University of Southern California Dissertations and Theses

Motivation and the meanings of health behavior as factors associated with eating behavior in Latino youth

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Motivation and the meanings of health behavior as factors associated with eating behavior in Latino youth

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MOTIVATION AND THE MEANINGS OF HEALTH BEHAVIOR AS FACTORS
ASSOCIATED WITH EATING BEHAVIOR IN LATINO YOUTH

by
Arianna D. McClain
_____________________________________________

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirement for the Degree
DOCTOR OF PHILOSOPHY
(PREVENTIVE MEDICINE: HEALTH BEHAVIOR)

August 2010

Copyright 2010 Arianna D. McClain
ii

Acknowledgements

I would like to thank the members of my Dissertation Committee, Drs. Donna Spruijt-
Metz, Mary Ann Pentz, Jennifer Unger, Chih-Ping Chou, and Florence Clark, for their assistance
in developing this manuscript. My deepest appreciation and thanks goes to Dr. Spruijt-Metz (my
supervisor) and Dr. Unger for their guidance and support throughout my doctoral training. To
my mom, dad, family, and friends I thank you for your encouragement and advice through it all.
I am beyond thankful.

iii

Table of Contents

Acknowledgements ii
Table of Contents iii
List of Tables iv
List of Figures v
Abstract vi
Chapter 1: Introduction 1
Significance 1
Background 4
Theory and Psychosocial Determinants of Eating Behavior in Children and Adolescents 4
Affective vs. Cognitive Processes 6
Neurobiological Data Supporting the Notion that Cognitive-Based Interventions May Be
Less Effective in Children and Adolescents 6
Motivation as a Determinant of Eating Behavior 7
Meanings of Health Behavior 12
Specific Aims 14
Chapter 2: Measuring the Meanings of Eating in Latino Youth 19
Chapter 3: Intrinsic Motivation Predicts Fiber and Fat Intake in Latina Youth 41
Chapter 4: Unique Contributions of the Meanings of Eating and Motivation to Fruit Intake in
Minority Youth 66
Chapter 5: Conclusion 82
Bibliography 97

iv

List of Tables

Table 1: Taxonomy of human motivation 11
Table 2: 58-item MEI Scale 26
Table 3: MEI factor structure 32
Table 4: Correlation matrix between MEI factors and dietary intake 34
Table 5: Baseline characteristics of the sample (total n=37) 50
Table 6: Summary results of model development for association between changes in
intrinsic motivation and changes in percent of total sugar intake 58
Table 7: Summary results of model development for association between changes in
intrinsic motivation and changes in percent of total fat intake 59
Table 8: Summary results of model development for association between changes in
intrinsic motivation and changes in soluble fiber intake 60
Table 9: Demographic, psychosocial and dietary characteristics of participants 75
Table 10: Correlation matrix of psychosocial and dietary variables (n = 251) 77
Table 11: Simple correlation coefficients (r) and standard regression coefficients by
hierarchical stepwise multiple regression (β) between psychosocial variables and fruit
intake (n = 251) 78

v

List of Figures

Figure 1: Theoretical model 14
Figure 2: Relationship between intrinsic motivation and whole grain intake 51
Figure 3: Relationship between intrinsic motivation and fiber per 1000 calories intake 52
Figure 4: Relationship between change in intrinsic motivation and percent of calories from
total sugar intake 54
Figure 5: Relationship between change in intrinsic motivation and total fat intake 55
Figure 6: Relationship between change in intrinsic motivation and soluble fiber per 1000
calories intake 56

vi

Abstract

Purpose:
This dissertation examined the roles of 1) motivation for healthy dietary habits, and 2)
the meanings of eating behavior on dietary intake in Latino youth. Multiple objectives were
addressed in the three studies that comprised this dissertation. Study 1 identified the affective
meanings of dietary intake among minority children, developed factor items for the Meanings of
Eating Index (MEI), validated the MEI, and explored whether the meanings identified were
related to healthy (or unhealthy) dietary behavior in Latino youth. Study 2 examined the effects
of intrinsic and extrinsic motivation for eating fruits and vegetables on dietary intake at baseline
and on changes in dietary intake between baseline and follow-up in a randomized controlled
trial in overweight Latina adolescents. Study 3 investigated whether motivation and the
meanings of eating uniquely contributed to fruit and vegetable consumption in a predominantly
Latino sample of elementary school children.
Methods:
The participants were mostly Latino youth (ages 8-18), participating in one of five
studies. Study 1 used focus group data from two studies conducted at USC as part of formative
research for multiple diet and physical activity interventions. The second part of Study 1 used
data from a school-based obesity prevention pilot study entitled Pathways.
vii

Study 2 used data from the Strength and Nutrition Outcomes for Latino Adolescents
(SANO LA) study, which was a 16-week, randomized controlled trial designed to examine the
effects of the following four intervention groups on dietary behavior, adiposity, and insulin
glucose regulation in overweight Latino adolescents girls aged 14-18 years: 1) control group
(delayed intervention), 2) modified carbohydrate nutrition, 3) modified carbohydrate nutrition
and strength training, and 4) modified carbohydrate nutrition and circuit and aerobic strength
training.
Study 3 used data from a school-based study that aimed to evaluate whether a set of
psychosocial scales, which were originally developed in non-minority middle school populations
and adapted by our group for minority children ages 8-11, would be easily comprehensible for
the younger minority sample and yield reliable data. Meanings were assessed with the
Meanings of Eating Index constructed and developed in Study 1. Participants rated the
frequency with which they acted on a specific meaning of eating behavior. A 3-point scale
ranging from 1 (never) to 3 (often) was used. Higher scores indicated higher levels of
performing that specific eating behavior. Motivation was assessed with the Motivation to Eat
Fruits and Vegetables scale, which is an adapted version of the Motivation for Healthy Behaving
measure from the Treatment and Self Regulation Questionnaire (TSRQ) measure developed by
Williams et al. (Williams & Deci, 2001; Williams, Freedman, & Deci, 1998).
Dietary intake was assessed by one of three instruments: Study 1) food intake items
taken from the Nurse’s Health Study survey (Willett et al., 1985), which is the instrument upon
which the Youth/Adolescent Food Frequency Questionnaire (YAQ) was based (Rockett et al.,
viii

1997), Study 2) three-day dietary records (O'Connor et al., 2001), and Study 3) a previous day
food checklist (F. E. Thompson et al., 2002).
Results:
In Study 1, in a sample of 74 Latino youth (Mean age = 9.5 years; 76% female), the initial
58-item Meanings of Eating Index was reduced to a 19-item, 5-factor scale which comprised the
final MEI. Factor 1: Personal Negative Emotions (7-items; α=0.84) Factor 2: Personal Well Being
(4-items; α=0.76) Factor 3: Social Eating (4-items; α=0.52) Factor 4: Disturbed Eating (2-items;
α=0.52) and Factor 5: Eating on Behalf of Others (2-items; α=0.55). The main findings were that
personal negative emotions were positively associated with junk food consumption (r=0.23;
p=0.02) and salty snack consumption (r=0.26; p=0.02), while personal well-being was positively
associated with eating fruits and vegetables (r=0.23; p<0.05), vegetables (r=0.24; p=0.04), and
vegetable soups (r=0.33; p<0.01).
In Study 2, in a sample of 37 overweight Latina girls (Mean age = 15.1 years), baseline
findings illustrated that intrinsic motivation was associated with higher whole grain intake and
extrinsic motivation was associated with lower fiber intake, after controlling for confounders. At
follow-up, increases in intrinsic motivation were associated with increases in total sugar,
decreases in total fat, and increases in soluble fiber, after controlling for confounders.
In Study 3, intrinsic motivation and one meanings of index factor (i.e. eating on behalf of
others) were independently associated with fruit intake in a predominantly Latino sample of 251
elementary school children (Mean age = 9.7 years; 57.8% female; 41% Latino). However, the
amount of variance accounted for by motivation and the meanings of eating behavior was small.
ix

Conclusion:
Both motivation and the meanings of eating behavior uniquely contributed to variance
in dietary intake in Latino youth and show some promise as tools for understanding the affective
determinants of dietary intake in minority children. Although the direct influence of motivation
and meanings may be small, the information gained from these and future studies may point to
new directions for interventions aimed at improving dietary choices.
1

Chapter 1: Introduction

Significance
The overall goal of this dissertation was to examine the roles of 1) motivation for
healthy dietary habits and 2) the meanings of eating behavior on dietary intake in Latino youth.
Over the last three decades, the prevalence of overweight and obesity in children has
dramatically increased in the United States (Ogden et al., 2006; US Department of Health and
Human Services, 2001). In 2007-2008, over 30% of children and adolescents ages 2 through 19
years were overweight (BMI percentile ≥ 85
th
percentile for age and gender) (Ogden, Carroll,
Curtin, Lamb, & Flegal, 2010). Nearly 16% were obese (BMI percentile ≥ 95
th
percentile for age
and gender) (Ogden et al., 2010). These numbers are substantially higher in Latino youth (38.2%
≥ 85
th
percentile, 20.9% ≥ 95
th
percentile), placing them at disproportionate risk for type 2
diabetes, metabolic syndrome, and a host of other diseases (Ogden et al., 2010).
In order to reduce obesity and its related disorders in any population, it is vital to
improve diet and increase physical activity (Mokdad et al., 2001; Spiegelman & Flier, 2001;
Summerbell et al., 2009). Only a handful of interventions have targeted physical activity and diet
in Latino youth to date (Fitzgibbon, Stolley et al. 2006; Davis, Kelly et al. 2009; Davis, Tung et al.
2009), and only a few of those interventions successfully reduced obesity (Summerbell et al.,
2009). Some recent literature suggests that altering diet may be most important for weight loss
(Sacks et al., 2009). Research also illustrates that Latinos in racially segregated neighborhoods
have less access to healthy foods (Galvez et al., 2008; Hosler, Rajulu, Fredrick, & Ronsani, 2008)
2

and Latino children’s intake of fruits and vegetables fall short of current recommendations
(Basch, Zybert, & Shea, 1994). Additionally, over 40% of Latino children report fast food
consumption on an average day (Paeratakul, Ferdinand, Champagne, Ryan, & Bray, 2003). The
ethnic differences in consumption patterns are not well understood. Therefore, this dissertation
focused on dietary intake as a major modifiable cause of obesity and focused specifically on
understanding the determinants of dietary intake in Latino youth.
To develop effective dietary interventions for children and adolescents, it is important
to understand the behavioral and psychosocial factors that determine eating behavior in these
populations. Research suggests that interventions that are guided by relevant behavioral
theories are more likely to significantly impact dietary behaviors in youth (Baranowski, Cullen, &
Baranowski, 1999; Baranowski, Lin, Wetter, Resnicow, & Hearn, 1997; Spruijt-Metz, 1999).
Theory is crucial because it provides a framework to examine the relationships among
constructs and delineates factors and determinants to be studied (Brown, DiClemente, &
Reynolds, 1991; Glanz, Lewis, & Rimer, 1990, 1997; Spruijt-Metz, 1999). Theoretical
understanding of health behavior can guide both development and evaluation of effective
interventions (Glanz et al., 1990, 1997; Green & Kreuter, 1991; Spruijt-Metz, 1999).
Furthermore, recent requests for applications from many NIH funding sources are requiring
theoretical underpinnings for any intervention (see
http://obesityresearch.nih.gov/funding/funding.htm).
To date, interventions that have attempted to improve dietary behavior and decrease
adiposity in youth have been relatively unsuccessful (Kamath et al., 2008; Summerbell et al.,
3

2005). More often than not, these interventions do not employ a theoretical framework
(Summerbell et al., 2009). However, even when interventions to change diet in adolescent
populations are based in theory, significant intervention effects remain inconsistent
(Summerbell et al., 2009). This may be related to the fact that most theory-based interventions
continue to use predominantly cognitive theories of behavior change. These theories posit that
cognitions are the primary determinant of behaviors. This assumption may not apply to
adolescent populations.
Some research suggests that less cognitively-based, more emotionally-based
determinants drive adolescent health-related behavior (Kirscht, 1983; Spruijt-Metz, 1999). For
children and adolescents, the immediate satisfaction of psychological needs and adherence to
the individual’s personal meanings were shown to be strong determinants of health behaviors
(Spruijt-Metz, Gallaher, Unger, & Johnson, 2004; Spruijt-Metz & Saelens, 2006). Two emerging
behavioral theories, the Self Determination Theory (SDT) and the Theory of Meanings of
Behavior (TMB), focus on affective determinants of behavior and have proven useful for
understanding and changing adolescent eating and physical activity behaviors (Trudeau, Kristal
et al. 1998; Spruijt-Metz, Spruijt et al. 2004). Therefore, this dissertation focused on intrinsic
motivation from the SDT (Ryan & Deci, 2000b) and meanings of behavior from the TMB (Spruijt-
Metz, 1995) as determinants of dietary intake in Latino youth.
4

Background
Theory and Psychosocial Determinants of Eating Behavior in Children and Adolescents
Theory-based research defines and specifies constructs that are hypothesized to
influence behavior (Glanz et al., 1990, 1997; Green & Kreuter, 1991; Spruijt-Metz, 1999),
assesses the impact of these various constructs (Bentler & Speckart, 1979; Brown et al., 1991;
Spruijt-Metz, 1999), and provides a framework to examine the relationships among constructs
(Brown et al., 1991; Glanz et al., 1990, 1997; Spruijt-Metz, 1999) . Behavioral theory adds
coherence and effectiveness to research because it identifies facilitating situations and relevant
processes, and guides the timing and sequencing of events (Glanz et al., 1990, 1997; Green &
Kreuter, 1991; Spruijt-Metz, 1999). Theoretical understanding of health behaviors guides the
development of effective methods of intervention and evaluation (Glanz et al., 1990, 1997;
Green & Kreuter, 1991; Spruijt-Metz, 1999). Research suggests that, for this reason, theory
based interventions are more likely to significantly impact dietary behaviors in youth
(Baranowski, Lin et al. 1997; Baranowski, Cullen et al. 1999; Spruijt-Metz 1999). In summary,
theory-based research is important to increasing knowledge in the field by providing a
framework to describe, explain, and predict behavior.
Dietary interventions in youth have been relatively unsuccessful to date (Kamath et al.,
2008; Summerbell et al., 2005). Nonetheless, there is evidence that theory-based interventions
can be effective in changing dietary behavior (Michie & Abraham, 2004). Despite extensive
research in the area of adolescent health, there are relatively few studies that test program
effects on psychosocial mediators of dietary change in adolescents (Baranowski et al., 1999;
5

Summerbell et al., 2009). With respect to pediatric nutrition interventions in large school
settings, most studies that incorporate theory utilize the Transtheoretical Model, the Theory of
Planned Behavior, or the Social Cognitive Theory (SCT) to explain and change dietary behavior
(AD McClain, Chappuis, Nguyen-Rodriguez, Yaroch, & Spruijt-Metz, 2009). The concept of
modeling to enhance dietary behavior inherent to SCT is the most commonly used concept in
the design of nutrition education interventions (Baranowski, Cullen, Nicklas, Thompson, &
Baranowski, 2003). However, research indicates that despite the use of this concept of
modeling by way of a family-based component in an intervention, the dietary intake of children
does not significantly change (Lytle et al., 1996; Sahota et al., 2001). Interventions based on SCT
tend to result in improved dietary-related knowledge without necessarily changing behavior (S.
M. Davis et al., 2003; K. D. Reynolds et al., 1998; Saksvig et al., 2005; Warren, Henry, Lightowler,
Bradshaw, & Perwaiz, 2003), although a few studies have resulted in modest changes in dietary
behavior (Baranowski et al., 2000; Sahota et al., 2001; Teufel & Ritenbaugh, 1998; Van Horn,
Obarzanek, Friedman, Gernhofer, & Barton, 2005). To date, no dietary interventions in youth
have resulted in decreased body weight and adiposity; however, some interventions resulted in
significantly slowed weight gain compared to the control population (Summerbell et al., 2009).
One reason for the lack of significant intervention effects on diet and adiposity might be
that the interventions utilized cognitive theories of behavior change and maintain that
cognitions determine behaviors. Generally, cognitive health behavior theories assume that a
person makes choices based on objective or scientific facts that are learned (Baranowski et al.,
2003; Nader, Sellers, Johnson, & Perry, 1996; Spruijt-Metz, 1999). These theories also assume
that behaviors occur in a predictable, chronological sequence in direct reaction to accrued
6

knowledge (Stacy, Bentler et al. 1994; Spruijt-Metz 1995; Spruijt-Metz 1999). However, these
assumptions may not apply to adolescent populations. Some research suggests that less
cognitively-based, more emotionally-based determinants drive adolescent health-related
behavior (Kirscht, 1983; Spruijt-Metz, 1999). Therefore, the current models used in changing
the dietary behavior of adolescents that were often originally developed for adults, may not be
appropriate. These models may not adequately address issues central to adolescence such as
cognitive and emotional development (Spruijt-Metz et al., 2004; Spruijt-Metz & Saelens, 2006).
Affective vs. Cognitive Processes
Affective (emotional) processes can be described as “intentions in action” (Searle,
1983). Research suggests that these affective processes do not necessarily interact with higher
cognitive processes (Panksepp, 2003). Instead, affective processes generate pressure or drive
and produce various positive or negative feelings that do not accompany pure cognitions to
guide behavior (Panksepp, 2003). Conversely, cognitive processes produce an “intention to act”
(Heyes & Dickinson, 1990) and allow individuals to navigate toward a goal (Panksepp, 2003).
Research suggests that affects (emotions) are more powerful and likely to be induced in youth
(Panksepp, 2003). Conversely, adults are more likely to engage in more sophisticated cognitive
activities (Panksepp, 2003).
Neurobiological Data Supporting the Notion that Cognitive-Based Interventions May Be
Less Effective in Children and Adolescents
Neurobiological evidence demonstrates age-related changes in cerebral functioning
from lower-order, emotionally-based sensory processing towards higher-order, more cognitive
7

and rational processing of stimuli by means of the prefrontal cortical systems that involve
reward anticipation, self-monitoring, and behavioral inhibition (Killgore & Yurgelun-Todd, 2005).
The ability to make beneficial nutritional choices and regulate behavior may be affected by the
neurobiological development of cognitive abilities that permit the inhibition of responses, delay
of gratification and voluntary change of behavior to bypass short-term rewards in favor of
longer-term goals (Killgore & Yurgelun-Todd, 2005). This research supports the hypothesis that
the current, cognitively-based health behavior models may not be appropriate for adolescents
who tend to be more driven by affect.
Motivation as a Determinant of Eating Behavior
We must eat for survival; however, in an industrialized society, survival is no longer the
only force that drives eating behaviors or dietary intake. People rely more heavily on
environmental, affective and cognitive cues. Dietary intake is more likely to be driven by
personal choices than by physiological signals. Most interventions attempt to influence personal
choice by supplying the participants with knowledge and skills to make healthy decisions.
However, research illustrates that knowledge and self-efficacy are not consistently associated
with dietary intake in children and adolescents (AD McClain et al., 2009), which may explain why
these interventions are not successful. Research is needed to better understand what motivates
youth to initiate positive changes in diet and what sustains engagement in this health behavior
for the long term (Wing et al., 2001). Although Self Determination Theory’s construct of
motivation has been cited as an important determinant of behavior change (Ryan & Deci,
2000b), few studies have examined the impact of motivation on dietary choice in youth.
8

