INTRA-PERSONAL CORRELATES OF DISORDERED EATING PATTERNS IN COLLEGE STUDENTS
Sarah Kaplan
A Thesis
Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of
Master of Family and Consumer Sciences
December 2006
Committee:
Julian H. Williford Jr., Advisor
Priscilla Coleman
Rebecca Pobocik
ii
ABSTRACT
Julian H. Williford Jr., Advisor
Eating disorders are interesting problems on college campuses that appear to be increasing. The purpose of this study was to identify correlations between EAT-26 scores and: Beck Depression Inventory (BDI) scores, General Self-Efficacy Scale
(GSES) scores, nutrients consumed in a self-reported 5-day diet record analyzed by Diet
Analysis Plus 6.0, and demographic information from college students.
A convenience sample of 390 volunteer college students from an introductory
nutrition course at Bowling Green State University (BGSU) completed a 5-day diet
record and surveys including the EAT-26, the BDI, the GSES, and demographic
information. EAT-26 scores >20 were considered positive for disordered eating patterns.
Approximately 15% of the sample had positive EAT-26 scores, which corresponds to
previous studies conducted at BGSU (14%-18% positive). Positive EAT-26 scores were
significantly correlated (p < .0001) with higher BDI scores. EAT-26 scores were
significantly correlated with lower energy, (p < .0001) fat, (p < .0001) and carbohydrate
intakes (p > .05) although EAT-26 scores were not related to the sample meeting the
RDA/DRI for each nutrient. RDA/DRI was not met by any gender and age group for
energy, magnesium, and potassium: and for females, neither the RDA/RDI for calcium or
phosphorus was met. Approximately 88% of the sample had inadequate intakes of
magnesium. Females had a higher percentage of inadequate intakes of measured
nutrients than males. Subjects from low income families had significantly lower (p <
.05) BDI scores. However, subjects from both low income families and high income
families had significantly (p < .05) higher self-efficacy than those from middle income iii
families. Subject’s age significantly correlated with both the EAT-26 scores (p < .05)
and with BDI scores (p < .05).
This study concluded that a substantial number of students scored positively on
the EAT-26 and that the female students had a higher percentage of inadequate intakes than the male subjects, which supports previous research done at BGSU and other
researchers. The RDA/DRI was not met for much of the sample for many nutrients and
approximately 88% of the subjects did not have adequate intake of magnesium. The
University should have programs available for both students and faculty to increase
awareness and knowledge of eating disorders and the many risks they can include.
iv
This thesis is dedicated to my papa. v
ACKNOWLEDGEMENTS
I would first like to express my gratitude to my committee. Dr. Joe: thanks for all of your support and assistance with this thesis. Your encouragement kept me motivated
to finally complete this project. BP: thank you so much for being a part of this, you have
been both a teacher and a friend to me over the last few years and all of your time and
effort is greatly appreciated. Dr. Coleman: a huge thank you for helping me with all of
the statistical data. I really appreciate all of the extra time you took to help me through
this and I really did have fun working with you. I also want to thank all of my family and
friends for supporting me, for putting up with my crazy mood swings, and for loving me
anyways. Special thanks to my Mom, Chris, and Amanda: you put up with the most and
you kept me sane; I really can’t thank you enough. Finally I would like to thank my
fellow thesis-writer and friend Erin Dawson for our daily venting sessions; I don’t know
if I could have made it through the last couple of years without you!! vi
TABLE OF CONTENTS
Page
CHAPTER I. INTRODUCTION...... 1
Statement of Problem...... 2
Significance of Problem...... 3
CHAPTER II. REVIEW OF LITERATURE ...... 5
Disordered Eating ...... 5
Anorexia Nervosa...... 6
Bulimia Nervosa...... 6
Eating Disorders Not Otherwise Specified...... 6
Eating Disorders in College...... 7
Negative Effects of Eating Disorders ...... 8
Physical Effects...... 8
Psychological Effects...... 8
Correlation Between Eating Disorders and Mental Health Disorders...... 9
Dissociative Symptomatology ...... 10
Seasonal Affective Disorder ...... 10
Depression...... 11
Correlation Between Eating Disorders and Self-Efficacy ...... 12
Dietary Reference Intakes (DRI’s) ...... 13
Macronutrients Deficiencies and Eating Disorders ...... 14
Carbohydrates ...... 15 vii
Fat ...... 16
Protein ...... 16
Energy ...... 17
Deficiencies of Macronutrients in Disordered Eating ...... 17
Micronutrients Deficiencies and Eating Disorders...... 19
Vitamin A...... 20
Iron ...... 20
Vitamin C...... 21
Calcium...... 22
Measuring Disordered Eating ...... 23
The Eating Disorder Inventory ...... 24
The Questionnaire for Eating Disorder Diagnosis...... 25
The Eating Attitudes Test ...... 25
Measuring Depression ...... 28
The Inventory to Diagnose Depression...... 29
The Diagnostic Inventory for Depression...... 29
The Beck Depression Inventory...... 30
Measuring Self-Efficacy ...... 31
Traditional Measurement...... 31
Likert-Type Measurement ...... 32
The General Self-Efficacy Scale...... 33
Food Intake Surveys ...... 33
Food Frequency Questionnaires ...... 34 viii
Food Records and Recalls...... 34
Diet Records Analysis Computer Programs ...... 35
Hypotheses ...... 36
CHAPTER III. METHODOLOGY ...... 38
Population ...... 38
Data Collection...... 38
Instruments ...... 39
Interpreting the Demographic Information...... 39
Coding the EAT-26...... 40
Coding the Beck Depression Inventory ...... 40
Coding the General Self-Efficacy Scale ...... 41
Coding the 5-day Food Record for Nutrients ...... 41
Associations with DRI’s...... 42
Measurements Used for Evaluation of Nutrients...... 42
Statistical Analysis...... 43
CHAPTER IV. RESULTS...... 45
Categorical Variable Frequencies...... 45
Continuous and Categorical Descriptive Statistics...... 45
Correlations Among Primary Variables...... 45
Reliability of Multiple Item Scales...... 46
EAT-26 Score Associations with the BDI and General Self-Efficacy Scale...... 52
EAT-26 Score Associations with Nutrient Intake ...... 52
EAT-26 Score Associations with Specific Nutrient Intakes...... 54 ix
The EAR Cut-Point Method for Nutrients with an Estimate Average Requirement. 58
Exploratory Analysis...... 60
CHAPTER V. DISCUSSION...... 62
CHAPTER VI. CONCLUSIONS AND RECOMMENDATIONS...... 73
Conclusions ...... 73
Recommendations for Further Research...... 75
REFERENCES ...... 77
APPENDIX A. CONSENT FORM ...... 90
APPENDIX B. DEMOGRAPHIC/BACKGROUND SURVEY ...... 92
APPENDIX C. THE EATING ATTITUDES TEST (EAT-26) ...... 101
APPENDIX D. THE BECK DEPRESSION INVENTORY (BDI) ...... 104
APPENDIX E. THE GENERAL SELF-EFFICACY SCALE ...... 107
APPENDIX F. SAMPLE AVERAGE 5-DAY FOOD RECORD ANALYSIS...... 109 x
LIST OF TABLES
Table Page
1 Frequencies of Categorical Variables for Age, Gender, Year in School, Full
or Part Time Student, Socio-Economic Status, Ethnicity and Regular
Participation in Sports for College Students...... 47
2 Descriptive Statistics for Age and EAT-26 Scores in College Students ...... 48
3 Descriptive Statistics for Self-Efficacy Scale Scores in College Students...... 49
4 Descriptive Statistics for Beck Depression Inventory Scores in College
Students ...... 49
5 Descriptive Statistics for Nutrient Intake in College Students ...... 50
6 Descriptive Statistics for Nutrients in College Students Scoring
(≤ 20) on the EAT-26...... 50
7 Descriptive Statistics for Nutrients in College Students Scoring
(> 20) on the EAT-26...... 50
8 Correlations Among Primary Study Variables: the EAT-26, the BDI,
the General Self-Efficacy Scale, Nutrients, and Age ...... 51
9 Results of MANOVAs Exploring Nutrient Intake Associations with the
EAT-26 ...... 54
10 Associations of Daily Intakes of Nutrients with the DRIs for College
Students Scoring Positively (>20) on the EAT-26 ...... 56
11 Associations of Daily Intakes of Nutrients with the DRIs for College
Students Scoring Negatively (≤20) on the EAT-26...... 57
xi
12 Results of the EAR Cut-Point Method in College Students for all
Nutrients with an Estimated Average Requirement (EAR)...... 59
13 Analysis of Variance Results for Socio-Economic Status and EAT-26
Scores, BDI Scores, and General Self-Efficacy Scale Scores ...... 61
1
CHAPTER I
Introduction
As high school comes to an end, students are faced with the prospect of their
futures. Some will choose to enter the work force, some may go for an internship in a
field of their interest, and some may choose to do nothing at all; living at home or taking
a break. However, there are many interesting choices for a high school graduate, and one
of those choices is to continue with their education and go to college.
College is a time for academic learning and a time for people to learn more about
whom they are as a person. Many students go to college to live on their own for the first time. Many will go to college in a new city or state, which is a time of great change for most. A lot of students adjust to this life change seamlessly, there are the students who easily go through their college years without any problems. However, there are also the obvious cases where a student has trouble adjusting and needs to talk to a counselor or figure out a working solution to his or her problem(s). What isn’t as obvious are those students who are hurting psychologically, but don’t show it in a healthy or easily controllable way.
Many of these students who are hurting either emotionally, physically, or socially are taking their pain into their own hands. Examples could include the depressed student, whose way of dealing with his or her feelings involves either not eating or eating too much; or, the student who always seems on top of everything, but feels the need to control even more in his or her life, and who may add food restriction to their behavior. 2
With the ever growing industry of diets and diet products, people are bombarded with information on how to “lose weight” and “get lean”. The media is pushing a too- thin ideal as well. Conceivably, an unhappy or depressed person might blame his or her unhappiness on his or her weight, even if the depression stemmed from another source.
Another possibility is that a depressed person might see weight as a controllable “evil”, which once under control could be the key to his or her happiness.
Statement of the Problem
Disordered eating is an increasing and widespread problem for college students
(Leibman, Cameron, Carson, Brown, and Meyer, 2001; Mann et al., 1997; Nelson,
Hughes, Katz, and Searight, 1999; Schwitzer, Bergholz, Dore, and Salimi, 1998; and
Schwitzer, Rodriguez, Thomas, and Salimi, 2001). Many of the diagnosed eating disorder cases co-exist (whether the disordered eating is secondary or primary) along with a mental disorder; such as, depression, dissociative symptoms, anxiety disorders, or bi-polar disease (Gleaves and Everenz, 1995; Keel, Klump, Miller, McGue, and Lacono,
2005; McElroy, Kotwal, Keck, and Akiskal, 2005; Ramaciotti et al., 2005; Ro,
Martinsen, Hoffart, Sexton, and Rosenvinge, 2005; Ruuska, Kalitala-Heino, Rantanen, and Koivisto, 2005; Speranza et al., 2005; and Wildes, Simon, and Marcus, 2005).
Not only do mental disorders, like depression, seem to correlate with disordered eating and attitudes, a person’s initiative, expectations, and persistence in new situations
(self-efficacy) has been reported to be related as well (Cooley and Toray, 2001; Cooper,
Rose, and Turner, 2005; Marquez and McAuley, 2001; Watkins et al., 2001; and Wolf and Clark, 2001). Since a low sense of self-efficacy may lead to depression and anxiety
(Bandura, 1994), this relationship is understandable. 3
Along with the mental disorders and attitudes associated with eating disorders, there are distinct physical effects as well. Eating disorders center around a person’s patterns of eating that are not considered normal by professionals. Whether by restrictive food intake measures, or binges and purges, nutrient intakes are disrupted in disordered eating (Alpers and Tuschen-Caffier, 2004; Gendall, Sullivan, Joyce, Carter, and Bulik,
1997; and Hadigan et al., 2000).
Significance of the Problem
While it is understood by professionals that persons with disordered eating patterns must be treated professionally, the patient’s resistance to treatments makes disordered eating a difficult problem to identify, treat, and solve. Primary and secondary eating disorder prevention programs exist on many college campuses and are sponsored both by campuses, private clubs and groups (Mann et al., 1997). Primary prevention programs are aimed at healthy individuals in order to prevent new cases of eating disorders from arising and secondary programs are aimed at those people who are in the early stages of a disordered eating illness (Mann et al.). These programs are not supported by all health professionals. Researchers have stated that the information shared with the students during the programs may unintentionally make disordered eating behaviors seem more possible, normal, or interesting (Mann et al.) By providing information about eating disorders to individuals, people may increase their knowledge of how to behave in an unhealthy manner, and this knowledge could motivate those who would have an eating disorder to start these unhealthy practices (Mann et al.).
Developing better treatments and prevention programs is a necessary step in the fight against eating disorders. Early diagnosis of eating disorders is associated with improved 4 prognosis, which highlights the importance of early detection and a more complete understanding of disordered eating (Perry et al., 2002).
Many studies have reported correlations between eating disorders and other mental problems (Gleaves and Everenz, 1995; Keel et al., 2005; McElroy et al., 2005;
Ramacciotti et al., 2005; Ro et al., 2005; Ruuska et al., 2005; Speranza et al., 2005;
Wildes et al., 2005; Cooley and Toray, 2001; Cooper et al., 2005; Marquez and McAuley,
2001; Watkins et al., 2001; and Wolf and Clark, 2001). Therefore, depression, self- efficacy, and nutrient intake should be studied together in order to identify any common interactive themes of these conditions. The purpose of this study is to gain new knowledge and identify those common interactive themes. Once researchers and practitioners better comprehend the depth and breadth of the many complications associated with eating disorders, the treatments may become more effective in treating these conditions.
5
CHAPTER II
Review of Literature
Disordered Eating The Eating Disorders Foundation of Victoria [EDFV] (2005) has established that
eating disorders have existed for hundreds of years. Greater than a century ago two
French psychiatrists, Louis-Victor Marcé and Charles Lasègue, studied patients with
“l’anorexie hysterique” and “l’anorexie mentale” in the mid 1800’s (Beumont, Garner,
and Touyz, 1994). Marcé wrote the first definitive descriptions of the illness in 1859 and
Lasègue continued with his ideas in 1873 (Beumont et al., 1994). These psychiatrists
were interested in the psyche of patients with deliberate emaciation and their desire for perfectionism (Beumont et al. and Halmi et al., 2000). William Gull, a physician in the
late 1800’s, distinguished those patients who were deliberately underweight from those who were cachectic patients suffering from physical ailments (Beumont et al.). Gull’s description was mostly related to the physical symptoms, however, and he did not touch on the mental aspects of the disease, as did Marcé and Lasègue (Beumont et al.). Over a
half a century later, in the early 1960’s, the psychopathology of eating disorders became the focus of how eating disorders were studied. A psychological diagnosis was proposed in 1961 by Bruch that described body image disturbances, loss of awareness of interoceptive cues, feelings of ineffectiveness, and the “relentless pursuit of thinness”
(Beumont et al.). In the late 1960’s and early 1970’s, standardized diagnoses for anorexia nervosa were developed, and the Diagnostic and Statistical Manual was established by the American Psychiatric Association in the 1980’s (Beumont et al.).
There are many different ways with which a person can show symptoms of
disordered eating. Anorexia nervosa, bulimia nervosa, binge eating, and other disordered 6 eating patterns are all considered forms of eating disorders (Schwitzer et al., 2001). The
Diagnostic and Statistical Manual, Fourth Edition (DSM-IV) requires that a certain set of symptoms be present to formally diagnose a patient with an eating disorder: behavior, duration, frequency, and severity (Schwitzer et al.).
Anorexia nervosa.
According to the DSM-IV, anorexia nervosa is defined as: the refusal to maintain normal body weight; maintenance of body weight at lower than 85% of expected body weight; experience amenorrhea (lack of menstrual cycle) for at least three consecutive cycles; a severe restriction of food intake; an extreme fear of gaining weight; and a distorted image of one’s self as being overweight even though they are clinically underweight (Schwitzer et al., 2001 and EDFV, 2005).
Bulimia nervosa.
According to the DSM-IV, bulimia nervosa is defined as engagement in binge eating and then vomiting, laxative use, or excessive exercise to compensate for the extra caloric intake, at least twice a week, for three consecutive months (Schwitzer et al.,
2001). Bulimia nervosa is also characterized by periods of restricted eating, use of diet pills, and use of laxatives as an attempt to compensate for both binge eating and possible weight gain (Schwitzer et al. and EDFV, 2005). Binge eating disorder is characterized as periods of binge eating without the compensatory behavior seen in bulimia nervosa
(EDFV, 2005).
Eating disorders not otherwise specified.
There are many eating disorders that don’t fall into the categories of anorexia nervosa, bulimia nervosa, or binge eating. These disordered eating patterns may show 7 some symptoms of eating disorders without meeting all of the necessary criteria previously mentioned describing anorexia nervosa, bulimia nervosa, and binge eating.
These disordered eating patterns are as serious as the full blown eating disorder, and need to be taken seriously by professionals.
Eating disorders in college.
Determining how many people are battling eating disorders in the United States, or any country, is difficult. There may be millions who do not speak out or receive treatment. Keeping this possibly enormous number of unknown persons with eating disorders in mind, the National Association of Anorexia Nervosa and Associated
Disorders [ANRED] (2005) stated that “approximately eight million people in the U.S. have anorexia nervosa, bulimia, and related eating disorders”, and this number is rising
(Mathieu, 2004; Nelson et al., 1999; and O’Dea and Abraham, 2002). The majority of these patients are most likely to be adolescent girls and women in their 20’s (Mathieu).
College students, especially females, have been singled out as a high-risk group for developing eating disorders and patterns of disordered eating (Mann et al., 1997;
Mathieu; Nelson et al.; O’Dea and Abraham; Pemberton, Vernon, and Lee, 1996;
Schwitzer et al., 1998; Schwitzer et al., 2001; and Sobal and Bursztyn, 1998). The majority of studies on eating disorders and disordered eating have been done with females, while the study of eating disorders in young men has been marginally covered.
