EMOTIONAL EATING AND RACE AS FACTOR

Running Head: FORSAKING THE BODY TO PROTECT THE MIND

FORSAKING THE BODY TO PROTECT THE MIND: EMOTIONAL EATING AND RACE AS FACTORS IN PREDICTING OBESITY

DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Psychology in the Graduate School Department of Psychology of Keiser University by Kim E. Joseph Keiser University 2018

FORSAKING THE BODY TO PROTECT THE MIND: EMOTIONAL EATING AND RACE AS FACTORS OF PREDICTING OBESITY by Kim E. Joseph July 2018

APPROVED:

KELLY SCHMITT, Ph.D., Chair

LISA FOX, Ph.D., Committee Member

___________________ CHERI HANSEN, Ph.D., Committee Member

ACCEPTED AND SIGNED:

Brian Esterling, MBA, Ph.D. Program Chair Digitally signed by Jin An DN: cn=Jin An, o=Keiser University, ou=Graduate School, [email protected], c=US Date: 2018.08.23 10:12:48 -04'00'

Jin An, Ph.D. Dean of the Graduate School

© 2018, Kim E. Joseph All Rights Reserved

FORSAKING THE BODY TO PROTECT THE MIND

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Dedication This work is dedicated to my family, my loving rock of support throughout my life. My parents, Jeannie and June Joseph, inspired me to have faith beyond what is imaginable and taught me the world is my oyster, to go out and find my pearl. To my siblings, Beryl, Henry, and Darren, you have been the shoulders I stand on; through your constant encouragement and acceptance that allowed me to have my way, you made me believe I could do and be whatever I wanted in life, knowing you would always love me. To my nieces Shana, Alena, Laci, and Jayla and my nephews Cyril, Gerald, Jeremy, and Darren Jr., I pray this work and my accomplishments are a lamp unto your feet and a light unto your path. Finally, to my sister and BFF Giovanna, your courage and bravery in facing your personal struggles inspired this work; your expertise kept me on point, you have been the wind beneath my wings, and I am eternally grateful to have you as my sister and my friend. To God Be the Glory!


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Acknowledgements I would like to acknowledge my committee members, giving thanks for their dedication and perseverance as my leaders and mentors. To my chair, Dr. Kelly Schmitt: without your constant support, encouragement, advice, and direction, this dissertation would not have been completed; you have been with me from the beginning, and I will always be grateful. To Dr. Cheri Hansen: your guidance, support, statistical expertise, and unwavering belief in me kept me on track. To Dr. Lisa Fox: your critique and input encouraged me to “kick it up a notch” and not be afraid to take it a step further. I could not have accomplished this work without the support of my coworkers, past and present, of the Palm Beach County School District; I especially extend a sincere thank you to Dr. Cheryl Smith-Brown (ret.), Shanda, and Krista (my BFF) for your flexibility, patience, encouragement, and friendship. To my sister from another mister, the trailblazer Dr. Elizabeth Reyes-Fournier, you already know; the bonds that tie us together are everlasting, and you and the Phoenix Center for Healing family helped me to breathe again. To my family of faith, especially my home girl Adrienne Hart, you knew when to answer the phone and when not to call; thank you for your words of encouragement and for going into “the closet” on my behalf. To my sister in Christ and co-laborer in ministry, Elder Karen J, your prayers have sustained me; through my darkest hours and challenging moments, I felt them. To my social media network, with a shout out to my Facebook friends and family, you kept calling me “Doc” so I would remember my mission; you responded with support to those late-night posts of frustration; and you came through on my survey. Facebook fam, you rock! I humbly thank each and every one of you.


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List of Tables Table

Page

1.

Frequencies and Percentages for Sample Race/Ethnicity, Education, and Income ......... 43

2.

Frequencies and Percentages Describing the Sample for Health and Physical Activity……………………………………………………………………………… .... 44

3.

Descriptive Statistics for the Continuous Variables Describing the Sample……… ....... 45

4.

Descriptive Statistics and Cronbach’s Alpha for the Study Measures (N = 343) ……...46

5.

One-Tailed Pearson Correlations between Key Study Variables (N = 343) ……… ...... 48

6.

Descriptive Statistics for BMI by Race…………………………………….… .............. 49

7.

Results of the ANOVA for BMI by Race……………………………………… ........... 49

8.

Results of the Independent t-test…………………………………………………. ........ 51

9.

Stepwise Regression Interaction Models for Race, EES, and BMI………………… ..... 52

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Stepwise Regression Interaction Model for EES, Race, CESD, and BMI……...… ....... 54


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List of Figures Figure 1. 2.

Page Plotted Means for Average BMI by Race…………………………………… ................50 Group Scatter of Emotional Eating Scale by BMI by Race…………………. ............... 53


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Abstract Obesity has been identified as a public health crisis across demographics; as such, it is especially a risk factor for disease in African American women. However, the factors that contribute to this risk continue to elude researchers. While obesity shares a relationship with depression—as depressive symptoms influence eating behaviors in some demographics—the pathways of influence between depression and eating behaviors on obesity are not as clear with regard to African American women. This study took a closer look at those pathways and examined the relationship between obesity, emotional eating, and depression in women. Race was examined as a moderator in the relationship between emotional eating and depressive symptoms predicting obesity. Emotional eating is defined as “overeating in response to negative emotional states” as well as “poor food choices in response to stress and negative mood.” To assess these goals, 345 women completed an online survey that included: demographic questions; 25 questions from the Emotional Eating Scale; 10 questions from the Perceived Stress Scale; 20 questions from the Center for Epidemiologic Studies Depression Scale-Revised; and height and weight to assess body mass index (BMI). Race was a significant predictor of obesity as African American women had greater BMI when compared to White women. In addition, women who engaged in more emotional eating were more likely to have higher BMI. The relationship between emotional eating and obesity was moderated by race; emotional eating was a strong predictor of obesity in White women but not African American women. Further research is warranted to identify factors related to obesity that include other measures for weight beyond BMI as well as the eating behaviors of African American women.


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Specific Aims Health care treatment costs for obesity-related illnesses for the non-institutionalized adult population total an estimated $190 billion annually in the United States. African Americans have a greater risk of developing obesity as well as poorer health when compared to non-Hispanic White counterparts at all socioeconomic status (SES) levels. However, they have a lesser risk for pathological eating disorders and mental health disorders such as depression. When comparing African Americans and Whites at lower SES and living under similar stressful environments, non-Hispanic Whites had better overall health but slightly higher rates of mental health disorders, particularly mood disorders. In addition, African American women (AAW) have a greater risk of stress-related illnesses and an increased rate of death resulting from obesity related illnesses when compared to their non-Hispanic White American counterparts (WW). Experiences of daily stress that may contribute to emotional eating as a factor of obesity present as a potential missing link in the literature. Aim 1: Examine the relationship between emotional eating, obesity, stress, and depressive symptoms. Depression is often comorbid with obesity. A meta-analysis of longitudinal studies reveals a bidirectional association between obesity and depression such that obesity increased the risk of depression, and depression increased the risk of obesity. The prevalence of depression occurred more than twice as much in middle-aged women with obesity when compared to those with a BMI of less than 30. Higher depression scores related to higher emotional eating, and each were related to higher rates of obesity. This study aims to examine the relationship between emotional eating, depression, and obesity. Hypothesis 1. There will be a positive correlation between emotional eating, obesity, stress, and depression.


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Aim 2: Examine race differences in predicting obesity. Research on obesity prevalence in women suggests certain demographics are at greater risk for obesity and obesity-related illness. Women with a BMI >25 are considered obese and may be at risk for metabolic disorders, cardiovascular disease, and type 2 diabetes. Current research addresses obesity prevalence across demographic groups. This study aims to examine whether a race difference exits in predicting obesity. Hypothesis 2. There will be a race difference in predicting obesity; AAW will demonstrate greater BMI rates than will White women. Aim 3: Examine the relationship between emotional eating and depression in predicting obesity to determine if there are variances by race groups. Health disparities persist along racial groups; there are links of race to stress exposure and experiences of chronic stress to obesity with negative health outcomes. Emotional eating as a response to negative mood from chronic stress may lead to obesity. This study aims to examine racial/ethnic correlations between emotional eating and obesity in African American women when compared to White women. Hypothesis 3. AAW will demonstrate a stronger positive relationship between emotional eating (EES) and obesity (BMI) when compared to WW. Aim 4: Examine whether depression interacts with race to predict differences in obesity. Analyses will examine whether depression significantly adds to the prediction of obesity and whether there is an interactive effect between depression and race/ethnic group to predict obesity. Research on depression and African American women is mixed. Some research suggests African American women have more depressive symptoms but less lifetime major depressive disorders. This study aims to examine whether race interacts with depression to predict obesity.

FORSAKING THE BODY TO PROTECT THE MIND Hypothesis 4. AAW will demonstrate a stronger positive relationship between depressive symptoms (CESD) and obesity BMI when compared to WW.

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Table of Contents Dedication ........................................................................................................................................ i Acknowledgements ......................................................................................................................... ii List of Tables ................................................................................................................................. iii List of Figures ................................................................................................................................ iv Abstract ........................................................................................................................................... v Specific Aims ................................................................................................................................. vi INTRODUCTION .......................................................................................................................... 1 Factors Contributing to Health Disparities.................................................................................. 2 Emotional Eating and Stress ....................................................................................................... 3 Coping and the Strong Black Woman......................................................................................... 4 Purpose of the Study ....................................................................................................................... 5 REVIEW OF THE LITERATURE ................................................................................................. 7 Obesity ........................................................................................................................................ 7 Obesity and race. ..................................................................................................................... 8 Obesity and health behaviors .................................................................................................. 9 Stress and eating.................................................................................................................... 11 Obesity, stress, and racism .................................................................................................... 12 Behavioral coping and perceived racism............................................................................... 12 Emotional Eating....................................................................................................................... 16


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Theories of emotional eating ................................................................................................. 16 Emotional eating and BMI .................................................................................................... 19 Depression and Depressive Mood ............................................................................................. 20 Depression and obesity ......................................................................................................... 21 Depression and race. ............................................................................................................. 23 Depression and emotional eating .......................................................................................... 25 Depression and SES .................................................................................................................. 26 Summary ................................................................................................................................... 28 Methodological Consideration .................................................................................................. 28 METHODS ................................................................................................................................... 31 Participant Characteristics ......................................................................................................... 32 Data Collection.......................................................................................................................... 32 Demographic questions (Appendix A).................................................................................. 32 Emotional eating scale (Appendix B) ................................................................................... 33 Center for Epidemiological Studies Depression Scale-20 (Appendix C) .............................. 33 Obesity measure (Appendix D) ............................................................................................. 34 Stress measure (Appendix E) ................................................................................................ 35 Recruitment. .......................................................................................................................... 35 Survey Monkey ..................................................................................................................... 35 Quantitative Statistical Tests for Data Analysis ........................................................................ 36


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Independent Variables ............................................................................................................... 36 Dependent Variables ................................................................................................................. 37 Data Analysis ............................................................................................................................ 37 Description of Data Analysis Procedures .................................................................................. 38 Ethical Considerations............................................................................................................... 38 DATA ANALYSIS AND RESULTS ........................................................................................... 40 Research Questions and Hypotheses ............................................................................................. 40 Research Question 1.................................................................................................................. 40 Research Question 2.................................................................................................................. 40 Research Question 3.................................................................................................................. 41 Research Question 4.................................................................................................................. 41 Description of the Sample ......................................................................................................... 41 Summary of Results .................................................................................................................. 42 Descriptive statistics .............................................................................................................. 42 Missing data and outliers....................................................................................................... 45 Descriptive statistics for the study variables ......................................................................... 46 Hypothesis 1 .............................................................................................................................. 47 Hypothesis 2 .............................................................................................................................. 48 Hypothesis 3 .............................................................................................................................. 51 Hypothesis 4 .............................................................................................................................. 53


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Conclusion .................................................................................................................................... 55 DISCUSSION ............................................................................................................................... 56 Overview of the Study .................................................................................................................. 56 Discussion of Results .................................................................................................................... 56 Discussion of the Conclusion ........................................................................................................ 58 Limitations .................................................................................................................................... 60 Recommendation for Future Research .......................................................................................... 63 Conclusion .................................................................................................................................... 64 REFERENCES.............................................................................................................................. 66 Appendix A. Demographic Questionnaire .................................................................................... 93 Appendix B. Emotional Eating Scale ............................................................................................ 98 Appendix C. Center for Epidemiologic Studies Depression Scale (CES-D), NIMH .................. 100 Appendix D: BMI and Current Health ........................................................................................ 101 Appendix E. Perceived Stress Scale ............................................................................................ 102 Appendix F. Recruitment Scripts ................................................................................................ 103


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CHAPTER 1 INTRODUCTION United States health care spending increased 4.3% in 2016, totaling $10,348 per person and accounting for 17.9% of the Gross Domestic Product (U.S. Centers for Medicare & Medicaid Services, 2018). Although improvements in increased life expectancy and a reduction in coronary heart disease are evident, health disparities persist across demographic groups (Agency for Healthcare Research and Quality, 2017; Braveman & Egerter, 2013). Researchers of the Robert Wood Johnson Foundation have examined the myriad of health disparities found across racial and socioeconomic lines. African Americans have the highest rate of poverty (25%) in the United States when compared to other demographics (Henry J. Kaiser Family Foundation, 2015), and they are more likely to live below the federal poverty level, report poor health, develop hypertension, and die of cardiovascular disease (Braverman, Egerter, An, & Williams, 2011; Beydoun et al., 2016). Across socioeconomic status (SES) levels, African Americans1 have the poorest health compared to their non-Hispanic, White counterparts and the highest ageadjusted mortality rates (Braveman et al., 2011; Moulton, 2009). In addition, African Americans lead in the risk of development of stress-related illnesses including hypertension and type 2 diabetes (Lackland, 2014). Mujahid, Roux, Cooper, Shea, and Williams (2010) suggested that living in environments with high stress, lack of healthy foods, or safe places to exercise also contributed to disparities in hypertension. In an earlier study, Thorpe, Brandon, and LaVeist

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For the purpose of this study, participants’ self-described race is categorized as follows: African American refers to Blacks in the United States of African descent; White refers to Whites born in the United States of European descent; and Caribbean descent refers to Blacks of Island descent. The terms Black and African American may be used interchangeably as well as White Americans interchangeably with non-Hispanic Whites. The terms from the original research will be used when referencing previous research.


