intake in heavy drinking populations (Babor et al., 1999). Upstream interventions generally relate to changes in policy aimed at the population level. Historically, policy changes have resulted in increases to alcohol pricing through taxation; establishment of drink driving and legal drinking age laws; as well as restricting hours of sale for alcoholic beverages (Babor et al., 1999; Pennay et al., 2014). Unfortunately, political and economic reasons often dictate the introduction (or otherwise) of strategies that aim to restrict the availability and accessibility of alcohol in Australia (Meyer et al., 2019; Pennay et al., 2014). Other upstream interventions include those that use media to challenge harmful behaviours. An example is the largely unsuccessful television-led breast cancer mass media campaign designed to raise awareness of alcohol as a risk factor for cancer in the United Kingdom (N. Martin et al., 2018).

Despite their mixed success at the population level, efforts should continue to be directed at shifting the prevailing social norm and cultural practices from the current favourable views on alcohol consumption are warranted. Hence policies that enforce mandatory caner warning labels on alcoholic beverages is one approach that could be adopted and has been suggested by women in this study and in others (Meyer et al., 2019). For example, a recent qualitative study involving women without breast cancer suggested that study findings could be used in the establishment of much needed “participant-driven population- and policy-level interventions” that related to breast cancer risk, either directly or indirectly (Meyer et al., 2019, p. 3). Meyer et al. (2019) considered women’s reasoning around alcohol consumption and the association with breast cancer risk in the Australian context (N = 35). Similar concerns to those reported in this thesis were identified. Participants’ comments revealed their low levels of awareness and knowledge regarding alcohol as a cancer risk factor, hence the need to increase education via various means was suggested. The women noted that the benefits of interventions focusing attention on

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the more significant and immediate effects of alcohol consumption instead of focusing on the longer-term health risks. This study also highlighted that social norms and established habits should be targeted due to their influence on alcohol consumption. The important role of industry in this context was noted, particularly in regard to the issue of mandatory labelling (Meyer et al., 2019).

Future iterations of the WWACP would benefit from not only strengthening the current messages that address the downstream determinants of alcohol behaviours, but also weaving in strategies to address mid- and upstream determinants. For example, elements of the group abstinence charity events could be incorporated into the intervention to encourage periods of abstinence among participants and their immediate support networks.

Future iterations of the WWACP would also need to account for potentially unsupportive environments, possibly by nudging cognitive appraisal of alcohol’s physiological (individual) rather than psychosocial (collective) consequences. Alcohol has pleasurable and adverse physiological consequences that can also reinforce or inhibit intake. Although it can induce pleasant feelings of relaxation and reduce inhibition, consumption can also lead to dehydration, diminished concentration, and poorer reflex responses (DrugAbuse.com, 2018; National Institute on Alcohol Abuse and Alcoholism, n.d.). Other effects include drowsiness, emotional changes, weakened immune responses, sleep disturbances, headaches, nausea, and vomiting (DrugAbuse.com, 2018; National Institute on Alcohol Abuse and Alcoholism, n.d.).

Hot flushes are objectively described as an “acute rise in skin temperature, peripheral vasodilatation, a transient increase in heart rate, fluctuations in electrocardiographic baseline and a pronounced decrease in skin resistance” (Sturdee, Wilson, Pipili, & Crocker, 1978, p. 79). They are subjectively unpleasant and even distressing, as they are often associated with discomfort, sleep disturbance, fatigue, and decrements in quality of life (Gallicchio et al., 2015; Smith et al., 2016). Several research studies have correlated alcohol intake with hot flush duration, frequency, and severity (Duché et al., 2006; Gallicchio et al., 2015; Riley, Inui, Kleinman, & Connelly, 2004; Schilling et al., 2007); however, these reports are inconsistent. Similarly, although the findings in relation to hot flushes and alcohol intake were not significant in the WWACP quantitative results, the interview

Chapter 7: Interpretation 187 comments supported the idea that alcohol induces vasomotor symptoms. Moreover, participants always found them deeply unpleasant. There might be a way that the intervention can utilise this (sparingly) as negative reinforcement (Richardson, 2013).

Conversely, alcohol often served as a coping mechanism that ameliorated the stress of diagnosis for some participants, especially those who had no adverse effects from alcohol even during chemotherapy. These findings accord with the literature, which often proposes that women are more likely to engage in self-regulatory behaviours to help cope with stress (Mezuk et al., 2017) and that they often use alcohol to do this (Drabble & Trocki, 2013; Lyons & Willott, 2008; Warren, 2009). A number of theories attempt to explain how alcohol consumption helps people cope. These theories often illustrate the complex interplay between cognitive and behavioural factors (Hasking et al., 2011). From this perspective, models of stress- coping, stressor-vulnerability, and motivational models have emerged to explain alcohol use (Hasking et al., 2011). All conclude that when consumed for the purpose of reducing stress or to moderate an unpleasant mood, alcohol is often effective in the short-term (an adaptive response) (Hasking et al., 2011), which acts as a reinforcing factor and sees the action repeated. Alcohol consumption also increases confidence and reduces tension in the short-term (Hasking et al., 2011). However, if the underlying stressor remains and the action continues, this pattern of intake becomes a maladaptive coping response that is less effective over time (Hasking et al., 2011). The association between stress and alcohol consumption is reportedly strengthened when combined with lack of alternative coping mechanisms and poor social supports (Bressert, 2016). In line with social cognitive theory, social supports are considered environmental factors that influence the individual’s behaviours and vice versa (reciprocal determinism). Social cognitive theory acknowledges the importance of social supports like the WWACP that effectively reinforce the individual’s beliefs in their ability to manage their personal life circumstances (Alexander & Ward, 2017; Bandura, 2004).

7.3 CONCLUSION

In summary, participation in the WWACP and usual care did not quantitatively alter alcohol-related behaviour, in that they did not significantly reduce the participants’ alcohol intake or sustain any reduction over time. Despite this, the

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qualitative data indicated that the WWACP was, to some extent, effective in both enabling and reinforcing less alcohol consumption in cases where participants were open to change. The fact that participants were potentially open to change prior to the intervention was highlighted as a predisposing factor in the previous section. The next chapter provides recommendations based on these findings.

Chapter 7: Interpretation 189

190 Chapter 7: Interpretation

Chapter 8: Conclusions

This chapter provides a final summary of the background and aims of this PhD (Section 8.1); the major findings (Section 8.2); and the implications of the study and recommendations, including practical suggestions for future research (Section 8.3). The chapter concludes with the limitations of the study (Section 8.4) and the strengths of the research (Section 8.5).

