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Psychosocial and behavioral ractors associated with emotional eating in adolescents
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Psychosocial and behavioral ractors associated with emotional eating in adolescents
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Content
PSYCHOSOCIAL AND BEHAVIORAL FACTORS ASSOCIATED
WITH EMOTIONAL EATING IN ADOLESCENTS
by
Selena Thi Michel
______________________________________________________________________________
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PREVENTIVE MEDICINE)
December 2006
Copyright 2006 Selena Thi Michel
ACKNOWLEDGEMENTS
I would like to thank the members of my Dissertation Committee, Drs. Donna Spruijt-Metz,
Jennifer Unger, Chih-Ping Chou, Michael Goran, and Margaret Gatz, for their assistance in
developing this manuscript. My deepest appreciation and thanks goes to Dr. Spruijt-Metz and Dr.
Unger for their guidance and support throughout my doctoral training. To my fiancée, Santiago
Rodriguez, my mom, uncle, family and friends I thank you for standing by my side through it all. I
know I’ve been blessed.
ii
TABLE OF CONTENTS
Acknowledgements ii
List of Tables iv
List of Figures v
Abstract vi
Chapter 1: Introduction 1
Chapter 2: Psychological Determinants of Emotional Eating in Adolescence 5
Chapter 3: Dietary Correlates of Emotional Eating in Adolescence 18
Chapter 4: BMI as a Moderator of Perceived Stress and Emotional Eating in Adolescence 28
Chapter 5: Conclusion 42
Bibliography 49
iii
LIST OF TABLES
Table 1. Characteristics of the Sample 11
Table 2. Correlations Among Emotions/Moods 12
Table 3. Gender Differences in Emotional Eating 13
Table 4. Associations Between Emotions/Moods and Emotional Eating 14
Table 5. Characteristics of the Sample 22
Table 6. Differences in Dietary Choices Between Emotional Eaters and Non-
Emotional Eaters 23
Table 7. Associations Between Emotional Eating and Food Categories 25
Table 8. Characteristics of the Sample 35
Table 9. Summary Results of Model Development and Tests of Invariance 37
Table 10. Parameters of Final Model 38
iv
LIST OF FIGURES
Figure 1. Theoretical Model 4
Figure 2. SEM Moderation Model 30
Figure 3. Stress, Emotional Eating, and BMI Status 36
Figure 4. Empirical Model 42
v
ABSTRACT
The present study sought to explore psychosocial and behavioral associations of emotional
eating in adolescence. Data from the Get Moving study were used to conduct multilevel regression
and structural equation model analyses. Participants were 617 minority middle school students in Los
Angeles County in the seventh and eighth grades. In girls, emotional eating was positively associated
with confused and depressed mood, and negatively associated with anger. In boys, emotional eating
was associated with worries. Both boys and girls were likely to eat salty high energy-dense foods and
boys were also likely to eat fruits and vegetables when emotional eating. Findings showed a
significant positive relationship between perceived stress and emotional eating, however this
association was not moderated by BMI. Results indicate that there is specificity in emotions and foods
associated with emotional eating. It also appears that several associations found in European and
Caucasian samples as well as in the adult literature generalize to a minority sample of adolescents.
Results emphasize the important role of affect as an influential factor in eating behavior. Thus, these
findings provide points of focus for interventions that may not have previously received ample
attention.
vi
CHAPTER ONE: INTRODUCTION
Emotions are believed,, both in the scientific literature as well as among the general public,
to sometimes drive eating behaviors—most often termed emotional eating (Thayer, 2001).
Specifically, negative affect has been associated with unhealthy eating patterns, predominantly
(over)eating as a reaction to negative emotions, although negative affect has also been studied in
relationship to undereating and appetite loss. These changes in eating behavior may lead to disturbed
eating patterns and contribute to obesity. Emotional eating is most often conceptualized as
(over)eating in response to negative mood without attention to specific emotions (Faith, Allison, &
Geliebter, 1997). However, there exists a body of literature on stress induced eating that focuses on
stress as the emotion driving eating behavior (typically in lab settings). The largely adult literature
focuses on overeating in response to negative affect, and this study will focus exclusively on eating or
overeating in response to negative emotional arousal.
Eating that occurs not in response to physiological hunger cues can lead to overeating and
present health problems for those engaging in this behavior. Obesity and problem eating behaviors are
major health concerns associated with overeating that affect adolescents. In light of the difficulty
involved in changing people’s eating habits despite the knowledge that certain patterns are unhealthy,
understanding emotional eating may be important. Closer examination of the psychological precursors
to overeating may uncover avenues through which both individual and environmental risk factors
influence the development of disturbed eating behaviors and adolescent obesity. These issues have
significant relevance to public health and health research because of the predisposing role of obesity
for later development of associated diseases, such as Type II diabetes, certain cancers, and
cardiovascular disease as well as the deleterious effects of unhealthy eating patterns. Adolescence is a
key time point for study since adolescence is often viewed as a time of marked stress (Kelly,
Ricciardelli, & Clarke, 1999), and has been identified as one of the critical periods in the development
of obesity (Dietz, 1994).
1
Key concepts that need to be considered in any discussion of emotional eating include affect-
regulation and/or self-medication. Khantzian’s (1987; 1997) self-medication hypothesis of addiction
proposes that people begin the use of specific substances in order to alleviate particular symptoms of
mental disorders or side-effects of drugs prescribed for these disorders. In the case of emotional
eating, certain foods are used to alleviate negative mood. Thayer (2001) has consistently encountered
self-regulation processes akin to self-medication in his research on mood. Although he reports that
one of the basic methods of self-regulation of mood used by people is eating, there are a wide range of
behaviors that people use to manage their moods, including eating, drinking, listening to music,
smoking and interacting socially, to name just a few. Eating in response to negative affect is
considered to be psychologically or emotionally driven, rather than driven by hunger and/or satiety.
Therefore, emotional eating can be seen as a strategy used to regulate emotions, where eating is used
in a self-medicating attempt to improve mood. Study of the use of eating as a coping mechanism is
warranted when we consider the deleterious health implications that result from overeating.
The significance of studying emotional eating during adolescence is that we may be able to
address health behaviors early enough to prevent negative outcomes, such as problem eating patterns
and obesity. Therefore, determination of whether or not findings from the more extensive adult
literature also apply to adolescents is needed. This will help to address unanswered questions that
have been identified in this previous research. Are female adolescents more likely to engage in
emotionally eating than boys? Do all negative emotional states or only a selective few lead to
emotional eating in this population? Do adolescents consume all foods or only particular types of
foods during bouts of emotional eating? Are obese adolescents more likely to emotionally eat than
their normal weight counterparts? Identification of modifiable behaviors is pertinent in the
development of effective prevention efforts—answers to these questions can provide this information,
and, in turn, specific points on which to focus interventions.
The proposed study will explore factors related to emotional eating in order to further inform
the literature on determinants and outcomes of emotional eating in adolescents. Cross-sectional
follow-up data from the “Get Moving” study, a larger physical activity intervention study, will be
2
employed. Seventh and eighth grade students from Los Angeles County middle schools participated
in this one-year study. Participant data will be used to address the following aims: (1) to assess if
gender differences in emotional eating exist in adolescents, (2) to identify psychological/emotional
predictors of emotional eating in adolescents, (3) to identify types of dietary intake in emotional
eating in adolescents, and (4) to investigate the moderating effect of weight (BMI) on the relationship
between perceived stress and emotional eating in adolescents. Results from this study may help to
elucidate avenues of effective prevention and treatment of obesity and problem eating behaviors
during adolescence.
Questions posed and covariates included for the present study are based on a review of the
literature. The following hypotheses will be tested:
(1) Levels of emotional eating as well as proportion of emotional eaters will differ by gender.
Specifically, females will score higher on emotional eating than males, and there will be more
female emotional eaters than male emotional eaters.
(2) Emotions/moods consistently found in the literature will be related to emotional eating, including
stress, tension/anxiety, depression, and anger.
(3) Emotional eating will be associated with higher intake of high energy-dense foods and not with
consumption of fruit or vegetable products.
(4) Those considered at risk for overweight and overweight will engage in emotional eating when
stressed, while those considered normal or underweight will not engage in emotional eating when
stressed.
The theoretical model is shown in Figure 1. In addition to demographics, covariates included body
image and weight concerns as these factors are likely to impact eating behavior (Akan & Grilo, 1995).
Intervention was also controlled for since follow-up data were used in analyses. This model does not
address all possible relationships among these factors. It may be possible that BMI or foods can affect
emotions and mood. Also, this model says nothing about the directionality of the relationship between
BMI and emotional eating. High BMI may lead to emotional eating or emotional eating may lead to
high BMI. Only prospective studies that followed children from a very young age would allow us to
3
truly understand which comes first. This model only shows what was tested in the present study,
which reflects the theoretical model most accepted in the literature.
COVARIATES
Age
Ethnicity
Gender
Body Image
Wt Concerns
Intervention
EMOTIONS
/ MOODS
P Stress
Worries
Tension
Depression
Anger
Confusion
Fatigue
Vigor
BMI
EMOTIONAL
EATING
DIETARY
CHOICES
Fruits and vegetables
Energy dense foods
Figure 1. Theoretical Model
4
CHAPTER TWO: PSYCHOLOGICAL DETERMINANTS OF EMOTIONAL EATING IN
ADOLESCENCE
A recurring scene used in television shows and movies when someone, particularly a female,
is down, upset, sad, stressed, anxious, etc., is to show this person devouring far more food than is
physiologically necessary. Usually, the extent of the negative mood, often resulting from a break-up
with a romantic partner or anticipation of stressful events, is exemplified by the amount of food eaten.
The food may be a whole box of candy, an entire bag of chips, a carton of ice cream, or all of the
above. Not just in the media, but in everyday life, we often see people eating in an attempt to deal
with stressful situations, bad news and/or moods. Although the general public seems to be aware of
the phenomenon of emotional eating, the scientific literature exploring this issue is not as wide,
especially in adolescents.
Emotional eating is most often defined as (over)eating in response to negative affect (Thayer,
2001), without specificity to particular moods or emotions (Faith, Allison, & Geliebter, 1997).
Because the study of emotional eating came about in an attempt to explain obesity, many studies
focus on obese populations (e.g., Faith, Allison, & Geliebter, 1997; Ganley, 1989). It has been argued
that the lack of specificity of emotion inhibits detailed study of the psychological precursors to
overeating (Arnow, Kenardy, & Agras, 1995). Further, studies that assess specific emotions tend to
measure eating behavior in response to specific emotions/moods, and do not use the construct of
emotional eating.
Thayer (2001) cites feelings of increased tension and low-energy, “tense tiredness,” as the
main culprit in emotional eating, as it underlies many of the negative moods (for example, depression
and anxiety) that have been found to be associated with overeating. Hence, food is used in an attempt
to self-medicate and self-regulate mood. Weingarten and Elston (1991) found that tension in
undergraduates often preceded urges to eat. In a study to identify triggers of overeating in women, the
researchers found that participants felt tired, bored, lonely, anxious, tense, and stressed before
overeating; further, these feelings improved after eating (Popless-Vawter, Brandau, & Straub, 1998).