Those studies that have included young men have shown that disordered eating among college aged men may be increasing in comparison to previous years (Nelson et al. and
O’Dea and Abraham). Researchers should study trends in eating disorders in both male and female college students. 8
Negative Effects of Eating Disorders
Eating disorders have many negative effects which may be both physical and
psychological (EDFV, 2005). Eating disorders can also disrupt daily life and
relationships. Eating disorders are serious issues, with serious consequences which
require professional help.
Physical effects.
Food restriction and starvation, as seen in both anorexia nervosa and bulimia
nervosa, can cause many different physical problems. Some of these physical problems include infertility, kidney dysfunction, reduced metabolic rate, headaches, dry and brittle hair, skin, and nails, cardiac irregularities, muscle wasting, constipation or diarrhea, hormonal irregularities, edema, easy bruising of the skin, anemia, fainting and lightheadedness, heartburn, stunting of growth, hypoglycemia, and reduced mental ability
(EDFV, 2005).
Vomiting, a common symptom of bulimia nervosa, can also cause many harmful
physical problems. Some of these include the erosion of tooth enamel, sore throat,
indigestion, heartburn, bloating, enlarged salivary glands, and electrolyte imbalances
(EDFV, 2005). Bulimia nervosa is characterized also by the misuse of diet pills and
laxatives. The misuse of laxatives can cause bowel problems, constipation, cramping,
dehydration, bowel weakening, bleeding, bowel disease, and electrolyte imbalances
(EDFV).
Psychological effects.
Eating disorders also have negative psychological effects. The propensity for
experiencing depression as well as other negative emotions is associated with disordered 9
eating (Cooley and Toray, 2001). Anxiety, depression, obsessive behavior, social
isolation, irritability, suicidal thoughts and behavior, drug misuse, increased sensitivity,
guilt, and impaired achievements are all associated with eating disorders (EDFV, 2005
and Watkins et al., 2001). Suicidal behavior has been linked with bulimia nervosa as
well as binge-eating/purging anorexia nervosa (Ruuska et al., 2005).
Personal relationships can be affected by eating disorders from both the person
with the eating disorder, and also the partner or close friend of that person. Psychological
effects and social isolation can cause obvious strains on current or future relationships.
One study concluded that more than 300 college students surveyed who had heard of
anorexia nervosa and bulimia nervosa, over 50% of both the men and women were either
“not very” or “not at all” comfortable with dating a person with anorexia nervosa or
bulimia nervosa (p < .01) (Sobal and Bursztyn, 1998).
Evans and Wertheim (2005) reported that both women with eating disorders and
women with depression self-reported more insecure attachment styles than those with no
disorders. These persons with eating disorders and self-reported depression reported
anxiety and avoidance in regard to intimate partner relationships, which was also
associated with negative feelings toward their current partners (Evans and Wertheim).
The results of this study reinforce how eating disorders and depression can disrupt daily
life and relationships.
Correlation Between Eating Disorders and Mental Health Disorders
Personality traits and personal idiosyncrasies in eating disorder patients have been
widely studied. The relationship(s) between eating disorders and mood disorders have been studied for over 25 years (Ramacciotti et al., 2005). Obsessive compulsiveness and 10 impulsivity are both common traits in persons with eating disorders (Claes,
Vandereycken, and Vertommen, 2005). Many mood disorders have been studied with eating disorder patients; however, this current study will focus mainly on depression and self-efficacy to cover both emotionally based mood disorders (depression) and cognitively based mood disorders (self-efficacy).
Dissociative symptomatology.
According to Gleaves and Everenz (1995), an above normal level of dissociative symptomatology occurs in groups of eating disordered patients. Dissociative symptomatology is the de-association from reality, meaning the lack of connection of thoughts, memories, feelings, actions, or identity. There are varying levels of dissociative symptomatology from “black out” periods and memory loss, to the creation of separate personas (multiple personality syndrome). Dissociative symptomatology has a positive correlation with depression, anxiety, self-mutilation, and suicidal behaviors
(Gleaves and Everenz). People with dissociative disorder may experience: depression, mood swings, suicidal tendencies, sleep disorders, panic attacks, alcohol and drug abuse, compulsions and rituals, psychotic-like symptoms, and eating disorders (Sidran Institute,
2003). While the severity of the dissociate symptoms do not vary with the severity of the eating disorders, the relationship between the two disorders is still important to consider
(Gleaves and Everenz).
Seasonal affective disorder.
Seasonal affective disorder (SAD) is another disorder that has been linked with eating disorders because there are often seasonal fluctuations in the symptomatology of eating disorders (Eagles, McLeod, Mercer, and Watson, 2000). Certain behaviors 11
common in SAD, including cravings, hyperphasia (excessive hunger/eating), and appetite
and weight disturbances are also common in patients with bulimia nervosa (Ghadirian,
Marini, Jabalpurwala, and Steiger, 1999). Ghadirian et al. (1999) conducted a study
determining seasonality in three groups of eating disorder patients; bulimia nervosa,
anorexia nervosa, and eating disorders not otherwise specified (N=259). This study used
one-way analyses of variance (ANOVA) to determine that 27% of the patients reported
seasonal prevalence. The group reporting seasonal prevalence weighed significantly
more than the group not reporting seasonal prevalence (p < 0.01). There were also
significant differences in sleep length, social activity, mood, weight, appetite, and energy level, with the group reporting seasonal prevalence experiencing a significant increase of these variables with seasonal change. The results from this study indicated that behaviors of patients with eating disorders, and bulimia nervosa especially, may follow a seasonal pattern in a subset of the patients (Ghadirian et al.).
Depression.
Researchers report a significant relationship between depression and dissociation
in patients with bulimia nervosa (Gleaves and Everenz, 1995 and Wildes et al., 2005).
Keel et al. (2005) had results that reported significant co-morbidity existing between
eating disorders and major depression, anxiety disorders, and nicotine dependence, which
reaffirmed numerous other studies connecting eating disorders with depression, anxiety
disorders, and substance abuse (McElroy et al., 2005 and Cooley and Toray, 2001).
While many studies have been conducted concentrating on the relationship between
depression and anxiety disorders with disordered eating patterns, only a few have
specifically looked at bipolar disorder (McElroy et al. and Ramacciotti et al., 2005). One 12 clinical study reported by McElroy et al. (2004) established that patients with eating disorders have elevated rates of bipolar disorder and vice versa. Another study had results that indicated that binge eating was common among bipolar disease patients and that the bipolar disease generally was present in the patient before the eating disorder
(Ramacciotti et al.). Because depression and anxiety can be a result of a low sense of self-efficacy (Bandura, 1994), self-efficacy should be included when looking at disordered eating and depression
Correlation Between Eating Disorders and Self-Efficacy
As previously mentioned, eating disorders are complex and often involve many different issues. One of these issues could be self-efficacy. Self-efficacy is a mediator between knowledge and action (Prachaska, Redding, Harlow, Rossi, and Velicer, 1994), and is a person’s belief of what they are able to accomplish, and also how well they can accomplish that particular behavior. Albert Bandura, a distinguished and learned
Stanford professor, described self- efficacy as “people’s beliefs about their capabilities to produce designated levels of performance that exercise influence over events that affect their lives” (Bandura, 1994 and Pajares, 2004). Recently, the trait-like general dimensions of self-efficacy have been studied (Chen, Gully, and Eden, 2001). Although studies of self-efficacy being correlated with eating disorders are not common, some studies have reported that low self-efficacy has an inverse relationship to successful weight control (Cooley and Toray, 2001 and Watkins et al., 2001). Watkins et al. determined that those with binge eating disorder, and those classified as having borderline binge eating disorder, had significantly lower self-efficacy than non-binge 13
eating disorder persons. Self-efficacy has been reported to be a predictor of clinical
response to treatment for alcoholism, smoking, and obesity (Wolf and Clark, 2001).
Body image is a very important factor when looking at eating disorders (Cooley
and Toray, 2001). One study (n = 225, p < .01) reported that figure dissatisfaction in
participants in the beginning of the study was associated with increasingly worse bulimia
and restraint scores throughout the study (Cooley and Toray). The core beliefs a person
has about him or herself can be linked to depression as well as bulimia nervosa (Cooper
et al., 2005). Cooper et al. concluded that the negative core beliefs “I’m stupid” and “I’m
ugly” were significantly related to (p < .05) eating disorder symptoms in a sample of 272
girls aged 17 and 18. As stated earlier, anxiety and depression disorders often have a positive correlation with eating disorders. Common traits shared by those with eating disorders include dependency, self-criticism, and low self-efficacy (Speranza et al.,
2005). When a person is not confident in his or her abilities with knowledge of behaviors
necessary to pursue goals (self-efficacy), this can also lead to anxiety (Marquez and
McAuley, 2001).
Understanding the psychological problems that are associated with eating
disorders is only half the battle. There are also physical problems that occur, mostly
stemming from nutrient deficiencies.
Dietary Reference Intakes (DRI’s)
The DRI’s are nationally accepted and the most complete and up to date reference
values for nutrients that can be used to evaluate the dietary intake of people. The DRI
was developed by the Food and Nutrition Board as a revision to the Recommended 14
Dietary Allowances (RDAs), which was published in 1941 and most recently revised in
1989 (Mahan and Escott-Stump, 2000).
The DRIs were developed in 1994 based on scientifically grounded relationships between nutrient intake and indicators of good health as well as the prevention of chronic
diseases in apparently healthy populations (Dietary Reference Intakes [DRI], 2002). The
DRIs include the original RDAs as well as three new additional reference values for healthy individuals. The Recommended Dietary Allowance (RDA) is the average daily dietary intake level sufficient to meet the nutrient requirement of nearly all (97% to 98%) the healthy individuals in a particular life-stage and gender group. One of the new reference values is Adequate Intake (AI) which is the recommended intake level based on observed or experimentally established estimates of nutrient intake by a group (or groups)
of healthy people, when there is not enough scientific evidence to calculate an RDA or
Estimated Average Requirement (EAR). The EAR is the nutrient intake estimated to
meet the requirement of half the healthy individuals in a particular life-stage and gender group. The third new reference values is the Tolerable Upper Intake (UL), which is the highest average daily nutrient intake level likely to pose no risk of adverse health effects to almost all of the individuals in the general population (Mahan and Escott-Stump). In
2002, the Food and Nutrition Board of the Institute of Medicine, The National
Academies, released a report presenting Acceptable Macronutrient Distribution Ranges
(AMDRs) for fat, carbohydrate, and protein (Dietary Reference Intakes [DRI], 2002).
Macronutrients Deficiencies and Eating Disorders
Mahan and Escott-Stump (2000) define macronutrients as: “those
macromolecules in plant (and animal) structures that can be digested, absorbed, and 15
utilized by another organism as energy sources and as substrate for synthesis of the
carbohydrates, fats, and proteins required to maintain cell and system integrity” (p. 32).
Macronutrients can be divided into three types of compounds: fat, protein and
carbohydrates.
To maintain an eating disorder, one either restricts from eating or binges and then
purges, both of which will not allow for the retention of the recommended amount of
nutrients one should consume daily (Alpers and Tuschen-Caffier, 2004 and Hadigan et
al., 2000). In anorexia patients, it is usually fat and fatty products that are eliminated
from the diet first (Wallin et al. 1997). In bulimia patients, although they do consume
more food during an eating binge, they will not receive the total amount of energy available from that food for metabolism, because the food doesn’t stay in their systems to be processed (Alpers and Tuschen-Caffier, 2004). Gendall et al. (1995) reported that
during non-binge eating, women with bulimia nervosa were significantly lower in energy,
protein, and carbohydrates than the control sample (median n = 50, p < .05).
Carbohydrates.
Carbohydrates are divided into monosaccharides, di- and oligosaccharides, and polysaccharides based on the number of sugars or saccharides in the molecule (Mahan and Escott-Stump, 2000). Carbohydrates provide our bodies with compounds that cells can use for energy and are necessary for many metabolic functions (Mahan and Escott-
Stump). The RDA for carbohydrates for both males and females ages 9 to 70 is 130 g/d
(Dietary Reference Intakes [DRI], 2002). The acceptable macronutrient distribution range (AMDR) for carbohydrate is 45%-65% of total energy (Dietary Reference Intake).
16
Fat.
Fat is also necessary in the diet for proper metabolic functioning (Mahan and
Escott-Stump, 2000). Fat can be classified into six major groups: fatty acids,
triglycerides, phospholipids, lipids with no glycerol, lipids combined with other
compounds, and synthetic lipids (Mahan and Escott-Stump). Fat is used in the digestive
process and for structural and metabolic functioning (Mahan and Escott-Stump). Fat is also used in vitamin absorption and if dietary fat intake is too low it can affect the bioavailability of other nutrients, such as the fat soluble vitamins (vitamins A, D, E, and
K) (Mahan and Escott-Stump). The Institute of Medicine reported that fat should make up 20-35% of a person’s daily caloric intake (National Academies, 2005). The acceptable macronutrient distribution range (AMDR) for fat is 25%-35% of total energy for those 4-
18 years of age and 20%-35% for those over 18 (Dietary Reference Intake).
Protein.
Protein is required to maintain bodily functioning (Mahan and Escott-Stump,
2000). Protein also plays important structural roles in body muscles and tissues (Mahan
and Escott-Stump), and protein is required for other roles in the body as well, such as the
metabolism of vitamin A, which requires the synthesis of retinol binding proteins (Mahan
and Escott-Stump). Although in many cases of eating disorders patients have normal protein levels, they are in the low to normal range (Castro, Deulofeu, Gila, Puig, and
Toro, 2003). The RDA for protein in males aged 14 to 18 is 52 g/d, and in males aged
19-30 the RDA is 56 g/d (DRI, 2002). The RDA for protein in females aged 14 to 70 is
46 g/d (DRI, 2002). The Acceptable Macronutrient Distribution Range (AMDR) for 17
protein is 10%-30% of total energy for those 4-18 years of age and 10%-35% for those
over 18 (Dietary Reference Intake).
Energy.
Energy intake is derived from the carbohydrates, proteins, and fats in foods that
are consumed. Energy is measured in calories (one calorie is the amount of heat energy
required to raise the temperature of 1 mL of water by 1 degree Celsius (Mahan and
Escott-Stump, 2000). A kilocalorie (kcal) is equal to 1000 calories and is commonly
used to measure energy in food. Calorie with a capital C is a commonly used
abbreviation for kilocalorie. To estimate energy need, the total energy expenditure of a
person needs to be determined. This involves finding a person’s resting energy
expenditure (REE) and then multiplying that by a physical activity level factor (Mahan
and Escott-Stump). The general recommendations for energy intakes in adults were
revised in 1989 by the Food and Nutrition Board, National Research Council, and
National Academy of Sciences, and were developed based on a light-to-moderate activity
level factor and REE (Mahan and Escott-Stump). The recommended dietary allowances
for energy for males 15-18 and 19-24 are 3000 kcal and 2900 kcal per day respectively.
The recommended dietary allowances for energy for females 15-18 and 19-24 are 2200
kcal per day.
Deficiencies of macronutrients in disordered eating.
Many studies have been reported concerning macronutrient deficiencies in
different eating disorders. Castro et al. (2003) studied nutritional status in adolescent
anorexia nervosa patients (N = 61) before and after short term refeeding. The study
results indicated that prealbumin levels (the most sensitive protein depletion marker) 18
were low in 16% of patients at the beginning of the study, and that number diminished to
3% after refeeding (Castro et al.). This study reported that it is the restriction of nutrients
that is affecting the albumin levels of the persons with anorexia, and that with proper
nutrition they can be restored to normal levels.
In a study assessing the energy and nutritional status of female athletes with
eating disorders (N = 48), Beals and Manore (1998) had results that demonstrated their group with eating disorders (N = 24) had significantly (p < .05) lower mean protein and fat intakes when compared with the control group. Alpers and Tuschen-Caffier (2004)
conducted a study to determine macronutrient intake in persons with bulimia nervosa
during binge and non-binge meals by using a formula to adjust for the amount of food
that would be passed to the digestive tract before purging occurred. These researchers
reported that while bulimia nervosa patients (n = 120) did consume more calories during
periods of binging, their total energy intake was significantly fewer (p < .05). This study
also reported a significantly lower intake of fat and carbohydrates when compared to
healthy controls during non-binge meals with p < .05 (Alpers and Tuschen-Caffier).
Individuals with bulimia nervosa and binge eating disorder receive a higher than
normal percentage of intake from fats and carbohydrates and a low percentage of intake from protein (Latner and Wilson, 2004). Latner and Wilson performed a study (N = 18) comparing binge eating, intake at a test meal, and self-reported satiety in women with bulimia nervosa or binge eating disorder. The study was conducted while regularly administering high-protein supplements over a 2-week period, versus administration of high-carbohydrate supplements over a 2-week period in a repeated measures, counterbalanced design. Latner and Wilson’s study reported that less time was spent 19
eating when taking the protein supplements versus the carbohydrate supplements (p <
0.005), and that the frequency of binge eating episodes was 62% lower during the two
week period of protein supplementation versus during the carbohydrate supplementation.
This type of study is important for understanding how to treat patients with bulimia
nervosa or binge eating disorder as well as understanding more about their diseases.
