Does body composition reflect eating attitudes?

Thèse de Master , 2019 , 66 Pages

Santé - Sciences de l'alimentation

The objective of the study is to examine and identify the relationship between eating attitudes and body composition among university students in nutrition majors versus non-nutrition majors. Eating Disorders (EDs) are a group of mental and physical illnesses that can influence individuals from every age, gender, ethnicity, and socioeconomic group, and that result in altered consumption or absorption of food, such as anorexia and bulimia nervosa.Disorder behavior eating (DBE) represents a range of irregular and abnormal eating behaviors that do not warrant a diagnosis of particular eating disorders (EDs) that result in altered consumption or absorption of food, such as anorexia and bulimia nervosa. ED symptoms are prevalent during teenage years, particularly in females who are college aged in the United States.

Evaluating eating attitudes and body composition in dietetic students versus non-dietetic students using simple and valid scales will provide insight into early detection between these components, which may be useful in preventing the developing of EDs in this population.

Table of Contents

Dedication

List of Figures

List of Tables

Acknowledgments

List of Abbreviations

Abstract

Chapter I. Introduction
Objective of the Study
Primary Scientific Research Questions

Chapter II. Review of Literature
Defining the Spectrum of Disorder Eating
Definition of EDs
Anorexia nervosa
Bulimia nervosa
Binge eating disorder
Avoidant restrictive food intake disorder
Orthorexia
Validation Tools to Assess Eating Attitudes
Eating Attitude Test 26 (EAT-26)
Tendency to Diet Scale (TDS)
Prevalence of EDs in the College Population
Risk Factors for EDs among College Students
Body image
Stress
Media
Disordered Eating in Dietetic Students
Body Composition
Body Composition Measurement Methods
Body Mass Index (BMI)
Waist circumference (WC)
Waist hip ratio (WHR)
Bioelectrical impedance analysis (BIA)

Chapter III. Methods
Participants
Instruments and Procedures
Ethical Approval
Statistical Analysis

Chapter IV. Results

Chapter V. Discussion
Strengths and Limitations
Conclusions

References

Dedications

"And lower to them the wing of humility out of mercy and say, My Lord, have mercy upon them as they brought me up when I was a child." QURAN SURAH AL ISRA (24)

This thesis is dedicated first to both my parents who only rise, nurture, and supports me.

I also dedicate this work to my beloved wife, who always encourages and supports me in my life.

List of Figures

Figure

1. Age distributions among the participants from Doctor of Chiropractic (DC), nutrition, and non-health related major students

2. Comparison of the mean of Eating Attitude Test 26 (EAT-26) scores between the three majors

3. Comparison the mean of Tendency to Diet Scale (TDS) scores between the three majors

4. Comparison of the mean of Eating Attitude Test 26 (EAT-26) scores between the Doctor of Chiropractic (DC) students in different years

5. Comparison of the mean of Eating Attitude Test-26 (EAT-26) scores between the nutrition students in different levels

6. Total of body mass index (BMI) Classification of Students in the three majors: Doctor of Chiropractic (DC), nutrition, and non-health related majors

7. Total cardio-metabolic risk according to waist circumference (WC) in the various degrees: Doctor of Chiropractic (DC), nutrition, and non-health related majors

8. Total cardio-metabolic risk according to Waist Hip Ratio (WHR) in Different Majors: Doctor of Chiropractic (DC), nutrition, and non-health related majors

9. Total Fat Mass Classification of Students in the Three Majors: Doctor of Chiropractor (DC), nutrition, and non-health related majors

10. Total of ≥32% of Fat Mass of Students in the Three Majors: Doctor of Chiropractic (DC), nutrition, and non-health related majors

List of Tables

Table

1. The International Classification of BMI for Underweight, Overweight and Obese Adults

2. Guidelines for WC measurement

3. Guidelines for WHR

4. Average percentages of fat

5. Mean of Demographic Data and Anthropometric Measurements of Students in Three programs

6. Comparison of EAT-26, TDS, and Body Composition Measurements between the Three Programs

7. Prevalence of Eating Disorders (EDs) between Students in the Three Majors

8. Comparison of EAT-26 and TDS Scores

9. Correlation between EAT-26 and Total TDS Scores.28

10. Comparison of EAT-26 and TDS between Students in Different Years

11. Classification and Comparison of BMI

12. Cardio-Metabolic Risk According to Waist Circumference in the Various Degrees

13. Waist-Hip Ratio Classification and Comparisons

14. Classification and Comparison of Fat Mass Percentages

15. Relation between body composition and EAT-26

16. Relation between body composition and TDS

17. Correlation between Body Composition Measurements, EAT-26, and TDS Scores

18. Comparison of Body Composition between DC Students in Different Years

19. Comparison of Body Composition between Undergraduate and Graduate Nutrition Students

20. Comparison of Body Composition between Non-Health Related Programs Students in Different Years

Acknowledgements

I would like to first acknowledge and express the most profound thankfulness to my advisor, Dr. Negin Navaei, for her support, mentorship, and endless patience throughout my entire graduate school career. I would also like to thank my thesis committee members, Dr. Beverley Demetrius and Dr. Sudhanva Char, for their constant support and advice throughout the thesis process. Thank you for your help, expertise, and leadership which has made this a fantastic experience. I would also like to thank Dr. Rama Mohan and Lorainne Rodriguez for the time they gave me throughout the thesis process. Finally, I would like to thank Jazan University and Saudi Arabian Cultural Mission (SACM) for supporting me to complete my graduate study in the United States.

List of Abbreviations

Abbildung in dieser Leseprobe nicht enthalten

Abstract

Background: Disorder behavior eating (DBE) represents a range of irregular and abnormal eating behaviors that do not warrant a diagnosis of particular eating disorders (EDs) that result in altered consumption or absorption of food, such as anorexia and bulimia nervosa. ED symptoms are prevalent during teenage years, particularly in females who are college aged in the United States. The objective of the study is to examine and identify the relationship between eating attitudes and body composition among university students in nutrition majors versus non-nutrition majors.

Methods: A cross-sectional was conducted among female students at Life University. Sixty female adult students were recruited from three groups in the following degree programs: the nutrition field, the Doctor of Chiropractic (DC) program, and non-health related majors. Tendency to Diet Scale (TDS) and Eating Attitudes Test 26 (EAT-26) were used to assess eating attitudes. All students were measured (weight, height, % fat mass, and waist circumference (WC)), and body mass index (BMI) and waist-hip ratio (WHR) were calculated.

Results: Although no statistically significant association was found, the prevalence was 10% of nutrition students and 5% of DC students depicting a tendency for EDs, as compared to the students of non-health related programs, who did not depict any occurrence of EDs. First- and second-year students in the DC program and graduate nutrition students were at significantly higher risk of developing EDs (p = 0.038; p = 0.002 respectively). There was a statistically significant association between TDS score, BMI (p = 0.026), and WC (p = 0.027).

Conclusion: There is a relationship between body composition and eating attitudes. Nutrition students had greater prevalence of EDs; graduate nutrition students had significantly higher mean of EAT-26 scores, and healthier body composition than undergraduate nutrition students.

Chapter I: Introduction

Eating Disorders (EDs) are a group of mental and physical illnesses that can influence individuals from every age, gender, ethnicity, and socioeconomic group, and that result in altered consumption or absorption of food, such as anorexia and bulimia nervosa (Erskine, Whiteford, & Pike, 2016). However, disorder behavior eating (DBE) represents a range of irregular and abnormal eating behaviors that do not warrant a diagnosis of particular EDs (Academy of Nutrition and Dietetics, 2018). DBE, such as binge eating and restrictive eating, emotional eating, overeating, strict eating, and controlling body weight and shape through inappropriate compensatory behaviors are all risk factors for EDs (Quick & Byrd-Bredbenner, 2013). Abnormal eating behavior using unhealthy weight control methods, such as eating very little food, skipping meals, and taking diet pills, has increased among students in universities (Rouzitalab et al., 2015). A higher prevalence of EDs is noted in females than in males. According to Yu and Tan (2016), female college students had a higher prevalence of EDs: 11.6 %compared to male college students at 5.7%.