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(2008) found similar health outcomes of hypertension for African Americans and non-Hispanic Whites living in similar social settings. From 2003 to 2006, type 2 diabetes affected an estimated 20 million American adults; the rate of occurrence for African Americans (14.9%) was nearly double that of White Americans (7.6%). Although a national survey conducted in 2008 recorded a decrease across ethnic groups in the rate of diabetes, that of African Americans (11%) remained higher than that of Asians (8.2%) and Whites (7.0%) (Tsenkova & Karlamangla, 2016; Katzmarzyk & Staiano, 2012). Factors Contributing to Health Disparities Seventy-five million adults in the United States have hypertension, and African Americans are affected disproportionately (i.e., 2:5 are diagnosed; Center for Disease Control [CDC], 2016). Currently, nearly 50% of African Americans with hypertension have uncontrolled hypertension (CDC, 2014), which increases the risk for cardiovascular disease (Booth III, et al., 2016). SES, stress, racial discrimination, unhealthy behaviors (Braveman et al., 2011; Jackson, et al., 2010; LaVeist, Pollack, Thorpe, Feshazion, & Gaskin, 2011; Moulton, 2009; Singleton, Robertson, Robinson, Austin, & Edochie, 2008) and environment (Cozier et al., 2007; Mujahid et al., 2010) are all associated with the prevalence of hypertension in African Americans. Although the overall death rate for African Americans has declined and those in this racial group are living longer, their life expectancy is four years less than that of Whites; younger African Americans are twice as likely to die from heart disease as are Whites (Center for Disease Control, 2017). Furthermore, health risks are worsened when coupled with perceived stress, unhealthy behaviors, and obesity (Richardson A. S., Arsenault, Cates, & Muth, 2015). Chronic stress has led to unhealthy eating behaviors, which are associated with emotional eating and weight gain (Chao, Grillo, White, & Sihna, 2015).


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Emotional Eating and Stress Adverse stress combined with daily living factors influencing chronic stress, such as SES, environment, racial discrimination, and unhealthy behaviors, exacerbates poor health conditions and increases the risk for weight gain, stress-related eating (Diggins, Woods-Giscombe, & Waters, 2015), and the development of other chronic illnesses such as hypertension and obesity (Moulton, 2009; Singleton, Robertson, Robinson, Austin, & Edochie, 2008). Perceived stress, stress associated with uncontrolled eating (Junne et al., 2017), and emotional eating all foster poor food choices and eating behavior, such that individuals prefer foods high in fat and sugar when eating in response to stress or sadness (Pickett & McCoy, 2017; Richardson A. S., Arsenault, Cates, & Muth, 2015). This emotional eating creates a response in the brain that produces feelings of pleasure and reduces feelings of discomfort and negative mood (Dallman M. F., 2010), alleviating stress and creating a pathway for emotional eating that leads to weight gain and obesity (Masih, Dimmock, Epel, & Guelfi, 2017). Emotional eating also plays a role in the relationship between anxiety and cholesterol levels (Mensorio et al., 2016). In a group of individuals with hypertension and obesity, emotional eating mediated the relationship between anxiety and total cholesterol as well as between anxiety and LDL cholesterol and was associated positively with fat body mass. In essence, participants who experienced anxiety and negative emotions increased emotional eating and poor food choices (Mensorio et al., 2016). The experience of daily stress associated with unhealthy eating behaviors (Hayman, Lee, Miller, & Lumeng, 2014), daily hassles, eating style and behavior, and emotional eating in response to stress increased snacking and reduced the desire for main meals and vegetable consumption (O'Conner, Jones, Conner, McMillian, & Ferguson, 2008). Stress-induced eating


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prompted a preference toward the consumption of foods high in saturated fats and calories as well as high-sugar sweetened snacks (Luckett, et al., 2015). Eating behavior patterns associated with perceived stress and contextualized stress also influenced BMI (Diggins, Woods-Giscombe, & Waters, 2015). Daily hassles that overwhelm lead to chronic stress, with a varying impact on individuals. Coping and the Strong Black Woman The experience of chronic stress is common to African American women; in fact, AAW experience greater levels of chronic stress than do their White counterparts (Mujahid, Roux, Cooper, Shea, & Williams, 2010; Perry, Harp, & Oser, 2013). Woods-Giscombe (2010) discussed African American women’s descriptions of their efforts to respond to high levels of chronic stress, calling it the Superwoman Schema (SWS), and analyzed the liabilities SWS presents to these women as well. The researchers characterize SWS as an obligation to maintain strength; the need to suppress emotions about problems, resistance to accepting help from others out of a fear of being vulnerable, relentless determination to succeed against a lack of resources, and an obligation to be there for others even at the cost of meeting their own individual needs. Women adhering to the SWS criteria were more likely to engage in emotional eating behavior as well as have sleep issues and postpone self-care (Woods-Giscombe, 2010). Adherence to the Strong Black Woman script (SBW), a construct rooted in the media’s perception and portrayal of expectations of Black women and closely related to the SWS, creates an additional personal-stress burden and a negative impact on mental health. Black women who ascribe to the construct of an expectation of meeting the needs of others while sacrificing or suppressing individual needs and balancing multiple responsibilities attempt to fulfill the SBW script (Black & Peacock, 2011). This manifests as managing roles through relying on herself,


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sacrificing individual needs, and the silent acceptance of these roles. Efforts to maintain an appearance of strength creates personal stress (Donovan & West, 2015), compounded by the potential effects of the pre-existing chronic stress of daily living discussed above; it contributes to a physiological stress response, which overloads the hypothalamic-pituitary-adrenal cortical (HPA) axis (Black & Peacock, 2011; Jackson, Knight, & Rafferty, 2010; Talleyrand, 2006). The SWS becomes a self-imposed expectation of trying to do it all in response to the societal/media perception of the SBW script; as this construct says that Black women can and should do it all, they may in turn experience a higher rate of risk for stress-related illnesses such as hypertension and obesity (Black & Peacock, 2011; Mitchell, Catenacci, Wyatt, & Hill, 2011; WoodsGiscombe, 2010). Exposure to stress has also represented an environmental risk for mental illness (Cattaneo & Riva, 2015), yet African American women are less often diagnosed (Black & Peacock, 2011; Jackson, Knight, & Rafferty, 2010; Rizzolo & Sedrak, 2010; Sohail, Bailey, & Riche, 2014) and may be less likely to seek mental health assistance (Watson & Hunter, 2015). Further studies within this specific population are warranted to examine unhealthy coping behaviors that may impact mental and physical health, given the lack of research explaining the disparity between physical and mental health; the complex relationship that socioeconomic status plays as a determinant of health in the lives of racial and ethnic minorities (Braverman, Egerter, An, & Williams, 2011; Williams, Priest, & Anderson, 2016); and the underrepresentation of African American women in mental health research (Torres, 2012). Purpose of the Study Obesity-related illnesses like hypertension and cardiovascular disease are on the rise in some demographics. In addition, the United States leads the developed world in health care expenditures, yet the health of Americans shows little improvement (Braveman & Egerter,


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2013). Health disparities continue to grow, with lower socioeconomic levels (Robinson, et al., 2015), minorities, and women reporting the poorest levels of health; women also have higher rates of obesity when compared to men across income levels (Braverman, Egerter, An, & Williams, 2011; Centers for Disease Control and Prevention, 2010), and women are more likely to emotionally eat than men (Peneau, Menard, Mejean, Bellisle, & Hechberg, 2013; Wansik, Cheney, & Chan, 2003). If the United States intends to improve the health of people by the year 2020 (U.S. Department of Health and Human Services, 2014), it will require more research on the associations of risk factors for major illnesses. While African American women represent a high-risk demographic for obesity and illnesses related to obesity, the reasons for this disparity remain unclear. This study proposed to expand the body of knowledge on disparities in the occurrence of obesity in women of different ethnic origins—possibly the result of emotional eating—that may have increased the risk factors for the development of major physical illness. The study also attempted to increase the body of knowledge of emotional eating as a risk factor of obesity in African American women, particularly those who respond to negative emotions by using emotional eating as an attempt to temporarily suppress depressive symptoms that may have deleterious effects on overall health. The need to improve health outcomes for all women and the significant disparities in the health of African American women compared to other women provided motivation to investigate possible correlations.


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CHAPTER 2 REVIEW OF THE LITERATURE This section begins with a review of obesity and its overall impact on health as well as a discussion of the presence of obesity across the lifespan as it relates to health care costs, risk of disease, and quality of life. The literature review included a brief discussion of stress as it related to unhealthy coping mechanisms and risk of illness. Next, the review examined emotional eating as a way of improving mood and reducing sad feelings as a potential coping response to stress as well as a way to reduce, if only temporarily, feelings of depression, a potential risk factor for obesity. Further, the review examined depression as it related to women’s health behaviors (obesity in particular). Finally, it explored the relationship between depression, obesity, and emotional eating, as well as possible influences on health outcomes. Obesity Obesity is an excessive accumulation of body fat (Taylor, 2009) defined by a body mass index (BMI) of greater than 30, which is calculated by weight in kilograms divided by height in meters squared (Buchowski et al., 2010). The U.S. annual estimated cost to treat only obesity in non-institutionalized adults ranged between $147 billion (Finkelstein, Trogdon, Cohen, & Diets, 2009) and $185.7 billion (Cawley & Meyerhoefer, 2010). Women make up just above 35% of the reported 78 million adults with obesity (American Heart Association, 2013). African Americans have the greatest percentage of risk of development of obesity (Krueger & Reither, 2015) and related illnesses such as hypertension and type 2 diabetes when compared to other U.S. cultural groups (Braveman & Egerter, 2013; Kahng, 2010). National trends in health, tracked by the National Health and Nutrition Examination Survey (NHANES; Fryar, Carroll, & Ogden, 2012) indicated that non-Hispanic Black women have higher rates of obesity than do


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Black men, White men, White women, Hispanic men, and Hispanic women (Centers for Disease Control and Prevention, 2014; Go et al., 2013; National Center for Health Statistics, 2012). African American women maintained the highest percentage of obesity increases from 1988 to 1994 and from 2009 to 2010, with a 20% total increase throughout those years, from 38% to 58% (Fryar, Carroll, & Ogden, 2012). African American women showed no improvement in obesity prevalence rates from 2011 to 2014, with a 57% prevalence of obesity compared to 45% for Mexican American women and 35% for White women (Ogden C. L., Carroll, Fryar, & Flegal, 2015). Although the data indicated no significant changes in adult obesity between 2003-2004 and 2011-2012, the prevalence of obesity remained high across all groups (Ogden C. L., Carroll, Kit, & Flegel, 2014). The significance of this lies in the obesity trend, which suggests a potential disproportionate impact on African American women. When there is an identified risk factor for increased presence of disease in a high-risk demographic, it is imperative researchers address the risk with the intent to reduce its impact on overall health. The literature supports African American women’s struggle with obesity risk, but the factors contributing to that risk are not as well defined. Obesity and race. The struggle with obesity is not restricted to African American women; White women also struggle with obesity, especially those considered disadvantaged (Robinson, et al., 2015). Black and White women living in similar low-income environments shared the same odds of being obese (Bleich, Thorpe, Sharif-Harris, Feshazion, & LaVeist, 2010; Richardson A. S., Arsenault, Cates, & Muth, 2015). Data examined from national level surveys indicated Black women were two times as likely to be obese compared to White women (LaVeist et al., 2007). When that data was compared to data collected from a racially integrated subsample population in southwest Baltimore, Black and White Women had similar odds of


