INFLUENCES ON SCHOOL-AGE CHILDREN’S MILK AND SOFT DRINK INTAKE

by

ARAX BALIAN

Submitted in Partial Fulfillment of the requirements

For the degree of Doctor of Philosophy

Dissertation Adviser: Dr Elizabeth Madigan

Frances Payne Bolton School of Nursing

CASE WESTERN RESERVE UNIVERSITY

January, 2009

i

CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

______Arax Balian candidate for the _____Ph.D______degree*.

(signed) ______Elizabeth Madigan ______(chair of the committee)

______Donna A. Dowling ______

______Susan Tullai-McGuinness______

______Hope Barkoukis ______

______

______

(date) ______November______14, 2008

*We also certify that written approval has been obtained for any proprietary material contained therein.

ii

Dedication

This dissertation is dedicated to the memory of my father who would have been so proud of my accomplishments. It is also dedicated to my husband, Armand, who has provided me with his love and support and my children, Areen and Ayk, who have been the source of my inspiration.

iii

Table of Contents

Page

Table of Contents ------iii

List of Tables ------vii

List of Figures ------viii

Acknowledgements ------ix

Abstract ------xi

CHAPTER I: Introduction ------1

Introduction ------1

Problem ------2

Background and significance ------3 Milk and soft drink intake among American children ------3

Effects of milk and soft drink intake on children’s health ------6

Purpose ------9 Theoretical framework ------9

Study concepts ------15

Definition of terms ------18

Research questions ------20

Significance of the study ------21

CHAPTER II: Review of the Literature ------23

Overview of Childhood Overweight and Obesity ------23

Dietary Recommendations and Guidelines for Children ------28

iv

Cognitive development ------31

School-age children’s cognitive development ------31

Cognitive development, health beliefs, and food choice ------34

Factors influencing school-age children’s milk and soft drink intake ------36

School-age children’s milk and soft drink intake ------36

Influencing factors investigated in this study ------46

Other influences ------63

Summary for literature review ------66

CHAPTER III: Methods ------68

Design ------68

Setting ------68

Sample ------69

Sample size determination ------70

Sampling procedure ------72 Measurement ------72

Questionnaires ------73

Outcome variables ------78

Influencing factors ------78

Covariates ------82

Procedure ------84

Pilot study ------84

Main study ------85

Data analysis ------86

v

Description ------86

Preliminary analysis ------86

Analysis of research questions ------87

Protection of human subjects ------90

CHAPTER IV: Results ------92

Description ------93

Demographic characteristics ------93

Description of eating behaviors and patterns of milk and soft drink

intake ------94

Description of study variables ------99

Preliminary analysis ------100

Analysis of research questions ------102

The Theory of Planned Behavior constructs ------102

Gender, ethnicity, and BMI differences ------110 Summary ------120

CHAPTER V: Discussion ------122

Discussion of Results ------122

Description of eating behaviors and patterns of milk and soft drink

intake ------122

Analysis of research questions ------127

Prediction of milk/soda drinking behaviors ------127

Prediction of intention ------129

vi

Prediction of attitude, Subjective norm, and perceived

behavioral control ------131

Gender, ethnicity, and BMI differences ------135

Implications for practice, theory and policy ------138 Implications for practice ------138

Implications for theory ------138

Implications for policy ------139

Limitations of the Study ------140

Recommendations for Future Research ------141

Summary ------142

APPENDIX A: Demographic Characteristics Form: Tell me about yourself------144

APPENDIX B: 24-hour dietary recall for milk and soft drink intake ------145

APPENDIX C: Milk Intake Questionnaire ------147 APPENDIX D: Soda Pop Intake Questionnaire ------154

BIBLIOGRAPHY ------161

vii

List of Tables

Page

Table 1: Summary of findings about school-age children’s milk and

soft drink intake ------39

Table 2: Summary of relationships between influencing factors and milk

and soft drink intake ------47

Table 3: Ranges of the items in the Milk Intake Questionnaire from the

Pilot study ------75

Table 4: Ranges of the items in the Soda Pop Intake Questionnaire from the

Pilot study------76

Table 5: Frequencies of demographic characteristics of the study sample ------94

Table 6: Frequencies of eating behaviors and patterns of milk and

soft drink intake ------97

Table 7: Summary of descriptive statistics of the study variables ------100

Table 8: Gender differences in the milk and soda pop variables 111

Table 9: Race/ethnicity differences in the milk and soda pop variables ------115

Table 10: BMI differences in the milk and soda pop variables ------118

viii

List of Figures

Page

Figure 1: Theoretical Model of the Theory of Reasoned Action and

Theory of Planned Behavior ------10

Figure 2: Theoretical Model of the Study Based on the Theory of Planned

Behavior ------16

Figure 3: Substruction diagram of the study ------17

Figure 4: Path diagram for explanation of milk intake ------108

Figure 5: Path diagram for explanation of soft drink intake ------109

ix

Acknowledgements

I would like to express my gratitude to the following people, without whom I

could not have completed my doctoral degree. First, I would like to thank Elizabeth

Madigan, PhD, RN, FAAN, my academic advisor and the chairperson of my dissertation

committee, for her insightful guidance during my doctoral study. Despite the obstacles

that we faced, she made all efforts to ensure the fruition of this study. I also would like to

thank my dissertation committee members: Donna Dowling, PhD, RN, Susan Tullai-

McGuinness, PhD, RN, and Hope Barkoukis, PhD, RD, LD for their valuable input based on their expertise. I am also grateful to Gail McCain, PhD, RN, FAAN, for her support and input during the initial phase of the development of this study.

Next, I wish to extend my appreciation to the staff of the research office at Kaiser

Permanente Ohio, Bedford Heights and their previous director, Mrs Lisaann Gittner, who

made all efforts to help me collect my dissertation data from their facilities. I also wish to thank the Midwest Nursing Research Society for the financial support they provided me

through the 2007 Dissertation Research Grant award.

Finally, I would like to extend my gratitude to the following people who always

stood by me and helped me fulfill my dreams: to my mother, who has guided me during

my undergraduate nursing education, has been my role model as a pediatric nurse, and has been a devoted mother to me and a grandmother to my children enabling me

accomplish my graduate studies; to my aunts, brothers and sister-in-law, who have all

provided genuine and affectionate care to my two children while I pursued my doctoral

education and dissertation defense. Last but not least, my deepest gratitude goes to my

x husband, Armand, without whose support, patience, and encouragement I could not have started and completed my doctoral degree and my two children, Areen and Ayk, whose emotional support and true patience have allowed me to pursue my doctoral degree.

xi

Influences on School-Age Children’s Milk and Soft Drink Intake

Abstract

By

ARAX BALIAN

Over the past decades milk intake among 6-to11-year-olds declined 24% among boys and 32% among girls. Concomitantly, consumption of calorie and carbohydrate-rich

soft drinks doubled, raising concerns related to overweight. Prevalence of childhood

overweight has more than tripled since 1976 leading to serious health conditions. A

cross-sectional descriptive correlational study was undertaken using the Theory of

Planned Behavior. The purposes were to: (1) determine influences on school-age

children’s milk and soft drink intake and (2) determine the effect of gender, ethnicity, and

body mass index (BMI) on behavioral beliefs, normative beliefs, control beliefs, attitude,

subjective norm, perceived behavioral control, intention, and milk/soft drink intake

behaviors. Using the roster of Kaiser Permanente Ohio, a random sample of six hundred

10 to 11-year-olds were asked to complete a mailed survey including a demographics form, 24-hour dietary recall, and Milk and Soda Pop Intake Questionnaires. Ninety-seven children responded.

xii

The majority of the children had ≥ 1 glass of milk on a usual day during the school

week and two-thirds consumed soft drinks. Multiple regression was used to explain the

relationships among the variables. Intention predicted milk and soda intake, attitude had

the strongest contribution, followed by perceived behavioral control in predicting soda

intention. Within the beliefs, taste and being healthy predicted attitudes, friends predicted

subjective norms, and availability of milk in home refrigerator predicted perceived

behavioral control to drink milk. No gender and BMI differences were found in the milk

and soda TPB variables and both behaviors. Compared to the minority group, white participants had: stronger intention to drink milk, stronger perception that drinking milk makes them healthy, that someone in their family thinks they should drink milk every day, and that having milk in home refrigerator makes it easier to drink it. Compared to

the white participants, the minority group had stronger perception that someone in their family and friends think they should drink soda every day and they drank significantly more soda. Ultimately, prospective studies assessing beverage patterns over time and replication of this study with various foods are needed to intervene and possibly halt the rising proportion of overweight children.

1

CHAPTER ONE

Introduction

Childhood is a dynamic period in human life during which rapid physical growth

creates an extraordinary demand for energy and nutrients. The physical, developmental, and social changes that occur in childhood can profoundly affect eating behaviors and nutritional health. Failure to consume a healthy diet during this time can potentially affect

children’s growth and cognitive development and constitutes a risk for immediate health

problems as well as for chronic diseases in later life (Nicklas & Hayes, 2008). In view of

evidence that many children in Western countries are overweight or obese and have a

poor diet, the eating behavior of these young people has become an increasing public

health concern (LaRowe, Moeller, & Adams, 2007).

Childhood offers a unique opportunity to positively influence the adoption or

maintenance of healthy eating behavior that could be sustained throughout adulthood.

However, an understanding of the factors influencing children’s eating behavior is a

prerequisite. In this study, influences on school-age children’s milk and soft drink intake

were investigated using the Theory of Planned Behavior (TPB) (Ajzen, 1988, 1991). The influence of gender, ethnicity, and body mass index (BMI) on the relationship among behavioral beliefs, normative beliefs, control beliefs, attitude toward the behaviors, subjective norm, perceived behavioral control, behavioral intention, and milk and soft drink intake were examined. This chapter includes the problem, the background and significance, the research questions and the theoretical framework.

2

Problem

Compared to meeting the Dietary Reference Intakes (DRIs) and diets characterized by variety, moderation, and proportionality, the food choices of children in

the U.S. do not meet the national recommendations (Nicklas & Hayes, 2008). Over the

past three decades, major changes have taken place in the beverage consumption patterns

of American children (Nicklas & Hayes, 2008). Specifically, insufficient intake of

calcium-containing foods such as milk, and excess carbohydrate consumption, mainly of

soft drinks, are critical concerns related to childhood obesity (Ariza, Chen, Binns, &

Christoffel, 2004; Ludwig, Peterson, & Gortmaker, 2001; Nicklas, Baranowski, Cullen,

& Berenson, 2001; Nicklas & Hayes, 2008).

Eating behaviors formed during childhood have immediate as well as potentially

lifelong effects on health. Over the past three decades, changes in children’s food intake,

specifically overconsumption of energy-dense, nutrient-poor foods and beverages have

raised concerns due to their possible link to overweight in children (Nicklas & Hayes,

2008). As overweight children become overweight adults, the diseases associated with obesity and health care costs are expected to increase (Wang & Dietz, 2002).

Consequently, there is an urgent need for American children to adopt and maintain healthy eating behaviors to become fit and healthy adults with low morbidity and adequate physical work capacity. This may be attained through nutrition education, physical fitness programs, and effective interventions for children. The design of interventions that yield desirable changes in behavior require a prior understanding of

children’s eating behavior and the factors that influence them. Despite considerable

research in the area of child health, there is a paucity of descriptive, theoretically-based

3

investigations to identify factors that influence eating behaviors, specifically milk and

soft drink intake of school-age children, the focus of the present study.

Background and Significance

Milk and soft drink intake among American children

The relationship between nutrition, maintenance of health, and development of chronic diseases has been well established. Despite the national initiatives to promote lifelong healthy eating behavior, the behavior patterns of American children point to a growing trend toward unhealthy lifestyles and practices and their overall dietary quality has been found to decrease as they grow into adulthood (Demory-Luce et al., 2004). This

is of great concern due to the rising prevalence of obesity and overweight and the

consequent obesity-related diseases in both adults and children (Ogden, Yanovski,

Carroll, & Flegal, 2007). These diseases result in around 300,000 deaths and account for

more than $100 billion per year treatment costs (Allison, Fontaine, Manson, Stevens, &

VanItallie, 1999; Blumenthal, 2001; Must et al., 1999).

As a result of the recommendations to decrease dietary fat, there has been an

increase in carbohydrate intake by children. In addition, it is believed that the type of

carbohydrates consumed by children have changed to those with a higher glycemic index, an indicator of the ease with which a carbohydrate is digested (Slyper, 2004). In the U.S.,

children’s daily food intakes are markedly high in added sugars, those eaten separately or

used as ingredients in processed or prepared foods and beverages such as white sugar,

brown sugar, raw sugar, corn syrup, high-fructose corn syrup that do not contribute to

intake of vitamins, minerals, or other essential nutrients (Sigman-Grant & Morita, 2003).

4

Soft drinks are a major source of added sugars and they constitute the primary

beverage leading to increase in carbohydrate intake among 2 to 17-year-old children

(Nicklas & Hayes, 2008). A soft drink is a soda made from carbonated water, added sugar, and flavors (Nestle, 2000). Diet sodas contain artificial sweeteners instead of sugar and are consumed to a lesser extent by children as compared to regular soda (French, Lin,

& Guthrie, 2003; Grimm, Harnack, & Story, 2004). Each 12-oz serving of carbonated sweetened soft drink contains the equivalent of 10 teaspoons of sugar (Murray et al.,

2004), 40 grams of added sugar and 160 Kcal (Nestle, 2000).

Trends in children’s and adolescents’ beverage consumption indicate the possibility

of soft drinks replacing more nutritious drinks such as fruit juices and milk (Ballew,

Kuester, & Gillespie, 2000; Bowman, 2002; Rampersaud, Bailey, & Kauwell, 2003).

Data from national dietary surveys indicate that within the latest decades there has been a

dramatic increase in soft drink consumption among children in the U.S. (Nielsen &

Popkin, 2004; Nielsen & Popkin, 2003; Nielsen, Siega-Riz, & Popkin, 2002; Rampersaud et al., 2003; Smiciklas-Wright, Mitchell, Mickle, Goldman, & Cook, 2003). In 20 years, soft drink consumption has increased 300% (Cavadini, Siega-Riz, & Popkin, 2000) and serving sizes have grown from approximately 6.5 oz in 1950 to 12 oz in the 60s and 20 oz by the end of the 90s (Murray et al., 2004).

Carbonated soft drink consumption has been found to increase with age, with a

striking increase beginning around age 8 years. Between 1977 and 1998, soft drink

consumption increased 48% among 6 to 17-year-olds (French et al., 2003). Soft drinks account for 8.5% of children’s daily energy intake in the U.S. (Adair & Popkin, 2005).

5

Among school-age children, 56% to 85% drink at least one soft drink daily (Rampersaud et al., 2003).

From 1965 to 1996, lower fat milk has replaced higher fat milk but total consumption of milk has dropped significantly by 36% among children and adolescents

(Cavadini et al., 2000; Rajeshwari, Yang, Nicklas, & Berenson, 2005). Between 1977 and

2001, daily milk consumption among 2-to-18-year-olds has decreased from 3.46 servings to 2.75 servings (Nielsen & Popkin, 2004). As milk intake decreased, soft drinks have become children’s preferred choice of beverage, with their consumption more than tripling as children move from third to eighth grades (Lytle, Seifert, Greenstein, &

McGovern, 2000).

The decrease in milk consumption has occurred among both school-aged girls and boys (Fiorito, Mitchell, Smiciklas-Wright, & Birch, 2006). Concomitantly, soft drink consumption almost doubled (Bowman, 2002), a phenomenon that has been positively associated with higher caloric intakes (Adair & Popkin, 2005). In a national sample of

Americans ages 2-60 years, the biggest decrease in milk consumption from 13.2% in

1977 to 8.3% in 2001 occurred in the 2-to 18-year age group (Nielsen & Popkin, 2004).

For this same age group during the same time period, soft drink intake increased from

3.0% to 6.9% and milk portion sizes decreased from 15.4 oz in 1977 to 13.6 oz in 1996

(Nielsen & Popkin, 2004). Due to the cross-sectional nature of the available studies, it is still unclear whether milk intake has become less popular or if soft drinks have been substituted for milk.

6

Effects of milk and soft drink intake on children’s health

Milk. Besides growth and intellectual development, unhealthy eating behavior can

increase a child’s risk for a number of immediate health problems (Nicklas & Hayes,

2008). In addition to essential nutrients such as phosphorus, riboflavin, vitamin B12,

protein and vitamin A, dairy products contain substantial amounts of Vitamin D and

calcium. Milk is the main source of calcium in the American diet (Nicklas, 2003).

Because of its importance in several metabolic processes such as bone remodeling,

vascular function, muscular contraction, and others, inadequate calcium intake is a

serious public health issue. Furthermore, there have been reports about the beneficial role

of adequate dairy foods and calcium intake in weight maintenance and moderating body

fat in children (Skinner, Bounds, Carruth, & Ziegler, 2003; Novotny, Daida, Acharya,

Grove, & Vogt, 2004), insulin resistance (Heaney, Davies, & Barger-Lux, 2002; Pereira

et al., 2002), and some chronic diseases of older age such as hypertension (Huth,

DiRienzo, & Miller, 2006; Resnick et al., 2000), some cancers (Grant, 2002) and

osteoporosis (Flynn, 2003).

In diets low in dairy products, diminished calcium intake prevents the

accumulation of maximal peak bone mass during early adolescence, a critical time in life

(More, 2008). Bones contain almost 100% of calcium in the human body and nearly 40% of peak bone mass is built up during adolescence (More, 2008). A 5% to 10% deficit in

peak bone mass has been reported to increase lifetime incidence of hip fracture by 50%, a

problem that can be prevented by ensuring adequate intake of calcium among

preadolescents and adolescents (Wyshak, 2000). Inadequate dietary calcium intake,

together with inactivity, may hinder maximal skeletal growth and bone mineralization,

7 increasing risk of developing osteoporosis in later life (Whiting et al., 2004).

Furthermore, in both clinical and epidemiologic studies, higher intakes of calcium and dairy products have been associated with lower body fat and weight in children and adults (Carruth & Skinner, 2001; Davies et al., 2000; Heaney et al., 2002; Zemel, Shi,

Greer, Dirienzo, & Zemel, 2000).

Soft drinks. In addition to being associated with multiple health problems such as obesity (Ludwig et al., 2001), damage of the gastric mucosa (Kapicioglu et al., 1998), decrease in esophageal pH (Rubinstein, Hauge, Sommer, & Mortensen, 1993), duodenal acidification and ulceration (McCloy, Greenberg, & Baron, 1984), frequent intake of carbonated soft drinks has been associated with dental caries in children (Mariri et al.,

2003) due to their high sugar content and acidity causing enamel erosion (Heller, Burt, &

Eklund, 2001). Despite considerable progress made in the U.S., the 2000 United States

Surgeon General’s report indicates that 45% of children ages 5 to 17 still have dental caries (Allukian, 2000).

Besides calories, carbonated soft drinks offer minimal or no nutritional value.

There have been negative associations between intakes of carbonated soft drinks and intakes of essential micronutrients such as calcium, riboflavin, vitamins A, C, and D, phosphorus, folate, and magnesium in preschool and school-age children and adolescents

(Bowman, 2002). Furthermore, soft drinks are the greatest source of caffeine in children’s diets, which is of concern due to the potential for addiction (Keast & Riddell,

2007).

In addition to contributing to a reduction in the quality of children’s diets and their chances of achieving nutritional adequacy, soft drink consumption is a significant

8

contributor to total caloric intake (French et al., 2003; Ludwig et al., 2001). After

adjusting for anthropometric, demographic, dietary, and lifestyle variables in a

longitudinal study, each 12-oz daily serving of sugared soft drink was associated with a

0.18-point increase in children’s BMI and 60% increase in risk of obesity (Ludwig et al.,

2001). In a more controlled clinical study in Denmark, similar results were found for an

association between sweetened beverage intake and considerable weight gain (Raben,

Vasilaras, Moller, & Astrup, 2002). Compared to normal weight children, overweight

children are more likely to become obese adults (Magarey, Daniels, Boulton, &

Cockington, 2003), which increases long-term risk of CHD, hypertension, type 2 diabetes

(Ferraro, Thorpe, & Wilkinson, 2003), gallbladder disease, osteoarthritis, and some forms of cancer (Bray, 2003).

In addition to other factors, high soft drink consumption is thought to be contributing to the increasing prevalence of overweight and obesity among children

(Malik, Schulze, & Hu, 2006) possibly due to excessive caloric content, as soft drinks

have been found to contribute an additional intake of 188 Kcal/day in consumers

compared to nonconsumers (St-Onge, Keller, & Heymsfield, 2003). Furthermore, these

beverages are ingested in addition to, and not as a replacement for, other dietary products,

contributing to a higher caloric intake (Bellisle & Rolland-Cachera, 2001).

Several other mechanisms have been suggested to explain the link between soft

drink intake as a high-glycemic-index carbohydrate and obesity: (1) decreased milk intake concurrent with the rise in soft drink consumption; (2) high-glycemic index

carbohydrates lead to postprandial hyperinsulinemia, which may result in excessive weight gain; (3) decreased resting energy expenditure with beverages of high-glycemic

9

index carbohydrates compared with that of mixed-nutrients, such as milk that contains fat, protein, and carbohydrate; or (4) beverages with high-glycemic-index promote increased food intake due to decreased satiety and fullness sensation (Slyper, 2004; St-

Onge et al., 2003).

Purpose

Many lifestyle habits, such as eating behavior, that affect risk factors for chronic diseases have their environmental and behavioral roots in childhood (Joseph & Kramer, 1996). Furthermore, in view of the seriousness of the health consequences of children’s low milk consumption and increased soft drink intake and due to scarcity of the literature on factors influencing these behaviors, this study aimed at identifying factors that influence school-age children’s milk and soft drink intake using the TPB. The purposes of this study were to: (1) determine influences on school-age children’s milk and soft drink intake and (2) determine the effect of gender, ethnicity, and body mass index (BMI) on behavioral beliefs, normative beliefs, control beliefs, attitude toward the behavior, subjective norm, perceived behavioral control, intention and behaviors about milk/soft drink intake in school-age children. In this study, the words soft drink, soda, and soda pop are used interchangeably.

Theoretical framework

Since Wicker’s (1969) conclusion that attitudes may not predict behavior, social

psychologists have worked on improving the predictive power of attitudes. Thereafter,

among the models used to predict and understand social behavior, the most widely used

ones have been the Theories of Reasoned Action (TRA) (Ajzen & Fishbein, 1980;

Fishbein & Ajzen, 1975) and Planned Behavior (Ajzen, 1988, 1991) which will be

10

guiding this study. The aim of both theories is to understand and predict one’s behavior

from other determinants (Ajzen & Fishbein, 1980). They assume that individuals are

“rational actors” who process information and are motivated to act on it (Ajzen &

Fishbein, 1980). Fishbein (1967) developed and first introduced the TRA in 1967 in an attempt to understand the relationship between attitudes and behavior. The TRA includes measures of attitude and social normative perceptions that determine behavioral intention, which, in turn affects behavior. Refer to Figure 1 for the theoretical model of

the TRA and the TPB.

Figure1 . Theoretical Model of the Theory of Reasoned Action and Theory of Planned Behavior

Behavioral beliefs Attitude toward and outcome the behavior evaluations

Normative beliefs and motivation to Subjective norm Behavioral Behavior comply intention

Control beliefs and Perceived perceived power behavioral control

Note. The shaded sections depict the TRA and the entire model depicts the TPB.

The TRA and the TPB assume that behavioral intention is the main direct

determinant of behavior. Thus the usefulness of this theory in explaining behavior

11

depends on the extent to which the concerned behavior is under volitional control, i.e.

situations in which individuals have a large degree of control and thus can decide

willingly to perform or not to perform the behavior (Ajzen, 1991; Montaño & Kasprzyk,

2002). The theory has performed poorly in explaining behaviors over which individuals

have incomplete volitional control i.e. under conditions where there are constraints on

action, rendering intention as insufficient to predict behavior. An example would be in

the case of a person who has high motivation to eat healthy foods (behavior) but does not

actually do so due to environmental conditions such as cost and availability of healthy

foods. A possible reason for this may be that there are multiple factors influencing one’s intention, which complicates prediction of behavior from intention.

According to Ajzen (1991), most behaviors are found along a continuum extending

from total control to complete lack of control. When faced with no constraints in

adopting a certain behavior, an individual would have total control. In contrast, if

resources and skills that are prerequisites to adoption of a behavior are lacking or

unavailable, an individual would have lack of control. Thus, to address incomplete

volitional control, Ajzen (1991) proposed the TPB that includes the concept of perceived behavioral control.

The TPB is a microlevel theory derived from expectancy-value theory, meaning

that people are likely to take action if they perceive that the action will lead to expected

outcomes or anticipated consequences they value or desire. As such, children may choose

to drink milk to maximize positive outcomes such as maintenance of health and body

weight and to minimize negative outcomes. As seen in the theoretical model in Figure 1, the TPB postulates that behavior is predicted indirectly from perceived behavioral control

12

through the mediating effect of behavioral intention, as well as directly from behavioral

intention and perceived behavioral control (Ajzen, 1991). Behavioral intentions

determine perceived likelihood of performing a certain behavior (Ajzen & Fishbein,

1980). They are thought to represent the motivational factors influencing behavioral

performance i.e. they are indications of how hard people are willing to try to perform a

behavior. In general, the stronger one’s intention to do a certain behavior, the more

probable would be its performance, provided the behavior is under volitional control

(Ajzen, 1991).

Perceived behavioral control reflects one’s perception of how easy or difficult it is

to perform a certain behavior (Ajzen, 1991). It is assumed to reflect external factors such

as availability of time, money, or social support and internal factors such as ability, skills,

and information (Ajzen & Timko, 1986). Perceived behavioral control, as a predictor of

behavior, is most useful under two conditions. First, when the behavior being predicted is

not under complete volitional control and second, when perceptions of behavioral control

reflect actual control with some degree of accuracy (Ajzen & Madden, 1986). The

addition of perceived behavioral control is meant to add knowledge about the potential

constraints on performing an action as perceived by the actor, thus explaining failure of

intentions to always predict behavior (Armitage & Conner, 2001). Therefore, in cases when one’s level of volitional control hampers prediction of behavior from intention, perceived behavioral control is expected to (1) ease the implementation of behavioral intentions into behavior, and (2) directly predict behavior. Ajzen (1991) proposed the direct relationship between perceived behavioral control and behavior because of lack of evidence for the interactive effect of perceived behavioral control on the intention-

13 behavior relationship. He further explains that in cases where behavioral intentions alone account for small amounts of variance in behavior, possibly due to problems in volitional control, perceived behavioral control should independently predict behavior.

According to the TPB, behavioral intention can be explained by three conceptually independent determinants: attitude toward the behavior, subjective norm, and perceived behavioral control. Attitude toward the behavior refers to “the degree to which a person has a favorable or an unfavorable evaluation or appraisal of the behavior in question”

(Ajzen, 1991). Subjective norm refers to “the perceived social pressure to perform or not to perform the behavior” (Ajzen, 1991). Perceived behavioral control, as described above, reflects one’s perceived ease or difficulty of performing a certain behavior. It is assumed to represent past experience and expected obstacles. The relative contribution of the three determinants in predicting intention is shown to vary across behaviors and situations (Ajzen, 1991). For example, with a certain behavior, attitudes may have a significant effect on intentions, while with other behaviors attitudes and subjective norms may account for the variance in intentions, and in still others, all three predictors may have independent impacts.

According to the TPB, individuals have the following three salient beliefs that are the main determinants of their intentions and actions: (1) behavioral beliefs, which are beliefs about the likely outcomes of the behavior and the evaluations of these outcomes;

(2) normative beliefs that are beliefs about the normative expectations of others and motivation to comply with these expectations, and (3) control beliefs about the presence of factors facilitating or impeding performance of the behavior and the perceived power of these factors in helping behavioral performance. In combination, behavioral beliefs

14

give rise to favorable or unfavorable attitude toward the behavior, normative beliefs

result in perceived social pressure or subjective norm, and control beliefs lead to

perceived behavioral control (Ajzen, 1991).

In summary, attitude toward the behavior, subjective norm, and perceived

behavioral control result in the development of a behavioral intention. According to

Ajzen (1991), the more favorable the attitude, subjective norm, and perceived behavioral

control toward a behavior, i.e. when people evaluate a behavior positively, believe that

important others think they should perform it, and perceive of having control over it, the

stronger should be their intention to engage in that behavior. Intention is considered the

direct antecedent of behavior because, given an adequate degree of actual control over a certain behavior, individuals are expected to carry out their intentions. However, given the fact that many behaviors are not under one’s complete volitional control, in addition to intention, perceived behavioral control can be considered.

The TPB is chosen as a framework to guide this study for two reasons. First, the

TPB includes the social influence which is very pertinent to this study because eating behaviors take place in a social context where other eaters such as parents, siblings, and peers act as models and children’s observations of these models’ eating behavior influence their own preferences and eating behaviors (Birch & Fisher, 1998). Second, the

TPB provides an opportunity to further investigate the salient beliefs underlying attitudes,

subjective norms, and perceived behavioral control, which may enable further

understanding of factors influencing children’s milk and soft drink intake, which is vital

for future behavior change initiatives.

15

Study concepts

The relationships among the concepts of this study are illustrated in Figure 2 and the substruction is diagramed in Figure 3. Contrary to the TPB, in this study a direct relationship between perceived behavioral control and both milk and soda drinking behaviors was tested. The rationale for this modification is that some children have no control over their milk and soft drink choices and thus the relationship between perceived behavioral control and the behaviors through intention may not hold true. In addition to

the concepts of the TPB, the influence of three covariates: gender, ethnicity, and BMI on

the theoretical constructs were investigated in this study. Gender and ethnicity were

selected because previous studies about attitudes and eating and physical activity behaviors using the TPB with children and adolescents have found gender differences in the predictors (Backman, Haddad, Lee, Johnston, & Hodgkin, 2002; Berg, Jonsson, &

Conner, 2000; Craig, Goldberg, & Dietz, 1996). The association between BMI and food choice has not been extensively investigated in children. Therefore, in this study, we wanted to investigate this relationship.

16

Figure 2. Theoretical Model of the Study Based on the Theory of Planned Behavior

Q6 Gender Q7 Q8 Ethnicity BMI Q6 Q7 Attitude Q6 Q3 Q8 Behavioral toward the Q2 Q7 beliefs behavior Q8 Behavior Q4 Intention to Q1 Q2 Daily milk Normative Subjective drink milk intake beliefs norm and soft drink Daily soft Q2 drink Q5 Perceived Control behavioral Q1 beliefs control

Figure 3. Substruction diagram of the study 17

18

Definition of terms

As depicted in the theoretical model in Figure 2, in this study there are two

behaviors to be investigated: daily milk intake and daily soft drink intake. Consequently,

in the following section, the definitions of each concept will refer to both behaviors.

Behavior. Behavior is actions taken by individuals. Based on United States

Department of Agriculture’s (USDA) recommendations, daily milk intake is defined as

drinking three cups of regular milk per day (USDA, 2005). Daily soft drink intake is the

amount of regular (not including diet) carbonated beverages ingested. Both behaviors

were measured by having the participants indicate the amount of milk and soda pop they

drink in the 24-hour dietary recall.