8.1 BACKGROUND AND AIMS

Breast cancer and alcohol consumption are common and pose significant public health concerns in Australia and internationally. In 2012, global alcohol- attributable breast cancers were estimated to account for 8.6% of all breast cancer incidence and 7.3% of breast cancer-specific mortality (Shield et al., 2016). In Australia, 5,785 deaths were attributed to alcohol in 2015; 36% (n = 2,106) of these deaths were cancer-related, with 7% (n = 397) breast cancer-related (Lensvelt et al., 2018). Breast cancer was the leading cause of alcohol-attributed deaths in Australian women in 2015 (Lensvelt et al., 2018).

There is a number of direct and indirect ways in which alcohol consumption can affect the human body and subsequently influence the development of breast cancer and potential recurrence risk. In brief, the ethanol content of alcohol is an active carcinogen. Alcohol ingestion can affect hormonal shifts that can increase breast cancer risk, recurrence, and the development of secondary primary cancers, as well as risk of other chronic diseases. Alcohol consumption can also adversely affect concurrent health conditions commonly seen in breast cancer populations. Hence, limiting alcohol consumption post-diagnosis and treatment is important, especially for middle-aged women diagnosed in Australia, where the five-year relative survival rate is over 90% (AIHW & AACR, 2017). Extended survivorship periods and decreased mortality rates are no doubt positive outcomes for this growing population; however, extended survival is likely to be offset by chronic disease risks, including the recurrence of cancer, many of which are mediated by alcohol, as outlined above. Fortunately, alcohol consumption is a modifiable health behaviour.

Chapter 8: Conclusions 191 Guidelines for alcohol consumption are inconsistent. More importantly, alcohol consumption thresholds to reduce the risk of harm are ambiguous in relation to cancer risk. Among the growing Australian population of women previously treated for breast cancer, quantification of alcohol consumption, the drivers for alcohol intake, and current knowledge levels of the alcohol-breast cancer link are largely unknown. This study aimed to explore these issues.

The Precede model (Green & Kreuter, 2005) provided the theoretical basis for this study, the aim of which was to determine the predisposing, enabling, and reinforcing factors associated with alcohol consumption in women previously treated for breast cancer.

This was achieved through two studies. Study 1 involved a secondary analysis of alcohol-related data (N = 269) from the WWACP randomised control trial to scope the extent and nature of the issue. Study 2 involved a qualitative investigation of a representative sub-sample of WWACP participants. Study 2 findings illuminated how and why the findings from Study 1 might have occurred.

8.2 SUMMARY OF MAJOR FINDINGS

The study revealed some significant quantitative results that were supported and expanded upon with use of qualitative data. The combined quantitative and qualitative findings were interpreted according to the Precede model (Green & Kreuter, 2005) as the predisposing, enabling, and reinforcing factors that influenced the participants’ alcohol-related behaviours and any changes to these behaviours that occurred over time. The major findings are summarised below:

1. The baseline alcohol intake pattern (frequency, quantity, type, and place) of participants reflected that of women in the general Australian population and was also similar to international breast cancer cohorts. There were significant differences between non-drinkers and drinkers. Participants who consumed alcohol at baseline were highly-educated, current or past smokers, and reported better quality of life scores across the social and family wellbeing domain. The odds of moving to a higher level of alcohol intake were associated with being a current or past smoker, greater physical activity levels, and better overall quality

192 Chapter 8: Conclusions

of life. Many of these findings were congruent with those in the literature.

2. Precede guided the identification of the predisposing, enabling and reinforcing factors that influenced alcohol-related behaviours in this cohort. In brief, predisposing factors largely provide the rationale for the behaviour. Enabling factors often provide the motivation for change and reinforcing factors likely support or provide momentum for the behaviour to continue (Glanz et al., 2008; Green & Kreuter, 2005; Simons-Morton et al., 2012). Australian social norms and a family history of alcohol use appeared to shape pre-cancer behaviours and beliefs, which in turn predisposed alcohol-related behaviours after diagnosis. Other significant predisposing factors identified included age, tobacco use, health-related quality of life, physical activity, baseline alcohol-related knowledge, exposure to alcohol education, and other socially-mediated beliefs about alcohol. WWACP-related behaviour change enablers identified in this cohort primarily related to the intervention content, timing, and delivery method. The factors that reinforced change included the physical consequences of alcohol consumption and partner support. Figure 8.1 presents Precede Phases 1 to 3 and the study-specific indicators or factors that were identified in this PhD study.

3. The qualitative data suggested that, overall, participants viewed alcohol consumption favourably and that this was strongly associated with social norms around drinking. Alcohol intake changed pre-intervention for most participants; that is, intakes reduced after cancer diagnosis and during the treatment period. However, some participants who reported drinking while undergoing chemotherapy treatment also noted that few alcohol-related effects (pleasant or aversive) were felt.

4. The qualitative data revealed inconsistencies when it came to the provision of alcohol-related information by health professionals and the acceptance of this information throughout the diagnosis and treatment period. This resulted in individual knowledge gaps and participants who were misinformed about alcohol as a cancer risk factor.

Chapter 8: Conclusions 193 PRECEDE

PHASE 3 Study Specific Educational and PHASE 1 Indicators ecological PHASE 2 Social assessment Epidemiological assessment assessment

Age, tobacco use, health-related quality of life, physical activity, baseline knowledge, exposure to Predisposing Genetics alcohol education and other socially- mediated beliefs about alcohol.

Physical consequences of alcohol consumption and social supports Reinforcing Behaviour including WWACP, peer influences, partner supports Health Quality of Life

Intervention content, timing and delivery method Enabling Environment

PROCEED

Figure 8.1. Precede (Phases 1-3) with study specific indicators

194 Chapter 8: Conclusions 8.3 IMPLICATIONS OF THE STUDY AND RECOMMENDATIONS

8.3.1 Implications for education across clinical and community settings The implications of this PhD study relate to the provision of education on the cancer-related harms that alcohol could entail for women previously treated for cancer.

The WWACP alcohol-related content was based on current literature, Australian alcohol intake recommendations, and in response to the outcomes of the pilot study (the Pink Women’s Wellness Program) (D. J. Anderson & Lang, 2011). The pilot study reported favourable outcomes for health-related quality of life and other components of health; however, change in alcohol intake over time was not specifically tested. Despite a stronger focus on alcohol in the WWACP and the provision of additional consultation nurse training, no significant reduction in alcohol intake was noted in this PhD. This indicates that a better understanding of alcohol behaviours could not only maximise intervention efforts but also enable individuals to make educated decisions about their health. This is especially important beyond the diagnosis and treatment period, when patients are less likely to be in regular contact with cancer professionals. However, for this to happen clinicians in both acute and community settings, as well as those involved in intervention delivery, need to be adequately educated with the most current evidence-based information to provide to their patients.