5
Fascinatingly, although the women also ate when angry and depressed, these feelings did not improve
after the eating episode; in obese participants anger and depression increased after eating (the authors
concluded these negative feelings after eating may have been a result of feelings of guilt and anger at
self for overeating). Steptoe, Lipsey, and Wardle (1998) found that nurses and schoolteachers
increased energy intake during stressful weeks vs. less stressful weeks, indicated by food diary
reports.
In an early review of this literature, the anxiety reduction model was proposed (Kaplan &
Kaplan, 1957) which posited that obesity was developed and maintained by overeating in an attempt
to reduce anxiety. Ganley’s (1989) subsequent review of emotional eating in obese adults (clinical,
non-clinical, and lab studies) revealed a more complex model that accounted for individual
differences. He found that obese persons often reported eating in response to anger, loneliness,
boredom, and depression. He further noted the importance of a comprehensive assessment of stress
and the need for attention to the specific mood states that led to overeating. Evident in this review was
the fact that much of the literature focused on females. Faith, Allison, and Geliebter’s (1997)
examination of the issues of obesity differences and the assessment and treatment of emotional eating
offered suggestions for further exploration of this construct. Among these suggestions were inclusion
of children as participants, study of chronic stressors, and exploration of specific emotions.
There is a body of literature that focuses on stress-induced eating, although a good portion of
these studies are performed on animals, not necessarily providing or with the intention of analogy to
humans (Greeno & Wing, 1994). Within this literature, there are two models of thought: General
Effects (almost entirely animal studies) and Individual Differences (only human studies). The
General Effects Model holds that stress will increase eating in all organisms, while the Individual
Differences Model states that eating in response to stress will depend upon certain factors of an
individual. Three major hypotheses have been tested within the Individual Differences Model: obesity
vs. normal weight, restrained vs. unrestrained eaters, and females vs. males, where the former group
in each of these comparisons is thought to be more prone to stress-induced eating. Greeno’s review
resulted in support for either model of stress-induced eating, therefore, it does appear that stress is
6
often a precursor to overeating. And considering the fact that many studies of individual differences
were significant, the authors suggest studies continue in the individual differences model. In addition,
several questions were put forth by the authors, including what types of stress lead to eating, and
whether or not this relationship applies to males and non-adult populations in non-lab settings.
In an effort to further elucidate the emotional eating literature, a study to identify specific
psychological determinants of emotional eating was undertaken in a school-based sample of minority
adolescents. We expect that all negative emotional and mood states will be associated with emotional
eating. It is also predicted that girls may be more likely to emotionally eat than boys, as has been the
found in the adult literature.
Methods
Sample
The present study uses cross-sectional follow-up data from a sample of 617 students from
seven Los Angeles County public and private (Catholic) middle schools. Students were in seventh and
eighth grades, participating in a larger intervention study of physical activity in Latina girls. Surveys
assessed demographic factors and employed psychosocial and behavioral measures, including mood,
perceived stress, and emotional eating.
School selection
School selection was designed to select schools with large Latino populations from Los
Angeles County. The ethnic distributions of schools were identified through data from the California
Board of Education and the Roman Catholic Archdiocese. Socioeconomic status (SES) for schools
was also identified in order to obtain schools across the range of SES. The principal investigator
approached nine schools with high proportions of Latino students and a variety of SES, eight of which
agreed to participate. Due to curriculum requirements of the school district, one school was unable to
participate, thus we collected data from seven schools.
Student recruitment
Physical education teachers at each school were contacted in order to identify classrooms to
take part in the study. Of the eighteen teachers who were asked to participate, only one refused due to
7
scheduling issues. All students in classrooms of teachers who agreed to participate were invited to
join the study. Student recruitment took place across five days (including the first day of data
collection). On the first day, the principal investigator explained the research project and distributed
parental consent forms. On the second and third days, consent forms were collected and, on the third
day, parent refusal forms were distributed (separate consent and refusal forms were used in order to
allow for “implied consent” if participants did not return active consent or active refusal forms). This
combined active/implied consent procedure was approved by the Institutional Review Board, the
school districts, and the Archdiocese. Parent consent, refusal, student assent forms, and surveys were
collected on the fourth day. All parent forms were available in Spanish and students were asked to
choose the appropriate language forms.
If a parent provided active written consent for a child to be a part of the study (i.e., signed
and returned the consent form), this student was eligible to participate. If a parent provided active
written refusal (i.e., signed and returned the refusal form), this made a child ineligible to fill out the
survey. Those students whose parents did not actively refuse permission on a parent refusal form
were eligible to complete only a portion of the survey. This shortened version of the survey contained
only those questions that, according to the regulations of the IRB and the California Board of
Education, could be administered without active consent.
Students who were eligible to participate were then asked for written assent to be a part of
the study. Those who had active parental consent or did not present active parental refusal and
provided active written assent took part in the study. Eighty-five percent of students participated in
the study (this included those that had active or implied consent).
Procedure
The surveys were delivered and picked up by trained data collectors, not acquainted with the
students, according to a data collection manual and script provided to each data collector. Students
filled out an English language paper-and-pencil survey during two class periods. Because classes are
taught in English, participants were assumed to have the ability to read English. In addition, our
previous research with Latino adolescents in Los Angeles has indicated that when presented with
8
surveys in English and Spanish, fewer than 1% of the students selected the Spanish version. Schools
did provide a translator during data collection when needed and/or possible, otherwise data collectors
were also available for translation. These surveys were identified by a number specific to each child in
order to maintain confidentiality of data.
Measures
Emotional eating. Emotional eating was measured with the Emotional Eating subscale of the
Dutch Eating Behavior Questionnaire (DEBQ) (van Strien, Frijters, Bergers, & Defares, 1986). This
13-item scale asks about eating in response to a variety of emotions. Participants gave responses
along a 5-point Likert scale from “never” to “very often.” Two different coding schemes were used in
our measurement of emotional eating: (1) Continuous scale scores were obtained by taking the mean
score of the thirteen items (Cronbach α = 0.95), and (2) To assess gender differences in proportions of
“emotional eaters,” students’ continuous emotional eating scores were categorized as emotional eaters
or not based on age and gender specific cut-points delineated in the DEBQ manual (van Strien, 2005).
Perceived stress. Stress was assessed via a modified version of the Perceived Stress Scale
(PSS) (Cohen, Kamarck, & Mermelstein, 1983). The PSS is a 14-item scale that inquires about
perception of stressful experiences in the last month. Response options range from “never” to “very
often” along a 5-point Likert scale. Based on feedback from short interviews with adolescents
reviewing this scale, language was modified for comprehension and three items were added to this
measure. These items addressed the following three issues: work load, keeping secrets, and social life.
A sum of the item scores is calculated to obtain a scale score (Cronbach α = 0.73).
Worries. Worries were measured via a worries scale developed by Spruijt-Metz and Spruijt
(1997). Worries are similar to measures of stress and anxiety. This measure inquires about how much
the person had worried about each item in the past month. Items related to several issues pertinent to
adolescent life including self-image, relationships and school. Worries scores were obtained by
computing the mean of the score on each item (Cronbach α = 0.88).
Mood. The Adolescent version of the Profile of Mood States (POMS-A) was used to assess
mood (Terry, Lane, Lane, & Keohane, 1999). This scale is made up of six subscales (4-items each):
9
Anger (Cronbach α = 0.80), Confused Mood (Cronbach α = 0.81), Depressed Mood (Cronbach α =
0.88), Fatigue (Cronbach α = 0.85), Tension (Anxiety) (Cronbach α = .79), and Vigor (Cronbach α =
0.72). Vigor is the one positive emotion included in the analyses. These scales asked respondents to
indicate how they felt at that moment, with 5-point Likert scale response options ranging from “not at
all” to “extremely.”
Body image. The Body Image States Scale (BISS) is a 6-item scale (Cronbach α = .69) that
assesses body image (Cash, Fleming, Alindogan, Steadman, & Whitehead, 2002). This scale has a 7-
point Likert response format where participants indicate their feelings of satisfaction with looks,
attractiveness, and comparison to others.
Weight concerns. To measure weight concerns (Tomeo, 1999), respondents indicated on a 4-
point range from “never” to “very often” how much they worried about or felt negatively or positively
about their looks or body (Cronbach α = .77).
Age. Age in months was used in all analysis. This was calculated using birth date obtained
from school administrative offices and test date.
Ethnicity. Phinney’s (1992) ethnicity scale was used to obtain information on ethnicity. A
wide range of ethnic backgrounds are presented and respondents are asked to mark which ethnicity
corresponds to their background. If participants marked more than one, they were categorized as
Multi-ethnic. Several groups had small numbers and were therefore combined into an “Other”
category.
Data analysis
Descriptive statistics were computed for all demographic variables. T-tests and Chi-square
tests were used to assess gender differences in emotional eating. Because the data were nested within
schools, multi-level model multiple regression was performed to test the associations between
emotional/mood states and emotional eating, while controlling for age, gender, ethnicity, body image,
weight concerns, and intervention group (cross-sectional follow-up data were used in analyses,
therefore we needed to control for any possible intervention effects, although we did not anticipate
any effects of intervention on emotional eating since the intervention focused only on physical
10
Table 1. Characteristics of the sample.
Variable Overall
N = 424
Male
N = 96 (23%)
Female
N= 328 (77%)
Range
M (SD) M (SD) M (SD)
Age in months 161.52 (7.69) 162.28 (8.78) 161.30 (7.34) 147 – 189
Ethnicity*
Asian/PI 75 (17.86) 30 (31.6%) 45 (13.8%)
Latino 260 (61.80) 51 (53.7%) 209 (64.3%)
Multi-ethnic 46 (10.95) 6 (6.3%) 40 (12.3%)
Other 24 (5.71) 5 (5.3%) 19 (5.8%)
White 15 (3.57) 3 (3.2%) 12 (3.7%)
Perceived Stress 33.09 (8.98) 30.67 (8.52) 33.80 (9.00) 0 – 68
Worries 2.36 (0.58) 2.18 (0.63) 2.42 (0.55) 1 – 4
Anger 0.86 (0.94) 0.57 (0.78) 0.94 (0.97) 0 – 4
Confused Mood 0.81 (0.88) 0.56 (0.72) 0.89 (0.90) 0 – 4
Depressed Mood 0.82 (0.99) 0.56 (0.84) 0.90 (1.02) 0 – 4
Fatigue 1.11 (1.03) 0.88 (0.83) 1.18 (1.07) 0 – 4
Tension 0.86 (0.84) 0.62 (0.67) 0.93 (0.87) 0 – 4
Vigor 1.92 (0.99) 2.11 (0.94) 1.86 (1.00) 0 – 4
Emotional eating
Emotional Eating Score 1.85 (0.86) 1.62 (0.81) 1.92 (0.87) 1 – 5
Emotional Eaters* 58 (20.0%) 12 (12.5%) 73 (22.3%)
Body Image 4.09 (1.08) 4.34 (0.91) 4.01 (1.11) 1 – 7
Weight Concerns 2.29 (0.68) 2.01 (0.58) 2.37 (0.69) 1 – 4
*N (%)
11
Table 2. Correlations among emotions/moods
Perceived
Stress
Worries Anger Confused
Mood
Depressed
Mood
Fatigue Tension Vigor
Perceived
Stress
--
Worries 0.491
**
--
Anger 0.422
**
0.324
**
--
Confused 0.417
**
0.366
**
0.688
**
--
Depressed 0.522
**
0.413
**
0.740
**
0.747
**
--
Fatigue 0.424
**
0.347
**
0.613
**
0.591
**
0.599
**
--
Tension 0.430
**
0.368
**
0.655
**
0.770
**
0.679
**
0.834
**
--
Vigor -0.148
*
0.052 -0.031 0.017 -0.095 -0.159
*
-0.056 --
*
p < 0.01
**
p < 0.001
12
Table 3. Gender differences in emotional eating.
activity). All variables were entered into one multivariate model. Interaction analyses (interactions of
gender X significant emotions) were performed using these same regression methods. Each variable
was standardized to a mean of 0 and a standard deviation of 1 in order to produce standardized
parameter estimates.