Intention to drink milk and soft drink. This variable refers to a person’s perceived

likelihood of drinking three cups of milk and regular carbonated beverages daily. These

concepts were measured by the Intention to drink milk and Intention to drink soda pop

scales.

Attitude toward the behavior. In this study, attitude toward the behavior refers to a

person’s feeling of favorableness or unfavorableness toward drinking milk and soft drink

daily. These concepts were measured by the Attitude toward milk intake and Attitude

toward soda pop intake scales.

Behavioral beliefs. Behavioral beliefs refer to a person’s conviction that drinking

milk and soft drink daily will be associated with certain positive or negative outcomes

and one’s evaluation of these outcomes. These concepts were measured by the

Behavioral beliefs about milk and Behavioral beliefs about soda pop scales.

19

Subjective norm. Subjective norm refers to a person’s perception of social pressure or approval to drink 3 cups of milk and soft drink daily. These concepts were measured by the Subjective norm to milk intake and Subjective norm to soda pop intake scales.

Normative beliefs. Normative beliefs refer to the likelihood that certain important people with whom the individual is motivated to comply would approve or disapprove of that person’s drinking milk and soft drink daily. These concepts were measured by the

Normative beliefs about milk intake and Normative beliefs about soda pop intake scales.

Perceived behavioral control. Perceived behavioral control is a reflection of how easy or difficult it is for someone to drink milk or soft drink daily. These concepts were measured by the Perceived behavioral control over milk intake and the Perceived behavioral control over soda pop intake scales.

Control beliefs. Control beliefs refer to the presence of particular factors and their power to make easy or difficult for a person to drink milk and soft drink daily. These will be measured by Control beliefs about milk intake and Control beliefs about soda pop intake scales.

Gender. Gender refers to the biological sex of the participants and was indicated by each participant on the demographic characteristics form.

Ethnicity. Ethnicity is one’s affiliation in terms of language, customs, and beliefs with a racial or cultural group. It will be indicated by each participant on the demographic characteristics form.

Body mass index. BMI is a measure of adiposity. It was operationalized using the free CDC child and teen calculator (CDC, 2006). In this study, BMI-for-age percentile

20 was categorized as follows: between 5th and < 85th percentile (healthy weight), between

th th 85 and < 95 percentile (at risk of overweight), ≥ 95th percentile (overweight). Research questions

Based on Figure 2 the theoretical model of this study, the following research questions were investigated and analyzed in this study.

1. Does intention about milk/soft drink intake and perceived behavioral control

influence behavior about milk/soft drink intake in school-age children? (Q1)

2. Does attitude toward the behavior, subjective norm, and perceived behavioral

control influence intention about milk/soft drink intake in school-age children?

(Q2)

3. Do behavioral beliefs about milk/soft drinks influence attitude toward milk/soft

drink intake in school-age children? (Q3)

4. Do normative beliefs about milk/soft drinks influence subjective norm about

milk/soft drink intake in school-age children? (Q4)

5. Do control beliefs about milk/soft drinks influence perceived behavioral control

about milk/soft drink intake in school-age children? (Q5)

6. Are there differences by gender on behavioral beliefs, normative beliefs, control

beliefs, attitude, subjective norms, perceived behavioral control, intention and

behavior about milk/soft drink intake in school-age children? (Q6)

7. Are there differences by ethnicity on behavioral beliefs, normative beliefs, control

beliefs, attitude toward the behavior, subjective norm, perceived behavioral

control, intention and behavior about milk/soft drink intake in school-age

children? (Q7)

21

8. Are there differences by BMI on behavioral beliefs, normative beliefs, control

beliefs, attitude toward the behavior, subjective norm, perceived behavioral

control, intention and behavior about milk/soft drink intake in school-age

children? (Q8)

Significance of the study

In order to fulfill its goal of providing health care for clients, the main aim of nursing science is to generate scientific knowledge to guide nursing practice (Hinshaw,

1989). The significance of this study is in its potential contribution to the advancement of the discipline of nursing through generation of knowledge for both science and practice.

The concepts of this study fit with the perspective of nursing and its metaparadigm. The concept of person includes the school-age children whose eating behavior is of concern in this study. Environment entails internal stimuli such as the factors influencing children’s milk and soft drink intake, and external stimuli in the social context in which eating behaviors are influenced and shaped. Health is the state that ultimately results from the children’s milk and soft drink intake. Nurses are concerned with understanding factors that influence children’s eating behavior in order to promote healthy eating behavior among them.

Nurses, as health professionals, have a responsibility for health education and behavior change interventions. Knowledge generated from this study will be pertinent to the nursing science as it falls under one of the 11 research priorities for the 21st century outlined in The American Nurses’ Association policy statement on nursing research which is: “Minimizing or preventing behaviorally and environmentally induced health problems that compromise the quality of life and reduce productivity” (ANA, 1985, p. 3-

22

4). By investigating the fundamental psychosocial mechanisms underlying children’s

acquisition of health promoting behavior, information gathered from this study will aid in

maintenance of healthy lifestyles for children.

The design of interventions that generate desirable changes in health behavior can

best be implemented with an understanding of the theories of behavioral change and their

proper use in practice (Glanz, Rimer, & Lewis, 2002). Knowledge generated from this

theoretically-based study will add to the limited body of knowledge about the

phenomenon of children’s eating behavior, which can aid them and other health

professionals to initially understand what factors influence children to drink milk and soft

drinks. Subsequently, this will contribute to the design and implementation of tailored programs to meet the health needs of children specifically by promoting healthy eating behavior, such as drinking milk and minimizing soft drink intake among children, at the individual or community level, to possibly halt the obesity epidemic and ultimately improve their health status.

23

CHAPTER TWO

Review of the Literature

This study is guided by a value expectancy theory, the TPB, and it was designed

within a cognitive developmental context. This chapter starts with an overview of

childhood obesity and dietary recommendations and guidelines to improve eating

behavior among children in the US. This section will be followed by an explanation of children’s health beliefs based on their cognitive developmental level. Finally, a review of the literature about factors influencing children’s milk drinking behavior and soft drink intake will be presented. Conceptual, theoretical, and methodological issues will also be addressed.

Human food consumption has been found to vary considerably by age. Factors affecting eating patterns of school-age children are different than factors affecting eating patterns of adolescents, which, in turn, are different than those of adults. One possible reason for these differences is because children have to rely more on their parents for food, whereas adolescents and adults are independent. Because there are few studies about food choices of school-age children (Baranowski, Cullen, & Baranowski, 1999),

the literature review also will include studies conducted with adolescents.

Overview of Childhood Overweight and Obesity

Historical nutrient deficiencies in the U. S. have been replaced by excesses such

as in the case of soft drinks and calories and imbalances of some other food components

(e.g. insufficient calcium) (Nicklas & Hayes, 2008). Consequently, according to the

United States Department of Health and Human Services (USDHHS), there has been an

alarming increase in the number of overweight and obese children, rendering obesity an

24

important public health problem (USDHHS, 2000). The age-adjusted prevalence of

obesity has doubled from 1976-1980 to 1988-1994 for children in the 6- to 11-year-old

age group (Ogden, Flegal, Carroll, & Johnson, 2002), and the prevalence of overweight in the same age group has continued to significantly increase from 1999 to 2004 (Ogden et al., 2006). Because of the serious sequelae of obesity and the alarming increase in prevalence, an overview of obesity is presented in this section.

The World Health Organization (WHO) has defined obesity as “an accumulation of excess body fat, to the extent that health might be impaired” (Clement & Ferre, 2003, p. 722). Because it is difficult to measure body fat directly, obesity often is defined as

excess body weight rather than as excess fat (Ogden et al., 2007). According to U.S.

Centers for Disease Control and Prevention (CDC), a BMI-for-age between 85th and

95th percentile for age and sex, which corresponds to a BMI of 25 kg/m2 and above, is considered “at risk of overweight” and a BMI at or above the 95th percentile, which corresponds to a BMI of at least 30 kg/m2, is considered overweight or obese (CDC,

2006).

The degree of body fat mass in children depends on ethnic background, gender,

age, and developmental stage (Rosner, Prineas, Loggie, & Daniels, 1998). Fat mass

increases from 14% of body weight at birth to around 25% at the age of 6 months, then

progressively decreases until the age of 6 to 7 years. Eventually the relative fat mass increases until maturity, reflecting individual differences in fat mass (Maffeis, 1999).

Consequently, the BMI charts available for children take into account age and gender

(Flegal, Wei, & Ogden, 2002).

25

Energy balance to maintain the body’s steady-state can be regulated at the level of

food intake or energy output. Obesity develops when there is a discrepancy between

energy intake and energy output, disrupting the original steady-state, which, after a period of positive energy balance, leads to the development of a new steady-state at a

higher level with an increase in body fat store (Wabitsch, 2000). In general, obesity

involves an increase in both the number and the size of adipocytes and different types of

obesity (android or gynecoid) can be determined based on the location of adipose tissue

deposition in the body (Spiegelman & Flier, 2001).

In a survey of 8165 children and adolescents from two through 19 years of age

with weight and height measurements obtained in 2003-2004 and 2005-2006 as part of

the National Health and Nutrition Examination Survey (NHANES), 11.3% were at or

above the 97th percentile (obese) of the BMI-for-age growth charts, 16.3 were at or above the 95th percentile (obese), and 31.9% were at or above the 85th percentile (overweight)

(Ogden, Carroll, & Flegal, 2008). Specifically among 10-year-olds, the percentage of

overweight had increased significantly (p < .0001) from 13% in 1973-1974 to 39% in

1993-1994 and the percentage of obesity in the same age group had also increased

significantly (p < .0001) from 4% in 1973-1974 to 21% in 1993-1994 (Ogden et al.,

2002). Trend analysis indicated no significant changes in the prevalence of high BMI-for-

age among children and adolescents between 2003-2004 and 2005-2006 and no

significant trends between 1999 and 2006 (Ogden et al., 2008).

During the last two decades, being overweight has been more common among

ethnic minorities, with Blacks and Mexican Americans experiencing greater increases

than whites (Ogden et al., 2002). Similarly, during the current decade, prevalence of

26

being overweight among non-Hispanic white girls aged 6-19 years has been significantly

lower than that of non-Hispanic black and Mexican American girls (Hedley et al., 2004).

For boys of the same age group, being overweight has been significantly more prevalent among Mexican Americans than in non-Hispanic white and black boys (Hedley et al.,

2004).

Obese children are more prone to experience psychological or psychiatric problems than their non-obese counterparts (Reilly et al., 2003). In many children, being

overweight or obese precedes low self-esteem, suggesting a causal relationship. In a

study in which relationships between BMI and self-esteem across the elementary school years were investigated, an increasingly strong association between lower self-esteem and higher BMI was found (Hesketh, Wake, & Waters, 2004). Also prejudice against obesity starts in children as young as 6 years (Kolotkin, Meter, & Williams, 2001).

Many studies have consistently shown associations between obesity and

cardiovascular risk factors such as high blood pressure; dyslipidemia; abnormalities in

left ventricular mass and/or function; abnormalities in endothelial function; and

hyperinsulinemia and/or insulin resistance (Berenson et al., 1998; Freedman, Khan,

Dietz, Srinivasan, & Berenson, 2001; Freedman, Dietz, Srinivasan, & Berenson, 1999;

Reilly et al., 2003). Ten percent of a sample (n = 3599) of 5 to10 year olds in the U.S. were overweight, with a significant risk for raised diastolic blood pressure (OR 2.4), raised systolic blood pressure (OR 4.5), raised low-density lipoprotein (LDL) cholesterol

(OR 3.0), decreased high-density lipoprotein (HDL) cholesterol (OR -3.4), raised

triglycerides (OR 7.1), and high fasting insulin concentrations (OR 12.1) (Freedman et

27

al., 1999). Fifty-eight percent of the total sample had at least one of these cardiovascular

risk factors (Freedman et al., 1999).

Several studies have also described associations between obesity and asthma in childhood (Castro-Rodriguez, Holberg, Morgan, Wright, & Martinez, 2001; Sulit,

Storfer-Isser, Rosen, Kirchner, & Redline, 2005). In a longitudinal study of children (n=

1246) participating in a birth cohort, it was reported that becoming obese increased the

risk of developing asthmatic symptoms in girls who were originally not known to be

asthmatic (Castro-Rodriguez et al., 2001).

Childhood obesity has also been found to be associated with type 1 diabetes

(Hypponen, Virtanen, Kenward, Knip, & Akerblom, 2000), low grade systemic

inflammation as indicated by elevated serum C-reactive protein concentration (Visser,

Bouter, McQuillan, Wener, & Harris, 2001), sleep apnea (Dietz, 1998), structural abnormalities of the foot in prepubescent children (Riddiford-Harland, Steele, & Storlien,

2000), reduced quality of life (Williams, Wake, Hesketh, Maher, & Waters, 2005), and poor academic performance (Taras & Potts-Datema, 2005).

From 1979-1981 to 1997-1999, hospitalizations for obesity-related diseases

among 6- to 17-year-olds have increased considerably, leading to significant increases in

costs (Wang & Dietz, 2002). Discharge diagnoses with obesity-related diseases such as

diabetes have almost doubled from 1.43% to 2.36%, gallbladder diseases have tripled

reaching 0.59%, and sleep apnea has increased fivefold from 0.14% to 0.75%. Asthma

and some mental disorders were the most common principal diagnoses when obesity was

listed as a secondary diagnosis. In this same period, annual hospital costs related to

obesity among 6- to 17-year-olds have increased more than threefold (Wang & Dietz,

28

2002). The direct medical costs of obesity in the United States have recently been

estimated as more than $92 billion in 2002 dollars (Finkelstein, Fiebelkorn, & Wang,

2003).

Dietary Recommendations and Guidelines for Children

Over the past decade, health professionals have begun to address the challenge of

promoting healthful dietary behavior to reduce the risk of chronic diseases. The

recommendations from national health organizations have become more focused,

identifying dietary excesses of caloric intake, physical activity patterns, and prevention of

chronic diseases among Americans. In this regard, the following is the position statement of the American Dietetic Association (ADA): “It is the position of the American Dietetic

Association that children ages 2 to 11 years should achieve optimal physical and

cognitive development, attain a healthy weight, enjoy food, and reduce risk of chronic

disease through appropriate eating habits and participation in regular physical activity”

(Nicklas & Johnson, 2004).

Currently, more than ten scientific organizations have established dietary

recommendations and guidelines for children over two year of age. In 2002, the

Recommended Dietary Allowances (RDAs) were updated and the Dietary Reference

Intakes (DRIs) for energy, carbohydrates including added sugars, protein, amino acids,

fiber, fat, fatty acids, and cholesterol were presented by the Institute of Medicine’s Food

and Nutrition Board. The acceptable macronutrient distribution ranges (AMDR) as a

percent of energy intake are as follows: carbohydrates 45% - 65%, fat 25% - 35% for children ages 4 to 18 years, and protein 5% - 20% for young children 1 to 3 years of age and 10% -30% for older children 4 to 18 years of age (Nicklas & Johnson, 2004).

29

Major sources of added sugars are soft drinks, fruit drinks, pastries, candy, and other sweets. The DRIs did not set an amount for daily intake of added sugars for a healthy diet for children; however, it was recommended that they not exceed 25% of total calories consumed (Nicklas & Hayes, 2008). This amount was based on findings that intakes of added sugars exceeding 25% of total energy were coupled with decreased intakes of key micronutrients, predominantly calcium. From 1988 to 1994, 21% of 9 to

13-year-old children had added sugar consumptions exceeding 25% of their total energy intakes ("Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids.," 2002).

In 1997, new DRIs for calcium were established by the National Academy of

Sciences. The DRI for calcium is 1300 mg for 9-18 year olds. The levels were raised 500 mg above the 1989 RDAs for calcium for 9-10 year olds and by 100 mg for 11-18 year olds based on findings that calcium intakes at levels higher than the 1989 RDA can increase bone mineral density in children (Chan, Hoffman, & McMurry, 1995; Johnston et al., 1992) decreasing their risk of developing osteoporosis later in life (Whiting et al.,

2004). However, concomitant with declines in milk intakes, calcium intakes have also dropped and are lower than recommended AIs (Cavadini et al., 2000; French et al., 2003;

Kranz, Lin, & Wagstaff, 2007).

The U.S. Department of Agriculture (USDA) Food Guide Pyramid is a nutrition education tool that was designed to help Americans eat more healthy diets (USDA,

1992). It was first introduced in 1992 as a guide to translate the nutrition recommendations of the Dietary Guidelines for Americans into types and amounts of food to eat daily. However, due to difficulty in putting the nutrition messages into

30

practice, The Food Guide Pyramid underwent a process of review and in 2005

MyPyramid was released symbolizing a personalized approach to healthy eating and

physical activity for Americans 2 years of age and older (USDA, 2005). It presents the

following concepts for maintenance of a good health: (1) Activity as a reminder of

importance of physical activity; (2) Moderation by limiting the consumption of foods

high in solid fats and added sugars; (3) Proportionality signifying eating greater amounts

of foods from the groups placed lower in the pyramid such as grains, vegetables, and

fruits and (4) Variety i.e. eating foods from all the five different food groups (USDA,

2005). For an estimate of what and how much one needs to eat from each food group, one

can enter one’s age, sex, and activity level in the MyPyramid Plan box to get results.

In another federal initiative, Healthy People 2010 presents 10 Leading Health

Indicators which reflect the major health concerns in the United States at the beginning of

the 21st century. Each health indicator has one or more objectives designed to guide

health promotion and disease prevention policy and programs at the federal, state, and

local levels in order to improve the health of Americans by the year 2010 (USDHHS,

2000). Overweight and obesity is one of the 10 leading health indicators. Among the

nutrition objectives, there are specific ones for consumption of calcium, fruits,

vegetables, grains, and saturated fat and quality snacks. The dietary guidelines mentioned

above are consistent with the dietary recommendations of the main health promotion

organizations such as the National Research Council, the National Cholesterol Education

Program, National Institutes of Health, the National Cancer Institute, the American

Cancer Society, and the American Heart Association. In addition to these initiatives, the

U. S. national health promotion and disease prevention objectives recommend that all

31 schools provide nutrition education from preschool through 12th grade ("Guidelines for school health programs to promote lifelong healthy eating," 1997).

Cognitive development

School-age children’s cognitive development

The traditional Piagetian view of child development is that of a biological stage theory, in which the child progresses linearly through four predictable stages based on cognitive structures: sensorimotor, preoperations, concrete operations, and formal operations (Phillips, 1975). Piaget has explained cognitive structures as patterns of physical or mental action underlying particular acts of intelligence that correspond to stages of child development. Through observations, Piaget studied children’s intrinsic abilities to think and learn and described this progression of cognitive development. This study will include 10 to 11 year old children; therefore, the design will be based on the characteristics of the concrete operational stage (7-11 years of age).

According to Piaget, true thinking begins in the third stage of cognitive development with concrete operations around 7 to 11 years of age. The cognitive structure during the concrete operational stage is logical but depends on concrete referents. Piaget characterizes this period as one in which there is a relationship between thinking and symbolic logic, where children can do “groupings” of “concrete operations

(Phillips, 1975). He further mentions the presence of egocentrism in reasoning and explains three models for the use of the term “because” by children: (1) causal explanation which involves cause and effect relationships between two facts, (2) psychological/motivational which entails cause and effect relationships between an

32 intention and an act, and (3) logical implication which involves reason and consequence relationships between two ideas or judgments (Phillips, 1975).

By age 7 or 8, the average child makes a distinction between psychological and causal explanations but has trouble with logical implications, often resorting to psychological explanations. At around 9 years of age, full fledged logical justifications are present. A possible reason for this is that children, at this age, are able to impose direction and order on their thinking (Phillips, 1975). Despite being interested in the development of operational knowledge, Piaget also talked about social knowledge as equally important. Neo-Piagetians also have emphasized the strong influence of social- emotional context and cultural meanings on children’s cognitive development (Suizzo,

2000).

Intentions. Despite the fact that attributions of intention are basic to understanding of human behavior, there is lack of research on children’s understanding of intention, specifically that of school-age children. Consequently, in the following section, the available data will be presented which also includes younger age-groups.

Rather than focusing on children’s understanding of intentions, most research on children’s knowledge about intentions has been conducted on their use or nonuse of intention in making moral judgments. Research has highly relied on Piaget’s (1932) finding that younger children differ from older ones and adults in their ability to distinguish between a wrongful act and the person’s intentions when assessing blame.

However, more recently, rather than drawing inferences from research on moral judgments, researchers have tried to investigate children’s understanding of intentions

33

more directly. Children as young as 4 to 5 years old have been found to possess an early

understanding of intention closely linked with action (Baird & Moses, 2001).

In studies assessing when children begin to understand intentions represented in

purposive, goal-directed behaviors, children as young as 3 and 4 years old were presented

with stories in which the characters’ intentions differed from their desires and the

outcomes of their efforts to carry out their intentions. Children between 3 ½ and 4 years

of age could, at a beginning level, differentiate among intention, desire and outcome

(Feinfield, Lee, Flavell, Green, & Flavell, 1999).

By age 4 or 5, children are able to clearly distinguish intentions from desires or

preferences and from the outcomes of intentional acts (Moses, 1993). They also become

aware of psychological determinants of behavior other than intentions such as emotions,

motives, knowledge, beliefs, and personality traits (Flavell, 1999). Children as young as 3

and 4 years old also have been found to differentiate psychological states like beliefs and

desires from biological processes like reflexes, and physical forces as possible

determinants of human actions (Schult & Wellman, 1997). In another study conducted

with older children, a story-book task was administered to four to 11-year-olds to find out

about their understanding of three facets of intention: trying, not-trying, and trying-not

(Barriault, 2003). A developmental effect was seen with a slow increase in children’s understanding of intention that plateaued from 8 to 10 years of age (Barriault, 2003). A

possible explanation from this study is that older children’s successful intention

attributions may be due to their understanding of goal-action consistencies and

inconsistencies (Barriault, 2003).

34

Cognitive development, health beliefs, and food choice

Research has concluded that children’s understandings of the concepts of health and illness change qualitatively with cognitive development in a manner following the progression from preoperations to concrete operations to formal operations suggested by

Piaget (Kalnins & Love, 1982). Their ability to classify and think causally begins in the early school years; however, their reasoning at this time is limited to concrete objects and particular experiences (Flavell, 1977; Inhelder & Piaget, 1958). In a study examining 5 to

11-year-olds (preoperational and concrete operational stage) children were found to rely heavily on dimensions of sweet versus nonsweet foods, and meal entrees versus drinks and breakfast foods. These findings suggest that, regardless of cognitive developmental level, children’s food classifications were influenced by perceptual, functional, and physical properties (Michela & Contento, 1984). It was further found that children in the concrete operations stage understood dimensions involving degree of processing of foods, their origin, and what happens to food in the body, implying that classification systems and understanding of nutrients became more sophisticated with cognitive developmental level. At this age, children also use more behavioral, concrete, and precise ideas to explain health (Mickalide, 1986).

Inborn preferences of human beings are modified by learning processes, which play a major role in the development of food preferences and food rejections (Rozin,

1989). Modification of food acceptance patterns in children have been explained through three different processes: (1) mere exposure to unknown food i.e. the repeated tasting of an unknown food reduces chances of rejecting it, a phenomenon known as neophobia, (2) social influences such as parents or peers, and (3) association of physiological

35

consequences of food taste (Birch & Fisher, 1998; Koivisto Hursti, 1999). These learning

processes result in cognitive structures and processes such as attitudes and beliefs toward

food and eating behavior which, in turn, affect food intake in later life (Pudel, 1986).

Cognitive-motivational processes begin to affect children’s food choices

beginning in the concrete operational stage (7-11 years of age) and continue into formal

operations (Contento, Michela, & Goldberg, 1988; Michela & Contento, 1986). Despite

these findings, the developmental studies that have been conducted have mostly

concentrated on the changes in children’s concept of illness or on changes in children’s

broad beliefs about health. Only a few developmental studies have been conducted about

school-age children’s health habits and beliefs. In one of the studies where the health

habits of children from grade 4 through 11 were assessed, girls were found to have

healthier food choices than boys and elementary school children also had healthier food

habits than older ones (Perry, Griffin, & Murray, 1985). In another study testing the

applicability of a cognitive-behavioral model of health behavior to children’s food

choices, those in the concrete operational stage tended to be in one of three groups

according to: health orientation in food choice, taste orientation, or multiple-motive orientation (Michela & Contento, 1986). The taste-oriented group had the poorest dietary quality. In addition to social/environmental influences on children’s food choices, children used a process of cognitive self-regulation. Their food choices were guided by their beliefs and values about the outcomes of their behaviors, and the level of cognitive development was related to the specific beliefs about food such that elementary schoolchildren at the concrete operational stage of cognitive development frequently

36

acted according to their motivations applicable to their food choice (Michela & Contento,

1986).

Using the “draw and write” techniques, findings indicate that children possess considerable health-related knowledge with a healthy diet and physical activity viewed as

the most important factors that keep them healthy (Bendelow, Williams, & Oakley,

1996). It has been reported that children between the ages of 9-11 also were aware of the

relationship between their diet and health, and considered a healthy diet as being one that

did not contain fat. They considered fat to be the cause of heart problems and obesity,

which were considered undesirable for social reasons (Dixey, Sahota, Atwal, & Turner,

2001).

The research on children’s health beliefs from the cognitive developmental

perspective has confirmed the fact that the quality of children’s thoughts about health and

illness changes with cognitive development in interaction with other variables such as

personality, family background, and the child’s personal experience with health (Kalnins

& Love, 1982). The studies provide evidence for cognitive-motivational models (i.e.

models that give a key role to cognition in the self-regulation of motivation to perform a

behavior) such as a Value x Expectancy model in explaining influences on children’s

food intake (Contento et al., 1988; Michela & Contento, 1986).

Factors influencing school-age children’s milk and soft drink intake

School-age children’s milk and soft drink intake

According to Ajzen and Fishbein (1980), behavior can consist of single actions, a

specific behavior performed by someone, or behavioral categories involving sets of

actions. In order to be able to measure a behavior, whether it is a single action or a

37 behavioral category, it has to be defined clearly so that its performance can be determined. The behavior of interest is defined in terms of its target, action, context, and time (Ajzen & Fishbein, 1980). The behaviors in this study are two distinct ones consisting of single actions: milk and soft drink intake. Milk intake in this study is defined as drinking three cups of regular milk every day. Soft drink intake is defined as the amount of regular (not including diet) carbonated beverages ingested per day.

Both behaviors, milk and soft drink intake of school-age children, have been measured at the national (U.S.) level, as well as at the individual study levels, either separately or with other groups of beverages. While some studies have focused on the increase in soft drink consumption, others have equally examined the decline in milk consumption and reduction in calcium intake of American children and adolescents

(Demory-Luce et al., 2004; French et al., 2003; Friedman et al., 2007; Nielsen & Popkin,

2004; Rajeshwari et al., 2005; Rampersaud et al., 2003; Storey, Forshee, & Anderson,

2004). Table 1 presents a description of past research about milk and soft drink intake.

Only data about milk and soft drink intake of subgroups of school-age children have been presented in Table 1.

The majority of the studies examining these behaviors at the national level have investigated trends or patterns of prevalence or change in milk and soft drink consumption between two time points, usually two decades apart (French et al., 2003;

Nielsen & Popkin, 2004; Rajeshwari et al., 2005). These studies have used nationally representative data from the 1977-1978 Nationwide Food Consumption Survey, the

1989-1991 and 1994-1996 Continuing Surveys for Food Intake by individuals (CSFII),

38 the National Health and Nutrition Examination Survey, and the Supplemental Children’s

Survey 1998.

Table 1

Summary of findings about school-age children’s milk and soft drink intake

Source Design Sample Data collection tool Major findings

Berg, Jonsson, Cross- N=1730 pupils in 7-day food record - 42% drank milk at breakfast every day, of whom & Conner sectional Sweden 78% drank ≥1 glass of milk (2000) Age = 11, 13, & - 93% drank milk some time during the week 15 years

Demory-Luce et Longitudinal N = 246 biracial 24-hour dietary - Greater milk consumption in childhood (p ≤ .0001) al. (2004) cohort study young adults recall than in adulthood followed up from - Compared to childhood, greater soft drink age 10 years consumption (P≤.001) in adulthood - No association found between food group consumption patterns and BMI

Fisher, Cross- N=197 girls and - Girls: Three 24- - 88% of girls reported drinking milk, average milk Mitchell, sectional their mothers hour dietary recalls intake was 314 ± 185g Smiciklas- Age = 5 years (2 weekdays, 1 - 91% of girls reported drinking soft drinks Wright, & weekend day) Birch (2000) - Mothers: FFQ

French, Cross- N= 8909 from 3 1-day dietary recall, - Between 1977/1978 and 1994/1998 prevalence of Lin, & sectional national surveys 2-day record soft drink consumption increased 48% and mean Guthrie (2003) Age = 6-17 years intake more than doubled - Increases over time were larger among boys than girls

39

Table 1 (continued ).

Source Design Sample Data collection tool Major findings

Friedman, Randomized N = 356 male and 5 sets of three 24- - 65% to 90% of the children reported consuming Snetselaar, controlled 297 females hour recalls soft drinks which increased more over time Stumbo, Van clinical trial Age = 7.9 to 10.9 - Males consumed more soft drinks than females Horn, Singh, & years at start - Soft drink consumption increased over time and Barton, (2007) (median 7.3 years total milk consumption dropped drastically follow up)

Grimm, Survey N =560 children - Questions about - 30% reported drinking soft drinks daily Harnack, & Age = 8-13 years type of soft drinks - 85% usually drank regular (nondiet) soft drinks Story (2004) consumed (diet Vs - Soft drink consumption was higher among boys regular) and compared with girls (p = .03) frequency of - Intake increased with age (p < .001) consumption

Gummeson, Cross- N = 218 children 4-day breakfast diary No milk consumption data presented Jonsson, & sectional in Sweden Conner (1997) Age =10, 13, & 16 years

Harnack, Stang, Cross- N = 5311 children 2 nonconsecutive 24- - 64.1% of school-age children drank soft drinks & Story (1999) sectional from national hour dietary recalls - Whites were more likely than blacks to consume survey soft drinks Age = 2-18 years - Compared to nonconsumers, soft drink consumers had higher energy intakes and consumed less milk

40

Table 1 (continued).

Source Design Sample Data collection tool Major findings

Kassem, Lee, Cross- N = 707 female Questions about - 96.3% drank soda, 50.1% drank ≥ 2 glasses per day Modeste, & sectional students amount and - Students drank regular soda more than diet soda Johnston (2003) Age = 13-18 years frequency of regular soda consumed

Kassem & Lee Cross- N = 564 male Questions about - 96.5% drank sodas, 60.2% drank ≥ 2 glasses per day (2004) sectional students amount and - Students drank regular soda more than diet soda Age 13-18 years frequency of regular soda consumed

Nielsen, Barry, & Cross- N = 73,345 24-hour food - In 2-18-year-olds, the largest drop in milk Popkin (2004) sectional individuals from 4 recall/record consumption occurred from 13.2% of total energy in national (self/interviewer- 1977 to 8.3% in 2001 and soft drink consumption independent administered) increased from 3% to 6.9 % for the respective years surveys - For same age group between 1977 and 1996, the Age ≥2 years greatest changes occurred in milk and soft drink portions: decrease from 15.4 oz in to 13.6 oz for milk

and increase from 13.6 oz to 21 oz for soft drinks

Rajeshwari, Cross- N = 1548 children 24-hour dietary - Percentage of children consuming sweetened Yang, sectional Age = 10 years recall beverages significantly decreased from 1973 to 1994, Nicklas, & survey especially for soft drinks (p < .01) with no change in Berenson (2005) mean gram amount of soft drinks consumed - Total gram amount of milk consumption was significantly lower in medium to high 41

Table 1 (continued).