The literature suggests that oncologists and specialist cancer nurses are ideally positioned to offer this information. Furthermore, by understanding the predictors of alcohol behaviours, health professionals can more accurately target and potentially foresee adverse behaviours before they become problematic and potentially harmful. This study has highlighted that alcohol education via intervention delivery post- treatment was particularly important for these women, given that any alcohol-related messages might have been missed or unwanted during the diagnosis and treatment phases. This information is likely to be more relevant to the individual immediately post-treatment, and will therefore potentially be better received, retained, and enacted during this ‘teachable moment’.

Given these implications, the following recommendations are made. First, alcohol education should ideally be clearly articulated throughout chemotherapy

Chapter 8: Conclusions 195 treatment so that the individual absorbs the message. An appropriate strategy for clinical service delivery would be to introduce alcohol-related education from the beginning and keep repeating messages throughout, as well as providing practical strategies for how people can do this. However for this to occur appropriate training of clinicians, which would overcome any barriers to alcohol-education delivery such as reluctance to blame the woman for any risky behaviour, should also be considered. Second, future iterations of the WWACP might utilise the findings from this study to strengthen the current alcohol-related content and to provide more extensive alcohol- related training for the program consultation nurses. To arm the consultation nurses with the appropriate skills, nurse education and training should cover: the alcohol- related predisposing, enabling, and reinforcing factors potentially at play in this population; the notion that an individual’s pre-cancer drinking behaviours likely influence their post-cancer actions; that alcohol behaviours could fluctuate over the course of survivorship; and that intakes are strongly influenced by the drinking behaviours of those around the individual (i.e. social norms). Hence, consultation nurses could inquire about pre-diagnosis alcohol consumption; the current supports available to the individual to establish whether additional support is necessary; and the participants’ previous interactions with health professionals regarding provision of alcohol-education. Furthermore, consultation nurses should reiterate the risks associated with alcohol consumption (both immediate and longer-term risks) throughout the course of the program and direct the participants to reputable information sources, should they request more detailed information.

8.3.2 Implications for health promotion practice, policy and industry The findings from this PhD also have a number of implications for health promotion practice, policy, and industry.

This study was framed by the constructs within Precede (Green & Kreuter, 2005) to identify the predisposing, enabling, and reinforcing factors that influenced alcohol behaviours after breast cancer and after participation in a multimodal healthy lifestyle intervention. Some participants in Study 2 had previously partaken in the WWACP intervention, which used the social cognitive theory construct of self- efficacy to reduce current alcohol intakes if deemed necessary by the participant. The construct of self-efficacy is important for health promotion practice in this context. Building participants’ self-efficacy via support with tailored goal setting and

196 Chapter 8: Conclusions development of a skillset that could effectively reduce alcohol intake could strengthen their ability to cope with the pressure to engage in risky alcohol behaviours.

Key researchers in the field have noted that questions often asked by patients to the clinicians who treat them, include “Does my drinking history predict the course of my disease and overall health?” and “Must I now abstain from alcohol?” (Demark-Wahnefried & Goodwin, 2013). This PhD indicates that, in an Australian cohort of women who had been treated for breast cancer, we need to determine whether these questions are asked at all and the underlying alcohol-cancer related knowledge levels required of the individual to prompt such questions. Whether the message of alcohol-related harm is accepted or not by the individual, women need to be informed of the risks associated with alcohol consumption.

In this growing population of women, self-care over the course of survivorship is particularly important to maintain quality of life; however, self-care cannot be achieved if women do not know the potential harms. Policies need to adequately address cancer-related harms, such as alcohol consumption, that contribute to burden of breast cancer and chronic disease in Australia.

The strengths of the WWACP were the multimodal approach, virtual delivery that provided access to a wider scope of participants, and the different ways in which the topic of alcohol was approached (i.e., printed and electronic materials, verbal discussions with trained personnel). The qualitative findings also indicated that other strategies might assist women to reduce their alcohol consumption. For example, to prompt behaviour change, participants considered that information on alcohol as a breast cancer risk factor was necessary from more than one source. Given that the global burden of breast cancer is large and that survivorship periods are now long, a multitude of actions from various avenues is likely necessary to support women post- breast cancer. This includes government policy makers and industry doing their part to alleviate the burden of this disease. Thus, industry has a duty of care to provide consumers with adequate warning labels on products (alcoholic beverages) that contain cancer-causing agents (ethanol). The need for appropriate warning labels was raised by women in this study and evidenced in the literature. Governments and their policy makers need to ensure that current “voluntary” health warnings are mandatory, with a focus on the cancer-related risks of consuming alcohol, instead of

Chapter 8: Conclusions 197 solely focusing on pregnancy-related consumption risks. Based on this, I recommend that policy makers finalise legislation to ensure health warning labels on alcohol containers are changed from voluntary to mandatory as a matter of urgency and that these warnings are extended to include cancer-related risks.

8.3.3 Implications for future research For large-scale repeated measures studies such as the WWACP, there appears to be no “gold standard” for assessment of dietary intake, especially when cost- effective, time-efficient, and user-friendly tools are also necessary considerations. Bias from self-reported measures of alcohol intake is well-documented in the literature and primarily linked to underestimations of intake; therefore, care should be taken when selecting a tool for collection of alcohol intake data. Technological advances in this area should be considered and capitalised upon where possible.

A number of researchers have noted the importance of understanding individual attitudes towards alcohol consumption and the influence of cultural and social factors on drinking behaviours (Scoccianti, Lauby-Secretan, Bello, Chajes, & Romieu, 2014). Thus, further research should be undertaken using the Precede model (Green & Kreuter, 2005), particularly the constructs of predisposing, enabling, and reinforcing factors that assist with the identification and explanation of alcohol- related behaviours in at-risk populations. The WWACP study and this PhD study were undertaken with participants who were primarily Australian-born Caucasians, middle-aged, well-educated, and on higher than average incomes. These characteristics clearly influenced their drinking behaviours. Hence, the qualitative findings from this study are transferable only to a narrow group of women who have been treated for breast cancer. Further research should be undertaken with breast cancer cohorts that not only differ demographically from these participants but that is also longitudinal in design. This will elicit greater detail on the ‘why’ and ‘how’ of alcohol behaviour change over extended survivorship periods. Furthermore, such research could determine whether the alcohol-related predisposing, enabling, and reinforcing factors exposed in different cohorts highlight distinctive needs over the course of survivorship. It might also be important to research these factors further in different alcohol-attributable cancer streams, such as bowel, stomach, liver, pancreatic, and throat-related cancers.