Self-Determination Theory (SDT) asserts that motivation drives behavior (Ryan & Deci,
2000a, 2000b). In SDT, five forms of motivation are placed on a continuum of self-determination
of the behavior from non self-determined (extrinsic motivation) to self-determined (intrinsic
motivation) (Spruijt-Metz & Saelens, 2006). These forms of motivation consist of extrinsic,
introjected, identified, integrated and intrinsic motivation (see Table 1). Extrinsic motivation
(EM) involves feeling pressured or coerced to perform a behavior by another person or external
force. Extrinsic motivation involves performing a behavior for external and tangible rewards or
in response to pressures, such as approval or money (Ryan & Connell, 1989). Introjected
motivation occurs when external pressures regulating a behavior become internalized into the
self. Therefore, behavior is regulated through introjections, or through guilt and ego
evolvement. Identified motivation is the first type of self-determined motivation. When a
behavior is driven by identified motivation, it is performed because it is valuable and important
to the individual. Integrated motivation is the second type of self-determined motivation that
emerges when the behavior performed is integrated with other aspects of the self. Lastly,
intrinsic motivation (IM) is the prototype of self-determination and the highest form of self-
determination. Intrinsic motivation involves experiencing a sense of choice, a sense of full will.
Intrinsic motivation originates from one’s self and is therefore considered self-determined.
Intrinsically motivated activities are carried out for no apparent reward except the activity itself
or the feelings which result from the activity. Intrinsic motivation leads to interest, excitement
and confidence that in turn enhance persistence and performance (Deci & Ryan, 1985; Ryan &
Deci, 2000a, 2000b; Sheldon, Ryan, Rawsthorne, & Ilardi, 1997). According to the SDT, extrinsic
motivation undermines intrinsic motivation (Deci, Koestner, & Ryan, 1999). Research in
9

achievement motivation has shown that intrinsic motivation for a specific behavior is associated
with higher performance, while extrinsic motivation is detrimental to sustained performance
(Deci & Ryan, 1985).
Several studies have found that health-related behaviors are associated with self-
reports of motivation for engaging in that behavior (Cox, Miller, & Mull, 1987; Ryan, Plant, &
O'Malley, 1995; Williams, Gagne, Ryan, & Deci, 2002; Williams, Grow, Freedman, Ryan, & Deci,
1996). Few studies have examined the relationship between intrinsic and extrinsic motivation
on dietary intake, and no studies have examined this relationship in children (AD McClain et al.,
2009). In a sample of 1,450 adults (Mean age = 44 years; 60% female; 88.5% white) intrinsic
motivation for healthy diet was associated with higher intake of fruit and vegetable
consumption (p<0.001) (Trudeau, Kristal, Li, & Patterson, 1998). Patterson et al found a positive
association between a type of perceived pressure to eat healthy (extrinsic motivation) and
healthy dietary practices (p=0.02) in a sample of 607 adults (Mean age = 48.5 years; 61.3%
female; 96.9% white) (Patterson, Kristal, & White, 1996). To our knowledge, no interventions to
date have aimed to change motivation for healthy diet in order to change dietary behavior.
However, in a sample of 28 adolescents (Mean age = 11 years; 61% female; 85% African
American), Wilson and colleagues (Wilson et al., 2005) used a student-centered intervention to
increase intrinsic motivation and behavioral skills for physical activity through teaching positive
coping strategies, taking ownership by selecting a variety of physical activities in which to
participate, and developing program ideas for promoting physical activity. Due to initial success
in Wilson et al’s study in physical activity and due to support that more emotion-based
determinants are important for understanding adolescent health behavior (Kirscht, 1983;
10

Spruijt-Metz, 1999), it was hypothesized that investigating motivation as a determinant of
dietary behavior may be an important addition to the current literature.

Table 1: Taxonomy of human motivation
(Based on the figure from Ryan and Deci 2000 (page 61) (Ryan & Deci, 2000a))
REGULATORY STYLE Amotivation Extrinsic Motivation Intrinsic
Motivation
External
Regulation
Introjection Identification Integration
ASSOCIATED PROCESSES

Perceived non-
contingency
Low perceived
competence
Non-relevance
Non-intentionality
Salience of
extrinsic
rewards or
punishment
Compliance/
Reactance
Ego
involvement
Focus on
approval from
self or others
Conscious
valuing of
activity
Self-
endorsement
of goals
Hierarchical
synthesis of
goals
Congruence

Interest/
Enjoyment
Inherent
satisfaction

PERCEIVED LOCUS OF
CONTROL
Impersonal External Somewhat
External
Somewhat
Internal
Internal
11

12

Meanings of Health Behavior
The Theory of Meanings of Health Behavior (TMB), developed by Spruijt-Metz (1999),
and based on prior work from several other researchers (Ikard, Green, & Horn, 1969; Ikard &
Tomkins, 1973; C. Perry, 1999; C. L. Perry & Kelder, 1992), was developed to supplement
existing cognitive behavioral models and to provide a more comprehensive perspective on
behavioral determinants in youth, accounting for affective and developmental factors particular
to adolescence. Because research shows that children and adolescents tend to be less
cognitively and more emotionally driven, TMB proposes that adolescents and young adults
infuse health-related behaviors with affective meanings (Spruijt-Metz, 1995, 1999), defined by
Jessor as the symbolic significance of behavior (Jessor, 1984; C. L. Perry & Kelder, 1992; C. L.
Perry et al., 1999). Meanings reflect an individual’s need for emotional balance and
psychological comfort (Spruijt-Metz, 1999), and influence behavior directly without the
cognitive weighting of pros and cons, thus bypassing knowledge, rationality and cognition.
Affective meanings are related to personal feelings, fantasies, and experiences in families,
communities, societies, and cultures (Spruijt-Metz, 1995, 1999). These meanings are not
necessarily fixed. Instead, they can be built, modified, and changed (Spruijt-Metz, 1995, 1999).
It is posited that changing the meanings of a behavior will lead to a change in that behavior.
Previous research has shown that TMB predicts smoking, eating, physical activity, and
sexual activity behaviors in adolescents (Giannotta, Ciairano, Spruijt, & Spruijt-Metz, 2009;
Jamner, Spruijt-Metz, Bassin, & Cooper, 2004; Spruijt-Metz et al., 2004). Hsia (2003) showed
that meanings of smoking are strongly influenced by cultural background, gender and
acculturation. Personal, functional, and socially relevant meanings emerged as powerful factors

13

that directly influenced smoking behavior. Gianotta (2009) showed in a sample of 201 Italian
adolescents (Mean age = 17.4 years; 47% female) that sexual behaviors were highly influenced
by negative social meanings, transgressional meanings, and personal meanings (Giannotta et al.,
2009). Adolescents were more likely to have sex in order to imitate peers, to cope with needs,
or to act out against the normative conditions in which the sexual intercourse is expected to
occur. Additionally, Spruijt-Metz (2005) developed the Meanings of Physical Activity Scale
(MPAS) that was tested for reliability and validity in a multi-ethnic sample of 1004 middle school
students (Mean age = 12.6 years; 84% female). 43% of the sample engaged in sports or
exercise; 57% engaged in no sports or exercise. The 12-item MPAS discriminated significantly
between the two groups (t = 3.98, df = 62, p < .001, d = 1.15). There was high internal
consistency of the MPAS that suggests that the MPAS is measuring a cohesive construct.
Notably, the MPAS did not correlate significantly with standardized benefits and barriers scales.
Finally, Spruijt-Metz found, in a sample of 416 Dutch secondary students (Mean age = 14 years;
51% female), meanings of eating classified subjects into three groups that represent youth who
ate junk food for lunch, ate healthy food for lunch, or skipped lunch (Spruijt-Metz, 1995) . These
findings suggest that constructs from the TMB have potential as mediators of health behavior
change in youth. Therefore, this thesis investigated the meanings of eating behavior and its
association with dietary intake in minority youth as a potential avenue for effective dietary
intervention.

14

Specific Aims
For purposes of intervention development, especially those that target children and
adolescents, there is a need to more clearly understand the relationship between affect and
dietary intake. Therefore, this dissertation utilized cross-sectional and intervention data from
five independent studies that investigated the determinants of eating behavior in minority
children to examine the TMB and part of the SDT (motivation) as determinants of dietary intake
in Latino youth. It was expected that both the meanings of eating behavior and motivation
would lead to consumption of specific foods in Latino youth. The hypothesized relationships
between dietary intake, TMB and SDT are pictured in Figure 1. The theoretical model depicted
here does not represent all possible relationships among these variables, but only those that
were tested in the three studies.
Covariates
Gender
Age
Ethnicity/Race
Meanings of Eating
Behavior
Dietary Intake
Fruits, Vegetables, Snacks
Consumption, Sugar, Fiber
Motivation
Intrinsic
Extrinsic
1
2
3
Intervention

Figure 1: Theoretical model

15

Study 1: Cross-sectional analysis on the development and validation of the Meaning of
Eating Index among minority children
The overall aims of the first study were to identify the affective meanings of dietary intake
among minority children, develop factor items for the Meanings of Eating Index (MEI), validate
the MEI, and explore whether the meanings identified facilitated or hindered healthy dietary
behavior in this population.
Aim 1: Identify the affective meanings of dietary intake behavior in minority adolescents and
develop the items for a Meanings of Eating Index (MEI).
Aim 2: Evaluate the MEI, explore its factor structure, and determine whether the items were
internally consistent.
Hypothesis 2A:
A three-factor structure, representing Social Meanings, Personal Meanings, and
Functional Meanings, which corresponds to the central aspects of the Meanings of
Health Behavior theory was expected to emerge in the exploratory factor analysis.
Hypothesis 2B:
The scores on the MEI and the emergent factor subscales were expected to show
evidence of internal consistency reliability.
Aim 3: Examine the association between MEI factors and dietary intake (Arrow #2).
Hypothesis 3A:
Personal meanings would be significantly associated with healthier dietary choices.
• Personal meanings would be associated with higher fruit and vegetable intake.
Hypothesis 3B:
Social meanings would be significantly associated with unhealthier dietary choices.
• Social meanings would be associated with lower fruit and vegetable intake.
• Social meanings would be associated with higher junk food consumption.

16

Hypothesis 3C:
Functional meanings would be significantly associated with healthier dietary choices.
• Functional meanings would be associated with higher fruit and vegetable intake.

Study 2: Intervention effects on the motivation to eat fruits and vegetables as a
determinant of eating behavior in Latina adolescent girls
This study was part of a larger randomized intervention in overweight Latino adolescents that
evaluated the effectiveness of a modified carbohydrate nutrition program, focused on
increasing fiber and decreasing sugar, combined with motivational interviewing in addition to
strength or circuit and aerobic strength training to reduce risk for Type II diabetes. Although
only modest intervention effects were found (J. N. Davis, Kelly et al., 2009; J. N. Davis, Tung et
al., 2009) there were considerable within-group variation in changes in dietary intake. The
overall objective of this study was therefore to understand why some participants made dietary
changes while others did not. This study examined the effects of intrinsic and extrinsic
motivation for eating fruits and vegetables on dietary intake at baseline and on changes in
dietary intake between baseline and follow-up in a sample of Latina females (Arrow #1).

Aim 1: Examine the associations between intrinsic and extrinsic motivation to eat fruits and
vegetables and dietary intake at baseline.
Hypothesis 1A:
At baseline, intrinsic motivation to eat fruits and vegetables would be associated with
healthier dietary intake.
• Intrinsic motivation would be associated with higher fruit and vegetable intake.
• Intrinsic motivation would be associated with lower sugar intake.
• Intrinsic motivation would be associated with higher fiber intake.

17

Hypothesis 1B:
At baseline, extrinsic motivation to eat fruits and vegetables would be associated with
unhealthier dietary intake.
• Extrinsic motivation would be associated with lower fruit and vegetable intake.
• Extrinsic motivation would be associated with higher sugar intake.
• Extrinsic motivation would be associated with lower fiber intake.

Aim 2: Examine the associations between changes in intrinsic and extrinsic motivation to eat
fruits and vegetables and changes in dietary variables at follow-up (Arrow #1).
Hypothesis 2A:
At follow-up, increases in intrinsic motivation would be associated with healthier dietary
intake.
• Increases in intrinsic motivation would predict increases in fruit and vegetable
intake.
• Increases in intrinsic motivation would predict decreases in sugar intake.
• Increases in intrinsic motivation would predict increases in fiber intake.
Hypothesis 2B:
At follow-up, increases in extrinsic motivation would be associated with unhealthier
dietary intake.
• Increases in extrinsic motivation would predict decreases in fruit and vegetable
intake.
• Increases in extrinsic motivation would predict increases in sugar intake.
• Increases in extrinsic motivation would predict decreases in fiber intake.

18

Study 3: Unique contributions of the meanings of eating and motivation to fruit intake
in Latino youth
The overall objective of the third study was to cross-sectionally examine whether the meanings
of eating behavior and motivation were independently associated with fruit and vegetable
intake in a predominantly Latino sample of elementary school students.

Aim 1: Examine the direct effects of meanings of eating on fruit and vegetable intake
(Arrow #2).
Hypothesis 1A:
The MEI factors would be significantly associated with fruit and vegetable intake.

Aim 2: Examine the direct effects of motivation on fruit and vegetable intake (Arrow #1).
Hypothesis 2A:
Intrinsic motivation would be positively associated with fruit and vegetable intake.

Hypothesis 2B:
Extrinsic motivation would be negatively associated with fruit and vegetable intake.

19

Chapter 2: Measuring the Meanings of Eating in Latino Youth

Abstract
Although Latino youth are at high risk for obesity and related diseases, the determinants of
eating in Latino youth are understudied. The purpose of this study was to develop and validate
the Meanings of Eating Index (MEI) in a sample of Latino youth. Exploratory factor analysis was
performed on MEI items significantly correlated with dietary intake. Items loading on multiple
factors or at less than 0.50 were discarded. Factors with eigenvalues above 1.0 were retained.
A 19-item, 5-factor scale comprised the final MEI: Personal Negative Emotions were positively
associated with junk food (r=0.23; p=0.04), Personal Well Being was positively associated with
eating fruits and vegetables (r=0.23; p<0.05). Eating on Behalf of Others was associated with
less frequent fruit juice consumption (r=-0.28; p<0.02). Social Eating and Disturbed Eating were
not associated with dietary intake. The MEI shows promise as a tool for understanding the
affective determinants of dietary intake in Latino youth.