ED symptoms are common during teenage years, particularly in females who are college-aged (over 18 years) in the United State (U.S.) (Lipson & Sonneville, 2017). University students have many risk factors of DEB that lead to an increased risk of developing EDs, including new environment, peer pressure, and academic stress during their studies at universities and colleges. This can lead to an expression of disordered eating behaviors (Rouzitalab et al., 2015; Sanchez-Ruiz, El-Jor, Kharma, Bassil, & Zeeni, 2017). The prevalence of restrictive eating has been demonstrated in dietetic students from various countries, such as Brazil, Portugal, Germany, and South Africa (Korinth, Schiess, & Westenhoefer, 2010; Bo et al., 2014; Poínhos et al., 2015; Kassier & Veldman, 2014). Research evaluating the relationship between eating attitudes and body composition measures in dietetic students in the U.S. is limited (Geitz, 2016). Studies that did assess eating attitudes found that dietetic students are more likely to have higher prevalence of EDs in comparison to other non-dietetic majors (Poínhos et al., 2015; Kassier & Veldman, 2014). Another study also showed undergraduate dietetic students had low mean of BMI and higher prevalence of eating concerns than non-dietetic undergraduate students. It suggests that increased risks for dietetic students may be because of their knowledge of food, weight control, or obsessions related to body image (Ozenoglu, Unal, Ercan, Kumcagiz, & Alakus, 2015). However, increasing nutrition knowledge in dietetic students may have a positive influence on eating attitudes and body composition (Kassier & Veldman, 2014). Students in the nutrition programs had higher physical activity, dietary fiber intake, and lower total fat and saturated fat intake than students in non-health programs (Mealha et al., 2013).

Therefore, evaluating eating attitudes and body composition in dietetic students versus non-dietetic students using simple and valid scales will provide insight into early detection between these components, which may be useful in preventing the developing of EDs in this population.

Objective of the Study

This descriptive, cross-sectional study was designed to examine and identify the relationship between eating attitudes and body composition among Life University students in both undergraduate and graduate nutrition majors versus non-nutrition majors.

Primary Scientific Research Questions

- What is the prevalence of EDs among university students?
- Do nutrition students have a high risk of EDs in comparison to students in other departments?
- Do graduate nutrition students have a high risk of EDs in comparison to undergraduate nutrition students?
- Is there a relationship between eating attitudes and body compositions?
- What is the difference in body composition between students in nutrition majors versus the other majors?

Chapter II: Review of the Literature

Defining the Spectrum of Disorder Eating

According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-V), all of the described criteria for an eating disorder must be met to be clinically diagnosed. Until 1994, anorexia nervosa (AN) and bulimia nervosa (BN) were the only two specified EDs. From 1994 to 2013, the DSM-IV also identified a generic category derivative of an eating disorder known as “eating disorders not otherwise specified” (EDNOS) which included disorders that did not completely meet strict diagnostic criteria for AN or BN. The DSM-V in 2013 renamed EDNOS to “Other Specified Feeding or Eating Disorders” (OSFED) and included binge eating disorder in addition to anorexia and bulimia (American Psychiatric Association, 2013).

In the majority of cases, patients are given a diagnosis of OSFED. According to the Center for Eating Disorders (2015), approximately 40-60% of cases in eating disorder treatment centers in the U.S. have the EDNOS or OSFED category (Center for Eating Disorders, 2015). People with OSFED may show dietary restraint as a symptom but do not meet the full diagnostic criteria of one of the specific EDs (Jong et al., 2016). Dietary restriction or restrictive eating refers to the conscious control of food consumption to improve body composition or body shape and weight (Kruger, Bray, Beck, Conlon, & Stonehouse, 2016). A study explored the relationship between body composition and eating behavior in New Zealand. The study included 116 women from 18 to 44 years of age. Three Factor Eating Questionnaire (TFEQ) was used to assess their eating behaviors. The results were that women with low dietary restraint and high dis-inhibition (over-consumption) had significantly higher body mass index (BMI) and body fat percentage (p -< 0.001) than women with high dietary restraint and low dis-inhibition (Kruger et al., 2016).

Definition of EDs

According to the National Health Institute (NIH, 2016), people with EDs may intake very small quantities of food (restriction), or they may consume large amounts of food (binging). EDs are serious and often fatal illnesses that lead to severe disturbances of eating behaviors. Obsessions with some foods, body weight, and shape may also signal EDs. (NIH, 2016). According to National Association of Anorexia Nervosa and Associated Disorders (ANAD, 2018), at least in the U.S., 30 million persons of all ages and genders suffer from EDs.

Anorexia Nervosa (AN).

Around 0.9% of American females suffer from AN in their lifetime (ANAD, 2018). AN is a type of ED that is characterized by low body weight and intense fear of gaining weight. Individuals with anorexia nervosa may see themselves as being overweight even if they are severely underweight. Moreover, they typically restrict the amount of food they eat and take small quantities of selected types of foods (NIH, 2016). AN is associated with various medical complications. In general, the medical complications are a direct result of weight loss and malnutrition. Starvation leads to protein and fat catabolism that leads to loss of cellular volume and function, resulting in adverse impacts on the body and atrophy of the heart, brain, liver, intestines, kidneys, and muscles (Mehler & Brown, 2015).

Bulimia Nervosa (BN).

In contrast to AN, individuals with BN are characterized by binge eating, which is consuming unusually excessive quantities of food in a short period followed by purging (National Eating Disorders Association [NEDA], 2018). The purging after overeating is accompanied by abnormal behaviors such as self-induced vomiting by inserting their fingers into their mouths, excessive use of laxatives or diuretics, fasting, excessive exercise, or a combination of these behaviors to maintain a healthy or relatively normal weight (NIH, 2016). Approximately 1.5% of American females suffer from BN (ANAD, 2018). BN is linked with several medical complications. The complications depend on the mode and frequency of purging. Diuretic and laxative use and self-induced vomiting can lead to acid-based and electrolyte abnormalities, dehydration, gastroesophageal reflux, dysphagia, and esophageal erosions and ulcers (Mehler & Rylander, 2015).

Binge Eating Disorder (BED).

BED is a disorder of eating and the most common ED in the U.S., and 2.8% of American adults suffer from BED in their lifetime (ANAD, 2018). BED is characterized by recurrent eating of large amounts of food more rapidly than normal with losing control during eating (NEDAa, 2018). Unlike BN, people with BED do not experience purging, and they are often overweight or obese (NIH, 2016). BED is associated with depression, disgust, or guilt after overeating. It is highly correlated with obesity, which leads to serious health problems (Klatzkin, Gaffney, Cyrus, Bigus, & Brownley, 2018).

Avoidant/Restrictive Food Intake Disorder (ARFID).

ARFID is an eating or feeding disorder that is characterized by a persistent failure to meet adequate nutritional and/or energy requirements, which leads to weight loss and severe nutrient deficiency (Katzman, Stevens, & Norris, 2014). According to the Center for Eating Disorders (2015), the signs of ARFID typically show up during infancy or childhood; however, they may also present into adulthood. The prevalence of ARFID is still being studied but around 3-5% of children have ARFID (ANAD, 2018). Individuals with ARFID have difficulty digesting certain foods, avoid certain colors or textures of food, consume only very small quantities of food, have no appetite, are afraid to eat after a frightening episode of choking or vomiting, or fear negative consequences from eating (NEDAc, 2018).

Orthorexia.