Results
Sixty-nine percent of the 617 participants who completed the surveys (N=424) provided
complete data for the variables of interest in this study. Significant differences were found for those
providing complete data versus those with incomplete data (only significant differences are reported
here); there were larger proportions of males (32% vs. 22%, respectively) and emotional eaters (29%
vs. 20%, respectively) in the group that did not provide complete data on the covariates, independent,
and dependent variables. Demographic characteristics of the sample are shown in Table 1 (all
numbers reported are before standardizing). Table 2 reports the correlations among the
emotions/moods included in the model.
Analyses reveal that emotional eating was more prevalent among girls than among boys
(Table 3). Chi-square tests indicate that there were significant differences in the proportions of
emotional eaters in boys vs. girls ( χ2 = 4.41, p = 0.036). Additionally, t-tests illustrated significant
differences (t = -3.08, p = 0.002) in the continuous emotional eating scores between males (M = 1.62,
SD = 0.81) and females (M = 1.92, SD = 0.87).
Table 4 reports the results of multilevel multivariate regression models of emotional eating
as a correlate of emotion and mood. Controlling for covariates and random effect of school, emotional
eating was found to be significantly associated with worries (Std. β = 0.1323, p = 0.015), depressed
mood (Std. β = 0.2236, p = 0.005) and confused mood (Std. β = 0.2070, p = 0.011). Covariates in the
model that were related to lower levels of emotional eating included being male (Std. β = -0.2417, p =
T-test
a
Chi-square
b
2
Mean t p-value % χ p-value
Males 1.62 -3.08 0.0022 12.5% 4.41 0.0357
Females 1.92 22.3%
a
Mean difference in emotional eating scores
b
Difference in proportion of students defined as “emotional eaters”
13
Table 4. Associations between emotions/moods and emotional eating.
Emotional Eating
Overall Males Females
Std. β p-value Std. β p-value Std. β p-value
Perceived Stress 0.0526 0.3380 -0.0639 0.6012 0.0801 0.1968
Worries 0.1323 0.0152 0.3710 0.0014 0.0862 0.1763
Anger -0.0992 0.1582 0.2431 0.1439 -0.1770 0.0268
Confused Mood 0.2070 0.0106 0.2442 0.1417 0.1895 0.0479
Depressed Mood 0.2236 0.0048 0.1829 0.3839 0.2312 0.0087
Fatigue 0.1377 0.1008 0.1081 0.5681 0.1269 0.1799
Tension -0.1055 0.2943 -0.3038 0.0014 -0.0197 0.8666
Vigor -0.0105 0.8272 0.0474 0.6315 0.0046 0.9331
Note. All parameter estimates (standardized betas) are adjusted for age, gender, ethnicity, weight
concern, body image, intervention group, and random effect of school
0.029), Latino (Std. β = -0.6017, p = 0.011), Multi-ethnic (Std. β = -0.6448, p = 0.015), and an
intervention participant (Std. β = -0.2404, p = 0.045), thus supporting their inclusion as model
covariates.
Because gender was a significant variable in the model, interaction analyses were performed
for worries, depressed, and confused mood to explore gender differences in these associations. A
worries by gender interaction term was added to the model, and results showed no interaction effect
(Std. β = 0.0879, p = 0.370). No significant interaction was found when depressed mood by gender
and confused mood by gender interaction terms were added to the model (Std. β = 0.0742, p = 0.530;
Std. β = 0.0638, p = 0.599, respectively). Although interactions were not significant, previous
literature supports gender differences in emotions and eating behavior, therefore, stratified multilevel
model regression analyses were performed. Results show that worries (Std. β = 0.3710, p = 0.001)
were associated with emotional eating in boys, while confused (Std. β = 0.1895, p = 0.048), depressed
(Std. β = 0.2312, p = 0.009), and angry (Std. β = -0.1770, p = 0.027) mood were all related to
emotional eating in girls (see Table 4). However, this inverse association with anger is likely an
artifact of data analyses due to the number of variables included in the model. Univariate analysis of
anger showed a positive relationship between anger and emotional eating.
Discussion
An exploration of the specific emotional/mood states associated with emotional eating
revealed that worries, depressed mood and confused mood were related to emotional eating in a
14
minority adolescent sample. In line with our hypotheses, there were significant gender differences in
the proportion of emotional eaters and level of emotional eating between boys and girls, where girls
were more likely to emotionally eat. We also found gender differences in the specific moods
associated with emotional eating. In boys, a higher score on worries was associated with more
emotional eating. Confused and depressed mood was positively related to emotional eating, and there
was a negative relationship between anger and emotional eating in girls. However, the association
with anger may not be a valid finding, considering the aforementioned issue of data analyses.
Surprisingly, emotional eating was not associated with many of the negative moods included
in the model. Based on the literature cited above, we expected that emotional eating would be
positively associated with perceived stress/tension/anxiety, anger and fatigue. This may be because
these previous findings were from adult studies. Perhaps the specific negative affect that leads to
emotional eating is different during adolescence. It could also be that worries, depressed, and
confused mood are more salient to adolescents, while these other negative moods/emotions are
experienced more frequently in adults. The fact that there was no significant association with the one
positive emotion in the model, vigor, offers further support for the notion that emotional eating occurs
in response to negative emotions. However feeling energized may be a specific emotion that would
not be associated with a need for increased energy intake, thus there was no negative association.
Additional research is needed to assess the associations between other positive emotions and
emotional eating among adolescents, as emotional eating might also follow positive affect.
The significant gender difference in emotional eating is also consistent with previous
literature, however, the fact that there were more males and emotional eaters among the participants
who did not supply complete data for these analyses may have bearing on this finding. It is quite
plausible that inclusion of these participants would have made these differences non-significant.
Another plausible explanation for higher emotional eating in girls is that it seems that there are more
psychological states associated with emotional eating in girls than boys, therefore possibly leading to
more incidents of emotional eating.
15
Although gender differences in eating behaviors have often been found, measurement issues
may be the reason that findings have more often pointed to this being a female issue. It may be that
detecting whether or not boys are affected may depend on what is measured and how it is asked. This
makes sense for the emotional eating literature because many adult studies often tested eating in
response to emotions versus differences in the construct of emotional eating itself. Additionally, in
light of recent literature that points to increasing body/weight concern in boys (Cohane & Pope Jr.,
2001), exploration of this issue merits further attention. Future studies should include or focus on
males in their samples in order to increase understanding of these issues in boys as it seems to be an
important issue not just for females.
The cross-sectional nature of the study did not allow us to determine if emotions or moods
were experienced prior to emotional eating, however the nature of the emotional eating scale makes
the direction of the association implicit. The emotional eating scale items ask if eating occurs when
feeling a certain way. Perhaps our findings tell us that adolescents who experience more worries,
confusion, and depressed mood are more prone to eat in response to a variety of emotions. The use of
ecological momentary assessment (EMA) methods could prove useful in order to determine whether
emotional eating episodes directly follow specific mood states. EMA allows for measurement of
events/factors as they occur (Stone & Shiffman, 1994); this has shown to be a useful assessment
method in the study of several health behaviors, including anxiety and eating behavior (Henker,
Whalen, Jamner, & Delfino, 2002) as well as attention deficit hyperactivity disorder (Whalen et al.,
2006) .
The validity of data could also be affected by the self-report nature of the study. However,
participants were ensured of the confidentiality of all data, and measures were taken to display this
confidentiality to all participants. These scales have also been validated for use with adolescent
populations. Therefore, there is no reason to believe that students were not honest in their answers.
Results from this study support the hypothesis that eating behavior is influenced by
negative affect. This study is unique in that it was conducted with a minority adolescent population
that included boys, and identified specific emotions related to emotional eating in a non-lab setting,
16
thereby increasing the generalizability of previous findings that negative affect leads to emotional
eating. We also found that not all negative affect leads to emotional eating in adolescents, thereby
providing a focus for intervention in this population. These conclusions bear potential implications
for the treatment and prevention of pediatric obesity and eating disorders because they suggest that
interventions would benefit from incorporation of stress-reduction techniques to ameliorate worries
and promote positive mood. Although it seems intuitive that removal of the “trigger” to emotional
eating would reduce emotional eating, future research is needed to determine whether these types of
interventions can reduce emotional eating.
17
CHAPTER THREE: DIETARY CORRELATES OF EMOTIONAL EATING IN
ADOLESCENCE
Despite a longstanding recognition of the occurrence of emotional eating (including stress-
induced eating), many questions remain regarding its associated precursors and outcomes. Review
papers have pointed to the need for additional research to identify specific dietary choices in
emotional eating (especially considering its assumed association with obesity) as well as studies of
males and youth populations (Faith, Allison, & Geliebter, 1997; Ganley, 1989; Greeno & Wing,
1994). As evidenced in these reviews, emotional eating has predominantly been studied in Caucasian
adults, usually females, often in the lab, and little is known about specific food preferences. Findings
from review papers on adults and the few studies in youth reporting on dietary outcomes are below.
In Ganley’s (1989) review of emotional eating and obesity, it was made apparent that
individual food choice is an important factor in this relationship, and that consumption often involved
“high-calorie or high-carbohydrate food” (p. 354). Another review of the same topic addressed the
issue of “carbohydrate cravers” (Faith, Allison, & Geliebter, 1997). The mechanism thought to be at
work here is based on animal studies, where increases in carbohydrates led to increases of serotonin
levels in rats. This would support a preference for emotional eaters to ingest carbohydrates to improve
mood.