Source Design Sample Data collection tool Major findings

sweetened-beverage consumption group (p < .01) - Higher percentage of white (66%) compared to black children (51%) consumed soft drinks - Mean BMI significantly increased (p < .001) from 1973 to 1994 within all sweetened-beverage consumption groups

Rampersaud, Cross- N = 21,662 2 nonconsecutive 24- - Soft drink intake increased dramatically with age Bailey, & sectional individuals from hour dietary recalls beginning around 8 years Kauwell (2003) national survey - Mean soft drink intake exceeded milk intake at age 13 Age = birth to 18 (p < .04) years Storey, Cross- N = 8758 24-hour dietary recall - Soft drink: In 9-18 year olds, 28.9% consumed Forshee, & sectional subjects from none, 30% consumed ≤1 serving, 25.4% Anderson (2004) national survey consumed 1-2 servings, 15.6% consumed >2 Age = groups 2- servings 3, 4-8, 9-13, & - In 9-13 year olds, milk consumption dropped 14-18 years more rapidly in girls compared to boys - Milk consumption in whites and African- Americans declined relatively slower than in Hispanics.

42

43

The data have been gathered mainly by single 24-hour dietary recalls and/or records except in 2 studies where they used two nonconsecutive 24-hour dietary recalls

(Harnack, Stang, & Story, 1999; Rampersaud et al., 2003). However, in some studies the dietary recalls were self-administered and in others, interviewer-administered. Twenty-

four hour dietary recalls are thought to be appropriate for short-term intake rather than long-term ones (Farris & Nicklas, 1993). Similar findings have been obtained across the population-based studies. In general, there is consistent evidence that among American school-age children, between 1970s and 1990s, there has been an alarming increase in the amount of soft drink consumption, thus adding to higher caloric intakes and affecting dietary quality, and a concomitant decline in milk intake, adversely affecting calcium intake (Demory-Luce et al., 2004; French et al., 2003; Harnack et al., 1999; Nielsen &

Popkin, 2004; Rajeshwari et al., 2005; Rampersaud et al., 2003; Storey et al., 2004).

However, contrary to the national trends, Rajeshwari and colleagues (2005) have found that in Bogalusa, LA, the percentage of children drinking soft drinks has decreased from

1973 to 1994. Despite the possible presence of important regional differences in soft drink consumption, there is the need for more studies to confirm these regional differences which may or may not be reflective of national trends.

The design of most of the research relying on national surveys is cross-sectional and relies on broad subgroups by age. This design does not allow follow-up of children’s milk and soda drinking behavior over time to gain an understanding of changing habits from early childhood onwards. In order to better assess changes in eating patterns during age transitions, Demory-Luce and colleagues (2004) collected dietary intake data from a longitudinal sample. They collected dietary intake data from young adults who had

44

participated in a previous cross-sectional survey at 10 years of age. According to Block

(1982) and Thomson et al. (2003), the use of one 24-hour dietary recall is insufficient for characterizing the usual eating patterns of an individual but is adequate for characterizing the eating patterns of large groups of children. Despite having a limitation of using single

24-hour dietary recall with each participant at two time points, the results from this study provided important longitudinal information on changes in food group consumption patterns and were consistent with some of the above cited studies (Demory-Luce et al.,

2004).

Children’s and adolescents’ milk and soft drink intake have also been measured either alone or in conjunction with other foods with smaller samples, mainly to identify factors that influence or predict these behaviors (Berg et al., 2000; Fisher, Mitchell,

Smiciklas-Wright, & Birch, 2000; Grimm et al., 2004; Gummeson, Jonsson, & Conner,

1997; Kassem & Lee, 2004; Kassem, Lee, Modeste, & Johnston, 2003). The data collection tools and intervals are quite different in all these studies. The studies in which influences on soft drink consumption by children and adolescents were examined, the behavior was measured by single questions about frequency of consumption during the past 12 months with preset response items and type of soft drinks usually consumed: diet versus regular and colas with or without phosphoric acid (Kassem & Lee, 2004; Kassem et al., 2003). In the Swedish studies where influences on milk intake by children and adolescents was assessed in addition to other food choices, the behavior was measured either by a precoded 7-day food record (Berg et al., 2000) or a 4-day breakfast diary

(Gummeson et al., 1997) in which participants were asked to mark the days they had consumed the type of foods. In the study where both milk and soft drink intake of both

45 mothers and their daughters was assessed, the behaviors of the daughters were measured by three 24-hour food recalls and that of the mothers by food frequency questionnaires

(FFQ) (Fisher et al., 2000).

From the findings in Table 1, it is difficult to compare prevalence of milk and soft drink intakes across studies. Gummeson et al. (1997) have presented no data about consumption of milk. The figures of milk intake from the other studies (Berg et al., 2000;

Fisher et al., 2000) are somewhat close. The figures about soft drink intake are also varying. Some of the reasons to the discrepancy may be the different methods of measuring the milk and soft drink intake, differences in age groups studied, and varying habits among regions in the U.S. and between countries: Sweden and U.S.

In all these studies, influences and behaviors were measured concurrently except for the Swedish studies (Berg et al., 2000; Gummeson et al., 1997), in which the food choices were measured prospectively by administering the food records two and 6 weeks, respectively, after the TPB questionnaire. In Gummeson et al.’s (1997) study, the rationale for delaying measurement of the behavior was to minimize recall bias from the questionnaire, which could have influenced breakfast choices. However, there was a weak prediction of behavior by the TPB variables. Intention explained only 12% of the variance in choice of milk (Gummeson et al., 1997). Despite efforts to minimize recall bias, the time lapse between responding to the questionnaire and measurement of behavior might be a possible reason for the weak prediction because during this time interval, children’s preferences may have changed. Although not explicitly stated, Berg et al.’s (2000) rationale for having a shorter time lapse between the two measures may have been to minimize the possibility of weak predictions. In their study, intention explained

46

40- 49% of milk choices. Whether the shorter time lapse is the reason for the better

predictions can be determined only by additional studies with prospective designs

investigating influences on children’s milk and soft drink intakes.

In summary, there is a lack of standardized way of measuring milk and soft drink

intake, which makes comparison of both behaviors difficult across studies. Despite this

fact, it is clear from the available research that among school-age children, over the past

thirty years, there has been a tradeoff between milk and soft drink intake with soft drink

consumption increasing with age.

Influencing factors investigated in this study

As stated earlier, despite the importance of understanding determinants of

children’s eating behaviors, few descriptive theoretically-based studies have been

undertaken to identify factors influencing the eating behavior of children. Some of the

atheoretical studies have used various quantitative methods such as questionnaires,

surveys, and interviews to investigate predictors of food-related behavior in general (Le

Bigot Macaux, 2001), for fat and fiber (Berg, Jonsson, Conner, & Lissner, 2002), for

milk and soft drinks (Fisher et al., 2000; Grimm et al., 2004; Lee & Reicks, 2003; May &

Waterhouse, 2003), and dietary milk fat intake by preschool children (Dennison, Erb, &

Jenkins, 2001). Despite the scarcity of the atheoretical and theoretically-based studies

investigating influences on milk and soft drink intake, in the next sections, only the few

studies conducted with school-age children and adolescents will be presented. Table 2

presents a description of past research about influences on school-age children’s and

adolescents’ milk and soft drink intake.

47

Table 2

Summary of relationships between influencing factors and milk and soft drink intake

Source Design Sample Major findings

Berg, Cross- N = 1730 - Correlations between intention Jonsson, & sectional pupils in and milk choices were .63 - .70 Conner Sweden - AT & descriptive norm (strongest (2000) Age =11, 13, predictors), injunctive norm, & & 15 years PBC explained 78-93% of the variance in IN to drink milk - Weight gain valued more negatively among users of lower fat milk - Other influences: father & mother, availability, taste & health - Motivation to comply with: 36% mother’s wishes, 32% father’s wishes - Gender differences: males preferred higher fat milk than girls

De Bruijn, Cross- N = 208 (80 - AT (β = .32, p < .001), SN (β = Kremers, de sectional boys, 128 .22, p = .001), & PBC (β = .16, p = Vries, van girls in .015) significantly associated with Mechelen, & Netherlands IN Brug, (2007) Mean age = - IN was significantly associated 15.2 years with soft drink consumption (β = - .20, p = .003), - The model explained 14% of variance in adolescent soft drink consumption

Fisher, Cross- N = 197 non- - Daughters’ intake of milk and soft Mitchell, sectional Hispanic, drink directly influenced by their Smiciklas- white girls mothers’ intake of these beverages Wright, & and their - Inverse relationship between Birch (2000) mothers mothers’ milk and daughters’ soft Age = 5 years drink intakes - Adjusted model explained 26% of variance in daughters’ milk intake

48

Table 2 (continued). Source Design Sample Major findings

Gummeson, Cross- N = 218 - AT strongest predictor of IN Jonsson, & sectional children in compared to SN and PBC to drink Conner Sweden low fat milk. (1997) Age = 10, - IN explained 2-38% of the variance 13, & 16 in food choice. IN significantly years predicted milk choice - Taste important influence on food choice, friends unimportant for milk choice

Kassem, Lee, Cross- N = 707 - IN & PBC explained 28% of the Modeste, & sectional female variance in soft drink intake Johnston students - AT (strongest predictor), PBC, & (2003) Age = 13-18 SN explained 64% of the variance in years IN to drink soda - Other Influences: taste, quenching thirst, became hyper or had a sugar rush, health, gaining weight, parents, friends, availability, access to vending machines, & enough money to buy soda - Motivation to comply with: doctors’ and parents’ wishes about their soda intake

Kassem & Cross- N = 564 - IN & PBC explained 15% of the Lee (2004) sectional male students variance in soft drink intake Age 13-18 - Attitude (strongest predictor), PBC, & years SN explained 61% of the variance in IN to drink soda - Other Influences: taste, quenching thirst, health, parents (strongest), followed by friends, teacher, doctor, fast food restaurants, availability, advertisement, access to vending machines, & enough money to buy soda - Motivation to comply with: doctors’ and parents’ wishes about soda intake

49

Table 2 (continued).

Source Design Sample Major findings

Lee & Reicks Cross- N = 105 girls, Influences on milk/calcium intake: (2003) sectional 60% Asian taste enjoyment, perception of lactose survey American intolerance, household size, Mean age = availability at home, meal or snack, 13.4 ± .9 years modeling by father (p = .05) & siblings (p < .05), father (p = .001), mother (p = .001), or friends (p = .05) trying to get them drink milk

May & Qualitative N = 32 - Preferred drink: fruit-flavored drinks Waterhouse schoolchildren for 8 to 9-year-olds, soft drinks for 13 (2003) in UK to 14-year-olds Age = 8-9 & - Taste: most important influence on 13-14 years drink choice - Influential factors: parents and friends for younger group, cost, availability, & thirst for older group Note. AT= attitude, SN= subjective norm, PBC= perceived behavioral control, IN= intention

50

Thomas and Znaniecki (1918), who first used the concept of attitude to explain social behavior, explained attitudes as mental processes that determine an individual’s actual and potential responses. Measurement of attitudes has stemmed from the thought that they are causally related to behavior and that they could explain human action because they were considered as behavioral dispositions. This relation holds true both in the common use of the term attitude and in the research literature in social psychology

(Eagly & Chaiken, 1993). However, the empirical evidence for this relationship has not always been supported even in the nutrition literature where studies have been conducted to explore the degree of relationship between attitudes and consumption of certain foods

(Shepherd, 1999). A meta-analysis of such studies revealed evidence for small correlations (r = .18) between attitudes and food choice behavior. In the late 1960s, similar findings in social psychology supported several structured attitude models such as the TRA and the TPB (Armitage & Conner, 2001).

Based on the assumptions that increased knowledge and favorable attitudes toward a specific outcome lead to a desired change in health behavior, the area of attitude change has received the most attention in school health education research (Parcel, 1984).

However, there have been claims that the relationship between attitudes and behavior is too complex to expect that attitudes will be a direct predictor of health behavior (Green,

1979). Consequently, the few studies that have investigated attitudes and milk and soft drink intake among children and adolescents have studied the influence of attitudes on these behaviors through their effect on behavioral intention. In the next section, the relationship between attitudes, subjective norm, and perceived behavioral control with

51

intention will be presented, following which the influence of intention on milk and soda

drinking behavior will be discussed.

The TPB has been used to understand determinants of various behaviors including smoking (Cote, Godin, & Gagne, 2004; Godin, Valois, Lepage, & Desharnais, 1992), alcohol consumption (Schlegel, D'Avernas, Zalma, & DeCourville, 1992), condom use

(Gebhardt, Kuyper, & Greunsven, 2003), breastfeeding (Khoury, Moazzem, Jarjoura,

Carothers, & Hinton, 2005; Swanson & Power, 2005), and physical activity (Craig et al.,

1996; Hagger, Chatzisarantis, Biddle, & Orbell, 2001; Mummery, Spence, & Hudec,

2000; Trost, Saunders, & Ward, 2002). In addition, TRA/TPB have been applied to food choices mostly with adults and adolescents (Backman et al., 2002; Booth-Butterfield &

Reger, 2004; Brewer, Blake, Rankin, & Douglass, 1999; Conner, Norman, & Bell, 2002; de Bruijn, Kremers, de Vries, van Mechelen, & Brug, 2007; Dennison & Shepherd, 1995;

Kassem & Lee, 2004; Kassem et al., 2003; Lien, Lytle, & Komro, 2002). There are only two studies that have used the TPB to predict milk choices of school age-children among other food choices (Berg et al., 2000; Gummeson et al., 1997).

Attitude. Attitudes of participants toward milk and soft drink intake have mostly been investigated in studies using the TPB. In some of the atheoretical studies, attitudes towards food in general and breakfast choices were investigated. Three successive surveys were carried out in 1993, 1995, and 1997 with a sample of 1000 French children aged 9-11 years and their mothers. The purpose was to assess trends in food-related behaviors and their attitudes toward food and nutrition (Le Bigot Macaux, 2001).

Attitudes toward food varied clearly between children and their mothers. In order of importance, children perceived food as vital to stay alive, essential for maintenance of the

52

body, and as a pleasure. In contrast, mothers’ understandings of the meaning of food for

their children were ranked in the opposite direction. Children’s enjoyment of healthy and

unhealthy foods was similar. French fries were the favorite food for the majority of the

children (92%) followed by pasta (89%). Fruits and candy were ranked similarly high (Le

Bigot Macaux, 2001).

Dietary knowledge and beliefs in relation to breakfast choices (mainly fat and

fiber content) of 181 Swedish schoolchildren aged 11 to 15 years were investigated (Berg

et al., 2002). Interviews were conducted using the “stacking box methodology” in which

subjects were asked to select food items among photographs of breakfast foods and to

create hypothetical breakfasts with low fat and high fiber. Knowledge about sources and

health attributes of fiber was related to choices of bread and cereals rich in fiber and,

similarly, a positive attitude toward low fat foods predicted choice of low fat milk products (Berg et al., 2002).

A meta-analysis by Godin and Kok (1996) revealed that the TPB constructs

accounted for 32% of the variance in intentions toward eating behaviors. For eating

behaviors, attitude toward the behavior was a significant predictor, accounting for most

of the explained variance in intention. Congruent with these findings, in studies

investigating determinants of adolescents’ soft drink consumption, attitude was a

significant predictor (p ≤ .0001) of intention to drink soft drinks and, jointly with

subjective norm and perceived behavioral control, explained 64% of the variance in

intention among the females (Kassem et al., 2003) and 61% among the males (Kassem &

Lee, 2004). Among both females and males, attitude was the strongest predictor of intention and had a high statistically significant positive relationship with intention to

53 drink regular soda for females (r = .76, p ≤ .0001) and for males (r = .72, p ≤ .0001).

Similarly, attitude was the strongest predictor of intention to consume soft drink in Dutch adolescents (de Bruijn et al., 2007).

The investigations of different types of milk choices with Swedish 10 to 16-year- old schoolchildren have yielded similar results. In one study, attitude was the most powerful significant predictor (p < .05) of intentions to drink milk with more positive attitudes toward milk being associated with stronger intention to consume it (Gummeson et al., 1997) and in the other study, together with descriptive norms, attitude was also the strongest predictor of intention to drink milk and had moderate to high positive correlations with intention ranging between .43 and .68 (Berg et al., 2000). Comparable to other food choice studies, together with the other TPB variables, attitude explained

66% of the variance in intention to drink milk (Gummeson et al., 1997) and 78-93% of the variance in intention to drink different types of milk (Berg et al., 2000).

Subjective norm. Subjective norm encompasses the influence of the social environment on intentions and behavior. In the TPB, this concept is more restricted than the concept of norms as viewed in sociology. In the TPB, subjective norm refers to particular behavioral direction ascribed to a generalized social agent, whereas in sociology, the term norm refers to a broad range of permissible behaviors (Ajzen &

Fishbein, 1980). Subjective norm refers to one’s perception of what significant others desire concerning the performance or nonperformance of a certain behavior. This perception may or may not be congruent with what significant others think one should do

According to the TPB, one’s intention to perform a behavior will be stronger when one

54 perceives that important others think one should perform a behavior and vice versa

(Ajzen & Fishbein, 1980).

It has been suggested that norms are the weakest construct in predicting behavioral intention (Armitage & Conner, 2001). In a review of the application of the

TPB to health behavior, Godin and Kok (1996) found that values of social norm varied by type of behavior with values being high for social norms about automobile-related and oral hygiene behaviors, yet low for social norms about eating and exercising behaviors.

Similar to Godin and Kok’s (1996) findings, the construct of subjective norm has yielded different results in the milk and soft drink studies, even though both are eating behaviors.

In the two soft drink studies, subjective norm was a significant predictor of intention but had the least contribution (after attitude and perceived behavioral control) in explaining intention, indicating the importance of influences of mainly parents and friends (Kassem

& Lee, 2004; Kassem et al., 2003). In Gummeson et al.’s (1997) and de Bruijn et al.’s

(2007) studies, subjective norm was a significant predictor of intention with stronger social pressure to drink milk associated with greater intention to do so and with those who perceived less subjective norm towards limited soft drink intake drinking less soft drink. However, subjective norm ranked second after attitude in strength of prediction.

Contrary to the above findings, together with attitude, descriptive norm (perception of significant others’ preferences) was the strongest predictor of intention in the choice of milk in Berg et al.’s study (2000).

According to Armitage and Conner (2001), the weak predictive power of the normative component may be partially explained by weaknesses in measurement. For example, overall, norms have been measured by a single item. In addition, subjective

55

norm is usually operationalized as an injunctive norm i.e. social approval by significant

others. However, responses to injunctive norm questions have resulted in low variability

due to high social desirability i.e. significant others are generally thought of as approving

of desirable behaviors and disapproving of undesirable ones (Ajzen, 2002). As a possible

solution, Ajzen (2002) recommends the inclusion of items tapping descriptive norms i.e. whether important others themselves perform the particular behavior or not. This is based on Social Learning Theory’s premise that observational learning through modeling is a significant determinant of behavior (Bandura, 1986). There is growing empirical support

for the addition of descriptive norm to the TPB and its predictive validity of behavioral

intention with older age groups; however, its use with children is not common. Berg et al.

(2000) incorporated the concepts of descriptive and injunctive norms, as subsets of

subjective norm, in their study. The use of descriptive norm in the statistical model

improved the predictive power of the other constructs on intents to consume the different

alternatives of milk. However, in the other study about milk choice and the soft drink

studies, this concept was operationalized as injunctive norm (Gummeson et al., 1997;

Kassem & Lee, 2004; Kassem et al., 2003). A possible reason for the varied results may

be the different ways the concept of subjective norm was operationalized in these studies.

Perceived behavioral control. Perceived behavioral control is assumed to tap

external factors such as availability of resources like time, money, social support, and

internal factors such as ability, skills, and information. Ajzen (1998) argued that

perceived behavioral control is synonymous with self-efficacy and he added perceived

behavioral control to TPB to address human behavior that is not under complete

volitional control. Self-efficacy refers to individuals’ beliefs in their capabilities to

56 achieve various levels of performance attainment (Bandura, 1977). According to Bandura

(1982), people’s beliefs in their efficiency influence the choices they make, the extent of effort they exert in their actions, the extent of perseverance when facing difficulties, and their vulnerability to stress and depression.

According to Ajzen (Ajzen, 2002), a direct measure of perceived behavioral control that reflects an individual’s confidence in performing a behavior should include both self-efficacy and controllability items. The self-efficacy items tap the idea of one’s ease or difficulty in performing a certain behavior and the controllability items tap the control one has over performing the behavior. However, Bandura (1986) has argued that self-efficacy and control are different concepts. Results for other health behaviors have been contradictory. A comparison of the TRA, TPB, and the Social Cognitive Theory in physical activity participation was conducted, and self-efficacy, rather than perceived behavioral control, was found to directly influence behavior (Dzewaltowski, Noble, &

Shaw, 1990). Others have found perceived behavioral control to influence behavior and self-efficacy to influence intention (Terry & O'Leary, 1995; White, Terry, & Hogg,

1994). These contradictory findings do not solve the issue whether self-efficacy and perceived behavioral control are the same or different constructs.

Inclusion of a measure of perceived behavioral control in studies of food choice may be an important predictor of some food choices. Within the studies of milk and soft drink intake, perceived behavioral control was a significant predictor of intention; however it ranked second (Berg et al., 2000; Gummeson et al., 1997; Kassem & Lee,

2004; Kassem et al., 2003) and third (de Bruijn et al., 2007) after attitude in strength of the prediction. When comparing the influence of perceived behavioral control on

57

behavior across the studies, similarly mixed findings were found. Contrary to Ajzen’s

(1991) suggestion, perceived behavioral control was not a significant independent

predictor of milk drinking and soda drinking behavior (Gummeson et al., 1997; Kassem

& Lee, 2004; Kassem et al., 2003). However, in Berg et al.’s (2000) study, intention

predicted choice of milk and perceived behavioral control added significantly to the

prediction. This finding is consistent with another study of Norwegian 10-12 year olds in

which, among other Social Cognitive Theory factors investigated, self-efficacy was a

strong correlate to fruit and vegetable consumption (Bere & Klepp, 2004). The lack of an

independent contribution of perceived behavioral control to behavior in these studies may

have been due to either inaccurate measurement of the concept or participants’ inaccurate

perception of their degree of control over their behavior.

Intention. Expressions of behavioral intention are expected to accurately predict a

corresponding volitional action, thus acting as close antecedents of overt behavior

(Ajzen, 1988). If this holds true, then intentions should correlate more strongly with

behavior than other antecedents. In a meta-analysis, the TPB was found to account for

34% of the variance in a variety of health behaviors across different age-groups (Godin &

Kok, 1996), which is considered to be a relatively good explanation for psychosocial variables (Baranowski et al., 1999). Intention was the most important predictor of behavior; however, in almost half the studies, perceived behavioral control significantly added to the prediction (Godin & Kok, 1996). In another review of studies conducted to investigate psychosocial correlates of dietary behavior, the predictiveness of the different models, including the TPB, has been found to be low (R2 < 0.3) with adults and

adolescents and even lower for children (Baranowski et al., 1999). The R2 values were

58

higher when the models were used to predict specific categories of foods, suggesting that influences vary by food types.

In the studies with children and adolescents, intention was a significant predictor of milk and soda drinking behavior (Berg et al., 2000; de Bruijn et al., 2007; Gummeson et al., 1997; Kassem & Lee, 2004; Kassem et al., 2003); however, despite all having specific categories of foods to be predicted, the predictive values were not better than the

34% variance accounted for health behaviors found in the meta-analysis (Godin & Kok,

1996). With the females, intention and perceived behavioral control explained more of the variance in soft drink intake (28%) than with males (15%) (Kassem & Lee, 2004;

Kassem et al., 2003). In Gummeson et al.’s (1997) study, prediction of actual food choices were relatively poor (2 to 38% variability) and behavior was only weakly to moderately related to intention; however, similar to the other studies of soft drink, intention was a significant predictor of milk choice, predicting 12% of this behavior. A possible reason for the relatively small predictions in Gummeson et al.’s (1997) study could be that intention changed in the 6 weeks interval between administration of the

TPB measures and then the behavior measures. Overall, the relatively low predictive power of intention for milk and soda drinking behavior may be because in these studies, the behavior being predicted was current use of soft drink or milk, whereas the intention measured was intention to drink milk/soft drink in the future.

Taste. Taste appears to be an important factor influencing children’s food choices

(Birch & Fisher, 1998; Michela & Contento, 1986), including their decision to drink milk and soft drink (Kassem & Lee, 2004; Kassem et al., 2003; Neumark-Sztainer, Story,

Perry, & Casey, 1999). Milk flavor influences children’s milk drinking behavior

59

(Nicklas, 2003). Children who drank flavored milk had lower intake of soft drinks and

higher calcium intake compared to those who did not drink flavored milk (Johnson,

Frary, & Wang, 2002). Findings have been the same with soft drink. In a sample of 560

children 8 to 13 years old, who completed mail-in surveys investigating factors associated with their consumption of soft drinks, the children reporting the strongest taste preference were 4.5 times more likely to consume soft drinks five or more times per week than subjects with less taste preference (Grimm et al., 2004). Consistent with these

findings, among both children and adolescents, taste enjoyment has been an important

factor influencing choice of soft drink (Grimm et al., 2004; May & Waterhouse, 2003),

attitude toward soft drink intake (Kassem & Lee, 2004; Kassem et al., 2003), choice of

milk (Lee & Reicks, 2003), and attitude toward milk intake (Berg et al., 2000;

Gummeson et al., 1997).

Concern about body weight. There is a misconception about that milk is fattening,

which may lead children, especially females, to limit their milk intake to lose or maintain

weight (Neumark-Sztainer et al., 1999; Wells, 1994). In a survey by Seaman and

colleagues (1997), children’s concerns about healthy diet were related to body size rather

than diet-related chronic diseases. Weight gain was an influential factor for attitude toward soft drink intake among only females (Kassem et al., 2003) and for attitude

toward milk intake in one of the Swedish studies (Berg et al., 2000). Together with

dieting, concerns about weight gain may be an important predictor of food choice.

Parents and friends. Parents have been found to play a crucial role in influencing their children’s food habits and preferences by making certain foods available, acting as

60

models, and by their own eating behavior (Birch & Fisher, 1998; Koivisto Hursti, 1999).

Parental soft drink intake has been positively related to children’s soft drink intake

(Grimm et al., 2004; Vereecken, Keukelier, & Maes, 2004). Children whose parents

regularly drank soft drinks were 2.8 times more likely to consume soft drinks five or

more times per week than children whose parents did not regularly drink soft drinks

(Grimm et al., 2004). In another study in which structural equation modeling was used to

predict 5- year-old girls’ milk and soft drink intake, similar results were obtained (Fisher

et al., 2000). Subjects whose mothers drank milk more frequently were found to consume

milk more frequently and drank less soft drinks. For both mothers and daughters, soft

drink consumption was negatively associated with milk and calcium intake (Fisher et al.,

2000). In a more recent study examining the association between family cohesion and

behaviors linked to health or overweight in adolescents girls, family cohesion was

significantly associated with less soda intake and was a predictor of a trend in milk

consumption (Franko, Thompson, Bauserman, Affenito, & Striegel-Moore, 2008).

Consistent with these findings, parents and friends have been important influences

on children’s and adolescents’ soft drink intake, milk intake, and subjective norm to soft

drink and milk intake (Berg et al., 2000; Grimm et al., 2004; Kassem & Lee, 2004;

Kassem et al., 2003; Lee & Reicks, 2003). In all these studies, parents were more

important influences than friends. With milk choice, mothers appeared to be slightly

more important than fathers with 36% of children wishing to comply with mother’s

choices about breakfast food and 32% with that of father’s (Berg et al., 2000). With soft

drink intake, participants were motivated to comply with doctors’ and parents’ wishes

about their soda intake (Kassem & Lee, 2004; Kassem et al., 2003).

61

Health. Feeling healthy by drinking milk and regular soda was a predictor of attitude toward milk and soft drink intake only in two studies using the TPB (Berg et al.,

2000; Kassem & Lee, 2004; Kassem et al., 2003)

Availability. Availability of food items has been found to be quite consistently an important predictor of consumption. Availability of milk at home and soft drink at home and school were consistently associated with intake of milk and soft drink (Berg et al.,

2000; Grimm et al., 2004; Kassem & Lee, 2004; Kassem et al., 2003; Lee & Reicks,

2003). This is particularly important in studies of school-age children who are less likely to be influential in the availability of milk and soft drinks.

Gender. Few studies have investigated how variations in gender influence food choice decisions among children and adolescents. Gender differences have been found in soft drink consumption with males consuming soda significantly more than females (p =

.03) (Grimm et al., 2004). Also, female gender has shown to have a negative association to milk and calcium intake among 10-18-year-olds (Novotny et al., 2003). Overall, Berg et al. (2000) found similar gender variations in milk intake to that of other studies among adolescents with other foods (Backman et al., 2002; Cusatis & Shannon, 1996; Dennison

& Shepherd, 1995; Lien et al., 2002). Only frequency of skim milk intake was higher among females than males. Gender differences were found in intentions and attitudes with males being more in favor of whole milk than females. No significant gender differences were found for the TPB constructs on intention and consumption. Compared to boys, girls evaluated high fat intake and weight gain more negatively. These differences are consistent with the literature about gender differences related to restriction

62

of food intake by female adolescents because of dissatisfaction with body shape and size

(Dennison & Shepherd, 1995).

Ethnicity. Compared to other food choices of adolescents, the effect of ethnicity

has been less investigated with children’s and adolescents’ actual milk and soda drinking

behavior and no studies have investigated the effect of ethnicity on the influences on

children’s milk and soft drink intake. The available data is about its effect on actual

consumption of milk. There have been consistent findings with milk intake of children

and adolescents. Asian ethnicity has been found to have a negative association with milk

and calcium intake and compared to Caucasians, Asians consumed less milk and calcium

(Novotny et al., 2003; Oshiro, Novotny, & Titchenal, 2003). African American males

ages 9 to 18 years and females 4 to 18 years have been also found to consume

significantly less milk than their non-African American counterparts (1.03 vs 1.61 and

.94 vs 1.32 servings per day respectively, p < .05) (Fulgoni et al., 2007). An important issue for ethnicity and milk consumption is the higher rates of lactose intolerance among

African Americans and Asian Americans (Nicklas, 2003). There have been consistent findings about ethnic differences in actual soft drinks intake of children. Whites have been more likely than blacks to consume soft drinks (Harnack et al., 1999; Rajeshwari et al., 2005).

BMI. The association of BMI and food choice has been investigated relatively little in children. In one study, no association was found between food group consumption patterns and BMI of young adults (Demory-Luce et al., 2004). For 10-year- olds, mean BMI significantly increased (P <.001) from 1973 to 1994 among sweetened-

beverage consumers (Rajeshwari et al., 2005). In a study evaluating diet quality and BMI

63

by beverage patterns, for children aged 6 to 11 years, BMI was found to be significantly

higher (p < 0.05) in the soda patterns (adjusted mean BMI = 18.7) compared to the high-

fat milk patterns (adjusted mean BMI = 17.8) (LaRowe et al., 2007). In another study examining associations between parent and child attitudes and behavior related to eating and weight, children’s BMI was positively related to attitudes to eating and their weight

(r = .22, p < .05) and their attitudes and behavior for themselves and their perceptions of their parents (r = .24, .23, respectively, p < .05) (Baker, Whisman, & Brownell, 2000).