20

Introduction
Over the last three decades, the prevalence of overweight and obesity in youth
dramatically increased in the United States (Ogden et al., 2006; US Department of Health and
Human Services, 2001). Latino youth are disproportionately affected (Ogden, Carroll, & Flegal,
2008). In 2003-2006, 38.0% of Mexican Americans ages 2 through 19 were at risk of overweight
or overweight, as compared to 30.7% of non-Hispanic whites and 34.8% of non-Hispanic blacks
(Ogden et al., 2008). Healthy diets are important for reducing risk for developing overweight,
obesity, and obesity related disorders such as heart disease and type-2 diabetes (D. Thompson,
Edelsberg, Colditz, Bird, & Oster, 1999).
Research illustrates that Latino youth’s intake of fruits and vegetables fall short of
current recommendations (Basch et al., 1994; Colon-Ramos et al., 2009; Munoz, Krebs-Smith,
Ballard-Barbash, & Cleveland, 1997). Latino youth have low levels of fruit and vegetable intake,
independent of the level of acculturation and food security (Dave, Evans, Saunders, Watkins, &
Pfeiffer, 2009). Additionally, over 40% of Latino youth reported fast food consumption in two
non-consecutive 24-hour dietary recalls over the course of three to ten days (Paeratakul et al.,
2003).
Dietary patterns and preferences are established during childhood (Mikkilä, Räsänen,
Raitakari, Pietinen, & Viikari, 2007). Therefore, it is important to intervene and establish healthy
dietary behaviors early in life to reduce risk for developing overweight and obesity, especially
among high risk minority youth (D. Thompson et al., 1999). However, few studies have
investigated the determinants of eating behavior in Latino youth (AD McClain et al., 2009). It is

21

important to study determinants of dietary intake in this population in order to find a point of
intervention to improve dietary habits and prevent obesity.
To date, dietary interventions in youth have been relatively unsuccessful (Kamath et al.,
2008; Summerbell et al., 2005), and few have specifically targeted Latino youth. Nonetheless,
there is evidence that theory-based interventions are more likely to significantly impact dietary
behaviors in youth (Baranowski et al., 1999; Baranowski et al., 1997; Spruijt-Metz, 1999). Theory
adds coherence and effectiveness to intervention research because it identifies factors and
facilitating situations to be studied, guiding the sequencing of events, which can direct the
development of effective methods of intervention (Glanz et al., 1990, 1997; Green & Kreuter,
1991; Spruijt-Metz, 1999).
Research suggests that less cognitively-based, more emotionally-based factors may
drive adolescent health-related behavior (Kirscht, 1983; Spruijt-Metz, 1999). Therefore, the
current health behavior theories used to assess and change the dietary behavior of adolescents
that were originally developed for adults, may not be appropriate. These theoretical models
may not adequately address the cognitive and emotional development of adolescents (Spruijt-
Metz et al., 2004; Spruijt-Metz & Saelens, 2006).
The current literature on the psychosocial correlates of eating behavior in Latino youth
is limited (AD McClain et al., 2009). To our knowledge, only two studies have examined the
psychosocial correlates of eating behavior in Latino youth (Ayala et al., 2007; Perez-Lizaur,
Kaufer-Horwitz, & Plazas, 2008). One study found that self efficacy was related to vegetable
consumption, however knowledge, outcome expectations and preferences were not associated

22

with fruit or vegetable consumption (Perez-Lizaur et al., 2008). The other study found that
family social support for healthy eating was positively associated with fiber intake and
negatively associated with eating high fat snacks in Latino youth (Ayala et al., 2007). However,
these studies may not have been based on theoretical models most appropriate for adolescents.
Therefore, assessment of affective predictors of health behaviors in this population is
warranted.
Theoretical Model
The Theory of Meanings of Health Behavior (TMB), developed by Spruijt-Metz (1999),
and based on prior work (Ikard et al., 1969; Ikard & Tomkins, 1973; C. Perry, 1999; C. L. Perry &
Kelder, 1992), is a theory created to supplement existing cognitive behavioral models,
accounting for affective and developmental factors particular to adolescence. Because research
shows that children and adolescents tend to be less cognitively and more emotionally driven
(Kirscht, 1983; Spruijt-Metz, 1999), TMB proposes that adolescents and young adults infuse
health-related behaviors with affective meanings (Spruijt-Metz, 1995, 1999), defined by Jessor
as the symbolic significance of behavior (Jessor, 1984; C. L. Perry & Kelder, 1992; C. L. Perry et
al., 1999). These affective meanings reflect an individual’s need for emotional balance and
psychological comfort (Spruijt-Metz et al., 2004) and influence behavior directly, bypassing
knowledge, rationality and cognition. It is posited that changing the meanings of a behavior will
lead to a change in that behavior (Spruijt-Metz, 1995, 1999).
Previous research has shown that TMB is predictive of smoking, physical activity, and
sexual activity behaviors in adolescents (Giannotta et al., 2009; Jamner et al., 2004; Spruijt-Metz

23

et al., 2004). Therefore, the meanings of eating behavior are hypothesized to be a viable avenue
for effective dietary intervention. Study I examined the development of an assessment tool to
measure meanings of eating. Study II tested the psychometric properties of the developed
Meanings of Eating Index in a sample of Latino youth.
Study I: Questionnaire Development
The purpose of Study I was to identify the affective meanings adolescents and young
adults infuse into dietary intake behavior and to develop the items for the Meanings of Eating
Index (MEI). Based on previous research on the meanings of health behavior, we expected
items related to three distinct factors of meanings to emerge (Spruijt-Metz, 1995, 1999):
personal meanings, which represent intrapersonal relations such as dealing with bad moods or
stress (Spruijt-Metz, 1995, 1999); functional meanings, which represent dealing with physical or
environmental problems; and social meanings, which represent interpersonal relations such as
peer group acceptance.
Methods
Participants: The qualitative data used to construct the MEI was taken from focus groups that
were conducted as part of formative research for multiple diet and physical activity
interventions. Qualitative analysis of focus group data from minority adolescents was
conducted. Information from these focus groups provided insight into adolescent experiences
and meanings of eating behavior which guided development of a tool to measure meanings of
eating behavior. For this formative phase, a diverse group of 102 girls and 28 boys participated
in 24 focus groups and 2 individual interviews. Participants ranged in age from 11 to 17 years.

24

Study Procedure: Qualitative research methodology was used because it is well-suited to
identify key issues from the perspective of respondents (Morse & Field, 1995). Focus groups
and interviews were guided by a semi-structured interview protocol. Interview protocols
included questions on favorite foods and physical activities, social and environmental cues to
eating and physical activity, and meanings of eating and physical activity. The duration of each
interview was between 50-75 minutes depending upon the subjects’ interest in continuing the
discussion and school schedules. All interviews were audio-taped with permission from the
participants and then transcribed for the purpose of analysis. Transcribers were instructed to
remove all names from the transcripts.
Written parental informed consent was obtained prior to inviting the participants to
participate in the interviews. Youth’s written assent was obtained before the interviews began.
Small gift items were given as compensation for participation. All study procedures were
approved by the Institutional Review Board (IRB) at the University of Southern California (USC)
as well as appropriate school boards.
Statistical Analysis: Transcripts were coded using content analysis to provide a framework for
analysis (Holsti, 1969). First, categories were identified and defined through an iterative process
of re-reading interview texts, informed by the interview protocol. The categories, which include
functional, social, or personal meanings of health behavior, were derived from factors that
emerged from the data and a priori research questions based on previous research on the
meanings of health behavior. QSR NVivo Version 7 software was used to code, store, retrieve,
display, and analyze the transcripts from the focus groups and interviews.

25

Several steps were taken to ensure the integrity of each category and item. A.D.M. and
S.N.R. consulted each other regarding the content, clarity and parsimony of each category and
subsequent items. Scale items were then written until it was determined that the group of
items comprehensively and adequately reflected the central characteristics of the interviews
and accurately used the words and language of the participants. D.S.M. was also consulted to
ensure that the categories and items accurately reflected the content domain. These evaluators
then agreed as a team which items should be retained.
Results
Qualitative data analyses resulted in an initial pool of 58-items for the MEI that assessed
the key aspects of the meanings of health behavior. These items fit into three thematic
categories of meanings of eating: Functional (17-items), Social (25-items), or Personal (16-
items). Functional meanings were associated with achieving physical or environmental goals,
such as sating hunger, feeling healthy, or giving in to an ‘urge’. Furthermore, functional
meanings included a factor of demonstration, such as compliance, autonomy or independence.
Social meanings consisted of eating in order to be close to someone or something such as
family, friends, or culture. Personal meanings include those that were instrumental for personal
pleasure and happiness. These also consisted of eating for compensation, meaning to assuage
anger, counteract boredom, and relieve loneliness or stress. Multiple items representing each of
these three factors were gleaned from the interview data. (See Table 2 for full 58-titem MEI
scale)

26

Table 2: 58-item MEI Scale
Items
Retained
Sometimes I eat because… Never Some-
times
Often
SOCIAL MEANINGS
it helps me to fit in.
1

2

3

* everyone else is doing it.
1

2

3

* so my friends/family members don’t have to eat

1

2

3

it is part of celebrating my traditions.
1

2

3

it makes me feel like it is a special occasion.
1

2

3

* it is part of hanging out with my

1

2

3


it feels good to share with my

1

2

3


* it is part of being with my family
1

2

3


I don’t want to make my family feel bad.
1

2

3


it makes talking to my friends easier.
1

2

3


it makes hanging out with my friends easier.
1

2

3


it makes me feel like I am part of the popular group.
1

2

3


going to get food together is part of friendship.
1

2

3


sharing foods makes me feel closer to my friends.
1

2

3


it is part of watching sports with my friends.
1

2

3


eating certain foods makes me and my friends stand

PERSONAL MEANINGS

to show that I can make my own decisions.
1

2

3


it makes me feel free.
1

2

3


it makes me feel like I can do whatever I want to do.
1

2

3


it makes me feel good
1

2

3


it makes me feel happy.
1

2

3


it makes me feel better.
1

2

3


it comforts me.
1

2

3


* it takes my mind off of bad stuff.
1

2

3


* it helps me deal with anger.
1

2

3


it helps me get over fights with friends or family.
1

2

3


it helps me deal with stress.
1

2

3


it helps me to relax.
1

2

3


it helps me deal with school.
1

2

3


* it helps me deal with loneliness
1

2

3


* it helps me deal with sadness.
1

2

3


it fills the emptiness inside.
1

2

3


* it makes me feel smart about my health.
1

2

3


* it helps me feel better when things are not going

1

2

3


* it makes me feel less sad
1

2

3


27

Items
Retained
Sometimes I eat because… Never Some-
times
Often
it makes me feel like I am getting back at my

1

2

3


it makes me feel satisfied.
1

2

3


* it makes me feel healthy.
1

2

3


* it makes me feel stronger.
1

2

3


it makes me feel more alive.
1

2

3


eating certain foods makes me feel like I am special.
1

2

3


FUNCTIONAL MEANINGS

* I can’t stop eating.
1

2

3


to show that I can make my own decisions.
1

2

3


I feel like I have no control.
1

2

3


* it makes me feel like I am in control.
1

2

3


it makes me feel more energetic.
1

2

3


* it makes me feel like I am taking care of myself
1

2

3


I feel like certain foods will make me look good.
1

2

3


it makes other people happy.
1

2

3


I don’t want to refuse food.
1

2

3


it makes me feel like I am showing good manners.
1

2

3


it makes me feel like I am showing respect.
1

2

3


* it distracts me from everything I have to do.
1

2

3


then my family will leave me alone about my weight.
1

2

3


it makes my family/people stop bothering me.
1

2

3


* I don’t want my parents to be mad at me.
1

2

3


I feel like I have to clean my plate.
1

2

3


I feel guilty for not eating the food my family/friends

1

2

3


28

Study II: Psychometric Testing
The purpose of Study II was to assess whether the MEI items were associated with
dietary intake, test the hypothesized factor structure of the MEI (Walsh & Betz, 1999),
determine if the scales items were internally consistent (Walsh & Betz, 1999), and then to assess
whether the final MEI factors were associated with dietary intake in a sample of Latino youth.
Several hypotheses were formulated. First, a three-factor structure corresponding to the
central aspects of the TMB was expected to emerge in the exploratory factor analysis. Second,
the scores on the MEI and the emergent factor subscales were expected to show evidence of
internal consistency reliability. It has been asserted that the TMB is predictive of health
behavior, reflecting an individual’s need for emotional balance and psychological comfort
(Spruijt-Metz, 1999), influencing behavior directly. Therefore, our third hypothesis was that the
MEI factors would be associated with dietary intake.
Methods
Participants: The initial pool of MEI items plus other behavioral and psychosocial self-report
measures were administered by way of a school-based obesity prevention pilot study (Riggs,
Kobayakawa-Sakuma, & Pentz, 2007). Pilot participants were a predominantly Latino sample of
353 4
th
grade students, from 17 classrooms, in five schools, in Southern California. Only the
Latino participants made up the current study sample. Of the 133 Latino participants, 74
completed the survey version that included both MEI items and food intake items. The mean
age of participants was 9.52 years (SD = 0.60) and 62% were female. There were no significant
age or gender differences between those who completed the survey and those who did not.

29

Study Procedures: The survey consisted of 143 items and took approximately 45-50 minutes to
complete. The survey was administered aloud by a trained data collector, with a second data
collector available to answer individual student questions about comprehension. All procedures
were approved by University of Southern California’s Institutional Review Board.
Measures
Meaning of Eating Index (MEI): The initial 58-MEI items were constructed and developed in
Study I. Participants rated the frequency with which they acted on a specific meaning of eating
behavior. A 3-point scale, ranging from 1 (never) to 3 (often) was used. Higher scores indicated
higher levels of performing that specific eating behavior.
Dietary Intake: Dietary intake was measured with food intake items taken from the Nurse’s
Health Study survey (Willett et al., 1985), which is the instrument upon which the
Youth/Adolescent Food Frequency Questionnaire (YAQ) was based (Rockett et al., 1997). Due to
constraints of survey length, we used an abbreviated version as it has been shown that short
versions of food frequency questionnaires for dietary assessment in school aged youth are valid
for capturing consumption of certain foods of interest, although not for capturing total energy
intake (A. E. Field et al., 1999). Participants indicated how often they had consumed a particular
food item in a typical week. Dietary choices included three items that assessed fruit intake (e.g.,
“How often do you eat any fruit, fresh or canned (not counting juice),” four items that assessed
vegetable intake (e.g., “How often do you eat green salad”), and five items that assessed high
calorie junk food intake (e.g., “How often did you eat corn chips, potato chips, popcorn, or
crackers?”). A 6-point scale, ranging from 1 (less than once a week) to 6 (2 or more of these a

30

day) was used. Higher scores indicated higher frequency of consumption for eating a specific
food.
Statistical Analysis: The first step taken to evaluate the psychometrics of the MEI was to explore
whether the meanings of eating items were associated with dietary intake. To do this,
correlations between food items and the 58 MEI items were run. All MEI items that were not
significantly correlated with food items were discarded. Second, to evaluate the factor structure
of the reduced item MEI, exploratory factor analysis (EFA) with an un-weighted least squares
factor extraction and Promax rotation was performed. This type of rotation was performed
because it was hypothesized that the factors would correlate and Promax rotates the factor
axes, allowing them to have an oblique angle between them. The number of factors was
determined by eigenvalues above 1.0. Items were then discarded if they loaded on multiple
factors or loaded at less than 0.50. EFA was then repeated with this subset of items and items
were again discarded if they loaded on multiple factors or loaded at less than 0.50. Third,
internal consistency reliability was assessed via Cronbach’s alpha, which is an index that
measures the variation accounted for by the true score of the underlying construct for each
factor. Finally, the association between the MEI factors and dietary intake was explored by
running a correlation between the MEI factors and food intake items. Analyses were performed
using SAS version 9.0 statistical software.

31

Results
After conducting correlation analyses between the original 58-item MEI and the food
intake items twenty items were discarded. 38-items for the MEI were retained.
Exploratory Factor Analysis: Exploratory factor analysis with the MEI on the 38-item MEI was
performed. The hypothesis that the MEI would result in a three-factor structure corresponding
to the central aspects of the Meanings of Health Behavior theory was not supported. Instead,
these procedures resulted in the identification of a 19-item MEI, comprised of 5 factors. Factor
1 consisted of 7-items interpreted as Personal Negative Emotions (e.g. “I eat because it takes my
mind off of bad stuff.”). Factor 2 consisted of 4-items interpreted as Personal Well Being (e.g. “I
eat because it makes me feel smart about my health.”). Factor 3 consisted of 4-items
interpreted as Social Eating (e.g. “I eat because it is part of being with my family.”). Factor 4
consisted of 2-items interpreted as Disturbed Eating (e.g. “I eat because I can’t stop eating.”).
Factor 5 consisted of 2-items interpreted as Eating on Behalf of Others (e.g. “I eat because
everyone else is doing it”). The results of the EFA are shown in Table 3.

32

Table 3: MEI factor structure
Sometimes I eat because Factor1 Factor2 Factor3 Factor4 Factor5
it takes my mind off of bad stuff. 0.61878
it helps me deal with anger. 0.69568
it helps me feel better when things are
not going well.
0.78122

it helps me deal with loneliness. 0.72042
it helps me deal with sadness. 0.70136
it makes me feel like I am in control. 0.62996
it makes me feel less sad. 0.80066
it makes me feel smart about my health. 0.67714
it makes me feel healthy. 0.80491
it makes me feel like I am taking care of
myself.
0.79467

it makes me feel stronger. 0.77196
so my friends/family members don't have
to eat alone.
0.74079

it is part of hanging out with my
brothers/sisters/friends.
0.63500

it is part of being with my family. 0.64531
it distracts me from everything I have to

0.59538
I can’t stop eating. 0.78381
then my family will leave me alone about
my weight.
0.55996

everyone else is doing it. 0.69934
I don’t want my parents to be mad at me. 0.72097

33

Internal consistency reliability evidence for the MEI: The internal consistency reliabilities
(alphas) for scores in the MEI subscales were: 0.89 for Factor 1, representing Personal Negative
Emotions (7 items); 0.84 for Factor 2, representing Personal Well Being (4 items); 0.76 for Factor
3, representing Social Eating (4 items); 0.52 for Factor 4, representing Disturbed Eating (2 items);
and 0.55 for Factor 5, representing Eating on Behalf of Others (2 items).
Association between MEI and dietary intake: Correlations between the MEI and food frequency
items are presented in Table 4. Correlations greater than 0.50 are considered large, 0.50-0.30
are moderate, and 0.30-0.10 are small (Cohen, 1977). Most of the factors were significantly and
positively correlated with one another, with significant correlations values ranging from 0.19 to
0.33. Three of the five subscales were significantly associated with frequency of food intake.
Factor 1: Personal Negative Emotions was positively associated with unhealthy dietary behavior.
Factor 2: Personal Well Being was positively associated with healthier dietary behavior. Factor 3:
Social Eating and Factor 4: Disturbed Eating were not significantly associated with dietary intake.
Factor 5: Eating on Behalf of Others was negatively associated with fruit juice consumption. The
relationships are small to moderate in size and consistent with theory and research, illustrating
relationships within the expected direction.