The term “orthorexia” was recognized in 1998 and indicates an obsession with proper or “healthful” eating (ANAD, 2018). In other words, orthorexia describes people with an obsession for proper nutrition who pursue this obsession through diet restriction, focus on meal preparation, and ritualized patterns of eating. Orthorexia is often linked with significant impairment, such as malnourishment and poor quality of life. People with orthorexia are typically concerned with the quality of the food rather than the quantity. Orthorexic individuals spend considerable time evaluating foods. For example, they assess whether vegetables have been exposed to pesticides, whether dairy products came from hormone-supplemented cows, whether nutritional content was lost through cooking, and whether nutrients, artificial flavoring, or preservatives were added. Also, they assess whether food may contain plastic-derived carcinogenic compounds and whether labels provide enough information about the ingredients (Koven & Abry, 2015).

Validation Tools to Assess Eating Attitudes

EAT-26 scale.

The EAT-26 scale is a tool that assess eating attitude and used to determine EDs, but it is not designed to make a diagnosis of EDs. The EAT-26 scale has three subscales with 26 questions: Dieting, Bulimia and Food Preoccupation, and Oral Control. Each question has six choices with a corresponding point value: always (3), usually (2), often (1), sometimes (0), rarely (0), and never (0). The total score of EAT-26 equals the sum of scores for the 26 items. A score of equal to or more than 20 is defined as being characteristic of a “disordered eating attitude” (Garner, Olmsted, Borh, & Garfinkel, 1982). EAT-26 scale used in different studies to assess EDs (Kassier & Veldman, 2014; Barnard, 2016; Saleh, et al., 2018).

Tendency to Diet Scale (TDS).

The Tendency to Diet Scale (TDS) is a self-report, self-assessment, and descriptive term for 15 attitude and behavior questions. The TDS is related to attitudes and behaviors that are particularly related to dieting. The higher scores indicate a greater tendency to diet. The TDS is considered a valid and reliable scale (Cronbach’s alpha= .79) (Jeor, 1997). TDS used commonly among group of Ohio State University researchers, and used with a study that assessed the eating attitudes and body composition with dietetic students at Ohio State University (Geitz, 2016).

Prevalence of EDs in the College Population

In the 2013 Collegiate Survey Project conducted by National Eating Disorder Association (NEDA), college students faced increased pressure and stress, possibly leading to mental health problems and a greater need for campus mental health services (NEDA, 2013). EDs are rising, and they are more common in college-aged students, mainly occurring among females. Data from one college showed that the total percentage of EDs increased from 23 to 32% in females and from 7.9 to 25% in males (NEDA, 2013). Lipson & Sonneville (2017) estimated the prevalence of EDs in many U.S. college students. The participants in this study were 9,713 students from 12 colleges and universities in the U.S. The results were that the prevalence of EDs among females was 49% versus 30 % in males (p- value < 0.001) (Lipson & Sonneville, 2017).

Risk Factors for EDs among College Students

Body image.

Body image dissatisfaction and a stressful atmosphere may influence the occurrence of disordered eating and increase during college. According to Lofrano-Prado, Prado, Barros, and Souza (2015), it is important to identify symptoms of EDs and body image dissatisfaction in college students and to verify the relationship between EDs and body image dissatisfaction. The study was a cross-sectional study conducted with 408 college students (283 females) aged between 18 and 23 years, enrolled in the first semester of health science in public universities in Recife, Brazil.

The symptoms of EDs and body image dissatisfaction were evaluated by self-report questionnaires: EAT-26, Bulimic Investigation Test Edinburgh (BITE), Binge Eating Scale (BES), and Body Shape Questionnaire (BSQ). The findings were that dissatisfaction with body image was independently correlated with a 22-fold increased risk of AN, 18-fold for BN, and 25-fold for BED. Female college students (32.5%; CI 95 % = 27.2-38.1 %) have greater symptoms of EDs than males (18.4 %; CI 95 % = 12.3-25.9 %). Particularly for bulimia, both females (26.1 %; CI 95 % = 21.3- 31.5 %) and males (21.6 %; CI 95 % =15.1-29.5 %) are at great risk of developing BN (Lofrano-Prado et al., 2015).

Another study was conducted by Macneill, Best, & Davis (2017) to investigate sex differences in the association between personality, EDs, and body image dissatisfaction. The participants were 238 female and 85 male undergraduate students at a Canadian university. Self-report surveys were used to assess personality, body image, and disordered eating. The study concluded that the association between disordered eating, body image dissatisfaction, and personality was higher in females than males (Macneill et al., 2017). Thus, females reported more body dissatisfaction and disordered eating than males.

Stress.

Stress could be the main factor leading to an increase in the risk of EDs. College is a stressful atmosphere and has overwhelming experiences for students. This increased stress could trigger disordered eating behaviors among students. Research into the psycho-behavioral factors related to disorder eating in college students reported that perceived stress and emotional eating correlated with overeating among females, while boredom and anxiety were crucial factors associated with overeating in men (Bennett, Greene, & Schwartz-Barcott, 2013).

Hootman, Guertin, & Cassano (2018) evaluated the relationship between sex differences in stress, emotional eating, tendency to overeat, restrained eating behavior, and body composition. The findings of the 264 participants were that freshmen students had a higher tendency to overeat in response to external cues and emotions, leading to higher weight, BMI, and waist circumference (WC) at the start of college. Male students with higher perceived stress at college gained significantly more weight in the first semester (p -value = 0.001) (Hootman et al., 2018).

According to Fragkos & Frangos (2013), the study was to assess factors predicting EDs risk in 1,865 undergraduate students. They used a structured questionnaire that included demographics, Sick, Control, One Stone, Fat, Food (SCOFF) scale, a questionnaire for screening EDs, the Achievement Anxiety Test, and the Depression, Anxiety and Stress Scale. The results were that 39.7% of the students were at risk for EDs and more frequent in female students than males. Depression, stress, female gender, being married and searching for a romantic relationship were risk factors of having EDs (Fragkos & Frangos, 2013).

Another study was conducted by Ngan et al. (2017) and explored the relationship between stress and EDs among undergraduate medical students. The study was cross-sectional in that it was conducted among 320 participants comprised of third-, fourth-, and fifth-year medical students in a private medical college in Malaysia. Self-administered questionnaires that consisted of social demographic data, EAT-26, and Cohen Perceived Stress Scale (CPSS) were used. The findings were that 11% of medical students were at risk of developing EDs and the students who had obese BMI status (25%) were significantly more likely to be at risk of EDs (95 % CI: 1.4 - 10.9; p value= 0.007). There was a significant correlation between those with an unsatisfactory social relationship with peers and friends (OR 2.5, 95 % CI 1.0 - 5.9; p value= 0.035) and risk of developing EDs (OR 3.9, 95 % CI 1.4 - 10.9; p value= 0.007). For CPSS, 75.5%of the respondents had high stress levels. There was no significant association between pressure or stress and the risk of EDs. However, the majority of undergraduate medical students were under stress and therefore at risk for developing EDs. However, Ngan et al. did not include any association of effect of medicals students' knowledge on EDs (Ngan et al., 2017).

Media.

Traditional media (such as television and magazines) or social media (such as Instagram, Facebook, Snapchat, and Twitter) have a substantial impact on an individual's relationship with food and fear of gaining weight. Many people in the media world are reporting about their body fitness, food selections, and exercise regimens. The constant streams of body image and food consciousness can increase the level of stress and anxiety, which leads to increased risks of EDs. Sidani, Shensa, Hoffman, Hanmer, and Primack (2016) studied the relationship between social media use and eating concerns among U.S. young adults. The design of the study was a cross-sectional survey of 1,765 young adults aged 19-32 years. Two validated measures of eating concerns scale were used: the SCOFF Questionnaire and the Eating Disorder Screen for Primary Care (ESP). Social media use (Facebook, Twitter, Google+, YouTube, LinkedIn, Instagram, Pinterest, Tumblr, Vine, Snapchat, and Reddit) was measured using both volume (time per day) and frequency (visits per week). The findings from this study indicated a strong and consistent association between using social media and eating concerns among young adults in the U.S. aged between 19 to 32 years. This association was apparent whether using social media was measured as volume or frequency.