Similarly, Kuchler and Lin (2002) established a positive correlation between BMI and attitudes towards diet in adults.

Other influences

There are multiple other community and environmental level influences on school-age children’s milk and soft drink intake some of which are breakfast consumption and school meals, meal patterns, fast food consumption, lactose intolerance, and marketing of soft drinks.

Breakfast consumption and school meals. Eating breakfast is important for the overall dietary quality and adequacy in school-age children and has a positive influence on milk intake (Chitra & Reddy, 2007). However, breakfast consumption among children has declined significantly between 1965 and 1991(Siega-Riz, Popkin, & Carson, 1998).

Participation in the School Breakfast Program (SBP) and the National School Lunch

Program (NSLP) has improved children’s daily nutrient intake, including milk and milk

products which, in turn, has been associated with significant improvements in student

academic performance and psychosocial functioning (Kleinman et al., 2002; Pearson,

Biddle, & Gorely, 2008). Children participating in the NSLP and the SBP and are less

64

likely than nonparticipants to consume soft drinks and are more likely to consume milk, consequently having higher intake of calcium and essential micronutrients (Gordon &

McKinney, 1995). Furthermore, breakfast skipping among children has been associated

with an increased likelihood of being overweight (Dubois, Girard, Potvin Kent, Farmer,

& Tatone-Tokuda, 2008); whereas, breakfast consumption has been associated with a

lower BMI (Delva, Johnston, & O'Malley, 2007).

Meal patterns. The traditional pattern of families eating meals together at the

kitchen table has changed. Currently, fewer families eat meals together (Nicklas &

Johnson, 2004) and with time, the frequency of family dinners have decreased (Gillman

et al., 2000). In a survey with children 9-15 years old in 1995, five percent less children reported eating with their families (Gillman et al., 2000). Increased frequency of family

dinners at home has been positively associated with healthful dietary intake patterns such

as substantially higher intake of several nutrients, including calcium and lower intake of

soft drinks, hence a lower glycemic load (Gillman et al., 2000; Neumark-Sztainer,

Hannan, Story, Croll, & Perry, 2003).

Fast-food consumption. The frequency of fast-food consumption has increased

considerably since the early 1970s and fast-food consumption has become especially

popular among children and adolescents (Lin, Guthrie, & Frazao, 1999). Fast-food

consumption, among other foods, has been associated with higher intake of soft drinks

and lower intake of milk (French, Story, Neumark-Sztainer, Fulkerson, & Hannan, 2001;

Paeratakul, Ferdinand, Champagne, Ryan, & Bray, 2003).

Lactose intolerance. In the U.S., Caucasians have the lowest prevalence of lactose

intolerance (15%), whereas African-Americans and Asian-Americans have the highest

65

prevalence (80% and 90% respectively) (Nicklas, 2003). Individuals with lactose

intolerance have a common misconception that they cannot consume dairy products

because they will have gastrointestinal symptoms, thus eliminating or minimizing intake

of milk and dairy foods ( Lee & Reicks, 2003). Despite these figures, their validity has

been challenged and the prevalence of lactose intolerance has been found to be

profoundly overestimated (Jarvis & Miller, 2002).

Marketing of soft drinks. The dramatic increase in soft drink consumption mainly

among children and adolescents has been associated with extensive unprecedented

marketing of soft drinks, using pop culture and sports icons to target specifically children

(Austin & Rich, 2001; Nestle, 2000). Television viewing and advertisements have been

found to influence children’s and adolescents’ higher soft drink intake (Grimm et al.,

2004; Kassem & Lee, 2004). Another means of advertising that promotes soft drink

intake among children is the recent phenomenon of “pouring rights” contracts, in which,

to assist with budget constraints, adverting and sales arrangements from soft drink

manufacturers are present at schools (Nestle, 2000).

Cost. Cost and enough money to buy soft drink were influences on mostly

adolescents’ soft drink consumption (Kassem & Lee, 2004; Kassem et al., 2003; May &

Waterhouse, 2003). This may be because, compared to children, adolescents are more independent in making food choices and purchasing items.

In summary, among influences on children’s and adolescents’ milk and soft drink

intake, attitudes, subjective, norm, and perceived behavioral control explained a

considerable amount of the variance in intention to drink milk and soft drinks, comparing

favorably to other food choice studies. However, the reason for the relatively low

66

predictive power of intention on actual behavior still remains unclear. Among the other

influences, relatively more consistent findings were obtained with taste, weight concerns,

health, availability, parents and friends. Due to the limited number of studies

investigating influences on children’s milk intake and no such study with children’s soft

drink intake, these influences will be investigated among American children in this study.

Summary for literature review

Childhood obesity has been on the rise over the past three decades and is reaching

epidemic proportions in the U.S. Some of the possible causes are thought to be the rise in

soft drink consumption, which is a significant contributor for total caloric intake for

especially children, and the accompanying decline in milk intake by American school-age

children. Despite the national initiatives and the ones by the health promotion

organizations to promote healthy eating behavior among children, American children do

not meet the dietary guidelines and recommendations mainly for added sugars and

calcium. An understanding of factors influencing milk and soft drink intake of school-age

children is essential to intervene properly and minimize associated health issues.

Numerous reasons at the community level for the increase in soft drink intake and

the accompanying drop in milk consumption among school-age children have been

suggested. Relatively little is known about the factors that influence school-age children’s milk and soft drink consumption. The few studies presented in the review of the literature have investigated soft drink consumption and intake of various types of milk among other foods in older age groups. In addition to a small number of research studies that have investigated influences on school-age children’s milk and soft drink behavior, there have been few theoretically-based research studies in which the researchers have developed

67 instruments for the purposes of the studies. This makes it difficult to compare findings across studies and draw conclusions.

Evidence suggests that more research is needed about psychosocial factors at the personal level that influence school-age children’s milk and soft drink intake in order to better understand reasons for the increased soft drink intake and decrease in milk intake.

Therefore this study adds to our understanding of influences on milk and regular soft drink intake among younger children in the U.S., which would assist in planning effective intervention strategies to improve the health and quality of life of American children.

68

CHAPTER THREE

Methods

This chapter describes the methods for this study. The following are the components to be discussed: design, sample, sample size determination, sampling procedure, setting, measurement, procedure, data management, data analysis, and protection of human subjects.

Design

Because there is a scarcity of available literature and the need for quantitative studies to understand factors influencing school age children’s eating behavior in the

U.S., a cross-sectional descriptive correlational design was used to determine whether the

TPB constructs predict behavioral intention about milk and soft drink intake which, in turn, predict milk and soft drink intake. The effects of gender, ethnicity, and BMI on the

TPB constructs also were studied. The ultimate aim of the description of the factors influencing children’s eating behavior is to generate knowledge which may serve as a basis for further research.

Setting

Children spend a considerable amount of their time per day in school and hence most research with school aged children is conducted in school settings (Bendelow et al.,

1996; Berg et al., 2000; Dixey et al., 2001; Edwards & Hartwell, 2002; Gummeson et al.,

1997; Hymovich, 1997; Seaman et al., 1997). Despite this fact, it was not possible to gain access to children in schools in Northeast Ohio. Instead, Kaiser Permanente Ohio (KPO) agreed to collaborate and based on their requirement, the researcher went through an

69

unpaid internship at KPO to familiarize herself with their computer system for the

sampling procedure.

Kaiser Permanente (KP) is an integrated managed care organization, and is a

consortium of the Kaiser Foundation Health Plan, Inc. and the Ohio Permanente Medical

Group. Participants were recruited from those receiving care from the 11 KP facilities in

6 counties that comprise the entire Northeast Ohio KP region. The population census

varies monthly; however, Kaiser Permanente Ohio (KPO) serves approximately 73,000 children each year. In the KPO region, there are 150,315 health plan members. While racial and ethnic data is not available for KP’s members, Greater Cleveland’s population is approximately 51% African American and in areas where KP’s medical center offices are located, such as Bedford, about 67% of the population is African American.

Sample

There is evidence that children’s food choices are established from the sixth to twelfth grade, so health promotion interventions should begin prior to sixth grade before these eating patterns become resistant to change (Kelder, Perry, Klepp, & Lytle, 1994).

Based on this evidence, a random sample of school-age children receiving care from a northeastern Ohio Health Maintenance Organization (HMO) was chosen to participate in

this study to generate data about milk and soft drink intake. The inclusion criteria were:

(a) 10-11 years old and (b) in generally good health. This age group was chosen to ensure

that the participants were at the concrete operations level of Piaget’s cognitive

developmental stages, a prerequisite to be able to fill out the questionnaires for this study.

Children with the following health conditions were excluded because the focus was on

healthy children: small for gestational age (SGA), low birth weight babies with poor

70 catch-up growth, chronic feeding difficulties from neurological and muscular disorders, food and milk intolerance or allergies, diabetes mellitus, diabetes insipidus, thyroid disease, parathyroid disease, polycystic ovary syndrome (PCOS), chronic inflammatory diseases i.e. Crohns Disease, colitis, short gut syndrome, gastroesophageal reflux disease

(GERD), gastrostomy feedings, chronic renal diseases, bulimia, anorexia nervosa, Prader-

Willi Syndrome, autism, childhood malignancies and chronic hematological disorders,

Trisomy 21, Trisomy 13-15, 18, Cri-du-chat, fetal alcohol syndrome, Williams

Syndrome, and global developmental delays.

Sample size determination

Sample size was calculated using the power analysis procedure based on Cohen

(1988). Four parameters are needed in power analysis: alpha, beta, power, and effect size.

Power is the probability that a statistical test will lead to the rejection of the null hypothesis

(Cohen, 1988). Based on the fact that understanding predictors of healthy eating behavior in children is a relatively new area of research, both type I and type II errors are important.

Alpha was set at .05 and power at .90.

Effect size (ES) is the degree to which the null hypothesis is false (Cohen, 1988) i.e. how much relationship there is between the dependent and the independent variables. Due to the presence of few studies investigating predictors of children’s eating behavior, determination of the parameters for sample size calculation in this study were based on

Gummeson et al.’s (1997) because their sample included 10 to 11-year-olds whose milk drinking behavior was investigated, in addition to other food choices, using the TPB. In this study, R2s between .02 and .38 have been obtained when explaining food choices

(behavior) by intention and perceived behavioral control, and R2s between .50 and .84 were

71

obtained when explaining intention by attitudes, subjective norm, and perceived behavioral

control (Gummeson et al., 1997). Using the mean of the R2 ranges obtained, an R2 = .44 is

translated into an ES f 2 = R2/ 1- R2 = .79 which is considered a large ES (Cohen, 1988).

However, since the ranges of the explained variances are so wide and this study is not

identical to the one cited above, to be conservative, a medium ES of .15 was used.

Based on Cohen (1988), sample size for multiple regression was determined as

2 2 follows: N= (L/f ) + K +1 where N is sample size, L is lambda, f is the effect size, and K

is the number of independent variables. With an ES of .15, a power of .90, a significance

level alpha = .05, Lambda = 19.4 (based on table value at a power of .90, significance alpha

level of .05, 7 independent variables) and 7 independent variables, the estimated total

sample size required for this study was (19.4/.15) + 7 + 1= 137. Based on GPower, with the

same parameters and 7 predictors, the required sample size is 130. The estimated total

sample size of 137 participants was chosen for this study. As noted below, we over- sampled to obtain sufficient sample size.

Using two groups for gender (male/female) and ethnicity (white/ethnic or racial minority), and Student’s t tests using the same parameters (alpha = .05, medium effect size for t test = .50), with 137 subjects, the power is .90. Based on our understanding of the population served by Kaiser Permanente Ohio (KPO), we did not anticipate having difficulty obtaining sufficient numbers of subjects by gender or ethnicity. Using three categories of BMI-for-age percentile (healthy weight, at risk of overweight, overweight),

analysis of variance with a medium effect size (medium effect size for ANOVA = .25)

and alpha = .05 with 137 subjects results in a power of .79.

72

Sampling procedure

The researcher identified the ICD-9 codes for the exclusion criteria. Using

the inclusion criteria and the ICD- 9 codes, an information analyst at KPO identified the

subject pool of children participating with KPO and obtained a list of 3355 male and

female participants. Simple random sampling technique was used to obtain participants

from the subject pool. The researcher did a draw of numbers one to five to indicate the starting point of random selection. From the list of 3355 members, starting number 1, every 5th child was chosen until a sample of 600 participants was obtained. A simple random sample was used to ensure representativeness of all 10 to 11-year-olds in

Cleveland, Ohio with diverse ethnic and socioeconomic backgrounds. We purposely over-sampled because of the expected response rate of 23% to a mailed survey (Dillman,

1978).

Measurement

In this study, four assessment measures were used: (1) demographic characteristics form: Tell me about yourself (Appendix A), (2) 24-hour dietary recall for milk and soft drink intake (Appendix B), (3) Milk Intake Questionnaire (Appendix C), and (4) Soda Pop Intake Questionnaire (Appendix D). All the above mentioned measures were designed for the purpose of this study. The direct measures of the TPB constructs in the Milk Intake Questionnaire and the Soda Pop Intake Questionnaire were constructed based on Ajzen’s (2002) recommendation to construct measures in the formative stages of an investigation and his guidelines. Due to time and cost constraints, it was not feasible to conduct a study to elicit children’s beliefs about milk and soft drink. Thus the beliefs for this study were determined from the most recurrent ones in previous

73

investigations about various food choices, including milk and soft drink with same or

similar age groups (Backman et al., 2002; Berg et al., 2000; Gummeson et al., 1997;

Kassem & Lee, 2004; Kassem et al., 2003) and the belief-based measures were developed based on Ajzen’s (2002) recommendations.

In a pilot study conducted with fifth grade students to develop and validate questionnaires measuring psychosocial determinants of physical activity, it was reported that fifth graders had difficulty with understanding 5-point scales, and thus they were reduced to 2-point scales (Saunders et al., 1997). Others have also used 3-point Likert scale with responses consisting of +1 (yes), -1 (no), and 0 (not sure) (Trost et al., 2002).

Although such modifications have been reported to be necessary when attempting to collect meaningful self-report data from preadolescents (Ware, 1989), reductions from a 5-point to a dichotomous (Yes or No) scale may minimize variance. To avoid this problem from occurring and to generate interval-level data that allow greater variance of responses, instead of using Likert-type scales, visual analogue scales (VAS) were constructed for the

Milk Intake and Soda Pop Intake Questionnaires.

Questionnaires

The study variables in the theoretical model were measured by separate but similar statements for milk and soft drink intake with bipolar adjectives as responses. In the initial stage of questionnaire development, content validity was evaluated by having two faculty members with expertise in the TPB and a registered dietitian review the questionnaires for relevance, representativeness, and specificity. Consequently, the questionnaires were modified based on their input. Internal consistency was determined by examining Cronbach’s alpha for questionnaire items.

74

Pilot study. The questionnaires of this study and the 24-hour dietary recall were pretested with 10 fifth graders during a pilot study to ensure their applicability and usefulness with this age group. The purposes of the pretesting of the questionnaires included the following as suggested by Converse and Presser (1986). First, the level of variation in responses was tested. Converse and Presser (1986) suggest that skewed distributions from a pretest can serve as a preliminary warning sign for lack of response variation. Second, detection of skip patterns was examined. Third, approximate timing required to accomplish the questionnaires was determined. Finally, task difficulty and respondent interest and attention were assessed by asking the participants’ feedback. The results of each research question in the pilot study were as follows:

1- What is the level of variation in responses?

In general, the items of The Milk Intake Questionnaire and The Soda Pop Intake

Questionnaire had an acceptable level of variation in the pilot study participants. Refer to

Table 3 and Table 4 for individual ranges of scores in both questionnaires.

2- Are there any skip patterns in responses?

No skip patterns were found. The only item that a participant skipped intentionally was the last question in the 24-hour dietary recall which asks the respondent to choose between milk and soda pop. The participant suggested to add a ‘none’ response category due to lack of choice from the available options.

3- What is the approximate timing required to accomplish the questionnaires?

The average time needed to complete the measures and the demographic

characteristics form was 15 minutes.

75

Table 3.

Ranges of the items in the Milk Intake Questionnaire from the pilot study

Item Range

I plan to drink 3 cups of milk every day for the next month -2 -1 How likely is it that you will drink 3 cups of milk every day for the next -2 -1 month For me drinking milk is important/not important 8 -100 For me drinking milk is good/bad 1 -100 For me drinking milk is enjoyable/unenjoyable 8 -100 How much do you like milk? 1 -100 Drinking milk makes me healthy 0 - 45 Milk tastes good/bad 0 -100 Drinking milk helps me gain weight 0 -100 For me being healthy is important/not important 0 - 41 For me taste of milk is important/not important 0 - 87 For me gaining weight is good/bad 36 -100 Important people think I should drink 3 cups of milk every day 0 - 69 Most people who are important to me drink 3 cups of milk every day 39 -100 My family thinks I should drink 3 cups of milk every day 0 - 84 My family drinks 3 cups of milk every day 8 -100 When it comes to drinking milk, I want to do as my family thinks 0 -100 Drinking 3 cups of milk every day would be easy/difficult 0 -100 I can decide if I drink 3 cups of milk every day 0 - 64 Do you always get to choose what you drink at home? 0 - 100 Is there milk in your refrigerator? 0 - 16 Having milk in the refrigerator makes it easier to drink 3 cups every day 0 - 80 My parents offer me milk with meals 0 - 82 Do you drink milk at school? 0 -100 Having milk available at school makes it easier to drink 3 cups every day 0 -100 Do you drink milk at school because it is given to you? 0 -100

76

Table 4.

Ranges of the items in the Soda Pop Intake Questionnaire from the pilot study

Item Range

I plan to drink soda pop every day -2 -2 How likely is it that you will drink soda pop every day? -2 -1 For me drinking soda pop every day for the next month is important/not 40 -100 important For me drinking soda pop is good/bad 0 – 69 For me drinking soda pop is enjoyable/unenjoyable 0 -100 How much do you like soda pop? 0 -100 Drinking sod pop makes me healthy 74 -100 Soda pop tastes good/bad 0 -100 Drinking soda pop helps me gain weight 0 – 57 For me taste of soda pop is important/not important 0 -100 Important people think I should drink soda pop every day 22 -100 Most people who are important to me drink soda pop every day 0 -100 My family thinks I should drink soda pop every day 0 -100 My family drinks soda pop every day 0 -100 When it comes to drinking soda pop, I want to do as my family thinks 0 -100 Drinking soda pop every day would be easy/difficult 0 -100 I can decide if I drink soda pop every day 0 – 78 How much control do you have over drinking soda pop every day? 0 -100 Do you always get to choose what to drink? 0 – 78 Is there soda pop in your refrigerator? 0 – 84 Having soda pop in the refrigerator makes it easier to drink every day 0 -100 My parents offer me soda pop with meals 0 -100 Do you drink soda pop at school? 100-100 Having soda pop available at school makes it easier to drink every day 0 -100

77

In addition to the above questions, responses to the following open ended question

were obtained from the participants:

4- Which questions were the most difficult for you to understand and to answer?

None of the participants indicated having difficulty with any question. The only one

they could not understand was the term “Caucasian” in the race/ethnicity item from the demographic characteristics form, which was changed to “White”. The participants further offered the following suggestions:

- Add response category ‘None’ for the choice of drink in the 24-hour dietary recall.

- “Indicate the midpoint on the VAS by a mark so we won’t be off the midpoint if we

need to indicate it.”

- “Responding to the VAS would be easier with set numbers on it Ex. On a scale of

one to 10 which one would you choose?”

However, recommendations on the use of VAS suggest keeping them free of

numerical labels in order to minimize bias seen in Likert-type scales where respondents

may easily associate higher scores with a more positive response and lower scores with

more negative ones (Lee & Kieckhefer, 1989). Consequently, it was opted to keep the VAS

questions for this study without numerical labels. To minimize possible response bias, the

positive and negative end anchors of the VAS items were initially counterbalanced. In the

pilot study results it was noted that the counterbalanced anchors raised confusion among

the respondents. Consequently, all the end anchors were changed with negative on the left

of the VAS and positive on the right.

78

Outcome variables

Behavior. In this study, milk intake is defined as drinking 3 cups of milk every

day and soft drink intake is defined as drinking regular carbonated beverages every day.

Both behaviors were measured by a 24-hour dietary recall (Appendix B). Despite having

some disadvantages, diet record keeping by children has become a popular approach to

involving them in modifying their own eating behaviors (Farris & Nicklas, 1993). The

quality of diet diaries may deteriorate over an extended recording period such as 7 days

(Farris & Nicklas, 1993). Therefore, instead of using extensive diaries for this study,

participants were asked to indicate by placing a sign (X) on preset responses if they had

lunch, supper, and breakfast during the 24 hours prior to data collection, whether or not

they had milk or soft drink that day, and how much milk and soda pop they drink on a

usual day during the school week. They were also asked to indicate their choice between

milk and soda pop. In the following section, the measures of each concept will be

presented with examples. Refer to Appendix C and D for the complete version of the

measures.

Influencing factors

To minimize subject burden especially with school-age children, we chose to use the least number of items for each construct in the questionnaires but still be able to measure the variables. For example, for referents, we chose to examine influences of only friends and family. The same is true with attitude (behavioral belief) and perceived behavioral control (control beliefs).

Behavioral intention. In this study, intention refers to a person’s perceived likelihood of drinking 3 cups of milk and drinking soda daily. The participants rated the

79

following two items from the Intention to Drink Milk and Soda Pop Scales: ‘I plan to

drink 3 cups of milk/drink soda pop every day,’ and ‘How likely is it that you will drink 3

cups of milk/drink soda pop every day.’ These items have 5 response categories ranging

from ‘Definitely not’ (-2) to ‘Definitely yes’ (2). ‘Don’t Know’ (0) was included to avoid

guessing of responses. Scores from the two items in each scale were added with total

possible scores ranging from -4 to 4. A total score of 0 indicates neutral intention to drink

milk or soda pop, negative scores indicate negative intention, and positive scores stronger

intention. The Cronbach’s alpha for the Intention to Drink Milk and Soda Pop Scales

were .85 and .88, respectively.

Attitude. In the proposed study attitude refers to a person’s feeling of

favorableness or unfavorableness toward drinking milk and soft drink daily. The Attitude

toward Milk Intake and Soda Pop Intake Scales consist of 4 VAS items each. Participants

were asked to place a sign (X) on a 100 mm horizontal line. The 3 items are ‘For me

drinking 3 cups of milk/drinking soda pop every day is,’ with bipolar response adjectives

as ‘Very unimportant/Very important,’ and ‘Very bad/Very good,’ and ‘Very

unenjoyable/Very enjoyable.’ The fourth item is ‘How much do you like milk?’ and

‘How much do you like soda pop?’ with responses as ‘Not at all/A lot.’ The total scores

from the Attitude scales can range from 0 to 400 with higher scores indicating more positive attitude toward milk and soda pop intake. The Cronbach’s alpha for the Attitude

toward Milk Intake and Soda Pop Intake Scales were .82 and .80, respectively.

Subjective norm. This concept refers to one’s perception of what significant others

desire concerning the performance or nonperformance of a certain behavior. It was

measured by the Subjective Norm to Milk Intake and Soda Pop Intake Scales which

80

contain the following two items: ‘My friends think that I should drink 3 cups of

milk/soda pop every day’ and ‘My friends drink 3 cups of milk/soda pop every day’ with

bipolar adjective responses as ‘Strongly disagree/Strongly agree.’ The total scores on

each scale can range from 0 to 200 with higher scores indicating stronger beliefs among

one’s friends about milk and soda pop intake. The Cronbach’s alpha for the Subjective

Norm to Milk Intake and Soda Pop Intake Scales were .57 and .56, respectively.

Perceived behavioral control. PBC reflects how easy or difficult it is for someone to drink 3 cups of milk or soft drink daily. This concept was measured by the following four items in the Perceived Behavioral Control over Milk Intake and Soda Pop Intake

Scales: ‘For me drinking 3 cups of milk/soda pop every day would be,’ ‘I can decide whether or not I drink 3 cups of milk/soda pop every day,’ ‘How much control do you think you have over drinking 3 cups of milk/soda pop every day?’ and ‘Do you always get to choose what to drink at home?’ The bipolar adjective responses to the respective items are ‘Very difficult/Very easy,’ ‘Strongly disagree/Strongly agree,’ ‘No control/Complete control’ and ‘Definitely no/Definitely yes.’ The total possible scores on each scale can range from 0 to 400 with higher scores indicating stronger perception of control over drinking milk/soda pop. The Cronbach’s alpha for the Perceived Behavioral

Control over Milk Intake and Soda Pop Intake Scales were .50 and .66, respectively.

Behavioral beliefs. These are beliefs about the likely outcomes of the behavior and the evaluations of these outcomes and are one of the salient beliefs which are main determinants of intentions and actions. They were measured by three behavioral beliefs about taste, health, and weight gain and their outcome evaluations. The following are the items: ‘Drinking milk/soda pop makes be healthy,’ ‘Milk/soda pop tastes,’ ‘Drinking

81 milk/soda pop makes me gain weight,’ ‘For me being healthy is,’ ‘For me taste of milk/soda pop is,’ ‘For me gaining weight is.’ The bipolar response adjectives are

‘Strongly disagree/Strongly agree,’ ‘Very unimportant/Very important’ and ‘Very bad/Very good.’

Normative beliefs. These are the second salient beliefs about the normative expectations of others and motivation to comply with these expectations. They were measured by three items about perception of what someone in one’s family thinks about both behaviors and compliance with that person’s thoughts: ‘Someone in my family thinks I should drink 3 cups of milk/soda pop every day,’ ‘When it comes to drinking milk/soda pop, I want to do as this person in my family thinks I should’ and ‘When it comes to drinking milk, I want to do as my friends think I should. The bipolar adjective responses to the respective items are ‘Strongly disagree/Strongly agree,’ ‘Never/Always,’ and ‘Not at all/Very much.’

Control beliefs. These are the third salient beliefs which refer to the presence of factors facilitating or impeding behavioral performance and the perceived power of these factors in behavioral performance. These were measured by six items for milk and for soda about availability of milk/soda pop at home and school. The items are as follows: ‘Is there milk/soda pop in your home refrigerator?’ ‘Having milk/soda pop in our home refrigerator would make it easier for me to drink 3 cups/it every day,’ ‘My parents offer me milk/soda pop with meals and snacks,’ ‘Being offered milk by my parents would make it easier for me to drink 3 cups every day,’ ‘Do you drink milk/soda pop at school?’ and ‘Having milk/soda pop available at school would make it easier for me to drink 3 cups/it every day.’ The bipolar adjective responses to the items are ‘Never/Always,’

82

‘Strongly disagree/Strongly agree,’ and ‘Definitely no/ Definitely yes.’ Each pair of

underlying influencing factors (behavioral beliefs/outcome evaluation, normative

beliefs/motivation to comply, and control beliefs/perceived power) was multiplied and

product scores were obtained for behavioral beliefs underlying attitude, normative beliefs

underlying subjective norms, and control beliefs underlying perceived behavioral control

(Ajzen, 1988, 1991).

Covariates

Gender. The gender of the participants was categorized as male or female and was

recorded on the demographic characteristics form by the participants.

Ethnicity. The participants indicated one of the following categories on the

demographic characteristics form: American Indian or Alaskan Native, Asian, Black or

African American (not of Hispanic Origin), Hispanic or Latino, Native Hawaiian or Other

Pacific Islander, and White or Caucasian (not of Hispanic Origin).

Body mass index. The BMI percentile indicates the relative position of the child’s

BMI number among children of the same sex and age. BMI, as a measure of adiposity

and body size, was operationalized using the free CDC child and teen calculator (CDC,

2006) for which child’s date of birth, date of measurement, gender, height, and weight are

required. For children who had been in to see their KP provider within three months of

the data collection letter, we planned to use the height and weight data obtained from the

record or Epic. Because of how quickly children at this age grow, we hesitated to use KP data that was older than three months. Our first approach with data collection was to obtain as many of these children as possible. In the event that we needed to rely on family obtained data due to insufficient measures from the existing data, we sent instructions in

83

which the child’s height, weight, and date of measurement were sought from the

parent/guardian who provided informed consent. In addition, date of birth and gender

were recorded by the participant on the demographic characteristics form.

BMI-for-age percentile was categorized as follows: between 5th and < 85th percentile (healthy weight), between 85th and < 95th percentile (at risk of overweight), ≥

95th percentile (overweight). We anticipated not obtaining sufficient subjects in the

underweight and overweight category. Thus, we planned to dichotomize the BMI-for-age

into two categories: healthy weight and at risk of overweight if there were insufficient

subjects in the underweight and overweight categories.

Body mass index, waist circumference, and skin-fold thickness are the most

common noninvasive measures used to define obesity (Kiess et al., 2001). Despite the

reported limitations of the BMI method, most of the reported studies on childhood obesity are based on this measure. Both lean mass and fat mass are highly correlated with

BMI, to the extent that it can act as a proxy for both, but can distinguish neither (Wells,

2001). It has been used extensively in studies because it lends itself to easy calculation in a clinical setting and correlates well with other measures of body fat such as subcutaneous and total body fat (Pietrobelli et al., 1998; Rosner et al., 1998). The validation of BMI as a measure of adiposity among children has been done in relation to total body fat (TBF) and percent of body weight as fat (PBF) estimated by dual energy x- ray absorptiometry (DXA). BMI has been shown to be strongly associated with TBF (r =

0.85 and 0.89 for boys and girls respectively) and PBF (r = 0.63 and 0.69 for boys and

girls respectively) (Pietrobelli et al., 1998). The instructions sent to parents included how

to calibrate the family scale using a known weight such as 10 pound pet food, five pound

84

bags of flour or sugar, or one gallon of milk (8.5 pounds). The instructions asked to

adjust the knob on the scale until the weight on dial reads the weight of the milk, bag of

sugar or flour, or pet food.

Procedure

Approval to conduct the pilot study was obtained from the Case Western Reserve

University Institutional Review Board (IRB). For the main study, approval was obtained

from the KPO IRB and the Case Western Reserve University IRB. Written consent was

obtained from the parents/guardians, and written assent from the participants.

Pilot study

This was a “participating” pretest that is the participants were told that this is a

practice run and that after completion, their feedback concerning their reactions to the

questions and answers will be sought (Converse & Presser, 1986). There were 10

participants in the pilot study who were from two groups: (1) six children who are

personal acquaintances of the investigator such as children of fellow doctoral students

from the FPB School of Nursing at Case Western Reserve University and some of their

friends and (2) four children from a church in Cleveland. For the first group, the

investigator contacted her fellow doctoral students by phone and explained to them the

nature of the study and that it only entailed testing of questionnaires about milk and soda

pop intake developed for this study. Furthermore, a colleague of the investigator

contacted the parents of her children’s friends by phone and explained to them the nature

of the study and that it only entailed administration and testing of questionnaires during

their children’s gathering. Children of all interested parents/guardians were invited to a

colleague’s house for a gathering. For the second group, the investigator contacted

85

parents of the children from the church by phone and similarly explained to them what

the study entails. Data collection took place in a Sunday school classroom in the church

with children of all interested parents and participants. Prior to data collection with both

groups, the investigator secured written consent from one parent/guardian of each child

and written assent from the children. All parents were given the choice to stay with their

children during data collection, but none did. The investigator explained to the

participants how to fill in the questionnaires by examples and asked them to individually

complete them. The participants were also allowed to ask questions at any time. After

completion of all questionnaires, all the participants as a group were asked an open ended

question. After providing responses, they were offered a healthy meal.