Micronutrients Deficiencies and Eating Disorders
Mahan and Escott-Stump (2000) refer to micronutrients as essential parts of a
healthy diet that are not energy yielding macronutrients. This group includes the
vitamins and minerals, which are nutrients that are essential for normal physiological
functioning, and in many cases cause a deficiency syndrome when not obtained in adequate dietary amount (Mahan and Escott-Stump, 2000). Studies have reported that
persons with anorexia nervosa are predisposed to vitamin and mineral deficiencies
(Hadigan et al., 2000 and Kovacs and Winston, 2003). One study reported that almost
one half of subjects (n = 50) with bulimia nervosa consumed less calcium than the RDA,
and more than one half of subjects (n = 50) with bulimia nervosa consumed less iron than
the RDA (Gendal et al., 1997). Mahan and Escott-Stump (2000) reported that the median
daily calcium intake for females in the United States established in 1998 is roughly just
over 50% of the Adequate Intake levels, which indicated that low intake is not just a
problem in those persons with eating disorders. In 2003, Kovacs and Winston reviewed
which investigations were routinely conducted by clinicians on patients with anorexia
nervosa and bulimia nervosa (N = 71). The results of this study reported that many
micronutrient levels were not routinely determined in these eating disorder patients. 20
Kovacs and Winston suggested that more emphasis should be given to the detection of
treatable micronutrient deficiencies.
As stated earlier, inadequate intakes of fat and protein can influence vitamin A
status. Another vitamin, vitamin C, is necessary for the absorption of iron. Iron is an essential micronutrient that is obtained from protein rich foods, as is calcium. As previously mentioned, lower levels of protein are consumed in eating disorder patients.
Vitamin A.
Vitamin A is a fat soluble vitamin and is the descriptor for compounds with the
biologic activity of retinol (Mahan and Escott-Stump, 2000). Vitamin A must have
efficient absorption and retinol yield in order to have high bioavailability. As previously
mentioned, the absorption can be affected by dietary factors such as levels of protein and
fat. Vitamin A is essential for vision, growth, and the development and maintenance of
epithelial tissues, immune functions, and reproduction (Mahan and Escott-Stump).
Vitamin A is measured in retinol equivalents (RE) for use in diet calculations, which accounts for the biopotencies of preformed vitamin A and major provitamin A carotenoids that occur in foods. The Recommended Dietary Allowance (RDA) for
vitamin A in males 14-30 is 900 mg/day; and, for females 14-30 is 700 mg/day (DRI,
2001). Dark green, leafy, and yellow-orange vegetables and fruits such as sweet potatoes, carrots, squash, peaches, and also animal foods such as liver, egg yolk, and
halibut are common sources of vitamin A (Mahan and Escott-Stump).
Iron.
Iron is an essential nutrient that is needed in the diet because it participates in
many oxidation and reduction reactions, which include; red blood cell functioning, 21 myoglobin activity, heme- and nonheme- enzymes, respiratory transport of oxygen and carbon dioxide, and cellular respiration (Mahan and Escott-Stump, 2000). Iron has also been reported to be involved in immune function and cognitive performance (Mahan and
Escott-Stump). As with calcium, iron is mostly absorbed in the small intestine, which creates the same absorption issues for patients with bulimia nervosa (iron-containing food reaching the small intestine and rapid intestinal transit time). Iron absorption is enhanced when consumed with vitamin C because the ascorbic acid in the vitamin C reduces ferric iron to ferrous iron which is more easily absorbed (Mahan and Escott-Stump). The impairment of iron status is frequent in patients with anorexia nervosa, often leading to anemia (Castro et al. 2004). Iron deficiency and iron deficiency anemia are common, with iron deficiency anemia being the world’s most common nutritional deficiency disease (Mahan and Escott-Stump).
The Recommended Dietary Allowance (RDA) for iron in males and females 14-
18 is 11 and 15 mg/day, respectively; and, for males and females 19-30 is 8 and 18 mg/day respectively (DRI, 2001). Meats, liver, green vegetables and dried beans, breads and other flour based foods are common sources of iron (Mahan and Escott-Stump,
2000).
Vitamin C.
Vitamin C is a water-soluble antioxidant vitamin that plays a role in forming collagen, which gives bones, cartilage, connective tissue, muscles, and blood vessels structure. Vitamin C also helps to maintain capillaries, bones, and teeth, and as previously mentioned, enhances the absorption of iron. Vitamin C (ascorbic acid) was originally isolated from adrenal tissue, oranges, and cabbage (Mahan and Escott-Stump, 22
2000). Low vitamin C intake is not commonly associated with eating disorders, however,
vitamin C deficiency is a risk for anyone who is decreasing or restricting their nutrient
intake.
The Recommended Dietary Allowance (RDA) for Vitamin C in males and
females 14-18 is 45 mg/day and 75 mg/day respectively; and, for males and females 19-
30 is 65 mg/day and 75 mg/day respectively (DRI, 2001). Many different fruits and
vegetables are common sources of vitamin C including; kiwi, broccoli, cantaloupe,
peppers, oranges, kale, strawberries, tomatoes, and mango. Citrus fruits and juices are
important sources for people who would not eat many other fruits or vegetables (Mahan
and Escott-Stump, 2000).
Calcium. Calcium is the most abundant mineral in the body, accounting for 1-2 percent of
adult body weight, and is needed for building and maintaining bone mass and density and
teeth (DRI, 1997 and Mahan and Escott-Stump, 2000). Calcium also plays a role in the
metabolic functioning of many cells and tissues (Mahan and Escott-Stump). Calcium is absorbed mostly in the small intestine. Calcium absorption is affected by decreased stomach acid (which would occur with an empty stomach or abuse of antacids) and decreased intestinal transit time (Mahan and Escott-Stump, 2000). A person with anorexia nervosa may have decreased stomach acid, and a person with bulimia nervosa could either purge before most food reaches the intestines or take laxatives which would reduce the intestinal transit time.
Nutritional status, including decreased body weight and inadequate dietary
calcium, is reported to have immense effects on bone metabolism related to osteoporosis
(Powers, 1999). In fact, Powers stated that osteoporosis occurs in more than 50% of 23
female anorexia nervosa patients. Osteoporosis is characterized by reduced bone mass,
increased bone fragility, and an increased risk of bone fracture (DRI, 1997).
The adequate intake (AI) for calcium in males and females 14-18 is 1,300
mg/day, and for males and females 19-30 is 1,000 mg/day (DRI, 1997). There are many
different food sources of calcium including dairy products, dried beans, dark green
vegetables like broccoli and spinach, and a number of calcium fortified foods such as
cereals. Dairy products supply 72% of the dietary food calcium in the U.S. diet (Mahan and Escott-Stump, 2000). Calcium supplements are also available and are absorbed as well as calcium from milk (Mahan and Escott-Stump).
Measuring Disordered Eating
Disordered eating trends are usually measured in one of two ways, a questionnaire
or a clinical interview (Al-Subaie et al., 1996). While a clinical interview is considered
to be the best way to truly diagnose disordered eating, a questionnaire is another
commonly used way to identify eating disordered patterns. If a person is identified as
having an eating disorder from a self-report questionnaire, a follow up interview is
recommended. The follow up interview would consist of a diagnostic evaluation by a
psychologist or psychiatrist who is trained to treat eating disorder patients. From this
interview, the interviewer would conclude whether or not the interviewee needed further
psychotherapy. There are many different self-report questionnaires used to gather
disordered eating information, including three popular ones: the Eating Disorder
Inventory (EDI) (Berland, Thompson, and Linton, 1986); the Questionnaire for Eating
Disorder Diagnosis (Q-EDD) (Kashubeck-West, Mintz, and Saunders, 2001); and the
Eating Attitude Test (EAT-40) and the EAT-26 (a shorter version), which are commonly 24
used and reliable questionnaires to identify disordered eating patterns (Al-Subaie et al.
and Koslowski et al., 1992).
The eating disorder inventory.
The Eating Disorder Inventory (EDI) is a self-report measure of behaviors and attitudes that are associated with anorexia nervosa and bulimia nervosa developed by
Garner, Olmstead, and Polivy in 1983 (Espelage et al., 2003) using their clinical
experience and research (Kashubeck-West et al., 2001). The EDI consists of 64 items
and generates eight subscales; Bulimia, Body Dissatisfaction, Drive for Thinness,
Perfectionism, Interoceptive Awareness, Interpersonal Distrust, Ineffectiveness, and
Maturity Fears (Joiner and Heatherton, 1998). The original scale was updated by Garner
in 1991 (EDI-2) to expand the clinical sample, as well as add three provisional scales to
the original eight which are still used frequently in research (Espelage et al.). The
original 64 items remain, and 27 additional items were added to them to produce the three
provisional scales: Asceticism, Impulse Regulation, and Social Insecurity (Kashubeck-
West et al., 2001). The EDI and the EDI-2 are self-report measures using a Likert-type
6-point “never to always” scale (Spillane, Boerner, Anderson, and Smith, 2004). Those
scoring above a 42 on the original eight subscales are considered to have a diagnosable
eating disorder (Kashubeck-West et al.)
Spillane et al. (2004) reported comparing the effectiveness of the EDI-2 in women
and men (N = 429; 214 undergraduate men and 215 undergraduate women), and the results
of the data analysis showed that the EDI-scale scores have lower reliability for men.
Research has proven to be contradictory when attempting to use the EDI to discriminate 25 between subgroups of eating disorder patients, and specificity has been reported to be low between subscales (Kashubeck-West et al., 2001).
The questionnaire for eating disorder diagnoses.
The Questionnaire for Eating Disorder Diagnoses (Q-EDD), developed by Mintz,
O’Halloran, Mulholland, and Schneider in 1997, is a 50 item self-report questionnaire using DSM-IV criteria for eating disorders. The Q-EDD categorizes into eating disordered (DSM-IV) diagnosed and non-eating disordered (Kashubeck-West et al., 2001 and Mintz et al.). These categories are further broken down into anorexia nervosa, bulimia nervosa, as well as four other types of eating disorders not otherwise specified from the eating disordered category, and asymptomatic and symptomatic (reporting some, but not enough to be DSM-IV diagnosed) from the non-eating disordered category
(Kashubeck-West et al.). Scoring of the Q-EDD used a flowchart method which places individuals into diagnostic groups (Kashubeck-West et al.)
In a college sample (N = 136) Mintz et al. (1997) reported a high overall accuracy rate (90%) and in the clinical sample (N ≈ 1400), a slightly lower one (78%). This questionnaire has proven reliable and accurate, but because of the more difficult scoring methods, the EAT test is a better choice if you are simply determining whether or not an individual has an eating disorder (Kashubeck-West et al., 2001).
The eating attitudes test.
The Eating Attitudes Test (EAT)-40 and the EAT-26 were tests developed by
Garner and Garfinkel in 1979 and 1982, respectively (Berland et al., 1986; Garner and
Garfinkel, 1979; and Garner, Olmstead, Bohr, and Garfinkel, 1982). The EAT tests are presumably the most frequently used scales in this line of paper and pencil questionnaire 26 testing (Koslowski et al., 1992). The EAT tests have been used widely in studies in the
United States, as well as other areas of the world (Al-Subaie et al., 1996; Eagles et al.,
2000; Koslowski et al.; Lee, Kwok, Liau, and Leung, 2002; and Scheinberg et al., 1993).
The EAT-40 was intended to measure the symptoms in anorexia nervosa, and today is used to identify persons with various levels and types of disordered eating, as well as persons with concerns about their weight and food intake (Garner and Garfinkel and
Kowlowski et al.). Using clinical literature, Garner and Garfinkel tested items reflecting a range of reported behaviors of anorexia nervosa patients on two independent groups of female anorexia nervosa patients and normal control groups (n = 158, p < .001). Garner and Garfinkel (1979) revised the original EAT test by discarding some items (questions) and adding others, the result being the final version of the EAT, the EAT-40. When testing the EAT-40, they established a validity coefficient of .87, which meant that the test was a consistent predictor of the scales measured (p ≤ .001) (Berland et al. and
Garner and Garfinkel).
Garner et al. did further research in 1982 by looking at the relationships between symptom areas and clinical features in a large sample of anorexia nervosa patients.
Garner et al. performed a factor analysis of the EAT-40 and three factors were extracted:
“dieting”, “bulimia and food preoccupation”, and “oral control”, to set apart those persons suffering from eating disorders and normal persons (Berland et al., 1986; Garner et al., 1982; Koslowski et al., 1992; and Pendley and Bates, 1996) “Dieting” is related to an avoidance of foods that are fattening; “bulimia and food preoccupation” is related to thoughts about food or those indicating bulimia; and “oral control” is related to both self- control with food intake, and also the perceived pressure to gain weight from others 27
(Garner et al.). From the original 40 items, 14 did not correspond with these three
factors, which created a new 26-item scale, hence the EAT-26 (Berland et al.; Garner et al.; and Koslowski et al, 1992). Correlational studies were conducted with 160 female eating disorder patients and a control group of 140 female university students between the EAT-40 and the EAT-26 and it was established that the EAT-26 correlated .98 with the EAT-40 and was a reliable substitute for the EAT-40 (Berland et al.; Garner et al.;
and Pendley and Bates, 1996). Murphy (1997) determined a high correlation between the
EAT-40 and the EAT-26 for both males and females (r = 0.96 and 0.98, p <0.0001) in
university students.
There are studies that have reported that the EAT tests are suboptimal for various reasons, including false positives and low effectiveness for screening severe eating disorders (Lee et al., 2002; Scheinberg et al., 1993; and Garner website, 2005). Neither
the EAT tests nor any other screening measure can give a specific diagnosis of an eating
disorder (Garner website). The EAT tests have been determined, however, to be highly
effective when used as a screening process or first step in the diagnosis of eating
disorders and disordered eating (Al-Subaie et al., 1996; Lee et al., 2002; and Scheinberg
et al.), which is why the EAT-26 was chosen to use to collect the data for this current
study.
The EAT-26, the questionnaire used in the current study, contains 26 self-report
questions (Al-Subaie et al., 1996 and Garner et al., 1982). These questions are scored on
a 6-point Likert scale, meaning that they force the participant to specify their level of
agreement to a statement (always, very often, often, sometimes, rarely, or never) (Al-
Subaie et al. and Garner and Garfinkel, 1979). The responses are then scored from zero 28
to three with a score of three for the “always” or “never” responses depending on if the
question is positively or negatively keyed (Garner website, 2005). A score above 20 is
commonly used in studies to identify disordered eating attitudes in persons completing
the EAT-26, who should be referred for a follow-up diagnostic interview (Al-Subaie et
al. 1996 and Garner website).
Researchers should also consider that “while the EAT data may indicate the
presence of disturbed eating patterns, the EAT score does not reveal the motivation or
possible psychopathology underlying the manifest behavior.” (Garner website, 2005) For
this reason, the individual’s predisposition to or current encounter with depression should be queried also.
Measuring Depression
Depressive and mood disorders are some of the most common mental health
problems in the United States (Greening, Stoppelbein, Dhossche, and Martin, 2005 and
Viinamaki et al., 2004). There are many ways of measuring depressive disorders in
persons. As with measuring disordered eating, self-report questionnaires can be used, as
well as diagnostic interviews. Once again, as with measuring disordered eating, while
questionnaires are important for first step screening processes, to truly diagnose a patient
with a mental illness or disorder including depression, a clinical and diagnostic interview
must take place (Sloan et al., 2002). This follow up interview would consist of a
diagnostic evaluation by a psychologist or psychiatrist who is trained to treat depression
patients, and who would determine whether or not further psychotherapy is needed.
There are many types of developed and tested questionnaires to screen for depression including: the Inventory to Diagnose Depression, the Diagnostic Inventory for 29
Depression, and the Beck Depression Inventory (Zimmerman, Sheeran, and Young,
2004).
The inventory to diagnose depression.
The Inventory to Diagnose Depression (IDD) was developed in 1986 by
Zimmerman to determine correspondence between diagnostic symptoms and self-report items (Hodgins, Dufour, and Armstrong, 2000 and Zimmerman et al., 2004). The IDD contains 22 self-report items that diagnose major depression diagnosis according to
DSM-III criteria (updated in 1994 to meet the standards of the DSM-IV) and quantify the severity of depression (Hodgins et al. and Zimmerman et al.) Studies have demonstrated good internal consistency for the IDD, with Cronbach’s alpha ranging from 0.79 to 0.94, and have reported consistently high correlations with other self-report measures, such as
the Beck Depression Inventory (ranging from 0.81 to 0.90) (Hodgins et al.) A limitation
of the IDD is that exclusion criteria such as grief reactions, medication-induced
depression, psychotic episodes, and substance-induced depression are not able to be
distinguished (Hodgins et al.).
The diagnostic inventory for depression.
The Diagnostic Inventory for Depression (DID) was developed by Zimmerman,
Sheeran, and Young in 2004 to assess the DSM-IV symptom inclusion criteria for major
depressive episodes, psychosocial impairment related to depression, and also the
subjective quality of life. The DID is made up of 38 items assessing symptom severity,
symptom frequency, psychosocial functioning, and quality of life using a Likert scale
(Zimmerman et al., 2004) The results of the initial validation study on the DID by
Zimmerman et al., suggested that it was both a reliable and valid test. The distinction 30
between the DID and other self-report depression surveys is the way in which the DID is
scored. Depression criteria, and specified cutoffs, are used to determine thresholds based
on the DSM-IV, rather than scores based on whether an individual scores above or below
certain scoring levels, which can lead to confusion as to where the cutoff should be
(Zimmerman et al.)
The Beck depression inventory.
The most commonly utilized questionnaire used to screen for depression, with
well-established reliability and validity, is the Beck Depression Inventory (BDI)
(Zimmerman et al., 2004). The BDI has been used in over 2000 empirical studies (Van
Hemert, Van De Vijver, and Poortinga, 2002), and the BDI has exhibited good internal consistency and acceptable test-retest reliability in clinical and nonclinical samples
(internal consistency mean = .86) (Devilly, 2004 and Weeks and Heimberg, 2005). For
these reasons the BDI was the questionnaire used in the current study.
The BDI was developed in 1961 by Beck, Ward, Mendelson, Mock, and Erbaugh
to examine evaluations of treatment produced change, and the “behavioral manifestations of depression” (Sloan et al., 2002 and Van Hemert et al., 2002). The BDI evaluates the
cognitive, affective, motivational, and physiological symptoms of depression, and
research data have demonstrated the BDI is effective in classifying individuals into
depressed and non-depressed groups based on their questionnaire scores (Sloan et al.).
The BDI is a 21-item self-report assessment of depression (Evans and Wertheim, 2005).