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obesity (LaVeist, Pollack, Thorpe, Fesahazion, & Gaskin, 2011). Obesity occurrence varied across racial groups when the data did not account for SES, individual health behaviors such as food choices, smoking, physical activity, and stress response—all factors that influence eating, weight gain, and/or maintenance (Jackson, Knight, & Rafferty, 2010; LaVeist et al., 2011). In summary, trends in obesity have not changed, and national data shows African American women at a greater risk for obesity. Yet when African American women share the same low SES income and social context as their White counterparts, they share the same risk for obesity (Rossen & Schoendorf, 2012; Vilaro, Barnett, Mathews, Pomeranz, & Curbow, 2016), confirming lower SES as a risk factor contributing to obesity. The significance of these results emphasizes the need to examine the factors known to influence the developmental risk of obesity in women and whether the factors associate with emotional eating and depression. In addition, a closer look at SES and obesity prevalence across racial groups may shed light on factors influencing obesity prevalence in women. Obesity and health behaviors. Obesity does not occur in isolation; unlike in illnesses with a specific etiology (i.e., breast cancer, sickle cell anemia), there are individual behaviors linked to weight gain. In a group of African American and White women with self-reported comorbidities of hypertension, type 2 diabetes, arthritis, emphysema, and depression, African American women were more likely to be overweight or obese than were White women (Buchowski et al., 2010). Across races, women with higher BMI were also less active, with higher sedentary behaviors. White women had higher predicted moderate and severe obesity (OR=4.03) than did African American women (OR=1.56) with the same sedentary behaviors (Buchowski et al., 2011). Obesity risk increased with sedentary behaviors (Compernolle, et al.,


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2015); White women in the highest sedentary group were moderately overweight, yet this trend was not seen in African American women (LaVeist et al., 2011). Although the sample of African American women demonstrated greater rates of obesity than did White women in the Buchowski and colleagues (2011) study, African American women had less visceral fat and smaller hip circumference, a difference in body compositon and BMI, compared to White women with similar waist circumference. Visceral fat, found under the skin in the abdominal area as well as surrounding internal organs, is linked to increased risk for coronary heart disease (Goldbacher & Salomon, 2005). Visceral fat also increases the risk for metabolic syndrome, a combination of imparied glucose, lipid metabolism, and hypertension (Raikkonen, Matthews, & Kuller, 2007). Taking the results of the previous studies into consideration, when examining the difference in visceral fat and the weakened relationship between sedentary behaviors and obesity one might expect African American women would continue to report higher BMIs and more obesity. In summary, when examining adults, data collected at the national level showed African American women as twice as likely to be obese. Yet when examining women within similar economic situations, a similar risk exists for both Black and White women to develop obesity. The Buchowski and colleagues’ study highlights the relationship between sedentary behaviors and BMI; however, it does not consider the effects of stress and coping on eating behaviors or the type of food consumption during sedentary behaviors. Additional research also found cultural, economic, and environmental factors attributed to racial differences in sedentary and active behaviors associated with obesity—with lower SES contributing to greater inactivity (Buchowski et al., 2010; Katzmarzyk & Staiano, 2012; Krueger & Reither, 2015). This study intended to build upon Buchowski and colleagues’ (2010) research and addressed a gap in the


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literature by examining women across socioeconomic levels and additional factors that may influence obesity outcomes; these factors include emotional eating in response to stress and eating during the presence of negative mood states, both of which impact the eating behaviors of women in higher socioeconomic levels. Stress and eating. Educated African American women living in stressful, low SES environments are at an increased risk for obesity when compared to their counterparts in higher SES neighborhoods (Coogan et al., 2010; Mujahid, Roux, Cooper, Shea, & Williams, 2010). Furthermore, the physiological effect of stress increases the likelihood of illness (Rizzolo & Sedrak, 2010). African American women not only experience more stress that challenges available resources for coping but also have a higher perceived ability to face the challenge from chronic stress of daily events than do their White counterparts (Diggins et al., 2015;Vines et al., 2006). Emotional eating, defined as the consumption of comfort foods in response to stress, may contribute to weight gain and unhealthy coping behaviors that can lead to a suppressed immune system (Dhabhar, 2009), which can lead to hypertension, cardiovascular disease, type 2 diabetes, and obesity (Dallman et al., 2003; Richardson et al., 2015). Further, increased exposure to chronic stress, as well as psychosocial attributions of stressful events, contribute to increased risk of type 2 diabetes, obesity, and hypertension (Goldbacher & Salomon, 2005; Raikkonen, Matthews, & Kuller, 2007; Rizzolo & Sedrak, 2010). Buchowski and colleagues (2011) found racial differences in the amounts of women’s abdominal fat, with Black women having less visceral fat than do White women. The study examined emotional eating in response to stress that may have supported the findings of Rizzolo and Sedrak (2010); for example, women who used emotional eating in an attempt to avoid depressive symptoms or respond to stress more likely experienced obesity.


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Obesity, stress, and racism. Stress occurs when someone is faced with challenges perceived to exceed the capacity to cope (Taylor S. E., 2009). The stress response includes problem-focused strategies or emotion-focused strategies (Folkman, Lazarus, Dunkel-Schetter, DeLongis, & Gruen, 1986). Problem-focused strategies attempt to alter or change the personenvironment relation, or the source of the stress; emotion-focused strategies attempt to regulate emotions triggered by the stressful event (Folkman et al., 1986). In their research, Folkman and colleagues agreed that no assumptions exist on what constitutes good or bad coping. However, efforts to cope that are maladaptive may help someone to respond in the moment but, in turn, may have deleterious health effects over time (Richardson ert al., 2015; Schneiderman, Ironson, & Siegel, 2005). Behavior coping responses are used to attenuate the effects of physical or psychological stress on health; African American women face additional race-related stress challenges not often experienced by their White counterparts (Williams, Priest, & Anderson, 2016). Similar to the affect regulation model, emotion-oriented coping attempts to regulate emotions by reducing or choosing less detrimental negative emotions associated with the problem (Spoor S. T., Bekker, Van Strien, & van Heck, 2006). In a community-based sample of healthy women and women with eating disorders, Spoor and colleagues (2007) found higher scores on negative affect were related to increased levels of emotion-oriented coping, and emotion-oriented coping and avoidance distraction were strongly related to higher levels of emotional eating. However, within this group, negative affect did not significantly contribute to emotional eating, supporting an individual difference model, the approach taken by the current study. Behavioral coping and perceived racism. Perceived racism can be a source of stress and difficult to manage (Chao, Longo, Wang, Dasgupta, & Fear, 2014). African American


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women’s attempt to cope with perceived racism takes on the form of behavioral coping responses as well as emotional coping responses, often in an effort to fulfill the SBW script (Watson & Hunter, 2015). Obese African American women used more active and passive behavioral coping in response to perceived racism when compared to normal-weight women; active behavioral coping responses include praying, actively speaking up, or trying to effect change while passive responses include avoidance or ignoring. In contrast, emotional coping responses such as feeling angry/frustrated, powerless, helpless, ashamed, and strengthened had no association with BMI (Mwenda et al., 2011). African American women experiencing perceived stress using maladaptive coping behaviors, such as increased food intake, emotional eating, intake of sweet foods, and poor appetite, also associates with BMI (Diggins et al., 2015). In addition, psychological stress associated with perceived racism is linked to high daily stress, which is linked to a higher level of abdominal fat in African American women and poorer health outcomes (Vines et al., 2007). In an attempt to meet the demands of daily stress and to respond to perceived racism, African American women may rely on the Superwoman Schema (Woods-Giscombe, 2010) under an obligation to fulfill the “Strong Black Woman” script (Black & Peacock, 2011). African American women feel an obligation to perform as superwomen, accomplishing everything for everyone without fail; today’s social and media perceptions of African American women as the “backbone of their communities” and the protector of the family “against racial, economic, and gender oppression” (Black & Peacock, 2011 p. 144) reinforce this historical expectation. This over-performance combined with a suppressed desire to embrace an alternative to the SBW script creates psychological distress, which leads to pathways for overeating, obesity, and hypertension (Beauboef-Lafontant, 2008; Watson & Hunter, 2015; Woods-Giscombe, 2010).


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Stress can impact everyday responsibilities such as meal preparation, and can interfere with advanced planning and preparation of balanced meals, resulting in reliance on fast food. Greater perceived stress is associated with a tendency toward unhealthy eating and haphazard meal planning (Richardson et al., 2015; Simms et al. (2008). This association was stronger in overweight and obese participants than the overall sample; African American women experienced greater perceived stress when compared to their male counterparts, reinforced by the belief that preparing a large Sunday meal is part of fulfilling the SWB script in African American families and often requires large amounts of comfort foods. In addition, the consumption of food is seen as a socially acceptable response to stress in the African American community. BMI was highest among stress-driven eaters whose food choices included sausages, hamburgers, pizza, and chocolate (Laitinen, Ek, & Sovio, 2002). Stress created a drive to eat, associated with a decrease in the eating of healthful food, and was linked to greater palatable or comfort food intake (Groesz et al., 2012). However, when examining “overweight/BMI” and emotional eating, directionality remains inconclusive; stress can cause poor food choices and overeating, and weight gain can increase stress as well, suggesting a possible bidirectional relationship (Groesz et al., 2012; Laitinen, Ek, & Sovio, 2002; Porter et al., 2014). Perceived stress is only one form of stress exposure that African American women experience. African American women exposed to traumatic events—as well as distress from those events—engage in an affect regulation model; they often binge eat to manage negative emotions associated with traumatic experiences (Harrington, Crowther, & Shipherd, 2010). Managing and escaping negative effects of trauma through binge eating is common amongst both African American and White women. However, Harrington and colleagues (2006) found


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African American women strongly adhering to the SBW script saw binge eating as a socially acceptable strategy to respond to trauma and its associated distress. Binge eating then becomes a coping mechanism with the potential to encourage emotional avoidance; in other words, eating for psychological reasons becomes a form of self-medication (Harris, 2015). This research is significant to the present study as it examined emotional eating as a coping behavior for African American women who attempted to temporarily avoid or reduce feelings of depression. Their efforts to ward off this stress and any accompanied negative mood may improve their mental health by reducing depressive symptoms in the moment, but these actions may put them at risk for increased weight gain, maintenance of adiposity, or obesity. The effects of African American women using emotional eating as a coping response to reduce depressive symptoms presented a gap in the literature this study attempted to fill. African American women in general, particularly those living in stressful, low SES environments, continue to represent a greater percentage of those with obesity when compared to other at-risk groups (Ailshire & House, 2011; Bleich et al., 2010; Kirby, Liang, Chen, & Wang, 2012; Mwenda et al., 2011; Richards et al., 2015; Wen & Kowaleski-Jones, 2012). When women are faced with the need to cope while living under challenging and stressful circumstances, their maladaptive coping responses can lead to the development of unhealthy coping mechanisms such as emotional eating. While some coping mechanisms may appear effective, they in turn may have deleterious effects on health over time. Coping strategies used to protect emotional and mental health may contribute to the development of health issues such as hypertension, type 2 diabetes, cardiovascular disease, obesity, and other obesity related illnesses (Jackson, Knight, & Rafferty, 2010; Mujahid et al., 2010; Mwendwa et al., 2011; Tsenkova & Karlamangla, 2016). By examining African American women and their use of emotional eating as a coping response


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to stress and negative mood states, this study endeavors to gain further insight into emotional eating and its association with obesity in women in general and African American women in particular. Emotional Eating Early studies of emotional eating focused on its role in relationship to obesity (Arnow, Kenardy, & Agras, 1995). Other research has addressed emotional eating in the context of eating disorders and depression. (Meyer, Leung, Barry, & De Feo, 2010). While research studies are sparse that address emotional eating in the African American community and possible associations with both obesity and depression, research is available concerning emotional eating and the consumption of sweets associated with obesity and waist circumference within elderly populations (Porter Starr, Fischer, & Johnson, 2014). Theories of emotional eating. In the literature, Nguyen-Rodriguez, Unger, and SpruijtMetz (2009) identified two theories or models of emotional eating: the general effects model, applied to animal subjects; and the individual differences model, applied to human participants. The general effects model suggested stress will increase food consumption in all organisms; the individual differences model suggested eating in response to stress will vary depending upon factors of individual participants. The individual differences model approach can be observed in previous research. Multiple studies addressing hypotheses from individual differences have been tested by observation of obese and non-obese participants (Masheb & Grilo 2005; Wiedemann & Saules, 2013), race and ethnic differences (Striegel-Moore et al., 2003), specific emotions and eating (Adriaanse, de Ridder, & Evers, 2010; Crockett, Myhre, & Rokke, 2015), and psychological factors of emotional eating. This study took the individual differences model approach and examined data from participants across weight and race demographics.