Main study

A packet containing the following items was mailed to the parent(s)/legal

guardians of the children: a self-addressed stamped envelope, a cover letter explaining about the study, two copies of a parent informed consent document (ICD), a copy of the child assent form, a copy of the questionnaires, instructions for height and weight measurement, a paper measuring tape that is currently used in provider practices and hospitals, and one U.S. dollar in cash as an incentive. All those who agreed their child to participate in the study were asked to sign the written informed consent form and have their child sign the written assent form. The parents were asked to measure their child’s height with the measuring tape and weigh their child following the instructions sent to them. They were asked to record the child’s height and weight on the parent ICD, have their child fill out the measures, and mail a copy of the signed ICD, the signed child assent form, and the measures in the self-addressed stamped envelope within one week.

86

The parents were to keep a copy of the consent form. All those who failed to return the envelope within a week were reminded by a letter to consider the study and they were referred to the principal investigator (PI) for any concern they may have had. The PI clarified the definition of each outcome behavior in the description of the study. She provided the participants with detailed written instructions about how to fill out the questionnaires with an illustrated example in them. Anyone who had questions was instructed to contact the PI by phone. The returned questionnaires were matched with the demographic data form and the 24-hour dietary recall by subject code numbers.

Data analysis

The data analysis consisted of three parts: description, preliminary analysis, and analysis of research questions. Prior to data analysis, reliability of the questionnaires of this study was tested using internal consistency reliability by Cronbach’s alpha. The data were analyzed using the Statistical Package for the Social Science (SPSS) 16.

Description

Descriptive statistics such as frequencies, means, medians, and standard deviations were used to describe the sample characteristics and the dependent variables.

Furthermore, ranges and means of the samples’ eating behaviors in terms of patterns of milk intake and soft drink intake are provided.

Preliminary analysis

Prior to analysis, the data were examined for missing values and outliers. Any value two standard deviations away from the mean was to be considered as outlier and was to be investigated further. Assumptions of multiple regression were examined to ensure that they were not violated and to ensure proper use of parametric statistics.

87

Linearity of the relationship between the independent and the dependent variable was assessed by examining the bivariate and residual scatterplots (Mertler & Vannatta, 2002).

Data from the descriptive statistics were screened and examined for normality. Normal distribution was evaluated by examining histograms, P-P plots, and values of skewness for each variable. In case of presence of outliers, data analysis was to be performed with and without the outliers. Homoscedasticity of the variance of the residuals was also evaluated by examining the residual scatterplots for even distribution of points across the reference line (Tabachnick & Fidell, 2001). In addition, Tolerance and Variance Inflation

Factor (VIF) were examined to determine muticollinearity. If the initial residual scatterplots were not to give satisfactory results, appropriate data transformation for one or more variables was to be considered (Tabachnick & Fidell, 2001). Examining the assumption of zero mean was done by determining a constant in the equation and by checking the mean and the standard deviation of the standardized residuals which should be 0 and approximately 1, respectively. Finally, independence was evaluated by examining the Durbin-Watson statistics.

Analysis of research questions

1. Does intention about milk/soft drink intake and perceived behavioral control

influence behavior about milk/soft drink intake in school-age children?

2. Does attitude toward the behavior, subjective norm, and perceived behavioral control influence intention about milk/soft drink intake in school-age children?

3. Do behavioral beliefs about milk/soft drinks influence attitude toward milk/soft

drink intake in school-age children?

88

4. Do normative beliefs about milk/soft drinks influence subjective norm about

milk/soft drink intake in school-age children?

5. Do control beliefs about milk/soft drinks influence perceived behavioral control

about milk/soft drink intake in school-age children?

Separate multiple regression analyses were run for the milk and soda data to answer research questions one through five. The following variables were used to examine how

well intention and milk intake and soft drink intake behaviors could be predicted from the

variables suggested by the TPB: (1) the dependent variables milk and soda intake

behaviors were regressed onto the independent variables intention and perceived

behavioral control, and (2) the dependent variable intention was regressed onto the

independent variables attitude, subjective norm, and perceived behavioral control.

Attitude was regressed on the product of behavioral beliefs and outcome evaluations.

Subjective norm was regressed on the product of normative beliefs and motivation to

comply with referents. Perceived behavioral control was regressed on the products of

control beliefs and perception of ease/difficulty of factors.

6. Are there differences by gender on behavioral beliefs, normative beliefs, control

beliefs, attitude, subjective norms, perceived behavioral control, intention and

behavior about milk/soft drink intake in school-age children?

7. Are there differences by ethnicity on behavioral beliefs, normative beliefs, control

beliefs, attitude toward the behavior, subjective norm, perceived behavioral

control, intention and behavior about milk/soft drink intake in school-age

children?

89

8. Are there differences by BMI on behavioral beliefs, normative beliefs, control

beliefs, attitude toward the behavior, subjective norm, perceived behavioral

control, intention and behavior about milk/soft drink intake in school-age

children?

Research questions 6 through 8 were analyzed using a series of independent samples t-tests to test for differences in each variable based on gender and ethnicity. The

Bonferroni correction procedure will be used to adjust the alpha level of .05 with each

test. Because we did not obtain sufficient subjects in the Asian, African American,

Hipanic/Latino, and Native Hawaiian categories as compared the White or Caucasian

category (2 Asians, 20 African Americans, 2 Hipanic/Latino, 1 Native Hawaiian, and 72

White/Caucasian), we opted to collapse the categories with small distributions and

dichotomized the ethnicity variable into Minority and White or Caucasian and ran

analyses with two categories.

Research question number 8 was planned to be analyzed using analysis of variance

(ANOVA) to test for differences in BMI-for-age for weight status categories [between 5th

and < 85th percentile (healthy weight), between 85th and < 95th percentile (at risk of

overweight), ≥ 95th percentile (overweight)]. The Scheffé post-hoc test, as the most

conservative one among the post-hoc tests, was to be used to simultaneously test the

multiple comparisons of the various groups. However, similar to the ethnicity variable,

we did not obtain sufficient subjects in the three categories (12 Overweight, 26 At risk of

overweight, Vs 59 Healthy weight). Consequently, we opted to collapse the overweight

category with small distribution into the At risk of overweight category and dichotomized

the BMI-for-age weight status categories and ran a series of independent samples t-tests

90

to test for differences in each variable based on Healthy weight and At risk of overweight

categories.

Protection of human subjects

The selection process in this study ensured fair selection that is, without specific

exclusion or inclusion of individuals based on gender, race, ethnicity, cultural, or social

values. All children from the selected list were eligible for the study. All subjects were

treated similarly with no discrimination. All parents of subjects participating in this study

were required to provide written informed consent. While the study population is

considered legally incompetent to give informed consent, children were asked to give

their written assent.

This study entailed only minimal risk consisting of a possible emotional discomfort

for some of the participants due to measurement of height and weight. To maintain

confidentiality of the data and to mask participants’ identity, data were coded with study-

specific identification numbers. The raw data will be kept in a cabinet, in a locked office for three years after the completion of the study. Only the investigator and her faculty advisor will have access to the data.

Because this study entailed only minimal risk, consent from only one parent/legal guardian was sufficient. Consent was obtained concurrently with enrollment in the study and was to be sought again in case any changes in the study procedures occurred. The consent/assent process included: (1) an explanation of the purpose and the nature of the study; (2) a clear statement of the potential risks/benefits of the study; (3) ensuring all

participants’ the right to privacy and confidentiality; (4) explanation of compensation;

and (5) ensuring a non-coercive nature of the study and the right to withdraw at any

91 point. A copy of the informed consent and assent was provided to the participant and parent/legal guardian, while another copy is kept with the investigator. Approval to conduct the study was sought from the IRBs at KPO and Case Western Reserve

University.

92

CHAPTER IV

Results

The results from this study will be presented in this chapter. The purposes of this study were to: (1) determine influences on school-age children’s milk and soft drink

intake and (2) determine the effect of gender, ethnicity, and BMI on behavioral beliefs,

normative beliefs, control beliefs, attitude toward the behavior, subjective norm,

perceived behavioral control, intention, and behaviors about milk/soft drink intake in

school-age children.

Of the total surveys mailed to 600 participants from October to December 2007, ninety eight completed surveys were returned by May 2008. One had considerable missing information and was excluded from the study. Thirty packets were returned as undeliverable mail and 4 unsigned consent forms were returned with notes about lack of participation because child was 12 years old. Thus there was complete data on 97 participants, yielding a response rate of 16%. Of the 97 respondents, three were allergic

to milk and three were not allowed to drink soda. These participants did not respond to

the Milk Intake Questionnaire and Soda Pop Intake Questionnaire respectively.

This chapter presents description of the demographic characteristics of the sample,

the patterns of milk and soft drink intake, and the study variables. In addition, results

from the preliminary analyses of statistical assumptions and analyses to answer the

research questions will be presented.

93

Description

Demographic characteristics

The demographic characteristics of the study participants are presented in Table 5.

The study sample consisted of school-age children (N=97). The gender distribution was similar with slightly over half of the participants being female (54%). The ages ranged from 10 to 12 with a mean of 11 years (SD = .64). Despite the fact that Greater

Cleveland’s population is approximately 51% African American, the majority of the participants were White or Caucasian (74%), followed by Black or African American

(21%). There were no American Indians or Alaskan Natives, and only 2 Asians (2%), 2

Hispanics (2%), and 1 Native Hawaiian (1%). The majority of the participants lived with both parents (86%). Eleven percent lived with mother only. One participant lived with grandmother and grandfather. Two participants (2%) in the ‘Other’ category lived with father and stepmother. None lived with father only, grandmother only, or grandfather only. The majority of the participants (61%) had a healthy weight, followed by 27% being at risk of overweight and 12% being overweight.

94

Table 5.

Frequencies of demographic characteristics of the study sample

Characteristics N %

Age (in years) 10 35 36 11 49 51 12 12 12 Gender Male 45 46

Female 52 54

Ethnicity American Indian or Alaskan Native - - Asian 2 2 Black or African American (not of Hispanic origin) 20 21 Hispanic or Latino 2 2 Native Hawaiian or Other Pacific Islander 1 1 White or Caucasian (not of Hispanic origin) 72 74 Who the child lives with Mother & father 83 86

Mother only 11 11 Grandmother & grandfather 1 1

Other 2 2 Father only - -

Grandmother only - -

Grandfather only - - BMI -for-age weight status category Healthy weight 59 61 At risk of overweight 26 27

Overweight 12 12

Description of eating behaviors and patterns of milk and soft drink intake

The patterns of milk and soft drink intake of the study participants are presented in Table 6. The data gathered about the eating behavior covers a 24-hour period. The majority of the respondents (86%) claimed to have had breakfast the morning they completed the questionnaire. Seventy two percent had breakfast at home, followed by

95

13% at school. A considerable number of respondents (36%, n= 35) did not drink milk during breakfast but the majority did (64%, n= 62). To obtain an estimate of total milk consumption per regular school day, we asked respondents to indicate both in glasses and cartons. We considered an 8-ounce carton similar to an 8-ounce glass and so we added them with equal weights. Half the sample (48%, n= 46) responded up to two glasses, followed by 3 glasses (20%, 20), and more than 3 glasses (26%, n= 25). Only six percent did not drink milk.

Almost all (99%) respondents had lunch the day prior to responding, and the majority (73%) had it at school, followed by 22% who had it at home. Half of the respondents (53%) got their lunch from home and 28% got it at school. The rest (16%) bought it and only 2 respondents (2%) got it from friends.

The majority (93%) had dinner, of which 90% had it at home. As expected at this age, 68% of respondents claimed family preparing meals and 32% claimed both family and themselves preparing meals. The majority (83%) had dinner with family, 11% with others, and only 6% ate alone.

There were twice as many consumers of soft drinks than nonconsumers (71%, n=

69 and 29%, n= 28 respectively). To obtain an estimate of total soda consumption on a usual school day, we asked respondents to indicate in cans, cups, and bottles. We coded a

12-ounce can as 1 and weighted the 16-ounce cup and bottle as 1.45. We added the values across the three measures of soda and came up with an estimate of number of cans/day. These figures yielded the following results about the amount of soda consumption during a school day: 41% drank none, followed by 27% claiming one can,

23% two to three cans, and 6% more than 3 cans. The nonconsumers of soda (41%) were

96 different than the ones (29%) in the previous question. This is because the question for the latter result had no time context to it, whereas the prior question had a clear time context: on a usual day during the school week. Less than half (40%) reported drinking milk with snacks and only 19% claimed to drink soda with snacks. When asked when given the choice, what respondents would choose to drink, the majority (45%) chose water, followed by 26% milk and 10% soda. Of the 16% that chose the “Other” category,

41% preferred juice (orange juice and lemonade) and one respondent preferred milk shake.

97

Table 6.

Frequencies of eating behaviors and patterns of milk and soft drink intake

Variable N %

Ate anything before school in the morning Yes 83 86 No 14 14 Where child ate morning food Home 70 72 School 12 13 Car 3 3 Another place 3 3 Did not eat anything 9 9 Drank milk before school in the morning Yes 62 64 No 35 36 On a usual school day how much milk child drinks None 6 6 Up to 2 glasses 46 48 3 glasses 20 20 >3 glasses 25 26 Ate lunch the day before Yes 96 99 No 1 1 Where child ate lunch Home 21 22 School 71 73 Another place 5 5 Did not eat lunch - - Where child got lunch from Bought it 16 16 Home 51 53 Friends 2 2 School 27 28 None 1 1 Had meal after school and before bedtime (dinner) Yes 90 93 No 7 7 Where child ate dinner Home 87 90

98

Table 6 (continued).

Variable N %

Another place 3 3 Did not eat anything before 7 7 Who prepares food at home My family 66 68 Me - - My family and me 31 32 Other people - - Had food last night With family 80 83 With others 10 11 By myself 6 6 Does child drink soda pop Yes 69 71 No 28 29 On a usual school day how much soda pop child drinks None 40 41 One can 26 27 2-3 cans 23 23 >3 cans 6 6 On a typical school day what child would choose to drink Soda pop 10 10 Milk 25 26 Water 44 45 Nothing 1 1 Other 16 17 On a usual school day does child drink milk with snacks Yes 39 40 No 58 60 On a usual school day does child drink soda pop with snacks Yes 18 19 No 79 81 Note. Percentages may not equal 100 due to missing values.

99

Description of study variables

As shown in Table 7, the respondents had neutral to probable intention to drink 3

cups of milk daily (M = .42, SD = 2.5, range -4 to +4); however, they had negative

intention to drink soda pop every day (M = -2, SD = 2.3). For attitude, the range of

responses was 0 to 400. They had moderately positive attitude towards drinking 3 cups of

milk daily (M = 250, SD = 85) and moderately negative attitude toward drinking soda

pop every day (M = 178.7, SD = 82.7). For subjective norm, the scale ranged from 0 to

200. They slightly disagreed that their friends think they should drink 3 cups of milk

daily (M = 87, SD = 48.3) and moderately disagreed that their friends think they should

drink soda pop every day (M = 66, SD = 41.6). For perceived behavioral control (range 0

to 400), they perceived drinking 3 cups of milk per day as moderately easy and that they

had moderate control over the behavior (M = 284, SD = 68). The perception of drinking soda was similar to that of milk intake but was slightly easier and they had slightly less control over the behavior (M = 229, SD = 100). The average consumption of milk was 3 glasses/day (SD = 2.42) and that of soda was 1.5 cans/day (SD = 2.5).

100

Table 7.

Summary of descriptive statistics of the study variables

Variable N Mean SD Possible Skewness range Milk subscales Behavioral Intention to drink milk 93 .42 2.5 -4 - 4 -.41 Attitude toward milk intake 93 250 85 0-400 -.52 Subjective norm to milk intake 93 87 48.3 0-200 .20 Perceived behavioral control 93 284 68 0-400 0 Milk intake behavior (glasses) 97 3 2.42 - 2.1 Soda pop subscales Behavioral Intention to drink soda 92 -2 2.3 -4 - 4 1 Attitude toward soda intake 89 178.7 82.7 0-400 -.21 Subjective norm to soda intake 92 66 41.6 0-200 .4 Perceived behavioral control 93 229 100 0-400 -.21 Soda intake behavior (cans) 95 1.5 2.5 - 4.1

Preliminary analysis

Prior to analysis, the data were examined for missing values and outliers. The missing values were minimal and random. Consequently, no imputations were needed.

Using the standardized deleted residuals and leverage values, no values were found two standard deviations away from the mean, suggesting absence of outliers. Outliers may or may not affect the estimation of a regression line; however, if they do so, they are called influential cases (Tabachnick & Fidell, 2001). Cook’s Distance of all the independent variables was < 1, indicating the absence of influential data points.

The following assumptions of multiple regression were examined to ensure that they were not violated and to ensure proper use of parametric statistics: (1) zero mean;

101

(2) independence; (3) normal distribution of residuals; (4) homoscedasticity; (5)

multicollinearity; and (6) linearity of the relationship between the independent and the

dependent variable. Zero mean and independence are two assumptions that cannot be

violated for multiple regression. The assumption of zero mean was met by determining a

constant in the equations and by checking the mean and the standard deviation of the

standardized residuals which were 0 and 1, respectively, for the milk and the soda

variables. Independence was evaluated by examining the Durbin-Watson statistic which

was 2.100 for the milk variables and 1.992 for the soda variables. These values were not

> 2.5 and < 1.5, indicating no violation of this assumption.

Data from the descriptive statistics were screened and examined for normality.

Normal distribution was evaluated by examining histograms, P-P plots, and values of

skewness for each variable. The shape of the histograms indicated normal distribution

and the P-P plots were close to straight lines. Homoscedasticity of the variance of the

residuals was also evaluated by examining the residual scatterplots for even distribution

of points across the reference line (Tabachnick & Fidell, 2001). This assumption was not

violated since there was an approximately even random scatter of values across the

reference line. Multicollinearity was examined by checking the tolerance and VIF for both the milk and soda variables. All milk and soda independent variables had Tolerance values >.10 and VIF <10, indicating no multicollinearity. Finally, the assumption of linearity was also met. The bivariate and residual scatterplots revealed linear relationships between the independent and the dependent variables.

102

Analysis of research questions

The Theory of Planned Behavior constructs

Separate multiple regressions were run to answer research questions one through

five. The results of the 5 multiple regression analyses for explanation of milk intake are

presented in Figure 4 and that of soda intake are presented in Figure 5.

1. Does intention about milk/soft drink intake and perceived behavioral control

influence behavior about milk/soft drink intake in school-age children?

Milk data. In explaining milk intake behavior among school-age children, both intention and perceived behavioral control were positively related to milk intake; however, only intention had a statistically significant positive correlation with milk intake

(r =.24, p = .01). These two independent variables together explained 4% of the variance in milk intake. The overall regression equation was statistically significant (F = 2.95, p

=.05). Only intention predicted milk intake (β = .26) and was statistically significant (t =

2.43, p = .02), with stronger intention to drink milk being more related to actual milk

intake. Perceived behavioral control was not a significant predictor of milk intake (t = -

.54, p = .59).

Soft drink data. In explaining soft drink intake behavior among school-age

children, similar to the milk intake results, both intention and perceived behavioral

control were positively related to soda intake; however, only intention had a statistically significant positive correlation with soda intake (r =.39, p < .0001). These two

independent variables together explained 14% of the variance in soda intake. The overall

regression equation was statistically significant (F = 8.18, p = .001). Similar to milk

intake results, only intention to drink soft drink predicted soft drink intake (β = .42) and

103 was statistically significant (t = 3.83, p < .0001), with stronger intention to drink soda being more related to actual soda intake. Perceived behavioral control was not a significant predictor of soft drink intake (t = -.54, p = .59).

2. Does attitude toward the behavior, subjective norm, and perceived behavioral

control influence intention about milk/soft drink intake in school-age children?

Milk data. Attitude, subjective norm, and perceived behavioral control had moderate to high statistically significant positive relationships with intention to drink milk (r =.69, p < .0001, r =.37, p < .0001, and r =.28, p = .003 respectively); however, all these relationships did not hold when they were assessed independently in a multiple regression analysis. The three independent variables yielded a good level of prediction and together explained 49% of the variance in intention to drink milk. However, attitude was the only significant independent predictor (β = .64) of intention to drink milk (t =

7.48, p < .0001), with more positive attitude towards milk intake being associated with stronger intention to drink milk. The overall regression equation was statistically significant (F = 30.55, p < .0001). Subjective norm and perceived behavioral control were not significant predictors of milk intake (t = .93, p = .36 and t = 1.66, p = .10 respectively).

Soft drink data. Attitude, subjective norm, and perceived behavioral control had moderate to high statistically significant positive relationships with intention to drink soda (r =.59, p < .0001, r =.39, p < .0001, and r =.47, p < .0001 respectively); however, all these relationships did not hold when they were assessed independently in a multiple regression analysis. The three independent variables yielded a good level of prediction and together explained 44% of the variance in intention to drink soft drink. Attitude and

104

perceived behavioral control were significant independent predictors (β = .45 and β = .30 respectively) of intention to drink soda (t = 4.96, p < .0001 and t = 3.53, p = .001 respectively) with attitude being the stronger predictor. More positive attitude towards soda intake and stronger perceived behavioral control were associated with stronger intention to drink soda. The overall regression equation was statistically significant (F =

23.04, p < .0001). Similar to the milk intake results, subjective norm was not a significant predictor of intention to drink soda (t = 1.43, p = .16).

3. Do behavioral beliefs about milk/soft drinks influence attitude toward milk/soft

drink intake in school-age children?

As attitudes were strong predictors of both intention to drink milk and soft drink, the importance of the underlying beliefs of the attitudes were examined using multiple regression analyses. See Figures 4 and 5 for the regression results.

Milk data. Regressions of attitude towards milk intake onto the behavioral beliefs accounted for 45% of the variability. The overall regression equation was statistically significant (F = 26.39, p < .0001). Being healthy by drinking milk and taste of milk had high statistically significant positive associations with attitude to drink milk (r =.48, p <

.0001, and r =.65, p < .0001 respectively). These relationships remained when they were assessed independently in a multiple regression analysis. Both being healthy and taste were significant independent predictors of attitude (t = 2.75, p = .007 and t = 6.28, p <

.0001 respectively) with taste being the stronger predictor (β = .55) than being healthy (β

= .24). The participants thought that drinking milk makes them healthy and taste was important for their choice of drinking milk. The third belief that drinking milk makes one

105 gain weight had no independent influence in predicting attitude toward milk intake (t =

1.13, p = .26).

Soft drink data. Regressions of attitude towards soft drink intake onto the behavioral beliefs accounted for 52% of the variability. The overall regression equation was statistically significant (F = 31.53, p < .0001). Similar to the milk intake beliefs, being healthy by drinking soft drink and taste of soft drink had moderate to high statistically significant positive associations with attitude to drink soft drink (r =.42, p <

.0001, and r =.67, p < .0001 respectively). These relationships remained when they were assessed independently in a multiple regression analysis. Both being healthy and taste were significant independent predictors of attitude toward drinking soda (t = 3.82, p <

.0001 and t = 7.92, p < .0001 respectively) with taste being the stronger predictor (β =

.61) than being healthy (β = .29). Similarly, the participants thought that drinking soda makes them healthy and taste was important for their choice of drinking soda. The third belief that drinking soda makes one gain weight had no independent influence in predicting attitude toward soft drink intake (t = 1.29, p = .20).

4. Do normative beliefs about milk/soft drinks influence subjective norm about

milk/soft drink intake in school-age children?

Milk data. The two independent variables (normative beliefs) yielded a good level of prediction and together explained 60% of the variance in subjective norms to milk intake. The overall regression equation was statistically significant (F = 70.88, p <

.0001). Both someone in one’s family and friends had moderate to high positive significant correlations with subjective norm (r =.31, p = .001, and r =.78, p < .0001 respectively). These associations were stronger for friends than family member. Unlike

106

the correlation coefficients, only friends was an independent significant predictor (β =

.84) of subjective norm (t = 10.93, p < .0001), with participants wishing to comply more

with what friends think they should be doing in terms of drinking milk than what

someone in the family thinks.

Soft drink data. The two independent variables (normative beliefs) yielded a

moderate level of prediction and together explained 30% of the variance in subjective

norms to soft drink intake. The overall regression equation was statistically significant (F

= 20.23, p < .0001). Both someone in one’s family and friends had positive significant

correlations with subjective norm (r =.32, p = .001, and r =.56, p < .0001 respectively).

Similar to the milk data results, these associations were stronger for friends than family member; however, the association between friends and subjective norm was slightly weaker than the one obtained in the milk data (r =.78, p < .0001). Unlike the correlation

coefficients, only friends was an independent significant predictor (β = .55) of subjective

norm (t = 5.20, p < .0001), with participants wishing to comply more with what friends

think they should be doing in terms of soft drink intake.

5. Do control beliefs about milk/soft drinks influence perceived behavioral control

about milk/soft drink intake in school-age children?

Milk data. The three independent variables (control beliefs) yielded a fair level of

prediction and together explained 14% of the variance in perceived behavioral control to

drink milk. The overall regression equation was statistically significant (F = 6.18, p =

.001). Only availability of milk in home refrigerator had a moderate statistically

significant positive association with perceived behavioral control to drink milk (r = .41, p

< .0001) and this relationship remained when it was assessed independently in a multiple

107 regression analysis. Availability of milk in home refrigerator was the only significant independent predictor of perceived behavioral control (t = 4.24, p < .0001). The participants thought that having milk available in their home refrigerator made it easier for them to drink it. The other two beliefs about availability at school and whether being offered milk by parents made it easier for them to drink milk had no independent influence in predicting perceived behavioral control to drink milk (t = -.62, p = .54 and t

= -.02, p = .99 respectively).

Soft drink data. The three independent variables (control beliefs) yielded a fair level of prediction and together explained 8% of the variance in perceived behavioral control to drink soda. The overall regression equation was statistically significant (F =

3.40, p = .02) and unlike the milk data, all three variables: availability of soda in home refrigerator, availability at school, and being offered by parents had low to moderate statistically significant positive associations with perceived behavioral control to drink soda (r =.28, p = .004, r =.20, p = .03, and r =.19, p = .04 respectively). However, none of these relationships remained when they were assessed independently in a multiple regression analysis. None of the control beliefs about availability of soda in home refrigerator, availability at school, and whether being offered soda by parents made it easier for them to drink it had significant predictions of perceived behavioral control to drink soda (t = 1.89, p = .06, t = 1.35, p = .18, and t = .89, p = .38 respectively).

Figure 4. Path diagram for explanation of milk intake

108

Figure 5. Path diagram for explanation of soft drink intake

109

110

Gender, ethnicity, and BMI differences

6. Are there differences by gender on behavioral beliefs, normative beliefs, control

beliefs, attitude, subjective norms, perceived behavioral control, intention and

behavior about milk/soft drink intake in school-age children?

The results of independent samples t tests revealed no statistically significant differences between males and females in terms of the milk and the soda pop TPB variables and both behaviors. The results are presented in Table 8. The scores of the behavioral beliefs, normative beliefs, and control beliefs are large. The reason for this is that each pair of underlying influencing factors (behavioral beliefs/outcome evaluation, normative beliefs/motivation to comply, and control beliefs/perceived power) was multiplied and product scores were obtained for behavioral beliefs underlying attitude, normative beliefs underlying subjective norms, and control beliefs underlying perceived behavioral control.

111

Table 8.

Gender differences in the milk and soda pop variables

Variable Gender n Mean SD Possible t range Milk variables Behavioral Intention Male 45 .56 2.37 -4 - 4 .52 Female 48 .29 2.55 -4 - 4 Attitude Male 45 247.33 84.07 0-400 -.30 Female 48 252.63 86.53 0-400 Behavioral beliefs: Milk makes me healthy Male 45 7036.29 2850.18 0-10,000 -.28 Female 48 7194.88 2643.10 0-10,000 Milk taste Male 45 4679.78 2850.31 0-10,000 -.65 Female 48 5067.43 2859.76 0-10,000 Milk makes me gain weight Male 45 1144.77 1835.56 0-10,000 .16 Female 48 1090.29 1456.53 0-10,000

Subjective norm Male 45 82.42 46.33 0-200 -.91 Female 48 91.56 50.13 0-200 Normative beliefs: Someone in family Male 45 4924.60 3248.31 0-10,000 1.15 Female 48 4177.75 3027.06 0-10,000 Friends Male 45 2951.73 2511.82 0-10,000 -.71 Female 48 3363.38 3056.83 0-10,000

Perceived behavioral control Male 45 289.40 74.33 0-400 .78 Female 48 278.27 61.53 0-400 Control beliefs: Milk in home refrigerator Male 45 6967.84 2938.81 0-10,000 -.67 Female 48 7393.4792 3181.61 0-10,000 Milk at school Male 45 4823.22 3652.84 0-10,000 .46 Female 48 4464.98 3894.72 0-10,000 Parents offering Male 45 3125.80 2827.27 0-10,000 -.33 Female 48 3335.65 3276.68 0-10,000

Milk intake behavior (glasses) Male 45 3.41 2.74 - 1.35 Female 52 2.75 2.09 - Soda pop variables Behavioral Intention Male 43 -1.91 2.41 -4 - 4 .40 Female 49 -2.10 2.24 -4 - 4

112

Attitude Male 42 188.24 81.46 0-400 1.03 Female 47 170.15 83.66 0-400 Table 8 (continued).

Variable Gender n Mean SD Possible t range

Behavioral beliefs: Soda makes me healthy Male 44 1050.09 1385.38 0-10,000 .32 Female 47 961.21 1295.65 0-10,000 Soda taste Male 44 4352.98 2463.64 0-10,000 -.16 Female 49 4450.78 3325.66 0-10,000 Soda makes me gain weight Male 44 2232.45 2466.94 0-10,000 1.22 Female 47 1704.53 1581.45 0-10,000

Subjective norm Male 43 68.72 44.46 0-200 .36 Female 49 63.53 39.19 0-200 Normative beliefs: Someone in family Male 44 491.09 810.75 0-10,000 1.03 Female 49 337.35 630.85 0-10,000 Friends Male 44 940.34 1234.02 0-10,000 1.24 Female 49 659.14 938.63 0-10,000

Perceived behavioral control Male 44 241.23 104.64 0-400 1.15 Female 49 217.31 95.21 0-400 Control beliefs: Soda in home refrigerator Male 0-10,000 .43 3524.43 2958.22 44 Female 49 3246.53 3297.47 0-10,000 Soda at school Male 43 823.93 1339.83 0-10,000 1.54 Female 49 455.14 946.26 0-10,000 Parents offering Male 44 1509.57 2090.31 0-10,000 .63 Female 48 1243.33 1969.50 0-10,000

Soda intake behavior (cans) Male 44 1.46 1.95 - -.09 Female 51 1.51 2.92 -

113

7. Are there differences by ethnicity on behavioral beliefs, normative beliefs, control

beliefs, attitude toward the behavior, subjective norm, perceived behavioral

control, intention and behavior about milk/soft drink intake in school-age

children?