A 2004 study of eating patterns of adolescents and adults found that 1) in adults emotional
eating was associated with higher intake of fatty fruits (e.g. peanuts) and cakes/pastries/biscuits, and
2) in adolescents and young adults (mean age 17 years old), there was a negative relationship with
fruits and vegetables and a positive association with yogurt in males (de Lauzon et al., 2004). In Braet
and van Strien’s (1997) study of eating style in children, relationships were found between emotional
eating and protein, fat, and carbohydrates. Wardle (1992) found that those scoring high on emotional
eating gave high ratings of liking fattening food as well as to their being “good for you.”
Greeno and Wing’s (1994) review of stress-induced eating found studies that supported a
preference for high-density foods in response to stress, specifically women preferred sweets
18
(Grunberg & Straub, 1992). An adolescent study of stress and dietary practices revealed that higher
stress increased the chances of unhealthy dietary practices, such as increased consumption of fatty
foods and decreased consumption of fruits and vegetables. Michaud’s (1990) study of high school
students showed that a stressful life event (major exam) did in fact change eating behavior. A gender
interaction showed that on the day of the exam, increased total energy intake was significant for girls,
and there was an increase in percentage from fat in the diet of boys.
A close look at the studies on emotional eating in youth also reveals that the large majority of
these studies have been conducted with European populations, with only a few in the United States.
National data show that over 30% of children and adolescents are ‘at risk for overweight’ and
‘overweight.’ Further, Latino youth in the U.S. have been shown to be at increased risk for obesity
compared to their Caucasian counterparts. Data from 1999-2002 show that nearly 40% of Mexican
American children and adolescents were reported to be at least at risk for overweight and more than
20% were overweight; these numbers were similar for African Americans, 35.4% and 20.5%,
respectively (Hedley et al., 2004). Considering these striking numbers, it seems that studies of
emotional eating in American minority adolescents are warranted, as this may help us to better
understand this increased risk.
The primary aim of this study was to identify dietary choices associated with emotional
eating in a predominantly Latino adolescent population. Based on outcomes of the literature
presented, the following hypotheses were tested. It was hypothesized that emotional eating would be
associated with higher consumption of sweet and salty high energy-dense items and soda, and not
with fruit and vegetable intake. Further, we expected that emotional eaters (vs. those not categorized
as emotional eaters) would be more likely to consume the high energy-dense choices than fruits and
vegetables. We also sought to explore gender differences in the associations between emotional
eating and dietary choices.
19
Methods
Sample
Cross-sectional data from 617 middle school students in Los Angeles County, collected as
part of a larger survey on physical activity and diet in Latino children, were employed for the present
study. Seventh and eighth grade students were from seven public and private middle schools.
Participants completed a psychosocial questionnaire that assessed emotional eating and dietary
choices, among other factors.
School selection
Sampling methods aimed to obtain a sample of predominantly Latino adolescents for the
parent intervention study. Data from the California Board of Education and the Roman Catholic
Archdiocese were used to identify schools with high numbers of Latino students and a range of
socioeconomic statuses (SES). The principal investigator approached nine schools fitting study
selection criteria and eight agreed to participate. At the time of data collection, one school was no
longer able to participate due to curriculum priorities of the district, therefore seven schools took part
in the study.
Student recruitment
The principal investigator contacted physical education teachers at each school to recruit
individual classrooms for participation. Seventeen of the eighteen teachers approached agreed to
participate, and all students from each classroom were invited to complete the surveys. Recruitment
and data collection took place across five days. The study was explained on the first day by the
principal investigator, and parental consent forms were distributed. Separate parent refusal forms were
given out on the third day of recruitment to those students who had not yet returned parental consent
forms. All forms were collected through four days (students were asked to choose the appropriate
language forms, English or Spanish, for their parents); on the fourth and fifth days, data collection
took place. All students who gave active personal assent, and active parental consent or whose parents
did not actively refuse participation completed surveys. Those students with implied consent (i.e.,
those who did not provide written parental consent or written parental refusal) filled out an
20
abbreviated version of the survey. All study procedures were approved by the University Institutional
Review Board and the appropriate boards of participating schools and school districts. Active or
implied consent was provided by 85% of students.
Procedure
Trained data collectors followed data collection manual procedures and scripts in distribution
and collection of surveys. Paper-and-pencil surveys were completed across two class periods.
Confidentiality was maintained by identifying surveys only by a unique identification number
assigned to each student. The survey took approximately 45 minutes to complete.
Measures
Dietary choices. Items from a validated food frequency questionnaire (FFQ) were used to
assess dietary choices. Due to constraints of survey length, we used an abbreviated FFQ as it has been
shown that short versions of FFQs for dietary assessment in middle school aged youth are valid (Field
et al., 1999). Items were taken from surveys used in the Nurse’s Health Study (Willett et al., 1985),
which is the instrument on which the Youth/Adolescent Food Frequency Questionnaire (YAQ) was
based (Rockett et al., 1997). Our dietary choices measure included a range of food items from fruits
and vegetables to cakes, chips, and ice cream. Items were categorized as: (1) fruits and vegetables (7
items), (2) salty high energy-dense (2 items), (3) sweet high energy-dense (4 items), and (4) soda (full
sugar; 1 item). Responses were summed in order to obtain intake scores for each of these categories
(Phillips et al., 2004).
Emotional eating. The Emotional Eating subscale of the Dutch Eating Behavior
Questionnaire (DEBQ) was employed to measure emotional eating (van Strien, Frijters, Bergers, &
Defares, 1986). Responses to 13-items range from “never” to “very often” across a 5-point Likert
scale. Two measures of emotional eating were used in analyses: the mean of items was calculated to
obtain a continuous score (Cronbach α = 0.95), and a categorical variable based on predetermined
age-and-gender-specific cut-points (van Strien, 2005).
Body image. Body image was assessed via Cash’s (2002) Body Image States Scale (BISS).
The mean of items was obtained to calculate a body image score (Cronbach α = .69) Participants rate
21
their feelings of satisfaction with looks, attractiveness, and how they compared to others along a 7-
point Likert scale.
Weight concerns. Weight concerns were measured using a 7-item scale (Tomeo, Field,
Berkey, Colditz, & Frazier, 1999) (Cronbach α = .77). Responses ranged from “never” to “very often”
across a 4-point Likert scale. Items assessed how much participants worried about or felt negatively
or positively about their looks or body.
Age. Using birth date (obtained from school administration) and test date, age in months at
the time of assessment was computed. All analyses included age as a covariate.
Ethnicity. Ethnic background was obtained using Phinney’s (1992) ethnicity scale. In a
“mark all that apply” format, participants were asked to check off, from a wide range of ethnic
backgrounds, which ethnicity (or ethnicities) corresponded to their ethnic background. Participants
were categorized as Multi-ethnic if they marked more than one option. Because of small numbers in
several groups, an “Other” category was created in which these ethnic backgrounds were combined.
Data analysis
Means and frequencies were obtained to report descriptive statistics of the sample. T-tests
were used to test differences in dietary intake by gender and in emotional eaters vs. non-emotional
Table 5. Characteristics of the sample (N = 514).
Variable M (SD) Range
Female* 393 (76.46)
Age in Months 161.39 (7.68) 147 – 189
Ethnicity*
Asian/PI 98 (17.86)
Latino 313 (61.49)
Multi-ethnic 56 (11.00)
Other 31 (6.09)
White 18 (3.54)
Emotional Eating
Emotional eating score 1.88 (0.90) 1 – 5
Emotional eaters* 113 (21.98)
Fruit & Vegetable Intake 2.83 (1.01) 1 – 42
Salty High Energy-dense Intake 5.61 (2.13) 1 – 10
Sweet High Energy-dense Intake 10.23 (3.75) 1 – 20
Soda (Full Sugar) 3.28 (1.72) 1 – 7
Body Image 4.02 (1.12) 1 – 7
Weight Concerns 2.31 (0.69) 1 – 4
*N (%)
22
eaters. To test relationships between emotional eating and dietary intake, multi-level model (MLM)
linear regression was performed. When data is nested, for example students nested within schools,
MLM is the appropriate method of analyses in order to control for random effect of school (otherwise,
the fundamental assumption of independence in regression analysis is violated). Separate models for
fruit and vegetable, salty high energy-dense, sweet high energy-dense, and soda intake were tested.
Emotional eating was the independent variable in the four models, with dietary intake as the
dependent variable. Age, gender, ethnicity, body image, weight concerns, and intervention were
included in all models as covariates. Although there was no reason to believe that the physical activity
intervention would impact emotional eating or dietary choices, it was controlled for because analyses
were conducted on follow-up data. Interaction analyses (interactions of gender X emotional eating)
were performed using these same regression methods. Standardized parameter estimates were
obtained by standardizing all continuous variables to a mean of 0 and a standard deviation of 1.
Results
Complete data on model variables were available for 512 (83%) of the 617 students who
completed the survey. There were more boys in the sample of those with incomplete data (35.9%)
compared to those who provided complete data (23.5%). Table 5 provides information on
characteristics of the sample. Participants were predominantly Latina girls, with an average age of
13.4 years old, and approximately 22% were categorized as emotional eaters. The food category with
the highest intake was sweet high energy-dense foods, while consumption of fruits and vegetables was
the lowest.
Table 6. Differences in dietary choices between emotional eaters and non-emotional eaters.
Non-emotional
Eaters
Emotional Eaters Dietary Choices
Category
t p Mean (SD) Mean (SD) -value
Fruit & Vegetable 2.99 (1.08) 2.78 (0.99) -1.95 0.0521
Salty Energy-dense 5.97 (2.26) 5.51 (2.09) -2.06 0.0395
Sweet Energy-dense 11.30 (3.80) 9.92 (3.68) -3.48 0.0005
Soda (Full Sugar) 3.51 (1.81) 3.22 (1.70) -1.57 0.1161
23
Table 6 shows that emotional eaters had significantly more intake of both salty (t = -2.06, p =
0.0395) and sweet (t = -3.48, p = 0.0005) high energy-dense foods than those not categorized as
emotional eaters. Fruit and vegetable intake as well as soda intake was similar in both groups. There
were no significant gender differences in dietary intake.
Results of multilevel regression analyses are found in Table 7. After controlling for model
covariates and random effect of school, emotional eating was significantly related to salty high
energy-dense (Std. β = 0.1611, p = 0.0005), sweet high energy-dense (Std. β = 0.2056, p < 0.0001),
and soda (Std. β = 0.1061, 0.0212) intake. The only model covariate associated with dietary intake
was (positive) body image with higher intake of sweet high energy-dense foods (p = 0.0372). The
following covariates approached significance: Asian ethnicity in the salty high energy-dense model
(less intake; p = 0.0501), “other” ethnicity (p = 0.0513) and body image (0.0699) in the soda model.
Variables significantly associated with lower fruit and vegetable intake were Latino (p = 0.0339) and
multiethnic (p = 0.0230), while weight concern (p = 0.0006) and positive body image (p = 0.0102)
were associated with higher intake.