Table 4: Correlation matrix between MEI factors and dietary intake
1 2 3 4 5
Personal Negative Emotions (1) 1.0000

0.42

0.58

0.56

0.37 **
Personal Well Being (2) 1.0000

0.43

0.09 0.24 *
Social Eating (3) 1.0000

0.29

0.42 **
Disturbed Eating (4) 1.0000

0.22 †
Eating on Behalf of Others (5) 1.0000

Junk Food 0.26 * 0.03 0.07 0.10 0.09
Fruits and Vegetables 0.01 0.23 * 0.08 0.01 -0.05
How often to you eat fruit juice, like orange—fresh, frozen or

-0.06 0.08 -0.04 -0.17 -0.28 *
How often do you eat any fruit, fresh or canned (not counting

0.09 0.11 0.03 0.05 -0.14
How often do you eat vegetable juice, like tomato juice, V-8,

0.02 0.06 0.12 0.05 -0.13
How often do you eat green salad? -0.19 0.07 -0.04 -0.11 -0.18
How often do you eat potatoes, any kind, including baked,

0.17 0.09 0.11 -0.07 -0.01
How often did you eat vegetable soups, or stew with

0.20 0.33 ** 0.11 -0.01 0.15
How often did you eat any other vegetables, including string

0.01 0.24 * 0.05 0.07 0.02
How often did you eat French fries, fried potatoes? 0.19 0.08 0.01 -0.03 0.06
How often did you eat corn chips, potato chips, popcorn, or

0.26 * 0.04 0.03 0.07 -0.03
How often did you eat doughnuts, pastries, cake, or cookies

0.09 -0.02 0.09 0.19 0.10
How often did you drink diet soda (1 can or glass)? 0.23 * -0.07 0.12 -0.08 0.10
How often did you drink soda – not diet (1 can or glass)? 0.18 0.03 0.02 0.18 0.10
*** p ≤ .0001; ** p≤ .01; * p ≤ .05

34

35

MEI and dietary intake: The eight-item scale: It may not always be possible to utilize the entire
19-item MEI in large studies. Therefore a short version of the scale, consisting of only three
factors and eight items was created to substitute the larger scale. The three factors most
significantly associated with dietary intake in the larger survey and the scale items with the
highest factor loadings were retained for the short version of the MEI. The final factor structure
for the abridged MEI resulted in three-items measuring Personal Negative Emotions (numbers
29, 35 and 56); three-items measuring eating for Personal Well Being (numbers 51, 52 and 53);
and two items measuring Eating on Behalf of Others (numbers 3 and 42). The internal
consistency reliability for the factors were 0.85 for Personal Negative Emotions, 0.80 for
Personal Well Being, and 0.55 for Eating on Behalf of Others.
Results of correlations between the three factors of the eight-item MEI scale and food
frequency items are presented here. The three-item Personal Negative Emotions Factor was no
longer significantly associated with any of the food items. The three-item Personal Well Being
factor was significantly associated with frequency of fruit and vegetable consumption (r=0.23,
p<0.05). The two-item Eating on Behalf of Others Factors had a significant association with
frequency of fruit juice consumptions (r= -0.28, p=0.02). However, the short eight-item MEI
factors were strongly associated with the full 19-item factors: Personal Negative Emotions
(r=0.93, p<.0001); Personal Well Being (r=0.97, p<.0001), and Eating Behalf of Others utilized the
same factors as in the full scale. These data suggest that the three-factor, eight-item scale may
provide a useful measure of the meaning of eating behavior for use in telephone interviews and
other situations where a very short scale is required.

36

Chapter 2: Discussion and Conclusion
This paper described the development and psychometric testing of a questionnaire to
measure the meanings of eating behavior. Study I included a qualitative component, utilizing
open-ended interviews with children to identify meanings and reasons why children eat. The
qualitative component was conducted via a series of focus groups with students in middle
schools, aimed to identify affective factors in school aged children that promote eating
behavior. This component offered the unique opportunity to hear from students about their
experiences and perspective on why they engage in various eating behaviors. This work
supported dimensions that had been identified in previous Meanings of Health Behavior
research and were used to create items for the MEI. In Study II, the items that constituted the
initial MEI were reduced to a 19-item scale which comprised the final MEI. This final scale was
then assessed for reliability and tested for its association with dietary intake among Latino
youth.
The MEI elicited more than the expected three factors of social, functional and personal
meanings found in prior literature on the meanings of health behavior (e.g. smoking, physical
activity, etc.). Rather, five factors were derived. This larger number of factors is indicative of the
multi-faceted and complex nature of eating behavior. The MEI captures the various personal
meanings associated with eating behavior in minority youth. Factor 1 represents Personal
Negative Emotions. This factor demonstrates how minority youth may use food as a distraction
from dealing with emotion. Factor 2 represents Personal Well Being. This factor indicates how
minority youth may create a personal identity of someone who is healthy and wants to eat

37

healthy. Factor 3 represents Social Eating. The Social Eating factor demonstrates how minority
youth may eat socially to feel approval as part of a group. They may engage in the experience of
eating with others because they do not want to feel left out. Eating may also be a way to
emotionally connect with others. The Social Eating factor also represents how eating behavior
among minority youth can be situational and collaborative, involving decisions regarding social
activities (Bisogni et al., 2007). Factor 4 represents Disturbed Eating. Research illustrates that
people may eat as a means of rebelling against harsh self or externally inflicted constraints (i.e.
family members who harass minority youth about their weight) (Loro & Orleans, 1981). Finally,
Factor 5 represents Eating on Behalf of Others. The factor represents cultural ideals (Furst,
Connors, Bisogni, Sobal, & Falk, 1996), which are the learned values in a society. In Latino
culture, the factor relates to the value of familismo and simpatia. A strong attitude towards
familismo (family) is a core characteristic in Latino culture. Latinos have a strong identification
with family and extended family (Suarez & Ramirez, 1999). Simpatia is the notion of being nice,
tolerable and agreeable. Consequently, Latinos may avoid conflict in social and cultural
situations (Suarez & Ramirez, 1999). Latino culture may ingrain the belief that it is
“unacceptable” to not eat a meal prepared for family meal time. Therefore, minority youth
may be likely to eat because everyone else in their family eats.
The moderate intercorrelation between these factors suggests that the respective
items are tapping distinct factors. The moderate to high internal consistencies suggest that
most factors are stable enough to use as a subscale. The exceptions were Factor 4 (Disturbed
Eating) and Factor 5 (Eating on Behalf of Others), which only had two items each, limiting the
test of reliability. However these factors were significantly related to some dietary intake.

38

The major impetus for the development of the MEI was to determine affective
meanings of eating behaviors in minority youth and to assess relationships between factors of
the MEI and eating behaviors in Latino youth. We found that three of the five scales had
significant associations with frequency of dietary intake in the expected direction. A positive
meaning of eating behavior, Personal Well Being (Factor 2), was associated with healthier
dietary behavior, while other more negative meanings of eating subscales were associated with
unhealthier dietary behavior. Personal Negative Emotions (Factor 1) were associated unhealthy
high energy-dense food, specifically French fries, chips and soda. Eating on Behalf of Others
(Factor 5) was associated with consuming less fruit juice. Even though Social Eating (Factor 3)
and Disturbed Eating (Factor 4) were not significantly associated with dietary intake in this
sample, it is possible these factors may differ by weight status or longitudinally. Although
research is needed to further substantiate its utility, these results suggest that the MEI shows
promise as a tool for understanding the affective determinants of dietary intake in minority
children.
The three-factor, eight-item version of the MEI provides a useful tool when data must
be collected over the phone or in settings where use of the larger scale is not feasible. The
shorter scale will allow for repeated measures of the meanings of eating behavior in large
samples. However, it should be noted that the abridged scale suffers in internal reliability and
therefore provides a weaker estimate of the meanings of eating behavior compared to the full-
length scale.

39

Limitations: A major limitation of this study is that it does not have a large enough study
population to perform confirmatory factor analysis. Second, data were collected in a school
setting which limits the generalizability of our findings. This study was validated in a Latino
population, therefore generalizability to other populations may be limited. Factor structure and
scale properties may be different in other populations. Meanings may differ depending on
weight status within the Latino population. Thirdly, there are limitations due to the self-report
nature of our measurements. This may especially be an issue with younger children who may
not be able to accurately recall details regarding dietary intake. Fourth, the food frequency
items used to measure dietary behavior were not comprehensive, so future research may
benefit from a more inclusive measure of dietary behavior such as 24 hour recalls or dietary
records. Finally, The MEI was validated in children, because we firmly believe that interventions
to promote healthy diet should begin early. However, middle school students were used in the
focus groups because their level of cognitive development allows for clearer verbalization of
their thoughts. Therefore, there may be important meanings of eating behavior for younger
children that were not elicited through interviews. Testing the properties of the MEI in a
younger population does provide confidence in comprehensibility and generalizability of the
scale to larger age ranges.
Future Directions: The MEI may permit significant advances in understanding affective
determinants of eating behavior in children in order to create successful theory based
interventions. The MEI may be useful in identifying the personal meanings attached to eating
behavior, which are modifiable factors that can be targeted in interventions. However, more
extensive validation of these scales in larger, more diverse study populations is needed. For

40

example, discriminant and predictive validity should be assessed as well as external validity. The
associations between subscales of the MEI and eating behaviors suggests that targeting
meanings of eating in order to change eating behaviors might offer new strategies to improve
dietary habits.
Implications: The observed correlations among the meanings of eating behavior and dietary
intake lend support to the importance of the meanings of eating behavior and the theory of
meanings of behavior as a possible target for interventions. Previous research in preventive
health for youth has focused mainly on cognitive health behavior theories and more cognitively
based factors such as knowledge, outcome expectations, and self-efficacy as factors to
intervene on. In contrast to cognitive models, affective meanings influence behavior directly
without the cognitive weighting of pros and cons, bypassing knowledge, rationality and
cognition. Since the latter is more salient to youth health behavior, the constructs from the TMB
may be more effective in increasing healthier dietary intake in this population. Consequently,
interventions designed to increase healthy dietary behavior should focus on increasing
children’s belief that eating healthy foods is good for their personal well being. Interventions
could also focus on finding healthy alternatives to eating unhealthy food when children are
feeling negatively about their selves.
Acknowledgements: This study was funded by NICHHD and NIDA (RO1 HD-052107-0182,Pentz,
PI), NIDDK (KO1 DK59293, Spruijt-Metz, PI), the Baxter Foundation (Spruijt-Metz, PI), the Creff
foundation (Wiegensberg, PI), and University of Southern California Center for Transdisciplinary
Research on Energetics and Cancer grant (U54 CA 116848) (Goran & Bernstein, PI’s).

41

Chapter 3: Intrinsic Motivation Predicts Fiber and Fat Intake in
Latina Youth

Abstract
Self Determination Theory was used to examine intrinsic motivation and extrinsic motivation to
eat fruits and vegetables on dietary intake at baseline and changes in dietary intake at follow-up
in a randomized controlled intervention. 37 overweight Latina girls (Mean age = 15.1 years)
were randomly assigned to one of four conditions. Because there were no significant group
effects, treatment groups were combined for analyses. At baseline, intrinsic motivation was
associated with higher whole grain intake (R
2
=0.50, p<0.0001), and extrinsic motivation was
associated with lower fiber intake (R
2
=0.22, p=0.02), after controlling for confounders. At follow-
up, increases in intrinsic motivation were associated with increases in total sugar (R
2
=0.45,
p<0.001), decreases in total fat (R
2
=0.36, p<.01) and increases in soluble fiber (R
2
=0.43, p=0.001)
after controlling for confounders. Changes in extrinsic motivation were not related to changes in
dietary intake. Baseline findings support previous research that illustrates intrinsic motivation is
associated with healthier dietary choices and extrinsic motivation is associated with unhealthier
dietary choices. At follow-up, results were mixed. Increased intrinsic motivation was associated
with increased fiber intake and decreased fat intake, but also to increased total sugar intake.
This study is important in expanding the current literature, and suggests that the Self
Determination Theory may offer insight into motivation for different types of dietary intake in
overweight Latina adolescent girls.

42

Introduction
Latinos have the highest rates of pediatric overweight and obesity (Ogden et al., 2010).
In 2007-2008, 38.2% of Latinos ages two through nineteen were overweight or obese, compared
to 35.9% of non-Hispanic blacks and 29.3% of non-Hispanic whites (Ogden et al., 2010). In the
Latino population, the increase in overweight prevalence rates has been closely paralleled by
increasing rates of type 2 diabetes and other metabolic risk factors (Rosenbloom, Joe, Young, &
Winter, 1999). Although Latinos are the fastest-growing segment of the US population (Bureau,
1996, 2000), they are arguably the most understudied. While there are numerous interventions
aimed at decreasing obesity (Caballero et al., 2003; Gortmaker et al., 1999; Pangrazi, Beighle,
Vehige, & Vack, 2003), few randomized control trials have targeted Latino adolescents (Spruijt-
Metz, Nguyen-Michel, Goran, Chou, & Huang, 2008).
To date, interventions to improve dietary behaviors in youth have been relatively
unsuccessful (Summerbell et al., 2005). Furthermore, despite extensive research in the area of
adolescent health, few studies test program effects on psychosocial mediators of dietary change
in adolescents (Baranowski et al., 1999). Several studies have used cross-sectional analyses to
look at constructs of Social Cognitive Theory and the Theory of Planned Behavior to identify
determinants of fat intake (Baranowski et al., 1999; Butler, Wing, Jeffery, & Jakicic, 1995)and
snack food consumption (Conner, 1993). However, motivation for dietary change, as delineated
in Self Determination Theory and cited as an important determinant of behavior change (Ryan &
Deci, 2000b), has not been widely studied as a determinant of dietary choices in youth.

43

Self Determination Theory (SDT) asserts that motivation drives behavior (Ryan & Deci,
2000b). In SDT, behavior is placed on a continuum of self-determination, from non self-
determined (extrinsic motivation) to self-determined (intrinsic motivation) (Spruijt-Metz &
Saelens, 2006). Intrinsically motivated activities originate from one’s self and are carried out for
no apparent reward except the activity itself or solely for the feelings that result from the
activity. Intrinsic motivation (IM) leads to interest, excitement, satisfaction and confidence that
in turn enhance persistence and performance (Deci & Ryan, 1985; Ryan & Deci, 2000a, 2000b;
Sheldon et al., 1997; Spruijt-Metz & Saelens, 2006). Extrinsic motivation (EM) occurs when
people perform a behavior for external and tangible rewards or pressures, such as money (Ryan
& Connell, 1989). Research in motivation has shown that IM for a specific behavior is associated
with better performance, while EM is associated with a weaker performance (Deci & Ryan,
1985). Several studies have found that health-related behaviors are associated with self-reports
of motivation for engaging in that behavior (Cox et al., 1987; Ryan et al., 1995; Williams et al.,
2002; Williams et al., 1996). Few studies have examined the relationship between motivation
and dietary intake. In a sample of 1,450 adults (Mean age = 44 years; 60% female; 88.5% white)
intrinsic motivation for healthy diet was associated with higher intake of fruit and vegetable
consumption (Trudeau et al., 1998).To our knowledge, no studies to date examine motivation as
a predictor of dietary change in Latino youth.
This study is a secondary data analysis of data originally collected as part of a larger
randomized intervention in overweight Latino adolescents that evaluated the effectiveness of a
nutrition education program, focused on increasing fiber and decreasing added sugar, combined
with strength, or circuit and aerobic strength training to reduce risk for Type II diabetes.

44

Although only modest intervention effects were found (J. N. Davis, Kelly et al., 2009; J. N. Davis,
Tung et al., 2009) there was considerable within-group variation in changes in dietary intake
(Ventura et al., 2009). Therefore, the objective of this study is to understand why some
participants made healthier dietary changes while others did not. The effects of changes in
intrinsic and extrinsic motivation at pre-intervention and on changes in dietary intake from pre-
intervention to post-intervention were examined in a sample of overweight Latina females. The
focus of the intervention was on increasing fiber and decreasing added sugar, therefore it was
hypothesized that at pre-intervention, intrinsic motivation would be associated with overall
healthier dietary intake and extrinsic motivation would be associated with unhealthier dietary
intake. At post-intervention, it was hypothesized that increases in intrinsic motivation would
lead to increases in fiber intake, decreases in added sugar intake, and overall increases in
healthier dietary intake. Extrinsic motivation at post-intervention was hypothesized to be
related to decreases in healthy dietary intake.
As a secondary data analysis, this study was limited to the data gathered by the primary
study, which was an intervention study powered to test changes in adiposity and insulin and
glucose regulation in Latino adolescents. However, this dataset is unique because, to our
knowledge, this is the first study to investigate the association between motivation and dietary
intake in Latina adolescents. Therefore, the present analyses are intended to explore possible
associations between motivation and dietary intake in Latina adolescents.