Each of the 21 items consists of four statements describing feelings and experiences. The
test taker is asked to pick which statement most accurately describes how they feel at that
moment (Sloan et al.). The total score can range from 0-63 (Viinamake et al., 2004). 31
Beck et al. reported that scores above 20 would identify persons with moderate to severe depression, and scores between 29 and 63 would identify persons with severe depression
(Evans and Wertheim).
Because the BDI was developed by Beck et al. (1961) to measure syndromes, instead of different classifications of depression, the BDI has potentially poor high-end specificity and accuracy (Sloan et al., 2002). Sloan et al. reported that the BDI scores are positive also for participants without depression, but who had other psychological disorders. These contradicting results and lack of accuracy reinforce the importance for a follow-up clinical interview.
Measuring Self-Efficacy Self-efficacy, people’s beliefs about their own capabilities, can be measured in self-report questionnaire scale fashion. There are self-efficacy self-report questionnaire scales for many different situations including exercise, teaching, jobs, and other areas of functioning, as well as general self-efficacy scales (Bandura, 1994 and Pajares, 2004).
Understanding a person’s level of self-efficacy is important because self-efficacy affects how people feel, think, become motivated, and behave as well as influencing life choices
(Bandura). A person with high self-efficacy will approach life choices in a positive manner and recover quickly from failures, while a person with low self-efficacy approaches life choices in a negative manner and will dwell on personal failures which can lead to an increased vulnerability to stress and or depression (Bandura).
Traditional measurements.
There are many ways of measuring self-efficacy. Traditionally, self-efficacy is measured by first asking participants to indicate whether or not they feel they have the knowledge and ability to successfully perform certain tasks at different levels of 32 difficulty, and then asking what their degree of confidence (e.g., 100%, 90%, etc.) is in their ability to perform the task (Maurer and Andrews, 2000). This measures both magnitude and confidence, the original measurements suggested by Bandura (Maurer and
Andrews).
A study by Lee and Bobko (1994), using the traditional format developed by
Wood and Locke in 1987, asked students (N = 207) to indicate if they could achieve a certain level of attainment (yes or no), and their degree of confidence in their ability to perform at that level (0 to 10 scale). The self-efficacy level was defined as the total number of yes answers divided by the total number of items, and the self-efficacy strength was determined by summing the 0 to 10 scores (of the yes answers) and dividing by the total number of items (Lee and Bobko). This type of study, while valid, is not as simple to assess as a Likert scale measure. The problem was, at the time, Likert scales were not considered to correspond to Bandura’s suggestions to assess strength and magnitude of self-efficacy (Lee and Bobko).
Likert-type measurements.
Maurer and Pierce did a study in 1998 to determine whether or not Likert-Type measurement formatting can be used as an alternative to the traditional ways of measuring self-efficacy. Their study (N = 128) reported that when comparing a Likert- type scale with the traditional scale measurement, there were no significant (p < .05) differences (Maurer and Pierce, 1998). Their results suggest that the Likert scale can be considered a measure of strength and magnitude, and is an acceptable alternative way of measuring self efficacy (Maurer and Pierce).
33
The general self-efficacy scale.
The self-efficacy scale used in this study is a general self-efficacy scale developed by Sherer and Adams in 1983. This scale was developed to measure individuals’ general initiative, expectations, and persistence concerning general self-efficacy in new situations
(Chen, Gully, and Eden, 2001). Over 200 studies have been published that have used or published the scale developed by Sherer and Adams (Chen et al., 2001). Sherer and
Adams reported acceptable internal consistency reliability [Cronbach’s α = .86], which measures the reliability of latent variables (those which must be inferred from other variables) (Coleman and Karraker, 2003). This scale has 17 items measured on a six- point Likert scale, and was modified from a previous study from a five-point Likert scale while still maintaining Cronbach’s alpha of .86 (Coleman and Karraker). Questions are both positively and negatively skewed for scoring throughout the scale. Scores are possible ranging from 17-102, with higher scores reflecting higher levels of general self- efficacy in relation to initiative, expectations, and persistence (Coleman and Karraker).
Those persons with anorexia nervosa might tend to report higher food-related self- efficacy, and those persons with bulimia nervosa might tend to report lower food-related self efficacy, based on what is known about control issues in different types of eating disorders. For these reasons a general self-efficacy scale might report general trends that incorporate all disordered eating patterns and this scale was used for the current study instead of specific food related self-efficacy scales.
Food Intake Surveys
When assessing food intakes in individuals, food frequency questionnaires
(FFQs), food records, and 24-hour or multiple 24-hour food recalls are often used by 34 professionals (Amanatidis Mackerras, and Simpson, 2000; Hebert, Hurley, Chiriboga, and Barone, 1998; and Jain and McLaughlin, 2000). The type of measurement used depends on the purpose of the assessment, available funding, and time restrictions of the particular study. Accuracy issues can occur in food intake assessments for a number of reasons including: human error, consciously or unconsciously over or underreporting which could be caused by guilt, social desirability, and or false estimates of quantities
(Amanatidis et al.; Hebert et al.; Johansson, Wikman, Ahren, Hallmans, and Johansson,
2000 and Pendley and Bates, 1996).
Food frequency questionnaires.
Food Frequency Questionnaires record the average frequency of consumption of specific lists or types of foods over a period of time (Jain and McLaughlin, 2000). FFQs are usually used to categorize a person according to his/her reported frequency of nutrient or food intake, as opposed to recording actual intake of food amounts (Jain and
McLaughlin). Food frequency questionnaires are useful, but have been reported to produce less accurate estimates of food intakes than either food records or recalls
(Amanatidis et al., 2000).
Food records and recalls.
Food records and recalls focus on a 24-hour period or group of 24-hour periods
(Hebert et al., 1998). Food records and recalls are used to describe the average dietary intake of a group and have been reported to produce acceptable results (Hebert et al.).
There are many advantages to using a 24-hour recall including precision and, when multiple days are assessed, validity (Rescinow et al., 2000). A food recall or record from one 24-hour period does not give an accurate estimate of a person’s usual daily intake. 35
However, recalls and records can be recorded for a group of days, and can therefore
provide dietary information concerning a specific period of time that accurately
represents a person's usual dietary eating habits (Hebert et al.).
Self-reported food intake is often underreported in regard to the amount of food consumed (Johansson et al., 2000 and Pendley and Bates, 1996). Factors such as age, body weight and concerns about diet and body weight all may affect the willingness to accurately report consumption of certain foods (Johansson et al.).
Diet Records Analysis Computer Programs
Once food records have been recorded, much time and effort is needed to convert
the measurements into individual nutrient amounts. There are different computer
programs available online or in downloadable versions that make this process much
easier and save the researcher time.
The computer analysis program used in this study is the Diet Analysis Plus (DA+)
version 6.0. DA+ is a widely used program that incorporates the latest dietary references
and a database of over 8,000 foods including beverages, fast-food and chain restaurant
meals, frozen foods, international foods, name brand products, ethnic dishes, sports
drinks and supplements, and vegetarian foods; there is an option to add customized foods to the database as well (DA+, 2002).
This program is based on the USDA database and allows users to enter these foods and drinks from the list of available choices in common units such as “each” or
“piece” for up to seven days and have the foods and drinks analyzed into individual nutrient amounts including alcohol, caffeine, and water (DA+, 2002). Missing data are recognized by either “n/a” or “-” in the printout. Users of the DA+ (2002) program enter 36
their height, weight, age, sex, and activity level so the program can personalize their intake results to their specific dietary needs. Once foods have been entered, the program analyzes the information and produces a daily report of nutrient intake. These nutrient reports can be presented as bar graphs, pie graphs, single nutrient lists, ratios and percentages, the previous Food Guide Pyramid, or collective spreadsheets (DA+, 2002)
The program can then take multiple daily records and average the results to give an estimate of the average nutrient intake, over the period recorded, of a person (DA+,
2002).
The increasing trend of disordered eating patterns on college campuses and the
inconsistent support of current treatments, along with the pattern of mood disorders co-
existing with eating disorders, create a need for increased knowledge of these disorders.
With the many different ways of measuring disordered eating patterns, depression, self-
efficacy, and nutrient intakes, increasing our knowledge of how these different
measurements correlate with each other is a good step towards finding more effective
treatment methods.
Hypotheses
Based on the previous review of literature, the following hypotheses were tested
using the objectives in the following chapter:
Ho1 Students with a positive EAT-26 score (>20) will score higher on the Beck
depression Inventory (BDI) than students with a negative EAT-26 score (≤20).
Ho2 Students with a positive EAT-26 score (>20) will score higher on the general
self-efficacy scale than students with a negative EAT-26 score (≤20). 37
Ho3 Students with a positive EAT-26 score (>20) will consume fewer
nutrients than students with a negative EAT-26 score (≤20).
Ho4 Students with a positive EAT-26 score (>20) will not meet the daily
requirements for energy, protein, carbohydrates, fat, vitamin A, vitamin C,
calcium, iron, magnesium, phosphorus, and potassium for college age students
from 18-24 years of age. 38
CHAPTER III
Methodology
Population
This study consists of a convenience sample from an existing set of data collected from 390 volunteer college students at a public university in Northwest Ohio. This state university is located in a city in Northwest Ohio with a population of just under 30,000
(City of Bowling Green, 2006). The school enrolls around 20,000 students (85 percent undergraduate) with approximately 7,000 of those students living on campus (Bowling
Green State University, 2006). Of the students attending this university, 10% come from outside the state of Ohio (with over 500 students coming from other countries), and almost 1,700 of these students are African American, Native American, Hispanic, or
Asian American (Bowling Green State University, 2006). The sample was taken during the spring semester of 2003 in an introductory nutrition course (Food and Nutrition 207).
The sample of volunteer subjects for this study was generated by two sections of students who took the course. Approximately 3% of the students did not complete the food record or surveys. This study data set was collected with approval of the Human Subject
Review Board and is located under the file number H03P126FE7.
Data Collection
The data set used in this study was collected using Blackboard, a web-based class organizational system used at the university. A 5-day food record was completed in the beginning of the semester. Students were instructed on portion sizes so that over- and under-reporting of nutrients could be minimized. A group of surveys including; a socio- demographic/background questionnaire, the EAT-26, the EAT-40, the Beck Depression 39
Inventory, and several self-efficacy scales were posted on Blackboard later in the
semester. The students taking the course were asked to voluntarily complete these
surveys. Students were aware of what the surveys and food records were measuring and
gave their individual consent by signing a form handed out in class. The students were
assigned code numbers to use on the surveys instead of names to protect their privacy.
The data collected were coded and entered into SPSS for further analysis. Out of a total
760 total points possible in the class, students were given an extra 30 bonus points to
complete the surveys and food records. The level of bonus points was not large enough
to be considered coercion.
Instruments
This study used data collected from a previous study including responses to a
demographic/background questionnaire, the EAT-26 questionnaire, the Beck Depression
Inventory, a general self-efficacy scale, and 5-day, 24-hour food records. The data are
part of a larger data set including the EAT-40 and other self-efficacy scales. The study used certain information from the demographic information questionnaire including: age, sex, year in school, whether or not the volunteer is a full or part time student, the
volunteer’s family socio-economic status, race, and whether or not they participate
regularly in any sports. Only completed surveys of undergraduate students who were
between 18-24 years of age were used (N = 390 for food record, N = 387 for the EAT-26,
N = 386 for the Self-Efficacy Scale, and N = 387 for the BDI).
Interpreting the Demographic Information
The demographic information was recorded based on answers given on the
survey. Survey answers regarding: age, sex, year in school, full or part time student, 40
family socio-economic status, ethnicity, and participation in a sport were coded and
entered into SPSS for further evaluation.
Coding the EAT-26
The responses to the 26 questions on the EAT-26 are based on a 6-point Likert
scale. The responses were scored from zero to three with a score of three for responses
farthest from the symptomatic direction (“always” or “never”) and a score of zero for the three responses closest to the symptomatic directions depending on if the question is positively or negatively keyed [the only negatively keyed question on the EAT-26 is question 26] (Garner website, 2005). Total scores were calculated by adding all of the individual question scores. A score greater than 20 was used in the current study to identify disordered eating attitudes, as this is the recommended score commonly used in prior studies to identify disordered eating attitudes in persons who should be referred for a diagnostic interview (Al-Subaie et al., 1996; and Garner website). Cronbach’s alpha was also tested for the EAT-26 scores.
Coding the Beck Depression Inventory
The responses to the 21 items were scored from zero to three. Total scores were
determined by adding all of the individual question scores. A score of greater than 20
was used to identify moderate to severe depression, as this is the score acknowledged by
Beck et al. (1961) and used in other studies to identify moderate to severe depression
(Evans and Wertheim, 2005). Cronbach’s alpha was also tested for the Beck Depression
Inventory scores.
41
Coding the General Self-Efficacy Scale
The responses to the 17 items on the general self-efficacy scale were scored on a scale from one to six depending on whether the survey item was positively or negatively keyed [the positively keyed items were: 2, 4, 5, 6, 7, 10, 11, 12, 14, 16, and 17, and the negatively keyed items were 1, 3, 8, 9, 13, and 15]. The total scores were tabulated by adding all of the individual question scores and then ranked from low to high with higher scores indicating less self-efficacy and lower scores indicating more self-efficacy. A median split procedure was used to determine high or low self-efficacy with scores at or above the median signifying low self-efficacy and those scores below the median signifying high self efficacy. Cronbach’s alpha was also tested for the General Self-
Efficacy scores.
Coding the 5-day Food Record for Nutrients
The 5-day food record was analyzed using the Diet Analysis Plus 6.0 Wadsworth computer program. This program was included with the textbook required for enrollment in the course. Students entered their personal data and the program analyzed nutrient composition for each day and then averaged the days to produce one average nutrient intake for the five days. The average values for energy (kcal), total fat (g), protein (g), and carbohydrates (g) as well as calcium (mg), iron (mg), vitamin A (RE), and vitamin C
(mg) were reported. The data were divided into male and female and age subgroups to analyze dietary adequacy and then re-grouped to analyze correlations with the EAT-26.
Cronbach’s alpha was used to further test the nutrient analysis data.
42
Associations with DRI’s
Because the Recommended Dietary Allowance (RDA) is an intake level
exceeding the requirement of nearly all individuals, it should not be used as a reference cut-point for assessing the nutrient intakes of groups. To use the RDA to assess groups would overestimate the proportion of the group at risk of inadequacy by a huge amount
(Murphy and Poos, 2002). The Tolerable Upper Intake Level (UL) can be used to assess
groups, however the purpose of this study was not to assess the risk of adverse health
effects from excessive nutrient intake.
The Estimated Average Requirement (EAR) is used for evaluating the nutrient
intake of populations (Mahan and Escott-Stump, 2000), which is why this reference was
chosen for use in this study to evaluate the nutrient data collected. The EAR cut-point method was used to assess each group of subjects; those scoring above and below the cut- off points for the EAT-26. The cut-point method will determine the frequency of usual intakes below the EAR. That proportion is the estimate of the individuals in the group with inadequate intakes (Murphy and Poos, 2002).
For calcium, which has no EAR because of limitations in evidence, Adequate
Intake (AI) was used to assess the groups in this study. This means that for the groups, a
usual intake at or above the AI implies a low prevalence of inadequate intakes. For those
nutrients not having an RDA or EAR, the AI is used in practice as the target intake
(Murphy, Barr, and Poos, 2002).
Measurements used for evaluation of nutrients
The DRI’s from the 2004 National Academy of Sciences was used to determine
the EAR and AI of each nutrient evaluated. For the macronutrients the general 43
recommendations for energy intakes in adults which were developed based on a light-to-
moderate activity level factor and REE was used to evaluate total energy intake. The
Acceptable Macronutrient Distribution Range (AMDR) was used to evaluate total fat
intake by percent of total intake. Both Protein and Carbohydrates were evaluated using
EAR. For the micronutrients, AI was used for calcium and potassium, and EAR was used for vitamin A, vitamin C, iron, magnesium, and phosphorus. All nutrients were compared to age and sex appropriate DRI’s which are presented in Appendix A.
Statistical Analysis
All data were entered into SPSS and statistically tested. Descriptive statistics
(mean, median, standard deviation) were conducted on primary variables. For
exploratory purposes, zero order correlational coefficients were conducted using the
primary study variables for the full sample, and separately for each EAT-26 score
category (>20 or ≤20).
In order to test the first two hypotheses, a multivariate analysis of variance
(MANOVA) was performed using the EAT-26 categorical scores as the independent
variable, and the BDI scores and self efficacy scale total scores as the dependent
variables. The multivariate F-test was examined to assess the relationship between EAT-
26 scores and the combined psychological measures. The univariate F-test was examined
for the explicit purpose of testing the hypotheses.
In order to test the third hypothesis, a second MANOVA was conducted using
EAT-26 categorical scores (>20 or ≤20) as the independent variable, and the set of
nutrients (carbohydrates, protein, fat, calcium, and iron) as the dependent variables. The 44 results of the multivariate and univariate F-tests were used to test this hypothesis. These
F-tests used mean nutrient intakes in order to complete the statistical analyses.
In order to test the fourth hypothesis mean and median intake of each nutrient for each gender were compared to DRI measurements for each measure. The EAR-cut point method used median intake to determine the proportion of individuals in each group with inadequate intake for each nutrient.
45
CHAPTER IV
Results
Categorical Variable Frequencies
Frequencies for all categorical variables for the full sample are presented in Table
1. The sample size of this study was 390 volunteer college students between 18 and 23
years of age, of which 83% were female. The majority of the subjects were sophomores
in college, and the overwhelming majority of the subjects were full time college students.
Most of the subjects were White/Non-Hispanic and came from middle to upper-middle
income families. Approximately 70% of the subjects participated regularly in some form
of sports.