Although gender did not have significant main effects on food intake, because the literature
has shown gender differences in dietary behavior (e.g., Wardle et al., 2004), gender interaction
analyses were performed for each model. Significant gender interactions were found for fruit and
vegetable (Std. β = 0.3601, p0.0005) as well as salty high energy-dense (Std. β = 0.2133, p = 0.0402)
intake. Gender stratified analyses of these two models reveal that emotional eating was associated
with fruit and vegetable intake in boys (Std. β = 0.3242, p = 0.0009), but not in girls; and was
associated with salty high energy-dense intake in both boys (Std. β = 0.2779, p = 0.0048) and girls
(Std. β = 0.1140, p = 0.0298).
Discussion
This study provides a significant contribution to the emotional eating literature, as it shows
that findings from previous literature may generalize to a Latino youth sample, and also provides
information about specific dietary correlates in this unique population. Emotional eaters were more
24
Table 7. Associations between emotional eating and food categories.
Fruit & Vegetable
Intake
Salty High Energy-
dense Intake
Sweet High Energy-
dense Intake
Soda Intake
(Full Sugar)
Std. β p-value Std. β p-value Std. β p-value Std. β p-value
Emotional Eating 0.0421 0.3552 0.1611 0.0005 0.2056 < 0.0001 0.1061 0.0212
Eth nicity
Asian -0.4252 0.0960 -0.5048 0.0501 0.0832 0.7442 -0.3470 0.1780
Latino -0.5115 0.0339 -0.2559 0.2924 0.0683 0.7784 -0.2746 0.2589
Multi-ethnic -0.6141 0.0230 -0.3769 0.1654 -0.0882 0.7429 -0.1676 0.5376
Other -0.2063 0.4821 -0.2546 0.3896 -0.0505 0.8633 -0.5783 0.0513
Emotional Eating X
Gender
0.3601 0.0005 0.2133 0.0402 0.1413 0.1703 0.1221 0.2414
Gender Stratified Analyses
Males 0.3242 0.0009 0.2779 0.0048 -- -- -- --
Females -0.0384 0.4382 0.1140 0.0298 -- -- -- --
Note. All parameter estimates (standardized betas) are adjusted for age, gender, ethnicity, weight concern, body image, intervention
group, and random effect of school
25
likely than non-emotional eaters to eat high energy-dense foods. In overall multilevel regression
analyses, emotional eating was shown to have a significant relationship with high energy dense
dietary choices as well as soda intake. However, gender stratified analyses revealed differences in
associations between dietary intake and emotional eating in boys and girls. Results showed a
significant association of emotional eating with increased fruit and vegetable intake in boys. This
positive association was not expected, and is an important finding as it shows that emotional eating
may not always lead to high caloric intake in adolescent boys. It appears that boys increase overall
intake, including fruits and vegetables, while girls increase intake of specific foods. This is similar to
the findings of Michaud (1990), however in that study, French girls increased overall intake while
boys increased fat.
These data provided the opportunity to explore dietary preferences and emotional eating in a
minority sample of adolescents. Results showed that Latino and Multi-ethnic students were more
likely to have a lower intake of fruits and vegetables. Minority populations often have a lower
socioeconomic status, which is also associated with less access to healthy foods (CDC, 1995-2003).
Insufficient fruit and vegetable consumption is associated with risk for overweight, therefore this
finding has important implications for obesity risk in minority populations.
One important limitation of this study is the measure of dietary choices. Although taken from
a larger validated measure, the number and breadth of items was abbreviated, and many food items
that may be associated with emotional eating were therefore likely to be missed. However, items were
chosen to reflect foods that are readily available to minority middle school populations, and thus
relevant to the current sample. Response options were modified to be consistent across items and to
increase understanding by the intended sample. Despite the limited range of dietary choices included,
these items provide valuable information on dietary choices of urban minority adolescents.
An additional limitation is that data are cross-sectional, hence disallowing assessment of
directionality. It is implicit that eating follows emotional stimulation in emotional eating. Therefore
the assumed relationships in these cross-sectional analyses are that emotional eating leads to
consumption of these particular foods, as this makes theoretical sense.
26
Another potential limitation of this study is that all data are self-reported. Therefore, it is
possible that dietary intake and/or emotional eating may have been over or underreported. However,
we employed previously validated questionnaires in our survey, which should yield legitimate results.
The generalizability of results to adolescent males may also be problematic since there was a smaller
percentage of boys in this sample than in the underlying population.
In this study, we found that emotional eating leads to consumption of high caloric food in a
minority adolescent population, thus replicating findings from adolescent and adult literature.
Importantly, this may be the first data to explore possible precursors to overweight in an American
population at significantly increased risk for obesity. We did not expect to find the gender specific
significant positive relationship between emotional eating and consumption of fruits and vegetables,
as this is contrary to previous literature. It does however provide support for being able to teach
healthy substitutions in emotional eating. Although emotional eating was associated with intake of
fruits and vegetables in boys, there were also associations with the other high energy dense items and
soda, which puts them at risk for overconsumption of unhealthy foods. As emotional eating has often
been examined as a possible risk factor for obesity (e.g., Faith, Allison, & Geliebter, 1997; Ganley,
1989; Greeno & Wing, 1994), the mechanisms by which they are related are important points of
intervention. Therefore, if emotional eating leads to obesity through overconsumption of energy-dense
foods, intervention programs that focus on substituting healthier snacks and teaching healthier coping
strategies for negative affect could help to reduce risk for obesity.
27
CHAPTER FOUR: BMI AS A MODERATOR OF PERCEIVED STRESS AND EMOTIONAL
EATING IN ADOLESCENCE
Within the domain of emotional eating there is a subset of literature on stress-induced eating.
Two models of thought exist within this body of literature, the General Effects and Individual
Differences models (Greeno & Wing, 1994). The former has been tested predominantly in animals
and the latter has exclusively been studied in humans. According to the General Effects Model, all
organisms will increase intake in response to stress. The Individual Differences Model posits that
certain factors of the individual will dictate whether or not stress leads to eating. One of the main
hypotheses of the Individual Differences Model that has been tested is that obese individuals are more
likely to engage in stress-induced eating than normal weight individuals (Greeno & Wing, 1994).
In their review of stress-induced eating, Greeno and Wing (1994) concluded that stress does
indeed often lead to overeating, and that future studies should continue on the individual differences
model and explore this phenomenon in samples with males, non-adult populations, and in non-lab
settings. Michaud and colleagues (1990) found that stress increased food intake in a sample of French
high school students and concluded that this behavior could bring about increased body weight over
time. A lab study of 8-11 year olds reported the moderating effect of restraint on the association
between stress and snacking, where those higher in restraint (conscious control of energy intake) were
more likely to eat in the face of stress, which could lead to weight gain when restraint was broken
(Roemich, Wright, & Epstein, 2002). Cartwright et al.’s (2003) examination of seventh graders in
London revealed that higher perceived stress was related to higher consumption of fat and unhealthy
amounts of snacking. The authors concluded that this behavior could result in obesity.
The theory from which most discussions of emotional eating stem is the Psychosomatic
Theory of Obesity. According to this theory, food is used as an emotional defense in the face of
negative affect, which causes overconsumption which, in turn, leads to obesity (Kaplan & Kaplan,
1957). In addition, it is posited that obese individuals excessively eat in response to these negative
emotional states, while normal weight persons do not eat in the face of distress and instead employ
28
other coping mechanisms (Faith, Allison, & Geliebter, 1997). For this reason, many emotional eating
studies focus on obese persons.
However, support for the Psychosomatic Theory has not been consistent. Emotional eating
was not related to body mass index (BMI) in a British adolescent sample, however findings did show
that perceived fatness was related to emotional eating (Wardle et al., 1992). A study of binge-eating
and obesity found that emotional eating was positively associated with binge-eating, and binge eating
was predictive of obesity (but negative affect alone was not related to BMI) (Stice, Presnell, &
Spangler, 2002). In a sample of 9-12 year olds from Belgium, Braet and van Strien (1997) found that
overweight and obese children scored significantly higher on emotional eating than normal weight
children.
Of the studies cited above, only two were conducted in the United States and none were
conducted within minority populations. Latinos are a population at high risk for overweight and
obesity (Hedley et al., 2004). Considering the focus on weight and body appearance in the U.S. and
the high risk for overweight in Latino populations, it seems warranted to explore emotional eating as a
potentially modifiable risk factor in this population.
In order to examine the Psychosomatic Theory and the Obesity Hypothesis of the Individual
Differences Model of Stress-induced Eating in a minority adolescent population, a cross-sectional
analysis of the moderating effects of BMI on the relationship between perceived stress and emotional
eating was undertaken. According to theory, it is expected that perceived stress will be significantly
associated with emotional eating in overweight and obese students only. This association is not
expected to be significant for the normal weight group. The conceptual model for these analyses is
presented in Figure 2. It was further anticipated that overweight participants would be more likely to
emotionally eat than those of normal weight and for there to be a larger proportion of emotional eaters
in the overweight group than the normal weight group.
29
Perceived
Stress (PS)
PS1
PS2
PS3
PS4
EE4
EE3
EE2
EE1
E1
E2
E3
E4
E14
E13
E12
E11
BMI
Category
Emotional
Eating (EE)
D2
Figure 2. SEM Moderation Model
PS5 E5
PS6 E6
PS7 E7
PS8 E8
PS9 E9
EE8
EE7
EE6
EE5
E18
E17
E16
E15
EE12
EE11
EE10
EE9
E22
E21
E20
E19
PS10
E10
EE13 E23
D1
Ethnicity
v24
30
Methods
Sample
Six-hundred and seventeen students from public and private Los Angeles County middle
schools provided data for the present analysis. Students were primarily of Latino ethnicity and were in
grades seven and eight. Confidential questionnaires were administered, assessing demographics,
psychosocial factors and behavior, including perceived stress and emotional eating.
School selection
School selection aimed to obtain a predominantly Latino sample from the underlying Los
Angeles County population. Data from the California Board of Education and Roman Catholic
Archdiocese were employed to identify ethnic distributions of the school. A school sample
representing the range of socioeconomic status was desired; therefore this data was also used in the
selection criteria for schools in order to obtain a sample of mostly Latino students and a wide range of
SES. Eight of nine schools that fit selection criteria agreed to participate. Just prior to the
commencement of data collection, one school was unable to participate due to school district
mandates for time spent in remedial reading, therefore, a total of seven schools participated in the
study.
Student recruitment
Classrooms were identified by approaching instructors of physical education classes. Only
one of the teachers approached was unable to participate due to scheduling issues. All students in the
seventeen classes that agreed to participate were invited to take part in the study. The study was
explained to potential participants and parental consent forms were distributed on the first day of
recruitment. On the third day of recruitment, separate parent refusal forms were distributed to those
that had not returned a parental permission consent form. On the fourth day, any remaining consent
and refusal forms were collected, and surveys were distributed to those eligible to participate. Data
collection continued the next day (day 5) at schools where extra time was needed to complete surveys.