45

Methods:
Participants: This study was conducted as part of a larger randomized control trial that has
been reported in detail elsewhere (J. N. Davis, Kelly et al., 2009; J. N. Davis, Tung et al., 2009;
Ventura et al., 2009). Participants were recruited from Los Angeles County and met the
following inclusion criteria: body mass index (BMI) in the 85th percentile or higher, Latino
ethnicity, and grades nine through twelve. Informed written consent from parents and assent
from the children were obtained. This study was approved by the institutional review board of
the University of Southern California, Health Sciences Campus.
Randomization: The main trial was conducted over two waves. In the first wave, participants
were randomized to one of three intervention groups: control (C group), nutrition education
only (N group), or nutrition education plus strength training (N+ST group). In the second wave,
participants were randomized to one of four intervention groups: C group, N Group, N+ST
group, or nutrition education plus circuit and aerobic strength training (N+CAST group).
Participants were over-sampled in the N+CAST group to account for the supplemental arm only
being run in one of the waves. Additionally, the N+CAST group only consisted of female
participants.
Fifty participants were randomized to one of the four groups and allocations were
concealed from participants until after pretesting was complete. Of the 50 participants who
were randomized, 37 completed the intervention. There were no statistically significant
differences in baseline demographics, anthropometrics, or body composition measures between

46

the 12 participants who dropped out of the program and the 37 participants who completed the
program.
Description of interventions: The nutrition education only group received one nutrition class
per week for 16 weeks. The dietary intervention targeted two goals: 1) a decrease in added
sugar intake to ≤ 10% of total daily calorie intake from added sugar, and 2) an increase in fiber
intake toward a goal of at least 14g/1000 kcal of dietary fiber a day. Participants in the nutrition
education plus strength training group received the same weekly nutrition classes along with
strength training two times per week for 16 weeks. Participants in the nutrition education plus
circuit training group received the same weekly nutrition classes along with circuit and aerobic
strength training two times per week for 16 weeks.
Participants randomized to the N group, N+ST group, and N+CAST also received
motivational interviewing (MI) sessions (approximately 10-15 minutes long) throughout the 16
week program by research staff trained in MI techniques. Staff received bi-weekly MI
supervision. MI is a client-centered counseling approach designed to enhance intrinsic
motivation for behavior change by creating, exploring and resolving ambivalence (W. R. Miller &
Rollnick, 2001). Participants in the N group received four individual MI sessions throughout the
16-week program by trained research staff in order to enhance intrinsic motivation for healthy
eating. The N+ST group and the N+CAST group received four individual and four group MI
sessions by trained research staff in order to enhance intrinsic motivation for healthy eating and
increased physical activity.

47

Participants randomized to the control group received no intervention over the 16
weeks between pre-intervention and post-intervention data collection. Periodically through the
16-week intervention, participants received non–health-related incentives, such as T-shirts,
water-bottles and regular telephone calls to enhance retention. After post-testing, participants
were offered an option of a month of bi-weekly nutrition and strength training classes
developed from the curriculum used in the intervention. No testing was done after this month.
Measures
Dietary intake: At both pre-intervention and post-intervention, participants were given three-
day diet records to complete. Participants were given a short lesson (10 minutes) on how to
estimate portion sizes and were given measuring cups and rulers to aid in accurate reporting.
Research staff, trained and supervised by a Registered Dietitian, clarified all dietary records.
Nutrition data were analyzed using the Nutrition Data System for Research (NDS-R version
5.0_35), a software program developed by the University of Minnesota.
Motivation to eat fruits and vegetables: Motivation to eat fruits and vegetables was assessed
with an adapted version of the Motivation for Healthy Behavior measure from the Treatment
and Self Regulation Questionnaire (TSRQ) measure developed by Williams et al (Williams & Deci,
2001; Williams et al., 1998). The 17-item measure generates two main subscales: (1)
autonomous/intrinsic motivation, (2) controlled/extrinsic motivation. Sample items from the
intrinsic scale include: “The reason I eat fruits and vegetables is because I personally believe it is
the best thing for my health.” A sample item from the extrinsic scale includes: “The reason I eat
fruits and vegetables is because I want others to approve of me.” The Cronbach's alpha for the

48

intrinsic motivation and extrinsic motivations scales in this sample were 0.83 and 0.87,
respectively. Motivation to eat fruits and vegetables was assessed because these measures
were available and had been tested previously, while measures of motivation to decrease sugar
and increase fiber were not available. Furthermore, the nutrition intervention stressed
substituting fruits and vegetables for foods high in sugar and low in fiber as a main means to
increase fiber and decrease sugar intake
Statistical analysis: Frequencies, means and standard errors were used to describe the sample.
Analysis of variance (ANOVA) and analysis of covariance (ANCOVA) was used with a Tukey
adjustment for multiple comparisons to compare characteristics across intervention groups. T-
tests were used to compare motivation scores between participants who received MI (N group,
N+ST group, and N+CAST group) and those who did not (C group). A change score was
calculated to examine both psychosocial and dietary measures by subtracting the post-
intervention score from the pre-intervention score. Partial correlations between changes in
psychosocial variables and dietary intake were then used to guide multiple linear regression
analyses. All statistical analyses were performed with SAS v.9.1 software (Cary, NC). Statistical
significance was set at 0.05.

49

Pre-Intervention Results:
Baseline descriptives: Out of the 50 participants randomized to one of four intervention groups,
37 (74%) completed the study: C group (n=6), N group (n=10), N+ST group (n=7), and N+CAST
group (n=14). There were no significant differences in baseline demographics, anthropometrics,
or body composition measures between participants who dropped out of the program and
participants who completed the program. Table 5 shows the baseline characteristics of the
evaluable participants. Participants were 37 overweight or obese Latina females with a mean
age of 15.1 years. There were significant differences in baseline intrinsic motivation and
baseline percent of calories from total sugar intake across groups. Therefore, intervention
group (IG) was controlled for in all of our subsequent analyses. The N+ST group was significantly
less intrinsically motivated compared to the other groups. The C group consumed significantly
more percent of calories from total sugar compared to the other groups. Using t-tests, there
were no significant differences between the participants who received MI and those who did
not for IM (p= 0.67) or EM (p= 0.40). There were no other significant baseline differences in any
variables of interest.

50

Table 5: Baseline characteristics of the sample (total n=37)
C Group

N Group N+ST Group N+CAST
Group
Between
Group

Total N 6 (16%) 10 (16%) 7 (23%) 14 (37%)
Mean Age (yrs) 15.2 ± 1.2 15.3 ± 1.1 15.4 ± 1.3 14.9 ± 1.0 NS
Baseline BMI 35.1 ± 8.0 33.8 ± 5.7 34.4 ± 8.3 34.2 ± 6.7 NS
Baseline BMI Z-Score 2.1 ± 0.5 2.0 ± 0.4 2.0 ± 0.6 2.1 ± 0.3 NS
Intrinsic Motivation 4.9 ± 1.0 4.65 ± 1.0 3.4 ± 1.3 5.4 ± 1.3 <.05
Extrinsic Motivation 3.4 ± 1.5 2.4 ± 1.7 2.3 ± 0.9 3.4 ± 1.5 NS
Total Energy (g/d) 1429.6 ±

1549.9 ±

1838.3 ±

1726.8 ±

NS
Total Fat (% of kcals) 28.6 ± 7.4 31.2 ± 6.1 35.1 ± 7.6 33.2 ± 5.5 NS
Total Protein (% of kcals) 13.3 ± 3.9 17.1 ± 2.6 16.5 ± 2.4 16.2 ± 2.2 NS
Carbohydrates (% of kcals) 59.7± 7.9 52.8 ± 6.2 50.0 ± 6.6 51.2 ± 5.8 NS
Total Sugar (% of kcals) 27.4 ± 7.6 20.1 ± 5.5 24.6 ± 4.7 21.0 ± 4.3 <.05
Added Sugar (% of kcals) 17.2 ± 6.2 12.9 ± 7.0 13.1 ± 5.9 12.4 ± 6.4 NS
Fiber (per 1000 kcals) 8.8 ± 3.6 10.0 ± 3.6 8.9 ± 2.4 8.3 ± 2.7 NS
Soluble Fiber (per 1000

2.3 ± 0.6 3.0 ± 1.0 2.5 ± 0.9 2.3 ± 0.7 NS
Insoluble Fiber (per 1000

6.3 ± 2.9 6.9 ± 2.9 6.4 ± 1.7 5.9 ± 2.2 NS
Whole Grain Intake

0.6 ± 0.5 0.4 ± 0.6 0.5 ± 0.5 0.8 ± 0.9 NS
Refined Grain Intake

1.2 ± 0.7 1.2 ± 0.7 0.8 ± 0.8 1.4 ± 1.0 NS
Fruit Intake (serv/day) 0.2 ± 0.4 0.2 ± 0.2 0.2 ± 0.1 0.3 ± 0.3 NS
Vegetable Intake

0.4 ± 0.3 0.4 ± 0.3 0.3 ± 0.2 0.4 ± 0.4 NS
Fruit & Vegetable Intake

0.6 ± 0.5 0.7 ± 0.4 0.5 ± 0.5 0.5 ± 0.4 NS

Baseline correlations: At baseline, IM to eat fruits and vegetables was positively associated with
whole grain intake (r= 0.46, p< 0.01) after controlling for baseline total energy intake and IG
(See Figure 2). IM was not associated with total energy, percent of calories from total sugar,
percent of calories from added sugar, total fiber per 1000 calories, soluble fiber per 1000
calories, insoluble fiber per 1000 calories, or percent of calories from total fat (p’s >0.05) after
controlling for IG. IM was also not associated with fruit intake, vegetable intake, fruit and
vegetable intake, or any other diet variable of interest after controlling for baseline total energy
and IG. Baseline EM to eat fruits and vegetables was negatively associated with total fiber

51

intake per 1000 calories (r= -0.38, p= 0.03) (See Figure 3), soluble fiber intake per 1000 calories
(r= -0.50, p< 0.01), and insoluble fiber per 1000 calories (r= -0.40, p= 0.02) after controlling for
IG. EM was not associated with total energy, percent of calories from total sugar, percent of
calories from added sugar, or percent of calories from total fat after controlling for IG. EM was
also not associated with fruit intake, vegetable intake, fruit and vegetable intake, whole grain
intake, or any other diet variable of interest after controlling for baseline total energy and IG.

Figure 2: Relationship between intrinsic motivation and whole grain intake
Partial Correlation (r= 0.46, p < 0.01; Model R
2
= 0.53, p < 0.0001) after controlling for baseline total
energy intake and intervention group.

52

Figure 3: Relationship between intrinsic motivation and fiber per 1000 calories intake
Partial Correlation (r= -0.38, p= 0.03; Model R
2
= 0.22, p= 0.02) after controlling for intervention group.

Baseline ANCOVA: At baseline, IM explained 9% of the variance of higher whole grain intake
after controlling for baseline total energy and IG (Model R
2
= 0.53, p< 0.0001). EM explained
20% of the variance of lower total fiber per 1000 calories after controlling for IG (Model R
2
=
0.22, p= 0.02). EM explained 24% of the variance of lower soluble fiber per 1000 calories after
controlling for IG (Model R
2
= 0.25, p< 0.01). EM also explained 16% of the variance of lower
insoluble fiber per 1000 calories after controlling for IG (Model R
2
= 0.17, p< 0.05).

53

Post-Intervention Results:
Differences across groups: Using ANOVA, changes in IM and EM were examined across
intervention groups. There were no significant differences in change scores for IM (p= 0.40), EM
(p= 0.65) or any other variable of interest. Using t-tests, there were no significant differences
between the participants who received MI and those who did not for changes in IM (p= 0.66) or
EM (p= 0.20).
Post- intervention correlations: At post-intervention, increases in IM to eat fruits and
vegetables were associated with increases in percent of calories from total sugar (r= 0.36, p=
0.03), increases in soluble fiber per 1000 calories (r= 0.35, p= 0.04) and decreases in percent of
calories from total fat (r= -0.34, p< 0.05), after controlling for IG, baseline IM and baseline
variable (See Figures 4, 5, and 6, respectively). Increases in IM were not associated with
changes in total energy, percent of calories from added sugar, total fiber per 1000 calories,
insoluble fiber per 1000 calories, fruit intake, vegetable intake, fruit and vegetable intake, or any
other diet variable of interest (p’s >0.05) after controlling for IG, baseline IM, and baseline diet
variable. Changes in EM were not associated with any variable of interest after controlling for
IG, baseline IM, and baseline diet variable.

54

Figure 4: Relationship between change in intrinsic motivation and percent of calories from total sugar
intake
Partial Correlation (r= 0.36, p= 0.03; β=0.04, Model R
2
= 0.45, p < 0.001) after controlling for intervention
group, baseline percent of calories from total sugar intake and baseline intrinsic motivation.

55

Figure 5: Relationship between change in intrinsic motivation and total fat intake
Partial Correlation (r = -0.34, p < 0.05; β= -0.02, Model R
2
= 0.36, p < 0.01) after controlling for baseline
intrinsic motivation, baseline percent of calories from total fat and intervention group.

56

Figure 6: Relationship between change in intrinsic motivation and soluble fiber per 1000 calories intake
Partial Correlation (r = - 0.35, p< 0.05; β=0.31, Model R
2
= 0.43, p= 0.001) after controlling for intervention
group, baseline soluble fiber per 1000 calories intake and baseline IM.

57

Post-intervention general linear modeling: Tables 6, 7, and 8 show the results of the regression
analyses predicting changes in food intake. Increases in IM explained 8% of the variance of
increases in percent of calories from total sugar after controlling for confounders (β= 0.04,
Model R
2
= 0.45, p< 0.001). Increases in IM also explained 7% of the variance of decreases in
percent of calories from total fat after controlling for confounders (β= -0.02, Model R
2
= 0.36, p<
0.01). Increases in IM also explained 5% of the variance of increases in soluble fiber per 1000
calories after controlling for confounders (β= 0.31, Model R
2
= 0.43, p= 0.001). Although IG did
not have significant main effects on motivation or dietary intake, intervention group interaction
analyses were performed for each model to assess possible group differences. There were no
significant interactions found between motivation and IG in any model (p’s > 0.05).

58

Table 6: Summary results of model development for association between changes in intrinsic
motivation and changes in percent of total sugar intake

Standardized
Beta
P-Value R
2
of Model
(Change)
P-Value

Model 1 0.0043 0.29
Intercept 0.048 0.29
Baseline Intrinsic Motivation -0.010 0.29
Model 2 0.08 (0.076) 0.09
Intercept -0.027 0.63
Baseline Intrinsic Motivation 0.004 0.73
Change in Intrinsic Motivation 0.020 0.06
Model 3 0.24 (0.16 ) 0.007
Intercept 0.085 0.20
Baseline Intrinsic Motivation 0.006 0.53
Change in Intrinsic Motivation 0.016 0.10
Baseline Sugar Intake -0.541 0.0068
Model 4 0.42 (0.18) 0.0002
Intercept 0.011 0.86
Baseline Intrinsic Motivation 0.003 0.72
Change in Intrinsic Motivation 0.019 0.03
Baseline Sugar Intake -0.376 0.03
Intervention group 0.028 0.002
Model 5 0.45 (0.03) 0.0002
Intercept -0.009 0.89
Baseline Intrinsic Motivation 0.002 0.85
Change in Intrinsic Motivation 0.038 0.01
Baseline Sugar Intake -0.303 0.09
Intervention group 0.033 0.0007
Change in Intrinsic Motivation x

-0.010 0.12

59

Table 7: Summary results of model development for association between changes in intrinsic
motivation and changes in percent of total fat intake

Standardized
Beta
P-Value R
2
of Model
(Change)
P-Value

Model 1 0.01 0.64
Intercept -0.030 0.52
Baseline Intrinsic Motivation 0.004 0.64
Model 2 0.08 (0.07) 0.21
Intercept 0.039 0.52
Baseline Intrinsic Motivation -0.008 0.49
Change in Intrinsic Motivation -0.019 0.10
Model 3 0.35 (0.27 ) 0.002
Intercept 0.260 0.002
Baseline Intrinsic Motivation -0.013 0.20
Change in Intrinsic Motivation -0.019 0.048
Baseline Fat Intake -0.608 0.0008
Model 4 0.36 (0.01) 0.005
Intercept 0.258 0.003
Baseline Intrinsic Motivation -0.012 0.23
Change in Intrinsic Motivation -0.020 0.048
Baseline Fat Intake -0.576 0.002
Intervention group -0.006 0.51
Model 5 0.36 (0.0) 0.01
Intercept 0.257 0.003
Baseline Intrinsic Motivation -0.013 0.23
Change in Intrinsic Motivation -0.015 0.39
Baseline Sugar Intake -0.575 0.003
Intervention group -0.006 0.59
Change in Intrinsic Motivation x

-0.003 0.74

60

Table 8: Summary results of model development for association between changes in intrinsic
motivation and changes in soluble fiber intake

Standardized
Beta
P-Value R
2
of Model
(Change)
P-Value

Model 1 0.0007 0.87
Intercept 0.189 0.80
Baseline Intrinsic Motivation 0.024 0.88
Model 2 0.05 (0.05) 0.37
Intercept -0.770 0.45
Baseline Intrinsic Motivation 0.197 0.32
Change in Intrinsic Motivation 0.260 0.17
Model 3 0.43 (0.39) 0.003
Intercept 1.847 0.07
Baseline Intrinsic Motivation 0.114 0.47
Change in Intrinsic Motivation 0.315 0.04
Baseline Soluble Fiber Intake -0.902 < 0.0001
Model 4 0.43 (0.0) 0.001
Intercept 1.860 0.08
Baseline Intrinsic Motivation 0.114 0.47
Change in Intrinsic Motivation 0.315 0.04
Baseline Soluble Fiber Intake -0.903 < 0.0001
Intervention group -0.007 0.96
Model 5 0.34 (0.1) 0.002
Intercept 2.949 0.09
Baseline Intrinsic Motivation -0.078 0.42
Change in Intrinsic Motivation 0.254 0.62
Baseline Soluble Fiber Intake -1.000 < 0.001
Intervention group 0.442 0.81
Change in Intrinsic Motivation x