198 Chapter 8: Conclusions Finally, the findings of this study support the virtually-delivered, multimodal approach taken by the WWACP. The scope of the WWACP was far-reaching and offered support to participants who had no other access to supports. In light of the need expressed for evidence-based alcohol-caner related tailored messages that assist the target population, future iterations of the WWACP should utilise the findings from this study to strengthen the alcohol-related component of the program.

8.4 STUDY LIMITATIONS

A number of study limitations should be acknowledged.

1. This study relied on self-reported data for alcohol intake, including self- estimations of what constituted a “glass”, which were then converted into grams of alcohol per day. These data were further categorised with cut- points representing a change of one Australian standard drink (10g/drink) per day. Self-reported data, especially for assessment of alcohol consumption, is notorious for being underreported and underestimated. A standardised data collection tool with measures that are congruent with Australian standard drinks would be more helpful, potentially with pictorials of actual drink sizes.

2. The literature indicates that there is a greater risk of breast cancer development in women with oestrogen receptor positive tumours as opposed to oestrogen receptor negative tumours where moderate alcohol intake is concerned. Therefore, including additional variables on breast cancer type (i.e., ER-, ER+) into data collection might have provided greater insight into the risks for this cohort. Hence, identification of participants with ER+ tumours and a history of alcohol behaviours that indicate moderate consumption pre-diagnosis could add to the existing knowledge in this area.

3. Participants were mostly self-referred into the study, which could indicate a pre-existing desire to improve health behaviours, and which might have skewed the data. For example, it is possible that participants had reduced their alcohol consumption prior to study enrolment, thereby making the alcohol-related components of the intervention appear less effective.

Chapter 8: Conclusions 199 4. The inclusion criteria required that participants were proficient with technologies such as computer applications, Skype and FaceTime, and that they owned or had access to a Smartphone and a reliable internet connection. Criteria such as these might have excluded women who experienced relative socioeconomic disadvantage.

5. The sample of women recruited for the study was homogenous and relatively affluent, which reduced the generalisability of the study findings to other at-risk populations.

6. Information on changes to alcohol-related behaviours, especially those described prior to diagnosis, might have been affected by recall bias given that some participants had been diagnosed several years prior to interview. Furthermore, social desirability regarding alcohol consumption might have altered responses.

8.5 STUDY STRENGTHS

This study also has a number of strengths.

1. The mixed methods study design provided greater insight into alcohol behaviours in this cohort than quantitative or qualitative data alone could provide.

2. Inclusion of data from a large, multi-faceted parent study provided access to a wide range of variables that potentially influenced or confounded alcohol intakes. This enabled thorough analysis of the PhD topic.

3. Virtual facilitation of the interviews increased the scope of participants reached and negated potential costs (travel related and time off work) for the participant as well as for the project.

4. Qualitative interviews were facilitated by an interviewer who had previously built rapport with most of the participants; hence, a supportive environment was provided for the participants to share detailed information on a sensitive topic.

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8.6 CONCLUSION

This mixed method study, underpinned by the respected Precede model (Green & Kreuter, 2005), contributes evidence to the limited knowledge on factors that might influence alcohol-related behaviours in Australian women previously treated for breast cancer. The alcohol-related behaviours and influencing factors identified in this cohort are reasonably generalisable to demographically similar Australian breast cancer cohorts, and in part, to women in the general Australian population, given that their patterns of alcohol intake were very similar. The findings have implications for education processes across cancer-related clinical and community-based settings; health promotion practice, policy, and industry; as well as for future research.

Research on alcohol-related behaviours in women who have undergone treatment for breast cancer can be difficult for a number of reasons. These include, but are not limited to: the availability of appropriate data collection tools for alcohol intake, problems that relate to self-reported measurement biases, and the care that must be taken by researchers to avoid creating or adding to feelings of self-blame for the participants’ cancer diagnosis. However, such research is invaluable if we wish to enhance care over the course of survivorship and reduce potential harms from alcohol consumption.

Chapter 8: Conclusions 201

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234 Reference List

Appendices

Appendix A Study design: Parent program and alcohol sub-study

Appendix B Overview of other theories used by Precede-Proceed

a) Bandura’s social cognitive theory (SCT) b) Health belief model (HBM) c) The transtheoretical model (TTM): Stages of change

Appendix C Patient information and consent form for parent program

Appendix D Study 1 data collection tools

a) Administered via Key Survey (Online) b) Administered by Research Assistant (Virtually) c) Food Frequency Questionnaire (modified version) d) International Physical Activity Questionnaire – Short Form

Appendix E Measurement instructions

Appendix F Study 2 ethical variation approval email

Appendix G Study 2 patient information, consent form and eligibility criteria for the sub-study

Appendix H Invitation to participate email

Appendix I Full interview protocol

Appendix J Study 2 interview stem questions

Appendix K Study 2 qualitative coding themes

Appendix L Bar charts for all of the alcohol types, grouped by intervention and control

Appendices 235 Appendix A Study design: Parent program and alcohol sub-study

Baseline (T1) 6 weeks 12 weeks (T2) 24 weeks (T3)

Intervention (WWACP n = 138) Outcome Sustained outcome

Qualitative Randomisation ALCOHOL Interviews SUB-STUDY N = 269 N = 17

Standard care n = 131 Outcome Sustained outcome

ALCOHOL Recruitment and consent Virtual consultation with nurse (intervention SUB-STUDY Key survey (post Key survey Key survey (before group only – weeks 0, 6 WWACP) Lifestyle randomisation) and 12 + week 3 progress PhD Candidate led Lifestyle Health Socio-demographics email) Virtual Qualitative Health Lifestyle Interviews

Health Virtual RA Appointment Virtual RA Appointment

Weight, waist/hip circ, Weight, waist/hip circ, FFQ, Virtual RA Appointment FFQ, IPAQ, med/surg Hx, IPAQ, med/surg Hx, meds, Height, weight, waist/hip meds, health costs. health costs. circ, FFQ, IPAQ, med/surg

Hx, meds, health costs.

Study One – Secondary Data Analysis of WWACP Study Two

236 Appendices

Appendix B Overview of other theories used by Precede-Proceed a) Bandura’s Social Cognitive Theory (SCT)

Efficacy Information Sources

Self-efficacy judgements are influenced by exposure to various forms of efficacy information. A tabulation of the alcohol-related efficacy information sources embedded in the parent study, as well as other potential information sources participants could encounter is provided below.