Disordered Eating in Dietetic Students

Constant exposure to food and nutrition, knowledge about diet and weight control, or obsessions related to body image may make dietetic students more susceptible to the adoption of abnormal eating behaviors than the general population (Kassier & Veldman, 2014). The Commission on Dietetic Registration through the Academy of Nutrition and Dietetics quantified the national census of registered dietitians to be 98,053 in 2018, of which 90.6% were females, 3.8% were males, and 5.6% were not reported (Academy of Nutrition and Dietetics, 2018).

Nutrition students have higher risks of EDs compared to other students in different majors. Research has demonstrated the prevalence of disordered eating in dietetic students. Most of the studies investigating EDs in this population have been conducted in various countries outside the U.S. (Geitz, 2016).

Mealha, Ferreira, Guerrra, & Ravasco (2013) examined the relationship between eating disorder development in undergraduate dietetics and nutrition students and other undergraduate degree programs in Portugal. They investigated eating behaviors using the EAT-26, Eating Disorder Inventory (EDI), International Physical Activity Questionnaire, a modified food frequency questionnaire, and body composition using WC and bioelectrical impedance analysis. All 189 participants were female and enrolled in either a nutrition degree or another academic area of study.

They did not find any significant differences in eating behavior between students from nutrition departments and students from other departments. They noted that compared to non-nutrition students, students in nutrition departments tended to restrict food consumption to control their weight, and they had highest percentages of normal weight according to WC and normal fat mass. Although they did not find differences, they concluded that nutrition students had double the prevalence of psychological and behavioral tendencies that are typically connected with EDs compared to students in other majors (Mealha et al., 2013).

Another study was conducted in South Africa in 2014 which included both undergraduate dietetic students versus undergraduate non dietetic majors who were mostly female. The study was cross-sectional and included 145 participants; 83 were non-dietetic majors and 62 were dietetic majors. The outcome measures that were used in the study were the TFEQ, EAT-26, and BMI. The mean of BMI of all of the participant categories was within the normal range. There was a significant difference between the two groups, and it was observed with regard to eating restraint (p value < 0.001). Disordered eating was found in 33% of dietetic students compared to 16% of non-dietetic students (Kassier & Veldman, 2014).

Poínhos et al. (2015) compared eating behaviors among 154 undergraduate nutrition students and 263 students attending other courses in Portugal. They assessed emotional and external eating using the Dutch Eating Behavior Questionnaire, dietary restraint using the flexible and rigid control of eating behavior subscales, binge eating by using the Binge Eating Scale, and eating self-efficacy by using the General Eating Self-Efficacy Scale. Similarly, the findings were that nutrition students from both genders have a higher practice of dietary restraint compared to students from other courses. Also, female nutrition students showed higher binge eating levels than students from other departments. Nutrition students in comparison with non-nutrition students were more likely to display characteristics of EDs (Poínhos et al., 2015).

Barnard conducted another study in 2016 in South Africa. A cross-sectional study was performed to determine and compare the BMI, eating behavior, and eating attitudes of undergraduate dietetic students and non-undergraduate dietetic students. The methods used in the study were BMI, SCOFF, and EAT-26 questionnaires for screening the presence of EDs. The results of the study were that non-undergraduate dietetic students had a higher mean BMI than undergraduate dietetic students. However, BN and BED were higher in undergraduate dietetic students than non-dietetic undergraduate students (Barnard, 2016). Thus, nutrition students have a higher prevalence of eating concerns than other students.

Geitz (2016) conducted a cross-sectional study to investigate the relationship between eating attitudes and body composition in undergraduate dietetic students in the U.S. Thirty female and five male dietetic students participated in the study. EDs examination questionnaire, TDS, Multi-Body Shape Relations Questionnaire, and Figure Rating Scale were used in the study. The findings were that the female dietetic students had greater restrictive eating attitudes, and the majority of all participants were within a normal BMI. Geitz concluded that there is a need to assess restrictive eating attitudes and body composition in dietetic students across the country amongst different universities in the U.S. and other countries (Geitz, 2016).

Body Composition

Body composition is used to determine the percentages of fat, bone, water, and muscle in human bodies and is influenced by EDs. Measuring body composition is used to describe either deficiencies or excesses of a component that can be related to health risks (Müller , Geisler, & Bosy-Westphal, 2018). EDs and lifestyle factors that contribute to changes in body fat distribution include dietary changes, with higher consumptions of saturated fats and simple sugars, and decreased physical activities, with less skeletal muscle mass and reduced strength (Hootman et al., 2018).

College students are at high risk of gaining fat mass, particularly first-year college students. The transition from high school to college is a critical period for establishing health-related behaviors, such as unhealthy eating and poor physical activity (Vadeboncoeur, Townsend, & Foster, 2015). Yu and Tan (2016) reported that 50% of first-year college students at U.S. universities were consuming high-fat food or fast food three or more times during the week. Therefore, the tendency to gain weight by increasing fat mass during college is well-documented; about 60-75% of first-year students gain weight, with an average gain of between 1.4 and 1.8 kilograms (Vadeboncoeur et al., 2015).

Body Composition Measurement Methods.

Several characteristics of body composition, in particular the amount and distribution of body fat and lean mass, are now understood to be essential health outcomes. The available measurement methods for body composition range from simple to complex ones, with all methods having limitations and some degree of measurement error. Therefore, an understanding of the body composition measurements is essential for effective intervention studies (Hillier, Beck, Petropoulou, & Clegg, 2014). The measurement of body composition gives a better understanding of nutrition and growth status assessment in disease states and their treatment in populations. Body BMI,WC, waist-hip Ratio (WHR), and bioelectrical impedance analysis (BIA) are common measurement methods that are important in clinical nutrition and research.

BMI

BMI is a metric method currently used for determining anthropometric height/weight characteristics in adults and for classifying and categorizing them into groups. The most common method used to estimate if a person is overweight or obese is to calculate their BMI. In addition, BMI is an estimation of fat in the body based on comparing a person’s weight to his or her height. It can be classified into different categories based on mathematic calculations (National Institutes of Health, 2013). According to World Health Organization (WHO) (2018), BMI is calculated by weight in kilograms divided by the square of height in meters (kg/m[2]), and the weight status is classified by using the index of weight-for-height as shown in Table 1.

Table 1

The International Classification of BMI for Underweight, Overweight and Obese Adults

Abbildung in dieser Leseprobe nicht enthalten

World Health Organization (WHO, 2018). Retrieved from http://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight

BMI is a simple and inexpensive technique to measure body fat. However, a problem with the BMI’s obesity index is that it usually does not differentiate between body fat mass and lean body mass. In other words, an individual can have high BMI and still have very low fat mass (Nuttall, 2015). For instance, athletes have plenty of the fat-free mass, which raises the weight on the gauge and will show a high BMI. The comparatively poor relationship between body fat mass percentage and BMI has been well known for numerous years and was revealed in research where the percentage of body fat was defined by the densitometry technique. Kassier and Veldman (2014) examined BMI and dietary restraint amongst dietetic and non-dietetic students. In the dietetic group, two thirds of participants were found to have a BMI within the normal range (BMI 19-25 kg/m[2]), based on WHO criteria.

Although not significant, nearly half of the non-dietetic group was considered overweight based on BMI. There was still a prevalence of disordered eating as assessed by the TFEQ and EAT-26 despite having a normal BMI. BMI may not be a typical trait analyzed when evaluating the prevalence of EDs. However, it may have an important role despite the lack of statistical significance. The similarity in BMI between dietetic and non-dietetic students contributes to the theory that necessary energy needs are being met through food consumption and that restrained eating does not necessarily mean consumption of food is less than caloric needs, resulting in weight loss or lower BMI (Kassier & Veldman 2014).