Continuous and Categorical Descriptive Statistics
Descriptive statistics for all continuous and categorical variables are presented in
Tables 2 through 5. Descriptive statistics were also determined for all nutrients separated by EAT-26 scores (either ≤20 or >20) and are presented in Tables 6 and 7. On the EAT-
26, the question that asks “Think about burning up calories when I exercise” had the highest mean and was the only question that had a mean above 1.00 (mean = 1.21). On the General Self-Efficacy Scale, the question that asks “One of problems is that I cannot
get down to work when I should” had the highest mean and is the only question that had a
mean above 3.00 (mean = 3.54).
Correlations Among Primary Variables
Correlations for the EAT-26, the Self-Efficacy Scale, and the Beck Depression
Inventory are presented in Table 8. Correlations for all nutrients as well as age are also included in Table 8. Significant correlations occurred between EAT-26 scores and BDI 46
scores, total calories, total fat, total carbohydrates, and age. Significant correlations
occurred between BDI scores and General Self-Efficacy Scale scores. The BDI was
significantly correlated with protein intake. There were correlations between every
nutrient. These correlations imply that each nutrient was affected by total food consumed.
Reliability of Multiple Item Scales
Cronbach’s alpha coefficient was used to assess the internal consistency reliability
of the three multiple item scales. For the EAT-26, the Self-Efficacy Scale, and the Beck
Depression Inventory the coefficients were equal to .89, .88, and .86 respectively,
suggesting moderately high reliability for all three scales as measures of predisposition to
eating disorders, depression and view of one’s self.
47
Table 1 Frequencies of Categorical Variables for Age, Gender, Year in School, Full or Part Time Student, Socio-Economic Status, Ethnicity, and Regular Participation in Sports for
College Students
Categorical Variable Frequency Percent Age 18 36 9.2 19 148 37.9 20 102 26.2 21 69 17.7 22 26 6.7 23 8 2.1 Missing 1 .3 Total 390 100.0 Gender Male 66 16.9 Female 324 83.1 Missing 0 0.0 Total 390 100.0 Year in School Freshman 80 20.5 Sophomore 186 47.7 Junior 81 20.8 Senior 43 11.0 Missing 0 0.0 Total 390 100.0 Full or Part Time Student Part Time 2 .5 Full Time 386 99.0 Missing 2 .5 Total 390 100.0 Socio-Economic Status Very Low Income 7 1.8 Low Income 18 4.6 Middle Income 155 39.7 Upper Middle Income 161 41.3 Upper Income 34 8.7 High Income 10 2.6 Missing 5 1.3 Total 390 100.0
48
Table 1 (Continued)
Categorical Variable Frequency Percent Ethnicity 354 90.8 White, Non-Hispanic 3 .8 Asian, Asian-American 18 4.6 Black, African-American 7 1.8 Mexican, Mexican-American 3 .8 Other Hispanic-Latino 3 .8 Native American Indian 2 .5 Other 0 0.0 Missing 390 100.0 Total Participates Regularly in Sports Participates Regularly 274 70.3 Does Not Participate Regularly 116 29.7 Missing 0 0.0 Total 390 100.0
Table 2 Descriptive Statistics for Age and EAT-26 Scores in College Students
Variable N Mean SD Observed Range Potential Range Age* 389 19.81 1.160 18-23 18-24 EAT-26 question 1 390 .63 .925 0-3 0-3 EAT-26 question 2 390 .50 .801 0-3 0-3 EAT-26 question 3 390 .46 .871 0-3 0-3 EAT-26 question 4 390 .82 .825 0-3 0-3 EAT-26 question 5 390 .23 .571 0-3 0-3 EAT-26 question 6 390 .22 .596 0-3 0-3 EAT-26 question 7 390 .78 1.076 0-3 0-3 EAT-26 question 8 390 .24 .653 0-3 0-3 EAT-26 question 9 390 1.21 1.169 0-3 0-3 EAT-26 question 10 390 .31 .762 0-3 0-3 EAT-26 question 11 390 .89 1.147 0-3 0-3 EAT-26 question 12 390 .65 .992 0-3 0-3 EAT-26 question 13 390 .72 1.005 0-3 0-3 EAT-26 question 14 390 .04 .297 0-3 0-3 EAT-26 question 15 390 .03 .271 0-3 0-3 EAT-26 question 16 390 .09 .383 0-3 0-3 EAT-26 question 17 390 .44 .860 0-3 0-3 EAT-26 question 18 390 .32 .751 0-3 0-3 EAT-26 question 19 390 .17 .581 0-3 0-3 EAT-26 question 20 390 .18 .526 0-3 0-3 EAT-26 question 21 390 .33 .746 0-3 0-3 EAT-26 question 22 390 .11 .451 0-3 0-3 EAT-26 question 23 390 .11 .435 0-3 0-3 EAT-26 question 24 390 .06 .307 0-3 0-3 EAT-26 question 25 390 .07 .322 0-3 0-3 EAT-26 question 26 390 .64 .895 0-3 0-3 EAT-26 total* 387 10.31 9.94 0-50 0-78 * One student did not record age and three students had incomplete surveys and were not counted in total
49
Table 3
Descriptive Statistics for Self-Efficacy Scale Scores in College Students
Variable N Mean SD Observed Range Potential Range
S-E question 1 390 2.10 .911 1-6 1-6 S-E question 2 390 3.54 1.493 1-6 1-6 S-E question 3 390 2.11 .960 1-6 1-6 S-E question 4 390 2.09 1.139 1-6 1-6 S-E question 5 390 2.22 1.153 1-6 1-6 S-E question 6 390 2.78 1.274 1-6 1-6 S-E question 7 390 2.39 1.148 1-6 1-6 S-E question 8 389 2.62 1.112 1-6 1-6 S-E question 9 390 2.82 1.170 1-6 1-6 S-E question 10 390 2.37 1.072 1-6 1-6 S-E question 11 389 2.67 1.237 1-6 1-6 S-E question 12 390 2.40 1.078 1-6 1-6 S-E question 13 390 2.52 1.129 1-6 1-6 S-E question 14 388 2.90 1.493 1-6 1-6 S-E question 15 390 2.32 1.199 1-6 1-6 S-E question 16 390 2.05 1.097 1-6 1-6 S-E question 17 390 1.87 1.079 1-6 1-6 S-E total** 386 41.66 11.78 17-93 17-102 ** Four students had incomplete surveys and were not included in total
Table 4 Descriptive Statistics for Beck Depression Inventory Scores in College Students
Variable N Mean SD Observed Range Potential Range BDI question 1 390 .26 .514 0-3 0-3 BDI question 2 390 .36 .517 0-3 0-3 BDI question 3 390 .17 .431 0-3 0-3 BDI question 4 390 .39 .570 0-3 0-3 BDI question 5 390 .28 .542 0-3 0-3 BDI question 6 390 .24 .488 0-3 0-3 BDI question 7 390 .42 .597 0-3 0-3 BDI question 8 389 .70 .666 0-3 0-3 BDI question 9 390 .17 .387 0-2 0-3 BDI question 10 390 .39 .658 0-3 0-3 BDI question 11 389 .63 .683 0-3 0-3 BDI question 12 390 .36 .550 0-2 0-3 BDI question 13 390 .43 .624 0-2 0-3 BDI question 14 389 .52 .762 0-3 0-3 BDI question 15 390 .37 .563 0-3 0-3 BDI question 16 390 .53 .615 0-3 0-3 BDI question 17 390 .65 .572 0-3 0-3 BDI question 18 390 .33 .575 0-3 0-3 BDI question 19 390 .36 .556 0-3 0-3 BDI question 20 390 .22 .491 0-3 0-3 BDI question 21 390 .27 .566 0-3 0-3 BDI total** 387 8.03 6.22 0-47 0-63 ** Three students had incomplete surveys and were not included in total 50
Table 5 Descriptive Statistics for Nutrient Intake in College Students
Variable N Mean SD Observed Range Potential Range Total Calories (kcal) 390 1796.40 701.48 512.14-5183.44 n/a Total Fat (g) 390 60.42 31.14 4.52-240.37 n/a Total Protein (g) 390 71.32 32.73 23.54-274.96 n/a Total Carbohydrate (g) 390 242.88 97.95 67.90-806.83 n/a Total Calcium (mg) 390 865.51 422.72 142.98-3286.98 n/a Total Iron (mg) 390 13.29 6.17 3.61-55.82 n/a Total Vitamin A (RE) 390 884.73 541.95 79.86-3769.23 n/a Total Vitamin C (mg) 389 107.60 79.28 1.25-736.38 n/a Total Magnesium (mg) 390 175.14 107.51 12.04-846.17 n/a Total Phosphorus (mg) 390 857.93 486.33 20.99-3958.26 n/a Total Potassium (mg) 389 1937.71 971.00 141.48-6796.52 n/a Total Alcohol (g) 390 2.11 7.55 0.00-65.12 n/a
Table 6 Descriptive Statistics for Nutrients in College Students Scoring ≤20 on the EAT-26
Variable N Mean SD Observed Range Potential Range Total Calories (kcal) 328 1838.57 719.25 512.14-5193.44 n/a Total Fat (g) 328 62.47 31.32 4.52-240.37 n/a Total Protein (g) 328 72.29 32.47 23.54-274.96 n/a Total Carbohydrate (g) 328 247.83 100.60 67.90-806.83 n/a Total Calcium (mg) 328 870.03 421.75 142.98-3254.27 n/a Total Iron (mg) 328 13.37 6.43 3.61-55.82 n/a Total Vitamin A (RE) 328 878.00 555.80 79.86-3769.23 n/a Total Vitamin C (mg) 328 106.86 81.02 1.25-736.68 n/a Total Magnesium (mg) 328 174.19 109.03 12.04-846.17 n/a Total Phosphorus (mg) 328 865.04 496.59 20.99-3958.26 n/a Total Potassium (mg) 328 1936.99 990.99 141.48-6796.52 n/a Total Alcohol (g) 328 2.07 7.42 0.00-65.12 n/a
Table 7 Descriptive Statistics for Nutrients in College Students Scoring >20 on the EAT-26
Variable N Mean SD Observed Range Potential Range Total Calories (kcal) 59 1560.36 546.82 704.81-3484.87 n/a Total Fat (g) 59 48.54 27.05 6.78-139.88 n/a Total Protein (g) 59 66.11 34.35 26.71-227.65 n/a Total Carbohydrate (g) 59 216.86 79.84 90.54-491.40 n/a Total Calcium (mg) 59 836.55 427.99 300.88-3286.98 n/a Total Iron (mg) 59 12.83 4.69 5.91-30.37 n/a Total Vitamin A (RE) 59 920.44 472.09 230.29-2463.89 n/a Total Vitamin C (mg) 59 114.35 70.39 15.92-273.34 n/a Total Magnesium (mg) 59 180.88 102.23 31.45-704.47 n/a Total Phosphorus (mg) 59 820.72 436.26 206.05-3193.32 n/a Total Potassium (mg) 59 1934.43 882.83 527.44-5983.79 n/a Total Alcohol (g) 59 1.93 7.54 0.00-49.80 n/a
51
Table 8 Correlations Among Primary Study Variables: The EAT-26, the BDI, the General Self-Efficacy Scale, Nutrients, and Age
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1. EAT total Pearson Correlation --- -.022 .275*** -.200*** -.227*** -.096 -.161* -.038 -.013 .078 .005 .049 -.020 .012 .010 -.116* 2. S-E total Pearson Correlation --- .326*** .019 .017 -.012 .035 -.029 .041 -.013 .068 -.039 -.048 .000 -.053 -.072 3. BDI total Pearson Correlation --- -.055 -.044 -.123* -.039 -.050 -.023 -.008 .013 -.077 -.085 -.035 .033 .042 4. Total calories (kcal) Pearson Correlation --- .854*** .765*** .892*** .602*** .637*** .374*** .419*** .566*** .635*** .631*** .286*** .094 5. Total fat (g) Pearson Correlation --- .641*** .580*** .425*** .392*** .216*** .233*** .346*** .450*** .410*** .223*** .063 6. Total protein (g) Pearson Correlation --- .550*** .671*** .584*** .466*** .311*** .657*** .728*** .671*** .186*** .094 7. Total carbohydrate (g) Pearson Correlation --- .546*** .670*** .369*** .487*** .554*** .566*** .604*** .167* .064 8. Total calcium (mg) Pearson Correlation --- .586*** .546*** .413*** .723*** .825*** .756*** .141* .074 9. Total iron (mg) Pearson Correlation --- .557*** .447*** .696*** 639*** .641*** .132* .096 10. Total Vit A (RE) Pearson Correlation --- .367*** .611*** .570*** .594*** .051 .023 11. Total Vit C (mg) Pearson Correlation --- .401*** .315*** .565*** .066 .004 12. Total magnesium (mg) Pearson Correlation --- .887*** .819*** .164* .087 13. Total phosphorus (mg) Pearson Correlation --- .813*** .177*** .105* 14. Total potassium (mg) Pearson Correlation --- .142* .033 15. Total Alcohol (g) Pearson Correlation --- .224*** 16. Age (years) Pearson Correlation --- *p<.05; **p<.001; ***p<.0001 52
EAT-26 Score Associations with the BDI and the General Self-Efficacy Scale
Ho1 and Ho2 predicted that students with a positive EAT-26 score (>20) would
score higher on the Beck Depression Inventory as well as higher on the General Self-
Efficacy Scale than those with a negative score on the EAT-26 (≤20). In order to test the
first two hypotheses, a multivariate analysis of variance (MANOVA) was conducted
using positive and negative EAT-26 scores (>20 vs. ≤20) as the independent variable, and
depression and self-efficacy scores as the dependent variables. Using Hotelling’s Trace
as the omnibus, F multivariate effect was significant, F (2, 377) = 11.37, p < .0001. The
univariate F for depression was examined to test the first hypothesis and it was
significant, F (1, 379) = 19.56, p < .0001. As hypothesized, the BDI score mean was
higher for those with a positive EAT-26 score (M = 11.28; SD = .80) compared to those
with a negative EAT-26 score (M = 7.45; SD = .34).
In order to explore the second hypothesis, the univariate F for self-efficacy was
examined and was concluded to be not significant, F (1, 378) = .026, p = .871. Therefore
the second hypothesis was not supported.
EAT-26 Score Associations with Nutrient Intake
Ho3 predicted that students with a positive EAT-26 score (>20) would not
consume as many nutrients as those with a negative EAT-26 score (≤20). Two
MANOVAs were conducted to test the third hypothesis. In the MANOVA, the EAT-26
categorical variable again served as the independent variable with the macronutrients;
energy, fat, protein, and carbohydrates as the dependent variables. The multivariate F
test was significant using Hotelling’s Trace, F (4, 382) = 2.94, p = .021. Examination of
the univariate F tests revealed significant effects for calories (F (1, 385) = 7.99, p = .005),
53 fat (F (1, 385) = 10.28, p = .001), and carbohydrate intakes (F (1, 385) = 10.28. p = .001).
The univariate F for protein was not significant, F (1, 385) = 1.78, p = .183. Students in the group with a positive EAT-26 score had means of 1560.36 (SD = 546.82), 48.54 (SD
= 27.05), and 216.86 (SD = 79.84) for total calories, fat, and carbohydrate, respectively.
Students in the group with a negative EAT-26 score had means of 1838.57 (SD =
719.25), 62.47 (SD = 31.32), and 247.83 (SD = 100.60) for total calories, fat, and carbohydrate, respectively.
The second MANOVA likewise used EAT-26 categorical variables as the independent variable, and micronutrients; iron, calcium, vitamin A, vitamin C, magnesium, phosphorus, and potassium as the dependent variables. Using Hotelling’s
Trace as the omnibus, F was not significant, F (7, 378) = 1.11, p = .359. Based on the above, hypothesis three was partially supported.
In order to explore the third hypotheses in more depth, six MANOVAs were conducted. Three of these analyses dealt with macronutrients as the dependent variables, and three of them employed the micronutrients. For four of the analyses, female data were used (two for females 19 years and older and two for females 18 years of age). The two remaining analyses were with male data, 19 and older only, because there were so few males who were 18 years old (n = 5). The results of these analyses are presented in
Table 9. As indicated, none of the multivariate F tests results were significant. The significant univariate F tests results were confined to students 19 years and older.
Among females tested, those with positive EAT-26 scores reported consuming significantly fewer calories and less fat (p = .03 and p = .008, respectively) than those with negative EAT-26 scores. Among the males, those with positive EAT-26 scores
54 reported consuming significantly more protein and results for the multivariate test p =
.069 approached significance, which could indicate a tendency toward having significant differences in macronutrient intake.
Table 9
Results of MANOVAs Exploring Nutrient Intake Associations with the EAT-26
Group Dependent Results of Significant Univariate Tests M (SE) Corresponding to Variable Multivariate Test Significant Univariate Tests
(Pillai’s Trance) Negative Positive Females Macro- 18 yrs. nutrients NS NS
Females Micro- 18 yrs. nutrients NS NS
Females Macro- Calories F(1,287) = 4.74, p = 19+ yrs. nutrients NS .03 1668.56 (34.29) 1479.81 (79.84) Fat F(1,287) = 7.11, p = .008 56.44 (1.75) 44.64 (4.07)
Females Micro- 19+ yrs. Nutrients NS NS
Approaching Males Macro- significance 19+ yrs. Nutrients F(4,56)=36.48, Protein F(1,59) = 4.64, p = .035 104.59 (5.87) 153.99 (22.16) p = .069
Males Micro- 19+ yrs. Nutrients NS NS
EAT-26 Score Associations with Specific Nutrient Intakes
The fourth hypothesis predicted that students with a positive EAT-26 score (>20)
would not meet the daily requirements for all of the nutrients for 18-24 year olds. In
order to test hypothesis four, the mean and median intake of each nutrient for each gender
and age group and the percentage of those students below the EAR (for those nutrients
with an EAR), split by EAT-26 scores (>20 or ≤20), was compared to the DRI
55 measurement for each nutrient. The results of this comparison are presented in Table 10 and Table 11. Total energy, calcium, magnesium, phosphorus, and potassium were the only nutrients for which groups did not meet the DRI measurements. Certain nutrient values were much higher than the DRI measurements for males. Males scoring both positively and negatively on the EAT-26 had mean intakes between two and three times the recommended DRI for protein, vitamin A, iron, and phosphorus. There was not much distinction in the DRI inadequacies between the positive or negative EAT-26 scoring groups, and there were no trends or tendencies.