Alcohol-related Examples Source Mode of Induction Potential positive effect Potential negative on behaviours (from effect on behaviour WWACP intervention) Performance • Participant • Negative • Positive Accomplishments modelling experiences with experiences with (past • Performance alcohol intake alcohol intake experiences) desensitisation (social events, • Performance celebrations) exposure • Experiences • Self-instructed with other high- performance risk health behaviours (smoking, inactivity) Vicarious • Live modelling • Discussion panel • Drinking Experience • Symbolic (via observation) behaviours of (modelling of modelling • Knowing that other friends, family, others) women are work colleagues participating in the • Advertisements program for alcohol sales • Health messages and • Social media images contained avenues within iBook • Social media avenues Verbal (social) • Suggestion • Nurse consultations • Peer pressure to Persuasion • Exhortation (i.e. counselling, drink at social (coaching and • Self-instruction goal setting and events evaluation • Interpretive strategy feedback) treatments development for alcohol minimisation). • Discussion panel (via interaction) • Perception of alcohol after cancer • Social media avenues

Appendices 237 • Self-guidance facilitated via iBook activities and suggestions Physiological and • Attrition • teachable moment • Desire to be like emotional states • Relaxation, everyone else biofeedback again and not • Symbolic ‘sick’ desensitisation • Symbolic exposure Table A. Adaptation of Efficacy Expectations: Major sources of efficacy information as described by Bandura (1977)

238 Appendices

b) Health Belief Model (HBM)

Alcohol-related examples for the four psychological variables:

Perceived susceptibility refers to “the beliefs about the likelihood of getting a disease or condition” (Glanz et al., 2008, p. 47). For example, the participant must believe that there is a real possibility that consuming alcohol above the recommended one drink per day will possibly result in breast cancer recurrence before she will consider reducing or abstaining from alcohol consumption.

Perceived seriousness or severity refers to the individuals’ feelings about the severity of developing the illness or of leaving it untreated (Glanz et al., 2008). For example, in this scenario, the seriousness of breast cancer recurrence would likely stir quite strong and unwelcome feelings in the participant. However, if the participant rarely consumes alcohol, they might perceive their personal susceptibility to recurrence as a result of alcohol consumption to be low, and therefore severity from this particular cause might not be an important influence on behaviour (Simons- Morton et al., 2012). It is from both of these variables—susceptibility and seriousness—that the perceived threat is felt.

Perceived benefits refers to the individual’s beliefs about the benefits of taking action to reduce the health threat (Simons-Morton et al., 2012). For example, a participant might believe there are benefits to reducing their alcohol intake from two drinks per night to one; conversely, another might see no benefits in reducing their intake if a close family relative lived a long healthy life and consumed more than three drinks each day.

Perceived barriers can refer to the “beliefs about the psychological, time, expense and other costs of action” (Simons-Morton et al., 2012, p. 115). For example, making the conscious decision not to binge drink on a night out with friends who usually drink heavily might result in the individual feeling like a social outcast. It is from both of these variables—perceived benefits and barriers—that the outcome expectations are felt.

Appendices 239 c) The Transtheoretical Model (TTM): Stages of Change

The following table briefly explains each stage of change and is accompanied by an alcohol related example. Stages of Description Extreme example: Individual consumes 1-2 bottles Change of wine per day and has recently completed her treatment for breast cancer. Pre- No intention to take Individual might be mis/under-informed about the contemplation action within the next consequences of high alcohol consumption or not six months believe the link between cancer and alcohol, the individual therefore does not want to talk about their high-risk behaviours. Equally, they could have previously tried and failed to reduce their alcohol consumption. Contemplation Intends to take action Individual may recognise that they consume too much within the next six alcohol and see the potential benefits of reduced intake; months however, they do not know how to, or lack the appropriate strategies and or support, to reduce their intake. Preparation Intends to take action Individual has recently signed up for a 12-week healthy within the next 30 days lifestyle program and is ready to make a change. and has taken some behavioural steps in this direction Action Changed overt Scenario 1: participant is allocated to the intervention behaviour for less than arm of the study, starts the program, and receives the six months appropriate advice and support to reduce their alcohol consumption. Actively reduces intake from two bottles to one bottle over five months. Scenario 2: participant is allocated to the control arm and does not commence the program. Participant attempts to reduce intake on their own and then look for other avenues (possible relapse to preparation and potentially contemplation) Maintenance Changed overt Scenario 1: individual continues to use strategies behaviour for more developed throughout the study and gains confidence in than six months prevention of relapse because they start to feel better. (is estimated to last Individual is currently maintaining their intake of less from six months to five than one bottle per night; however, is aware that more years) action is required. Termination No temptation to Scenario 1: Intake was steadily reduced over four years relapse and 100% and now individual prefers to abstain from alcohol confidence altogether. Individual is certain that regardless of how they feel (depressed, stressed, bored etc) or the social company they keep, they will not return to unhealthy behaviours. Table C. Stages of change constructs adapted from Glanz et al.(2008)

240 Appendices

Appendix C Patient information and consent form for parent program

Appendices 241

242 Appendices

Appendices 243 Appendix D Study 1 data collection tools

a) Administered via Key Survey (Online)

Domain Variable Measurement

Age Age in whole years Socio- demographic Marital Status Married, de facto, separate, divorced, widowed, single.

Post Code Post Code

Aboriginal or Torres Strait Yes – Aboriginal, Yes - Torres Strait Islander Islander, Yes - both, No

Country of Birth Australia, Other (please specify)

Ancestry Oceanian, North-West European, Southern and Eastern European, North African and Middle Eastern, South-East Asian, North- East Asian, Southern and Central Asian, People of the Americas, Sub-Saharan African, Other (please specify).

Language other than Yes (please specify), No English

Education No schooling; primary school; junior high school (Year 10); senior high school (Year 12); trade, technical certificate or diploma; university or college degree; post graduate degree.

Employment Status Employed full-time; employed part-time; (before cancer diagnosis) home duties; unemployed; full-time student; part-time student; retired, permanently ill/unable to work.

Gross Household Income Less than $20,000; $20,001 - $40,000; $40,001 - $60,000; $60,001 - $80,000; $80,001 - $100,000; $100,001 - $120,000; $120,001 - $150,000; $150,001 - $250,000; $250,001 - $500,000; $500,001 or more; don’t know.