Milk data. The results of a two-tailed independent samples t test revealed a significant difference between the races with respect to behavioral intention t (91) = -2.0, p = .05. There was a stronger disposition to drink milk among white participants (M =

.68, SD = 2.56) than the ones in the minority group (M = -.33, SD = 1.97). Similarly, a statistically significant difference was found in one behavioral belief about health t (91) =

-1.93, p = .05) where white participants perceived that drinking milk makes them healthy

(M = 7435.55, SD = 2660.13) more than the minority (M = 6205.58, SD = 2783.77).

Within the normative beliefs, white participants (M = 5031.14, SD = 3124.58) had a significantly stronger perception than the minority (M = 3124.58, SD = 2082.96) that someone in their family thinks they should drink 3 cups of milk every day t (91) = -3.27, p = .002. Within the control beliefs, white participants (M = 7581.28, SD = 3028.01) had a significantly stronger perception than the minority (M = 6055.50, SD = 2912.10) that having milk in home refrigerator would make it easier for them to drink 3 cups of milk every day t (91) = -2.15, p = .03. There were no statistically significant differences between minority and white participants in terms of the rest of the TPB variables about milk. The results are presented in Table 9.

114

Soft drink data. Within the soda pop variables, differences were found in both

normative beliefs and soda intake behavior. Contrary to the milk normative belief about

family, the minority participants (M = 700.63, SD = 877.64) had a significantly stronger perception than the white participants (M = 309.03, SD = 635.57) that someone in their family thinks they should drink soda pop every day t (91) = 2.01, p = .05. Similarly, the minority participants (M = 1163.67, SD = 1237.67) had a significantly stronger perception than the white participants (M = 662.97, SD = 1013.93) that their friends think they should drink soda pop every day t (91) = 1.97, p = .05. As for the soda intake, the minority participants (M = 2.60, SD = 3.56) drank significantly more soda than the white participants (M = 1.09, SD = 1.88) t (93) = 2.65, p = .009. There were no statistically significant differences between minority and white participants in terms of the rest of the TPB variables about soda pop.

115

Table 9.

Race/ethnicity differences in the milk and soda pop variables

Variable Race n Mean SD Possible t range Milk variables Behavioral Intention Minority 24 -.33 1.97 -4 - 4 -2.00* White 69 .68 2.56 -4 - 4

Attitude Minority 24 229.42 65.18 0-400 -1.39 White 69 257.25 90.10 0-400 Behavioral beliefs: Milk makes me healthy Minority 24 6205.58 2783.77 0-10,000 -1.93* White 69 7435.55 2660.13 0-10,000 Milk taste Minority 24 4903.46 2970.66 0-10,000 .05 White 69 4871.65 2824.13 0-10,000 Milk makes me gain weight Minority 24 1392.54 1692.97 0-10,000 .96 White 69 1020.70 1625.37 0-10,000

Subjective norm Minority 24 78.54 35.23 0-200 -1.01 White 69 90.13 51.96 0-200 Normative beliefs: Someone in family Minority 24 3124.58 2082.96 0-10,000 -3.27* White 69 5031.14 3306.35 0-10,000 Friends Minority 24 2453.33 2602.71 0-10,000 -1.45 White 69 3411.45 2840.28 0-10,000

Perceived behavioral control Minority 24 268.88 66.46 0-400 -1.24 White 69 288.80 68.09 0-400 Control beliefs: Milk in home refrigerator Minority 24 6055.50 2912.10 0-10,000 -2.15* White 69 7581.28 3028.01 0-10,000 Milk at school Minority 24 4216.96 3777.19 0-10,000 -.64 White 69 4784.88 3775.17 0-10,000 Parents offering Minority 24 2523.38 2566.08 0-10,000 -1.48 White 69 3481.32 3184.44 0-10,000

Milk intake behavior(glasses) Minority 25 2.56 1.35 - -1.61 White 72 3.23 2.68 -

116

Table 9 (continued).

Variable Race n Mean SD Possible t range

Soda pop variables Behavioral Intention Minority 24 -1.67 2.32 .47268 .40 White 68 -2.13 2.31 .28024

Attitude Minority 21 193.33 73.96 16.13947 .36 White 68 174.16 85.17 10.32778 Behavioral beliefs: Soda makes me healthy Minority 23 1296.13 1443.95 0-10,000 1.22 White 68 905.44 1289.66 0-10,000 Soda taste Minority 24 4856.79 2997.85 0-10,000 .88 White 69 4247.19 2917.42 0-10,000 Soda makes me gain weight Minority 23 1973.91 1666.84 0-10,000 .04 White 68 1955.01 2192.04 0-10,000

Subjective norm Minority 23 73.26 35.22 0-200 .33 White 69 63.52 43.46 0-200 Normative beliefs: Someone in family Minority 24 700.63 877.64 0-10,000 2.01* White 69 309.03 635.57 0-10,000 Friends Minority 24 1163.67 1237.67 0-10,000 1.97* White 69 662.97 1013.93 0-10,000

Perceived behavioral control Minority 24 202.08 92.97 0-400 .13 White 69 237.86 101.28 0-400 Control beliefs: Soda in home refrigerator Minority 24 2858.92 2628.07 0-10,000 -.94 White 69 3558.57 3281.93 0-10,000 Soda at school Minority 24 1041.63 1414.23 0-10,000 2.08 White 68 481.35 1021.56 0-10,000 Parents offering Minority 23 2050.26 2118.47 0-10,000 1.89 White 69 1144.13 1951.33 0-10,000

Soda intake behavior (cans) Minority 25 2.60 3.56 - 2.65* White 70 1.09 1.88 - * p ≤ .05

117

8. Are there differences by BMI on behavioral beliefs, normative beliefs, control

beliefs, attitude toward the behavior, subjective norm, perceived behavioral

control, intention and behavior about milk/soft drink intake in school-age

children?

The results of independent samples t tests revealed no statistically significant differences between participants in the ‘healthy weight’ category and those in the ‘at risk of overweight’ category in terms of the milk and the soda pop TPB variables and both behaviors. The results are presented in Table 10.

118

Table 10.

BMI differences in the milk and soda pop variables

Variable BMI n Mean SD Possible t category range Milk variables Behavioral Intention Healthy 58 .33 2.48 -4 - 4 -.46 At risk 35 .57 2.44 -4 - 4

Attitude Healthy 58 241.14 86.39 0-400 -1.31 At risk 35 264.86 81.51 0-400 Behavioral beliefs: Milk makes me healthy Healthy 58 7173.53 2687.33 0-10,000 .25 At risk 35 7026.34 2840.09 0-10,000 Milk taste Healthy 58 4680.22 2774.26 0-10,000 -.87 At risk 35 5210.69 2972.88 0-10,000 Milk makes me gain weight Healthy 58 1287.90 1844.32 0-10,000 1.44 At risk 35 832.89 1208.09 0-10,000

Subjective norm Healthy 58 93.67 48.43 0-200 1.70 At risk 35 76.31 46.72 0-200 Normative beliefs: Someone in family Healthy 58 4552.83 3038.91 0-10,000 .05 At risk 35 4516.43 3349.56 0-10,000 Friends Healthy 58 3374.79 2807.71 0-10,000 .93 At risk 35 2815.20 2789.377 0-10,000

Perceived behavioral control Healthy 58 281.47 68.03 0-400 -.40 At risk 35 287.29 68.48 0-400 Control beliefs: Milk in home refrigerator Healthy 58 7450.03 2933.67 0-10,000 1.07 At risk 35 6752.51 3248.52 0-10,000 Milk at school Healthy 58 4427.24 3772.12 0-10,000 .60 At risk 35 4988.11 3777.28 0-10,000 Parents offering Healthy 58 3381.47 3095.37 0-10,000 -.69 At risk 35 2989.91 3009.16 0-10,000

Milk intake behavior(glasses) Healthy 60 3.10 2.62 - .22 At risk 37 2.99 2.10 -

119

Table 10 (continued).

Variable BMI n Mean SD Possible t category range Soda pop variables

Behavioral Intention Healthy 57 -1.93 2.48 -4 - 4 .43 At risk 35 -2.14 2.03 -4 - 4

Attitude Healthy 53 179.47 73.88 0-400 .11 At risk 36 177.53 95.22 0-400

Behavioral beliefs: Soda makes me healthy Healthy 56 983.14 1395.09 0-10,000 -.19 At risk 35 1037.86 1246.41 0-10,000 Soda taste Healthy 57 4498.56 2634.55 0-10,000 .37 At risk 36 4255.58 3389.48 0-10,000 Soda makes me gain weight Healthy 55 2042.84 1968.09 0-10,000 .46 At risk 36 1832.92 2222.91 0-10,000

Subjective norm Healthy 56 62.00 37.05 0-200 -1.14 At risk 36 72.11 47.70 0-200 Normative beliefs: Someone in family Healthy 57 320.44 554.54 0-10,000 -1.36 At risk 36 552.03 918.24 0-10,000 Friends Healthy 57 668.6 955.08 0-10,000 -1.3 At risk 36 987.72 1268.01 0-10,000

Perceived behavioral control Healthy 57 225.04 95.22 0-400 -.43 At risk 36 234.31 108.17 0-400 Control beliefs: Soda in home Healthy 0-10,000 -.13 refrigerator 57 3345.09 3098.08 At risk 36 3430.14 3217.60 0-10,000 Soda at school Healthy 57 1216.35 1859.97 0-10,000 .14 At risk 35 1621.97 2265.61 0-10,000 Parents offering Healthy 56 641.02 1182.92 0-10,000 -.93 At risk 36 606.50 1127.59 0-10,000

Soda intake behavior (cans) Healthy 59 1.60 2.89 - .56 At risk 36 1.30 1.74 - Note. ‘Healthy’ refers to healthy weight and ‘At risk’ refers to at risk of overweight

120

Summary

The purposes of this study were to: (1) determine influences on school-age children’s milk and soft drink intake and (2) determine the effect of gender, ethnicity, and

BMI on the TPB variables and behaviors about milk/soft drink intake in school-age children. Of the total surveys mailed to 600 participants from October to December 2007, ninety seven completed surveys were returned by May 2008.

For the demographic variables, slightly more than half of the participants were female (54%). The ages ranged from 10 to 12 with a mean of 11 years (SD = .64). The majority of the participants were White or Caucasian (74%). The majority of the participants lived with both parents (86%) and 11% lived with mother. Concerning the eating behaviors, the majority did drink milk during breakfast (64%, n= 62), of which

20% had 3 glasses per day. Only 6% did not drink milk. There were twice as many consumers of soft drink than nonconsumers (71%, n= 69 and 29%, n= 28 respectively).

Forty percent drank milk with snacks and 19% drank soda.

Multiple regression was used to answer research questions one through five. In

question one, only intention significantly predicted milk and soda intake. In question two,

attitude was the only significant predictor of intention to drink milk and in the soda data,

both attitude and perceived behavioral control significantly predicted intention with

attitude being the stronger predictor. For the behavioral beliefs, both behaviors, being

healthy and taste, were predictors of attitude with taste being the stronger predictor. For

normative beliefs, friends were a significant predictor of subjective norm in both

behaviors. For control beliefs, availability of milk in home refrigerator was the only

121 significant predictor of perceived behavioral control for milk. None of the control beliefs were significant predictors of soda intake.

For research questions 6 and 8, the results of independent samples t tests revealed no statistically significant gender and BMI differences in terms of the milk and the soda pop TPB variables and both behaviors. For question 7, significant differences were found between the races with respect to the milk behavioral intention, behavioral belief about health, normative belief about family, and control belief about availability of milk in home refrigerator, with white participants scoring higher in these variables. Within the soda pop variables, differences were found in both normative beliefs and soda intake behavior. The minority participants had a significantly stronger perception than the white participants that someone in their family and friends think they should drink soda pop every day. The minority participants drank significantly more soda than the white participants.

122

CHAPTER V

Discussion

The beverage consumption patterns of American children have changed over the past decades. Daily milk consumption among 6 to 11-year-olds has dropped and, during the same time period, soft drink consumption has doubled, leading to potential serious health conditions (Nicklas & Hayes, 2008). Understanding eating habits of children early in life is important for planning effective intervention strategies. This cross-sectional

descriptive correlational study is the first one to use the TPB to investigate influences on

school-age children’s milk and soft drink intake in the U.S. The purposes were to: (1)

determine influences on school-age children’s milk and soft drink intake and (2) determine the effect of gender, ethnicity, and BMI on behavioral beliefs, normative beliefs, control beliefs, attitude toward the behavior, subjective norm, perceived behavioral control, intention, and behavior about milk/soft drink intake in school-age children. This chapter includes discussion of the study results, implications for practice, theory and policy, the limitations of the study, and recommendations for future research.

Discussion of Results

Description of eating behaviors and patterns of milk and soft drink intake

Breakfast. Despite the substantial decline in breakfast consumption among children since 1965, reported as between 64.7% and 74.9% depending on gender (Siega-

Riz, Popkin, & Carson, 1998), the majority of the respondents in this study (86%) had breakfast the morning they completed the questionnaire. This finding is consistent with

Berg et al.’s (2000) study where 96% of Swedish children were reported to eat breakfast

123

at home and Bellisle and Rolland-Cachera’s (2007) study, in which 97% of the 9 to 11-

year-old French children had breakfast. The 14% proportion of the participants in our

study who skipped breakfast is consistent with the 10% proportion that was reported

recently (Nicklas & Hayes, 2008). These findings could point towards a slowdown or a

reversal of the previously reported decline in breakfast consumption.

Of the children who consumed breakfast, 72% had breakfast at home and 13% at

school. These figures are different from the ones found in another study in which, 49% of

children from nationally representative samples obtained from the Nationwide Food

Consumption Surveys of 1965, 1977–1978 and 1989–1991 ate breakfast at home, and

51% ate breakfast at school (Siega-Riz et al., 1998). This last study reports on behaviors

that took place 17 years ago which may have shifted significantly since. Although both

studies utilized the same recall method to collect eating behavior data, another possible

reason for the discrepancy in these figures may reside in the fact that our study relied on

mailed surveys and, despite being asked about ‘breakfast before school today,” the

participants may have filled them on a weekend when breakfast is consumed at home,

hence increasing the number of ‘at home’ responses.

Lunch. Almost all (99%) respondents had lunch the day prior to responding, and

the majority (73%) had it at school, followed by 22% at home. A study conducted in the

U.S. to analyze children's meal patterns over 2 decades similarly found that only 2% of

children skipped lunch (Nicklas, Morales et al., 2004). Moreover, in Bellisle and

Rolland-Cachera’s (2007) study, almost all French children (96% in 1997 and 97% in

1995) had lunch; however, two-thirds consumed it at home and one-third at the school. It is unclear if this discrepancy could be attributed to cultural differences. From 1973-1974

124

to 1993-1994, the percentage of American children eating a school lunch declined from

89.7% to 78.2% and those eating lunch brought from home increased from 5.9% to

11.1% (Nicklas, Morales et al., 2004). Half of the respondents (53%) in our study got their lunch from home and 28% got it at school, possibly confirming both trends.

Dinner. The majority (93%) of respondents had dinner, of which 90% had it at home. The majority (83%) had dinner with family, 11% with others, and only 6% ate alone. Similarly, almost all French children (99%) had dinner, most often at home and in the company of all family members (73-87%) (Bellisle & Rolland-Cachera, 2007). Our findings are contrary to other findings indicating an increase in meals and snacks eaten away from home (Nicklas, Morales et al., 2004). From the 1970s to the 1990s, consumption of dinner at home has been been reported to decrease from 89.2% to 75.9

(Nicklas, Morales et al., 2004). Further studies are needed to determine whether our findings represent a reversal of this trend.

Milk intake. In our study, almost all children (91%) had ≥ 1 glass of milk on a

usual day during the school week. This result is consistent with the findings of Berg et

al.’s (2000) study, in which 93% of the Swedish children and adolescents drank milk

some time during the week. However, only 42% of the Swedish children drank milk at

breakfast, while nearly two-thirds of the respondents (64%) in our study reported

drinking milk during breakfast. This discrepancy could be attributed to the fact that Berg

et al.’s (2000) findings were drawn from 7- day food records, while our study used a 24-

hour dietary recall. Moreover, Berg et al. assessed milk intake for breakfast ‘everyday’

which could have resulted in exclusion of children who drank milk at breakfast on some,

but not all days of the week. Another possible explanation may be that the sample in Berg

125

et al.’s study consisted of adolescents (11 to 15 years) and ours included younger children

(10 to 12 years). Based on prior research looking into trends of eating behavior in the

U.S., milk consumption has been found to decrease with increasing age (Demory-Luce et al., 2004; Friedman et al., 2007), suggesting that higher milk consumption in the present study may be associated with age.

Forty percent reported drinking milk with snacks. From 1977 to 1996, snacking among children ages 6 to 11 years has increased considerably from 76% to 91% (Jahns,

Siega-Riz, & Popkin, 2001). However, the mean consumption of milk reported as snacks has significantly decreased (Nicklas, Demory-Luce et al., 2004). In Canadian children 4 years and older, only 15.8% of snack calories were from milk products (Roblin, 2007).

The afternoon snack, a traditional meal for French children, was consumed by 86-88% of

the 1000 children sampled by Bellisle and Rolland-Cachera (2007). In 1993, 1995, and

1997 milk was consumed in 21%, 18%, and 16% of afternoon snacks, respectively.

However, we could not find data from other studies about proportion of children drinking

milk with snacks. This data may suggest that as snacking is on an increasing trend,

promoting milk intake during snacks may prove to be a successful strategy to improve on

calcium intake and weight parameters.

Soft drink intake. Overall, two-thirds of our sample (71%) consumed soft drinks.

Our findings are consistent with findings from other studies in which around 65% of

school-age children drank soft drinks (Friedman et al., 2007; Harnack et al., 1999).

However, in one study, only 30% of 8 to 13-year-olds reported drinking soft drinks

(Grimm et al., 2004) while others found higher proportions of female and male

adolescents (96%) drinking soda (Kassem & Lee, 2004; Kassem et al., 2003). The lower

126

proportion of soda consumers in Grimm et al.’s (2004) study and the higher proportions

in Kassem and Lee’s (2004) and Kassem et al.’s (2003) studies may be explained by the

fact that the former studied both school-age children and adolescents (8 to 13 years) whereas the latter studied adolescents 13 to 18 years of age. All three studies had age groups different than ours and it was repeatedly shown that soft drink consumption varies dramatically by age, increasing beginning around 8 years (Friedman et al., 2007; Grimm et al., 2004; Rampersaud et al., 2003).

When asked “On a usual day during the school week, how much soda pop do you

drink,” 41% reported none, 27% one can, 23% two to three cans, and 6% more than 3

cans. Our findings are similar to those of Storey et al.’s (2004), in which 28.9%

consumed no soda, 30% consumed ≤ 1 serving, 25.4% consumed 1 to 2 servings, and

15.6% consumed > 2 servings. Again, as discussed previously, with older age groups,

more females (50%) and males (60%) drank ≥ 2 glasses of soda per day (Kassem & Lee,

2004; Kassem et al., 2003).

Only 19% of our respondents claimed drinking soda with snacks. When asked

what the respondents would choose to drink, the majority (45%) chose water, followed

by milk (26%) and soda (10%). Nearly half of the respondents choosing the “Other”

category preferred orange juice and lemonade. These findings are again consistent with

findings from the sample of French children, whose drinks during their snack were

reported being mostly water, juices and sodas (Bellisle & Rolland-Cachera, 2007). The difference lies in the preference of milk in our study when given the choice, whereas milk was not mentioned in the French study. Again, this finding points to a potential means to increase milk consumption through promoting it with snacks.

127

It is interesting to note that while only 10% of respondents stated they would

choose to drink soda, nearly double consume it with snacks. This may be attributed to

social desirability, where, despite not being in contact with the researchers, they may

have wanted to impress us by choosing less soda as it may be perceived as an inferior

choice to milk. Alternatively, soda may be more available to children during snack time.

On the contrary, although almost all claimed to drink ≥ 1 glass of milk daily, only 26%

reported to drink it if they were given the choice. This may imply that there are factors

beyond the children’s control leading them to consume milk and, if given the choice, not

all of them would choose to drink it. Further research will be needed to explore the

reasons behind these discrepancies.

Analysis of research questions

Prediction of milk/soda drinking behaviors.

1. Does intention about milk/soft drink intake and perceived behavioral control

influence behavior about milk/soft drink intake in school-age children?

In explaining milk and soft drink intake behaviors among school-age children, only intention predicted milk and soda intake behavior, with stronger intention to drink milk/soft drink being more related to actual intake. Our findings about the contribution of intention compare favorably with other studies about milk and soft drink consumption in adolescents (Berg et al., 2000; de Bruijn et al., 2007 ; Gummeson et al., 1997; Kassem &

Lee, 2004; Kassem et al., 2003).

In this study the presence of a direct relationship between perceived behavioral

control and both milk and soda drinking behaviors was tested. The rationale for this

modification was that some children have no control over their milk and soft drink

128

choices and thus the relationship between perceived behavioral control and the behaviors

through intention may not hold true. Contrary to Ajzen’s (1991) suggestion, perceived

behavioral control was not significantly correlated with either behavior nor was it an

independent predictor of them. These findings are congruent with other studies about

milk and soda drinking behaviors (Gummeson et al., 1997; Kassem & Lee, 2004; Kassem et al., 2003), the exception being Berg et al.’s (2000) study, in which perceived behavioral control added significantly to the prediction of milk choice by intention. The lack of an independent contribution of perceived behavioral control to the prediction of both behaviors in this study may be possibly due to children’s inaccurate perception of their degree of control over their milk/soda drinking behavior. Alternatively, this age group may lack actual control over their food choices through either parental decisions over availability of milk and soda or through school-based meal programs where milk or water may be the only options. Again, further research is needed to determine the etiologies behind this finding.

The predictiveness of the TPB has been found to be low (R2 < 0.3) with adults and adolescents and even lower for children (Baranowski et al., 1999). The R2 values have

been higher when the models were used to predict specific categories of foods,

suggesting that influences vary by food types. Despite having chosen specific food

categories, the predictive values of intention and perceived behavioral control were

relatively low for milk intake (4%) and for soda intake (14%). However, our results are

comparable with findings from other milk and soda prediction studies (Gummeson et al.,

1997; Kassem & Lee, 2004; Kassem et al., 2003). Similar to the other studies, the

relatively low predictive power of intention for milk and soda drinking behaviors may

129

have been because the behaviors being predicted were current intake of milk and soft

drink, whereas the intention measured was intention to drink milk/soft drink in the future.

Despite the low predictive power of intentions for both behaviors, these findings point

out the importance of intention as a worthwhile target for future investigations to

ascertain its significance.

Prediction of intention.

2. Does attitude toward the behavior, subjective norm, and perceived behavioral

control influence intention about milk/soft drink intake in school-age children?

The three independent variables yielded a good level of prediction and together explained 49% of the variance in intention to drink milk and 44% of the variance in intention to drink soft drink supporting the use of the TPB with children. Attitude was a significant independent predictor of intention to drink both milk and soda and explained most of the variance in both behavioral intentions. This predictive pattern of attitudes is congruent with the other studies using the TPB in which attitudes have been the strongest predictors of intentions (Berg et al., 2000; de Bruijn et al., 2007; Gummeson et al., 1997;

Kassem & Lee, 2004; Kassem et al., 2003).

Despite the influences by parents and friends found on children’s and adolescent’s

milk and soda drinking behaviors, subjective norms surprisingly did not contribute to the

prediction of both behaviors in our study. There have been mixed findings about

subjective norm in the other TPB studies (Berg et al., 2000; de Bruijn et al., 2007;

Gummeson et al., 1997; Kassem & Lee, 2004; Kassem et al., 2003). A possible reason

for the weak or lack of contribution of subjective norm may be because it is usually operationalized as an injunctive norm i.e. social approval by significant others, responses

130

to which have resulted in low variability due to high social desirability. As a possible

solution, Ajzen (2002) recommended the inclusion of items tapping descriptive norms i.e. whether important others themselves perform the particular behavior or not. Despite not being commonly used with children, Berg et al.’s (2000) incorporation of the concept of

descriptive norm as a subset of subjective norm improved the predictive power of the

other constructs on intents to consume the different alternatives of milk.

Interestingly, perceived control was able to predict intention to drink soda in our

study, but not intention to drink milk; however, it ranked second after attitude in the

strength of the prediction. This supports findings from all previously mentioned studies

about milk and soda consumption. Our findings are possibly not related to weak

measurement of this concept because perceived behavioral control was measured with

identical items for the milk and soda intake behaviors. A possible explanation may be

that school-age children have different perception of control over drinking milk versus

drinking soda or these behaviors may even be habitual.

These results point to the importance of attitudes in determining intentions and the

relatively minor influence of perceived behavioral control and subjective norm with this

population. Future research should: focus on promoting positive attitudes towards healthy

foods, investigate both injunctive and descriptive norms concurrently, and investigate the

extent to which the lack of influence in one behavior and not another is attributable to

measurement issues versus actual differences in perception of control with varying

behaviors or due to preformed habits.

131

Prediction of attitude, Subjective norm, and perceived behavioral control

The strength of predictions of attitude, subjective norm, and perceived behavioral control from their underlying beliefs varied considerably. The behavioral beliefs accounted for a considerable amount of variance in milk intake and soft drink intake

(45% and 52%, respectively). The amount of explained variance by normative beliefs was even more for subjective norms to milk intake (60%). However, the strength of the predictions weakened considerably for subjective norms to soft drink intake, perceived behavioral control to drink milk and soft drink (30%, 14%, and 8%, respectively). Few studies have investigated the underlying belief components; however, the variations in the strength of the predictions are consistent with the soft drink consumption studies conducted with males and females (Kassem & Lee, 2004; Kassem et al., 2003). A possible explanation for the weak predictions is that, contrary to Ajzen’s recommendation of eliciting the salient beliefs from participants using a qualitative method, we included the most commonly elicited ones from previous TPB studies. In addition, in order to minimize subject burden with children, we used only the beliefs that were found to have a significant influence. Not all the included beliefs may have matched our participants’ individual beliefs. For example, instead of the control beliefs that we chose: availability at home and at school, and being offered by parents, some others such as quenching thirst, seeing advertisements, and having enough money to buy soda may have matched the children’s beliefs thus yielding higher predictions.

132

3. Do behavioral beliefs about milk/soft drinks influence attitude toward milk/soft

drink intake in school-age children?

Taste of milk and soda were one of the stronger predictors of attitude toward milk/soda intake. Taste was an important influence on the participants’ choice of milk/soda. The importance of taste has been repeatedly found to be the strongest or one of the strongest influences on children’s and adolescents choice of milk and soda (Berg et al., 2000; de Bruijn et al., 2007; Gummeson et al., 1997; Kassem & Lee, 2004; Kassem et al., 2003). Future interventions promoting healthy eating behavior should incorporate the importance of taste to entice children to choose milk as the tastier beverage.

Being healthy ranked second in predicting attitude toward milk and soda intake.

The participants thought that drinking milk/soda makes them healthy. This finding supports findings from previous milk and soda studies (Berg et al., 2000; Kassem & Lee,

2004; Kassem et al., 2003). It is interesting to find that children think drinking both milk and soda makes them healthy. This may explain the increasing consumption of soft drinks over the past couple of decades. These findings point to the importance of further investigating the reasons that lead children to think soda consumption makes them healthy and emphasizing programs that underscore the unhealthy consequences of soda consumption.

The third belief that drinking milk/soda makes one gain weight had no independent

influence in predicting attitude toward milk/soda intake. Weight gain was an influential

factor for attitude toward soft drink intake only among females (Kassem et al., 2003) and

for attitude toward milk intake in Swedish adolescents (Berg et al., 2000). These findings run against the misconception about milk being fattening, which may lead

133

children, especially females, to limit their milk intake (Neumark-Sztainer et al., 1999).

However, it is alarming to find that children have a misconception about soft drink not

making them gain weight and thus not influencing their soda intake. Milk and soda intake

and their relation to weight gain should be addressed in health promotion programs. In

addition, a better understanding of “diet” soft drinks needs to be included as it is not clear

that children of this age always understand which sodas are low in calories and which are

not.

4. Do normative beliefs about milk/soft drinks influence subjective norm about

milk/soft drink intake in school-age children?

Only friends were an independent significant predictor of subjective norm to milk

and soft drink intake, with participants wishing to comply more with what friends think

they should be drinking than what someone in the family thinks. In other studies, friends

have been found to influence adolescents’ milk and soda intake; however these influences

have mostly been weaker than those of parents (Berg et al., 2000; de Bruijn et al., 2007;

Gummeson et al., 1997; Kassem & Lee, 2004; Kassem et al., 2003). Contrary to our

findings, Gummeson et al. (1997) have found friends to be unimportant in children’s and

adolescents’ milk choice, which may be because their sample included both children and

adolescents whose perception of referents as influences on their food choices may vary.

A possible reason for the lack of consistency between our findings and the others is that

we have asked about ‘someone in my family’ as a referent, while the other studies

mention parents or mother and father. Future studies about children’s food choices should further investigate the influence of parents, as parents role in influencing their children’s

food choices has been well documented (Birch & Fisher, 1998; Koivisto Hursti, 1999).

134

This is important in order to involve proper referents in promoting children’s healthy food choices. As well, our wording choice was deliberate to capture the diversity of living arrangements where grandparents and other family members may be more influential than parents.

5. Do control beliefs about milk/soft drinks influence perceived behavioral control

about milk/soft drink intake in school-age children?

Availability of milk in home refrigerator was the only predictor of perceived behavioral control to drink milk. The participants thought that having milk available in their home refrigerator made it easier for them to drink it. This finding supports a number of other studies about milk and soda intake (Berg et al., 2000; de Bruijn et al., 2007;

Gummeson et al., 1997; Kassem & Lee, 2004; Kassem et al., 2003). The other two beliefs about availability at school and whether being offered milk by parents made it easier for them to drink milk did not predict perceived behavioral control to drink milk.

This underscores the importance of making milk available at home in order to promote its consumption.

It is interesting to note that, despite having significant positive associations with perceived behavioral control to drink soda, none of the control beliefs about availability of soda in home refrigerator, availability at school, and being offered by parents predicted perceived behavioral control to drink soda. Since the control beliefs are not multicollinear, this discrepancy may be because the strength of the prediction of perceived behavioral control to drink soda (8%) is weak. The lack of influence of availability of soda at school may also be explained by the fact that most elementary schools in Cleveland no longer make soft drinks available on their premises.

135

Gender, ethnicity, and BMI differences

6. Are there differences by gender on behavioral beliefs, normative beliefs, control

beliefs, attitude, subjective norms, perceived behavioral control, intention and

behavior about milk/soft drink intake in school-age children?

No significant differences were found between males and females in terms of the milk and the soda pop TPB variables and both behaviors. Only Berg et al. (2000) have investigated gender differences in the TPB constructs and milk intake. Our findings concerning the lack of gender differences in the TPB variables and milk consumption are consistent with Berg et al.’s findings. Gender differences were only found in intentions and attitudes with males being more in favor of higher fat milk than females. No significant gender differences were found for the TPB constructs on intention and consumption and, compared to boys, girls evaluated weight gain more negatively (Berg et al., 2000). Soft drink consumption has been found to be higher among males than females

(Friedman et al., 2007; Grimm et al., 2004). Our findings indicate the need for further investigation of gender differences within school-age children. Findings from such studies are essential in developing tailored interventions based on gender differences.