The poor connection between body fat mass percent and BMI also seemingly has been revealed in the National Health and Nutrition Examination Survey (NHANES) III databank. Here, the bioelectrical impedance technique was applied to approximate the fat element of body composition. In the course of measurement of people with BMI of around 25 kg/m[2], body fat percentage in males ranged between 14 and 35%, while in females it ranged between 26 and 43% (Gonnelli et al., 2013).

While sometimes used as a surrogate for obesity, BMI describes a ratio between height and weight and is not an indicator of body composition. Individuals with a healthy BMI currently dieting or with a dieting history can have a higher body fat percentage compared to those who have never dieted (Ntalla et al., 2013). People may have a high average body fat percentage despite having what is considered a healthy or normal BMI. This concept is known as normal weight obesity (NWO), which describes a person who has a normal body weight when defined by BMI but also has a high amount of body fat (Oliveros, Somers, Sochor, Goel, & Lopez-Jimenez, 2014). Individuals with NWO can have increased risks of metabolic syndrome (Oliveros et al., 2014). Given many reports of NWO, analysis of body composition such as the BIA method will be more accurate than the use of BMI.

WC

WC is a measure of body composition and is most widely used to assess central obesity (Ma et al., 2013). People with central obesity are at a higher risk of several chronic conditions, including type 2 diabetes, hypertension, and dyslipidemia, which lead to a high prevalence of cardiovascular disease and an increased risk of morbidity (Fan et al., 2016). WC provides a more reliable indicator than BMI in measuring central obesity. WC measurement is an easy method, more strongly correlated with intra-abdominal fat composition and several chronic conditions (Ma et al., 2013).

Hou et al. (2018) compared the associations of obesity indicators with the risks of prediabetes and diabetes in 42,918 Chinese adults aged between 20 and 88 years, using WC, waist- hip ratio (WHR), and BMI. They found that WC and WHR were more strongly associated with diabetes than BMI among Chinese adults. Hence, measuring WC can help screen for potential health risks associated with being overweight and obese. The American Heart Association (2015) provides guidelines for WC measurement in Table 2.

Table 2

Guidelines for WC measurement

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American Heart Association (2015). Retrieved from https://newsarchive.heart.org/waist-size-matters-in-diagnosing-obesity-among-minorities/

WHR

WHR is one of the body composition measurements that is used to measure both the circumference of waist and hips. The WHR is a speedy and easy technique to approximate body composition. WHR is a popular measurement method which shows the level of an individual’s abdominal fat that is associated with many health conditions (Hassan, Ahmad, Adam, Nawi, & Ghazi, 2016).

WHR is used as a substitute for visceral body fat measurement. Gadekar, Dudeja, Basu, Vashisht, & Mukherji (2018) studied the correlation between visceral fat with WHR, WC, and BMI in healthy young adults. The study was descriptive and cross-sectional, conducted on 215 healthy adults. They found that there was a strong correlation between visceral body fat and WHR compared to the other measurements in the study (Gadekar et al., 2018). According to Hassan et al. (2016), WHO provides guidelines for WHR measurement as shown in Table 3.

Table 3.

Guidelines for WHR

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Hassan et al. (2016). Abdominal obesity indicators: waist circumference or waist- hip ratio in Malaysian adults’ population. International Journal of Preventive Medicine, 7(1), 82. doi:10.4103/2008-7802.183654

BIA

BIA is a method that is regularly used for estimating body composition in two components: fat mass and fat-free mass (Silva, Fields, & Sardinha, 2013). It can also be used to estimate components including intracellular and extracellular body water, fat mass and fat-free mass (Kyle, Earthman, Pichard, & Coss-Bu, 2015). BIA measures the impedance or resistance to small electrical currents as they travel through the body’s water pool. The estimation of various components through BIA assumes that 73% of fat-free mass is body water, which is considered a good electrical conductor. On the contrary, fat mass is a poor conductor of electricity because of its lower water constitution (Kyle et al., 2015). Table 4 from the body fat chart shows how average percentages differ according to the specified groups and categories.

Table 4

Average percentages of fat

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Body Fat Calculator – Calculate your body fat percentage. (2018). Retrieved from http://www.freebodyfatcalculator.org/body-fat-percentage-chart/

The advantages of BIA are that it is relatively simple, quick, and low cost, it requires little or no technician/user experience, and it is safe. Another significance is that the common BIA devices that are used in the field or at home are hand-to-hand and foot-to-foot models. Consequently, the BIA technique of estimating body fat percent to determine adiposity risk is valid for use in large epidemiological studies (Aldosky, Yildiz, & Hussein, 2018).

Chapter III: Methods

Participants

This cross-sectional study aimed to identify the relationship between eating attitudes and body composition among students at Life University. This study was carried out during the fall quarter of 2018 and included 60 female adult students (20 students in each group) who were age 18 and over were recruited from three groups in the following degree programs: the nutrition field, the DC program, and non-health related majors (e.g., business, biology, and psychology programs). Students younger than 18 years, male students, students from other mentioned programs, and students who were pregnant were excluded from the study.

Instruments and Procedures

Students were asked to complete a demographic questionnaire to obtain information about age, study year or level, and study major. Students were asked also to complete the following validate questionnaires to assess their eating attitudes: Tendency to Diet Scale (TDS) and Eating Attitudes Test 26 (EAT-26) questionnaires. TDS is a self-report, self-assessment, and descriptive term for 15 attitude and behavior questions. The TDS is related to attitudes and behaviors that are particularly related to dieting. The higher scores indicate a greater tendency to diet. The TDS is considered a valid and reliable scale (Cronbach’s alpha= .79) (Jeor, 1997). TDS used commonly among group of Ohio State University researchers, and used with a study that assessed the eating attitudes and body composition with dietetic students at Ohio State University (Geitz, 2016).

The EAT-26 scale measure to determine EDs, but it is not designed to make a diagnosis of EDs. The EAT-26 scale has three subscales with 26 questions: Dieting, Bulimia and Food Preoccupation, and Oral Control. Each question has six choices with a corresponding point value: always (3), usually (2), often (1), sometimes (0), rarely (0), and never (0). The total score of EAT-26 equals the sum of scores for the 26 items. A score of equal to or more than 20 is defined as being characteristic of EDs (Garner et al., 1982). EAT-26 scale used in different studies to assess EDs (Kassier & Veldman, 2014; Barnard, 2016; Saleh, et al., 2018).

Body composition was assessed by using a calibrated scale for the measurement of weight to the nearest 0.1 kg and measuring tape to measure waist and hip circumferences. WHR was calculated, and BIA was used to assess fat percentage. Height without shoes was measured using a wall-mounted stadiometer to the nearest 0.5 cm. BMI was calculated as kg/m[2].

Ethical Approval

This study was approved by IRB. Participants signed an informed consent form after being informed of the nature and scope of the study. Students were asked to complete the questionnaires EAT-26 and TDS to assess their eating attitudes. Height without shoes was measured using a wall-mounted stadiometer; body composition was assessed by using a calibrated scale for the measurement of weight, measuring tape to measure waist and hip circumferences and BIA. The participants were told that participation in this study was voluntary, that they may stop participating at any time, and that their names would not be disclosed when the study findings were reported. The participant's information was obtained according to IRB regulations.

Statistical Analysis

The quantitative variables were described by mean, median, mode, deviation standard, minimum and maximum; absolute and relative frequencies were calculated for the qualitative variables. Associations between categorical independent variables were performed by Chi-square test, Pearson correlation t test and Coefficients regression analysis to facilitate comparison between the groups. All analyses were performed using SPSS and Microsoft Office Excel. p < 0.05 considered statistically significant.