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The affect regulation model explains emotional eating—the tendency to binge eat to decrease negative mood states and increase positive mood states (Bohon, Stice, & Spoor, 2009). Observing the presence of neural activity in response to food intake was a method of differentiating emotional from non-emotional eaters. Measuring depressive symptoms, negative and positive mood, and eating disorder symptoms, Bohon and colleagues found greater neural sensitivity to food reward in emotional eaters across all mood states, suggesting emotional eaters received more pleasure from food reward than did non-emotional eaters. More specifically, during both positive and negative moods of emotional eaters, brain activity increased in response to anticipation and consumption of a food reward (milk shake). However, in a systematic review of the literature on the association between positive and negative affect and emotional eating, food consumption occurred during positive affect; yet during stress, sadness and depression, greater amounts of unhealthy food were consumed (Devonport, Nicholls, & Fullerton, 2017). In the Bohon (2009) study, the participants consumed more food, experienced a reduction of negative mood, and saw an increase in positive mood. Such findings support the construct of emotional eaters experiencing better mood when consuming food considered rewarding (in the case of this study, a milk shake), which reinforces the consumption of food to maintain positive mood states and prevent the experience of negative ones. The affect regulation model suggested individuals who experienced negative mood used food consumption to decrease uncomfortable feelings and increase positive feelings. When the emotional eater experiences pleasure from eating, there is a risk of overeating to maintain that feeling, which in turn, may increase the risk of obesity and obesity related illness (Katzmarzyk & Staiano, 2012).


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Individuals use emotional eating to improve feelings associated with negative mood (Taylor, 2009; Thayer, 2001). Experiencing negative emotions and daily life stressors affects eating behavior (Bohon, Stice, & Spoor, 2009; Dallman M. F., 2010) as individuals prefer to eat high fat and sweet food items in response to feelings of sadness (Jackson, Knight, & Rafferty, 2010), which in turn creates a pleasure response in the brain and reduces feelings of discomfort (Bohon, Stice, & Spoor, 2009). Although the physiological response to stress and negative mood may be a loss of appetite (Bohon et al, 2009; Hernendez-Hons & Wolley, 2012), individuals using emotional eating as a response to negative mood continue to seek comfort through eating to increase positive moods temporarily (Parker, Parker, & Brotchie, 2006). This pattern of eating has the potential to lead to compromised physical health while attempting to protect mental health. When examining eating disorders and eating behaviors, research concurs that African American women have a lower occurrence of diagnosed eating disorders when compared to White women (Taylor, Caldwell, Baser, Faison, & Jackson, 2007). Yet, research also found that African American women who hold a negative view of their body image (Harris, 2015) and lower ethnic identity have an increased risk for disordered eating (Hart, Sbrocco, & Carter, 2016). However, the presence of overeating behaviors in African American women remains a cause for concern. The tendency for African American women to overeat may be an emotional response to societal stressors. For those who are members of two oppressed groups (Blacks and females), efforts to cope with the combined race and gender-related stress may become internalized, with the potential to exacerbate feelings of negative mood. If unchecked, these feelings can develop into depressive symptoms (Spoor S. T., Bekker, Van Strien, & van Heck, 2006; Talleyrand, 2006; van Strein et al., 2016). Those depressive symptoms may, in turn,


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prompt an increase in emotional eating, which may create a cycle of eating that protects the mind from negative feelings and depressive symptoms while forsaking the health of the body— increasing obesity and associated disease risk. This study expanded on this body of knowledge and examined the role of emotional eating in women and correlations to depressive symptoms and weight. Emotional eating and BMI. Multiple factors influence the relationship between emotional eating and weight gain, such as sex (male or female), eating habits (diet), types of food consumed when emotionally eating, and the amount of physical activity occurring during times of distress (van Strien, 2018). Peneau, Menard, Mejean, Bellisle, & Hercberg (2013) examined the relationship between emotional eating and weight status and whether that relationship is modified by the subject’s gender and dieting. They found a strong association between emotional eating scores, BMI, and weight status across men and women, and across most dieting statuses as well. They also found women had higher emotional eating than did men; women demonstrated a strong association between emotional eating and being overweight/high BMI, and women who have never dieted demonstrated the strongest association when compared to women who were currently or had previously dieted. Knottinen and colleagues (2010) found emotional eating and depressive symptoms had a positive relationship in both men and women, and both emotional eating scores and depressive symptoms were related to higher BMI. They also found that men and women with lower emotional eating scores had depressive symptoms that related to eating non-sweet foods. In addition, they noted the type of foods consumed (sweet vs. non-sweet) had no significant interaction with levels of emotional eating in participants with high levels of depressive symptoms, as it appeared depressive symptoms were more likely to


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promote unhealthy food choices than emotional eating. Still other studies (Davenport, Nicholls, & Fullerton, 2017) suggested emotional eating prompted poor nutritional food choices. Depressive symptoms and depressed mood related to emotional eating also decreased the desire toward physical activity. For example, Konttinen and colleagues (2010) found the effect of depressive symptoms significantly mediated emotional eating and physical activity; also a negative correlation existed between physical activity and adiposity indicators. Although emotional eating occurred significantly more in women than men, emotional eating positively associated with depressive symptoms and adiposity indicators in both groups. These studies together support a Psychosomatic Theory of Obesity, which may be stronger in women. Taken together, the studies of Konttinen and colleagues (2010) and Peneau and colleagues (2013) provided support that women demonstrated greater levels of emotional eating than did men, and BMI and weight gain are significantly associated with emotional eating in women (van Strien et al., 2016), putting them at risk for obesity and its associated illnesses. These studies highlight the strong relationship between emotional eating and depression (Ouwens & van Strien, 2009), which has a bidirectional relationship with obesity (Atlantis & Baker, 2008; van Strein et al., 2016). Taken together, these factors, which one can see as stronger in African American women (Beauboef-Lafontant, 2008; Buchowski, et al., 2010), increase the risk of poorer health outcomes; this elevates the importance of increasing understanding of the relationship between depression and BMI in African American women. Depression and Depressive Mood Some of the symptoms that characterize depression include sad or depressed mood, a loss of interest in activities once enjoyed, fatigue, changes in weight, low self-esteem, and difficulty concentrating (American Psychiatric Association, 2013; CDC, 2013). Depression presents a


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serious concern to the quality of life for individuals living with it (Frank, Matza, Revicki, & Chung, 2005; Sohail et al., 2015), to the public health system, and to health care costs (Reese, Thorpe, Bell, Bowie, & LaVeist, 2012). Notably, depression and depressive symptoms have inhibiting effects on necessary physical activity to prevent or reduce obesity (Schneider, Baldwin, Mann, & Schmitz, 2012). These symptoms support a bidirectional association of comorbidity in diagnosed depression and morbid obesity (Luppino et al., 2010). Depression and obesity. Depression is one of the most widely reported mental health disorders and affects 8.1% of adults over the age of 20, with women twice as likely as men to have depression (Brody, Pratt, & Hughes, 2018); over 25% of adolescents also report at least mild symptoms (Knopf, Park, & Mulye, 2008). Further, one-third of female high school students experienced debilitating sadness and were depressed compared with one-fifth of male students. In adolescence and childhood, obesity is a risk factor for depression in adolescent samples in clinical populations (Eremis, et al., 2004); however, in the general population of national and community samples of adolescents, obesity is not generally associated with depression (Frisco, Houle, & Lippert, 2013). Goodman and Whitaker (2002) found depressed adolescents who were obese at baseline were at an increased risk for the development and persistence of obesity, which predicts adult obesity, which, in turn, is strongly associated with depression. Transitions in development from adolescence to adulthood present challenges when accompanied by mental health issues like depression (Frisco, Houle, & Lippert, 2013). Consistent depression from adolescence to adulthood was common in young women consistently obese during the transition to adulthood; however, this association was reduced when adjusted for the young women’s background characteristics, suggesting the relationship between weight change and depression is unique to young women. No relationship was found between weight gain and depression in men.


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In summary, Frisco and colleagues (2013) suggested weight gain is pivotal for the development of depression during key life-course transitions. Cross-sectional evidence supports that depression is associated with an 18% increased risk of being obese; this association was found in women but not men (Atlantis & Baker, 2008; de Wit, Luppino, van Straten, & Cuijpers, 2010). The exact mechanisms of this association are not detailed within cross-sectional studies. Depressed persons may experience greater psychological distress (Atlantis & Baker, 2008) or lead unhealthy lifestyles that contribute to obesity over time. The experience of being obese may lead to a negative self-image, resulting in depression over time (Conceicao, Utzinger, & Pisetsky, 2015). A meta-analysis of 15 longitudinal research studies demonstrated a bidirectional relationship between depression and obesity (Luppino et al., 2010). Participants with obesity had a 55% increased risk for developing depression, whereas participants with depression had a 58% increased risk of becoming obese (Luppino et al., 2010; Schneider, Baldwin, Mann, & Schmitz, 2012); previous research established an association between depression and obesity (Cooke & Wardle, 2007; Luppino et al., 2010). The meta-analysis also showed Americans had a stronger association between obesity and depression severity (i.e., depressive symptoms or a clinical diagnosis of depression) than did Europeans. This association was stronger when a diagnostic clinical interview was used to measure depression rather than a self-report symptom checklist. The meta-analysis also supported a bidirectional association between depression and obesity. The negative effects of depression on obesity development were present in women and may be reinforced over time. Furthermore, weight gain has been shown to activate inflammatory pathways (O' Rourke, 2009); as inflammation associates with depression, it may act as a possible mediator between


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obesity and depression. Obesity also has been linked to HPA axis dysregulation (Incollingo Rodriguez, et al., 2015), which is also linked to depression (de Carvalho Tofoli, Von Werne Baes, Severi Martins, & Juruerna, 2011). However, during a 16-year follow-up study, obese patients at risk for cardiovascular disease (CVD) had a higher risk of depressive symptoms than overweight or obese participants not at risk for CVD (Hinnouho, et al., 2017). Overweight and obese participants without additional cardiac risk demonstrated less depression, suggesting an individual differences model approach may shed light on differences in measures. Examining clinical subtypes of major depressive disorder (MDD) and adverse health conditions, Cizza and colleagues (2012) found pre-menopausal women with undifferentiated, atypical features of MDD demonstrated greater BMI, as well as whole body and abdominal fat mass, when compared to control groups. Further, Atlantis and Baker (2008), in a systematic review of epidemiological studies on obesity’s effects on depression, found U.S. studies supported the association between obesity and prevalence of depression outcomes in women but not men. Depression and race. The prevalence of depression has changed little in the last decade. The Center for Disease Control and Prevention (CDC) has documented that the race differences in depression are seen in women and are associated with obesity (Pratt & Brody, 2014). African American women continue to have the highest prevalence of obesity and depression compared to Mexican American and White women, respectively. Additional studies on the association of obesity and depression found race/ethnic differences in adolescents and adults. That is, Whites experienced more days of depression, major depressive disorder, and depression over their lifetime than did their Black counterparts (BeLue, Francis, & Colaco, 2009; Gavin, Rue, & Takeuchi, 2010; Heo, Pietrobell, Fontaine, Sirey, & Faith, 2006). Obesity associated with


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depression for lifetime mood disorder in Whites but not in Blacks and Hispanics (Simon et al., 2006). The literature on obesity includes studies using self-report data to assess depressive symptoms, which this study approached with caution. The use of self-report measures of depressive symptoms increases risk that the responses will be influenced by negative societal views on depression, which can lead to underreporting of symptoms (Bleich et al., 2010; Buchowski et al., 2010; Coogan et al., 2010; Fryar et al., 2012). Conducting research with populations in non-clinical settings may provide insight into overweight participants experiencing depressive symptoms but who have not reached diagnostic criteria Diagnostic and Statistical Manual of Mental Disorders-5th ed. (DSM-5; American Psychiatric Association, 2013). Data on depression in African American women is mixed; however, research within this demographic shows greater prevalence of depressive symptoms and dysthymia and less lifetime major depressive disorder (Riolo, Nguyen, Greden, & King, 2005). This study examined emotional eating and depressive symptoms in African American women, who continue to report higher levels of depression than their non-Hispanic White and Hispanic female counterparts (Brody, Pratt, & Hughes, 2018). Previous research has linked depression in African American women to financial difficulties and problems meeting basic needs (Plant & Sachs-Ericsson, 2004; Riolo et al., 2005). What is less known is the prevalence of depression in African American women who may not share the same struggles (i.e., those who are able to meet their basic needs without serious financial struggle) as women in lower SES. This study also examined depression prevalence in African American women at higher SES levels and the relationship with emotional eating and depression.