7. Are there differences by ethnicity on behavioral beliefs, normative beliefs, control

beliefs, attitude toward the behavior, subjective norm, perceived behavioral

control, intention and behavior about milk/soft drink intake in school-age

children?

Compared to the minority group, white participants were found to have stronger

intention to drink milk, had stronger perception that drinking milk makes them healthy,

had stronger perception that someone in their family thinks they should drink 3 cups of

136

milk every day, and that having milk in home refrigerator would make it easier for them

to drink 3 cups every day. Contrary to the racial differences found in the milk data,

compared to the white participants, the minority group had stronger perception that

someone in their family and their friends think they should drink soda pop every day and

they drank significantly more soft drinks.

To our knowledge, no studies have investigated the effect of ethnicity on the

influences on children’s milk and soft drink intake. The available data focuses on its

effect on actual consumption of milk and soda. Our findings are supported by previous

studies that have shown significant racial differences in these behaviors. Asian ethnicity

has been described to be negatively associated with milk and calcium intake (Novotny et al., 2003; Oshiro, Novotny, & Titchenal, 2003). Similarly, African American males aged

9 to 18 years and females 4 to 18 years have also been found to consume significantly less milk than their non-African American counterparts (Fulgoni et al., 2007). Black non-

Hispanic and low-income children were reported to be more likely to consume soft drinks

at school if they were made available to them (Fernandes, 2008). These ethnic differences

in milk and soft drink consumption could be partially explained by the higher prevalence

of lactose intolerance among African Americans and Asian Americans (Nicklas, 2003).

Whether differences in socioeconomic status among different ethnicities confound our

observations needs to be ascertained in further studies.

137

8. Are there differences by BMI on behavioral beliefs, normative beliefs, control

beliefs, attitude toward the behavior, subjective norm, perceived behavioral

control, intention and behavior about milk/soft drink intake in school-age

children?

Our findings indicated no significant differences between participants in the

‘healthy weight’ category and those in the ‘at risk of overweight’ category in terms of the milk and the soda pop TPB variables and both behaviors. Few studies have investigated associations of BMI and beverage consumption in children. For 10-year-old sweetened-

beverage consumers, mean BMI has been found to significantly increase over two decades (Rajeshwari, Yang, Nicklas, & Berenson, 2005). In a study evaluating diet quality and BMI by beverage patterns, the BMI of children aged 6 to 11 years was found to be significantly higher in the soda patterns compared to the high-fat milk patterns

(LaRowe, Moeller, & Adams, 2007). To our knowledge, our study is the first to investigate the influence of BMI differences on the TPB constructs in school-age children. Since prolonged exposure to soft drink is needed to impact BMI, the lack of association between BMI and TPB variables in both behaviors could be attributed to the fact that our study population consisted of younger children. Future research should follow school-age children longitudinally to determine if factors influencing milk and soda consumption may vary by the different BMI categories.

138

Implications for practice, theory and policy

Implications for practice

The epidemic of childhood overweight and obesity is expected to progress into a significant public health burden as dietary patterns persist through adulthood, leading to chronic disease. Early interventions in children’s lives are needed to promote healthy eating behaviors that will be maintained through adulthood. To date, most interventions have been aimed at limiting availability, as illustrated by regulations banning soft drink sale on school premises. Our study offers a glimpse into understanding factors that determine school-age children’s soft drink and milk drinking behavior. It underscores the

lack of influence of availability on soda intake and the importance of taste in the choice

process. Moreover, it was alarming to find that children believe milk and soft drink intake

both contribute positively to their health and do not relate soda intake to weight gain.

Such an understanding will allow devising behavior-altering interventions that will have

the advantage of being independent of the traditional school versus home context. For

example, health education programs should focus more on explaining the health effects

of various drinks to school age children.

Implications for theory

To our knowledge, our study is the first to use the TPB to explain milk and soft

drink intake in school-age children in the U.S. It was also the first to introduce VASs into

the TPB concepts. The TPB was able to explain most of the influences on milk and soda

intake, pointing to its usefulness and applicability in this context. However, since some of

the variance in both behaviors could not be accounted for by the TPB, further

operationalization of the concepts will be needed.

139

Implications for policy

Several policy interventions currently aim at restricting availability of soda to promote healthier drinking patterns. However, such interventions are effective only as long as the restrictions are in place and fall short when children are outside the restrictive environment. For example, “pouring rights” contracts have been the subject of many controversies and the focus of many health advocacy groups for their potential contribution to the epidemic of childhood overweight and obesity. A simple screening tool about nutritional habits for the child to fill may be designed for regular use in HMOs such as Kaiser Permanente, as well as in pediatric clinics. Such a tool would serve as a trigger for targeted interventions for clinicians in providing health promotion guidance.

As well, consideration needs to be given as to the age at which children complete a simple dietary recall as the study findings indicate that children are capable of completing a recall of their “normal day” intake.

Through better understanding children’s attitudes, social norms, behavioral controls, beliefs and intentions, our results provide a means to develop interventions that will shape behavior in a more permanent manner. A policy aimed at thoroughly explaining to children the effects of various drinking items on their health might prove beneficial. Nurses, through their presence in schools, can assume a leading role as educators and actively participate in state mandated health education programs tailored to children. As well, nurses, with a focus on health promotion, may be able to influence other groups where there is the potential to impact child health, such as YMCA/YWCA programs, through Boy and Girl Scouts and other youth organizations.

140

Limitations of the Study

There were some limitations in this study with respect to research design and

measurement. First, this study utilized a cross-sectional design which does not allow determining directions and possible causality of associations. With the conflicting views on the beverage –milk displacement hypothesis, these trends in beverage and milk consumption need to be determined in longitudinal studies. In the absence of a randomized control design, longitudinal data provide the best means to establish that observed effects are causal and not due to confounding, selection bias, or reverse causality (Lee & Frongillo, 2001).

Second, we relied on self-reported data for the milk and soft drink intake which

may have been affected by memory issues and insufficient recall. Thus the behavioral

measures in this study may have been somewhat biased because of self-report. Third, we

did not address the possible effect of social desirability which may have affected the

responses. The effect of social desirability should have been minimized by the use of an

anonymous mailed survey and the fact that parents and participants were informed that

the researchers would not know what participants would report. However, since we are

not aware if the participants filled the questionnaires on their own or in the presence of

parents, we would suspect that some of them may have wanted to impress parents by

indicating more preference to milk as the healthier beverage. Fourth, 86% of our sample lived in a two parent family and all had health insurance, both of which is not

representative of 10 to 11-year-olds in Cleveland, Ohio or other American children in

general. Finally, all the measures were developed for the purpose of this study and

reliability and validity established with this sample. The Cronbach’s alpha for the

141

Subjective Norm to Milk Intake and Soda Pop Intake Scales was .57 and .56,

respectively, and for the Perceived Behavioral Control over Milk Intake and Soda Pop

Intake Scales was .50 and .66, respectively. Further development/refinement of these

scales is needed to improve their reliability scores.

Recommendations for Future Research

Despite the limitations of this study, our findings are valuable in serving as preliminary steps for further research to allow nurses and health care workers to ultimately plan effective interventions to promote healthy beverage choices by school-age

children thus reducing risk factors for overweight. The following are recommendations

for further research:

1- Longitudinal studies to determine whether milk has simply become less popular

or whether soft drinks have been substituted for milk.

2- Study other food items to further validate the applicability of the TPB with

school-age children.

3- Investigate whether school-age children distinguish carbonated beverages from

sports drinks and flavored/vitamin water.

4- Study the impact of the media on the selection of milk or soft drink by school-age

children.

5- Design of a qualitative study to generate emerging beliefs specific to American

school-age children and replication of the study with a larger sample size to

generate more compelling evidence for associations between influences on

children’s milk and soft drink intake and their actual consumption.

142

6- Planning effective interventions to promote healthy beverage choices by school-

age children to possibly reduce risk factors for overweight.

7- Replication of the study in Lebanon and design of a comparative study to explore

cultural differences in milk and soft drink intake among American and Lebanese

school-age children.

Summary

This study was conducted to investigate influences on school-age children’s milk

and soft drink intake behaviors. The results indicated that the majority of the children had

≥ 1 glass of milk on a usual day during the school week and two-thirds consumed soft

drinks. Within the TPB variables, intention predicted milk and soda intake, attitude had

the strongest contribution, followed by perceived behavioral control only in predicting

soda intention. Subjective norms did not contribute to the prediction. Within the beliefs,

taste and being healthy predicted attitudes, friends predicted subjective norms, and

availability of milk in home refrigerator predicted perceived behavioral control to drink

milk. When investigating differences in terms of the milk and the soda pop TPB variables

and both behaviors, none were found between males and females and between

participants in the ‘healthy weight’ and the ‘at risk of overweight’ categories. Compared

to the minority group, white participants were found to have stronger intention to drink

milk, perceived that drinking milk makes them healthy, had stronger perception that

someone in their family thinks they should drink 3 cups of milk every day and that

having milk in home refrigerator would make it easier for them to drink it. Compared to

the white participants, the minority group had stronger perception that someone in their family and their friends think they should drink soda pop every day and they drank

143 significantly more soft drink. These findings have many implications for practice, theory and policy; however, the study has some limitations with respect to cross-sectional design, self-report of behaviors, effect of social desirability, and measurement issues.

Ultimately, prospective studies assessing beverage patterns over time and replication of this study with larger samples and various foods are needed to validate the exact influences and intervene to possibly halt the rising proportion of overweight children.

144

APPENDIX A

Date: ………………. ID: ……………..

Influences on School-age Children’s Milk and Soft Drink Intake

Tell me about yourself

Birth Date: ______

Place a (X) in the space beside your response. Respond to questions only above the black line.

1- I am a (1) Boy (2) Girl ______

2- I am: (1) American Indian or Alaskan Native ______(2) Asian ______(3) Black or African American (not of Hispanic Origin) ______

(4) Hispanic or Latino ______(5) Native Hawaiian or Other Pacific Islander ______(6) White or Caucasian (Not of Hispanic origin) ______

3- Who do you live with? (1) Mother only ______(5) Grandfather only ______(2) Father only ______(6) Grandmother & Grandfather ______(3) Mother & Father ______(7) Other ______(4) Grandmother only ______

For researchers’ use

Height (in inches): ______

Weight (in pounds): ______

BMI: ______

145

APPENDIX B

Date: ………………. ID: ……………..

Influences on School-age Children’s Milk and Soft Drink Intake

24-Hour Dietary Recall

Place a (X) in the space beside your answer

1- Did you eat anything before school today? (1) Yes ____ (2) No ____ 2- Where did you eat this morning’s food? (1) Home ____ (2) School ____ (3) Car ____ (4) Another place ____ (5) I did not eat anything before school today ____ 3- Did you drink milk before school this morning? (1) Yes ____ (2) No ____ 4- On a usual day during the school week how much milk do you drink? (Write the number of glasses or cartons. Place a 0 if you don’t drink any milk). Glasses ______Cartons ______

5- Did you eat lunch yesterday? (1) Yes ____ (2) No ____ 6- Where did you eat lunch? (1) Home ____ (2) School ____ (3) Another place ____ (4) I did not eat lunch yesterday ____ 7- Where did you get your lunch from? (1) Bought it ____ (2) Home ____ (3) Friends ____ (4) School ____ (5) None ___ 8- Did you have a meal after school and before bedtime (1) Yes ____ (2) No ____ yesterday?

9- Where did you eat this meal before bedtime? (1) Home ____ (2) Another place ____ (3) I did not eat anything before bedtime _____

146

10- Who prepares food for you at home? (1) My family ____ (2) Me ____ (3) My family and me____ (4) Other people ____

11- Had food last night (1) By myself ____ (2) With family ____ (3) With others _____ 12- Do you drink soda pop? (1) Yes ____ (2) No ____ 13- On a usual day during the school week how much soda pop do you drink? (Write the number of cans, cups or 16 oz bottles. Place a 0 if you don’t drink any soda pop).

______

14- On a typical school day what would you choose to drink (circle one)?

OTHER NOTHING (please write what) ______

15- On a usual day during the school week, while (1) Yes ____ (2) No ____ eating snacks do you drink milk?

16- On a usual day during the school week, while (1) Yes ____ (2) No ____ eating snacks do you drink soda pop?

147

APPENDIX C

Date: ………………. ID: ……………..

Influences on School-age Children’s Milk and Soft Drink Intake

Milk Intake Questionnaire

Place a (×) in the space beside your response

Does drinking milk ever make you sick? ____ Yes ____ No

If you marked no, please go on to the next page.

If you marked yes, please go on to the next section marked Soda Pop Intake Questionnaire.

148

Intention to Drink Milk Scale

Place a (×) in the square below your response

Definitely Maybe Don't Maybe yes Definitely

not not know yes

1. I plan to drink 3 cups of milk      every day

Very Don’t Unlikely Likely Very likely unlikely know

2. How likely is it that you will drink      3 cups of milk every day

149

Directions: Tom and his friend were playing in the snow. Tom’s mom came over and told them: ‘I will ask you a question and you will answer me by placing a (×) for your response on the line. The vertical line is the middle of the horizontal line.

CORRECT example How much do you like playing in the snow?

X Not at all Very much

Incorrect example How much do you like playing in the snow?

Not at all X Very much

Attitude Toward Milk Intake Scale

3-5. For me drinking 3 cups of milk every day is

Very unimportant Very important

Very bad Very good

Very unenjoyable Very enjoyable

6. How much do you like milk?

Not at all A lot

150

Behavioral Beliefs about Milk Intake Scale

7. Drinking milk makes me healthy

Strongly disagree Strongly agree

8. Milk tastes

Very bad Very good

9. Drinking milk makes me gain weight

Strongly disagree Strongly agree

10. For me being healthy is

Very unimportant Very important

11. For me taste of milk is

Very unimportant Very important

12. For me gaining weight is

Very bad Very good

151

Subjective Norm to Milk Intake Scale

13. My friends think that I should drink 3 cups of milk every day

Strongly disagree Strongly agree

14. My friends drink 3 cups of milk every day

Strongly disagree Strongly agree

Normative Beliefs about Milk Intake Scale

15. Someone in my family thinks I should drink 3 cups of milk every day

Strongly disagree Strongly agree

16. When it comes to drinking milk, I want to do as this person in my family thinks I should

Not at all Very much

17. When it comes to drinking milk, I want to do as my friends think I should

Not at all Very much

152

Perceived Behavioral Control over Milk Intake Scale

18. For me drinking 3 cups of milk every day would be

Very difficult Very easy

19. I can decide whether or not I drink 3 cups of milk every day

Strongly disagree Strongly agree

20. How much control do you think you have over drinking 3 cups of milk every day?

No control Complete control

21. Do you always get to choose what you drink at home?

Definitely no Definitely yes

153

Control Beliefs about Milk Intake Scale

22. Is there milk in your home refrigerator?

Never Always

23. Having milk in our home refrigerator would make it easier for me to drink 3 cups every day

Strongly disagree Strongly agree

24. My parents offer me milk with meals and snacks

Strongly disagree Strongly agree

25. Being offered milk by my parents would make it easier for me to drink 3 cups every day

Strongly disagree Strongly agree

26. Do you drink milk at school?

Never Always

27. Having milk available at school would make it easier for me to drink 3 cups every day

Strongly disagree Strongly agree

154

APPENDIX D

Date: ………………. ID: ……………..

Influences on School-age Children’s Milk and Soft Drink Intake

Soda Pop Intake Questionnaire

Place a (×) in the space beside your response

Are you allowed to drink soft drinks and pop? ____ Yes ____ No

If you mark no here, you can stop and hand this in.

155

Intention to Drink Soda Pop Scale

Place a (×) in the square below your response

Definitely Maybe Don't Maybe yes Definitely

not not know yes

1. I plan to drink soda pop every      day

Very Don’t Unlikely Likely Very likely unlikely know

2. How likely is it that you will drink      soda pop every day

156

Attitude Toward Soda Pop Intake Scale

Directions: Place a (×) for your response on the line

3-5. For me drinking soda pop every day is

Very unimportant Very important

Very bad Very good

Very unenjoyable Very enjoyable

6. How much do you like soda pop?

Not at all A lot

Behavioral Beliefs about Soda Pop Intake Scale

7. Drinking soda pop makes me healthy

Strongly disagree Strongly agree

8. Soda pop tastes

Very bad Very good

157

9. Drinking soda pop makes me gain weight

Strongly disagree Strongly agree

10. For me taste of soda pop is

Very unimportant Very important

Subjective Norm to Soda Pop Intake Scale

11. My friends think that I should drink soda pop every day

Strongly disagree Strongly agree

12. My friends drink soda pop every day

Strongly disagree Strongly agree

Normative Beliefs about Soda Pop Intake Scale

13. Someone in my family thinks I should drink soda pop every day

Strongly disagree Strongly agree

158

14. When it comes to drinking soda pop, I want to do as this person in my family thinks I should

Not at all Very much

15. When it comes to drinking milk, I want to do as my friends think I should

Not at all Very much

Perceived Behavioral Control over Soda Pop Intake Scale

16. For me drinking soda pop every day would be

Very difficult Very easy

17. I can decide whether or not I drink soda pop every day

Strongly disagree Strongly agree

18. How much control do you think you have over drinking soda pop every day?

No control Complete control

19. Do you always get to choose what to drink at home?

Definitely no Definitely yes

159

Control Beliefs about Soda Pop Intake Scale

20. Is there soda pop in your home refrigerator?

Never Always

21. Having soda pop in our home refrigerator would make it easier for me to drink it every day

Strongly disagree Strongly agree

22. My parents offer me soda pop with meals and snacks

Definitely no Definitely yes

23. Being offered milk by my parents would make it easier for me to drink

Strongly disagree Strongly agree

24. Do you drink soda pop at school?

Never Always

25. Having soda pop available at school would make it easier for me to drink it every day

Strongly disagree Strongly agree

160

Thank you for your participation. Please review to make sure you have completed all questions.

161

BIBLIOGRAPHY

Adair, L. S., & Popkin, B. M. (2005). Are child eating patterns being transformed

globally? Obesity Research, 13(7), 1281-1299.

Ajzen, I. (1988). Attitudes, personality, and behavior. Chicago: Dorsey Press.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human

Decision Processes, 50, 179-211.

Ajzen, I. (1998). Models of human social behavior and their application to health

psychology. Psychology and Health, 13, 735-739.

Ajzen, I. (2002). Constructing a TpB questionnaire: Conceptual and methodological

considerations. Retrieved April 24, 2005, from the World Wide Web:

http://www.people.umass.edu/aizen/pdf/tpb.measurement.pdf

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior.

Englewood Cliffs, NJ: Prentice-Hall.

Ajzen, I., & Madden, T. J. (1986). Prediction of goal-directed behavior: Attitudes,

intentions, and perceived behavioral control. Journal of Experimental Social

Psychology, 22, 453-474.

Ajzen, I., & Timko, C. (1986). Correspondence between health attitudes and behavior.

Journal of Basic and Applied Social Psychology, 7, 259-276.

Allison, D., Fontaine, K., Manson, J., Stevens, J., & VanItallie, T. (1999). Annual deaths

attributable to obesity in the United States. Journal of the American Medical

Association, 282(16), 1530-1538.

162

Allukian, M., Jr. (2000). The neglected epidemic and the surgeon general's report: A call

to action for better oral health. American Journal of Public Health, 90(6), 843-

845.

American Nurses Association (Council of Nurse Researchers). (1985). Directions for

nursing research: Towards the twenty-first century. Kansas City, MO: American

Nurses Association.

Ariza, A. J., Chen, E. H., Binns, H. J., & Christoffel, K. K. (2004). Risk factors for

overweight in five- to six-year-old Hispanic-American children: A pilot study.

Journal of Urban Health, 81(1), 150-161.

Armitage, C., & Conner, M. (2001). Efficacy of the Theory of Planned Behaviour: A

meta-analytic review. British Journal of Social Psychology, 40, 471-499.

Austin, S. B., & Rich, M. (2001). Consumerism: Its impact on the health of adolescents.

Adolescent Medicine, 12(3), 389-409.

Backman, D., Haddad, E., Lee, J., Johnston, P., & Hodgkin, G. (2002). Psychosocial

predictors of healthful dietary behavior in adolescents. Journal of Nutrition

Education & Behavior, 34(4), 184-193.

Baird, J., & Moses, L. (2001). Do preschoolers appreciate that identical actions may be

motivated by different intentions? Journal of Cognition and Development, 2(4),

413-448.

Baker, C. W., Whisman, M. A., & Brownell, K. D. (2000). Studying intergenerational

transmission of eating attitudes and behaviors: methodological and conceptual

questions. Health Psychology, 19(4), 376-381.

163

Ballew, C., Kuester, S., & Gillespie, C. (2000). Beverage choices affect adequacy of

children's nutrient intakes. Archives of Pediatric and Adolescent Medicine,

154(11), 1148-1152.

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavior change.

Psychological Review, 84, 191-215.

Bandura, A. (1982). Self-efficacy mechanism in human agency. The American

Psychologist, 37, 122-147.

Bandura, A. (1986). Social Foundations of Thought and Action. Englewood Cliffs:

Prentice-Hall, Inc.

Baranowski, T., Cullen, K., & Baranowski, J. (1999). Psychosocial correlates of dietary

intake: Advancing dietary intervention. Annual Review of Nutrition, 19, 17-40.

Barriault, T. L. (2003). Trying, not-trying, and trying-not: School-age children's

understanding of intention. Dissertation Abstracts International, 64(4-B), 1923.

Bellisle, F., & Rolland-Cachera, M. F. (2007). Three consecutive (1993, 1995, 1997)

surveys of food intake, nutritional attitudes and knowledge, and lifestyle in 1000

French children, aged 9-11 years. Journal of Human Nutrition and Dietetics,

20(3), 241-251.

Bellisle, F., & Rolland-Cachera, M. (2001). How sugar-containing drinks might increase

adiposity in children. The Lancet, 357(9255), 490-491.

Bendelow, G., Williams, S. J., & Oakley, A. (1996). It makes you bald: Children's

knowledge and Beliefs about health and cancer prevention. Health Education, 3,

12-19.

164

Bere, E., & Klepp, K. (2004). Correlates of fruit and vegetable intake among Norwegian

schoolchildren: Parental and self-reports. Public Health Nutrition, 7(8), 991-998.

Berenson, G., Srinivasan, S., Bao, W., 3rd, N. W., Tracy, R., & Wattigney, W. (1998).

Association between multiple cardiovascular risk factors and atherosclerosis in

children and young adults. The Bogalusa Heart Study. New England Journal of

Medicine, 338(23), 1650-1656.

Berg, C., Jonsson, I., & Conner, M. (2000). Understanding choice of milk and bread for

breakfast among Swedish children aged 11-15 years: An application of the

Theory of Planned Behaviour. Appetite, 34(1), 5-19.

Berg, M., Jonsson, I., Conner, M., & Lissner, L. (2002). Relation between breakfast food

choices and knowledge of dietary fat and fiber among Swedish schoolchildren.

Journal of Adolescent Health, 31(2), 199-207.

Birch, L. L., & Fisher, J. O. (1998). Development of eating behaviors among children and

adolescents. Pediatrics, 101, 539-549.

Block, G. (1982). A review of validations of dietary assessment methods. American

Journal of Epidemiology, 115(4), 492-505.

Blumenthal, D. (2001). Controlling health care expenditures. New England Journal of

Medicine, 344(10), 766-769.

Booth-Butterfield, S., & Reger, B. (2004). The message changes belief and the rest is

theory: The"1% or less"milk campaign and reasoned action. Preventive Medicine,

39(3), 581-588.

165

Bowman, S. A. (2002). Beverage choices of young females: Changes and impact on

nutrient intakes. Journal of the American Dietetic Association, 102(9), 1234-

1239.

Bray, G. A. (2003). Risks of obesity. Endocrinology and Metabolism Clinics of North

America, 32(4), 787-804, viii.

Brewer, J., Blake, A., Rankin, S., & Douglass, L. (1999). Theory of Reasoned Action

predicts milk consumption in women. Journal of the American Dietetic

Association, 99(1), 39-44.

Carruth, B., & Skinner, J. (2001). The role of dietary calcium and other nutrients in

moderating body fat in preschool children. International Journal of Obesity &

Related Metabolic Disorders, 25(4), 559-566.

Castro-Rodriguez, J. A., Holberg, C. J., Morgan, W. J., Wright, A. L., & Martinez, F. D.

(2001). Increased incidence of asthmalike symptoms in girls who become

overweight or obese during the school years. American Journal of Respiratory

and Critical Care Medicine, 163(6), 1344-1349.

Cavadini, C., Siega-Riz, A. M., & Popkin, B. M. (2000). US adolescent food intake

trends from 1965 to 1996. Archives of Disease in Childhood, 83(1), 18-24.

CDC. (2006). BMI -- Body Mass Index: About BMI for children and teens. Centers for

Disease Control and Prevention: Department of Health and Human Services.

Retrieved May 11, 2006, from the World Wide Web:

http://www.cdc.gov/nccdphp/dnpa/bmi/childrens_BMI/about_childrens_BMI.htm

Chan, G. M., Hoffman, K., & McMurry, M. (1995). Effects of dairy products on bone

and body composition in pubertal girls. Journal of Pediatrics, 126(4), 551-556.

166

Chitra, U., & Reddy, C. R. (2007). The role of breakfast in nutrient intake of urban

schoolchildren. Public Health Nutrition, 10(1), 55-58.

Clement, K., & Ferre, P. (2003). Genetics and the pathophysiology of obesity. Pediatric

Research, 53(5), 721-725.

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.).

Hillsdale, NJ: Lawrence Erlbaum Associates.

Conner, M., Norman, P., & Bell, R. (2002). The theory of planned behavior and healthy

eating. Health Psychology, 21(2), 194-201.

Contento, I., Michela, J., & Goldberg, C. (1988). Food choice among adolescents:

Population segmentation by motivations. Journal of Nutrition Education, 20, 289-

298.

Converse, J., & Presser, S. (1986). Survey Questions: Handcrafting the Standardized

Questonnaire. Beverly Hills, CA: Sage.

Cote, F., Godin, G., & Gagne, C. (2004). Identification of factors promoting abstinence

from smoking in a cohort of elementary schoolchildren. Preventive Medicine,

39(4), 695-703.

Craig, S., Goldberg, J., & Dietz, W. (1996). Psychosocial correlates of physical activity

among fifth and eighth graders. Preventive Medicine, 25(5), 506-513.

Cusatis, D., & Shannon, B. (1996). Influences on adolescent eating behavior. Journal of

Adolescent Health, 18(1), 27-34.

Davies, K., Heaney, R., Recker, R., Lappe, J., Barger-Lux, M., Rafferty, K., & Hinders,

S. (2000). Calcium intake and body weight. The Journal of Clinical

Endocrinology and Metabolism, 85(12), 4635-4638.

167

de Bruijn, G. J., Kremers, S. P., de Vries, H., van Mechelen, W., & Brug, J. (2007).

Associations of social-environmental and individual-level factors with adolescent

soft drink consumption: results from the SMILE study. Health Education

Research, 22(2), 227-237.

Delva, J., Johnston, L. D., & O'Malley, P. M. (2007). The epidemiology of overweight

and related lifestyle behaviors: racial/ethnic and socioeconomic status differences

among American youth. American Journal of Preventive Medicine, 33(4 Suppl),

S178-186.

Demory-Luce, D., Morales, M., Nicklas, T., Baranowski, T., Zakeri, I., & Berenson, G.

(2004). Changes in food group consumption patterns from childhood to young

adulthood: The Bogalusa Heart Study. Journal of the American Dietetic

Association, 104(11), 1684-1691.

Dennison, B., Erb, T., & Jenkins, P. (2001). Predictors of dietary milk fat intake by

preschool children. Preventive Medicine, 33(6), 536-542.

Dennison, C., & Shepherd, R. (1995). Adolescent food choice: An application of the

theory of planned behaviour. Journal of Human Nutrition and Dietetics, 8, 9-23.

Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol,

protein, and amino acids. (2002). Panel on Macronutrients. National Academy of

Sciences. Institute of Medicine of the National Academies. National Academy

Press.

Dietz, W. H. (1998). Health consequences of obesity in youth: Childhood predictors of

adult disease. Pediatrics, 101(3 Pt 2), 518-525.

Dillman, D. A. (1978). Mail and telephone surveys. New York: John Wiley & Sons.

168

Dixey, R., Sahota, P., Atwal, S., & Turner, A. (2001). Children talking about healthy

eating: Data from focus groups with 300 9-11 -year-olds. British Nutrition

Foundation, 26, 71-79.

Dubois, L., Girard, M., Potvin Kent, M., Farmer, A., & Tatone-Tokuda, F. (2008).

Breakfast skipping is associated with differences in meal patterns, macronutrient

intakes and overweight among pre-school children. Public Health Nutrition, 1-10.

Dzewaltowski, D., Noble, J., & Shaw, J. (1990). Physical activity participation -- Social

cognitive theory versus the theories of reasoned action and planned behavior.

Journal of Sport and Exercise Psychology, 12, 388-405.

Eagly, A., & Chaiken, S. (1993). The Psychology of Attitudes. San Diego, CA: Harcourt,

Brace and Jovanovich.

Edwards, J., & Hartwell, H. (2002). Fruit and vegetables--attitudes and knowledge of

primary school children. Journal of Human Nutrition and Dietetics, 15(5), 365-

374.

Farris, R., & Nicklas, T. (1993). Characterizing children's eating beahvior. In R. Suskind

& L. Lewinter-Suskind (Eds.), Textbook of Pediatric Nutrition (2nd ed., pp. 505-

516). New York: Raven press.

Feinfield, K., Lee, P., Flavell, E., Green, F., & Flavell, J. (1999). Young children's

understanding of intention. Cognitive Development, 14(3), 463-486.

Fernandes, M. M. (2008). The effect of soft drink availability in elementary schools on

consumption. Journal of the American Dietetic Association, 108(9), 1445-1452.

169

Ferraro, K. F., Thorpe, R. J., Jr., & Wilkinson, J. A. (2003). The life course of severe

obesity: Does childhood overweight matter? The Journals of Gerontology. Series

B, Psychological Sciences and Social Sciences, 58(2), S110-119.

Finkelstein, E. A., Fiebelkorn, I. C., & Wang, G. (2003). National medical spending

attributable to overweight and obesity: how much, and who's paying? Health

Affairs (Millwood), Suppl Web Exclusives, W3-219-226.

Fiorito, L. M., Mitchell, D. C., Smiciklas-Wright, H., & Birch, L. L. (2006). Dairy and

dairy-related nutrient intake during middle childhood. Journal of the American

Dietetic Association, 106(4), 534-542.

Fishbein, M. (Ed.). (1967). Readings in attitude theory and measurement. New York:

Wiley.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An

introduction to theory and research. Reading, MA: Addison-Wesley.

Fisher, J., Mitchell, D., Smiciklas-Wright, H., & Birch, L. (2000). Maternal milk

consumption predicts the tradeoff between milk and soft drinks in young girls'

diets. The Journal of Nutrition, 131(2), 246-250.

Flavell, J. (1977). Cognitive Development. Englewood Cliffs, N.J.: Prentice-Hall.