Chapter IV: Results

The mean of demographic data and anthropometric measurements of participants among 60 female students in three different programs (DC, nutrition, and non-health related programs) are reported in Table 5. The students had normal mean of BMI, no risk of WC, low risk of WHR, and average fat mass percentages. Nutrition students had the lowest mean of body weight (129.5 ±22.5) and BMI (23.2 ± 3.29) according to WHO criteria, which outlines that a person’s normal BMI should be between 18.5-24.9 kg/m[2], compared to the other programs. Age distributions among the participants from DC, nutrition, and non-health related major students are shown in Figure 1. There are 37 participants who are 25 years old and below, 15 between the ages of 26 and 30, five participants between the ages of 31 and 35 only one participant in the age group 36 to 40, and two above the age of 40, making a tally of 60 participants.

Table 5

Mean of Demographic Data and Anthropometric Measurements of Students in Three Programs

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Values are reported as mean±SD. Abbreviations: BMI: Body Mass Index, DC: Doctor of Chiropractic, WC: Waist Circumference, WHR: Waist-Hip Ratio.

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Figure 1. Age distributions among the participants from Doctor of Chiropractic (DC), nutrition, and non-health related major students.

The comparison of EAT-26, TDS, and body composition measurements between the three programs is depicted in Table 6. Nutrition students had a little lower mean of EAT-26 score (6.85) (see Figure 2) and a lower mean of BMI (23.2), along with a little higher mean of fat mass percentage (26.1) than DC and non-health program students. DC students had a little higher mean of TDS than nutrition and non-health program students (see Figure 3), but non-health program students showed a little lower mean of WC (29.7) and WHR (0.76). However, there were no statistically significant differences in the comparison of EAT-26, TDS, and body composition measurements between students in the three groups of degrees.

Table 6

Comparison of EAT-26, TDS, and Body Composition Measurements between the Three Programs

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Values are reported as mean±SD and analyzed by chi-square test. Abbreviations: BMI: Body Mass Index, DC: Doctor of Chiropractic, EAT-26: Eating Attitudes Test 26,NS; Not Statistically Significant (p = ˃0.05),TDS: Tendency to Diet Scale, WC: Waist Circumference, WHR: Waist-Hip Ratio.

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Figure 2. Comparison of the mean of Eating Attitude Test 26 (EAT-26) scores between the three majors. The Y-axis represents Doctor of Chiropractic (DC), nutrition, and non-health related major students. The X-axis represents the mean scores of EAT-26 for the students.

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Figure 3. Comparison the mean of Tendency to Diet Scale (TDS) scores between the three majors. The Y-axis represents, Doctor of Chiropractic (DC), nutrition, and non- health related major students. The X-axis represents the mean scores of TDS for the students.

The prevalence of EDs among students that were randomly selected from three different majors is reported in Table 7. Although there were no statistically significant differences between students in the three groups of degrees and the EAT-26 total score (p = 0.349), 5% of students were identified with EDs from the three programs. Five percent were identified with EDs in DC students, 10% of nutrition students indicated with EDs, and no students were identified with EDs in non-health related majors.

Table 7

Prevalence of Eating Disorders (EDs) between Students in the Three Majors

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Values are analyzed by chi-square test. Abbreviations: DC: Doctor of Chiropractic, EAT-26: Eating Attitudes Test 26, NS; Not Statistically Significant (p = ˃0.05).

Comparison of EAT-26 and TDS scores between the three groups of different majors are reported in Table 8. There was a highly statistically significant association between EAT-26 and TDS (p = 0.000), which is ˂ 0.01. The results indicate students in all groups had much greater tendency to diet. Moreover, the correlation between EAT-26 and total TDS scores are depicted in Table 9. The domain of correlation helps to define the statistical relationship between two variables. The result found that there was a negative correlation between EAT-26 and TDS scores, which indicated there is an inverse association between two variables, EAT-26 scores decrease, TDS scores increase. The result was not statistically significant association exist between the features of EAT-26 and TDS scores. Further analysis of EAT-26 and TDS comparisons between students in different years are reported in Table 10. The mean of EAT-26 score in the first- and second-year DC students was significantly higher than third- and fourth-year students (p = 0.038) (see Figure 4). However, the mean of TDS scores in the DC students in different years was not significant. In the nutrition students, the mean of graduate students was highly significant compared to undergraduate students (p = 0.002) (see Figure 5), while the TDS did not show significant differences between the nutrition students. The mean of EAT-26 and TDS scores in non-health related major students in different years did not show statistical significance.

Table 8

Comparison of EAT-26 and TDS Scores

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Values are reported as mean±SD and analyzed by chi-square test. Abbreviations: EAT-26: Eating Attitude Test 26, **HS: Highly statistically significant (p = ˂ 0.01), TDS: Tendency to Diet Scale

Table 9

Correlation between EAT-26 and Total TDS Scores

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Values are analyzed by Pearson Correlation. Abbreviations: EAT-26: Eating Attitude Test -26, NS; Not Statistically Significant (p = ˃0.05), TDS: Tendency to Diet Scale

Table 10

Comparison of EAT-26 and TDS between Students in Different Years

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Values are reported as mean±SD and analyzed by chi-square test. Abbreviations; DC: Doctor of Chiropractic, EAT-26: Eating Attitude Test 26, **HS: Highly statistically significant (p = ˂ 0.01), * statistically significant (p = ˂ 0.05), TDS: Tendency to Diet Scale

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Figure 4. Comparison of the mean of Eating Attitude Test 26 (EAT-26) scores between the Doctor of Chiropractic (DC) students in different years. The Y-axis represents different years of DC students. The X-axis represents the mean scores of EAT-26 for the DC students.

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Figure 5. Comparison of the mean of Eating Attitude Test-26 (EAT-26) scores between the nutrition students in different levels. The Y-axis represents different levels of nutrition students. The X-axis represents the mean scores of EAT-26 for the nutrition students.

The comparison of BMI categories between the three majors (DC, nutrition, and non-health related majors) is illustrated in Table 11. Nutrition students had 75% of normal BMI and no obesity was identified, compared to 65% of normal BMI and 15% obesity in DC students and students in non-health related programs. However, there was no statistical significance found between the groups (p = 0.501). Most of the students (68.3%) in the three groups were in the normal category of BMI, and only 15% of the students were obese (see Figure 6). The cardio-metabolic risk according to waist circumference (WC) in the various degree programs is reported in Table 12. The majority of students (63.3%) were not at risk (see Figure 7). Among the groups, 65% of students in nutrition and non-health related programs and 60% of DC students were not at risk. The results showed no statistical significance between the groups (p = 0.931).

Further analyses about the comparison of the body composition measurements between students in different majors are explained. The classifications and comparisons of WHR among the three majors are illustrated in Table 13. The results show that there was no statistical significance found between the groups (p = 0.908). The majority of students (65%) in the groups were in the low category of WHR (see Figure 8). Non-health related program students were the majority that had the lowest category (70%), followed by nutrition students at 65% and DC students at 60%. Fifteen percent of nutrition students had high WHR compared to 10% of students from DC and non-health related majors. The comparison of fat mass percentage between the three programs is illustrated in Table 14. Around half of the students in the three programs had ≤24% fat mass, which indicated that 51.7% students were fitness participants (see Figure 9). Among the groups, 50% of nutrition and non-health related major students had ≤24% fat mass, compared to 55% of DC students. Obese students who had ≥32% of fat mass were observed more in non-health related majors (25%), in contrast to DC and nutrition students, which were 15% (see Figure 10). No significant differences were found in fat mass percentages among the three groups (p = 0.890).

Table 11

Classification and Comparison of BMI

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Values are analyzed by chi-square test. Abbreviations: BMI: Body Mass Index, DC: Doctor of Chiropractic, NS; Not Statistically Significant (p = ˃0.05).

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Figure 6: Total of body mass index (BMI) Classification of Students in the three majors: Doctor of Chiropractic (DC), nutrition, and non-health related majors. The X-axis represents the BMI classifications, and the Y-axis represents the total percentage of students in BMI classifications.

Table 12

Cardio-Metabolic Risk According to Waist Circumference in the Various Degrees

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Values are analyzed by chi-square test. Abbreviations: DC: Doctor of Chiropractic, NS; Not Statistically Significant (p = ˃0.05).