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Demographic characteristics associated with risk factors for depression include low SES, minority race, and female gender (Frank et al., 2005; Ward et al., 2009). Depression impairs the quality of life for an already marginalized population such as African American women. The stigmatization of depression as a personal weakness, as with other mental illnesses, inhibits treatment-seeking and use of mental health services (Ward & Heidrich, 2009). In addition, as discussed above, African Americans may underreport depressive symptoms due to stigmas associated with mental health disorders and treatment. The association of depression and low SES populations is well established (Frank, Matza, Revicki, & Chung, 2005; Heard et al., 2011; Jackson, Knight, & Rafferty, 2010; Luppino et al., 2010). However, there is less research about women of color who are in higher SES levels and have experiences of depression. Whether it results from underrepresentation in research or participants’ underreporting, a gap exists in the literature, with its reasons remaining unclear. Moreover, the use of self-reporting measures with participants who may feel stigmatized by acknowledging depressive symptoms can lead to the underreporting of symptoms and may not reveal accurate data. For some, the adherence to the Superwoman Schema (Black & Peacock, 2011; Woods-Giscombe, 2010; Watson & Hunter, 2015), along with cultural stigmas surrounding depression, lead African American women to underreport symptoms (Heard et al., 2011; Waite & Killian, 2008). Depression and emotional eating. Overcoming cultural stigmas surrounding depression continues to be a challenge to the efforts of investigators in researching relationships between depression and emotional eating. Ouwens and van Strien (2009) investigated the possible relationship between depression and emotional eating and found a positive and direct relationship. Ouwens and van Strien (2009) noted participants who engaged in emotional eating


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experienced a reduction in the awareness of negative or distressful emotions. In more recent research, van Strein and colleagues (2016) found depressive symptoms associated with increased emotional eating, which predicted increases in obesity (BMI). Knottinen et al. (2010) examined emotional eating, depressive symptoms, and the consumption of high sweet/high-caloric foods; they found emotional eating and depressive symptom scores associated with higher BMI. Women with higher emotional eating consumed more high-sweet/high-caloric food items as did women with higher depressive symptoms. In a population seeking bariatric surgery, Fischer and colleagues (2007) found that high emotional eaters tended to have higher levels of depression prior to surgery than did low emotional eaters. Although the results in these studies vary with nonclinical populations, an established relationship exists between emotional eating and depressive symptoms; this study aims to examine the nature of that relationship in a nonclinical sample of women in general and African American women in particular. Depression and SES Beydoun and colleagues (2016) examined the mechanisms by which SES differences in adiposity were mediated by depressive symptoms and unhealthy eating behavior. They found elevated depression scores had a positive association with higher waist-to-hip ratio (WHR) in African American women, and elevated depression scores associated with poor quality of food consumption across sex-ethnicity groups, with the exception of African American men. Beydoun and colleagues also noted African American men and women showed a positive association of SES and central adiposity; however, SES was inversely related to central adiposity in white women. The overall positive association of SES with healthy eating was significantly mediated by depressive symptoms among Whites but not African Americans. The positive relation of


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CES-D scores and WHR in African American women was independent of SES and not mediated by quality of food consumption. In summary, the Beydoun and colleagues (2009) study highlighted major differences in sex and ethnicity in the pathways linking SES, depressive symptoms, and eating factors to central adiposity measures, with women most greatly influenced. Poor quality food choices in eating behaviors may not have been the culprit in the association between depression and central adiposity; depressive symptoms and unhealthy eating may have contributed to SES disparities in central adiposity of White women and the measures of waist circumference and WHR. The former may have been attributed to lack of physical activity, and the latter may have been attributed to lack of access to healthy foods in low SES neighborhoods. In cross-sectional studies, data supporting this remains limited. In an attempt to understand the relationship between socioeconomic position (SEP) and depressive symptoms, Hudson, Neighbors, Geronimus, and Jackson (2010) examined the relationship between specific indicators of socioeconomic status and major depressive episodes (MDE) in African Americans. African Americans at higher income levels report poorer health than their White counterparts at the same SES (Braverman et al. 2011). In keeping with this, Hudson and colleagues found male respondents with higher incomes (>$80k) reported greater depression than male respondents in the lowest income category. However, African American women with higher income ($45k -$79k) had lower odds of 12-month MDE than women in the lowest income category. The significance of these result to the present study is women in lower income levels may be more vulnerable to depressive symptoms; however, income alone is not conclusive evidence of a relationship between SEP and depression as stress and environmental factors also play a significant role in the experience of depressive symptoms (Heard, Whitfield,


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Edwards, Bruce, & Beech, 2011). The purpose of this study is to build on this research to examine the role of depression and eating behaviors in women of different race/ethnic groups and at different SES levels. Summary Prior research has established a bidirectional association between obesity and depression (Cooke & Wardle, 2007; Luppino et al., 2010); this association varies by race. In one study, White women demonstrated a positive association between depression and obesity, but African American and Mexican American women did not show the same positive association between depression and obesity (Hicken et al., 2013). While emotional eating is a potential risk factor of obesity and depression, what remains unknown is the nature of the relationship between these three variables. The role of race and emotional eating as a factor in obesity and depression outcomes remains unclear. The focus of this study is to look deeper into the relationship between emotional eating, obesity, and depression through the lens of race. The goal is to gain a better understanding of the possible effects each of these variables may have on women’s health and to expand the body of research on obesity and depression as they relate to the health of African American women. Methodological Consideration This study intends to use self-report measures to gather data from respondents for the final analysis. Self-report measures have limitations: the honesty of respondents concerned about their image, lack of introspect or understanding of self or concepts/questions, and response bias may each influence the accuracy of responses and the overall data (Hoskin, 2012). Luppino et al. (2010) have noted differences when comparing diagnostic interview results to self-report measures, which supports the difficulty and limitations when using self-report assessments for


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measuring depressive symptoms. As such, the risk of underreporting symptoms on selfassessments remains a potential limitation of this study. Because of the stigma of a mental illness diagnosis like depression, coupled with the views of African American women, in particular, of depression as a sign of personal weakness, underreporting on self-report depression scales may result (Ward, Clark, & Heidrich, 2009). Thus, this study chose a depression measure with excellent reliability and validity, to attempt to minimize concerns about the assessment of depression. In addition to concerns about self-report surveys, certain variables are difficult to measure, such as emotional eating, which is often discussed in terms of eating in response to negative emotions; however, studies have shown emotional eating also occurs in response to positive emotions and involves many factors; some at the individual level create contradictions and inconsistencies in the research (for a review, see Bongers & Jansen, 2016). It has been suggested that high emotional eating scores reflect changes in emotional arousal (Penaeu et al, 2013) and are highly individual, according to different characteristics of the person, and may be learned coping responses (Canetti et al, 2002). When considering self-report measures like the Emotional Eating Scale (Arnow, Kenardy, & Argas, 1995), which provides rating scales drawn from respondent replies to a range of options (“not at all” to “extremely likely”), the extreme responder can influence the data results in extreme directions. Since there is no clear consensus on a diagnosis of emotional eating, as well as no specific cut-off scores on the EES, there is a limitation to specifying the presence of emotional eating (B. Arnow, personal communication, June 22, 2016). Despite this limitation, the EES was chosen by this researcher as the most appropriate option to measure a wide range of emotions across a diverse population. Measuring of emotional eating is as much of


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a challenge as measuring stress, which also has no agreed upon definition. Like the EES, the Perceived Stress Scale (PSS) has no cut-off score, only comparisons within the sample; however, it remains a widely used and well-regarded tool for measuring stress (Cohen, Deverts, Chin, & Zajdel, 2015). BMI is another variable when self-reported data has limitations. There is some disagreement as to the accuracy of BMI and body fat percentage. For example, Nuttal (2015) suggested it does not accurately capture body fat mass in different body regions. The tendency to underreport height and weight, which women do more often than men, also limits the accuracy of the data collected for analysis (Visscher, Vielt, Kroesbergen, & Seidell, 2006). Nonetheless, this remains the best measure for obesity (Mitchell, Catenacci, Wyatt, & Hill, 2011) and continues to be an appropriate way to gather this type of data in a self-report setting.


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CHAPTER 3 METHODS This study examined the relationship between levels of depressive symptoms, emotional eating, obesity, race, and stress. Specifically, the study sought to determine if race moderated the relationship between emotional eating and depression. It also examined if emotional eating was positively associated with obesity (as measured by BMI); if it was more so for African American women than in women of other ethnic and race groups; and whether a stronger relationship between emotional eating, depression symptoms, and obesity have placed African American women at a greater health risk than women of other race/ethnic groups. Women over the age of 18 across racial and ethnic groups were surveyed. Attempts were made to target a minimum of 115 respondents per race group and to achieve a medium effect size with three of the five variables. Participants were recruited by email as well as social media (Facebook, local churches, and civic organizations). Participants were also recruited from the student body at Keiser University via email and from a post on the Hanover University Psychology blog page. Participants began the survey with an expressed informed consent statement explaining the voluntary nature of the study, the anonymity of responses, and that no identifiable information would be collected. The consent also noted no financial gain resulted from completing the survey; however, participants had the option of providing an email address to be entered into a drawing for one of five $25 gift cards. This study was open to all adult women, with the intention that the results further the body of knowledge on women’s health issues. This study excluded women younger than 18 years old and individuals who did not selfidentify as a woman.


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Participant Characteristics The sample population consisted of women ages 18 to 81. Women self-described as White made up the majority of respondents. Participation by African American women was less than anticipated; reasons for this will be discussed in the limitation section. Asian women and those responding as Other made up the remaining sample. Self-identification as Hispanic was also less than non-Hispanic. A small number of respondents chose to self-identify as mixed race (Black/White, Native American/White, multi-ethnic), or by designating countries or regions of origin (European, Middle Eastern, or Caribbean). A total of 83 respondents chose not to identify any specific race from the full sample of 414 participants. The participants were asked to report height and weight to determine BMI; the average participant had a BMI >30, which is considered obese. Data Collection Participants completed a 67-question survey that contained three scales: the Emotional Eating Scale (25), the Center for Epidemiological Studies-Depression Scale (20), and the Perceived Stress Scale (10), in addition to demographic questions, a BMI calculation, and health conditions. The final question asked participants how they found out about the survey. Average survey completion time was nine minutes. Demographic questions (Appendix A). Demographic data collected included gender, race, income, education level, number of people in household, marital status, number of children in the home, and employment status. The initial question asked the participant’s gender; female respondents proceeded to the survey; male respondents were thanked and prevented from proceeding any further. Participants provided an estimate of height and weight for BMI


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calculation. Race and ethnicity data collection followed a two-question format as found in the U.S. Department of Health and Human Services Regulatory information (see Appendix A; U.S. Food and Drug Administration, 2014). Participants also provided their annual household income, which was used to calculate SES; however, SES was not significantly correlated with BMI within this sample (Braverman, Egerter, An & Williams, 2011). Emotional eating scale (Appendix B). The Emotional Eating Scale (EES, Arnow, Kenardy, & Agras, 1995) measured emotional eating. The EES is a 25-item scale with three subscales measuring states of Anger/Frustration (0-44), Anxiety (0-36), and Depression (0-20); the maximum overall score is 100. Although this data is based on ordinal variables, overall emotional eating scores were grouped to describe the sample (e.g., score > 25 indicates an unhealthy use of food consumption to cope with negative mood). Transformations were performed on non-normal data. The EES demonstrated strong internal consistency: coefficient alpha for the total scale was α =.81; the subscales Anger/Frustration α =.78; Anxiety α = .78; and Depression α =.72 (Arnow, Kenardy, & Agras, 1995). Waller and Osman (1998) found the Emotional Eating Scale has an α =.93 in a non-clinical group of women; it demonstrated emotional eating was linked to bulimic eating attitudes and is considered valid and reliable in nonclinical populations, with the potential for early identification of eating problems (Waller & Osman, 1998). This study examined the correlations between the EES subscales, BMI, and depression, and performed additional analyses. Center for Epidemiological Studies Depression Scale-20 (Appendix C). The Center for Epidemiological Studies Depression Scale (CES-D-20) is a self-report scale developed to measure depressive symptoms in the general population for epidemiological studies, reporting an internal consistency of α = 0.85 for the total scale (Radloff L. S., 1977). The CES-D 20-item