244 Appendices

Weight and What is your weight? Weight in kilograms (kg) Height

What is your height? Height in centimetres (cm)

Diet Do you currently eat 5 Yes, I have been eating 5 serves of serves of vegetables every vegetables daily for more than six months; day? Yes, I have been eating 5 serves of vegetables daily for less than six months; 1 serve of vegetables = 1 No, but I am planning to start eating 5 medium potato; ½ cup serves of vegetables daily in the next 30 cabbage/ spinach/ days; No, but I am planning to start eating 5 broccoli/ cauliflower/ serves of vegetables daily in the next six brussel sprouts; or 1 cup months; No, and I don’t plan to start eating of lettuce or salad 5 serves of vegetables daily in the next six vegetables months

How many serves of 1 serve; 2 serve; 3 serve; 4 serve; 5 serve; 6 vegetables do you usually serve; less than one serve; don’t eat eat each day? vegetables

Do you currently eat 2 Yes, I have been eating 2 serves of fruit serves of fruit every day? every day for more than six months; Yes, I have been eating 2 serves of fruit every day 1 serve of fruit = 1 apple; for less than six months; No, but I am 1 orange; 1 mandarin; 2 planning to start eating 2 serves of fruit plums; 2 apricots; 8 every day in the next 30 days; No, but I am strawberries; 1 cup diced planning to start eating 2 serves of fruit or canned fruit; 20 every day in the next six months; No, and I grapes or cherries don’t plan to start eating 2 serves of fruit every day in the next six months

How many serves of fruit 1 serve; 2 serve; 3 serve; 4 serve; 5 serve; 6 do you usually eat each serve; less than one serve; don’t eat fruit day?

Do you currently Yes/No consume any beverage or substances containing caffeine either regularly or occasionally?

How many drinks of Number of caffeine-containing beverages caffeine-containing per week beverages did you have during the past week? (1 drink = cups of shots of coffee; cups of tea; cans/drinks of cola; 'energy' drinks containing caffeine)

Appendices 245

On average, how much Number of drinks (soft drink/flavoured soft drink, flavoured mineral water/sports drink) in ml per day mineral water, or sports drinks (in mL) would you have each day? (Do not include 'diet' drinks)

If the amount of soft drink per day varies greatly across the week, please calculate an average for one day of the week (1 can = 375mL; 1 glass = 200- 250mL; 1 bottle = 600mL or 1.25L or 2L)

Smoking Do you currently smoke No, never smoked; no, smoked regularly (at cigarettes? least once a day) in the past; yes, regular (at least once a day) smoker; yes, casual (not every day) smoker.

1. Approximately how many cigarettes do you usually smoke per week? (one package contains 20) ______cigarettes/ week

2. Approximately how many years have you been/ were you a cigarette smoker? ______years

FACT G Below is a list of statements that other people with your illness have said (Functional are important. Please tick one number per line to indicate your response Assessment of as it applies to the past seven days. Cancer Therapy – Response options: Not at all/a little bit/somewhat/quite a bit/very much. General) Physical well-being

5. I have a lack of energy 6. I have nausea 7. Because of my physical condition, I have trouble meeting the needs of my family 8. I have pain 9. I am bothered by the side effects of treatment 10. I feel ill 11. I am forced to spend time in bed Social well-being

246 Appendices

12. I feel close to my friends 13. I get emotional support from my family 14. I get support from my friends 15. My family has accepted my illness 16. I am satisfied with family communication about my illness 17. I feel close to my partner (or the person who is my main support) 18. I am satisfied with my sex life. Emotional well-being 19. I feel sad 20. I am satisfied with how I am coping with m y illness 21. I am losing hope in the fight against my illness 22. I feel nervous 23. I worry about dying 24. I worry that my condition will get worse Functional well-being

25. I am able to work (include work at home) 26. My work (including work at home) is fulfilling. 27. I am able to enjoy my life 28. I have accepted my illness 29. I am sleeping well 30. I am enjoying the things I usually do for fun 31. I am content with the quality of my life right now CES-D Below is a list of the way you might have felt or behaved. Please tell us how often you have felt this way during the past week by placing a tick (Centre for in one box per row. Epidemiologic Studies Questions: Depression Scale) 32. I was bothered by things that usually don’t bother me 33. I did not feel like eating; my appetite was poor 34. I felt that I could not shake off the blues even with the help from my family or friends 35. I felt that I was just as good as other people 36. I had trouble keeping my mind on what I was doing 37. I felt depressed 38. I felt everything I did was an effort 39. I felt hopeful about the future 40. I thought my life had been a failure

Appendices 247 41. I felt fearful 42. My sleep was restless 43. I was happy 44. I talked less than usual 45. I felt lonely 46. People were unfriendly 47. I enjoyed life 48. I had crying spells 49. I felt sad 50. I felt that people dislike me 51. I could not get “going”

Response options:

52. Rare Rarely or none of the time (<1 day) 53. Some or little of the time (1-2 days) 54. Moderate amount of the time (3-4 day) 55. Most/all of the time (5-7 days) PSQI The following questions relate to your usual sleep habits during the past (Pittsburgh month only. Your answers should indicate the most accurate reply for the Sleep Quality majority of days and nights in the past month. Index) 56. During the past month, what time have you usually gone to bed at night? 57. During the past month, how long (minutes) has it usually taken to fall asleep? 58. During the past month, what time have you usually gotten up in the morning? 59. During past month, how many hours of actual sleep did you get at night? 60. During the past month, have you had trouble sleeping because you … (options: not during the past month/less than once a week/once or twice a week/three or more times a week) a. Cannot get to sleep within 30 minutes b. Wake up in the middle of night or early morning c. Have to get up to use bathroom d. Cannot breathe comfortably e. Cough or snore loudly f. Feel too cold g. Feel too hot

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h. Had bad dreams i. Have pain j. Other reason k. Other reason, describe How often during the past month have you had trouble sleeping because of this? (Not during the past month/less than once a week/once or twice a week/three or more times a week) 61. During past month, how would you rate sleep quality? (very good/fairly good/fairly bad/very bad) 62. During the past month, how often have you taken medicine to help you sleep (prescribed or over the counter)? (Not during the past month/less than once a week/once or twice a week/three or more times a week) 63. During past month, how often have you had trouble staying awake while driving, eating meals, or social activity? (Not during the past month/less than once a week/once or twice a week/three or more times a week) 64. During the past month, how much of a problem has it been to keep up enough enthusiasm to get things done? (not a problem at all/only a very slight problem/somewhat of a problem/a very big problem) 65. Do you have a bed partner or roommate? (no bed partner or roommate/partner or roommate in other room/ partner in same room, but not same bed/partner in same bed) If you have a roommate or bed partner, ask him/her how often you have had…. (options: not during the past month/less than once a week/once or twice a week/three or more times a week) a. Loud snoring b. Long pauses between breaths while asleep c. Legs twitching or jerking while asleep d. Episodes of disorientation or confusion during sleeping e. Other restlessness while you sleep f. Other, please specify Greene Please indicate the extent Only the following items relate to Climacteric to which you are bothered vasomotor symptoms: Scale - at the moment by any of Vasomotor theses symptoms by 66. Hot flushes (not at all/a little/quite Subscale placing a tick in the a lot/extremely) appropriate box 67. Sweating at night (not at all/a little/quite a lot/extremely)

Appendices 249 b) Administered by Research Assistant (Virtually)

Domain Variable Measurement

Medical History Date of diagnosis What month and year were you diagnosed with cancer? What type of cancer have you been treated Cancer type for? Breast, Ovary, Uterus, Cervix, Vulva, Blood cancer, Other (please specify)

Treatment history Which of the following treatments have you received? (Yes/No) a. Surgery – please specify: b. Chemotherapy c. Radiotherapy - localised d. Radiotherapy – to whole body e. Stem cell transplant f. Other therapies? (e.g. Femarra, Trastuzumab - Herceptin , Tamoxifen, Anastrozole - Arimidex , Goserelin - Zoladex ). If you answered ‘yes’, please specif:

Have YOU been diagnosed with any of the Co-morbidities following health problems?