Flavell, J. (1999). Cognitive development: Children's knowledge about the mind. Annual

Review of Psychology, 50, 21-45.

Flegal, K. M., Carroll, M. D., Kuczmarski, R. J., & Johnson, C. L. (1998). Overweight

and obesity in the United States: Prevalence and trends, 1960-1994. International

Journal of Obesity and Related Metabolic Disorders, 22(1), 39-47.

170

Flegal, K. M., Wei, R., & Ogden, C. (2002). Weight-for-stature compared with body

mass index-for-age growth charts for the United States from the Centers for

Disease Control and Prevention. American Journal of Clinical Nutrition, 75(4),

761-766.

Flynn, A. (2003). The role of dietary calcium in bone health. The Proceedings of the

Nutrition Society, 62(4), 851-858.

Franko, D. L., Thompson, D., Bauserman, R., Affenito, S. G., & Striegel-Moore, R. H.

(2008). What's love got to do with it? Family cohesion and healthy eating

behaviors in adolescent girls. The International journal of eating disorders, 41(4),

360-367.

Freedman, D., Khan, L., Dietz, W., Srinivasan, S., & Berenson, G. (2001). Relationship

of childhood obesity to coronary heart disease risk factors in adulthood: the

Bogalusa Heart Study. Pediatrics, 108(3), 712.

Freedman, D. S., Dietz, W. H., Srinivasan, S. R., & Berenson, G. S. (1999). The relation

of overweight to cardiovascular risk factors among children and adolescents: the

Bogalusa Heart Study. Pediatrics, 103(6 Pt 1), 1175-1182.

Friedman, L. A., Snetselaar, L., Stumbo, P., Van Horn, L., Singh, B., & Barton, B. A.

(2007). Influence of intervention on beverage choices: trends in the dietary

intervention study in children (DISC). Journal of the American Dietetic

Association, 107(4), 586-594.

French, S., Lin, B., & Guthrie, J. (2003). National trends in soft drink consumption

among children and adolescents age 6 to 17 years: Prevalence, amounts, and

171

sources, 1977/1978 to 1994/1998. Journal of the American Dietetic Association,

103(10), 1326-1331.

French, S. A., Story, M., Neumark-Sztainer, D., Fulkerson, J. A., & Hannan, P. (2001).

Fast food restaurant use among adolescents: Associations with nutrient intake,

food choices and behavioral and psychosocial variables. International Journal of

Obesity and Related Metabolic Disorders, 25(12), 1823-1833.

Fulgoni, V., 3rd, Nicholls, J., Reed, A., Buckley, R., Kafer, K., Huth, P., et al. (2007).

Dairy consumption and related nutrient intake in African-American adults and

children in the United States: continuing survey of food intakes by individuals

1994-1996, 1998, and the National Health And Nutrition Examination Survey

1999-2000. Journal of the American Dietetic Association, 107(2), 256-264.

Gebhardt, W., Kuyper, L., & Greunsven, G. (2003). Need for intimacy in relationships

and motives for sex as determinants of adolescent condom use. Journal of

Adolescent Health, 33(3), 154-164.

Gillman, M. W., Rifas-Shiman, S. L., Frazier, A. L., Rockett, H. R., Camargo, C. A., Jr.,

Field, A. E., Berkey, C. S., & Colditz, G. A. (2000). Family dinner and diet

quality among older children and adolescents. Archives of Family Medicine, 9(3),

235-240.

Glanz, K., Rimer, B., & Lewis, F. (Eds.). (2002). Health Behavior and Health Education:

Theory, Research, and Practice (3rd ed.). San Francisco: Jossey-Bass.

Godin, G., & Kok, G. (1996). The theory of planned behavior: A review of its

applications to health-related behaviors. American Journal of Health Promotion,

11(2), 87-98.

172

Godin, G., Valois, P., Lepage, L., & Desharnais, R. (1992). Predictors of smoking

behaviour: An application of Ajzen's theory of planned behaviour. Bja (British

Journal of Addiction), 87(9), 1335-1343.

Gordon, A. R., & McKinney, P. (1995). Sources of nutrients in students' diets. American

Journal of Clinical Nutrition, 61(1 Suppl), 232S-240S.

Grant, W. B. (2002). An ecologic study of dietary and solar ultraviolet-B links to breast

carcinoma mortality rates. Cancer, 94(1), 272-281.

Green, L. (1979). Should health education abandon attitude change strategies?

Pespectives from recent research. Health Education Monographs, 1(30), 25-47.

Grimm, G., Harnack, L., & Story, M. (2004). Factors associated with soft drink

consumption in school-aged children. Journal of the American Dietetic

Association, 104(8), 1244-1249.

Guidelines for school health programs to promote lifelong healthy eating. (1997). Journal

of School Health, 67(1), 9-26.

Gummeson, L., Jonsson, I., & Conner, M. (1997). Predicting intentions and behaviour of

Swedish 10-16-year-olds at breakfast. Food Quality and Preference, 8(4), 297-

306.

Hagger, M., Chatzisarantis, N., Biddle, S., & Orbell, S. (2001). Antecedents of children's

physical activity intentions and behaviour: Predictive validity and longitudinal

effects. Psychology and Health, 16, 391-407.

Harnack, L., Stang, J., & Story, M. (1999). Soft drink consumption among US children

and adolescents: Nutritional consequences. Journal of the American Dietetic

Association, 99(4), 436-441.

173

Heaney, R., Davies, K., & Barger-Lux, M. (2002). Calcium and weight: Clinical studies.

Journal of the American College of Nutrition, 21(2), 152S-155S.

Hedley, A. A., Ogden, C. L., Johnson, C. L., Carroll, M. D., Curtin, L. R., & Flegal, K.

M. (2004). Prevalence of overweight and obesity among US children, adolescents,

and adults, 1999-2002. Journal of the American Medical Association, 291(23),

2847-2850.

Heller, K., Burt, B., & Eklund, S. (2001). Sugared soda consumption and dental caries in

the United States. Journal of Dental Research, 80(10), 1949-1953.

Hesketh, K., Wake, M., & Waters, E. (2004). Body mass index and parent-reported self-

esteem in elementary school children: Evidence for a causal relationship.

International Journal of Obesity and Related Metabolic Disorders, 28(10), 1233-

1237.

Hinshaw, A. S. (1989). Nursing science: The challenge to develop knowledge. Nursing

Science Quarterly, 2(4), 162-171.

Huth, P. J., DiRienzo, D. B., & Miller, G. D. (2006). Major scientific advances with dairy

foods in nutrition and health. Journal of Dairy Science, 89(4), 1207-1221.

Hymovich, D. (1997). Measurement issues with children and adolescents. In M. Frank-

Stromborg & S. Olsen (Eds.), Instruments for Clinical Health-Care Research

(2nd ed., pp. 31-43). Sudbury, Massachusetts: Jones and Bartlett Publishers.

Hypponen, E., Virtanen, S. M., Kenward, M. G., Knip, M., & Akerblom, H. K. (2000).

Obesity, increased linear growth, and risk of type 1 diabetes in children. Diabetes

Care, 23(12), 1755-1760.

174

Inhelder, B., & Piaget, J. (1958). The Growth of Logical Thinking from Childhood to

Adolescence: An Essay on the Construction of Formal Operational Structures.

New York: Basic Books.

Jahns, L., Siega-Riz, A. M., & Popkin, B. M. (2001). The increasing prevalence of

snacking among US children from 1977 to 1996. The Journal of Pediatrics,

138(4), 493-498.

Jarvis, J. K., & Miller, G. D. (2002). Overcoming the barrier of lactose intolerance to

reduce health disparities. Journal of the National Medical Association, 94(2), 55-

66.

Johnson, R. K., Frary, C., & Wang, M. Q. (2002). The nutritional consequences of

flavored-milk consumption by school-aged children and adolescents in the United

States. Journal of the American Dietetic Association, 102(6), 853-856.

Johnston, C. C., Jr., Miller, J. Z., Slemenda, C. W., Reister, T. K., Hui, S., Christian, J.

C., & Peacock, M. (1992). Calcium supplementation and increases in bone

mineral density in children. The New England Journal of Medicine, 327(2), 82-

87.

Joseph, K. S., & Kramer, M. S. (1996). Review of the evidence on fetal and early

childhood antecedents of adult chronic disease. Epidemiologic Reviews, 18(2),

158-174.

Kalnins, I., & Love, R. (1982). Children's concepts of health and illness-- and

implications for health education: An overview. Health Education Quarterly, 9(2-

3), 104-115.

175

Kapicioglu, S., Baki, A., Tekelioglu, Y., Arslan, M., Sari, M., & Ovali, E. (1998). The

inhibiting effect of cola on gastric mucosal cell cycle proliferation in humans.

Scandinavian Journal of Gastroenterology, 33(7), 701-703.

Kassem, N., & Lee, J. (2004). Understanding soft drink consumption among male

adolescents using the theory of planned behavior. Journal of Behavioral

Medicine, 27(3), 273-296.

Kassem, N., Lee, J., Modeste, N., & Johnston, P. (2003). Understanding soft drink

consumption among female adolescents using the Theory of Planned Behavior.

Health Education Research, 18(3), 278-291.

Keast, R. S., & Riddell, L. J. (2007). Caffeine as a flavor additive in soft-drinks. Appetite,

49(1), 255-259.

Kelder, S., Perry, C., Klepp, K., & Lytle, L. (1994). Longitudinal tracking of adolescent

smoking, physical activity, and food choice behaviors. American Journal of

Public Health, 84(7), 1121-1126.

Khoury, A. J., Moazzem, S. W., Jarjoura, C. M., Carothers, C., & Hinton, A. (2005).

Breast-feeding initiation in low-income women: Role of attitudes, support, and

perceived control. Womens Health Issues, 15(2), 64-72.

Kiess, W., Galler, A., Reich, A., Muller, G., Kapellen, T., Deutscher, J., Raile, K., &

Kratzsch, J. (2001). Clinical aspects of obesity in childhood and adolescence.

Obesity Reviews, 2(1), 29-36.

Kleinman, R., Hall, S., Green, H., Korzec-Ramirez, D., Patton, K., Pagano, M., &

Murphy, J. (2002). Diet, breakfast, and academic performance in children. Annals

of Nutrition & Metabolism, 46 Suppl 1, 24-30.

176

Koivisto Hursti, U. (1999). Factors influencing children's food choice. Annals of

Medicine, 31 (Suppl 1), 26-32.

Kolotkin, R. L., Meter, K., & Williams, G. R. (2001). Quality of life and obesity. Obesity

Reviews, 2(4), 219-229.

Kranz, S., Lin, P. J., & Wagstaff, D. A. (2007). Children's dairy intake in the United

States: too little, too fat? The Journal of Pediatrics, 151(6), 642-646.

Kuchler, F., & Lin, B. H. (2002). The influence of individual choices and attitudes on

adiposity. International Journal of Obesity and Related Metabolic Disorders,

26(7), 1017-1022.

LaRowe, T. L., Moeller, S. M., & Adams, A. K. (2007). Beverage patterns, diet quality,

and body mass index of US preschool and school-aged children. Journal of the

American Dietetic Association, 107(7), 1124-1133.

Le Bigot Macaux, A. (2001). Eat to live or live to eat? Do parents and children agree?

Public Health Nutrition, 4(1A), 141-146.

Lee, J. S., & Frongillo, E. A., Jr. (2001). Understanding needs is important for assessing

the impact of food assistance program participation on nutritional and health

status in U.S. elderly persons. The Journal of Nutrition, 131(3), 765-773.

Lee, K. A., & Kieckhefer, G. M. (1989). Measuring human responses using Visual

Analogue Scales. Western Journal of Nursing Research, 11(1), 128-132.

Lee, S., & Reicks, M. (2003). Environmental and behavioral factors are associated with

the calcium intake of low-income adolescent girls. Journal of the American

Dietetic Association, 103(11), 1526-1529.

177

Lien, N., Lytle, L. A., & Komro, K. A. (2002). Applying theory of planned behavior to

fruit and vegetable consumption of young adolescents. American Journal of

Health Promotion, 16(4), 189-197.

Lin, B., Guthrie, J., & Frazao, E. (1999). Quality of Children's diets at and away from

home: 1994-96. Food Review, 22, 2-10.

Ludwig, D., Peterson, K., & Gortmaker, S. (2001). Relation between consumption of

sugar-sweetened drinks and childhood obesity: A prospective, observational

analysis. The Lancet, 357(9255), 505-508.

Lytle, L., Seifert, S., Greenstein, J., & McGovern, P. (2000). How do children's eating

patterns and food choices change over time? Results from a cohort study.

American Journal of Health Promotion, 14(4), 222-228.

Maffeis, C. (1999). Childhood obesity: The genetic-environmental interface. Baillière's

Best Practice & Research. Clinical Endocrinology & Metabolism, 13(1), 31-46.

Magarey, A. M., Daniels, L. A., Boulton, T. J., & Cockington, R. A. (2003). Predicting

obesity in early adulthood from childhood and parental obesity. International

Journal of Obesity and Related Metabolic Disorders, 27(4), 505-513.

Malik, V. S., Schulze, M. B., & Hu, F. B. (2006). Intake of sugar-sweetened beverages

and weight gain: a systematic review. The American journal of clinical nutrition,

84(2), 274-288.

Mariri, B. P., Levy, S. M., Warren, J. J., Bergus, G. R., Marshall, T. A., & Broffitt, B.

(2003). Medically administered antibiotics, dietary habits, fluoride intake and

dental caries experience in the primary dentition. Community Dentistry and Oral

Epidemiology, 31(1), 40-51.

178

May, J., & Waterhouse, P. J. (2003). Dental erosion and soft drinks: A qualitative

assessment of knowledge, attitude and behaviour using focus groups of

schoolchildren. A preliminary study. International Journal of Paediatric

Dentistry, 13(6), 425-433.

McCloy, R., Greenberg, G., & Baron, J. (1984). Duodenal pH in health and duodenal

ulcer disease: Effect of a meal, Coca-Cola, smoking, and cimetidine. Gut, 25(4),

386-392.

Mertler, C. A., & Vannatta, R. A. (2002). Advanced and Multivariate Statistical Methods

(2nd ed.). Los Angeles, CA: Pyrczak Publishing.

Michela, J., & Contento, I. (1984). Spontaneous classification of foods by elementary

school-aged children. Health Education Quarterly, 11(1), 57-76.

Michela, J., & Contento, I. (1986). Cognitive, motivational, social, and environmental

influences on children's food choices. Health Psychology, 5(3), 209-230.

Mickalide, A. (1986). Children's understanding of health and illness: Implications for

health promotion. Health Values, 10, 5-21.

Montaño, D., & Kasprzyk, D. (2002). The theory of reasoned action and the theory of

planned behavior. In K. Glanz & B. Rimer & F. Lewis (Eds.), Health behavior

and health education: Theory, research, and practice. San Francisco: Jossey-

Bass.

More, J. (2008). Children's bone health and meeting calcium needs. The Journal of

Family Health Care, 18(1), 22-24.

Moses, L. (1993). Young children's understanding of belief constraints on intention.

Cognitive Development, 8, 1-25.

179

Mummery, W., Spence, J., & Hudec, J. (2000). Understanding physical activity intention

in Canadian school children and youth: An application of the theory of planned

behavior. Research Quarterly for Exercise & Sport, 71(2), 116-124.

Murray, R. D., Taras, H. L., Frankowski, B. L., McGrath, J. W., Mears, C. J., & Young,

T. L. (2004). American Academy of Pediatrics. Committee on School Health. Soft

drinks in schools. Pediatrics, 113(1), 152-154.

Must, A., Spadano, J., Coakley, E., Field, A., Colditz, G., & Dietz, W. (1999). The

disease burden associated with overweight and obesity. Journal of the American

Medical Association, 282(16), 1523-1529.

Nestle, M. (2000). Soft drink "pouring rights": Marketing empty calories to children.

Public Health Reports, 115(4), 308-319.

Neumark-Sztainer, D., Hannan, P., Story, M., Croll, J., & Perry, C. (2003). Family meal

patterns: associations with sociodemographic characteristics and improved dietary

intake among adolescents. Journal of the American Dietetic Association, 103(3),

317-322.

Neumark-Sztainer, D., Story, M., Perry, C., & Casey, M. (1999). Factors influencing

food choices of adolescents: Findings from focus-group discussions with

adolescents. Journal of the American Dietetic Association, 99(8), 929-937.

Nicklas, T., Baranowski, T., Cullen, K., & Berenson, G. (2001). Eating patterns, dietary

quality and obesity. Journal of the American College of Nutrition, 20(6), 599-608.

Nicklas, T. A., Demory-Luce, D., Yang, S. J., Baranowski, T., Zakeri, I., & Berenson, G.

(2004). Children's food consumption patterns have changed over two decades

180

(1973-1994): The Bogalusa heart study. Journal of the American Dietetic

Association, 104(7), 1127-1140.

Nicklas, T. A., & Hayes, D. (2008). Position of the American Dietetic Association:

nutrition guidance for healthy children ages 2 to 11 years. Journal of the

American Dietetic Association, 108(6), 1038-1044, 1046-1037.

Nicklas, T., & Johnson, R. (2004). Position of the American Dietetic Association: Dietary

guidance for healthy children ages 2 to 11 years. Journal of the American Dietetic

Association, 104(4), 660-677.

Nicklas, T. A. (2003). Calcium intake trends and health consequences from childhood

through adulthood. Journal of the American College of Nutrition, 22(5), 340-356.

Nicklas, T. A., Morales, M., Linares, A., Yang, S. J., Baranowski, T., De Moor, C., et al.

(2004). Children's meal patterns have changed over a 21-year period: the

Bogalusa Heart Study. Journal of the American Dietetic Association, 104(5), 753-

761.

Nicklas, T. A., O'Neil, C. E., & Berenson, G. S. (1998). Nutrient contribution of

breakfast, secular trends, and the role of ready-to-eat cereals: A review of data

from the Bogalusa Heart Study. American Journal of Clinical Nutrition, 67(4),

757S-763S.

Nielsen, S., & Popkin, B. (2004). Changes in beverage intake between 1977 and 2001.

American Journal of Preventive Medicine, 27(3), 205-210.

Nielsen, S. J., & Popkin, B. M. (2003). Patterns and trends in food portion sizes, 1977-

1998. Journal of the American Medical Association, 289(4), 450-453.

181

Nielsen, S. J., Siega-Riz, A. M., & Popkin, B. M. (2002). Trends in energy intake in U.S.

between 1977 and 1996: Similar shifts seen across age groups. Obesity Research,

10(5), 370-378.

Novotny, R., Daida, Y. G., Acharya, S., Grove, J. S., & Vogt, T. M. (2004). Dairy intake

is associated with lower body fat and soda intake with greater weight in

adolescent girls. The Journal of nutrition, 134(8), 1905-1909.

Novotny, R., Boushey, C., Bock, M. A., Peck, L., Auld, G., Bruhn, C. M., Gustafson, D.,

Gabel, K., Jensen, J. K., Misner, S., & Read, M. (2003). Calcium intake of Asian,

Hispanic and white youth. Journal of the American College of Nutrition, 22(1),

64-70.

Ogden, C. L., Carroll, M. D., & Flegal, K. M. (2008). High body mass index for age

among US children and adolescents, 2003-2006. Journal of the American Medical

Association, 299(20), 2401-2405.

Ogden, C. L., Yanovski, S. Z., Carroll, M. D., & Flegal, K. M. (2007). The epidemiology

of obesity. Gastroenterology, 132(6), 2087-2102.

Ogden, C. L., Carroll, M. D., Curtin, L. R., McDowell, M. A., Tabak, C. J., & Flegal, K.

M. (2006). Prevalence of overweight and obesity in the United States, 1999-2004.

Journal of the American Medical Association, 295(13), 1549-1555.

Ogden, C. L., Flegal, K. M., Carroll, M. D., & Johnson, C. L. (2002). Prevalence and

trends in overweight among US children and adolescents, 1999-2000. Journal of

the American Medical Association, 288(14), 1728-1732.

Oshiro, C. E., Novotny, R., & Titchenal, C. A. (2003). Calcium intake of Asian and

Caucasian adolescents in Hawaii. Hawaii Medical Journal, 62(12), 272-276.

182

Paeratakul, S., Ferdinand, D. P., Champagne, C. M., Ryan, D. H., & Bray, G. A. (2003).

Fast-food consumption among US adults and children: Dietary and nutrient intake

profile. Journal of the American Dietetic Association, 103(10), 1332-1338.

Parcel, G. (1984). Theoretical models for application in school health education research.

The Journal of School Health, 54(6), 39-49.

Pearson, N., Biddle, S. J., & Gorely, T. (2008). Family correlates of breakfast

consumption among children and adolescents: a systematic review. Appetite, In

press

Pereira, M., Jacobs, D., Jr, Van Horn, L., Slattery, M., Kartashov, A., & Ludwig, D.

(2002). Dairy consumption, obesity, and the insulin resistance syndrome in young

adults: The CARDIA Study. Journal of the American Medical Association,

287(16), 2081-2089.

Perry, C. L., Griffin, G., & Murray, D. M. (1985). Assessing needs for youth health

promotion. Preventive Medicine, 14, 379-393.

Phillips, J. J. (1975). The Origins of Intellect. Piaget's Theory. USA: W. H. Freeman &

Company.

Piaget, J. (1932). The Moral Judgment of the Child. New York: Harcourt Brace.

Pietrobelli, A., Faith, M. S., Allison, D. B., Gallagher, D., Chiumello, G., & Heymsfield,

S. B. (1998). Body mass index as a measure of adiposity among children and

adolescents: A validation study. Journal of Pediatrics, 132(2), 204-210.

Pudel, V. (1986). Psychologie der Ernährung. Monatsschr Kinderheilkd, 134, 393-396.

Raben, A., Vasilaras, T., Moller, A., & Astrup, A. (2002). Sucrose compared with

artificial sweeteners: Different effects on ad libitum food intake and body weight

183

after 10 wk of supplementation in overweight subjects. American Journal of

Clinical Nutrition, 76(4), 721-729.

Rajeshwari, R., Yang, S. J., Nicklas, T. A., & Berenson, G. S. (2005). Secular trends in

children's sweetened-beverage consumption (1973 to 1994): The Bogalusa Heart

Study. Journal of the American Dietetic Association, 105(2), 208-214.

Rampersaud, G., Bailey, L., & Kauwell, G. (2003). National survey beverage

consumption data for children and adolescents indicate the need to encourage a

shift toward more nutritive beverages. Journal of the American Dietetic

Association, 103(1), 97-100.

Reilly, J. J., Methven, E., McDowell, Z. C., Hacking, B., Alexander, D., Stewart, L., &

Kelnar, C. J. (2003). Health consequences of obesity. Archives of Disease in

Childhood, 88(9), 748-752.

Resnick, L., Oparil, S., Chait, A., Haynes, R., Kris-Etherton, P., Stern, J., Clark, S.,

Holcomb, S., Hatton, D., Metz, J., McMahon, M., Pi-Sunyer, F., & McCarron, D.

(2000). Factors affecting blood pressure responses to diet: The Vanguard study.

American Journal of Hypertension, 13(9), 956-965.

Riddiford-Harland, D. L., Steele, J. R., & Storlien, L. H. (2000). Does obesity influence

foot structure in prepubescent children? International Journal of Obesity and

Related Metabolic Disorders, 24(5), 541-544.

Roblin, L. (2007). Childhood obesity: Food, nutrient, and eating-habit trends and

influences. Applied Physiology Nutrition and Metabolism, 32(4), 635-645.

184

Rosner, B., Prineas, R., Loggie, J., & Daniels, S. R. (1998). Percentiles for body mass

index in U.S. children 5 to 17 years of age. Journal of Pediatrics, 132(2), 211-

222.

Rozin, P. (1989). The role of learning in the acquisition o food preferences by humans. In

R. Shepherd (Ed.), Handbook of Psychophysiology of Human Eating. Chichester:

Wiley.

Rubinstein, E., Hauge, C., Sommer, P., & Mortensen, T. (1993). Oesophageal and gastric

potential difference and pH in healthy volunteers following intake of coca-cola,

red wine, and alcohol. Pharmacology & Toxicology, 72(1), 61-65.

Saunders, R., Pate, R., Felton, G., Dowda, M., Weinrich, M., Ward, D., Parsons, M., &

Baranowski, T. (1997). Development of questionnaires to measure psychosocial

influences on children's physical activity. Preventive Medicine, 26(2), 241-247.

Schlegel, R., D'Avernas, J., Zalma, M., & DeCourville, N. (1992). Problem drinking: A

problem for the theory of reasoned action? Journal of Applied Social Psychology,

22, 358-385.

Schult, C., & Wellman, H. (1997). Explaining human movements and actions: Children's

understanding of the limits of psychological explanation. Cognition, 62(3), 291-

324.

Seaman, C., Woods, M., & Grosset, E. (1997). Attitudes to healthy eating among Scottish

school children. Health Education, 1, 19-22.

Shepherd, R. (1999). Social determinants of food choice. Proceedings of the Nutrition

Society, 58(4), 807-812.

185

Siega-Riz, A. M., Popkin, B. M., & Carson, T. (1998). Trends in breakfast consumption

for children in the United States from 1965-1991. American Journal of Clinical

Nutrition, 67(4), 748S-756S.

Sigman-Grant, M., & Morita, J. (2003). Defining and interpreting intakes of sugars. The

American Journal of Clinical Nutrition, 78(4), 815S-826S.

Skinner, J. D., Bounds, W., Carruth, B. R., & Ziegler, P. (2003). Longitudinal calcium

intake is negatively related to children's body fat indexes. Journal of the American

Dietetic Association, 103(12), 1626-1631.

Slyper, A. H. (2004). The pediatric obesity epidemic: Causes and controversies. Journal

of Clinical Endocrinology and Metabolism, 89(6), 2540-2547.

Smiciklas-Wright, H., Mitchell, D. C., Mickle, S. J., Goldman, J. D., & Cook, A. (2003).

Foods commonly eaten in the United States, 1989-1991 and 1994-1996: Are

portion sizes changing? Journal of the American Dietetic Association, 103(1), 41-

47.

Spiegelman, B. M., & Flier, J. S. (2001). Obesity and the regulation of energy balance.

Cell, 104(4), 531-543.

St-Onge, M. P., Keller, K. L., & Heymsfield, S. B. (2003). Changes in childhood food

consumption patterns: A cause for concern in light of increasing body weights.

American Journal of Clinical Nutrition, 78(6), 1068-1073.

Storey, M. L., Forshee, R. A., & Anderson, P. A. (2004). Associations of adequate intake

of calcium with diet, beverage consumption, and demographic characteristics

among children and adolescents. Journal of the American College of Nutrition,

23(1), 18-33.

186

Suizzo, M. (2000). The social-emotional and cultural contexts of cognitive development:

Neo-Piagetian perspectives. Child Development, 71(4), 846-849.

Sulit, L., Storfer-Isser, A., Rosen, C., Kirchner, H., & Redline, S. (2005). Associations of

Obesity, Sleep-disordered Breathing, and Wheezing in Children. American

Journal of Respiratory and Critical Care Medicine, 171(6), 659-664.

Swanson, V., & Power, K. G. (2005). Initiation and continuation of breastfeeding:

Theory of planned behaviour. Journal of Advanced Nursing, 50(3), 272-282.

Tabachnick, B. G., & Fidell, L. S. (2001). Using Multivariate Statistics (4th ed.). Boston:

Allyn and Bacon.

Taras, H., & Potts-Datema, W. (2005). Obesity and student performance at school. The

Journal of School Health, 75(8), 291-295.

Terry, D., & O'Leary, J. (1995). The theory of planned behavior: The effects of perceived

behavioural control and self-efficacy. British Journal of Social Psychology, 34,

199-220.

Thomas, W., & Znaniecki, F. (1918). The Polish Peasant in Europe and America (Vol.

1). Boston: Badger.

Thomson, C. A., Giuliano, A., Rock, C. L., Ritenbaugh, C. K., Flatt, S. W., Faerber, S.,

Newman, V., Caan, B., Graver, E., Hartz, V., Whitacre, R., Parker, F., Pierce, J.

P., & Marshall, J. R. (2003). Measuring dietary change in a diet intervention trial:

Comparing food frequency questionnaire and dietary recalls. American Journal of

Epidemiology, 157(8), 754-762.

Trost, S., Saunders, R., & Ward, D. (2002). Determinants of physical activity in middle

school children. American Journal of Health Behavior, 26(2), 95-102.

187

USDA. (1992). The food guide pyramid. A guide to daily food choice. Washington DC:

US Department of Agriculture, Human Nutrition Information Service.

USDA. (2005). Inside the pyramid. How much food from the milk group is needed daily?

Retrieved April 8, 2006, from the World Wide Web: http://www.mypyramid.gov

USDHHS. (2000). Healthy people 2010: United States Department of Health and Human

Services. Washington, DC: U.S. Department of Health and Human Services.

Vereecken, C. A., Keukelier, E., & Maes, L. (2004). Influence of mother's educational

level on food parenting practices and food habits of young children. Appetite,

43(1), 93-103.

Visser, M., Bouter, L. M., McQuillan, G. M., Wener, M. H., & Harris, T. B. (2001).

Low-grade systemic inflammation in overweight children. Pediatrics, 107(1),

E13.

Wabitsch, M. (2000). Overweight and obesity in European children: Definition and

diagnostic procedures, risk factors and consequences for later health outcome.

European Journal of Pediatrics, 159 Suppl 1, S8-13.

Wang, G., & Dietz, W. H. (2002). Economic burden of obesity in youths aged 6 to 17

years: 1979-1999. Pediatrics, 109(5), E81.

Ware, J. (1989). Methodological considerations in the selection of health status

assessment procedures. In N. Wenger & M. Mattson & C. Furvery & J. Elinson

(Eds.), Assessment of Quality of Life in Clinical Trials of Cardiovascular

Therapies (pp. 87-117). New York: Le Jacq.

Wells, D. (1994). Children in focus. Nutrition & Food Science, 94, 29-31.

188

Wells, J. C. (2001). A critique of the expression of paediatric body composition data.

Archives of Diseases in Childhood, 85(1), 67-72.

White, K., Terry, D., & Hogg, M. (1994). Safer sex behavior: The role of attitudes,

norms, and control factors. Journal of Applied Social Psychology, 24, 2164-2192.

Whiting, S. J., Vatanparast, H., Baxter-Jones, A., Faulkner, R. A., Mirwald, R., & Bailey,

D. A. (2004). Factors that affect bone mineral accrual in the adolescent growth

spurt. Journal of Nutrition, 134(3), 696S-700S.

Wicker, A. W. (1969). Attitudes versus actions: The relationship of verbal and overt

behavioral responses to attitude objects. Journal of Social Issues, 25, 41-47.

Williams, J., Wake, M., Hesketh, K., Maher, E., & Waters, E. (2005). Health-related

quality of life of overweight and obese children. Journal of the American Medical

Association, 293(1), 70-76.

Wyshak, G. (2000). Teenaged girls, carbonated beverage consumption, and bone

fractures. Archives of Pediatrics & Adolescent Medicine, 154(6), 610-613.

Zemel, M., Shi, H., Greer, B., Dirienzo, D., & Zemel, P. (2000). Regulation of adiposity

by dietary calcium. The FASEB journal: Official publication of the Federation of

American Societies for Experimental Biology., 14(9), 1132-1138.