34

scale is a composite of items selected from several established inventories, including the Zung Self-Rating Depression Scale, the Beck Depression Inventory, the Raskin Scale, and the Minnesota Multiphasic Personality Inventory Depression Scale (Radloff & Locke, 2000). The CES-D-20 measures current symptoms of depression aligned with the DSM-5 (American Psychiatric Association, 2013) symptom groups. The nine symptom groups include sadness, (items 2, 4, 6), loss of interest (items 8, 10), appetite (items 1, 18), sleep (items 5, 11, 19), thinking/concentration (items 3, 20), guilt/worthlessness (items 9, 17), fatigue (items 7, 16), movement/agitation (items 12, 13), and suicidal ideation (items 14, 15). The CES-D-20 calculates a summary score ranging from 0-60. Items are rated on a four-point scale, in which participants are asked to rate how often those feelings have occurred in the past week: 0= rarely/less likely than a day; 1=sometimes/couple of days; 2=moderate/a few days; and 3=most of the time/up to a week. For descriptive purposes, it is worth noting that a cut-off score of 30 (Simon et al., 2008); BMI is calculated by dividing weight in kilograms divided by height in meters squared (Buchowski et al., 2010); this was calculated using SPSS. Although analyses will include this variable as a continuous measure, it is worth noting that “overweight” is indicated by a BMI of >25 – 29.9. Respondents entered their actual weight and height and their ideal height and weight. Self-reports of weight and height were used to calculate body mass index (BMI). Stress measure (Appendix E). The Perceived Stress Scale (PSS) measured individual perceptions of experienced stress and the degree in which respondents viewed their life as out of control, overloaded, and unpredictable (Cohen, Kamarck, & Mermelstein, 1983). Internal reliabilities for the PSS-10 were Cronbach’s α =.78 and .91 in national samples of the eNation Survey in 2006 and 2009 (Cohen & Janicki-Deverts, 2012). Recruitment. The researcher posted an approved announcement on social media pages (Facebook & Twitter) of various groups and her own page that included a link to the survey. Participants followed the embedded website link/URL to the survey. The announcement and link were also shared via Keiser University email to graduate and undergraduate female students and on the Hanover University Psychology blog. A total of 421 participants responded to the survey, and of those who identified how they found out about the survey, 146 were from Facebook, 68 from “a friend,” 43 from Hanover University Psych Blog, 39 from Keiser University, 19 via email, and 17 indicated “other.” Survey Monkey. Data were collected using the online survey software on Surveymonkey.com. The participants accessed the survey site via a computer, tablet, or smart phone and an internet connection. This web-based survey service provided the platform to create and distribute the survey through integration with various social media platforms. It also provided data analysis and integration with Statistical Package for Social Science (SPSS, IBM


36

Corp, 2013). The integrated data encryption allowed the researcher to collect data anonymously while the data were kept secure on a password-protected computer. Upon completion of the survey, participants were given the opportunity to enter into a random drawing for one of five Amazon gift cards. The email addresses collected for this drawing were used solely for the gift card drawing. Participants also completed a short response on their experience and reflections on their participation in the survey. Quantitative Statistical Tests for Data Analysis This study used a cross-sectional survey design; an anonymous online survey platform collected data at one point in time. This researcher conducted a multiple regression/correlation and performed significance testing at α=.05; for the f-test of the multiple R3, this researcher anticipated a medium effect size (ES), that is f = .15. The study included five variables; the G power analysis indicated a CI=95% and a p =.05; the moderate ES necessitated a sample size of 115 (Cohen, J., 1992). The online survey service, Survey Monkey, hosted the survey and was fully integrated with IBM, SPSS-25, which allowed for analysis of quantitative data analysis on the following variables: Independent Variables Emotional Eating (EES) – Ordinal variable; scores above the mean on the EES indicate an unhealthy use of food consumption to cope with negative mood (maximum score of 100). Subscale scores for Anger/Frustration (maximum score of 44), Anxiety (maximum score of 36), and Depression (maximum score of 20) may also be analyzed in relation to obesity if correlations are significant. Scores were summed, and normality was assessed.


37

Depression (CESD-20) – Ordinal variable; scores use a 4-point ordinal scale with a range from 0-60; a cut-off score of 16 indicates “significant” or “mild” depressive symptomatology. Scores were summed and transformed to obtain normal distributions. Stress (PSS) – Ordinal variable; scores are reversed scored (0 = 4, 1 = 3, 2 = 2, 3 = 1, 1 = 4) for positively stated items (4, 5, 7, & 8) and summed across remaining scale items. Scores above the group mean indicated an increased perception of stress. Scores were summed and transformed to obtain normal distributions. Race – Categorical (AA, Other). An insufficient number of Hispanics completed the survey; women responding to the question of ethnicity were grouped according to how they responded to the question of race. Thus, race was collapsed into three categories: African American (AA), Asian, and White. Caribbean/Black was also collapsed into AA. Dependent Variables Obesity (BMI) – Interval variable; BMI is calculated as weight in kilograms divided by height in meters squared. Formula to convert weight: (X) lbs. x 0.45 = (Y) kilograms. To convert height: 1 inch = 0.25 meters. (X) in. x 0.25 = (Z) meters; Z is then squared, Y/Z2 =BMI. Example: weight 184 x 0.45359237 = 8.34kg, height 63x0.254= 162, 8.34kg/16m2 = 30.6 BMI Data Analysis For H1 conducted Spearman’s rho correlation analyses and ascertained the strength and direction of the relationship between overall emotional eating score (EES), depression (CESD20), stress (PSS), and obesity (BMI). For H2 conducted a Multiple Regression of the independent variable race on the dependent variable obesity (BMI), and determined there are variances in race group in predicting obesity.


38

For H3 conducted a moderated multiple regression of the independent variables, Emotional Eating (EES), and determined an interaction with race in predicting the dependent variable obesity (BMI). For H4 conducted a multiple regression moderation of the independent variables of race/ group and depression (CESD-20), which predicted obesity (BMI). Description of Data Analysis Procedures Survey responses were collected via Survey Monkey, which has enhanced security. Individual raw data were downloaded and coded for analysis. The completed data collection was integrated through downloading into SPSS. Frequency distribution and other descriptive statistics were used to screen the data for outliers and missing cases (Field, 2009). Data graphs using boxplots and histograms with a criterion of > 3 standard deviations from the mean detected no outliers (Field, 2009), and missing values analysis highlighted patterns of missing values (Tabachnick & Fidell, 2006). Additional analysis of skewness and kurtosis were run to ensure homogeneity of variance and normal distribution (Field, 2009). The researcher conducted a Pearson product-momentum correlation coefficient (Field, 2009) and measured the strength of the relationship between all scale variables, and Spearman’s rho for ordinal variables. Regressions allowed for interactions between race, SES, and other variables predicting the dependent variable, obesity (Field, 2009). Ethical Considerations This study complied with the standards established by the APA (2010) to ensure that the researcher protected the confidentiality and privacy of respondents who consented to participate in this dissertation research. The researcher adhered to these ethical standards by following research guidelines that apply to human research as set forth in the Collaborative Institutional


39

Training Initiative. In so doing, this researcher provided and maintained voluntary informed consent; protected the welfare and dignity of participants by responding to concerns from potential participants; and provided a survey that preserved the participants' freedom to accept or decline participation, to respond or skip questions, and to discontinue the survey at any point. The researcher included a written explanation of the responsibilities of the participants as well as the researcher's responsibilities in the study, protecting participants from physical and psychological discomfort or danger; in addition, the researcher provided an explanation of the nature and purpose of the study, without creating a bias in participant responses or misleading the participants in the purpose and nature of the research. The data used for this study were not stored on cloud storage but on a password-protected computer in a password-protected file.


40

CHAPTER 4 DATA ANALYSIS AND RESULTS The purpose of the study was to investigate the relationship between the variables of emotional eating, stress, depression, and obesity in women across ethnic and race groups. The four aims and their corresponding hypotheses of the study were to: (a) examine the relationship between emotional eating (EES), obesity (BMI), stress (PSS), and depressive symptoms (CESD); (b) examine the relationship between emotional eating and obesity and determine if there are variances by race groups; and (c) examine race differences in emotional eating and depression that predict obesity in women. Research Questions and Hypotheses The study tested the following four research questions and hypotheses: Research Question 1 RQ1. Is there a relationship between emotional eating (EES), stress (PSS), obesity (BMI), and depression (CESD)? H10a

There will not be a positive correlation between EES, BMI, PSS, and CESD

H11a

There will be a positive correlation between EES, BMI, PSS, and CESD

Research Question 2 RQ1

Is race a significant predictor of obesity (BMI)?

H20a

There will be no statistical significance in race in predicting obesity (BMI).

H21a

There will be a statistical significance in race predicting obesity (BMI).

H20b

AAW will not demonstrate greater rates of obesity (BMI) in comparison to White women.

FORSAKING THE BODY TO PROTECT THE MIND H21b

41

AAW will demonstrate greater rates of obesity (BMI) in comparison to White women.


Will AAW demonstrate a stronger positive relationship between emotional eating (EES) and obesity (BMI) when compared to WW?

H30a

WW will demonstrate a weaker positive relationship between emotional eating (EES) and obesity (BMI) when compared to AAW.

H31a

AAW will demonstrate a stronger positive relationship between emotional eating (EES) and obesity (BMI) when compared to WW.


Will AAW demonstrate a stronger positive relationship between depressive symptoms (CESD) and obesity (BMI) when compared to WW?

H40a

WW will demonstrate a weaker positive relationship between depressive symptoms (CESD) and obesity (BMI) when compared to AAW

H31a

The positive relationship between depressive symptoms (CESD) and obesity (BMI) will be stronger in AAW when compared to WW.

Description of the Sample The sample of participants was recruited using social media; most respondents replied to a post on Facebook, either on their own timeline or one shared by others. The survey was also posted in group pages within Facebook, targeting African American women to increase respondent rates from that population. Additional respondents completed the survey by following a link from a webpage dedicated to psychology research as hosted by Hanover University Psychology Department; an email containing a survey link sent to Keiser University students; or


42

via direct email or social media requests from friends. In addition, some responded to an email request with the survey link directly from the researcher. The original sample was 416, of which 71 were eliminated due to the number of incomplete responses (n=69) or being male (n = 2). Thus, the final sample for analysis was 345 females. The findings in Table 1 reveal that the largest racial ethnic group was White (n = 142, 42.9%), followed by African American (n = 114, 34.4%), Asian (n = 10, 3.0%), and Other (n = 65, 19.6%), and 3 respondents did not identify. Most participants identified as not Hispanic (n = 291, 87.1%), and 12.9% (n = 43) identified as Hispanic. The sample was well-educated, with approximately half of the sample graduating from college (n = 92, 48.7%); from graduate school with a master’s degree (n = 93, 27.8%); or from graduate school with a doctoral degree (n = 21, 6.3%). Income varied with approximately onehalf of participants reporting a household income over $70,000 (n = 162, 49.4%). Slightly above half of the sample reported no diseases or disorders (n = 177, 60%). In terms of exercise, 39.2% (n = 131) reported walking regularly; 22.7% (n = 76) occasionally riding a bike; and 11.7% (n = 39) regularly doing a sport. Summary of Results Descriptive statistics. The analysis included descriptive statistics, correlations, and multiple regression. In the current chapter, descriptive statistics are presented as well as the results of regressions. Demographic data is reported in Table 1 and Table 2.


43

Table 1 Frequencies and Percentages for Sample Race/Ethnicity, Education, and Income n Variable Female gender 345

%

100

Ethnicity Hispanic Not Hispanic Total

43 291 334

12.9 87.1 100.0

Race African American White Asian Other Total

114 142 10 65 331

34.4 42.9 3.0 19.6 100.0

26 103 70 22 114

7.5 30.9 20.9 6.6 34.1

335

100.0

14 16 20 30 22 30 34 32 17 16

4.3 4.9 6.1 9.1 6.7 9.1 10.4 9.8 5.2 4.9

Highest Level of Education 9th grade-HS Graduate 1-5 years of college Graduated from college Some graduate school Completed graduate school with master's degree or higher (e.g., M.S., M.A., Ph.D, M.D, J.D) Total 2016 Household Income Before Taxes and Deductions less than $10,000 $10,000 to $19,999 $20,000 to $29,999 $30,000 to $39,999 $40,000 to $49,999 $50,000 to $79,999 $60,000 to $69,999 $70,000 to $79,999 $80,000 to $89,999 $90,000 to $99,999

FORSAKING THE BODY TO PROTECT THE MIND $100,000 to $149,999 $150,000 or more Total

44 53 44 328

16.2 13.4 100.0

Table 2 Frequencies and Percentages Describing the Sample for Health and Physical Activity

Variable

n

%

Health Issues Diabetes (Type 1 or Type 2) Meningitis Epileptic seizures Cardiovascular disease High blood pressure None of these diseases and disorders Other metabolic disorders Total

6 4 1 2 78 177 27 295

2.0 1.4 .3 .7 26.4 60.0 9.2 100.0

How Often Walks/Hikes Rarely Occasionally Regularly Total

80 123 131 334

24.0 36.8 39.2 100.0

Rides a Bicycle No Occasionally Regularly Total

244 76 15 335

72.8 22.7 4.5 100.0

Does a Sport No Occasionally Regularly Total

255 40 39 334

76.3 12.0 11.7 100.0

The descriptive statistics for the demographic variable, age of household members, and hours worked for pay are presented in Table 3. Average age was 44.9 (SD = 13.82). Average


45

number of people living in the household was 3.01 (SD = 1.61). Average number of children was 1.81 (SD = 1.15). Most (281, 81%) worked for pay. On average, participants worked 35.54 hours (SD = 13.32) each week for pay; the remainder were retired or did not work for pay. Table 3 Descriptive Statistics for the Continuous Variables Describing the Sample Characteristic N Range M Age in years 341 18 to 83 44.9 336 1 to 10 3.01 Number of people living or staying at address (including self) Number of children 333 1 to 8 1.81 Weekly hours worked for pay 281 1 to 50 35.54

SD 13.8 1.61 1.15 13.32

Missing data and outliers. Missing data were investigated by running frequency counts. Sixteen participants were eliminated from further analyses due to missing data for BMI; these were not replaced. Missing data was defined as missing one or more questions from the scales measuring depressive symptoms (CESD-20) and emotional eating (EES). There were eight cases of missing data for the depressive symptoms (CESD-20) and eight cases missing for emotional eating (EES). Missing cases for depressive symptoms (CESD-20) and emotional eating (EES) were replaced with the overall mean score. A test for univariate outliers was conducted, and a few were found within the distributions. Univariate outliers were sought by converting observed scores to z-scores and then comparing case values to the critical value of ±3.29, p < .001. Case z-scores that exceed this value are greater than three standard deviations from the normalized mean and should be investigated and potentially removed. For emotional eating (ESS), one case had a z-score of 3.44; this case was removed. For obesity (BMI), three cases had z-scores of 3.46, 3.39, and 3.38; these cases were removed.