Students were eligible to complete the full survey if they provided active parental consent
and personal assent. If students did not return an active parent refusal and gave student assent, they
31
were allowed to take an IRB approved abbreviated survey. Those that had active refusal or did not
provide personal assent did not take part in the study. All parent forms were available in both English
and Spanish. These consenting and all other procedures were approved by the University Institutional
Review Board as well as appropriate school boards. Eighty-five percent of students provided either
active (i.e., parent provided written consent) or implied (i.e., parent did not provide written refusal)
consent to participate in the study.
Procedure
According to procedures outlined in data collection manuals and scripts, trained data
collectors measured height and weight, and distributed and picked up all surveys. On average, the
survey took approximately 45 minutes to complete, however data collection may have taken place
across two class periods depending on length of the class period. All surveys were in English only;
this is because students were assumed to be sufficiently proficient in English since all classes are
taught only in English and, in our experience with Latino adolescents in Los Angeles, less that 1% of
have chosen a Spanish language survey when given a choice. In the few cases where it was needed,
schools did provide a translator if possible, otherwise data collectors could also translate. All surveys
were completely confidential, identified only by a unique ID number.
Measures
Emotional eating. The subscale from van Strien’s (1986) Dutch Eating Behavior
Questionnaire (DEBQ) was used to measure emotional eating. This scale assesses eating in response
to specific and diffuse emotions with 13 items, using a 5-point Likert scale response format, ranging
from “never” to “very often.” Scale scores are obtained by calculating the mean of responses
(Cronbach α = 0.95).
Perceived stress. The Perceived Stress Scale was employed to assess perceived stress
(Cohen, Kamarck, & Mermelstein, 1983). This scale was modified based on short interviews
conducted with adolescents, where wording changes were made to increase understanding and 3 items
were added. Therefore, a total of 17 items were included, with responses ranging from “never” to
“very often” in a Likert response format. A sum of items is used as the perceived stress score
32
(Cronbach α = 0.72). This scale was reduced to 10-items (rationale for obtaining a smaller number of
items is explained below) in order to optimize structural equation model analysis. Reliability analyses
showed that Cronbach’s alpha ( α = 0.89) was better for this reduced scale.
Body Mass Index. A Tanita body composition analyzer (TBF-300A) was used to measure
weight in kilograms. A standard measuring instrument (stadiometer) was used to measure height in
centimeters (nearest whole number). Body Mass Index (BMI) was calculated from these
measurements (weight in kilograms divided by height in meters squared). The 2000 Centers for
Disease Control and Prevention (CDC) age and gender growth charts were used to calculate BMI
percentiles (Pietrobelli et al., 1998). BMI percentiles are used in pediatric populations in lieu of BMI
in order to take into account age and gender factors of BMI while children are still growing and
developing. According to the CDC cut-points, those below the 5
th
percentile are underweight, while
those at the 85
th
and below the 95
th
percentiles are considered at risk for overweight and those at or
above the 95
th
percentile are categorized as overweight. Because there were very few underweight
participants in this sample, they were excluded from analyses. BMI was categorized into normal
weight vs. at risk for overweight and overweight participants combined (Wang, 2001). For ease of
discussion, the latter group is referred to as the overweight group throughout this paper, although this
should not indicate that those at risk for overweight should be labeled as overweight.
Ethnicity. Ethnic background of participants was determined using an ethnicity scale
developed by Phinney (Phinney, 1992). This instrument presents a range of ethnicities and
participants are asked to mark all that apply. If more than one ethnicity was checked off, the
participant was categorized as Multi-ethnic. Ethnic groups with very small numbers were combined
into an “Other” category. To obtain similar group sizes for analyses, groups were combined and coded
as “Latino” and “non-Latino.”
Data analysis
Descriptive statistics for the sample were obtained by computing means and frequencies of
demographic data. T-tests were used to test for mean differences between weight groups in perceived
stress and emotional eating. Differences in the proportion of emotional eaters in the BMI groups were
33
tested using chi-square analyses. Multilevel regression analyses were used to identify covariates to be
included in the structural equation model (SEM) analyses. Exploratory factor analyses were used to
reduce the number of items for each construct, as Bentler & Chou (1987) suggest limiting the number
of variables in a data set in order to minimize issues of poor measurement structure, with caution not
to exclude key variables.
Although moderation can be assessed via regression analyses, SEM offers an approach to
obtain results with reduced bias. Due to measurement error not accounted for and inability to estimate
reciprocal effects in regression analysis (potentially resulting in underestimated effect sizes), SEM is
ideal because measurement error in the variables are statistically controlled, it allows for the
measurement of reciprocal effects, and it assesses overall model fit (Peyrot, 1996). Model fit is
important because it gives us information about the suitability of the model to our data. SEM allows
for a simultaneous test of all variables in the model in order to assess model fit. If a good model fit is
found, this offers support for the theorized model (Byrne, 1994).
In addressing the issue of nested data, multilevel SEM was considered for analyses.
Intraclass correlation (ICC) coefficients provide an estimate of the degree of commonality in
observations within a given unit, in this case school. Conventional single-level analytical approach is
inadequate to obtain valid statistical results when the ICC is large. Murray and Blitstein (2003) have
reported that the school-level ICCs for the majority of health behaviors is less than 0.05 indicating
low degree of dependence in observations. Therefore, ICCs were calculated for variables in the model
and all were found to be less than 0.05. Furthermore, techniques for multi-level SEM are still under
development and not yet well understood. Therefore single level SEM was the method of analyses for
this study.
Confirmatory factor analysis. The relationship of the indicators to their respective latent
factors was empirically assessed through confirmatory factor analyses. Empirical data were used to
statistically test the hypothesized model in order to confirm the adequacy of indicator variables used
to represent the proposed latent factors. Factor loadings of indicator variables were expected to have
high loadings on their respective factors and to be significant in order to show evidence of convergent
34
validity. Following inspection of confirmatory factor analysis results to verify the presence of distinct
constructs, causal pathways were inserted to delineate the relationship between perceived stress and
emotional eating. Testing of the structural model was then performed.
2
Model fit. Assessment of model fit was performed using the goodness-of-fit χ test statistic
as well as the comparative fit index (CFI) and the root mean squared error of approximation
(RMSEA); a CFI of 0.9 or more and an RMSEA of < 0.05 is considered to be a good fit (Browne &
Cudeck, 1993; Kline, 1998). The maximum-likelihood estimation procedure was employed as a
global test of the model (Bentler, 1990). Distributions of all variables were checked for normality via
kurtosis and skewness statistics. Robust estimates were obtained in order to deal with violations of
the normal distribution assumption. The Lagrange Modifier (LM) test was utilized to identify
parameters that would improve model fit. To preserve the theoretical factor structure of all the
measures, only error covariances that were consistent with theory were added to the model.
Testing for model invariance. Good model fit was obtained for the two weight groups
separately. Using the multiple group approach SEM procedure, these models were combined to test
for invariance of factor loadings between the normal weight and overweight groups. Invariance of the
factor loadings assures that factors can be treated the same between the two weight groups. Upon
Table 8. Characteristics of the sample.
Variable Overall Normal Weight Overweight
N = 517 N = 301 (58%) N= 216 (42%) Range
M (SD) M (SD) M (SD)
Age 12.51 (0.66) 12.54 (0.68) 12.47 (0.63) 11–15
Female* 392 (75.8) 236 (78.4) 156 (72.2)
Ethnicity*
Asian/PI 91 (17.6) 66 (21.9) 25 (11.6)
Latino 319 (61.7) 165 (54.8) 154 (71.3)
Multi-ethnic 57 (11.0) 41 (13.6) 16 (7.4)
Other 31 (6.0) 20 (6.6) 11 (5.1)
White 19 (3.7) 9 (3.0) 10 (4.6)
Perceived
Stress
15.98 (9.17) 17.04 (9.08) 16.89 (9.30) 0–40
Emotional
Eating
1.90 (0.92) 1.96 (0.96) 1.81 (0.86) 1–5
BMI
Percentile
68.95 (27.67) 50.82 (22.69) 94.21 (4.20) 5.1–
99.8
*N (%)
35
obtaining good model fit, invariance of the regression weight between the two groups was the focus of
the test for moderation. In order to do this, the path between perceived stress and emotional eating
was constrained to be equal across groups (Peyrot, 1996). If the model fit remained, there was no
moderating effect, since this would indicate that the same model fits for both groups, with the same
path coefficients. However, if the model fit was lost with this added constraint, this would mean that
there was a significant weight status interaction. The Chi-square difference test was used to test the
significance of the change in model fit.
Results
517 (84%) of the 617 students who participated in the survey had complete data on perceived
stress and emotional eating and were of at least normal weight. There were significant differences
between those with complete data vs. those without complete data; among those with
complete data there were less boys (23.9% vs. 34.6%), and they scored higher on perceived stress
(M
complete
= 17.45, SD = 8.72; M
incomplete
= 9.67, SD = 11.00; p = 0.005) and emotional eating (M
complete
= 1.92, SD = 0.92; M
incomplete
= 1.58, SD = 0.82; p < 0.001) than those with incomplete data. There
were no significant differences in BMI percentile between those included in analyses and those
excluded (p = 0.527). Table 8 and Figure 3 display sample characteristics.
Figure 3. Stress, emotional eating and BMI status.
0
50
100
150
200
250
300
350
400
450
500
High Stress Emotional Eater Overweight
Number Participants
Yes
No
36
T-tests revealed no significant differences in perceived stress (t = 0.19, p = 0.85) or
emotional eating (t = 1.84, p = 0.07) between the normal weight group and the overweight group.
Further, chi-square analyses revealed that there was a larger proportion of emotional eaters in the
normal weight group than in the overweight group ( χ
2
= 4.24; p = 0.03). Twenty-six percent of normal
weight participants were categorized as emotional eaters vs. 18% of overweight participants; 66% of
those categorized as emotional eaters were of normal weight. Results from the regression analyses
showed that ethnicity should be included in the SEM model analysis.
Factor analysis
The measurement model was tested using confirmatory factor analyses. Results show that all
variables loaded high on their respective factors and all factor loadings were significant. This serves
as evidence of construct and convergent validity. Distributions of all variables appeared to be normal,
as kurtosis and skewness statistics were quite low. Nonetheless, robust estimates were also computed
and showed no significant differences; therefore results from ML procedures were used.
Table 9. Summary results of model development and tests of invariance.
Group
2
χ df p CFI RMSEA
Normal Weight
M 478.85 250 < .00001 .934 .055
N0
M 406.44 249 < .00001 .955 .046
N1
Overweight
M 417.41 250 < .00001 .915 .056
O0
M 386.66 249 < .00001 .930 .051
O1
M 377.91 248 < .00001 .934 .049
O2
Combined
M0 784.63 497 < .00001 .946 .047
M1 812.90 518 < .00001 .945 .047
M1-M0 28.27 21 .1327
M2 815.03 519 < .00001 .944 .047
M2-M1 2.13 1 .1440
Note. M
N0
and M
O0
= basic theoretical models for normal weight (N = 301) and overweight (N =
216) groups, respectively. M
N1
and M
O1
= modified models with common correlated errors
suggested by the LM test on M
N0
and M , respectively. M
O0 O2
and M
O3
= modified models with
correlated errors suggested by the LM test on M
O1
. M0 = basic model combining normal and
overweight groups. M1 = model with all factor loadings constrained to be equal across groups.