56
Table 10
Associations of Daily Intakes of Nutrients with the DRIs for College Students Scoring
Positively (>20) on the EAT-26
Nutrient and DRI Gender* and DRI Standard Measurement Age Group Measurement % Below EAR Mean Deviation Median
M 15-18 3000 n/a n/a n/a n/a Energy M 19-24 2900 n/a 2531.66 652.19 2290.47 (kcal/day) F 15-18 2200 n/a 1536.58 310.02 1585.14 (**) F 19-24 2200 n/a 1479.30 502.86 1363.33 M 14-18 100 n/a n/a n/a n/a M 19-30 100 0.00 278.27 84.03 278.26 Carbohydrate F 14-18 100 0.00 219.61 56.32 228.63 (g/day) (EAR) F 19-30 100 4.44 210.78 83.01 196.55 M 14-18 44 n/a n/a n/a n/a M 19-30 46 0.00 153.99 75.30 149.85 Protein (g/day) F 14-18 38 10.00 57.46 16.68 60.50 (EAR) F 19-30 38 10.25 60.22 18.91 60.48 M 14-18 25-35% n/a n/a n/a n/a Fat (% daily M 19-30 20-35% n/a 30.37 29.30 30.88 intake) F 14-18 25-35% n/a 29.84 13.26 29.62 (AMDR) F 19-30 20-35% n/a 26.43 26.88 24.45 M 14-18 630 n/a n/a n/a n/a M 19-30 625 0.00 1340.61 524.69 1376.41 Vitamin A F 14-18 485 20.00 936.27 438.85 879.90 (μg/day) (EAR) F 19-30 500 20.00 879.58 466.70 787.29 M 14-18 63 n/a n/a n/a n/a Vitamin C M 19-30 75 25.00 123.40 48.54 132.75 (mg/day) F 14-18 56 10.00 138.27 74.04 119.53 (EAR) F 19-30 60 31.11 108.24 71.23 87.89 M 14-18 1300 n/a n/a n/a n/a Calcium M 19-30 1000 n/a 1531.85 1188.97 1080.52 (mg/day) F 14-18 1300 n/a 866.97 295.55 953.98 (AI) F 19-30 1000 n/a 767.98 279.71 712.90 M 14-18 7.7 n/a n/a n/a n/a M 19-30 6.0 0.00 15.36 4.00 15.75 Iron (mg/day) F 14-18 7.9 10.00 11.47 2.93 10.85 (EAR) F 19-30 8.1 22.22 12.90 5.02 12.05 M 14-18 340 n/a n/a n/a n/a Magnesium M 19-30 330 75.00 342.12 248.35 259.63 (mg/day) F 14-18 300 100.00 179.64 71.48 156.80 (EAR) F 19-30 255 86.67 166.82 77.19 157.49 M 14-18 1055 n/a n/a n/a n/a Phosphorus M 19-30 580 0.00 1478.69 1143.67 925.07 (mg/day) F 14-18 1055 90.00 772.00 239.24 754.06 (EAR) F 19-30 580 33.33 773.07 328.28 797.85 M 14-18 4700 n/a n/a n/a n/a Potassium M 19-30 4700 n/a 3290.15 1863.86 2605.73 (mg/day) F 14-18 4800 n/a 1981.65 680.77 1880.19 (AI) F 18-30 4800 n/a 1803.42 1803.42 1687.97 * M = Male F = Female ** General recommendations for energy intakes in adults revised by the Food and Nutrition Board, the National Research Council, and National Academy of Sciences, and were developed based on a light-to- moderate activity level factor and REE.
57
Table 11
Associations of Daily Intakes of Nutrients with the DRIs for College Students Scoring
Negatively (≤20) on the EAT-26
Nutrient and DRI Gender* and DRI Standard Measurement Age Group Measurement % Below EAR Mean Deviation Median
M 15-18 3000 n/a 2678.03 437.72 2651.96 Energy M 19-24 2900 n/a 2580.70 932.46 2386.76 (kcal/day) F 15-18 2200 n/a 1642.12 1642.12 1484.98 ** F 19-24 2200 n/a 1668.56 1827.82 1613.11 M 14-18 100 0.00 336.89 47.64 344.38 M 19-30 100 0.00 330.94 147.35 281.06 Carbohydrate F 14-18 100 0.00 225.67 73.93 214.16 (g/day) (EAR) F 19-30 100 1.64 228.97 35.91 229.03 M 14-18 44 0.00 140.73 54.00 129.3 M 19-30 46 1.75 104.59 42.02 99.4 Protein (g/day) F 14-18 38 9.52 63.19 20.48 57.81 (EAR) F 19-30 38 10.25 64.21 28.18 60.28 M 14-18 25-35 n/a 28.67 27.20 29.51 Fat (% daily M 19-30 20-35 n/a 31.81 35.54 31.72 intake) F 14-18 25-35 n/a 29.62 21.33 31.02 (AMDR) F 19-30 20-35 n/a 29.75 27.35 29.76 M 14-18 630 0.00 1793.25 1256.90 1537.98 M 19-30 625 29.82 1050.78 769.42 760.50 Vitamin A F 14-18 485 28.57 792.92 462.84 624.15 (μg/day) (EAR) F 19-30 500 23.77 826.47 455.67 766.65 M 14-18 63 40.00 157.03 157.10 140.52 Vitamin C M 19-30 75 26.32 148.52 96.11 135.60 (mg/day) F 14-18 56 23.81 109.36 62.22 94.15 (EAR) F 19-30 60 34.84 96.15 73.40 83.63 M 14-18 1300 n/a 1435.33 571.54 1413.32 Calcium M 19-30 1000 n/a 1099.64 609.61 946.17 (mg/day) F 14-18 1300 n/a 707.79 280.58 651.44 (AI) F 19-30 1000 n/a 819.41 343.47 765.29 M 14-18 7.7 0.00 19.47 7.75 18.19 M 19-30 6.0 0.00 18.53 9.18 15.34 Iron (mg/day) F 14-18 7.9 14.29 11.09 3.96 9.65 (EAR) F 19-30 8.1 19.26 12.25 5.01 11.44 M 14-18 340 60.00 352.21 295.25 245.55 Magnesium M 19-30 330 78.95 253.71 170.89 198.10 (mg/day) F 14-18 300 95.24 141.30 62.81 125.98 (EAR) F 19-30 255 90.88 154.91 70.01 144.32 M 14-18 1055 40.00 1794.46 1274.38 1355.39 Phosphorus M 19-30 580 19.30 1206.71 718.88 1013.09 (mg/day) F 14-18 1055 90.48 695.74 256.72 659.59 (EAR) F 19-30 580 30.74 781.45 354.71 764.26 M 14-18 4700 n/a 3520.65 1451.64 3372.13 Potassium M 19-30 4700 n/a 2592.20 1400.42 2203.33 (mg/day) F 14-18 4800 n/a 1913.71 931.55 1612.65 (AI) F 19-30 4800 n/a 1755.88 759.46 1610.69 * M = Male F = Female ** General recommendations for energy intakes in adults revised by the Food and Nutrition Board, the National Research Council, and National Academy of Sciences, and were developed based on a light-to- moderate activity level factor and REE.
58
The EAR Cut-Point Method for Nutrients with an Estimated Average Requirement
To further explore the nutrient intakes, the Estimated Average Requirement
(EAR) cut-point method was used to determine how many individuals in each group have usual intakes below the EAR. This method estimates the proportion of individuals in the group with inadequate intake. The results of the EAR cut-point method are presented in
Table 12.
The results of the EAR cut-point method determined that for many of the different groups distinguished by age, gender, and EAT-score, there was a proportion of individuals with inadequate nutrient intake, however there was not much distinction between the positive and negative EAT-26 scoring groups for most nutrients. Those individuals who scored positively on the EAT-26 had consistently higher percentages of inadequate intakes for carbohydrates, inconsistently higher and lower percentages for protein, iron, magnesium, and phosphorus, and consistently lower percentages of inadequate intakes for both vitamins A and C than those scoring negatively on the EAT-
26. This tendency for those scoring positively on the EAT-26 to consume fewer carbohydrates and more vitamins A and C could be explained by the inclination of those who are dieting or trying to eat healthier, to cut complex starch carbohydrates from the diet and increase their fruit and vegetable consumption.
The entire female sample had higher percentages of inadequate intakes compared to the entire male sample for all of the nutrients measured other than vitamin A. Males who scored negatively on the EAT-26 had a higher percentage of inadequate intakes than those scoring positively on the EAT-26 for protein, vitamin A, vitamin C, magnesium, and phosphorus. For the entire sample, each nutrient had at least some percentage of
59 individuals with inadequate intake, the least being carbohydrates (1.56%) and the most being magnesium (88.6%).
Table 12
Results of the EAR Cut-Point Method in College Students for all Nutrients with an
Estimated Average Requirement (EAR)
Gender/Age Protein Carbohydrate Iron Vitamin A Vitamin C Magnesium Phosphorus EAT-score* % under % under % under % under % under % under % under Group the EAR the EAR the EAR the EAR the EAR the EAR the EAR
Males 18 yrs (-) 0.00 0.00 0.00 0.00 40.00 60.00 40.00
Males 18 yrs (+) n/a n/a n/a n/a n/a n/a n/a
Males 19+ yrs (-) 1.75 0.00 0.00 29.82 26.32 78.95 19.30
Males 19+ yrs (+) 0.00 0.00 0.00 0.00 25.00 75.00 0.00
Females 18 yrs (-) 9.52 0.00 14.29 28.57 23.81 95.24 90.48
Females 18 yrs (+) 10.00 0.00 10.00 20.00 10.00 100.00 90.00
Females 19+ yrs (-) 10.25 1.64 19.26 23.77 34.84 90.88 30.74
Females 19+ yrs (+) 11.11 4.44 22.22 20.00 31.11 86.67 33.33
Males 18-24 yrs (-) 1.61 0.00 0.00 27.42 27.42 77.42 20.97
Males 18-24 yrs (+) 0.00 0.00 0.00 0.00 25.00 75.00 0.00
Females 18-24 yrs (-) 10.19 1.51 18.87 24.15 33.96 91.32 35.47
Females 18-24 yrs (+) 10.91 3.64 20.00 20.00 27.27 89.10 43.64
All Males 1.51 0.00 0.00 25.76 27.27 77.27 19.70
All Females 10.31 1.88 19.06 23.44 32.81 90.94 36.88
All Males and All Females 8.81 1.56 15.80 23.83 31.87 88.60 33.94
* (-) = negative EAT score (≤ 20) (+) = positive EAT score (>20)
60
Exploratory Analyses
Using only the segment of the student sample who was over 21, an exploratory analysis independent t-test was conducted to see if students with positive EAT-26 scores were less inclined to drink alcoholic beverages. The results indicated that they were in fact less likely to consume alcohol, t (385) = 3.93, p < .0001. The mean for the group scoring positive for disordered eating patterns was equal to .31 (SD = 1.07); whereas the mean for the group scoring negative for disordered eating patterns was equal to 5.16 (SD
= 11.41).
A second exploratory independent t-test was conducted to see if there was a difference in EAT-26 scores based on regular participation in sports. The effect was not significant, t (385) = .32, p = .749.
Third and fourth sets of exploratory analyses revealed no significant differences in EAT-26 scores, BDI scores, or General Self-Efficacy Scale scores based on ethnicity and gender.
A fifth set of exploratory analyses was conducted using self-reported socio- economic family status as the independent variable and EAT-26, BDI, and General Self-
Efficacy Scale scores as the dependent variables. The results are summarized in Table
13. As indicated by the data presented, no significant differences were observed based on
EAT-26 scores. There were a number of significant group differences using General
Self-Efficacy Scale scores as the dependent variables, and those in the low income group were determined to have higher BDI scores than those in the middle income group.
61
Table 13
Analysis of Variance Results for Socio-Economic Status and EAT-26 Scores, BDI Scores, and General Self-Efficacy Scale Scores.
Dependent Income Significant Group Variable Univariate F Levels M (SD) Differences
EAT-26 Scores NS
1) Low Income 10.60 (9.43) 2) Middle Income 7.69 (6.01) BDI NS 3) Upper Middle Income 8.01 (5.88) 1 and 2 Scores 4) Upper Income 8.07 (5.91)
1) Low Income 38.20 (10.57) General Self- 2) Middle Income 42.07 (12.95) 1 and 2 Efficacy Scale F=(3,378) = 3.68, 3) Upper Middle Income 42.62 (11.52) 2 and 3 Scores p = .012 4) Upper Income 39.07 (8.94) 2 and 4
A sixth set of exploratory analyses using age as the independent variable revealed
significant differences between the 18 year olds and the students 19 and older on EAT-26
scores, t(384) = 2.36, p = .019, and BDI scores, t(52) = -2.22, p = .031 (equal variances
not assumed). The 18 year old students had a mean of 14.03 (SD = 10.72) on the EAT-
26 compared to the mean of 9.94 (SD = 9.80) observed in the students 19 years and older.
On the BDI, the students 19 years and older reported higher scores (M = 8.21; SD = 6.36)
than the 18 year old students (M = 6.44; SD = 4.33). No differences were determined for
the General Self-Efficacy Scale scores based on age.
62
CHAPTER V
Discussion
Hypothesis one predicted that students with a positive EAT-26 score (>20) would score higher on the Beck Depression Inventory (BDI) than students with a negative EAT-
26 score (≤20). The results of this study support this hypothesis because the Beck
Depression Inventory score mean was significantly higher for those with a positive score on the EAT-26 than those with a negative score. These results were similar to numerous other studies that report significant relationships between eating disorders and depression
(Cooley and Toray, 2001 and Watkins et al., 2001). Neither the mean BDI score for those scoring positively on the EAT-26 (11.28) or the mean BDI score for those scoring negatively (7.45) on the EAT-26 was positive for depression, even though the BDI score was significantly higher in the positive EAT-26 group. This implies that persons with disordered eating patterns might tend to have more signs of depression than someone without disordered eating patterns, even if they are not determined to be clinically depressed.
A study conducted by Troop, Serpell, and Treasure (2001) categorized their subjects scoring less than 10 on the BDI as non-depressed, and those scoring between 10-
19 on the BDI as being mildly depressed. The researchers set these categories as standard groupings. If these categories were used for the current study, those scoring positively on the EAT-26 would be categorized as mildly depressed, while those scoring negatively on the EAT-26 would be categorized as non-depressed. Further studies using multiple eating disorder surveys and multiple depression surveys should be conducted to further explore this correlation between eating disorders and depression, and try to
63 determine causality between the two. Other associations, such as seasonality or severity of the disorders, should also be explored with further research.
The second hypothesis predicted that students with a positive EAT-26 score (>20) would score higher on the general self-efficacy scale than students with a negative EAT-
26 score (≤20). The results of this study did not support hypothesis two because the results did not support a significant difference between Self-Efficacy scores and a positive EAT-26 score (p = .871). These results were not consistent with previous research which stated that low self-efficacy is a common trait in those persons with diagnosed eating disorders (Speranza et al., 2005). Because self-efficacy has been correlated with depression and depression has been correlated with eating disorders, it is unclear whether self-efficacy is in fact correlated with eating disorder symptoms rather than just depressive symptoms. For the current study, the observed range for the survey was from 17-93 and the survey had a potential range of 17-102. Although there was a wide range of self-efficacy scores in the current study, they were not statistically related to disordered eating patterns. A larger sample size may indicate different or more representative results. Correlation of the EAT-26 with specific self-efficacy scales or questions, to see if there would be correlations, may indicate some relationships with the
EAT-26 scores.
The third hypothesis predicted that students with a positive EAT-26 score (>20) would consume fewer measured nutrients than students with a negative EAT-26 score
(≤20). The results of this study do not support all of hypothesis three because while students with positive EAT-26 scores (>20) consumed significantly fewer calories, fat, and carbohydrates compared to those with negative EAT-26 scores (≤20), students with
64 positive EAT-26 scores did not consume significantly less protein, iron, calcium, vitamin
A, vitamin C, magnesium, phosphorus, or potassium than those students with a negative
EAT-26 score. These results could be related to the consumption of nutrient dense food or current trends in low carbohydrate/high protein diets. Low carbohydrate/high protein diets [LC-HP] diets consist of significant amounts of animal protein and relatively low amounts of carbohydrates in order to put the dieter into a state of ketosis, which uses fat stores for energy in a disproportionate amount, causing most such dieters to lose weight quickly (Kappagoda, T., Hyson, D., and Amsterdam, E., 2004). The results of the current study could be explained by a higher intake of lean meats and dairy products, and a lower intake of starchy carbohydrates, which would support a low carbohydrate/high protein diet.
When these results were explored further, significant results for less energy and fat intake (p = .005 and .001, respectively) were determined for females 19 years and older who had positive EAT-26 scores. These results are comparable to previous studies that reported significantly lower macronutrient intakes, but not micronutrient intakes, in eating disorder groups compared to control groups (Beals and Manore, 1998 and Castro et al., 2004).
One interesting result was that males with positive EAT-26 scores had significantly higher intakes in protein than those with negative EAT-26 scores. This idiosyncrasy may be caused by the possibility that males scoring positively on the EAT-
26 may have done so not because they have disordered eating to lose weight, but have disordered or abnormal eating patterns and thoughts in order to gain muscle or gain weight. This result is logical because the EAT tests are intended to identify not only
65 persons with various levels and types of disordered eating, but also those persons with concerns about their weight and food intake (Garner and Garfinkel (1979) and Kowliwski et al (1992). More research into male responses to the EAT-26 may be necessary to determine if the EAT-26 is a valid and reliable measure for both males and females. The small sample size of males (n= 66) and the even smaller sample size of those males with a positive score on the EAT-26 (n= 4) make the results of this current study concerning male subjects less conclusive.