If yes, in which year were you diagnosed? (Yes/No/year diagnosed)

a. Headaches/ migraine b. Stroke c. High blood pressure d. Leaking urine when coughing or sneezing (stress incontinence) e. Back problem f. Coronary heart disease (angina, heart attack, bypass surgery, angioplasty) g. Other heart disease (irregular beat, heart failure) h. Irritable bowel problem i. Thyroid disorder j. Diabetes k. Cancer (any other type) l. Arthritis or rheumatism m. Osteoporosis n. Bone or joint problem other than arthritis or osteoporosis o. Clinical depression

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p. Anxiety disorder q. Alzheimer’s disease r. Other mental health problem (please specify): ______

Family history Have your parents or siblings been diagnosed with any of the following health problems? (Yes, parent/Yes, sibling/No)

a. Stroke b. High blood pressure c. Coronary heart disease (angina, heart attack, bypass surgery, angioplasty) d. Other heart disease (irregular beat, heart failure) e. Thyroid disorder f. Diabetes g. Breast cancer h. Ovarian cancer i. Endometrial cancer j. Cancer (any other type) k. Arthritis or rheumatism l. Osteoporosis m. Bone or joint problem other than arthritis or osteoporosis n. Clinical depression o. Anxiety disorder p. Alzheimer’s disease q. Other mental health problem (please specify): ______Please list any prescription medications you Current medications have taken on a regular basis in the last month. (These are medications that a doctor has prescribed for you to take).

Biophysical Height In centimetres to the nearest 0.1cm Measurements Weight In kilograms to the nearest 0.1 kg

BMI calculated kg/m2

Waist circumference In centimetres to the nearest 0.1cm

Hip circumferences In centimetres to the nearest 0.1cm

Waist to Hip Ratio WHR (WHR) calculated

FFQ 74 food items grouped Please refer below for a detailed copy of into four main categories the FFQ. with alcohol intake reported separately 1. Cereal foods, sweets and snacks 2. Dairy products, meats and fish

Appendices 251 Preceding 1 month 3. Fruit considered 4. Vegetables 5. Alcohol

Additional On which days in the last None; All; Monday; Tuesday; Wednesday; Alcohol-related 7 days did you have Thursday; Friday; Saturday: Sunday; None, questions drinks that contained I didn’t drink this week. alcohol? (can select multiple answers)

Where do you usually In my own/spouse’s/partner’s home; At a drink alcohol? (please friend’s house; At a party at someone’s mark all that apply) house; At raves/dance parties; At restaurants/cafés; At licensed premises (e.g. pubs, clubs); At school, TAFE, university, etc.; At my workplace; In public places (e.g. parks, beaches); In a car or other vehicle; Somewhere else.

IPAQ – Short Please refer to below for a Five areas are covered: form detailed copy of the IPAQ Part 1: Job-related physical activity

Part 2: Transportation physical activity

Part 3: Housework, house maintenance, and caring for family

Part 4: Recreation, sport, and leisure-time physical activity

Part 5: Time spent sitting

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c) Food Frequency Questionnaire (modified version)

Appendices 253

254 Appendices

Appendices 255

256 Appendices

d) International Physical Activity Questionnaire – Short Form

Appendices 257

258 Appendices

Appendix E Measurement instructions

Appendices 259 Appendix F Study 2 ethical variation approval email

260 Appendices

Appendix G Study 2 patient information, consent form, and eligibility criteria for the sub- study

Appendices 261

262 Appendices

Appendices 263 264 Appendices

Appendix H Invitation to participate email

Dear ......

I hope this email finds you well.

I am writing to you today to ask if you would like to contribute to a small PhD research project that is linked to the Women’s Wellness after Cancer Study.

As you know, we have met many times while you were assisting us with data collection for the Women’s Wellness after Cancer Study.

What you may not know is that I am also a qualified dietitian who is currently completing my PhD studies.

As a dietitian, I have a particular interest in how your dietary intake can influence difference aspects of wellbeing. In particular, I have a special interest in alcohol consumption and any associated health behaviours. My PhD study aims to identify how alcohol consumption is viewed by women who have undergone treatment for breast cancer. In addition to analysing data obtained from the Women’s Wellness after Cancer Study, I will be undertaking one-off interviews with participants who have completed the study and wish to discuss their views on alcohol.

If you are interested in assisting with my PhD research, please read the attached participant information sheet, which has a consent form on the final page. I have also included an eligibility checklist for completion should you wish to participate.

This PhD research project has been granted full ethics approval (QUT Ethics Approval Number: 1300000335)

Thank you, and I hope to hear from you soon.

Warm regards Sarah Balaam [INSERT CONTACT DETAILS]

Appendices 265 Appendix I Full interview protocol

Interview Protocol

1. Welcome and thank participant for their time (re-establish rapport with participant). 2. Remind participant that permission to record the session has been granted. 3. Provide overview of session. 4. Explain the purpose of the study and check if any questions before continuing. 5. Semi-structured interview questions will cover the following: • Perception of alcohol consumption after treatment for cancer. • Alcohol consumption patterns pre- and post-diagnosis – reason for change if any. • Dietary-related behaviours when consuming alcohol. • Physical activity-related behaviours and alcohol consumption. • Social influences around participant that either encourage or discourage alcohol consumption (i.e., motivators such as occasions, locations or individuals). • Source of and provision of alcohol-related knowledge, if any during the course of diagnosis and treatment (oncologist, nurse, allied health professional, GP, internet) • Identification of self-management strategies currently in place for long term health care. 6. Wrap up interview, answer any final questions. 7. Thank participant for their time.

NOTE: In the case of an adverse event, the appropriate health professional will be contacted and/or patient referred on as necessary.

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Appendix J Study 2 interview stem questions Perception of alcohol consumption after treatment for cancer 1. Describe for me how you view alcohol? …… and is this view the same as what you had before your breast cancer diagnosis? 2. Can you list 3 SEPARATE DESCRIBING WORDS for how alcohol fits into your world? ….. can you provide an example for why this particular word came to mind? Alcohol consumption patterns pre- and post-diagnosis – reason for change if any.