M2 = model with all factor loadings and regression weight constrained to be equal across groups.
37
Multiple group approach structural equation model analyses
Results of the development of the models are summarized in Table 9. The basic theoretical
model was tested for normal weight and overweight groups separately. These base models did not
yield a statistically satisfactory fit to the data nor good fit based on the fit indices. Therefore, model
modification was guided by the LM test to obtain good model fit for the two groups.
Common correlated errors were identified from the LM test to begin modification. After the
first modification, the model for the normal weight group showed satisfactory model fit, based on the
fit indices. SEM Model 1 for the normal weight group yielded a χ
2
= 406.44 (df = 249), p < .00001,
Table 10. Parameters of final model.
Item Factor Loading
(SE)
†
Error Variance (SE) Reliability
**
Normal
Weight
Over- Normal
Weight
Over-
weight weight
Perceived Stress
PS1
1.000
*
0.828 (.073) 0.942 (.097) 0.553 0.568
PS2 1.148 (.076) 0.641 (.060) 0.692 (.076) 0.421 0.423
PS3 1.226 (.082) 0.874 (.080) 0.791 (.087) 0.465 0.423
PS4 0.981 (.066) 0.588 (.054) 0.511 (.056) 0.477 0.426
PS5 1.101 (.077) 0.775 (.070) 0.878 (.093) 0.488 0.502
PS6 0.736 (.073) 1.216 (.102) 1.307 (.129) 0.770 0.771
PS7 1.234 (.077) 0.529 (.053) 0.581 (.067) 0.341 0.347
PS8 1.052 (.076) 0.958 (.084) 0.766 (.081) 0.564 0.491
PS9 0.997 (.080) 1.125 (.097) 1.237 (.126) 0.628 0.635
PS10 0.997 (.077) 0.976 (.085) 1.064 (.109) 0.594 0.598
Emotional Eating
EE1
1.000
*
0.783 (.066) 0.423 (.044) 0.520 0.402
EE2 0.908 (.069) 1.208 (.101) 1.008 (.100) 0.669 0.660
EE3 1.219 (.066) 0.674 (.059) 0.475 (.051) 0.385 0.337
EE4 1.111 (.060) 0.556 (.048) 0.405 (.043) 0.384 0.343
EE5 1.197 (.061) 0.361 (.033) 0.414 (.044) 0.258 0.315
EE6 1.021 (.058) 0.522 (.045) 0.454 (.047) 0.409 0.409
EE7 1.161 (.057) 0.235 (.023) 0.391 (.042) 0.194 0.315
EE8 1.070 (.059) 0.547 (.048) 0.406 (.043) 0.398 0.360
EE9 1.116 (.057) 0.402 (.036) 0.310 (.034) 0.308 0.283
EE10 0.930 (.053) 0.434 (.038) 0.480 (.049) 0.409 0.469
EE11 1.128 (.057) 0.316 (.029) 0.399 (.043) 0.255 0.332
EE12 1.220 (.063) 0.385 (.035) 0.583 (.061) 0.264 0.383
EE13 1.017 (.070) 1.066 (.090) 0.971 (.097) 0.588 0.599
*
Factor loading for first variable of each factor was fixed at one
**
Reliability is equal to 1 – R-squared
†
Corresponding factor loadings were constrained equal between the two weight groups. Only
one factor loading now reported………………………………………………
38
CFI = .955, and RMSEA = 0.046. The LM test did not show any more common correlated errors,
therefore the above SEM model was treated as the final model for the normal weight group, and was
used for subsequent multiple group approach. One more correlated error, consistent with theory, was
added to the model for the overweight group to obtain good model fit. The final SEM for the
overweight group yielded a χ
2
= 377.91 (df = 248), p < .00001, CFI = .934, and RMSEA = 0.049.
Tests of model invariance followed.
A base model combining the final models for each the two groups was tested, yielding χ
2
=
784.63 (df = 497), p < .00001, CFI = .946, and RMSEA = 0.047. An M1 model was then developed
with all factor loadings constrained to be equal across groups, yielding χ
2
= 812.90 (df = 518), p <
.00001, CFI = .945, and RMSEA = 0.047. A likelihood ratio test on the change in chi-square was
performed and there was no significant difference between the base model and the fully constrained
M1 model (p = 0.13), therefore the fully constrained model was retained. Acceptance of M1 model
indicated that the measurements are invariant between the two weight groups.
In order to test for the moderating effect of BMI category, the regression weight between the
two factors was constrained to be equal across groups. The M2 model yielded χ
2
= 815.03 (df = 519),
p < .00001, CFI = .944, and RMSEA = 0.047. There was no change in chi-squares showing that there
was no significant difference between Model 1 and Model 2 (p = 0.14). Therefore, the fully
constrained Model 2 was retained as the final model; estimates for this model are presented in Table
10. These analyses indicate that perceived stress is significantly related to emotional eating, but that
BMI was not a significant moderator of the relationship.
Discussion
This test of the effects of weight status on the relationship between perceived stress and
emotional eating showed that BMI is not a moderator of this significant positive relationship.
Furthermore, there were no differences found in level of emotional eating by overweight and normal
weight participants; and the proportion of emotional eaters was higher in the normal weight group
than in the overweight group. These findings are inconsistent with those that have found an
39
association between weight and emotional eating, e.g., Ganley (Ganley, 1989). They do not support
the conjectures of the Psychosomatic Theory nor of the Obesity hypothesis of the Individual
Differences Model of Stress-induced Eating, nor with the a priori hypothesis stated for this paper.
Results do lend further support though for the Individual Differences Model which states that
individual differences account for why some people eat in the face of stress while others do not.
These findings indicate that the relationship between emotional eating and weight may be
more complicated than previously thought. It seems that eating may be a coping strategy used by
people of all weights, however those who keep their weight within the normal range may possess
certain protective qualities or engage in other compensatory behaviors. For example, when one
overeats due to stress, they may eat less the next day, or take a walk to make up for the extra caloric
intake. Another possibility is that people of normal weight overeat to a lesser degree than people who
are overweight, i.e. the amount of intake during overeating may differ between these groups. Yet
another possibility is that persons in this sample have not yet become overweight, but if this emotional
eating behavior continues, it may still lead to overweight. Future studies could make a substantial
contribution to the literature through exploration of potential mediating factors of the relationship
between BMI and emotional eating.
The fact that perceived stress and emotional eating scores were significantly higher for
participants in these analyses than those not included in the analyses may have affected results of the
study. These differences may have resulted if those scoring lower on stress were more likely to not
experience negative emotions therefore may have skipped items on the emotional eating scale that
they did not think applied to them. It is possible that those with lower scores on the constructs of
interest may have been distributed across weight groups in such a way that, if included in analyses,
may have made the relationship between perceived stress and emotional eating non-significant in one
or both of the weight groups. However, this seems unlikely, and there were also no significant
differences in BMI percentile between those with complete data vs. those that did not.
Causality cannot be deduced from these analyses due to their cross-sectional nature. It is
quite plausible that emotional eating may lead to the experience of stress. However, as indicated by
40
the definition of emotional eating, eating in response to negative affect, it makes theoretical sense to
look at the association as perceived stress leading to emotional eating.
Another potential limitation of this study was that the psychological data are self-reported
which can threaten the validity of the data. Because we used validated self-report instruments widely
used in the literature we feel confident that we obtained good data from our participants. Data
collectors also emphasized the confidentiality of all answers; therefore we do not believe that
participants would not have accurately reported data.
An important strength of this study is that it addressed the issues of BMI and emotional
eating in a predominantly Latino adolescent sample, a community at high risk for obesity (Hedley
2004). The analysis methods used also increase confidence in findings as SEM analyses provide
estimates based on a simultaneous test of all model parameters with reduced measurement bias.
Findings further support the association between perceived stress and emotional eating, and
highlight the fact that emotional eating is not an issue only for overweight and obese persons. This
brings to light the importance of providing our youth with more adaptive coping skills than turning to
food in the face of stress. Learning healthy coping strategies during this time period is crucial as this
is a critical time point at which health behaviors begin to solidify and may track into adulthood. This
study shows that some children in this population at increased risk for obesity and related chronic
disease have already incorporated emotional eating as a learned response to stress by the time that
they enter adolescence. Therefore, further investigation of means of ameliorating this problem eating
behavior certainly seems warranted.
41
CHAPTER FIVE: CONCLUSION
Based on reviews of the emotional eating literature, each of the three preceding studies
addressed an issue meriting further investigation in order to elucidate the literature in adolescent
populations. The first study provided information on specific moods/emotions associated with
emotional eating, instead of a whole range of negative psychological states. The second study
identified particular food types that were related to emotional eating (vs. increased consumption of
any or all foods). The third study assessed the impact of BMI on the relationship between perceived
stress and emotional eating. Results of these studies only partially supported a priori hypotheses.
Figure 4 shows the revised theoretical model based on empirical findings from these studies.
In the assessment of psychological determinants of emotional eating, we found that only
worries, depressed mood, and confused mood were associated with emotional eating. Gender
differences were also found in these analyses. Stratified analyses showed that only worries was
associated with emotional eating in boys, while, in girls, anger was negatively associated and
confused and depressed mood were positively related to emotional eating. It was hypothesized that all
negative emotions would have a positive relationship with emotional eating, as it is defined as eating
in response to negative affect, however these analyses reveal that emotional eating in adolescents may
only occur in response to a particular set of emotions, and further that the precipitating emotions may
COVARIATES
Ethnicity
Gender
Body Image
Wt Concerns
Intervention
EMOTIONS
/ MOODS
P Stress
Worries
Depression
- Anger
Confusion
EMOTIONAL
EATING
DIETARY
CHOICES
Fruits and vegetables
Energy dense foods
Figure 4. Empirical Model
Gender
42
be different for boys vs. girls. The significant findings are in line with that found in the adult
literature; the fact that not all of the same emotions were found to be significantly related may be a
result of the age of the sample. It is possible that the precursors to emotional eating are different for
adolescents vs. adults. It may also be possible that once emotional eating is a learned as a response to
certain emotions, this learning generalizes to overall negative affect. It may also be the case that
feelings of worry, confusion, and depression are more salient to adolescents.
In testing the associations between dietary choices and emotional eating, it was expected that
emotional eating would be positively correlated with soda and high energy-dense dietary choices. An
interesting outcome was that Latinos and Multi-ethnic students were less likely than Whites to
consume fruits and vegetables (there were no other ethnic differences in dietary choices). Findings
also showed gender differences in dietary choices associated with emotional eating. Overall,
emotional eating was related to intake of high energy-dense food and soda; the gender specific
findings were quite interesting. Both genders showed an association with salty high energy-dense
foods, but boys also showed a positive association with fruits and vegetables. Therefore, it appears
that boys may not necessarily favor the “unhealthy” food items. Previous literature supports gender
differences in eating behavior (Rolls, Fedoroff, & Guthrie, 1991), therefore the present findings seem
to support these differences.