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Figure 7: Total cardio-metabolic risk according to waist circumference (WC) in the various degrees: Doctor of Chiropractic (DC), nutrition, and non-health related majors. The X-axis represents cardio-metabolic classification according to WC, and the Y-axis represents the total percentage of students in cardio-metabolic classification according to WC.

Table 13

Cardio-Metabolic Risk According to Waist-Hip Ratio in the Various Degrees

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Values are analyzed by chi-square test. Abbreviations: DC: Doctor of Chiropractic, NS; Not Statistically Significant (p = ˃0.05).

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Figure 8: Total of cardio-metabolic risk according to waist-hip ratio (WHR) in the various degrees: Doctor of Chiropractic (DC), nutrition, and non-health related majors. The X-axis represents the WHR classifications, and the Y-axis represents the total percentage of students in WHR classifications.

Table 14

Classification and Comparison of Fat Mass Percentages

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Values are analyzed by chi-square test. Abbreviations: DC: Doctor of Chiropractic, NS; Not Statistically Significant (p = ˃0.05).

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Figure 9: Total Fat Mass Classification of Students in the Three Majors: Doctor of Chiropractor (DC), nutrition, and non-health related majors. The X-axis represents the fat mass classifications, and the Y-axis represents the total percentage of students in fat mass classifications.

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Figure 10: Figure 10: Total of ≥32% of Fat Mass of Students in the Three Majors: Doctor of Chiropractic (DC), nutrition, and non-health related majors. The X-axis represents the different degrees, and the Y-axis represents the total percentage of fat mass between the students.

The relation between body composition, EAT-26, and TDS scores are estimated between the groups in the different programs. The results found no significant association between the response of EAT-26 scores and the body composition measurements, BMI (p = 0.502), WC (p = 0.571), WHR (p = 0.313) and fat % (p = 0.645) (see Table 15). Moreover, the results found no significant association between the response of TDS scores and the body composition measurements, BMI (p = 0.451), WC (p = 0.380), WHR (p = 0.694) and fat % (p = 0.560) (see Table 16). The correlation between body composition measurement, EAT-26, and TDS scores between the participants in the three groups are illustrated in Table 17. Although there was no significant correlation between EAT-26 and body composition measurements, there were small correlations found in BMI (0.13; p = 0.924), WC (32; p = 0.811), and WHR (0.118; p = 0.367). The results showed a significant correlation between TDS and BMI (0.287; p = 0.026), and in WC (0.286; p =0.027). TDS showed a small correlation in fat mass percentage (0.199), but the correlation was not significant (p = 0.127)

Table 15

Relation between body composition and EAT-26

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Values are analyzed by coefficients regression analysis. Abbreviations: BMI: Body Mass Index, EAT-26: Eating Attitude Test 26, WC: Waist circumference, WHR: Waist-Hip Ratio.

Table 16

Relation between body composition and TDS

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Values are analyzed by coefficients regression analysis. Abbreviations: BMI: Body Mass Index, TDS: Tendency to Diet Scale, WC: Waist circumference, WHR: Waist-Hip Ratio.

Table 17

Correlation between Body Composition Measurements, EAT-26, and TDS Scores

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Values are analyzed by Pearson Correlation. Abbreviations: BMI: Body Mass Index, EAT-26: Eating Attitudes Test 26,NS; Not Statistically Significant (p = ˃0.05), * Statistically significant (p = ˂ 0.05), TDS: Tendency to Diet Scale, WC: Waist Circumference, WHR: Waist-Hip Ratio.

Comparisons of body composition between the groups of students in different years are estimated. Comparisons of body composition between DC students in different years are reported in Table 18. The first- and second-year group and the third- and fourth-year group of DC students had a significant association in BMI (p = 0.049) and in WC (p = 0.05). The BMI of third- and fourth-year DC students were normal (22.3) and lower in WC (30.6) compared to first- and second-year of DC students, which were overweight (=26.4) and higher in WC (33.6). The comparison of body composition between undergraduate and graduate nutrition students are illustrated in Table 19. The graduate students had a lower mean of BMI (22), WC (29), and fat percentage (24) than undergraduate students. However, the results of nutrition students did not show any significant association in body composition measurements between undergraduate and graduate students. The last group of body composition comparisons between students in different years is non-health related majors (see Table 20). There was a significant association between BMI and the first- and second-year and third and fourth-year students from non-health related majors (p =0.033). The third- and fourth-year students from non-health related majors had a lower mean of BMI (23.2) and fat (23%) than the first- and second-year students. Although there no statistical significance was found, first- and second-year students had a lower mean of WC (29.4) and WHR (0.75) than third- and fourth-year students in non-health related majors.

Table 18

Comparison of Body Composition between DC Students in Different Years

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Values are reported as mean±SD and analyzed by chi-square test. Abbreviations: BMI: Body Mass Index, DC: Doctor of Chiropractic, NS; Not Statistically Significant (p = ˃0.05), * Statistically significant (p = ˂ 0.05), WC: Waist Circumference, WHR: Waist-Hip Ratio.

Table 19

Comparison of Body Composition between Undergraduate and Graduate Nutrition Students

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Values are reported as mean±SD and analyzed by chi-square test. Abbreviations: BMI: Body Mass Index, NS; Not Statistically Significant (p = ˃0.05), WC: Waist Circumference, WHR: Waist-Hip Ratio.

Table 20

Comparison of Body Composition between Non-Health Related Programs Students in Different Years

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Values are reported as mean±SD and analyzed by chi-square test. Abbreviations: BMI: Body Mass Index, NS; Not Statistically Significant (p = ˃0.05), * Statistically significant (p = ˂ 0.05), WC: Waist Circumference, WHR: Waist-Hip Ratio.

Chapter V: Discussion

The main objective of this study was to examine and identify the relationship between eating attitudes and body composition among university students in both undergraduate and graduate nutrition majors versus non-nutrition majors. The research study compared the prevalence of EDs among the students from three different fields: the DC program, non-health related programs, and nutrition programs. The results, as mentioned in table 3, depicted that there were no statistically significant differences among EDs of the students from the three programs. However, 10% of nutrition students and 5% of DC students depicted the tendency of EDs, as compared to the students in non-health related programs, who did not depict any occurrence of EDs. Research conducted by Geitz (2016) highlighted the fact that dietetic and nutrition students become more concerned about their weight and body mass index, which impacts their eating attitudes as well as EDs. Kassier & Veldman, (2014) also provides justification of lower-occurrence of EDs among students in non-nutrition programs, who are less conscious about their body mass and weight. They found 33% of dietetic students had EDs compared to 16% of non-dietetic students.

The study compared EAT-26 and TDS scores between students in three programs. The findings were that all students had low score of EAT-26 and high score of TDS, which indicated that low EAT-26 scores significantly associated with high TDS scores (p = 0.00) ;and the correlation was statistically not significant ,and showed disagreement between the EAT-26 and TDS scores. In the context of this study, Saleh, Salameh, Yhya, and Sweileh (2018) compared EAT-26 and The Sick, Control, One Stone, Fat, Food (SCOFF) screening questionnaires between female students. The results showed association between EAT-26 and SCOFF scores, indicated a high degree of agreement between the two scales, and showed a high score of SCOFF associated with high EAT-26 scores.

The research study also compared the EAT-26 and TDS of the students from the first to the fourth year of the DC and non-health related, and undergraduate and graduate nutrition programs. The comparison provided a great deal of insight about the research, as it revealed that the EAT-26 score of the DC students was statistically significant (p= 0.038) as compared to their TDS score which was not statistically significant. On the other hand, the EAT-26 score of the nutrition students was statistically highly significant (p= 0.002), and their TDS score was not statistically significant. The same comparison of students of non-health related fields was not statistically significant in both of the cases of EAT-26 score and TDS score. The overall point highlighted by the research is that the EAT-26 score of the nutrition program students is more highly significant (p= 0.002) than that of the DC program students (p= 0.038). Moreover, the mean score of EAT-26 in the first- and second-year students of the DC program was higher and statistically was significant as compared to the third- and fourth-year students (p= 0.038). The TDS score showed was not statistically significant differences of the students of all the years in the DC program. In the case of nutrition students, the mean of EAT-26 score of graduate students was higher and highly statistically significant as compared to that of undergraduate students (p= 0.002), while the TDS score did not show any statistically significant differences for the students in different levels. In the case of non-health related students, the EAT-26 score and the TDS score of students of all years was not statistically significant.