3. During treatment did you drink, if not what stopped you? Fear of sickness? 4. Since completing treatment, would you say that your alcohol consumption patterns have returned to what they were before treatment? If no, how have they differed…. Can you provide an example? .... how does this compare to before diagnosis? If no, how have they differed? …. Example? 5. How frequently would you purchase of alcohol, either for home or when outside of the home? Social influences around participant that either encourage or discourage alcohol consumption i.e. motivators such as occasions, locations or individuals. Dietary-related behaviours when consuming alcohol 6. Do you find there are certain times of the day that you are more likely to have a drink? …..what is your main desire for having a drink at this time? 7. Are there any situations in which you are more likely to drink or not drink? ..… why do you think this is ………….example? 8. Have you encountered any social influences or reactions to not drinking? ………….example? 9. Do you have any rituals that you do when you have a drink? i.e. sit down and put your feet up / recap the day with your partner / consume some cheese and crackers. 10. When you think about your USUAL diet, how is alcohol factored in? ………. Do you think about the calorie/energy intake from alcohol? 11. When thinking about your family life, either current or past, would you say that alcohol has influenced family life? Been a prominent factor in your family history? Any family history of alcoholism? Physical activity-related behaviours and alcohol consumption 12. Do you think that having a drink affects how physically active you are? ie the next day.

Source of and provision of alcohol-related knowledge, if any during the course of diagnosis and treatment (oncologist, nurse, allied health professional, GP, internet) 13. Have you ever been provided information about alcohol consumption and breast cancer? If so, can you identify where or from who you received information? 14. Would information at time of diagnosis/during treatment/post treatment, have been useful for you? Why/why not?

Appendices 267 Appendix K Study 2 Qualitative coding themes

First Pass – Codes consistent with interview questions

Code Description PerceptionGeneral General perception of alcohol, regardless of any pre/post cancer intake changes PerceptionPre Perception of alcohol pre cancer PerceptionPost Perception of alcohol post cancer Descript1 First describing word for how alcohol fits into current lifestyle Descript2 Second describing word for how alcohol fits into current lifestyle Descript3 Third describing word for how alcohol fits into current lifestyle HolisticApproach Participant comments on the holistic approach to health IntakeReason Reason for current intake pattern. Not related to any pre/post cancer changes IntakePre Alcohol consumption pre cancer IntakePost Alcohol consumption post cancer IntakeChangeEx Alcohol consumption change example IntakeChangeReason Alcohol consumption reason for change IntakeChangeStrategies Alcohol consumption strategies IntakeConsequences Alcohol consumption consequences IntakeDuringTreat Intake during treatment ChemoTasteChange Notes taste changes as a result of chemo FreqPurHome How frequent is alcohol purchased for home QuantPurHome Quantity purchased for home FreqPurOutside How frequent is alcohol purchased outside home QuantPurOutside Quantity purchased outside of home PurchasePerson Who purchases the alcohol IntakeTime When is alcohol likely consumed IntakeSit Likely drinking situation IntakeSitReason Likely drinking situation reason UnlikelyIntakeSit Unlikely drinking situation NoDrinkReaction Reactions encountered if not drinking SocialDrinking Social drinking general comments SocialInflDrink Social influences to drink SocialInflNoDrink Social influences to not drink DesignatedDriver Use the excuse of being the designated driver to avoid drinking / deflect questions about not drinking / allows an out / reduce the social pressure to drink DrinkRitualsAction Drinking Rituals and Actions DrinkRitualsPpl Drinking rituals and people DrinkRitualsDiet Drinking rituals and diet or food items DrinkRitualsTime Drinking rituals and time of day EtohConsidDiet Is alcohol considered by participant as part of their usual diet EtohConsidkJ Is the kilojoule content considered EtohQuant Usual glass size of drink EtohType Type of alcohol consumed most often

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Code Description WaterIntake Water intake has changed since diagnosis/treatment. AlcoholDietChanges Have there been any alcohol dietary changes InfluencedCurrFam Has alcohol influenced current family life InfluencedPastFam Has alcohol influenced past family life EtohPromFactor Alcohol is a prominent factor in the family Alco’ismImmedFam Alcoholism immediate family Alco’ismImmedFamChild Alcoholism immediate family, childhood Alco’ismImmedFamAdult Alcoholism immediate family, adulthood Alco’ismExtendFam Alcoholism extended family PartnerDrinks Notes partners drinking habits Stimga Stigma attached to consumption of alcohol EtohAffectPlans Consuming alcohol the night/day before affects plans for the following day PAchange Physical activity changes due to consumption PAchangeReason Physical activity changes due to consumption, reason SleepAffected Alcohol consumption affects sleep SleepNoAffect Alcohol consumption has no affect on sleep InfoSourceNil No information provided regarding alcohol and breast cancer InfoSource Information source alcohol and cancer InfoSourceTimeDx When provided Information source, at diagnosis InfoSourceTimeTreat When provided Information source, during treatment InfoSourceTimePost When provided Information source, post treatment InfoProviders Information source providers SelfInfoHunt Self knowledge hunt InfoQuality Quality of information source AppreciateInfoYes Would have appreciated more information AppreciateInfoYesReason Would have appreciated more information, reason AppreciateInfoNo Would not have appreciated more information AppreciateInfoNoReason Would not have appreciated more information, reason SmokingAwareness Situation likened to risks of smoking and awareness campaigns BCaAwarenessAdvocate Includes informing others about reducing alcohol consumption SupportForWWACP Comment supports the need for WWACP/any program immediately post treatment

Appendices 269 Second Pass – Codes arising from Precede-Proceed (Phase 1 and 2). Re-analysis and alignment of first pass data, where possible, into below codes.

Code Description QoLSocialAssess Quality of Life – Precede Phase 1 social assessment HealthEpiAssess Health – Precede Phase 2 epidemiological assessment GeneEpiAssess Genetics – Precede Phase 2 epidemiological assessment BehavEpiAssess Behaviour – Precede Phase 2 epidemiological assessment EnviroEpiAssess Environment – Precede Phase 2 epidemiological assessment

Third Pass – Codes arising from Precede-Proceed (Phase 3). Re-analysis and alignment of second pass data, where possible, into below codes.

Code Description PredisposingAssess Predisposing – Precede Phase 3 educational and ecological assessment ReinforcingAssess Reinforcing – Precede Phase 3 educational and ecological assessment EnablingAssess Enabling – Precede Phase 3 educational and ecological assessment

270 Appendices

Appendix L Bar charts for all of the alcohol types, grouped by intervention and control

Appendices 271

272 Appendices

Appendices 273