-0.727 0.40

61

Chapter 3: Discussion and Conclusion
This study examined the effects of changes in intrinsic and extrinsic motivation for
eating fruits and vegetables on dietary intake at pre-intervention and on changes in dietary
intake at post-intervention in overweight Latina adolescents. We hypothesized that because the
intervention focused on increasing fiber and decreasing added sugar intake, at pre-intervention,
higher intrinsic motivation would be related to higher overall healthy dietary consumption and
extrinsic motivation would be related to unhealthier dietary consumption. At post-intervention,
it was hypothesized that increases in intrinsic motivation would be related to increases in fiber
intake, decreases in added sugar intake, and overall increases in healthy dietary consumption.
Extrinsic motivation was hypothesized to be related to unhealthy dietary consumption. At
baseline, this study demonstrated that those who were intrinsically motivated to eat fruits and
vegetables had higher whole grain intake (i.e. healthy dietary behavior) while those who were
extrinsically motivated had lower total fiber intake at baseline (i.e. unhealthy dietary behavior).
At follow-up, the findings were conflicting. Increases in intrinsic motivation were associated
with increases in fiber intake and decreases in fat intake (i.e. healthy dietary behavior).
However, increases in intrinsic motivation were not related to decreases in added sugar intake.
Unexpectedly, increases in intrinsic motivation were associated with increases in total sugar
intake (i.e. unhealthy dietary behavior). Changes in extrinsic motivation were not associated
with any changes in dietary intake.
This study partially supports a priori hypotheses and previous research on Self
Determination Theory demonstrating that intrinsic motivation is associated with healthy

62

behavior, namely higher whole grain intake, increased fiber and decreased fat intake. Trudeau
et al found that intrinsic motivation for eating healthy was associated with higher intake of fruit
and vegetable consumption in adults (Trudeau et al., 1998). Additionally, several studies have
found that health-related behaviors are associated with self-reports of intrinsic motivation for
engaging in that behavior (Cox et al., 1987; Ryan et al., 1995; Williams et al., 2002; Williams et
al., 1996).
The unexpected association between increases in intrinsic motivation and increases in
total sugar consumption might be related to findings showing that sugar intake is inversely
associated with fat intake, also known as the sugar-fat seesaw (Gregory, Foster, Tyler, &
Wiseman, 1990; McColl, 1988). The sugar-fat seesaw posits that high fat diets tend to be low in
sugar and low fat diets tend to be high in sugar (McColl, 1988). This reciprocal relationship
between the reported percentage of sugar and percentage of fat consumed is apparent in large-
scale surveys (Bolton-Smith & Woodward, 1994). Low-fat diets, which are popular, have had a
great deal of media coverage, are often followed among girls the age of this cohort (A. Field,
Haines, & Willett, 2008), and may be ingrained in participants’ minds as a healthy choice,
particularly because they are often taught in schools and in national campaigns. This might
partially explain the finding that positive changes in intrinsic motivation were associated with
decreases in fat consumption.
This study did not consistently support a priori hypotheses and previous research on Self
Determination Theory suggesting that extrinsic motivation is associated with unhealthy
behavior. Cultural values that largely influence behavior in Latinos may be one possible

63

explanation for this finding. Familismo (importance of family togetherness), respeto (respect
for elders) and simpatia (of potential conflict) may drive Latino youth to eat what is served to
the entire family without question (Suarez & Ramirez, 1999). Not eating what is served may be
considered disrespectful and cause conflict. Therefore, in Latino culture, extrinsic motivation
may not be related to detrimental health outcomes due to salient cultural values that often lead
to child conformity rather than rebellion (Suarez & Ramirez, 1999).
Although increases in intrinsic motivation to eat fruits and vegetables were related to
increased fiber intake, there was no significant relationship between intrinsic motivation and
fruit, vegetable, or fruit and vegetable intake. This might be due to the fact that one of the
primary intervention goals was to increase dietary fiber. Increasing fruit and vegetable
consumption was taught in nutrition classes as only one of the many possible ways to increase
fiber consumption. Research has shown that availability (Befort et al., 2006; Granner et al.,
2004; Reinaerts, de Nooijer, Candel, & de Vries, 2007; K. Reynolds, Hinton, Shewchuck, &
Hickey, 1999; Young, Fors, & Hayes, 2004) and accessibility (Bere & Klepp, 2004; Cullen et al.,
2003; Reinaerts et al., 2007) are strongly positively associated with fruit, juice and vegetables
consumption. Thus, although the participants were involved in an intervention to increase fiber
and lower sugar intake, their environment may have prevented specific behavior changes and
facilitated others. Therefore, while we were able to assess changes in fiber intake as measured
collectively via fruits, vegetables, and whole grains, we did not see significant changes in fruit
and vegetable consumption.

64

Limitations: This study has a number of limitations. First, as a secondary data analysis, it was
limited to the data gathered by the primary study, which was an intervention study powered to
test changes in adiposity and insulin and glucose regulation in Latino adolescents. Therefore, the
present research is intended as a pilot study to test the association between motivation and
dietary intake in Latina adolescents. Second, this study is limited because both the psychological
and dietary intake data are self-reported which can threaten the validity of the data. Third, we
measured intrinsic and extrinsic motivation with the motivation to eat fruits and vegetables
questionnaire, rather than a general measure of motivation for healthy dietary behavior or a
more specific measure of motivation to decrease sugar and increase fiber, which were the
specific goals of the intervention. Fourth, the homogeneity may prevent generalizing these
findings to other adolescents who belong to a different racial or ethnic group.
Implications for research and practice: This study is important in expanding the current
literature, supporting Self Determination Theory by demonstrating that intrinsic motivation may
be an important predictor of healthy dietary intake in overweight Latina adolescent girls. This
study suggests a plausible avenue for the development of interventions. The promotion of
intrinsic motivation in the development and implementation of nutrition education
interventions may assist in improving dietary intake among minority youth. For example, future
interventions could focus on the promotion of intrinsic motivation because increases in intrinsic
motivation were associated with increases in healthier dietary behavior. However, further
research is still needed. Intrinsic motivation was also associated with increased total sugar
intake. Therefore, future interventions should study whether intrinsic motivation may have a

65

negative effect on healthy behavior and examine different modalities for motivating overweight
Latino youth to improve dietary behaviors.
Acknowledgements: We would like to thank project managers Kami McClure and Christina
Ayala, and the SANO LA team. Finally, we are grateful for our study participants and their
families for their involvement. This work was supported by the NCI Centers for Transdisciplinary
Research on Energetics and Cancer (TREC) (U54CA116847, U54CA116848, U54CA116849,
U54CA116867, U01CA116850). All authors were funded by NCI as part of the TREC initiative.
The opinions or assertions contained herein are the private ones of the authors and are not
considered as official or reflecting the views of the National Institutes of Health.

66

Chapter 4: Unique Contributions of the Meanings of Eating and
Motivation to Fruit Intake in Minority Youth

Abstract
This study investigated whether motivation and the meanings of eating uniquely contributed to
fruit and vegetable consumption in a predominantly Latino sample of elementary school
children Participants were 251 predominantly through fifth grade elementary school children
(Mean age = 9.7 years; 57.8% female; 41% Latino) in Los Angeles County. Meanings were
assessed with the Meanings of Eating Index. Motivation was assessed with the Motivation to Eat
Fruits and Vegetables scale, which is an adapted version of the Motivation for Healthy Behaving
measure from the Treatment and Self Regulation Questionnaire (TSRQ). Intrinsic motivation
and the meanings of index factor, eating on behalf of others, were independently associated
with fruit intake in a predominantly Latino sample of elementary school children. However, the
amount of variance accounted for by motivation and the meanings of eating behavior was small.
In conclusion, both motivation and the meanings of eating behavior uniquely contributed to
dietary intake in Latino youth. Although the direct influence of motivation and meanings may
be small, the information gained from these and future studies may point to new directions for
interventions aimed at improving dietary choices.

67

Introduction
Research illustrates that a diet high in fruit and vegetables promotes health by
contributing to bone development (Prentice et al., 2006) and to the prevention of several
chronic diseases such as cardiovascular disease, obesity, and certain cancers (Holt et al., 2009;
Hung et al., 2004; Maynard, Gunnell, Emmett, Frankel, & Davey Smith, 2003; Paolini, Sapone,
Canistro, Antonelli, & Chieco, 2003). Additionally, increases in fruit and vegetable intake are
associated with decreases in intake of high-fat and high-sugar foods in children (Epstein et al.,
2001). Therefore, increasing fruit and vegetable consumption is a priority to help support
health, prevent future disease, and reduce pediatric overweight and obesity (Ogden et al., 2010;
US Department of Health and Human Services, 2001).
Minority youth are disproportionately affected by the current epidemic of childhood
obesity (Ogden et al., 2010). In 2007-2008, 38.9% of Mexican Americans and 35.9% of non-
Hispanic blacks, ages two through nineteen were overweight (BMI percentile ≥ 85
th
percentile
for age and gender) or obese (BMI percentile ≥ 95
th
percentile for age and gender), compared
to 29.3% of non-Hispanic whites (Ogden et al., 2010). In addition, the majority of Mexican
American and non-Hispanic black children are not meeting recommendations for fruit and
vegetable intake (Lorson, Melgar-Quinonez, & Taylor, 2009) . Interventions must be targeted to
address these health and behavioral disparities.
In order to develop effective dietary interventions for children and adolescents, it is
important to understand the factors that determine eating behavior in these populations.
Research has repeatedly shown that theory-based interventions that are guided by relevant

68

behavioral theories are more likely to significantly impact dietary behaviors in youth
(Baranowski et al., 1999; Baranowski et al., 1997; Spruijt-Metz, 1999). Theory is crucial because
it provides a framework to examine the relationships among constructs and delineates factors
and determinants to be studied (Brown et al., 1991; Glanz et al., 1990, 1997; Spruijt-Metz,
1999). To date, most studies utilize the Transtheoretical Model, the Theory of Planned Behavior
or the Social Cognitive Theory (SCT) as health behavior theories to explain and change dietary
behavior (AD McClain et al., 2009). However, research illustrates that the constructs from these
theories, such as knowledge and self-efficacy, are not consistently associated with dietary intake
in children and adolescents (AD McClain et al., 2009). This may explain why interventions that
have tried to improve dietary behavior in youth have been relatively unsuccessful (Kamath et al.,
2008; Summerbell et al., 2005). This study examines the Theory of Meanings of Health Behavior
(TMB) and Self Determination Theory (SDT), two less explored, but promising theories.
The Theory of Meanings of Health Behavior (TMB), developed by Spruijt-Metz (1999),
and based on prior work from several other researchers (Ikard et al., 1969; Ikard & Tomkins,
1973; C. Perry, 1999; C. L. Perry & Kelder, 1992), is a behavioral theory that supplements existing
models by highlighting affective and developmental factors in adolescence. TMB proposes that
adolescents and young adults infuse health-related behaviors with affective meanings (Spruijt-
Metz, 1995, 1999), defined by Jessor as the symbolic significance of behavior (Jessor, 1984; C. L.
Perry & Kelder, 1992; C. L. Perry et al., 1999). Meanings are influenced by both conscious and
subconscious experiences of self, family, friends, community, society, and culture (Hsia &
Spruijt-Metz, 2003; Spruijt-Metz, 1995, 1999). Meanings are invoked in particular situations and
can, for instance, reflect an individual’s need for psychological comfort (Spruijt-Metz, 1999).

69

Meanings are thought to influence behavior directly without the cognitive weighing of pros and
cons, bypassing knowledge, rationality and cognition. Previous research on the meanings of
health behavior illustrate that the meanings of eating behavior are associated with dietary
intake in Latino youth (A McClain et al., 2009, (in review)).
Self Determination Theory’s (SDT) construct of motivation has been cited as an
important determinant of behavior change (Ryan & Deci, 2000a). In SDT, various forms of
motivation are placed on a continuum of self-determination of the behavior from non self-
determined (extrinsic motivation) to self-determined (intrinsic motivation) (Spruijt-Metz &
Saelens, 2006). These various forms of motivation consist of extrinsic, introjected, identified,
integrated and intrinsic motivation. Extrinsic motivation is a form of motivation regulated by
external pressures and incentives. When people are extrinsically motivated, they perform a
behavior to obtain a reward or to avoid a negative outcome. Introjected motivation occurs
when external pressures regulating a behavior become internalized into the self. Therefore,
behavior is regulated through introjections, or through guilt and ego evolvement. Identified
motivation is the first type of self-determined motivation. When a behavior is identified, it is
performed because it is valuable and important to the individual. Integrated motivation is the
second type of self-determined motivation that emerges when the behavior performed is
integrated with other aspects of the self. Lastly, intrinsic motivation is the prototype of self-
determination and the highest form of self-determination. When intrinsically motivated, people
engage in the behavior just for the pleasure and satisfaction derived while performing the
activity. Research illustrates that more self determined forms of motivation (i.e. intrinsic
motivation, integration and identification) are positively associated with long-term maintenance

70

of weight loss and generally higher levels of well-being (Williams et al., 1996). Conversely, non
self-determined forms of motivation (i.e. external regulation, and introjections) are associated
with negative health and well-being outcomes (Deci & Ryan, 2000).
Purpose of Study: The association between meanings, motivation, and dietary intake in
minority youth has not been previously investigated. Therefore, the purpose of this study was to
investigate whether motivation and the meanings of eating uniquely contribute to fruit and
vegetable consumption in a predominantly Latino sample of elementary school children. It was
hypothesized that both meanings of eating and motivation to eat fruits and vegetables would
contribute uniquely to fruit and vegetable consumption in this ethnically diverse sample of
elementary school children.
Methods
Recruitment and Procedures: This study uses data from a school-based study that aimed to
validate a set of psychosocial scales that were originally developed in non-minority middle
school populations for use in minority children ages 8-11. Participants were elementary school
children from public and parochial schools in Los Angeles County. Third through fifth grade
minority children provided cross-sectional data for the study. In order to obtain a
predominantly Latino & African American sample, school selection was based upon the ethnic
makeup of the school. The ethnic breakdown of each school was assessed via the Archdiocese’s
of Los Angeles’ or the individual school’s website. If the information was not provided from
either website, schools were contacted directly and asked for the ethnic breakdown of their
student population. Elementary school principals were contacted to gauge interest in their

71

school’s participation. Interested principals presented the study to third through fifth grade
teachers and asked if they would like their classes to participate in the survey. Study description
letters were provided to principals and teachers. We attempted to contact ten school principals.
Two were unable to spare the time to participate and two did not respond after initial contact.
Thus, a total of six schools participated in the present study, four Catholic schools and two
public schools. The Catholic schools provided three classes from each grade level (third through
fifth) for a total of twelve classrooms. The two public schools provided a total of fourteen
classrooms.
All children in the classrooms of interested teachers were invited to participate. Using a
standardized script, the study coordinator explained the study and provided parent refusal
forms. Forms were available in both English and Spanish. Students were instructed to provide
the refusal forms to parents that included a description of the study. Parents were asked to
return the forms if they did not want their children to participate. Student assent forms were
distributed to students that did not return parent refusal forms on the first day of data
collection. Students who did not have active parental refusal and who provided written student
assent were allowed to complete the survey. 73% of students participated in the study.
The paper-and-pencil confidential survey was administered at a time of day most
convenient to the teacher. The survey included questions on dietary intake as well as
psychosocial constructs, such as the meanings of eating behavior and motivation. Time to
complete study ranged from 30 minutes to 90 minutes, usually with younger students taking
longer to complete the survey. Data collectors instructed participants on completing the survey
and were available to answer questions. Bilingual education has been removed from public

72

schools, therefore the surveys were in English language only, however Spanish speaking data
collectors were available to answer questions. All procedures were approved by the Institutional
Review Board, the school districts, and the Archdiocese.
Measures
Fruit and Vegetable Intake: Fruit and vegetable intake was assessed with a previous day
food checklist (F. E. Thompson et al., 2002). The food checklist is a form of a food record that is
comprised of a list of foods. The respondent makes a check besides each food item he or she
ate the day before. The checklist method is most appropriate in settings for assessment of a
limited set of foods or nutrients (F. Thompson & Subar, 2001). The strength of the previous day
checklist is the ease of checking off a food item as opposed to recording a complete description
of the food, and this strength is especially relevant when gathering data in young children (F.
Thompson & Subar, 2001). In this case, the checklist was developed to assess the ‘‘core foods’’
of fruit and vegetables. The checklist stated: “Please tell us if you ate any of these foods
yesterday”. To assess fruit intake the participants needed to check the box that stated “fruit
cocktail, fruit salad” and/or “all other fruits (other than juice)”. To assess vegetable intake the
participants needed to the check the boxes that stated “salad greens, such as lettuce and
spinach”, “lettuce in other mixtures, such as sandwiches”, and/or “all other vegetables alone or
in mixtures, such as salads”. Fruit and vegetables scores were assessed by the sum of items
checked. A machine scannable version of the checklist is publicly available at
http://riskfactor.cancer.gov/diet/screeners/daily.html.