46

Univariate normality was then assessed via the variables’ skewness and kurtosis indices (see Table 3). Per Kline (2011), a variable is univariate normal if its skewness index (i.e., skewness statistic/standard error) is less than three and if its kurtosis index (i.e., kurtosis statistic/standard error) is less than 2.0. These variables are within the acceptable range for skewness and kurtosis. Descriptive statistics for the study variables. Per Nunnally and Bernstein (1994), a measure is reliable if Cronbach’s alpha is .70 or higher. As shown in Table 4, Cronbach’s alpha for the CESD-20 was .91; therefore, the CESD-20 was considered internally consistent. The mean CESD-20 score was 15.43 (SD = 10.63) and scores ranged from 0 to 52.00. Cronbach’s alpha for the PSS was .89; therefore, the PSS was deemed internally consistent. The mean PSS score was 17.14 (SD = 7.19), and scores ranged from 2.00 to 37.00. Cronbach’s alpha for the EES was .95; therefore, the EES was also considered internally consistent. The mean EES score was 27.23 (SD = 17.74), and scores ranged from 0 to 80.00. Finally, mean BMI was 30.67 (SD = 7.97) and ranged from 15.88 to 55.90. Outlier analyses indicated that 3 extreme instances of BMI needed to be removed. Table 4 Descriptive Statistics and Cronbach’s Alpha for the Study Measures (N = 343) Scale Range M SD α Kurtosis CESD-20 0 to 52.00 15.4 10.6 .91 .51 PSS 2.00 to 37.00 17.4 7.19 .89 .35 EES 0 to 80.00 27.2 17.7 .95 .16 EES Anger/Frustration 0 to 39.00 10.9 8.80 -.23 EES Anxiety EEs Depression BMI

0 to 31.00 0 to 18.00 3.64 to 55.90

9.1 7.4 30.7

6.9 4.4 7.9

----

.09 .66 1.1

Note. SE for the kurtosis statistic was .26; SE for the skewness statistic was .13.

Skewness .91 .23 .62 .82 .70 .12 .60


47

Hypothesis 1 Aim 1 was to examine the relationship between emotional eating, obesity, stress, and depressive symptoms. Hypothesis 1 predicted a positive correlation between emotional eating (EES), obesity (BMI), stress (PSS), and depressive symptoms (CESD-20). Pearson correlation coefficients were used to ascertain the strength and direction of the relationship between overall emotional eating (EES), depressive symptoms (CESD-20), stress (PSS), and obesity (BMI), given that these data were approximately normally distributed. As can be seen in Table 4, all but one correlation pair (BMI and PSS) were statistically significant. There was a small, positive correlation between emotional eating (EES) and depressive symptoms (CESD-20), which was statistically significant (r (341) = .22, p < .01), indicating women with high emotional eating also endorsed depressive symptoms. There was also a small, positive correlation between emotional eating (EES) and obesity (BMI) (r (341) = .20, p < .01), indicating higher emotional eating was positively related to depressive symptoms. Further, there was a weak, positive correlation between depressive symptoms (CESD-20) and obesity (BMI) (r (341) = .10, p = .05), indicating depressive symptoms related to obesity (BMI) in some women. Finally, stress (PSS) was positively and significantly correlated with emotional eating (EES) (r (341) = .19, p = .01) and highly correlated with depressive symptoms (CESD-20) (r = .71, p = .001), indicating stress and high emotional eating was significantly related to depressive symptoms. However, stress (PSS) was unrelated to obesity (BMI) (r (341) = .04, p = .22) as there was not a significant relationship. The assumption of lack of multicollinearity was met for most of the variables as most did not exceed 0.21; however, depressive symptoms (CESD-20) and stress (PSS) had a strong correlation (r (341) = .71, p < .001). Due to multicollinearity and the need to only use depressive symptoms (CESD-20) or stress (PSS) in analyses, depression was deemed the more appropriate


48

variable to include in subsequent analyses, due to both prior research and slightly stronger correlations with the other variables. Table 5 One-Tailed Pearson Correlations between Key Study Variables (N = 343) EES CESD-20 BMI EES

PSS

1

CESD-20

.22**

1

BMI

.20**

.10*

1

PSS

.19**

.71**

.04

1

SES

-.080

-.239**

-.018

-.174

Note. * indicates p < .05, ** indicates p < .01. Given these findings, the hypothesis that there would be a positive correlation between emotional eating (EES), stress (PSS), obesity (BMI) and depression (CESD-20) was supported. Stress highly correlated with depression; due to multicollinearity with depression and depression having a stronger relationship to obesity, stress was not examined further. Income (SES) had no significant relationship with obesity (BMI) or emotional eating (EES) and was not analyzed any further. Hypothesis 2 To assess mean differences in BMI by race, an ANOVA was conducted. Levene’s test was used to determine homogeneity of variance and the null hypothesis that the population variances are equal. A Levene’s p value less than .05 indicates homogeneity of variances has not been achieved. Levene’s test of equality of error variances was not statistically significant (F(3, 327) = 2.53, p = .06), indicating this assumption of normality was not violated. Table 5 shows


49

the descriptive statistics for BMI by race. As seen in Table 6, there was a statistically significant difference in average BMI by race (F(3, 327) = 7.69, p = .001). The Tukey post hoc test indicated that African American women (M = 33.26, SD = 7.58) had significantly higher average BMIs than did White women (M = 29.23, SD = 7.36), Others (M = 30.06, SD = 9.37), and Asians. The mean score for each racial group is plotted in Figure 1. Table 6 Descriptive Statistics for BMI by Race 95% CI Group

N

African American

SD

SE

114 33.26

7.58

0.7

31.8

34.6 16.20

51.1

White

142 29.23

7.36

0.6

28.1

30.4 15.88

55.9

Asian

10 24.77

7.27

2.3

19.6

29.9

5.98

30.7

Other

65 30.06

9.37

1.2

27.7

32.4

3.64

55.8

331 30.64 8.103

0.4

29.7

31.5

3.64

55.9

Total

M

Lower Upper Min

Max

Table 7 Results of the ANOVA for BMI by Race Sum of Squares

Df

Mean Square

F

P

Between Groups

1430.14

3

476.7

7.7

.001

Within Groups

20254.46

327

61.9

Total

21684.61

330


50

Figure 1. Plotted means for average BMI by race. Given these findings, the null hypothesis was rejected; African American women had significantly higher average BMIs in comparison to other ethnic groups. Due to difficulty interpreting findings for the “other” race category and the small number of Asian participants, it was deemed most appropriate for the remaining race analyses to focus on African American and White women only. An independent sample t-test was conducted to compare BMI in African American and White women. As can be seen in Table 8, there was a significant difference for African American women (M = 33.3, SD = 7.6) and White women (M= 29.2, SD = 7.4) in obesity (BMI); t(251) = 4.28, p = .001. There was also a significant difference in depressive symptoms (CESD-20), with AAW reporting less depressive symptoms than White


51

women, t(240) = -2.70, p= .008. White women reported more stress (PSS) than AAW, t(254) = 3.28, p= .001. However, for emotional eating (EES) there was little difference between the two groups t(219) = .63, p = .533. These results support previous studies that suggest African American women are more likely to be obese than White women (Heymsfield, Peterson, Thomas, Heo, & Schuna Jr.., 2016; Johnson, Carson, Affuso, Hardy, & Baskin, 2014; McEniry, Samper-Ternent, Florez, Pardo, & Cano-Guiterrez, 2018), and White women are more likely to be stressed and depressed (Richardson A. S., Arsenault, Cates, & Muth, 2015b; Weaver, Himle, Taylor, Matusko, & Abelson, 2015). Table 8 Results of the Independent t-test Race

N

Mean

SD

BMI

AAW WW

114 139

33.3 29.2

7.6 7.4

EES

AAW WW

98 123

26.2 24.6

18.2 17.6

CESD

AAW WW

107 135

13.6 17.4

10.1 11.5

PSS

AAW WW

114 142

15.5 18.5

6.5 7.8

Hypothesis 3 Aim 3 was to examine the relationship between emotional eating (EES) and obesity, and to determine if this relationship was moderated by race. Hypothesis 3 was there will be race/ethnic variances in the relationship between emotional eating and obesity African American women who use emotional eating will demonstrate greater rates of obesity in comparison to other race groups. To test this hypothesis, the researcher first centered the emotional eating data and then computed an interaction between the centered emotional eating and race variables. This


52

was done to avoid multicollinearity. Next, a moderated multiple regression analysis was conducted to determine whether the relationship between emotional eating and obesity differed for African American and White women. The independent variables emotional eating (EES) and race were entered first, followed by the interaction between emotional eating (EES) and race; the dependent variable was BMI. That is, results of the analysis indicated that there was a positive relationship between emotional eating and obesity (B = 0.11, p < .001), and race moderated this relationship (B = 0.13, p = .022) such that emotional eating predicted obesity in White women, but not in African American women. As seen in Table 9, the best fit was the model with EE, race, and the interaction between EE and race predicting BMI (F (3, 252) = 13.83, p < .001), which accounted for 13% of the variance in BMI. Figure 2 shows 6.1% of the variability in BMI is accounted for by emotional eating. Table 9 Stepwise Regression Interaction Models for Race, EES, and BMI Model B SE β t (Constant)

37.24

1.5

Centered EES

.110

.03

Race_2

-3.88

.91

(Constant)

36.99

1.5

Centered EES

-.083

.088

Race_ 2

-3.7

CenteredEExRace_2

.13

P

Tol.

VIF

25.0

.001

.23

4.0

.001 1.0

1.0

-.25

-4.3

.001 1.0

1.0

25.0

.001

-.18

-.94

.35

.09

10.6

.90

-.24

-4.1

.00

.99

1.0

.05

.44

2.97

.022 .09

10.63


53

Figure 2. Scatterplot of Emotional Eating Scale and BMI by Race. Hypothesis 4 Aim 4 was to examine race differences in emotional eating and depression, predicting obesity. Hypothesis 4 was there will be a race difference in emotional eating and depression in predicting obesity. The researcher also hypothesized that AAW with high levels of emotional eating and depressive symptoms will demonstrate greater rates of obesity in comparison to White women. To test this hypothesis, a moderated multiple regression analysis was conducted to determine variances in race in the relationship between emotional eating and depression in predicting BMI. The researcher centered the depression data and then computed an interaction between the centered depression and race variables; also, these variables were centered, and an interaction (Race x EES) was computed. This was done to avoid multicollinearity.


54

In block 3 (table 10), the centered depression was added to the variables (EES and Race_2); depression alone was not a significant predictor of BMI (B = -0.07, p =. 094), indicating depressive symptoms were not related to obesity (BMI). Next, the interaction variables (CESD, race_2) were added to block 4 of the moderated multiple regression as a moderator with the variables (EES, race_2) to determine if the relationship between depression and BMI were moderated by race; the dependent variable was BMI. Results of the analysis (table 10) indicated that depression without the interaction was not significant in predicting obesity (B = 0.17, p= .24); with the interaction of race_2, depression had a negative relationship (B = -.064, p = .47) and was not significant in predicting BMI. As seen in Table 9, the best model was the model with EE, race, and the interaction between EE and race predicting BMI, (F (3, 252) = 13.83, p < .001), which accounted for 13% of the variance in obesity (BMI), indicating obesity (BMI) tended to be higher in White women with higher emotional eating scores (see figure 2). Table 10 Stepwise Regression Interaction Model for EES, Race, CESD and BMI Model

T SE 1.5

Β

(Constant)

B 37.4

Centered EES

.-.90

.09

Race_2

-3.9

CenteredEExRace_2

P Tol.

VIF

25.0

.001

-.19

-1.1

.30 .09

10.7

.92

-.26

-4.3

.001 .09

1.04

.13

.05

.44

2.3

.02 .09

10.8

Centered CESD

.17

.15

.24

1.17

.24 .08

12.7

CntdCESDxRace_2

-.06

.09

-.15

-.72

.47 .08

12.7


55

Also, depression did not interact with race to predict BMI (p =.163). Similar to Hypothesis 2, race and EEs were statistically significant predictors of BMI. Depression was not statistically significant. Given these findings, the null hypothesis was retained. Conclusion The aim of this study was to examine the relationship between demographic variables (race), independent variables, stress (PSS), emotional eating (EES), and depression (CESD) in predicting obesity (BMI) in women. The analyses revealed emotional eating predicted obesity (BMI), but this relationship was stronger in White women than African American women. It also revealed a significant relationship between emotional eating and BMI, which was moderated by race. Stress, income, and depression were not significant predictors of obesity (BMI).


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CHAPTER 5 DISCUSSION This chapter provides an interpretation of the study results and compares these findings with existing literature. It also addresses the limitations of the study and provides recommendations for future research. The chapter concludes with an overall summary of the research findings and a general summary of comments by study respondents as anonymously shared with the researcher. Overview of the Study The significance of this study was the examination of factors related to women’s health in the areas of emotional eating, depression, stress, and obesity and how these factors were influenced by race. This study examined the relationship between emotional eating, obesity, stress, and depressive symptoms in women—to better understand these disparities and add to the current body of literature. This study attempted to confirm prior research on race variances in obesity. An additional aim of this study examined whether race moderated the relationship between emotional eating and obesity. The final aim of this study determined if race interacted with depression to predict differences in obesity. African American women were of particular interest to this study as research on emotional eating is limited for women from this demographic and the research on depression is mixed. Discussion of Results First, it was determined that a small positive relationship existed between emotional eating and depression; as prior research suggested, depressive symptoms associated with increased emotional eating (van Strien, 2018). Earlier research supports these results in which emotional eating and depressive symptoms associated with higher BMI and influenced food


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choices (Knottiten, Mannisto, Sarlio-Lahteenkorva, Silventoinen, & Haukala, 2010; van Strein et al., 2016); in a smaller study of obese individuals, elevated depression scores associated with greater emotional eating (Goldschmidt et al., 2014), and more recent research found emotional eating acted as a mediator between depression and weight gain in Caucasian women (van Strien, Knottinen, Homberg, Engels, & Winkens, 2016). Emotional eating was a significant positive predictor of obesity (BMI); this is consistent with previous studies that found emotional eating associated with individuals who are overweight or obese (Escandon-Nagel, Pero, Grau, Soriano, & Feixas, 2018; Ogden C. L., Carroll, Fryar, & Flegal, 2015). The data also revealed depression had a small, positive relationship with obesity (BMI), but was not significant when emotional eating was also included as a predictor. Although the relationship was not significant, the results are consistent with prior research that obesity and depression share a bidirectional relationship (Centers for Disease Control and Prevention, 2010; Li, Gower, Shelton, & Wu, 2017; Pratt & Brody, 2014); obesity is found to be comorbid with depression (Carey et al., 2014); and increased depression scores have been associated with lower healthy eating and larger body frames, along with an increase in waist and hip circumference for African American women (Beydoun et al., 2009). Emotional eating has been identified as a stronger predictor of weight gain than depression in some populations (Konttinen, Silventoinen, Sarilo-Lahteenkorva, Haukkala, & Mannisto, 2010); these findings concur with the research of van Strien and colleagues (2016), supporting emotional eating as a strong predictor of high BMI in White women. Next, the relationship between race and obesity was examined to determine if African American women have higher levels of obesity than other race/ethnic groups. Findings indicate statistically significant differences in obesity across race groups, with the average BMI


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significantly higher for African American than White and Asian women. These results support prior research, namely that African American women are more likely to be overweight or obese than non-Hispanic White women (Johnson, Carson, Affuso, Hardy, & Baskin, 2014; Kirby, Liang, Chen, & Wang, 2012; McEniry, Samper-Ternent, Florez, Pardo, & Cano-Guiterrez, 2018; Ogden C. L., Carroll, Kit, & Flegel, 2014; Pratt & Brody, 2014). Third, emotional eating (EES) and obesity was examined to determine the presence of variances by race. Being African American and the emotional eating score were statistically significant and positive predictors of obesity. However, emotional eating predicted obesity at a significant level in White women only, more so than for African American women. While emotional eating was not a significant predictor of obesity for African American women, it did, however, predict obesity in White women. Our results found emotional eating had positive correlation with stress (PSS), supporting previous research that found stress was associated with emotional eating in African Americans that were both overweight and obese (Sims et al., 2008). Although we found stress did not have a significant relationship with obesity (BMI), recent research has found stress-related eating associated with obesity, and mediated the feelings of stress in obese individuals (Cotter & Kelly, 2018; Koski & Nsaukarinen, 2017). Stress also demonstrated a positive correlation with depression (CESD). However, stress (PSS) and income (SES) were unrelated to obesity (BMI); therefore, this study did not examine those variables further. Discussion of the Conclusion The results of this study support the current literature on the rates of obesity in African American women with reported significantly higher median BMI when compared to other women (see table 6). Previous research supports increased rates of obesity within the African


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American community, especially African American women (Johnson, Carson, Affuso, Hardy, & Baskin, 2014; Pratt & Brody, 2014). The African American community focuses celebrations around large meals and emphasizes traditional Sunday family dinners that may include comfort foods high in carbs and fat (Hargreaves, Schlundt, & Buchowski, 2002; Jackson, Knight, & Rafferty, 2010). This tradition can encourage behaviors that result in weight gain. Emotional eating was a stronger predictor of obesity than depression. This is surprising considering the bidirectional relationship between obesity and depression found in prior research. For African American women, this may be understood through the acceptance of a more positive body image (Cameron, Mudrow, & Stefani, 2018; Hart, Sbrocco, & Carter, 2016; Schaefer, Thibodaux, Krenik, Arnold, & Thompson, 2015) as full-figured women are considered attractive within the African American culture. It may also be a result of a lower response rate to the survey’s depression questions by African American women, a potential limitation addressed later. We also examined if socioeconomic status (income) was related to emotional eating or obesity. Although income (SES) was positively related to both depression and stress, we found no direct correlation with emotional eating or obesity. Women at both ends of the economic spectrum can struggle with stress for different reasons (Dallman et al., 2003). Women earning an insufficient salary struggle to meet their financial needs and those of their family (Heard, Whitfield, Edwards, Bruce, & Beech, 2011) whereas women with a higher income may experience stress related to a profession’s high demands. Both groups can experience the stress of balancing work responsibility with family responsibility (Vines A. I. et al., 2006). Women earning more money are viewed as the financial savior of extended family and expected to provide for those in need (Black & Peacock, 2011); this remains a common occurrence within


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the African American community as part of the Superwoman role. This level of stress associates with depression, emotional eating, and obesity (Cotter & Kelly, 2018; Koski & Nsaukarinen, 2017; Rizzolo & Sedrak, 2010). In addition, emotional eating is a far more complex issue as it relates to obesity, which has not been thoroughly researched (Bongers & Jansen, 2016; Mensorio, et al., 2016; Wiedemann & Saules, 2013; van Strien, Knottinen, Homberg, Engels, & Winkens, 2016). Limitations The participants completed the main questionnaire (covering depression, stress, emotional eating, and BMI); however, after excluding additional missing data, the majority of respondents were African American and White. The respondents were middle- to high-income earners employed fulltime and who had completed some post graduate study. Thus, this sample does not reflect the larger U.S. female population, which limits generalization of the results. This study used self-report measures to gather data from respondents for the final analysis. Self-report measures have limitations: the honesty of respondents concerned about their image; lack of introspect or understanding of self or concepts/questions; and response bias each may influence the accuracy of responses and the overall data (Hoskin, 2012). Luppino et al. (2010) noted differences when comparing diagnostic interview results to self-report measures, which supports the difficulty and limitations when using self-report measures for measuring depressive symptoms. As such, the risk of underreported symptoms on self-assessments remains a limitation of this study. Because of the stigma of a mental illness diagnosis like depression, combined with African American women’s views in particular of depression as a sign of personal weakness (Ward, Wiltshire, Detry, & Brown, 2013), this could have led to the underreporting on self-report depression scales and on self-identification as African American


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(Ward, Clark, & Heidrich, 2009). Even though the researcher chose a depression measure with excellent reliability and validity to minimize concerns about the assessment of depression, the overall results may not generalize to the public because of these concerns. In addition to self-report surveys, certain variables were difficult to measure, such as emotional eating. Emotional eating is often discussed in terms of eating in response to negative emotions; however, studies have shown emotional eating occurs in response to positive emotions and involves many factors, some at the individual level, creating contradictions and inconsistencies in the research (for a review see Bongers & Jansen, 2016). It has been suggested that high emotional eating scores reflect changes in emotional arousal (Penaeu et al, 2013) and are highly individual, according to different characteristics of the person, and may be learned coping responses (Canetti et al, 2002). When considering self-report measures like the Emotional Eating Scale (Arnow, Kenardy, & Argas, 1995), which provided rating scales drawn from respondent replies to a range of options (“not at all” to “extremely likely”), the extreme responder may have influenced the data results in extreme directions. Since there is no clear consensus on diagnosing emotional eating, there are no specific cut-off scores on the EES; this presented a limitation to specifying the presence of emotional eating (B. Arnow, personal communication, June 22, 2016). The resulting overall emotional eating scores may not generalize to the public. Despite this limitation, the EES was used by this researcher as the most appropriate option to measure a wide range of emotions across a diverse population. Measuring emotional eating is as much of a challenge as measuring stress, which also has no agreed upon definition. Like the EES, the Perceived Stress Scale (PSS) has no cut-off score, only comparisons within the sample. Stress responses vary at the individual level (Cohen &


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Janicki-Deverts, 2012; Cotter & Kelly, 2018). Stress is also difficult to recognize; the inability to recognize stress symptoms would lead to inaccurate assessment (Cohen, Deverts, Chin, & Zajdel, 2015). BMI is another variable with limitations when self-reported. Within the study population, some respondents declined to provide this data. There is some disagreement as to the accuracy of BMI and body fat percentage. For example, Nuttal (2015) suggested it does not accurately capture body fat mass in different body regions. The tendency to underreport height and weight, which women do more often than men, also limits the accuracy of the data collected for analysis (Visscher, Vielt, Kroesbergen, & Seidell, 2006). While this underreporting can also create inaccurate data results and limit the generalization of the study (Mitchell, Catenacci, Wyatt, & Hill, 2011), the use of self-report height and weight remains an appropriate way to gather this type of data in a self-report setting. Demographic data collection proved to be equally as challenging when it came to race. The option to choose from federal categories might not have captured the view or selfdescription of participants. Some respondents chose not to provide their self-described race and skipped the question, whereas some chose the “other” option, and provided a combined race, or nationality/regional designation (i.e., Caribbean). This can occur in surveys that target women of color, who for various reasons, personal and political, choose not to identify a race (Hughes, Kiecolt, Keith, & Demo, 2015). During the initial data collection, the lack of self-identification of race created the appearance of a lack in response by African American women, which prompted an extension of the survey window. Yet this study’s researcher is confident the number of respondents across all groups provided an adequate sample for the data gathered. The use of social media presented a unique limitation, in that the fear of fraud or identity theft also limited


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the response numbers during the first window of data collection. However, this limitation was overcome within a few weeks of the survey period. Recommendation for Future Research This study expands the body of knowledge as it found emotional eating significantly predicted obesity in White women more so than in African American women. African American women, who are more often overweight than their White counterparts (Centers for Disease Control and Prevention, 2010; Pratt & Brody, 2014), had a weaker relationship with emotional eating. Research on emotional eating in African American women is limited (Meyer, Leung, Barry, & De Feo, 2010; Striegel-Moore, et al., 2003); emotional eating and obesity research has focused on morbidly obese and eating-disordered populations (Fischer, et al., 2007; Masheb & Grilo, 2005; Mensorio, et al., 2016). To further understanding of emotional eating in overweight populations (BMI >24 but