The fourth hypothesis predicted that students with a positive EAT-26 score (>20) would not meet the daily requirements for energy, protein, carbohydrates, fat, calcium, iron, vitamin A, vitamin C, magnesium, phosphorus, and potassium for college age students from 18-24 years of age. The results of the current study do not support all of hypothesis four. There was little difference in meeting the DRI requirements between the different age and gender groups scoring positively and negatively on the EAT-26, and the different age and gender groups scoring negatively on the EAT-26. The nutrients for which DRI measurements were not met (energy, calcium, magnesium, phosphorus, and potassium) were in general not met by each age/gender group/EAT-26 score group.
These data demonstrate that although there are significant differences between nutrient intake of groups scoring positively on the EAT-26, and groups scoring negatively on the
EAT-26 (as reported by Ho3), nutrient deficiencies for energy, calcium, magnesium,
phosphorus, and potassium existed in both samples. Males reported data was high in
intakes of protein, vitamin A, iron, and phosphorus. These high dietary intakes could be the result of diets eaten with the intention to gain muscle. These diets could consist of
higher meat content, which would be high in protein, iron, and phosphorus. These
66 calculated high intakes could also be caused by inaccurate recording during the 5-day food intake records. Although portion sizes were explained before the students completed their daily dietary records, previous studies have reported portion estimation accuracy rates of only about 60%, even after instruction with portion size measurement aids (Byrd-Bredbenner and Schwartz, 2004).
Little distinction was determined between positive and negative EAT-26 groups when using the EAR cut-point method, however, the EAR cut-point method did yield interesting findings. All of the groups with positive EAT-26 scores had higher percentages of inadequate intake for carbohydrates and lower percentages for vitamins A and C than those groups with negative EAT-26 scores. These results, as well as the inconsistent higher and lower percentages of inadequate intake for protein, iron, magnesium, and phosphorus for both groups scoring positively and negatively on the
EAT-26, indicate the need for further study with larger samples. All female students in this study had higher percentages of inadequate intake for each nutrient with an EAR
(protein, carbohydrates, iron, vitamin A, vitamin C, magnesium and phosphorus) than all male students, which is consistent with females being more likely than males to have eating disorders (Mann et al., 1997). Males scoring negatively on the EAT-26 had higher percentages of inadequate intake for protein, vitamin A, vitamin C, magnesium, and phosphorus. This could be caused by those male students scoring positively on the EAT-
26 having an increased desire to gain weight or muscle and eating more protein based foods. The entire study sample had percentages of individuals with inadequate intake for each nutrient with an EAR. The most extreme of these was magnesium, for which 88.6% of the sample studied was under the EAR. Diets that are high in refined foods, meat, and
67 dairy products (such as average college diets) are usually lower in magnesium, which might explain why so many of the students are not meeting the EAR for magnesium
(Mahan and Escott-Stump, 2000).
Descriptive Statistics
On the EAT-26, the question that asks “Think about burning up calories when I exercise” had the highest mean of all the questions and was the only one with a mean above 1.00. This implies that a greater number of subjects responded to that question that they “always”, “very often” or “often” think about burning up calories when they exercise. This could be a result of understanding more about nutrition and how physical activity affects the body. On the General Self-Efficacy Scale, the question that asks “One of problems is that I cannot get down to work when I should” had the highest mean and is the only question that had a mean above 3.00. This implies that a greater number of subjects responded to that question that they “agree strongly”, “agree somewhat”, or
“agree slightly” with the statement. This could be a result of the subjects being college students and having many opportunities for activities and new experiences as well as having to complete class-work.
Exploratory Analyses
There was a significant relationship between alcohol consumption and EAT-26 scores. Those students (21 years and older) with positive EAT-26 scores were less likely to drink alcohol. These results are not consistent with previous research that report elevated rates of alcohol (and drug) use in patients with eating disorders (Von Ranson,
Iancono, and McGue, 2001). These associations between substance abuse and eating disorders are especially strong in those with binge eating and purging behaviors (Von
68
Ranson et al.). The current study sample could have had a higher rate of restrictive disordered eating behaviors rather than binge eating and purging behaviors which could account for the lower alcohol intake of those students with positive EAT-26 scores.
There was no relationship between EAT-26 scores and regular participation in sports, which was interesting because there are many studies that have established correlations between sports and eating disorders (Johnson, Powers, and Dick, 1999).
Certain sports including long distance running, cheerleading, gymnastics, dance, and ballet have a tendency to have higher rates of eating disorders (Sudi et al., 2004 and
Smolak, Murnen, and Ruble, 1999). Eating disorders have been reported in male athletes as well as female athletes, especially in sports where low body fat is advantageous (cross country) or where there is a need to be at a certain weight (wrestling or horse jockeying)
(Baum, A., 2006). Further research with students who participate regularly in sports, exercise regularly, and participate in college level athletics should be conducted. The self-report nature of the demographics question about regular participation in sports was not specific. Specification of what exactly regular participation in sports means and differentiation between sports should be defined for further studies.
Neither ethnicity nor gender made a significant difference on EAT-26 scores, BDI scores, or General Self-Efficacy scores. These results do not support previous research stating that Black and White college students differ on their views of body image
(Aruguete, DeBord, Yates, and Edman, 2005), or that females tend to have higher rates of disordered eating patterns than males (Mathieu, 2004). A larger sample size with more variation in ethnicity (this sample was approximately 91% White, Non-Hispanic), and
69 gender (this sample was approximately 83% female) might have given different or more complete results.
Although no significant difference were observed between positive and negative scores on the EAT-26 with socio-economic family status, both BDI scores and General
Self-Efficacy Scale scores did have significant differences. The students in the low income group reported significantly higher BDI scores than those in the middle income group and significantly higher self-efficacy than those in the middle income group. The students in the middle income group reported significantly higher self-efficacy than those in the upper middle income group and significantly lower self-efficacy than those in the upper income group. These results could be related to low income families being more self-sufficient and thus having higher self-efficacy, and high income families not having to worry as much financially and having higher self-efficacy. Middle income families may have more perceived day-to-day life struggles than the lower and higher income families. Thomas, James, and Bachmann (2002) speculated that income or the effect on family life of parental unemployment might be a risk factor for high EAT-scores and that low self-esteem and depression may links between economic and cultural stresses and eating attitudes. Further studies on socio-economic status and self-efficacy might benefit society.
Significant differences were established between those students 18 years old and those students 19 years and older on EAT-26 scores and BDI scores. These results are supported by previous study results reporting that the frequency of positive EAT scores were greatest for freshman females (Murphy, 1997). The results of the current study report that 18 year old students had higher EAT-26 scores, but lower BDI scores, and
70 students 19 years and older had lower EAT-26 scores, but higher BDI scores. These are slightly contradicting results. Because of the positive correlations between EAT-26 scores and BDI scores, it would be expected for one age group to have lower EAT-26 and
BDI scores than the other. These differences between the age groups could be explained by the many changes a college freshman (typically the 18 year old students) goes through in their first year of college. A college freshman might initially enter college fearing gaining weight or fearing acceptance based on appearance so the EAT-26 scores might be higher. As a student spends more time in college, the stress of deciding their future plans or increasingly difficult college courses might cause depression which could increase
BDI scores.
Previous Studies at Bowling Green State University
Similar data sets have been collected in previous research at Bowling Green State
University with similar results. Previous research indicated that between 14-19% of females and 0-6% of men scored positively on the EAT-26 or the EAT-40 (the EAT-26 correlates .98% with the EAT-40 [Berland et al., 1986; Garner et al., 1982; and Pendley and Bates, 1996]) (Allen, 1998; Lindway, 1999; Murphy, 1997; Pandya, 2004; and
Wakefield, 1998). These scores are similar to and, in three of the five cases (Allen;
Lindway; and Pandya), slightly less than the current study, which determined that approximately 17% of the females and approximately 6% of the males scored positively on the EAT-26. These results could indicate that eating disorders are an increasing trend at Bowling Green State University, which supports data reporting increasing disordered eating trends on college campuses (Leibman et al., 2001; Mann et al., 1997; Nelson et al.,
1999; Schwitzer et al., 1998; and Schwitzer et al., 2001).
71
Pandya (2004) reported no significant relationships between nutrient intakes of: protein, iron, calcium, vitamin D and zinc, and EAT-26 scores. This is consistent with the current study in which only energy, fat, and carbohydrates, and not protein or the micronutrients, were significantly lower in those scoring positively on the EAT-26.
Pandya also determined that there were no dietary deficiencies in the intakes of calories for subjects with a positive EAT score. This is similar to the current study which reports that while the intake of calories was fewer than the recommended amount, the amount reported did not differ between those scoring positively or negatively on the EAT-26.
Limitations This study is interesting in the finding’s correlations, the sample size (N = 390), and the 5-day length of the self-reported food record provided good accuracy and validity. However, there are several limitations which must be addressed. The self- report nature of the study may be a limitation. When dealing with self-reported data, one must take into consideration the tendency of persons with eating disorders to deny their illnesses and hide their disordered eating traits (Pendley and Bates, 1996); or, the possibility of persons wanting to be socially accepted and desirable, and not recording the correct intake, which create the possibility of inaccurate reports. Because there was a lack of a random sample of student participants, this study alone can not be seen as representative of the students on this or another college campus. The questionnaires and dietary reports were completed during separate times (and climates) in the school year, in which time the students could have changed their dietary habits or their responses could have been affected by the weather. Cost limitations and time required that nutrient analysis was completed using a self-reported diet record, rather than using a plate waste
72 for an accurate calculation of food consumed, and either blood or urine tests to validate body values of nutrients.
73
CHAPTER VI
Conclusion and Recommendations
Conclusions
The purpose of this study was to gain knowledge and identify common themes focusing on disordered eating patterns specified by the EAT-26 and correlations between
EAT-26 scores and; depression specified by the Beck Depression Inventory scores, self- efficacy specified by the General Self-Efficacy Scale scores, nutrient intake specified by the Diet Analysis Plus 6.0 computer program reports, and demographic information based on self-report surveys. The current study reported that approximately 15% of the sample scored positively (>20) on the EAT-26.
A positive EAT-26 score had a significant (p < .0001) correlation with higher BDI scores. This agreed with the hypothesis and theory that those persons with eating disorders would tend to have problems with depression. The current results did not report any significance between EAT-26 scores and General Self-Efficacy Scale scores.
This does not mean that there is no correlation between eating disorders and self-efficacy; however, in this particular study the scores did not have significant correlations, possibly caused by the smaller sample size.
Only some nutrient intakes were determined to be significantly correlated with positive EAT-26 scores. Energy (calories), fat, and carbohydrates had significantly lower intakes when compared to the students with negative EAT-26 scores. While these three nutrient values were different based on EAT-26 scores, EAT-26 scores were not related to whether or not the study sample met the RDA/DRI for each nutrient. The RDA/DRI was not met for energy, calcium, magnesium, phosphorus, and potassium in most of the
74 age and gender groups, regardless of the EAT-26 score. This current study determined that the female study sample had a higher percentage of inadequate intakes (not meeting the EAR) than the male study sample, and that 88.6% of the entire study sample had inadequate intake of magnesium, which could be caused by a typical college diet that includes mostly refined foods. This is similar to National data which reports 56% of
Americans have inadequate magnesium intake (Moshfegh, Goldman, and Cleveland
(2005).
While these results may only be typical of students on this campus, the results are significant and should be shared with the Bowling Green Student Wellness Connection at the Student Health Center. These results, as well as previous study results using Bowling
Green State University students, report a high percentage of EAT-26 positive students at
Bowling Green State University, 15% of the sample, compared with an estimated 2.7% (8 million out of approximately 299,367,044 people in the U.S.) (U.S. Census Bureau,
2006). Spear and Meyers (2006) of the American Dietetic Association reported that an estimated 85% of eating disorders have their onset during the adolescent period, which would occur before college. The results of the current study also report inadequate essential nutrient intake, not only in the EAT-26 positive students but in the entire sample, which needs to be addressed throughout campus. The Wellness Connection could plan eating disorder and nutrient intake awareness events during campus-wide events or at the Student Union, and possible collaborations could be made with the
Student Dining Services to promote certain foods high in the inadequately consumed nutrients.
75
There were not many significant correlations between demographics and EAT-26 scores. Participation in sports, ethnicity, gender, and socio-economic status all had no significance with positive or negative EAT-26 scores. Although it did not have any correlations with EAT-26 scores, socio-economic status had significant correlations with both BDI scores and General Self-Efficacy Scale scores. The only demographic that significantly correlated with EAT-26 scores was age, and it also significantly correlated with BDI scores. The results from this study concerning age, EAT-26 scores and BDI scores, should be shared with the Bowling Green Student Health Center personell, who could screen more carefully for these disorders, and also, they could be discussed in freshman and upper-class courses accordingly.
Recommendations for Further Research
As mentioned previously, this study had limitations. The following recommendations would improve/expand this study for a greater understanding of eating disorders and the correlations between eating disorders, depression, self-efficacy, and nutrient intake:
1. The sample size of the current study was comparatively smaller (n=390) than other
larger scale studies. A larger sample size would provide less statistical error, be
more representative, and possibly yield more compelling results.
2. This study used a convenience sample from one class at one university. A non-
convenience sample with volunteers from other large classes and other universities
would make this study more generally representative of all college campuses and
thus college students in general.
76
3. The five day dietary intake record was completed in the beginning of the semester
while the questionnaire including the EAT-26, the BDI, the General Self-Efficacy
Scale, and the demographic information were completed later in the semester. The
dietary intake record was completed early in the semester so that the students
would not have biased answered because of the nutrition information learned while
taking the class. Dietary habits may have changed from the time the students
completed the intake record to the time the questionnaires were completed because
of knowledge gained in the introductory nutrition class or other classes and other
possible habit changes for different times of the year. The questionnaires and
dietary reports should be completed at the same time for the most accurate results.
4. This study used self-report measures for all surveys including the food intakes. In
a more controlled study, the use of plate waste or blood and urine tests to determine
food intakes and nutrient deficiencies would be more accurate.
5. This study uses the Beck Depression Inventory (BDI) to screen for depression.
The Diagnostic Inventory for Depression (DID) is a more recent survey which is
scored using DSM-IV thresholds. While the BDI is well established, reliable and
valid, the DID might be an interesting tool to use in future research.
6. Eating disorders in males are not as widely studied as in females. Future research
should be conducted with large samples of males to better understand the types of
eating disorders and mood disorders that are associated with males.
7. The increasing problem of eating disorders on college campuses identifies the need
for more studies concerning college age students eating habits and attitudes.
77
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Appendix A: Consent Form
FN 207 Special Class Project Student Participation Consent Form
The purpose of this FN 207 volunteer student project is to enable Julian H. Williford, Jr., PhD. to collect volunteer college student self-reported data concerning college students’ eating habits, behaviors, self-efficacy, self-esteem, and dietary. We will collect this information over a one -year period, conduct statistical analysis of the data, and report the findings in an appropriate referred journal.
Students in the FN 207 class who choose to voluntarily participate will receive 30 points for the entire survey completed on-line as a part of the total points earned in the FN 207 course Students choosing not to participate will be allowed to count 3 more class quiz grades as a part of their final total points in the class [ a total of 30 points].
Students volunteering to participate in this study will be required to complete 4 different surveys on-line (found as a part of your MY BGSU FN 207 course listing, under special project). Each student will be assigned a 9-digit code to be used in completing each part of this project. Failure to properly use the assigned code will mean that the student will not receive the appropriate credit for his/her effort in completing the questionnaire(s). All collected data will remain confidential.
The benefit to the student in participating in this project is to enable each volunteer to review in more depth the concepts, thoughts, and ideas that appear to affect the eating habits of persons in US society. In addition, the researchers will be able to collect enough self-reported data from the students to develop a report on eating habits and attitudes from a cross sectional group of college students.
Any volunteer participant may withdraw from this study at anytime without penalty. If you have any questions about the requirements of this project, please contact Dr. Julian Williford (372-7833). If you have questions or concerns about rights as a research participant, you may contact the Chair of the Bowling Green State University’s Human Subjects Review Board, Richard Rowlands at 372-7716.
Sincerely,
______
Julian H. Williford, Jr. (372-7833)
91
I have read the above information and have asked questions and received answers. I consent to participate in the study.