73

Motivation to Eat Fruits and Vegetables: Motivation to Eat Fruits and Vegetables was
assessed with an adapted version of the Motivation for Healthy Behaving measure from the
Treatment and Self Regulation Questionnaire (TSRQ) measure developed by Williams et al.
(Williams & Deci, 2001; Williams et al., 1998). The 17-item measure generates four main
subscales: (1) extrinsic (Cronbach’s alpha = 0.67), (2) introjected (Cronbach’s alpha = 0.69), (3)
identified (Cronbach’s alpha = 0.81), and (4) intrinsic motivation (Cronbach’s alpha = 0.71).
Meanings of Eating Index (MEI): Meanings of eating was assessed with a scale
previously validated in minority youth (A McClain et al., 2009, (in review)). Subjects rated the
frequency with which they act on a specific meaning of eating behavior. The 19-item measure
generates four main subscales: (1) Eating on Behalf of Others (Cronbach’s alpha = 0.50), (2)
Social Eating (Cronbach’s alpha = 0.85), (3) Disturbed Eating (Cronbach’s alpha = 0.76), (4) Eating
for Personal Well Being (Cronbach’s alpha = 0.91), and (5) Personal Negative Emotions
(Cronbach’s alpha = 0.94). A 3-point scale, ranging from 1 (never) to 3 (often) was used. Higher
scores indicate higher levels of performing that specific eating behavior. Overall, the reliability
data indicate that the internal consistencies are moderate to high.
Statistical analysis: Descriptive statistics were calculated to present demographic data.
Pearson’s simple correlation test was performed to search for associations between
psychosocial and dietary variables. A hierarchical multiple regression analysis was performed as
a model for predicting previous day fruit and vegetable intake. The following sequence was
followed: first, demographic variables were entered as model 1; second, all of the meanings of
eating variables were entered into the model as model 2; third, because intrinsic, introjected,
identified, and extrinsic motivation had relatively high correlations, only intrinsic motivation and

74

extrinsic motivation were entered into the model as model 3 to avoid multicolinearity. In order
to find the best model, only variables left in the model that were significant at the 0.050 level
were retained. Because regression analysis cannot account for the clustering of data in schools
that violates the assumption of independence, multilevel regression (MLM) analysis was then
conducted on the best model fit to control for the random effect of school. Cronbach’s alpha
was used to assess the internal consistency of the total score of eat psychological test. All
statistical analysis were performed with SAS v.9.2 software (Cary, NC). Statistical significance
was set at 0.05.
Results
Table 9 shows the demographic, psychosocial and dietary characteristics of our
participants. Participants were 251 predominantly Latino (41.2%) and African American (23.6%)
third through fifth grade elementary school children from two public and four private schools in
Los Angeles County. The mean age of participants was 9.7 years (SD = 1.0), 57.8% were female.
Previous day fruit and vegetable intake was reported by 60% and 15%, respectively. There were
no significant age or ethnic differences in any of the variables used in these analyses (data not
shown).

75

Table 9: Demographic, psychosocial and dietary characteristics of participants
Variable N (%)

Gender
Male 106 (42.2%)
Female 145 (57.8%)
Age (years) 9.7 (1.0)
Ethnicity
Latino 103 (41.2%)
Asian 44 (17.6%)
Black 59 (23.6%)
Multi-racial 30 (12.0%)
Other 14 (5.6%)
Previous Day Fruit Intake
Yes 101 (60%)
No 150 (40%)
Previous Day Vegetable Intake
Yes 38 (15%)
No 213 (85%)
Personal Well Being 3.0 (1.6)
Social Eating 2.7 (1.6)
Disturbed Eating 2.3 (1.8)
Eating on Behalf of Others 2.5 (1.9)

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Table 10 shows a correlation matrix of the psychosocial and dietary variables for the 251
participants. The four motivation scales were significantly and positively associated. The five
meanings of eating index scales were also significantly and positively associated. However, the
motivation and meanings of eating scales were not significantly associated with each other.
Intrinsic motivation, introjected motivation, and identified motivation had significant, positive
correlations with fruit intake. The meanings of eating index factors of personal well being,
disturbed eating and eating on behalf of others had significant, negative correlations with fruit
intake. Neither the motivation scales, nor the meanings of eating index scales were associated
with previous day vegetable intake in this sample.
Table 11 displays the results of correlation coefficients by Pearson’s simple correlation
test and standard regression coefficients (β) by hierarchical stepwise multiple regression for the
demographic, psychosocial, and fruit intake. The hierarchical stepwise multiple regression
revealed that ethnicity, intrinsic motivation, and eating on behalf of others were significantly
and independently associated with previous day fruit intake (Table 3, final model, R
2
= 0.051),
after controlling for age and gender. However, no more than 5.1% of the variance in previous
day fruit intake was accounted for by age, gender, ethnicity, motivation, and meanings.
Using multilevel regression analyses to control for the random effect of school on the
best model fit also revealed significant associations motivation, meanings and previous day fruit
intake. MLM showed intrinsic motivation (ß = .108, p =0.046) and eating on behalf others (ß = -
0.063, p < .024) are statistically and independently associated with previous day fruit intake
when controlling for age, gender, ethnicity (p > .05) and the random effect of school.

Table 10: Correlation matrix of psychosocial and dietary variables (n = 251)

Variables
1 2 3 4 5 6 7 8 9 10 11
1 Intrinsic Motivation -
2 Introjected Motivation 0.348

-
3 Identified Motivation 0.780

0.399

-
4 Extrinsic Motivation 0.381

0.612

0.320

-
5 Personal Negative

-0.014 0.021 0.016 0.073 -
6 Personal Well Being 0.122 -0.013 0.118 0.042 0.811

-
7 Social Eating -0.010 0.098 0.008 0.058 0.789

0.749

-
8 Disturbed Eating -0.094 0.044 -0.107 0.040 0.784

0.693

0.680

-
9 Eating on Behalf of

-0.061 0.102 -0.056 0.060 0.745

0.643

0.749

0.701

-
10 Fruit 0.171

0.160

0.158

0.131 -0.120 -

-0.106 -

-

-
11 Vegetables -0.005 0.082 0.037 0.102 -0.056 -0.105 -0.071 -0.030 0.014 0.282

-
Value is correlation coefficient (r). * p<0.05 ** p<0.01.
77

Table 11: Simple correlation coefficients (r) and standard regression coefficients by hierarchical stepwise multiple regression (β) between
psychosocial variables and fruit intake (n = 251)

Hierarchical stepwise regression analysis
Variable Simple

Model 1 Model 2 Model 3 Final Model
r P β P β P β P β P
Gender -0.018 NS NS NS NS NS NS NS NS NS
Age 0.0145 NS NS NS NS NS NS NS NS NS
Ethnicity -0.137 0.030 -.067 0.032 NS NS -0.066 0.048 -0.065 0.049
Intrinsic Motivation 0.171 0.011 - - 0.119 0.026 0.113 0.037 0.109 0.040
Extrinsic Motivation 0.131 NS - - - - - - - -
Personal Negative

-0.120 NS - - - - - - - -
Personal Well Being -0.128 0.042 - - - - NS NS - -
Social Eating -0.106 NS - - - - - - - -
Disturbed Eating -0.134 0.034 - - - - NS NS - -
Eating on Behalf of Others -0.153 0.015 - - - - -0.066 -.050 -0.71 0.009
Adjusted R
2
0.007 0.025 0.043 0.051
F 1.57 -0.197 2.40 0.05 2.40 0.022 3.34 0.006
R2, multiple coefficient of determination. Only significant findings are reported. NS, not significant. Model 1, adjusted for
gender, age, and sex; Model 2, adjusted for Model 1 and intrinsic motivation; Model 3, adjusted for Model 2 and personal
negative emotions, personal well being, social eating, disturbed eating, and eating on behalf of others. Final model, includes
intrinsic motivation and eating on behalf of others, adjusting for gender, age and ethnicity.

78

79

Chapter 4: Discussion and Conclusion
This was the first study to examine both motivation and the meanings of eating together
on dietary intake in minority youth. In the present study, the relation between motivation,
meanings of eating behavior, and fruit and vegetable intake was investigated cross-sectionally in
a predominantly Latino sample of elementary school students. Intrinsic motivation and the
meanings of eating factor, eating on behalf of others, were independently associated with fruit
intake. However, the amount of variance accounted for by motivation and the meanings of
eating behavior in predicting fruit intake was small. Additionally, neither motivation, nor the
meanings of eating were associated with vegetable intake in this sample.
The positive association between intrinsic motivation and fruit intake has been
supported in the literature. Trudeau et al found that intrinsic motivation for eating healthy was
associated with higher intake of fruit and vegetables in adults (Trudeau et al., 1998).
Additionally, several studies have found that health-related behaviors are associated with self-
reports of motivation for engaging in that behavior (Cox et al., 1987; Ryan et al., 1995; Williams
et al., 2002; Williams et al., 1996).
The negative association between eating on behalf of others and fruit intake might be
related to the tendency for children to eat what is given to them. Eating on behalf of others
represents how children abide by the learned cultural values in a society. In Latino culture,
familismo and respeto are core values (Suarez & Ramirez, 1999). Familismo represents a strong
identification with family and extended family (Suarez & Ramirez, 1999). Respeto represents
respect of people based on age, gender and social position of authority. This notion may

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encourage members of the Latino community not to question orders from people of authority
(e.g. parents) (Suarez & Ramirez, 1999). Therefore, out of respect for parents, children may only
eat the foods provided for them. Children may also not ask for additionally foods, such as fruit.
If healthy foods such as fruit are not provided by parents or those in authority positions,
children’s consumption of fruit will also be low.
There are several possible explanations for the lack of association between motivation,
meanings and previous day vegetable intake in this sample. First, most of the children in this
study did not report eating any vegetables at all. Only 15% of the participants reported
consuming any vegetables the previous day. Indeed, current literature suggests only 17% of
children are meeting recommended daily vegetable intake (Lorson et al., 2009). Therefore,
there might not be enough variance in this behavior to allow for stable predictions. Second, it is
possible that the dietary instrument did not accurately capture fruit and vegetable intake. We
only assessed whether or not fruits and vegetables were consumed in the previous day. Volume
and amount were not taken into account. Additionally, because the instrument only measured
one previous day of intake, it is unclear if the data can generalize to usual intake. In a study
comparing one-day records of intake to seven-day records of intake for usual intake,
correlations with usual intake ranged from 0.43-0.64 for one-day records compared to 0.71-0.90
for seven-day records (Freudenheim, Johnson, & Wardrop, 1987). One day records did not
capture as much information as multiple days of measurement. Therefore, the one day food
checklist may not accurately capture usual fruit and vegetable intake. It may be necessary to
assess fruit and vegetable intake over the course of multiple days in order to confidently
estimate the true average intake (Basiotis, Welsh, Cronin, Kelsay, & Mertz, 1987).

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The present study has several limitations. First, the measure of dietary intake may not
accurately assess usual fruit and vegetable intake, as discussed above. Second, the amount of
variance accounted for by both the meanings of eating and motivation was very small.
However, the statistical significance in this study illustrates that meanings and motivation may
be one piece of the puzzle. Therefore, even though the amount of variance accounted for by
motivation and the meanings of eating behavior in predicting dietary intake may be small, these
and other affective determinants may still be important. Third, the cross-sectional nature of this
study does not inform the direction of the relationships among motivation, the meanings of
eating, and fruit and vegetable intake. It is possible that meanings may be shaped by what
people eat. Future studies may benefit from using a different method of dietary intake and
looking at the relationship among motivation, the meanings of eating behavior, and dietary
intake longitudinally.

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Chapter 5: Conclusion

Summary of Findings
This dissertation examined the relationship between motivation, meanings of eating,
and dietary intake in Latino youth. Study 1 identified the affective meanings of dietary intake
among minority children, developed factor items for the Meanings of Eating Index (MEI),
validated the MEI, and explored whether the meanings identified facilitate or hinder healthy
dietary behavior in Latino youth. Study 2 examined the effects of intrinsic and extrinsic
motivation for eating fruits and vegetables on dietary intake at baseline and on changes in
dietary intake between baseline and follow-up in a randomized intervention in overweight
Latina adolescents. Study 3 investigated whether motivation and meanings of eating uniquely
contribute to fruit and vegetable consumption in a predominantly Latino sample of elementary
school children.
In Study 1, a 58-item Meanings of Eating Index was developed from focus groups in
ethnically diverse elementary, middle and high school students. The initial 58-item MEI was
then reduced to a 19-item, 5-factor scale which comprised the final MEI. In Latino youth, three
of the five scales (Personal Negative Emotions, Personal Well Being, and Eating on Behalf of
Others) had significant associations with frequency of dietary intake in the expected direction.
Personal negative emotions were positively associated with unhealthy dietary intake. Personal
well-being was positively associated with healthier dietary intake. Additionally, the moderate
intercorrelation between these factors suggested that the respective items were tapping distinct

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factors. The moderate to high internal consistencies suggested that most factors were stable
enough to use as a subscale. This study indicated that although research is needed to further
substantiate its utility, the MEI shows promise as a tool for understanding the affective
determinants of dietary intake in minority children.
Study 2 demonstrated that intrinsic motivation was associated with higher whole grain
intake and extrinsic motivation was associated with lower fiber intake, after controlling for
confounders in a sample of overweight Latina adolescents. At follow-up, increases in intrinsic
motivation were associated with increases in total sugar, decreases in total fat, and increases in
soluble fiber after controlling for confounders. This study indicated that intrinsic motivation may
be an important determinant of healthy dietary behavior. Considering the high risk for
overweight and obesity among Latinos, it seems warranted to explore intrinsic motivation as a
potentially modifiable factor for healthy dietary intake in this population. The association
between intrinsic motivation and increased total sugar intake may be a result of increased
consumption of foods high in natural sugar, such as fruit and milk. Therefore, this study also
brought to light the importance of providing clear guidelines about hidden sugars in food,
clarifying mixed messages the participants may have received from other sources, and
highlighting the unique advantages of the intervention versus previous approaches.
In Study 3, intrinsic motivation and one MEI factor, eating on behalf of others, were
independently associated with fruit intake in a predominantly Latino sample of elementary
school children. However, the amount of variance accounted for by motivation and the
meanings of eating behavior was small. Additionally, neither the meanings of eating nor
motivation were associated with vegetable intake. This study partially supported Studies 1 and

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2, showing that motivation and meanings of eating were associated with healthy dietary choices
in Latino youth. The low prevalence of vegetable intake in this sample may explain null
associations between motivation, meanings of eating, and vegetable intake. Nevertheless, this
study indicated that even when an abbreviated measure of dietary intake is used, these
constructs still demonstrated significant relationships with diet. However, further research using
more valid and reliable assessment of dietary intake is needed.
Limitations
The studies presented in this dissertation have several limitations. First, both the
psychosocial and dietary intake data are self-reported which can threaten the validity of the
data. This may especially be an issue with younger children who may not be able to accurately
recall details regarding dietary intake. However, research suggests that from the age of 8 years
there is a rapid increase in the ability of children to self-report food intake (Livingstone &
Robson, 2000). Furthermore, surrogate child measures of dietary intake accrued from parents
or other adults might yield accurate data on food consumed in the home, but are less accurate
for food consumed outside the home(Livingstone & Robson, 2000). Therefore, we felt that self-
reported dietary intake would provide the best possible data, short of doubly labeled water.
Second, due to the cross-sectional nature of Studies 1 and 3, the temporal relationships cannot
be established. Third, this dissertation focused on investigating the psychosocial determinants
of Latino youth, an understudied population. Therefore, the study samples in this dissertation
are a relatively homogenous group (i.e. majority Latino). The homogeneity may prevent
generalizing the findings to other adolescents who belong to a different racial or ethnic group.
Factor structure and scale properties may also be different in other populations. Future research

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may benefit from examining the meanings of eating behavior longitudinally in larger, more
diverse samples using more valid and reliable measures of dietary intake.
Fourth, because this dissertation used broad theories of health behavior change that
focus on determinants of individual behavior, important key values in Latino culture may have
been missed. Key values in Latino culture known to impact health include familismo, simpatia,
respeto, and fatalism (Suarez & Ramirez, 1999). A strong attitude towards familismo (family) is
a core characteristic in Latino culture. Latinos have a strong identification with family and
extended family (Suarez & Ramirez, 1999). Simpatia is the notion of being nice, tolerable and
agreeable. Consequently, Latinos may avoid conflict in social and cultural situations (Suarez &
Ramirez, 1999). Respeto represents respect of people based on age, gender and social position
of authority. This notion may encourage members of the Latino community not to question
orders from people of authority (e.g. parents) (Suarez & Ramirez, 1999). Fatalismo is the belief
that people do not have control over personal health, an example of an external locus of
control. Instead, the Latino culture believes that health is about destiny, fate and God’s will
(Suarez & Ramirez, 1999). These key values may have a significant impact on the meanings of
eating, motivation, and subsequent dietary intake. Latino youth may eat because their family is
eating (representing the MEI factor Disturbed Eating), illustrating respect towards family
(familismo and respeto) and avoidance of potential conflict (simpatia). Future research may
benefit from measuring meanings, motivation and acculturation concurrently. The value of
fatalismo may influence intrinsic motivation to eat healthy. If community members believe
their personal food choices, weight gain, and subsequent deleterious health outcomes are
driven by fate, intrinsic motivation to eat healthy may never develop. Future research should

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incorporate how these important dimensions of acculturation may influence motivation and the
meanings of eating behavior in Latino youth. Despite the potential limitations of this
dissertation, the findings from these studies take the field closer to understanding the affective
determinants of dietary intake in Latino youth.