Psychosomatic theory supported a moderating effect of BMI on the relationship between
perceived stress and emotional eating, where overweight persons would be more likely to show a
significant relationship than normal weight participants. Results suggest that there is indeed a
significant relationship between perceived stress and emotional eating; however, analyses revealed
that this association was present in persons of all weights, not just the overweight group.
Additionally, there were no differences in level of emotional eating between the two weight groups,
and there were actually more normal weight emotional eaters than overweight emotional eaters. This
suggests that the relationship between weight and emotional eating is not as straightforward as stated
in the Psychosomatic Theory or Obesity Hypothesis of the Individual Differences Model. Wardle and
colleagues (1992) had previously concluded that emotional eating may not in and of itself be
43
problematic since it was only related to positive evaluations of food and would only pose a problem if
one was trying to lose weight. However Braet & van Strien (1997) found that it was problematic as it
led to increased caloric intake, thus having the potential for weight gain in the long run. It seems that
there are individual differences that may lead to maintaining a normal weight or that can lead to
becoming overweight. It may be that persons of normal weight engage in behaviors that compensate
for overconsumption or perhaps do not overeat to the degree necessary to result in weight gain.
Outcomes from the first and third study also bring to light a discrepancy in the relationships
between psychological factors and emotional eating. In the first study, perceived stress was not related
to emotional eating, however, in the third study, this relationship was found to be significant.
Although the reliability of the full 17-item scale was not maximal (Cronbach α = 0.73), it was used for
analyses in the first study, as it is advised that validated scales should be used in their entirety. In the
third study, however, the number of items needed to be reduced in order to provide the optimal
number of items for SEM. This reduced scale turned out to have higher reliability (Cronbach α =
0.89) than the complete scale used in study one. Therefore, the significant findings in the third study
may have resulted from using a factor with better reliability. Additionally, the regression model tested
in study one included all of the other moods/emotions that were being tested, therefore the
associations with these other variables may have attenuated the effect of perceived stress on emotional
eating in this model.
Limitations
Because these data came from a larger study with a main focus on physical activity,
instruments used to measure diet and BMI may have been less than optimal to be able to fully
examine the various hypotheses. BMI is not the best marker of body composition available. Other
more accurate measures of body fat, such as waist circumference, waist-to-hip ratio, skinfolds or dual-
energy x-ray absorptiometry (DEXA) may have produced different results. A more advanced dietary
assessment, for example data from multiple pass 24-hour diet recall or three-day dietary records could
have resulted in a more accurate delineation of associations with specific dietary components. Also,
because of the main focus of the parent study, Latina girls were oversampled. A sample that included
44
equal numbers of males and females may have enhanced the validity of analysis and results of these
studies. Thus, a major issue in these analyses has to do with the fact that the sample was
predominantly female (consistently about 70%) which may affect the generalizability (external
validity) of results to males.
Important to note is the fact that in each of these studies, there were differences between
those participants included (i.e. those with complete data on constructs of interest) and excluded from
analyses on key factors for each analysis. In the first study, there were fewer boys and fewer
emotional eaters in the sample on which analyses were conducted (i.e. there were fewer boys and
fewer emotional eaters with complete data on the key variables in analyses). This is significant
because gender differences in emotional eating were tested in this study. The second study also
examined gender differences, however, there were also less boys in the included group (less boys had
complete data) than in the excluded group. In the analysis sample for the third study, there were less
boys and scores were higher on perceived stress and emotional eating than in the excluded sample
(there were less boys in the group with complete data and those with complete data also scored higher
on the variables of interest). These differences have the potential to have influenced outcomes for
each of these studies. For example, the gender differences in emotional eating in the first study may
have been a result of having more females and more “emotional eaters” in the included sample.
Nonetheless, these studies do provide information on a sizable sample of boys. Studies that replicate
these findings with equal numbers of boys and girls and a wider range of emotional eaters would be
helpful in further validating these findings. Despite the potential influence of these differences, these
findings certainly hold merit as important steps in answering key questions in the adolescent
emotional eating literature.
Implications
The large majority of emotional eating studies have been conducted with adult females, often
in lab settings. Several findings from these studies have replicated that of the adult literature in a
minority adolescent population that included males. These studies make a significant contribution to
the literature on two key points 1) Examination of eating behaviors at a critical time point in
45
development, and 2) Examination of a modifiable risk factor for problem eating behavior in a
population at-risk for obesity. Adolescence poses an interesting point of study because adolescence is
often viewed as a time of marked stress (Kelly, Ricciardelli, & Clarke, 1999), and has been identified
as a critical period in the development of obesity (Dietz, 1994). Adolescence has also long been
viewed as the age of onset for eating disorders (American Psychiatric Association Task Force on
DSM-IV-TR, 2000; Attie & Brooks-Gunn, 1989; Bruch, 2001; Minuchin, Rosman, & Baker, 1978;
Steele, 1980). An exploration of the 13 English language studies of emotional eating in children and
adolescents showed that most of these studies were conducted in Europe, with only three in the U.S.,
and none of them using predominantly minority samples. The studies presented here examined
emotional eating in a largely Latino sample, a population with 40% prevalence of risk for overweight
and overweight vs. 28% in whites (Hedley et al., 2004). These studies addressed other gaps in the
literature identified by reviews by detecting specific emotions and dietary choices particular to
adolescents. They also provided an additional test of the Psychosomatic Theory’s supposition that
overweight persons emotionally eat while normal weight persons do not in an adolescent sample.
Interestingly, our data did not support this component of the theory.
This study was undertaken in order to further explore emotional eating as a risk factor for
problem eating behavior (Kagan & Squires, 1984; Paxton, 1993; Stice, Presnell, & Spangler, 2002)
and obesity (Braet & Van Strien, 1997; Stice, Presnell, & Spangler, 2002). Studies one and two seem
to support the notion that emotional eating is not a healthy behavior. It is an emotion-focused coping
strategy that may help in the short term, however this may keep adolescents from learning effective
problem-focused coping strategies, and in the long term, be a disservice to their mental well-being.
This behavior also leads to eating in response to emotional cues rather than physiological hunger cues,
thus leading to overeating. It is also associated with consumption of high energy-dense foods which
implies increased caloric intake. For these reasons, it seems implicit that with emotional eating comes
increased risk for obesity.
However, findings from study three do not provide evidence that emotional eating leads to
overweight. It appears that these minority adolescents can engage in emotional eating and still
46
maintain a normal weight. Therefore, it seems that emotional eating may be a risk factor for obesity
only for some people. It may be that emotional eating exacerbates some personal trait that puts some
people at increased risk for weight gain if they emotionally eat. Emotional eating may still pose a
health risk even if it does not necessarily lead to weight gain. The findings reported here tell us
nothing about body composition. It is possible that these normal weight individuals may have an
unhealthy percent body fat resulting from high caloric intake. Emotional eating is largely viewed as a
negative health behavior in the literature (Faith, Allison, & Geliebter, 1997; Ganley, 1989; Thayer,
2001) and although the present findings did not support a direct link to overweight, it merits further
exploration as many other studies have found this association.
The significance of the study of emotional eating lies in the fact that this behavior offers a
point of intervention for disturbed eating, particularly in adolescence. Many health behaviors have
their beginnings in childhood, tend to solidify in adolescence, and carry through to adulthood.
Understanding the emotional underpinnings to problem eating behaviors in pediatric populations may
help us to develop more meaningful and effective interventions aimed at producing and maintaining
healthy eating habits for youth. The studies conducted here suggest several possibilities for the
development of interventions. First, teaching effective coping strategies to improve negative mood
and to reduce stress may be useful components in prevention of overeating, as this may reduce
overconsumption that results from the use of food to self-regulate emotions (emotional eating). Being
able to find healthy substitutions for snacks may also prove fruitful. The finding that emotional eating
is associated with consumption of fruits and vegetables in boys supports the notion that adolescents
may be willing to exchange healthy dietary choices for less-healthy and energy dense foods. Further
investigation of how and/or why normal weight individuals are able to maintain a healthful weight in
spite of the fact that they are emotional eaters, and assumed to thus be overeating, may prove to be an
important direction for future studies. An issue of note for interventions is awareness of emotional
eating.
Despite the long standing recognition of the existence of emotional eating, the current
literature examining emotional eating in pediatric populations is still in its infancy, and the
47
information on its relationship to obesity is even scarcer. Extending investigations through
transdisciplinary methods seems to be the next important step for this literature. For example,
identifying interactions between biological and psychological mechanisms seems warranted. These
types of studies may help in the understanding of why or in what groups emotional eating may pose a
risk for obesity. In van Strien’s (1995) defense of the Psychosomatic Theory, she points to the fact
that obesity is largely a result of physiological mechanisms at work, and therefore, the variance
explained by emotional eating should be expected to be small. Although the direct influence of
emotional eating may be small, the knowledge to be gained from answering key questions is quite
significant, as this information may point to new directions for intervention to help youth to make
healthy lifestyle choices. In light of the problems of decreasing metabolic health and maladaptive
eating behaviors in adolescence, continued investigation of emotional eating in this population would
be helpful in improving the health of our youth and into adulthood.
48
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52
Abstract (if available)
Abstract
The present study sought to explore psychosocial and behavioral associations of emotional eating in adolescence. Data from the Get Moving study were used to conduct multilevel regression and structural equation model analyses. Participants were 617 minority middle school students in Los Angeles County in the seventh and eighth grades. In girls, emotional eating was positively associated with confused and depressed mood, and negatively associated with anger. In boys, emotional eating was associated with worries. Both boys and girls were likely to eat salty high energy-dense foods and boys were also likely to eat fruits and vegetables when emotional eating. Findings showed a significant positive relationship between perceived stress and emotional eating, however this association was not moderated by BMI. Results indicate that there is specificity in emotions and foods associated with emotional eating. It also appears that several associations found in European and Caucasian samples as well as in the adult literature generalize to a minority sample of adolescents. Results emphasize the important role of affect as an influential factor in eating behavior. Thus, these findings provide points of focus for interventions that may not have previously received ample attention.
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Asset Metadata
Creator
Michel, Selena Thi
(author)
Core Title
Psychosocial and behavioral ractors associated with emotional eating in adolescents
School
Keck School of Medicine
Degree
Doctor of Philosophy
Degree Program
Preventive Medicine (Health Behavior)
Publication Date
10/24/2006
Defense Date
08/01/2006
Publisher
University of Southern California
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Tag
Adolescents,BMI,emotional eating,,Latino,OAI-PMH Harvest,stress-induced eating
Language
English
Advisor
Unger, Jennifer B. (
committee chair
), Chou, Chih-Ping (
committee member
), Gatz, Margaret (
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), Goran, Michael I. (
committee member
), Spruijt-Metz, Donna (
committee member
)
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selenang@usc.edu
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Michel, Selena Thi
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BMI
emotional eating,
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stress-induced eating