I am over 18 years
Yes No
Student Name (please print): ______
Signature ______Date ______
92
Appendix B: Demographics/Background Survey
Demographic/Background Measure CODE
How old are you? ______
Circle your gender: Male Female _____
Circle year in college: Freshman _____ Sophomore Junior Senior Other
Circle your current enrollment status: Part-time student Full-time student _____
Circle which of the following categories fits your family’s socio-economic background most accurately? _____ a. Very low income b. Low income c. Middle income d. Upper-middle income e. Upper income f. High income
Print your declared major or intended major at BGSU? ______
Do you work in addition to attending college? Yes ____ No ______
If yes, how many hours do you work per week? ______
If this is not your first semester in college, what is your current GPA? ______
Circle the following group(s) to which you consider yourself to be a member. _____
a. White, non-Hispanic b. Asian, Asian-American c. Black, African-American d. Mexican, Mexican-American e. Other Hispanic-Latino f. Native American Indian g. Other______(please indicate)
Do you participate regularly in any sports? Yes ___No___. _____
Please indicate which sport? ______
93
CODE Do you participate regularly in any dance activities? Yes ___No___. _____
Please indicate the type of dance? ______
Do you participate regularly in any drama activities? Yes ___No___. _____
Please indicate the kind of drama? ______
Circle the one response that most accurately reflects your current friend network. _____
a. I really don’t have any close friends b. I have a few close friends c. I have a moderate size circle of close friends d. I have many close friends
Circle the response that most accurately reflects your level of satisfaction with your current network of friends. _____
a. Not at all satisfied b. Somewhat satisfied c. Moderately satisfied d. Very satisfied
Circle the response that most accurately reflects the serving of regular meals in your home. (Prior to coming to college) _____
a. Never have regular meals served b. Occasionally have regular meals served c. Usually have regular meals served d. Always have regular meals served
Is this serving of meals a recent development [Yes ____ No ____] or has this always been true [Yes ____ No ____]? _____
Explain______
Circle the response that most accurately reflects the serving of regular meals in your home. (While you are in college) _____
a. Never have regular meals served b. Occasionally have regular meals served c. Usually have regular meals served d. Always have regular meals served
Is this serving of meals a recent development [Yes ____ No ____] or has _____
this always been true [Yes ____ No ____]? _____
Explain______
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CODE
Circle the response that most accurately reflects your frequency of eating regular meals with your family in your home. (Prior to coming to college) _____
a. Never eat regular meals with my family b. Occasionally eat regular meals with my family c. Usually eat regular meals with my family d. Always eat regular meals with my family
Is this eating of regular meals with your family a recent development [Yes ___No __] or has this always been true [Yes ____ No ____]? _____
Explain______
Circle the response that most accurately reflects your frequency of eating regular meals with your family in your home. (While you are in college) _____
a. Never eat regular meals with my family b. Occasionally eat regular meals with my family c. Usually eat regular meals with my family d. Always eat regular meals with my family
Is this eating of regular meals with your family a recent development [Yes ___No __] or has this always been true [Yes ____ No ____]? _____
Explain______
Circle the response that most accurately reflects your frequency of eating alone _____ in your home. (Prior to coming to college) a. I never eat alone b. I occasionally eat alone c. I usually eat alone d. I always eat alone
Is your frequency of eating alone a recent development [Yes ____ No ____] _____
or has this always been true [Yes ____ No ____]? _____
Explain ______
Circle the response that most accurately reflects your frequency of eating alone _____ where you live while you are in college) a. I never eat alone b. I occasionally eat alone c. I usually eat alone d. I always eat alone Is your frequency of eating alone a recent development [Yes ____ No ____] _____ or has this always been true [Yes ____ No ____]? _____
Explain ______
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CODE
Circle when your family served regular meals, did you feel pressure to join them? _____
a. No, never b. Yes, occasionally c. Yes, usually d. Yes, always
Is this level of pressure a recent development [Yes ____ No ____] or has this always been true [Yes ____ No ____]? _____
Explain______
Who is the primary mother/father figure in your life?
a. Mother b. Step –mother c. Aunt d. Sister e. Other ______
Who is the primary father figure in your life? _____
f. Father g. Step – father h. Uncle i. Brother j. Other ______
Do your parents emphasize nutrition and development of positive eating habits? _____
a. No, not at all b. Not, not often c. Yes, sometimes d. Yes, frequently e. Yes, always
Is this a recent development [Yes ____ No ____], or has this always been true [Yes ___ No ____]? _____
Explain______
Circle how important is your physical appearance to you? _____
a. Not important at all b. Slightly important c. Somewhat important d. Very important e. Extremely important
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CODE Circle do your parents put pressure on you regarding your physical appearance? _____ a. No, not at all b. Not, not often c. Yes, sometimes d. Yes, frequently e. Yes, always
Is this level of pressure a recent development [Yes ____ No ____]or has this always been true [Yes ____ No ____]? _____
Explain______
Circle do your close friends put pressure on you regarding your physical appearance? _____
a. No, not at all b. Not, not often c. Yes, sometimes d. Yes, frequently e. Yes, always
Is this level of pressure a recent development [Yes ____ No ____] or has this always been true [Yes ____ No ____? _____
Explain______
Circle the response that most accurately reflects your primary mother figure’s expectations regarding your academic achievement. _____
a. Very high, she is only satisfied when I earn top grades b. Usually high, but she is satisfied as long as I’m trying c. Moderate, she is interested but she doesn’t apply much pressure d. Low, she has expressed very few expectations e. None, she really don’t seem to have any expectations
Circle the response that most accurately reflects your primary father figure’s expectations regarding your academic achievement. _____
a. Very high, he is only satisfied when I earn top grades b. Usually high, but he is satisfied as long as I’m trying c. Moderate, he is interested but he doesn’t apply much pressure d. Low, he has expressed very few expectations e. None, he really don’t seem to have any expectations
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CODE
Circle your primary mother figure’s level of formal education: _____ a. Competed 8th grade or less education b. Some high school c. High school graduate d. Some college e. Graduated from a 2-year college f. Graduated from a 4-year college g. Some graduate school h. Graduated from a master’s program i. Graduated from a doctoral program j. Received a law degree k. Received a medical degree l. Other ______(please explain)
Circle your primary father figure’s level of formal education: _____
a. Competed 8th grade or less education b. Some high school c. High school graduate d. Some college e. Graduated from a 2-year college f. Graduated from a 4-year college g. Some graduate school h. Graduated from a master’s program i. Graduated from a doctoral program j. Received a law degree k. Received a medical degree l. Other ______(please explain)
Circle the response that most accurately conveys how difficult it was for you to leave home to attend college. _____
a. Extremely difficult b. Very difficult c. Moderately difficult d. Slightly difficult e. Not at all difficult f. Not applicable, never left
If it was difficult for you, explain the nature of the difficulty______
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CODE
Circle the response that most accurately reflects your current level _____ of smoking cigarettes.
a. I do not smoke b. I smoke fewer than 5 cigarettes per day c. I smoke more than 5, but fewer than 10 cigarettes per day d. I smoke more than 10, but fewer than 20 cigarettes per day e. I smoke a pack a day f. I smoke more than one pack a day, but less than 2 packs g. I smoke two packs or more per day
If you do smoke cigarettes, what is (are) the reason(s) you started? ______
If you do smoke cigarettes, how old were you when you started? ______
If you do smoke cigarettes, what is (are) the reason(s) you continue to smoke? ______
If you are female, circle all that apply to you ______
a. Delivered only one child. What was the date? ______b. Delivered more than one child. What were the dates? c. Gave up only one child for adoption. What was the date? d. Gave up more than one child for adoption. What were the dates? e. Experienced only one miscarriage. What was the date? f. Experienced more than one miscarriage. What were the dates g. Experienced only one abortion. What was the date? h. Experienced more than one abortion. What were the dates? i. Experienced only one stillbirth. What was the date? j. Experienced more than one stillbirth. What were the dates? k. Experienced only one Ectopic pregnancy. What was the date? l. Experienced more than one Ectopic pregnancy. What were the dates? m. NONE
If you are male, circle all that apply to pregnancy outcomes of sexual partners ______you have had in the past (only pregnancies that you fathered).
a. Fathered only one child. What was the date? b. Fathered more than one child. What were the dates? c. Gave up only one child for adoption. What was the date? d. Gave up more than one child for adoption. What were the dates? e. Experienced only one miscarriage. What was the date? f. Experienced more than one miscarriage. What were the dates g. Experienced only one abortion. What was the date? h. Experienced more than one abortion. What were the dates?
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CODE i. Experienced only one stillbirth. What was the date? j. Experienced more than one stillbirth. What were the dates? k. Experienced only one Ectopic pregnancy. What was the date? l. Experienced more than one Ectopic pregnancy. What were the dates? m. NONE
How tall are you? _____ feet ____ inches. ______
How much do you weigh? ______pounds. ______
What is the most you have ever weighed? ______
How much would you like to weigh? ______pounds ______
How would you describe your meat diet? ______1) ______red meat (beef, pork, lamb) typical of most Americans. 2) ______limit red meat (beef, pork, lamb) 3) ______avoid red meat (beef, pork, lamb) 4) ______vegetarian 5) If you limit red meat (beef, pork, lamb) intake, WHY? ______
How would you describe your diet 3 months ago? ______
(1)______same as present diet (2) ______more red meat eaten (3) ______less red meat eaten (4) ______limit red meat (beef, pork, lamb) (5)______avoid red meat (beef, pork, lamb) (6) ______Other changes. Describe______
Are you trying to lose weight? _____ (1) Yes (2) No
If yes, describe how you are trying to lose weight.______
______
If you diet to lose weight, approximately how many calories do you consume?
(1)______Calories
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(2) ______Do not know
Do you use herbal supplements to help lose weight? _____ yes _____no.
If yes, what type(s)? ______
______
Has your diet been successful? Yes ______No______
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Appendix C: The Eating Attitudes Test (EAT-26)
EAT-26 Please circle the letter corresponding to the choice that applies best to you. CODE
1. Engage in dieting behavior A B C D E F ______Always Very often Often Sometimes Rarely Never
2. Eat diet foods A B C D E F ______Always Very often Often Sometimes Rarely Never
3. Feel uncomfortable after eating sweets. A B C D E F ______Always Very often Often Sometimes Rarely Never
4. Enjoy trying new rich foods. A B C D E F ______Always Very often Often Sometimes Rarely Never
5. Avoid foods with sugar in them. A B C D E F ______Always Very often Often Sometimes Rarely Never
6. Particularly avoid foods with high carbohydrate content. ______A B C D E F Always Very often Often Sometimes Rarely Never
7. Am preoccupied with a desire to be thinner. A B C D E F ______Always Very often Often Sometimes Rarely Never
8. Like my stomach to be empty. A B C D E F ______Always Very often Often Sometimes Rarely Never
9. Think about burning up calories when I exercise. A B C D E F ______Always Very often Often Sometimes Rarely Never
10. Feel extremely guilty after eating. ______A B C D E F Always Very often Often Sometimes Rarely Never
11. Am terrified about being overweight. ______A B C D E F Always Very often Often Sometimes Rarely Never
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Eat-26 continued Please circle the letter corresponding to the choice that applies best to you CODE
12. Am preoccupied the thought of having fat on my body. A B C D E F ______Always Very often Often Sometimes Rarely Never
13. Aware of the calorie content of foods that I eat. ______A B C D E F Always Very often Often Sometimes Rarely Never
14. Have the impulse to vomit after meals. ______A B C D E F Always Very often Often Sometimes Rarely Never
15. Vomit after I have eaten. ______A B C D E F Always Very often Often Sometimes Rarely Never
16. Have gone on eating binges where I feel that I may not be able to stop. ______A B C D E F Always Very often Often Sometimes Rarely Never
17. Give too much time and thought to food. ______A B C D E F Always Very often Often Sometimes Rarely Never
18. Find myself preoccupied with food. ______A B C D E F Always Very often Often Sometimes Rarely Never
19. Feel that food controls my life. ______A B C D E F Always Very often Often Sometimes Rarely Never
20.Cut my food into small pieces ______A B C D E F Always Very often Often Sometimes Rarely Never
21.Take longer than others to eat meals. ______A B C D E F Always Very often Often Sometimes Rarely Never
22. Other people think that I am too thin. ______A B C D E F Always Very often Often Sometimes Rarely Never
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Eat-26 continued Please circle the letter corresponding to the choice that applies best to you CODE
23. Feel that other would prefer if I ate more. ______A B C D E F Always Very often Often Sometimes Rarely Never
24.Feel that others pressure me to eat. ______A B C D E F Always Very often Often Sometimes Rarely Never
25. Avoid eating when I’m hungry. ______A B C D E F Always Very often Often Sometimes Rarely Never
26. Display self-control around food. ______A B C D E F Always Very often Often Sometimes Rarely Never
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Appendix D: The Beck Depression Inventory (BDI)
Beck Depression Inventory: On this questionnaire are groups of statements. Please read the entire group of statements of each category, and then pick the one statement in that group which best describes the way you feel today - that is, right now! Circle the number beside the statement that you have chosen. Be sure to read all of the statements in each group before making your choice. CODE
1. 0 I do not feel sad. _____ 1 I feel sad. 2 I am sad all the time and I can’t snap out of it 3 I am so sad or unhappy that I can’t stand it.
2. 0 I am not particularly discouraged about the future. _____ 1 I feel discouraged about the future. 2 I feel I have nothing to look forward to. 3 I feel that the future is hopeless and that things cannot improve.
3. 0 I do not feel like a failure. _____ 1 I fell I have failed more than the average person. 2 As I look back on my life, all I see is a lot of failures. 3 I feel I am a complete failure as a person.
4. 0 I get as much satisfaction out of thing as I use to. _____ 2 I don’t enjoy things the way I use to. 3 I don’t get satisfaction out of anything anymore. 4 I am dissatisfied or bored with everything.
5. 0 I don’t feel particularly guilty. _____ 1 I feel guilty a good part of the time. 2 I feel quite guilty most of the time. 3 I feel guilty all of the time.
6. 0 I don’t feel I am being punished _____ 1 I feel I may be punished. 2 I expect to be punished. 3 I expect to be punished all of the time.
7. 0 I don’t feel disappointed in myself. _____ 1 I am disappointed in myself. 2 I am disgusted with myself. 3 I hate myself.
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Beck Depression Inventory (continued) CODE 8. 0 I don’t feel I am any worse than anybody else. _____ 1 I am critical of myself for my weaknesses or mistakes. 2 I blame myself all the time for my faults. 3 I blame myself for everything bad that happens.
9. 0 I don’t have any thoughts of killing myself. _____ 1 I have thoughts of killing myself, but I would not carry them out. 2 I would like to kill myself. 3 I would kill myself if I had the chance.
10. 0 I don’t cry any more than usual. _____ 1 I cry more now than I use to. 2 I cry all the time now. 3 I use to be able to cry, but now I can’t even though I want to.
11 0 I am no more irritated now than I ever am. _____ 1 I get annoyed or irritated more than I used to. 2 I feel irritated all the time now. 3 I don’t get irritated at all by the things that use to irritate me.
12 0 I have not lost interest in other people. _____ 1 I am less interested in other people than I use to be. 2 I have lost most of my interest in other people. 3 I have lost all of my interest in other people and don’t care about them at all.
13. 0 I make decisions about as well as ever. _____ 1 I try to put off making decisions. 2 I have great difficulty in making decisions. 3 I can’t make any decisions at all any more.
14. 0 I don’t feel I look any worse than I use to. _____ 1 I am worried that I am looking old or unattractive. 2 I feel that there are permanent changes in my appearance and they make me look unattractive. 3 I feel that I am ugly or repulsive looking.
15. 0 I can work about as well as before. _____ 1 It takes extra effort to get started at doing something. 2 I have to push myself very hard to do anything. 3 I can’t do any work at all.
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Beck Depression Inventory (continued) CODE 16. 0 I can sleep as well as usual. _____ 1 I don’t sleep as well as I use to. 2 I wake up 1-2 hours earlier than usual and find it hard to get back to sleep. 3 I wake up several hours earlier than I use to and cannot get back to sleep.
17. 0 I don’t get any more tired than usual. _____ 1 I get tired more easily than I use to. 2 I have to push myself very hard to do anything. 3 I get too tired to do anything.
18. 0 My appetite is no worse than usual. _____ 1 My appetite is not as good as it use to be. 2 My appetite is much worse now. 3 I have no appetite at all anymore.
19. 0 I am no more worried about my health than usual _____ 1 I am worried about physical problems such as aches and pains; or upset stomach; constipation. 2 I am very worried about physical problems, and it’s hard to think of much else. 3 I am so worried about my physical problems that I cannot think about anything else.
20. 0 I have not noticed any recent changes in my interest in sex. _____ 1 I am less interested in sex than I use to be. 2 I am much less interested in sex now. 3 I have lost interest in sex completely
21. 0 I haven’t lost much weight, if any, lately. _____ 1 I have lost more than 5 pounds lately 2 I have lost more than 10 pounds lately 3 I have lost more than 15 pounds lately
I am purposely trying to lose weight by eating less Yes___ No___
_____
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Appendix E: The General Self-Efficacy Scale
“Self-Efficacy Scale”
Please circle your level of agreement with each item. CODE
1) When I make plans, I am certain I can make them work. ______
1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
2) One of the problems is that I cannot get down to work when I should. ______
1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
3) If I can’t do a job the first time, I keep trying until I can. ______1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
4) When I set important goals for myself, I rarely achieve them. ______1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
5) I give up on things before completing them. ______1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
6) I avoid facing difficulties. ______1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
7) If something looks too complicated, I will not even bother to try it. 1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
8) When I have something unpleasant to do, I stick to it until I finish it. ______1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
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“Self-Efficacy Scale” (continued) Please circle your level of agreement with each item. CODE
9) When I decide to do something, I go right to work on it. ______1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
10) When trying to learn something new, I soon give up if I am not initially successful. ______1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
11) When unexpected problems occur, I don’t handle them well. ______1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
12) I avoid trying to learn new things when they look too difficult for me. ______1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
13) Failure just makes me try harder. ______1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
14) I feel insecure about my ability to do things. ______1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
15) I am a self-reliant person. ______1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
16) I give up easily. ______1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
17) I do not seem capable of dealing with most problems that come up in my life. ______1 2 3 4 5 6 Agree Agree Agree Disagree Disagree Disagree Strongly Somewhat Slightly Slightly Somewhat Strongly
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Appendix F: Sample Average 5-day Food Record Analysis
Student Name: Sample Student Student ID #: Instructor Name: Class Days: Class Time:
Bar Graph Nutrient Value Goal % 00 25 50 75 100 Basic Components Calories xx.xx xx% Water xx.xx g Protein xx.xx g xx% Carbohydrates xx.xx g xx% Dietary Fiber xx.xx g xx% Fat – Total xx.xx g xx% Saturated Fat xx.xx g xx% Mono Fat xx.xx g xx% Poly Fat xx.xx g xx% Cholesterol xx.xx mg xx% Vitamins Vitamin A RE xx.xx RE xx% Thiamin-B1 xx.xx mg xx% Riboflavin-B2 xx.xx mg xx% Niacin-B3 xx.xx mg xx% Vitamin B-6 xx.xx mg xx% Vitamin B-12 xx.xx mcg xx% Folate xx.xx mcg xx% Vitamin C xx.xx mg xx% Vitamin D mcg xx.xx mcg xx% Vit E-Alpha Equiv. xx.xx mg xx% Minerals Calcium xx.xx mg xx% Iron xx.xx mg xx% Magnesium xx.xx mg xx% Phosphorus xx.xx mg xx % Potassium xx.xx mg xx% Sodium xx.xx mg xx% Zinc xx.xx mg xx% Other Caffeine xx.xx mg Alcohol xx.xx g