The research conducted by Rocks, Pelly, Slater, and Martin (2016) provided the insight that students enrolled in nutrition programs experience a change, which is usually positive, in their eating attitudes and diet tendencies, which is the main reason for the highly significant score depicted by nutrition students. The research also supported the point of view that nutrition programs help students to amend their eating attitudes and diet tendencies as they learn about nutritional needs, keeping in balance nutritional intakes, maintaining BMI, and developing a healthy body image.

Another aspect explored by the research study was the comparison of the BMI score of nutrition, non-health related, and DC program students while categorizing them into normal, overweight, and obese. The results revealed that the nutrition students had 75% normal BMI, as compared to students of the other two groups. On the other hand, the nutrition students did not reveal any tendency of obesity, which was found at a lower rate (15%) among the students of non-health related and DC programs. The overall comparison of students of three different programs in the BMI category was not statistically significant. The research conducted by Kolka and Abayomi (2012) supported the study in that it also revealed the majority numbers of normal BMI score were found among the nutrition students; however, it also revealed the satisfaction of the students with that, as they wanted to become leaner. This also provides insight about the lack of obesity among nutrition students, as they are more concerned about their image and health compared to the students in other programs.

In addition to the comparison of BMI score, the research study also compared the classification of WC, in the categories of risk and non-risk, among the students of the three programs under study. The comparison highlighted that a greater ratio of students in all three programs (DC, nutrition, and non-health related studies) were not at risk of being obese or developing chronic diseases such as heart diseases and type 2 diabetes mellitus. However, a lower ratio of the students belonging to all three programs was at a little risk of developing obesity. A research study was conducted by Leone, Morgan, and Ludy (2016) which also highlighted that most of the students had healthy waist circumference and depicted lesser risks of being obese.

The research study also compared the WHR of students in the three programs, categorizing it into low, moderate, and high. The comparison highlighted the point that the greater number of students (65%) in all the programs, which included DC, nutrition, and non-health related programs, depicted a low level of WHR. However, a significantly low number of students (11.7%) from all the three programs depicted a high WHR. Moreover, the average number of students (23.3%) also depicted a moderate level of waist-hip ratio. The overall comparison of students of three programs was not significant. The research study by Leone et al. (2016) also highlighted the point that WHR was lower among the students who depicted a lower ratio of WC. The lower WHR depicted less chance of developing obesity and becoming prone to chronic health issues.

Another aspect explored in the research study was the comparison of the fat mass percentage among students of DC, nutrition, and non-health related programs. The comparison revealed that more than half of the students from all the study groups depicted less than or equal to 24% fat mass percentage, which shed light on the fitness of the students. On the other hand, a significantly smaller number of students (18%) also depicted equal or greater than 32 of fat mass percentage, highlighting the obesity of the students. Moreover, 25% of students belonging to the non-health related programs were obese, compared to 15% of the students in other programs. The research study is supported by the study conducted by Yahia, Brown, Rapley, and Chung (2016), which highlighted the point that knowledge about nutrition and diet decreases the tendency of health issues, such as fat mass among the students of dietetic programs. On the other hand, the fat mass percentage among non-health related students is higher as they do not have advanced knowledge about the matter and show less concern about their health, in comparison to nutrition students.

In addition to all of this, the research study also compared the body composition and EAT-26 among the students of the three different programs. The scores obtained regarding BMI, WS, WHR, and fat percentages were not statistically significant. The research study also compared the body mass composition and the score of the TDS, which also did not depict any statistically significant result in terms of the measurements of BMI, WS, WHR, and fat percentage. Moreover, the research study compared the correlation of body composition measurement along with EAT-26 and TDS scores, which highlighted that the correlation between EAT-26 and body composition measurement was not statistically significant; however, it was statistically significant in the case of TDS score with BMI and WC. Rouzitalab, et al., (2015) studied the relationship between disordered eating attitudes and body composition indices in college students. They noted some body composition measurements such as BMI and central obesity indices were correlated with the increase of disordered eating attitude.

The research study conducted the comparison of undergraduate and graduate students in nutrition programs. Graduate students had a lower mean of the body composition measurements than undergraduate students. However, the results highlighted not statistically significant association between BMI, WS, WHR, and fat percentage. Kassier and Veldman (2014) also supported the point that dietetic students become more concerned about their eating attitudes, health, and body mass index as they get to know the concepts in detail and try to apply them to their lives.

On the other hand, the same comparison of the DC program students highlighted that third- and fourth-year students had lower mean of BMI and WC and were statistically significant in BMI (p= 0.049) and WS (p= 0.05). Also, the mean fat percentages of the third- and fourth-year students were lower as compared to those of first- and second-year students, but there was no statistical significance found. The research study also conducted the comparison of body composition between different year students of non-health related majors. The mean of BMI of third- and fourth-year students were lower than first- and second-year students and were statistically significant (p= 0.033). However, there were no statistically significant differences found in WC, WHR, and fat mass percentages among the students. The research conducted by Vadeboncoeur et al. (2015) supported that first-year college students are at high risk of gaining fat mass. The transition from high school to college is a critical period for establishing health-related behaviors, such as unhealthy eating and poor physical activity.

The research conducted by Kassier and Veldman. (2014) highlighted the fact that students in the non-dietetic or non-health programs were less conscious about BMI, WS, and WHR. This sheds light on the point that students of dietetic and nutrition programs become more aware of health issues and become concerned about their eating attitudes and EDs (Ashton, 2017).

Strengths and Limitations

The strength of this study is including graduate nutrition students that have advanced knowledge about food and nutrition. Also, this study included DC students that are part of health-related program, and they focus more on healthy diet and healthy lifestyles than on using medications. This study has limitations that need to be mentioned. The descriptive nature of cross sectional study is a clear limitation. The study included small sample sizes and recruited only female participants because there were not enough male students in nutrition programs. These results may not reflect eating attitudes among students since the participants were recruited from one university. The measurement of bioelectrical impedance is affected by body hydration status. Also, the scales used in this study to assess EDs cannot provide an accurate diagnosis, and the lack of experts in mental health assessment made the study valuable only as an initial screening method.

Conclusions

There is a relationship between body composition and eating attitudes. Although differences were not statistically significant, nutrition students showed a high prevalence EDs compared to students from other degree programs. The graduate nutrition students had significantly higher mean of EAT-26 scores and healthier body composition than undergraduate nutrition students, which may be related to the effect of advanced knowledge about diet and health that graduate students had. First- and second-year DC students had had significantly higher mean of EAT-26 scores and higher body composition than third- and fourth-year DC students. Non-health related majors did not show differences in EAT-26 but showed that first- and second-year students had significantly higher body composition than third- and fourth-year students.

The research study provided the insight that the students in the DC program and nutrition programs are more concerned about their nutrition and body composition as compared to the students in the non-health related programs. Moreover, the DC program and nutrition program students had the chance to improve their knowledge, becoming more aware of the impact of nutrition on body composition and health. Future research is needed to assess eating attitudes and body composition in nutrition students versus non-nutrition majors includes genders, marital status, and ethnicity across the country in different universities and in other countries.

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• Auteur: Abdullah Otayf (Auteur)
• Année de publication: 2019
• Pages: 66
• N° de catalogue: V465285
• ISBN (ebook): 9783346040343
• ISBN (Livre): 9783346040350
• Langue: anglais
• mots-clé: does