Implications
To develop effective dietary interventions for children and adolescents, it is important
to understand the factors that determine eating behavior in these populations. Research has
repeatedly shown that theory-based interventions guided by relevant behavioral theories are
more likely to significantly impact dietary behaviors in youth (Baranowski et al., 1999;
Baranowski et al., 1997; Spruijt-Metz, 1999). However, even when interventions to change diet
in adolescent populations are solidly based in theory, significant intervention effects remain
elusive (Summerbell et al., 2009). It was hypothesized that this may be related to the fact that
most theory-based interventions continue to use predominantly cognitive theories of behavior
change, which may not be suitable for children and adolescents. Research suggests that less
cognitively-based, more emotionally-based determinants may drive adolescent health-related
behavior (Kirscht, 1983; Spruijt-Metz, 1999). Therefore, this dissertation addresses gaps in the
literature by investigating two behavioral theories, Self Determination Theory and the Theory of
Meanings of Behavior, which focus on affective determinants of behavior. These studies make a
significant contribution to the current literature because they examined these affective theories

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of health behavior to determine and understand modifiable risk factors for healthy dietary
intake in a population at-risk for obesity.
The studies presented here examine motivation, the meanings of eating behavior and
dietary intake in a predominantly Latino sample, a population with an almost 40% prevalence of
risk for overweight and overweight compared to 29% in whites (Ogden et al., 2010). These
studies tested the Theory of the Meanings of Health Behavior hypothesis that meanings are
associated with dietary intake. They also tested Self Determination Theory’s hypotheses that
intrinsic motivation is related to healthy dietary intake and extrinsic motivation is related to
unhealthy dietary intake. The findings from these studies partially support these theories.
Study 1 partially supports a priori hypotheses and previous research on the Meanings of
Health Behavior. In Study 1, the Meanings of Eating Index was developed and more than the
expected three factors of social, functional and personal meanings found in prior literature on
the meanings of health behavior (e.g. smoking, physical activity, etc.) emerged. Rather, five
factors were derived. This larger number of factors may be indicative of the multi-faceted and
complex nature of eating behavior. There are biological and neurological factors that shape and
inform the psychological states that guide eating behavior (Wardle & Carnell, 2009; Zald, 2009).
There are decisions regarding time (Bisogni et al., 2007) and the social, physical, and macro-
environment in which people live (Kumanyika, 2008; Larson & Story, 2009; Story, Neumark-
Sztainer, & French, 2002). Additionally, personal development, personal relationships, and
changes in life situations and settings over a lifespan alter an individual’s eating behavior
(Devine, 2005; Devine, Connors, Bisogni, & Sobal, 1998; Gillespie & Johnson-Askew, 2009; AD
McClain et al., 2009; Sobal & Bisogni, 2009; Wethington & Johnson-Askew, 2009).

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In previous research on food choice, Furst et al (Furst et al., 1996) identified five main
categories that influence food choice decisions: cultural ideals, personal factors, resources,
social factors, and present contexts. Cultural ideals represent an individual’s learned system of
values and rules shared by a group of people (Spradley, 1972). These values allow people to
judge certain eating behaviors as “right”, “normal”, or “unacceptable”. For example, people in
the depression era were taught that it was “unacceptable” to leave food on their plate. Personal
factors represent individual characteristics of a person that influence eating behavior. Personal
factors can be biological (e.g. hormones and satiety signals), psychological (e.g. food preferences
and personality), and social (e.g. gender and social role). For example, people may develop a
personal identity with their eating behavior such as a being someone who is “vegetarian” or a
“healthy eater” (Bisogni, Connors, Devine, & Sobal, 2002). Resources represent financial assets
(e.g. income), material capital (e.g. cooking equipment), human capital (e.g. skills to cook), and
social capital (e.g. relationships). For example, a person may not have the financial assets to
afford healthy food or the human capital (e.g. skills) to prepare healthy food. Social factors
represent the relationships that influence eating behavior. Most eating occurs in groups, rather
than individually, where the interests of many people are negotiated (Sobal, 2000; Sobal &
Nelson, 2003). Different relationships allow for different opportunities. For example, a family
may be supportive in encouraging healthy choices or may hinder healthy eating behavior by
serving more unhealthy options. Present contexts represent the social and physical
environments that influence eating behavior. For example, the built environment can provide
infrastructure (e.g. liquor store or grocery store location) that may shape food choice decisions.

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Similar to Furst et al’s research on food choice (Furst et al., 1996), the Meanings of
Eating Index (MEI) was designed to identify factors that influence eating behavior. However, the
MEI was specifically developed for minority youth. Additionally, the MEI’s sole focus is to
understand the emotional/affective determinants of eating behavior, rather than the cognitive
or material factors associated with eating behavior. Therefore, the MEI overlaps with some of
Furst’s work; however the focus on emotional determinants in minority youth provides
additional information. The factor, “Personal Negative Emotions” represents how minority
youth may use food as a distraction from dealing with emotions. The factor, “Personal Well
Being” is similar to Furst’s personal factor (Bisogni et al., 2002), representing a personal identity
that minority youth may develop with eating certain foods. In this case, the identity created is
of someone who is healthy and wants to eat healthy. The factor, “Social Eating” represents how
minority youth may eat socially to feel approval as part of group and to emotionally connect
with others, rather than feeling left out and isolated. The factor, “Disturbed Eating” represents
how eating may be a form of rebellion against harsh self or externally inflicted constraints (i.e.
family members who harass minority youth about their weight) (Loro & Orleans, 1981). Lastly,
the factor “Eating on Behalf of Others” relates to Furst’s cultural ideals factor (Furst et al., 1996),
representing the learned values in a society. In Latino culture, the factor may relate to the
values of familsmo and respeto. Therefore, Latino children may eat when they are not hungry
because Latino culture ingrains the belief that it is disrespectful to refuse a meal prepared for
family meal time.
In summary, previous research demonstrates the multi-faceted factors that drive food
choice in adult behavior (Furst et al., 1996). The Meaning of Eating Index expands upon

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emotional personal factors related to eating behavior in youth. The larger than hypothesized
number of factors derived from the MEI supports previous findings regarding the complexity of
dietary choices (Bisogni et al., 2007; Devine, 2005; Devine et al., 1998; Furst et al., 1996;
Gillespie & Johnson-Askew, 2009; AD McClain et al., 2009; Sobal & Bisogni, 2009; Wardle &
Carnell, 2009; Wethington & Johnson-Askew, 2009; Zald, 2009). This finding provides evidence
that the complex nature of eating behavior also applies to minority youth, specifically personal
determinants.
Studies 1 and 3 partially support the hypothesis that meanings of eating behavior are
associated with dietary intake, with some (but not all) MEI factors showing a significant
association with dietary intake. In Study 1, several meanings of eating factors were significantly
correlated in the expected directions with dietary intake in Latino youth. Personal Negative
Emotions was positively associated with junk food and salty snack consumption. Personal Well
Being was positively associated with eating fruits and vegetables. Conversely, in Study 3 these
relationships were not significant. In Study 3, Eating on Behalf of Others was negatively
associated fruit intake and did not account for much variance. Although the associations
between the meanings of eating and dietary intake vary, the Meanings of Eating Index did
demonstrate significant relationships in both studies, using different dietary measures. These
findings suggest that the meanings of eating may help to understand affective dietary behavior
in minority youth, however further investigation is needed.
Studies 2 and 3 partially support a priori hypotheses and previous research on Self
Determination Theory (Cox et al., 1987; Ryan et al., 1995; Trudeau et al., 1998; Williams et al.,
2002; Williams et al., 1996) that intrinsic motivation is associated with healthy dietary intake.

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The relationship between extrinsic motivation and dietary intake was not consistent. In Study 2,
baseline findings support previous literature that illustrates intrinsic motivation is associated
with healthier dietary choices and extrinsic motivation is associated with unhealthier dietary
choices. However, at follow-up, results were mixed. Increased intrinsic motivation was
associated with both increased fiber intake and decreased fat intake (i.e. healthy dietary
behavior), but also to increased total sugar intake (i.e. unhealthy dietary behavior). In Study 3,
intrinsic motivation was associated with fruit intake, but not vegetable intake in a majority
Latino sample of elementary school children. Extrinsic motivation was not significantly
associated with either fruit or vegetable intake after controlling for confounders. The
inconsistent findings between motivation and dietary intake may be due to the use of different
dietary measures across studies. Nevertheless, intrinsic motivation was significantly associated
with healthier dietary behavior in both studies, using dietary measures. Therefore, these studies
are important in expanding the current literature, supporting Self Determination Theory by
demonstrating that intrinsic motivation may be a predictor of healthy dietary intake in minority
youth.
In this dissertation, dietary intake was measured using three different instruments.
Study 1 used a food frequency questionnaire (Willett et al., 1985), Study 2 used three-day
dietary records (O'Connor et al., 2001), and Study 3 used a previous day food checklist (F. E.
Thompson et al., 2002). Each instrument used in this study has its respective strengths and
weaknesses.
A food frequency questionnaire (FFQ) estimates a respondent’s usual intake of food
over a specific period of time. Information is collected on frequency; however little to no details

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are collected regarding food characteristics, such as meal preparation (e.g. fried or grilled) or the
combination of foods in a meal. Food frequency questionnaires have two major strengths. First,
respondent burden is low for FFQs compared to multiple diet records or recalls (F. Thompson &
Subar, 2001). Second, FFQs are inexpensive to administer and process. However, there are also
many limitations to this instrument. First, using a FFQ results in a substantial amount of
measurement error (Kipnis et al., 2003; Subar et al., 2003; F. Thompson & Subar, 2001) and may
yield inaccurate estimates of average intake because many details of dietary intake are not
measured. These inaccuracies result from an incomplete listing of all possible foods and from
errors in estimation of frequency and usual serving size (F. Thompson & Subar, 2001).
Dietary records record the type and amounts of foods and beverages consumed by a
respondent over three consecutive days. In Study 2, the amounts consumed were measured
using scales, such as rulers, cups and tablespoons. At the end of the recording period, a trained
interviewer reviewed the records with the respondent to clarify entries and to probe for
forgotten foods. The major strength of dietary records is that they provide fairly accurate
information on the quantity of food consumed during the recording period (Gibson, 2005).
However, there are also many limitations. First, dietary records are subject to reporting bias.
Recording foods as they are being eaten may affect quality and quantity of dietary choices and
lead to selective reduction of consumption of certain foods or overall caloric intake (Rebro,
Patterson, Kristal, & Cheney, 1998) if respondents know that the tool is intended to measure
their usual intake (Gibson, 2005). Second, dietary records are subject to recall bias if
respondents record only once per day rather than at each meal. Underreporting on food
records may be the results of incomplete recording. Research illustrates that in adults reported

93

energy intake on diet records are underestimated 4% to 37% when compared to energy
expenditure measured by doubly-labeled water (Goris, Westerterp-Plantenga, & Westerterp,
2000; Hill & Davies, 2001; Sawaya et al., 1996; Trabulsi & Schoeller, 2001). Because of these
findings, dietary records are considered the “imperfect gold standard” (F. Thompson & Subar,
2001).
A previous day food checklist uses a yes-no approach on close-ended food list and asks
whether a respondent ate particular food items on the previous day. Many of the strengths and
limitations of the previous day food checklist are similar to that of food frequency
questionnaires. The strengths of the instrument are that respondent burden is low and it is
inexpensive to administer and process. However, the limitation of the checklist is that it does
not capture information about the entire diet and in this case, only captures consumption of the
previous day, rather than 3 days (as in recalls and records) or in the last week or month (as is
sometimes asked in FFQs). Little to no detail is collected regarding characteristics or amounts of
foods consumed.
In summary, when measuring dietary intake, the serving size of foods consumed is often
difficult for respondents to evaluate and is thus problematic for all assessment instruments (F.
Thompson & Subar, 2001). Additionally, every dietary instrument has its respective strengths
and weaknesses. Therefore, the inconsistent results in the studies may be related to the fact
that different dietary measures were used across studies. Nevertheless, intrinsic motivation was
significantly associated with healthier dietary behavior and meanings of behavior were
associated with dietary intake in all studies where measured, using multiple dietary measures.

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This dissertation is important in expanding the current literature on affective behavior
theories. These studies suggest that the Self Determination Theory and the Theory of Meanings
of Health Behavior can provide further insight into how and why Latino youth engage in
particular dietary behaviors, and provide avenues for interventions. Although the amount of
variance accounted for by motivation and the meanings of eating behavior in predicting dietary
intake may be small, these and other affective determinants may still be important. Knowledge
of both affective and cognitive determinants of eating behavior in youth may inform the
development of more comprehensive and effective interventions aimed to improve and
maintain eating habits. For instance, Social Cognitive Theory’s is one of the most commonly
used theories in the design of nutrition education interventions (Baranowski et al., 2003).
However, in a study investigating the psychosocial predictors of fruit and vegetable intake
among elementary school children, Social Cognitive Theory’s constructs only accounted for 2%
of the variance in vegetable intake and did not account for any variance in predicting fruit intake
(Domel et al., 1996). These and other theories have not been entirely successful in changing
behavior, however, collectively they could contribute to develop a body of knowledge to help us
better understand and predict dietary behavior and thus provide stepping stones to the
development of more effective interventions in minority youth.
The studies conducted in this dissertation suggest plausible avenues for the
development of interventions. Intrinsic motivation was associated with healthier dietary
behavior. Additionally, meanings of eating influenced dietary intake. Therefore, promoting
intrinsic motivation and addressing the meanings of eating behavior in the development and

95

implementation of nutrition education interventions may make for more successful dietary
interventions in minority youth. Potential intervention strategies are discussed below.
Intervening on intrinsic motivation: Motivational interviewing (W. Miller, 1983; W. Miller
& Rollnick, 1991) is one method that may be used to increase intrinsic motivation in an
intervention setting. Motivational interviewing is a directive, client-centered counseling style
that elicits behavior change by helping patients explore and resolve ambivalence. Patients
identify aspects of their behavior that they would like to (or are ready to) change and then
explore the benefits and difficulties of making that change (W. Miller & Rollnick, 1991, 2002). By
helping clients understand situations, emotional influences, and previous eating experiences
while encouraging personal choice, the principles of motivational interviewing promote intrinsic
motivation to eat healthy (Markland, Ryan, Tobin, & Rollnick, 2005).
Another method researchers have used to increase motivation for healthy behavior is
by having participants give presentations outlining their personal strategies to change the
specific behavior. This motivational approach facilitates high levels of personal involvement to
enhance the participants’ self-motivation for increasing the health behavior. For instance,
Wilson and colleagues were relatively successful at increasing motivation to be physically active
by asking participants to develop, discuss, and present in video-tape format their own strategies
for improving their physical activity habits (Wilson et al., 2005).
Intervening on Meanings of Eating: Two strategies have been suggested for changing
meanings of eating in youth: 1) increasing salient meanings connected with the behavioral
objective to eat healthy, and 2) reducing negative salient meanings connected to unhealthy

96

eating (Spruijt-Metz, 1999). For instance, a lesson plan might be developed where participants
are taught to achieve stress relief by methods other than unhealthy eating, such as talking with
friends, taking a nap, or engaging in physical activity to address the meanings of eating due to
personal negative emotions. One method to address the meaning of eating on behalf of others
would be to have participants create a storybook about the importance of healthy eating to be
presented to their family members. The storybook could include information about pathways to
healthy eating, such as cooking at home using whole grains, eating more fruits and vegetables,
or reducing portion sizes. This method could facilitate a family discussion and may encourage
the family members to work together to improve dietary practices or at least understand and be
more accepting of the participant’s desire to change his/her eating behavior.

Chapter 5: Conclusion
In light of the increasing rates of overweight, obesity and type-2 diabetes among
minority youth over the past three decades, novel methods to understand and change dietary
behavior are warranted. This dissertation illustrates that both motivation and the meanings of
eating behavior uniquely contribute to variance in dietary intake in Latino youth and show some
promise as innovative tools for understanding the affective determinants of dietary intake in
minority children. Although the direct influence of motivation and meanings may be small, the
information gained from these and future studies point to new directions for interventions
aimed at improving dietary choices.

97

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Abstract (if available)

Abstract Purpose:This dissertation examined the roles of 1) motivation for healthy dietary habits, and 2) the meanings of eating behavior on dietary intake in Latino youth. Multiple objectives were addressed in the three studies that comprised this dissertation. Study 1 identified the affective meanings of dietary intake among minority children, developed factor items for the Meanings of Eating Index (MEI), validated the MEI, and explored whether the meanings identified were related to healthy (or unhealthy) dietary behavior in Latino youth. Study 2 examined the effects of intrinsic and extrinsic motivation for eating fruits and vegetables on dietary intake at baseline and on changes in dietary intake between baseline and follow-up in a randomized controlled trial in overweight Latina adolescents. Study 3 investigated whether motivation and the meanings of eating uniquely contributed to fruit and vegetable consumption in a predominantly Latino sample of elementary school children.

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Asset Metadata

Creator McClain, Arianna D. (author)

Core Title Motivation and the meanings of health behavior as factors associated with eating behavior in Latino youth

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Preventive Medicine (Health Behavior)

Publication Date 06/16/2010

Defense Date 04/13/2010

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag dietary intake,Latino,meanings of health behavior,Motivation,OAI-PMH Harvest,self determination theory,Youth

Place Name California (states)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Pentz, Mary Ann (committee chair), Spruijt-Metz, Donna (committee chair), Chou, Chih-Ping (committee member), Clark, Florence A. (committee member)

Creator Email admcclai@usc.edu,arianna.mcclain@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m3138

Unique identifier UC1480122

Identifier etd-McClain-3841 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-349328 (legacy record id),usctheses-m3138 (legacy record id)

Legacy Identifier etd-McClain-3841.pdf

Dmrecord 349328

Document Type Dissertation

Rights McClain, Arianna D.

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu