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Relationships among Health Self-Empowerment Theory Variables, Health Promoting Behaviors, and BMI in a Sample of Cultura...

Permanent Link: http://ufdc.ufl.edu/UFE0042645/00001

Material Information

Title: Relationships among Health Self-Empowerment Theory Variables, Health Promoting Behaviors, and BMI in a Sample of Culturally Diverse Adults
Physical Description: 1 online resource (83 p.)
Language: english
Creator: Grandoit, Delphia
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: coping, efficacy, empowerment, health, motivation, obesity
Psychology -- Dissertations, Academic -- UF
Genre: Psychology thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Given the high rates of overweight and obesity in the U.S., finding controllable psychological variables and behaviors that can prevent/modify overweight and obesity is critical. Health Self-Empowerment Theory (HSET) has been set forth as a theory to guide research aimed at addressing the problems of overweight and obesity. HSET asserts that the following five controllable psychological variables impact health promoting behaviors: (a) health motivation, (b) self-praise of health promoting behaviors, (c) adaptive coping styles, (d) health responsibility, and (e) health self-efficacy. This study tests two hypotheses regarding the theoretical integrity of the HSET and its usefulness in predicting health promoting behaviors. The first hypothesis states that HSET is a unified theory; therefore, each of five variables constituting this theory should be positively correlated. The second hypothesis states that engagement in health promoting behaviors will mediate the relationship between the variables constituting the HSET and an overweight/obesity-related health indicator (i.e., Body Mass Index; BMI). Additionally, this study addresses the following research question: Are there differences in levels of the HSET variables, levels of engagement in health promoting behaviors, and levels of BMI in association with sex and race/ethnicity? Study participants consisted of 365 culturally diverse adults, most of whom lived in low-income households and were overweight or obese or had a relative who was overweight or obese. To test the first hypothesis (i.e., the HSET variables will be positively correlated), a Pearson Product Moment Correlation was conducted. The results provided partial support for the first hypothesis in that most of the HSET variables had significant positive correlations with each other. However, there were two exceptions: (a) motivation was not significantly correlated with most of the other HSET variables and (b) the investigated coping style (i.e., use of social support) and health self-efficacy were not significantly correlated. To test the second hypothesis (i.e., health promoting behaviors will mediate the relationship between the HSET variables and BMI), a path model with bootstrapped estimates of standard error was conducted. The results provided partial support for the second hypothesis in that the investigated health promoting behaviors only mediated the relationship between self-praise of health promoting behaviors and BMI. Other findings from the path model show that self-praise and health responsibility were significant predictors of health promoting behaviors. Finally, results show significant differences in some of the theory variables, health promoting behaviors, and BMI in association with race/ethnicity and/or sex. The implications of the findings from this study for health promotion and reversing and preventing obesity are discussed.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Delphia Grandoit.
Thesis: Thesis (M.S.)--University of Florida, 2010.
Local: Adviser: Tucker, Carolyn M.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2012-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042645:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042645/00001

Material Information

Title: Relationships among Health Self-Empowerment Theory Variables, Health Promoting Behaviors, and BMI in a Sample of Culturally Diverse Adults
Physical Description: 1 online resource (83 p.)
Language: english
Creator: Grandoit, Delphia
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: coping, efficacy, empowerment, health, motivation, obesity
Psychology -- Dissertations, Academic -- UF
Genre: Psychology thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Given the high rates of overweight and obesity in the U.S., finding controllable psychological variables and behaviors that can prevent/modify overweight and obesity is critical. Health Self-Empowerment Theory (HSET) has been set forth as a theory to guide research aimed at addressing the problems of overweight and obesity. HSET asserts that the following five controllable psychological variables impact health promoting behaviors: (a) health motivation, (b) self-praise of health promoting behaviors, (c) adaptive coping styles, (d) health responsibility, and (e) health self-efficacy. This study tests two hypotheses regarding the theoretical integrity of the HSET and its usefulness in predicting health promoting behaviors. The first hypothesis states that HSET is a unified theory; therefore, each of five variables constituting this theory should be positively correlated. The second hypothesis states that engagement in health promoting behaviors will mediate the relationship between the variables constituting the HSET and an overweight/obesity-related health indicator (i.e., Body Mass Index; BMI). Additionally, this study addresses the following research question: Are there differences in levels of the HSET variables, levels of engagement in health promoting behaviors, and levels of BMI in association with sex and race/ethnicity? Study participants consisted of 365 culturally diverse adults, most of whom lived in low-income households and were overweight or obese or had a relative who was overweight or obese. To test the first hypothesis (i.e., the HSET variables will be positively correlated), a Pearson Product Moment Correlation was conducted. The results provided partial support for the first hypothesis in that most of the HSET variables had significant positive correlations with each other. However, there were two exceptions: (a) motivation was not significantly correlated with most of the other HSET variables and (b) the investigated coping style (i.e., use of social support) and health self-efficacy were not significantly correlated. To test the second hypothesis (i.e., health promoting behaviors will mediate the relationship between the HSET variables and BMI), a path model with bootstrapped estimates of standard error was conducted. The results provided partial support for the second hypothesis in that the investigated health promoting behaviors only mediated the relationship between self-praise of health promoting behaviors and BMI. Other findings from the path model show that self-praise and health responsibility were significant predictors of health promoting behaviors. Finally, results show significant differences in some of the theory variables, health promoting behaviors, and BMI in association with race/ethnicity and/or sex. The implications of the findings from this study for health promotion and reversing and preventing obesity are discussed.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Delphia Grandoit.
Thesis: Thesis (M.S.)--University of Florida, 2010.
Local: Adviser: Tucker, Carolyn M.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2012-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042645:00001


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1 RELATIONSHIPS AMONG HEALTH SELF EMPOWERMENT THEORY VARIABLES, HEALTH PROMOTING BEHAVIORS, AND BMI IN A SAMPLE OF CULTURALLY DIVERSE ADULTS By DELPHIA J. GRANDOIT A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2010

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2 2010 Delphia J. Grandoit

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3 In m emory of Dereck Chiu

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4 ACKNOWLEDGMENTS I have many to thank for their contributions to this thesis. Foremost, I thank my chair, Dr. Carolyn M. Tucker, whose brilliant ideas, support and empowering guidance have led to the creation of this work. I also wish to thank my committee members, Dr. Bonnie Moradi and Dr. Mary Fukuyama. Their knowledge a nd feedback significantly improved this paper and the I wish to thank the Health Psycholo gy Research team for all of the support they have given me throughout this process, and particularly, Sarah Nolan for her constant encouragement and humor as we both faced the struggle of completing theses together. I also would like to thank my family for somehow managing to provide support from a distance. Finally, and perhaps most importantly, I thank my partner, Janathan Grandoit, for his continual comfort and uplifting words, for holding me up when I was weary, and bringing joy, relaxation, and excitem ent when I needed it most.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ ........... 4 LIST OF TABLES ................................ ................................ ................................ ...................... 7 LIST OF FIGURES ................................ ................................ ................................ .................... 8 ABSTRACT ................................ ................................ ................................ ............................... 9 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ ............. 11 Nature of Problem ................................ ................................ ................................ .............. 11 Theoretical Framework ................................ ................................ ................................ ...... 14 Study Aims, Hypotheses, and Research Question ................................ ............................... 15 2 LITERATURE REVIEW ................................ ................................ ................................ ... 17 Social Cognitive Theory ................................ ................................ ................................ ..... 17 Social Cognitive Theory and Physical Activity ................................ ............................ 17 Social Cognitive Theory and Diet ................................ ................................ ................ 18 Social Cognitive Theory Interventions ................................ ................................ ......... 19 Health Self Empowerment Theory ................................ ................................ ..................... 21 Motivation and Health Promoting Behaviors ................................ ............................... 22 Self Praise and Health Promoting Behaviors ................................ ............................... 23 Adaptive Coping Styles and Health Promoting Behaviors ................................ ........... 23 Health Responsibility and Health Promoting Behaviors ................................ ............... 24 Self Efficacy and Health Promoting Behaviors ................................ ............................ 24 Study Overview ................................ ................................ ................................ ................. 24 3 METHOD ................................ ................................ ................................ .......................... 25 Participants ................................ ................................ ................................ ........................ 25 Measures ................................ ................................ ................................ ............................ 26 Demographic Data Questionnaire (DDQ) ................................ ................................ .... 26 Health Behaviors Goal Agreement (HBGA) Rating Form ................................ ........... 26 Health Self Praise Questionnaire (HSPQ) ................................ ................................ .... 27 Coping Questionnaire (COPE) ................................ ................................ .................... 28 Health Promoting Lifestyle Profile (HPLP) ................................ ................................ 28 Weight Efficacy Lifestyle Questionnaire (WEL) ................................ ......................... 29 Body Mass Index (BMI) ................................ ................................ .............................. 31 Procedure ................................ ................................ ................................ ........................... 31

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6 4 RESULTS ................................ ................................ ................................ .......................... 33 Preliminary Analyses ................................ ................................ ................................ ......... 33 Descriptive Statistics for the Major Investigated Variables ................................ ................. 35 Results of Pearson Correlations to Test Hypothesis One ................................ ..................... 35 Results of Bootstrapped Path Analysis to Test Hypothesis Two ................................ .......... 36 Model Specification ................................ ................................ ................................ .... 36 Significance Tests ................................ ................................ ................................ ....... 37 Direct effects ................................ ................................ ................................ ........ 38 Indirect effect ................................ ................................ ................................ ....... 38 Total effects ................................ ................................ ................................ ......... 38 Results of the ANOVAs to Test Research Question One ................................ .................... 39 5 DISCUSSION ................................ ................................ ................................ .................... 48 Summary of Results ................................ ................................ ................................ ........... 48 Hypothesis One ................................ ................................ ................................ ........... 48 Hypothesis Two ................................ ................................ ................................ .......... 49 Direct and total e ffects ................................ ................................ .......................... 49 Indirect effects ................................ ................................ ................................ ...... 50 Theoretical considerations ................................ ................................ .................... 51 Resear ch Question ................................ ................................ ................................ ....... 52 Limitations and Future Directions ................................ ................................ ...................... 55 Sample Limitations ................................ ................................ ................................ ..... 55 Measure Limitations ................................ ................................ ................................ .... 55 Limitations in Study Design ................................ ................................ ........................ 56 Future Directions ................................ ................................ ................................ ......... 56 Conclusions ................................ ................................ ................................ ........................ 57 APPENDIX : MEASURES ................................ ................................ ................................ ........ 59 LIST OF REFERENCES ................................ ................................ ................................ .......... 74 BIOGRAPHICAL SKETCH ................................ ................................ ................................ ..... 83

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7 LIST OF TABLES Table page 4 1 Selected descriptive statistics for all investigated variables using raw data set ................ 41 4 3 Pearson correlations of Health Self Empowe rment Theory variables using raw data set ................................ ................................ ................................ ................................ .. 43 4 4 Pearson correlations of Health Self Empowerment Theory variables using imputed dat a set ................................ ................................ ................................ .......................... 43 4 5 Direct, indirect and total effects in the path model ................................ ......................... 44 4 6 Statistics of ANOVAs using sex as the independent variable ................................ ......... 45 4 7 Statistics of ANOVAs using race/ethnicity as the independent variable ......................... 46

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8 LIST OF FIGURES Figure page 4 1 Full path model. ................................ ................................ ................................ ............. 47 4 2 Path model showing significant total and direct effects. ................................ ................. 47

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9 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science RELATIONSHIPS AMONG HEALTH SELF EMPOWERMENT THEORY VARIABLES, HEALTH PROMOTING BEHAVIORS, AND BMI IN A SAMPLE OF CULTURALLY DIVERSE ADULTS By Delphia J. Grandoit December 2010 Chair: Carolyn M. Tucker Major: Psychology Given the high rates of overweight and obesity in the U.S., finding controllable psychological variables and behaviors that can prevent/modify overweight and obesity is critical. Health Self Empowerment Theory (HSET) has been set forth a s a theory to guide research aimed at addressing the problems of overweight and obesity. HSET asserts that the following five controllable psychological variables impact health promoting behaviors: (a) health motivation, (b) self praise of health promoting behaviors, (c) adaptive coping styles, (d) health responsibility, and (e) health self efficacy. This study tests two hypotheses regarding the theoretical integrity of the HSET and its usefulness in predicting health promoting behaviors. The first hypothes is states that HSET is a unified theory; therefore, each of five variables constituting this theory should be positively correlated. The second hypothesis states that engagement in health promoting behaviors will mediate the relationship between the variab les constituting the HSET and an overweight/obesity related health indicator (i.e., Body Mass Index; BMI). Additionally, this study addresses the following research question: Are there differences in levels of the HSET variables, levels of engagement in he alth promoting behaviors, and levels of BMI in association with sex and race/ethnicity? Study participants consisted of 365 culturally diverse adults, most of

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10 whom lived in low income households and were overweight or obese or had a relative who was overwe ight or obese. To test the first hypothesis (i.e., the HSET variables will be positively correlated), a Pearson Product Moment Correlation was conducted. The results provided partial support for the first hypothesis in that most of the HSET variables had significant positive correlations with each other. However, there were two exceptions: (a) motivation was not significantly correlated with most of the other HSET variables and (b) the investigated coping style (i.e., use of social support) and health sel f efficacy were not significantly correlated. To test the second hypothesis (i.e., health promoting behaviors will mediate the relationship between the HSET variables and BMI), a path model with bootstrapped estimates of standard error was conducted. The r esults provided partial support for the second hypothesis in that the investigated health promoting behaviors only mediated the relationship between self praise of health promoting behaviors and BMI. Other findings from the path model show that self praise and health responsibility were significant predictors of health promoting behaviors. Finally, results show significant differences in some of the theory variables, health promoting behaviors, and BMI in association with race/ethnicity and/or sex. The impl ications of the findings from this study for health promotion and reversing and preventing obesity are discussed.

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11 CHAPTER 1 INTRODUCTION Nature of Problem The promotion of health and the prevention and reversal of obesity and related diseases have become major foci among government, academic, health, and business organizations in the U.S. today. One would think that with so much attention to these h ealth related issues, the majority of people living in the U.S. would be living healthy lives. This, however, is not the case as is indicated by the high prevalence of overweight and obesity among children and adults in the U.S. In fact, approximately one third of the adults in the U.S. are overweight, and 34% of the adults in the U.S. are obese (National Center for Health Statistics, 2009). These statistics are based on the Body Mass Index (BMI) cutoffs. According to the National Heart, Lung and Blood Ins titute (2010), someone who has a BMI under 18.5 is considered underweight, a BMI between 18.5 24.9 is considered normal weight, a BMI between 25 29.9 is considered overweight, and a BMI of 30 and above is considered obese. Individuals who are overweight or obese can face numerous long term conditions as a result of their BMI including, but not limited to hypertension, coronary heart disease, stroke, and kidney disease (National Heart, Lung and Blood Institute, 2009). Racial and ethnic minorities are particu larly at risk for being overweight or obese. In decreasing order of prevalence, the highest rates of obesity among women are in African American, Mexican American, and European American groups. Among men, the highest rates of obesity in decreasing order of prevalence are in Mexican American, European American, and African American groups (National Heart, Lung and Blood Institute, 2009). There are clearly differences in the breakdown of race/ethnicity by sex in rates of overweight and obesity, but there are also overall sex differences in the prevalence of overweight and obesity. According to

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12 there were higher rates of obese women than men, but higher rates of overweight men than women, highlighting the importance of examining the subtleties of the impact of sex on BMI. Obesity is also highly prevalent and problematic among low income groups. Low income individuals are 50% more likely to be obese than other socioeconomic groups (National Heart, Lung and Blood Institute, 2009). The problems of obesity among racial and ethnic minorities, groups with a low socioeconomic status, and women are compounded by the other health disparities that these groups face health dispariti es for which obesity is often a contributor, product, or both. According to the National Institutes of Health (2008), health disparities are long standing differences how frequently a disease affects a group, how many people g et sick, or how often the disease causes death health disparities are racial and ethnic minorities, inhabitants of rural areas, those with disabilities, women, children, and the elderly. Major contributors to overwei ght and obesity are not consistently engaging in physical activity and not eating a healthy diet. Inconsistent or no engagement in physical activity has been shown to result in higher rates of overweight and obesity as assessed by Body Mass Index (BMI) (Ya ng, Telama, Viikari, & Raitakari, 2006). Studies have also shown that levels of engagement in physical activity can vary by race/ethnicity and sex (Kim, Bramlett, Wright, & Poon, 1998; Kruger, Yore, Solera, & Moeti, 2007; Marshall, Jones, Ainsworth, Reis, Levy, & Macera, 2007; Whitt Glover, Taylor, & Heath, 2007). For example, in a study in which the CDC analyzed a survey on engagement in regular physical activity (i.e., engaging in moderate to intense physical activity 5 or more days per week) among adults it was found that the highest rates of regular

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13 physical activity were among men. Additionally, among the men in the study, highest rates of regular physical activity were among non Hispanic white men, followed by men classified as of other race, non Hisp anic black men, and Hispanic men. Among women, the highest rates of regular physical activity were among non Hispanic white women, followed by women classified by other race, Hispanic women, and non Hispanic black women (Kruger & Miles, 2007). These findin gs suggest that various demographic groups may have different barriers to engaging in physical activity, and thus interventions aimed at promoting physical activity among culturally diverse groups may need to be customized. Eating a healthy diet (e.g ., eat ing whole grains and eating a healthy breakfast each day) has been found to be negatively associated with BMI (Rose, Hosig, Davy, Serrano, & Davis, 2007; Kant, Andon, Angelopoulos, & Rippe, 2008). As is the case with engaging in physical activity, eating a healthy diet can also vary by race/ethnicity and sex In a study that assessed dietary behaviors in 23 countries, results suggested that women as compared to men were significantly more likely to avoid high fat foods and eat high fiber foods, more likely to eat fruit daily, and less likely to add salt to their food (Wardle, Haase, Steptoe, Nillapun, Jonwutiwes, & Bellisle, 2004). Additionally, using the same data that was used in the Kruger and Miles (2007) study, researchers found that non white men (i.e. racial/ethnic minorities) as compared to non Hispanic White men consumed significantly more fruits and vegetables and engaged in more physical activity. Additionally, non Hispanic white women consumed significantly more fruits and vegetables and engaged in more physical activity than non Hispanic black women and Latina women (Kruger, Yore, Solera, & Moeti, 2007). Even with what seems to be the widespread knowledge that consistently eating a healthy diet and engaging in physical activity can have desirable effects on BMI (although it is arguable

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14 whether the knowledge is truly widespread), there continues to be high rates of overweight and obesity. It is possible that groups who experience health disparities do not consistently engage in physical activity an d eating a healthy diet because of the barriers to these behaviors that these groups face barriers that include unsafe neighborhoods that deter physical activities such as walking and jogging, and the lack of neighborhood food markets that carry fresh fr uits and vegetables that are important components of a healthy diet. Given that many of these barriers are intractable, research is needed to identify modifiable psychological empowerment variables that may enable engaging in physical activity and eating a healthy diet even in the face of environmental, social, economic and other barriers to health promoting behaviors. Theoretical Framework This study examines how the Health Self Empowerment Theory variables (HSET; Tucker, Butler, Loyuk, Desmond, & Surrenc y, 2009) are associated with engagement in health promoting behaviors and BMI. The HSET acknowledges the often negative impact of social, economic, and environmental variables (e.g., absence of bike routes in low income communities, eating fast food becaus e of working two jobs and thus having limited time to cook, and not having access to health education programs that enable individuals to understand food labels) on engaging in health promoting behaviors and ultimately on health indicators such as BMI; how ever, this theory asserts that self empowerment oriented variables can enable individuals to engage in these behaviors under whatever conditions that exist in their lives. several modifiable personal factors that individuals can change to promote their own health. The HSET specifically sta tes that there are five personal/psychological variables that influence health promoting behaviors such as consistent engagement in physical activity and eating a healthy diet. These five variables are (a)

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15 motivation to engage in health promoting behaviors such as eating a healthy diet and engaging in physical activity, (b) self praise of health promoting behaviors, (c) adaptive coping styles to (health responsib ility), and (e) health self efficacy. The author of the HSET added health responsibility as a component of this theory subsequent to its publication (see Tucker, Butler, Kaye, Grandoit, Marsiske, Bragg, et al., 2010). While there is some research support f or the HSET (see Chapter 2), more research needs to be done that examines the usefulness of the theory in its entirety for understanding the health promoting behaviors of individuals who have low family incomes and/or are racial/ethnic minorities groups who often have little social or economic power in society and are often overweight or obese, and thus particularly need to be consistently engaging in physical activity and eating a healthy diet. Study Aims, Hypotheses, and Research Question The aims of t he planned study are (a) to determine if the variables constituting the Health Self Empowerment Theory, including the recently added health responsibility variable, are significantly correlated and thus form a unified theory, and (b) to examine the associa tions among the HSET variables, two obesity related health promoting behaviors (i.e., eating a healthy diet and engaging in physical activity), and BMI. Of major importance in the framing of these aims is that they will be examined using a sample with an overrepresentation of overweight and obese individuals, racial/ethnic minority individuals, and individuals who live in low income households. Such individuals could potentially benefit from the present study given that it may have clear implications for p sychological empowerment based interventions that can enable them to increase their health promoting behaviors and reduce their BMI. The ultimate benefit of such interventions could be elimination of obesity related health disparities of national concern. Using a cross sectional design, the following research hypotheses will be investigated:

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16 1. The HSET variables (i.e., motivation to engage in health promoting behaviors, self praise of health promoting behaviors, coping through the use of instrumental social support, health responsibility, and weight management self efficacy) will have significant positive correlations among each other. 2. The HSET variables will positively predict health promoting behaviors (i.e., eating a healthy diet and engaging in physical activity) and negatively predict BMI; the health promoting behaviors will negatively predict BMI; and the health promoting behaviors will mediate the relationship between the HSET variables and BMI (see Figure 1 in the Results chapter for a graphical depi ction of this hypothesis). The following research question will also be addressed: Are there differences in levels of the HSET variables, levels of engagement in physical activity and eating a healthy diet, and levels of BMI in association with sex and ra ce/ethnicity? Exploring sex and race/ethnicity differences may inform the application of the HSET to various groups, particularly groups that are most negatively impacted by he alth disparities such as racial/ ethnic minorities and women.

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17 CHAPTER 2 L ITERATURE REVIEW Social Cognitive Theory psychological theories for understanding the occurrence of health promoting behaviors such as engaging in physical activity and eating healthy foods. When applied to such b ehaviors, SCT explains that these health promoting behaviors are a result of social and environmental influences (i.e., perceived sociostructural facilitators or impediments) on health and personal and cognitive control over health (Bandura, 1998). Accordi ng to SCT, self efficacy and outcome expectations are major factors in understanding behavior. Self efficacy is the belief that one is capable of performing a certain behavior. Outcome expectations are the beliefs that the behavior will lead to desired ph ysical, social, and/or self evaluative outcomes. Additionally, Bandura (2004) explains that knowledge of health risks and benefits and health goals are critical components of engaging in health promoting behaviors. SCT specifically states that self efficac y directly impacts health behavior and also indirectly impacts health behavior through outcome expectations, sociostructural factors, and goals. Outcome expectations and sociostructural factors can also impact goals (Bandura, 2004). Social Cognitive Theory and Physical Activity The literature is extensive on SCT in general, but also in the application of SCT on health prevention and promotion. Regarding exercise and physical activity behaviors, one literature review found that all of the investigated studie s showed a significant relationship between self efficacy and exercise behavior, with explained variance ranging from 4 26% (Keller, Fleury, Gregor Holt, & Thompson, 1999). Furthermore, most of the investigated studies in the review showed a significant re lationship between outcome expectations and exercise behavior. Additionally, the studies in the literature review mostly involved participants who were yo ung, Caucasian, or from a high socioeconomic status Another study examined the factors that

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18 contribut ed to decline in physical activity in low income communities; low self efficacy was Among white older adults in the U.S. and Spain, self efficacy significantly p redicted physical activity; however, outcome expectations did not have a significant effect (Perkins, Multhaup, Perkins, & Barton, 2008). In a study examining multiple predictors of physical activity among black and white adults, perceptions of the physica l environment (e.g., perceiving the neighborhood as safe) had a significant effect on physical activity. Furthermore, motivation to engage in health promoting behaviors and self efficacy mediated the relationship between social and physical environmental f actors and physical activity (Haughton McNeill, Wyrwich, Brownson, Clark, & Kreuter, 2006). In th at study, self efficacy had the strongest relationship with physical activity out of all of the predictors as levels of self efficacy increased, engagement i n physical activity increased. Social Cognitive Theory and Diet There is also extensive research examining eating behaviors in the context of SCT. In a systematic review of literature on SCT and fruit and vegetable intake, Guillaumie, Godin, and V zina Im (2010) found that SCT variables accounted for 23% of the variance in fruit and vegetable intake, 19% of the variance in fruit intake alone, and 14% of vegetable intake alone. Another study conducted in Canada found that both self efficacy with regard to f ruit and vegetable consumption and nutrition knowledge were significantly correlated with fruit and vegetable intake (Strachen & Brawley, 2009). A study involving church participants from various demographic backgrounds by Anderson, Winett, & Wojcik (2007) found that self efficacy had a significant indirect effect on fat, fiber, and fruit and vegetable intake through outcome expectations. Social support and self regulation were also important components in determining eating behaviors in this study. In focu s groups using health club members, it was

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19 found that the knowledge component of SCT seemed to be the biggest factor in whole grain intake (Croy & Marquart, 2005). In a study using African Americans and European Americans from rural communities, multiple factors were found to impact eating behaviors, and the impact of these factors varied by sex (Hermstad, Swan, Kegler, Barnette, & Glanz, 2010). Specifically, home nutrition environment (i.e., what food is available in the home) mediated the relationship be tween self efficacy and healthy eating/dietary fat intake among women. Further, self efficacy was significantly related to both family support for healthy eating and eating out habits among both men and women. Social Cognitive Theory Interventions In add ition to using SCT to understand health promoting behaviors, this theory has been used to inform health interventions. One such intervention improved levels of fat, fiber, fruits, and vegetables; increased nutrition self efficacy (i.e., sense of personal c ontrol over eating habits); and increased both physical and social outcome expectations among participants who completed a computerized nutrition behavior intervention in supermarkets (Anderson, Winett, Wojcik, Winett, & Bowden, 2001). The computerized int ervention consisted of primarily white, middle income participants. In an intervention study to test the effects of a weight management program with overweight men and women in Turkey, it was found that self efficacy was significantly associated with weigh t loss (Bas & Donmez, 2009). In another weight loss intervention with primarily white, educated, and married women, self efficacy was significantly associated with weight loss behaviors at baseline and predicted weight control behaviors and weight loss dur ing the intervention stages of the study (Linde, Rothman, Baldwin, & Jeffery, 2006). Weight control behaviors mediated the relationship between self efficacy and weight change.

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20 A set of three longitudinal studies all used an SCT based exercise adherence intervention with cardiac and orthopedic reha bilitation patients (Schwarzer, Luszczynska, Ziegelmann, Scholz, & Lippke, 2008). The authors found that the SCT variables were significantly related to health behavior at post measurement. Risk perception, self efficacy, intention, and planning were significantly associated with health behavior in all three studies and outcome expectancies were significantly associated with health behavior in one of the three studies. In a church based study, two SCT based inte rventions were implemented (one internet intervention alone and one internet plus social support intervention) to improve health promoting behaviors among church members from various demographic backgrounds (Winett, Anderson, Wojcik, Winett, & Bowden, 2007 ). The internet plus social support intervention was effective in ble, and fiber intake and steps walked per day. There are many more studies examining SCT and health promoting behaviors, but the studies cited above sufficiently make the point that SCT is useful for understanding the occurrence of and developing interve ntions to significantly increase health promoting behaviors. While the present study recognizes the credibility of SCT, it will instead test an alternative theory that has an empowerment framework This framework may be particularly useful in understandin g the occurrence of health promoting behaviors among groups who because of their low socioeconomic status and/or racial/ethnic minority status have limited power over the social and environmental factors that influence these behaviors. Because SCT was no t developed using such groups but rather was developed using racial/ethnic majority groups (i.e., European American groups), it may not be a particularly appropriate theory for understanding the health promoting behaviors of groups with a low socioeconomic status and/or a racial/ethnic minority status such as those in the present study. Thus, an aim of the present study is to explore the

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21 usefulness of Health Self Empowerment Theory for understanding the health promoting behaviors and BMI status of the adul ts in the present study adults who are racial/ethnic minorities and/or have a low family income and who are most affected by health disparities. Health Self Empowerment Theory While there are clearly factors in SCT that are within the control of the in dividual seeking to change behavior, social and environmental factors are typically beyond the complete control of an individual and thus are much more difficult to modify. Although SCT is a sound and plausible theory and has clear implications for promoti ng health behaviors, interventions based implement with populations that have several social and environmental barriers (Tucker, Butler, Loyuk, Desmond, & Surrency, 2009). In response to the possible limitations of SCT for use with groups who face health disparities, Tucker and her colleagues posit an alternate culturally sensitive theory that recognizes the intractableness of many social, economic, and environmenta l barriers to health promoting behaviors. This theory is entitled the Health Self Empowerment Theory (HSET) emphasizing several modifiable personal factors that individuals can change to promote their own health. The HSET specifically states that there are five personal/psychological variables that influence health promoting behaviors such as consistent engagement in physical acti vity and eating a healthy diet. These five variables are (a) motivation to engage in health promoting behaviors such as eating a healthy diet and engaging in physical activity, (b) self praise of health promoting behaviors, (c) adaptive coping styles to ma nage emotions that negatively impact efficacy. Using a sample of low income African American and white mothers of chronically ill

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22 children, Tucker et al. (2009) have demonstrated that the HSET variables as a whole, excluding motivation to engage in health promoting behaviors and health responsibility, show significant associations with an overall health promoting lifestyle. Motivation may not have been predictive of a health promoting lifestyle because of the measure of motivation used in the study. Health responsibility was not assessed in the study. As discussed in the following sections, other studies have shown that the variables constituting HSET theory are l inked to health promoting behaviors and/or weight loss. Motivation and Health Promoting Behaviors Motivation, which is often defined as having a sense of purpose or desire to accomplish something, is a broad concept. For the purposes of this study, motiv ation is operationalized in terms of level of commitment to goals a person sets for him/herself. Previous studies have examined the role of motivation in the occurrence of health promoting behaviors. For example, Jayanti and Burns ( 1998) found that motivat ion and self efficacy were associated with engagement in preventative health behaviors. Moorman and Matulich ( 1993) found that motivation was tied to eating behaviors. Specifically, when motivation was high, levels of diet restriction was high. Another st udy found that general self determination and exercise motivation significantly predicted self regulation of eating behaviors (Mata, Silva, Vieira, Carraa, Andrade, Coutinho, et al., 2009). Another study by Hearty, McCarthy, Kearney, and Gibney (2007) dem onstrated that motivation was significantly associated with eating practices (i.e., eating breakfast cereals, vegetables, fruits, and poultry dishes). In an intervention that promoted self determination, goal setting, and motivation, intervention participa nts showed more weight loss and higher levels of engagement in physical activity compared to controls (Silva, Vieira, Coutinho, Minderico, Matos, Sardinha, et al., 2010). When controlling for explicit motivation, implicit motivation has also been shown to significantly predict physical activity (Conroy, Hyde, Doerksen, & Ribeiro, 2010).

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23 Self Praise and Health Promoting Behaviors Self praise is defined as verbal or non verbal messages of affirmation created by and for the self. It has been linked to openn ess to receiving health information (Harris, Mayle, Mabbott, & Napper, 2007; Harris & Napper, 2005; Jessop, Simmonds, & Sparks, 2009; Sherman, Nelson, & Steele, 2000; van Koningsbruggen & Das, 2009; van Koningsbruggen, Das, & Roskos Ewoldsen, 2009). Howeve r, the literature on the relationship between self praise and healthy eating or physical activity is sparse (Harris & Epton, 2009). The author is only aware of one study examining such a relationship. This study was conducted by Epton and Harris (2008) and revealed that participants who used self affirmation techniques ate significantly more fruit and vegetable servings than those who did not self affirm. Adaptive Coping Styles and Health Promoting Behaviors For the purposes of this study, coping through t he use of instrumental social support, an adaptive coping style, is the primary focus. A daptive coping styles have been linked to health promoting behaviors in the existing research literature. Poor coping styles may play a critical role in behaviors relat ed to weight maintenance and obesity (Byrne, 2002). Freeman and Gil styles they implemented in their daily life. In a health intervention study, it was found that participants who created action and coping plans, which reflect an adaptive coping style, had significantly more fruit and vegetable intake and marginally higher levels of physical activity than participants in the control group (Luszczynska & Haynes, 2009). Additionally, among overweight and obese participants in this study, intervention participants had significantly lower post intervention BMI than the control participants, controlling for baseline BMI. It is important to note that the intervention was only effective for participants who reported strong self efficacy beliefs at baseline. In another intervention that focused on proactive coping, which is another style of adaptive coping, for

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24 diabetes management behaviors, participants who completed th e intervention significantly improved their eating and physical activity behaviors and had significant weight loss over a 12 month period (Thoolen, de Ridder, Bensing, Gorter, & Rutten, 2009). Health Responsibility and Health Promoting Behaviors Health r being involved in personal health care and health practices. Generally, health responsibility has been conceptualized as an outcome or health promoting behavior (Coulson, Str ang, & Minichiello, 2004; Duffy, 1988; Hong & Li, 2008; Luca, Orshan, & Cook, 2000; Speake, Cowart, & Stephens, 1991; Starke & Brinkley, 2007). However, in this study health responsibility is being examined as a predictor or psychological variable. Because of this re conceptualization, there is no known research demonstrating the link between health responsibility and health prom oting behaviors. Self Efficacy and Health Promoting Behaviors The self efficacy component of the HSET is taken from the Social Cognitive Theory. As can be seen in previous sections of this paper, there is ample evidence linking self efficacy to health promoting behaviors. This study focuses on weight management self efficacy, or the sense of control over weight management behaviors, with a particular focus on eating behaviors. Study Overview The present study will explore the relationships among the HS ET variables, health promoting behaviors (i.e., eating a healthy diet and physical activity), and Body Mass Index (BMI). Specifically, the study will test whether health promoting behaviors mediate the relationship between the HSET variables and BMI.

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25 CHA PTER 3 METHOD Participants This study uses secondary data from a larger study that tested the intervention effects of a health promotion workshop series called the Family Health Self Empowerment Project (referred see Tucker, Butler, Kaye, Grandoit, Marsiske, Bragg, et al., 2010 for a description of the larger study). Participant inclusion criteria for the larger study of which the present study is a part were as follows: (a) age 18 or older, (b) self report as bein g overweight or obese or self report as being a family member of someone who is overweight or obese, (c) provide written consent to participate in the larger study, and (d) communicate in written or verbal form in English or Spanish. Participant exclusion criteria were as follows: (a) self report being pregnant and/or (b) self report having to adhere to a special diet due to diabetes or other reasons. The exclusion criteria were selected because pregnancy and special diets would confound the predictor vari ables and likely impact the outcome variables (e.g., diet and Body Mass Index [BMI]) of interest. A total of 365 volunteer adults enrolled in the present study. Of the 365 participants, 340 provided sufficient data for all of the necessary analyses. Adult program participants ranged in age from 18 to 85 years old, with a mean age of 41.36 years old ( SD= 13.08). There were 262 (71.8%) females, 93 (25.5%) males, and 10 (2.7%) participants who did not report their sex. One hundred twenty four (34.0%) adult part icipants self identified as African American/Black (hereafter referred to as African American), 107 (29.3%) self identified as non Hispanic Caucasian/White/European American (hereafter referred to as European American), 66 (18.9%) self identified as Hispan ic/Latino(a), and 35 (9.6%) self identified as Asian American. Additionally, 17 (4.7%) adult participants self identified as other (i.e., Caribbean, American Indian, Multi Racial, Middle Eastern, South Asian, Guyanese, and West Indian) and 16 participants did not report their race/ethnicity

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26 One hundred nine (29.9%) participants reported an annual household income below $20,000, 121 (33.1%) participants reported an annual household income between $20,000 and $40,000, and 80 (21.9%) participants reported an annual household income above $40,000. Fifty five participants did not report an annual household income. Given the above household income distribution data, the sample of participants in this study is described as being a low income skewed sample. Accor ding to the Body Mass Index values, one participant (0.3%) was considered underweight, 31 (8.5%) participants were considered normal weight, 53 (14.5%) participants were considered overweight, and 106 (29.0%) participants were considered obese. One hundred seventy four (47.7%) participants did not provide data (i.e., weight and height measurements) for determining BMI. Measures All of the instruments constituting the Assessment Battery (AB) for this study had a 5 th grade reading level as determined by Micr osoft Word 2007. Instruments in the AB were counter balanced to reduce order effects. Below are brief descriptions of the instruments that constitute the AB. Demographic Data Questionnaire (DDQ) The DDQ was designed to obtain the following information: con tact information, race/ethnicity, sex, age, language preference, and annual household income. Health Behaviors Goal Agreement (HBGA) Rating Form Th is Form was constructed by the investigators of the larger study, and it was used as a measure of motivation to engage in health promoting behaviors. Rawsthorne and Elliott (1999) provided support for using a goal assessment to measure motivation; their research indicated that having behavioral goals increases motivation to achieve those goals. At the time the s tudy occurred, the research investigators were unable to find a health motivation measure that seemed

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27 appropriate for the study so they created the HBGA. The specific items on the HBGA are based on the specific health promoting behaviors that were the focu s of the larger study. The HGBA Form assesses the degree to which health promoting behaviors are goals for an individual and was used as a measure of motivation to engage in those behaviors. Thus, each participant was asked to rate their l evels of agreement that eight health promoting behaviors (e.g., exercising, eating healthy foods and snacks, restricting television and other screen time, and drinking healthy drinks) are goals for her/him. The specific instruction on the HBGA Rating Form is to rate how much you agree that each item is a personal goal using a 4 point Likert scale ranging from 1 ( strongly disagree ) to 4 ( strongly agree ). A sample item on e calculated by taking the mean of all of the items in the scale. Higher scores indicate more motivation to engage in health promoting behaviors. Validity and reliability of scores generated by the HBGA were not obtained prior to the present study, which i s a limitation of this study. Health Self Praise Questionnaire (HSPQ) The 10 item HSPQ was constructed by the investigators of the larger study, and it is designed to assess the extent to which an individual engages in self praise of specific health promoting behaviors ( e.g ., exercising, eating healthy foods and snacks, restricting screen time, and drinking healthy drinks). The instruction on the HSPQ is to indicate how often you praise yourself for the behaviors listed using a 4 point Likert type sc ale, ranging from 1 ( never ) to 4 ( always calculated by taking the mean of al l of the items in the scale. Higher scores indicate higher levels of engagement in self praise of health promoting behaviors. A similar questionnaire was used in the Tucker, Butler, Loyuk, Desmond, and Surrency (2009) study; however, additional items were added for this study to target the health promoting behaviors that were the focus of the larger

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28 study. Validity and reliability were not assessed prior to the use of the HSPQ in this study, which is a limitation of this study. Coping Questionnaire (COPE) The COPE (Carver, Scheier, & Weintraub, 1989) is a 60 item questionnaire that is used subscales, one of which was used in the present study. The subscale used in t his study measures use of instrumental social support, which is an adaptive coping style. The instruction on the COPE is to indicate how frequently you use particular coping styles using a 4 point Likert type scale, ranging from 1 ( all ) to 4 ( usually do this a lot ). A sample item calculated by summing the ratings of the items in each individual subscale. There is no overall score. Higher scores indicate more frequent utilization of each coping style. reported to be .75 in the scale development study (Carver et al., 1989). The authors of the COPE demonstrated validity by examining the relationship between the COPE scales and personality measures. As Carver et al. (1989) predicted, coping styles that were conceptualized as functional were significantly associated with personality types that are cons idered beneficial, and coping styles that were conceptualized as less functional were inversely associated with beneficial personality types. Furthermore, the COPE has been used in studies pertaining to various physical health issues such as breast cancer (Culver, Arena, Antoni, & Carver, 2002), pregnancy (Rudnicki, Graham, Habboushe, & Ross, 2001), and overall physical health (Scheier & Carver, 1992). Health Promoting Lifestyle Profile (HPLP) The HPLP (Walker, Sechrist, & Pender, 1987) is a 52 item self re port inventory that measures level of engagement in an overall health promoting lifestyle. Six HPLP subscales

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29 assess level of engagement in specific health promoting behaviors that constitute a health promoting lifestyle; however, only the following three subscales were used in the present study: displaying health responsibility, consistently engaging in physical activity, and eating a healthy diet (nutrition). The health responsibility subscale was used to assess the health responsibility component of the Health Self Empowerment Theory (HSET). The other two subscales (physical activity and nutrition) were used to assess obesity related health promoting behaviors. The instruction on the HPLP is to indicate how frequently you engage in specific health promoti ng behaviors using a 4 point Likert type scale, ranging from 1 ( never ) to 4 ( routinely ). A sample item on this profile is Scores are calculated by taking the mean of all of the items in each subscale. Higher scores indicate a higher level of engagement in a health promoting lifestyle. (physical activity subscale), and .76 (nutrition subscale) in th e scale development study (Walker et al., 1987). The HPLP has been widely used and the resulting scales have been validated in research (e.g., in multiple medical settings, with many different populations, in various work settings, etc.). For a listing of research that has used the HPLP, see Walker, Sechrist, and Pender (n.d ). Weight Efficacy Lifestyle Questionnaire (WEL) The WEL (Clark, Abrams, Niaura, Eaton, & Rossi, 1991) is a 20 item questionnaire that is used to measure how confident individuals ar e in controlling their eating behaviors. The WEL questionnaire is used as a measure of health self efficacy, one of the variables constituting the HSET. The WEL questionnaire produces an overall eating self efficacy score based on five subscales: (a) negat ive emotions, (b) availability, (c) social pressure, (d) physical discomfort, and (e) positive activities. Only the overall eating self efficacy score was used in this study. The instruction on the WEL questionnaire is to indicate how confident you are in resisting the listed

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30 behaviors using a 10 point Likert type scale, ranging from 1 ( not confident ) to 10 ( very confident by summing the ratings of the items in each individual subscale and summing the subscales scores for an overall scale score. Higher scores indicate more confidence in controlling eating behaviors. The autho (see Clark et al., 1991 for subscale alphas); however, in a study that used the WEL questionnaire he overall scale was reported (Fontaine & Cheskin, 1997). The authors of the WEL questionnaire demonstrated validity of scores on it by correlating the subscales with relevant measures. For example, Clark et al. (1991) demonstrated that scores on the entir e WEL questionnaire were significantly correlated with scores on the Eating Self Efficacy Scale (Glynn & Ruderman, 1986). Clark et al. (1991) also demonstrated that the WEL questionnaire was useful in assessing behavior change in a weight management progra m. Furthermore, the WEL questionnaire has been used with various groups and for various purposes. Some examples of how the WEL has been used include, but are (b) a ssessing weight management among individuals who received bariatric surgery (Batsis, Clark, Grothe, Lopez Jimenez, Collazo Clavell, Somers, et al., 2009); (c) assessing the effectiveness of a diabetes prevention program (Delahanty, Meigs, Hayden, Williamso n, Nathan, & DPP Research Group, 2002); (d) assessing the effectiveness of Cognitive Behavior Therapy with individuals diagnosed with binge eating disorder (Wolff & Clark, 2001); and (e) other uses (Chang, 2007; Dutton, Davis Martin, Rhode, & Brantley, 200 4; Warziski, Sereika, Styn, Music, & Burke, 2008).

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31 Body Mass Index (BMI) formula: weight (lbs) / [height (in)] 2 x 703 (Center for Disease Control and Prevention, 2008). Calib Procedure The Principal Investigator for the present study was also a research associate for the larger study. The larger study and thus the present study were approved by the University of Florida Institutional Review Board. The larger study was conducted in two small cities in the Southeast and was implemented at community sites (i.e., churches, schools, community centers, and YMCAs) by academic and community researchers. Undergraduate research assistants and community member research partners recruited participants after being trained to do so in a culturally sensitive manner. The participant recruiters used multiple recruitment methods including tabling, distributing flye rs, and giving presentations about the study at community events. One or more of these methods were implemented in a variety of venues (i.e., community centers, community parks, multicultural festivals, hospitals, churches, schools, and supermarkets or con venience stores) in diverse neighborhoods across each city. Participant recruitment lasted 3 months. After participant recruitment was complete, participants attended one of several baseline data collection sessions. At these sessions participants signed an I nformed Consent Form, and then were given the Assessment Battery (AB) to complete, with the option of completing it at the data collection session or completing it at home within the two weeks following the data collection session. Participants who chose the latter option were given pre addressed and stamped envelopes in which to mail their completed AB to the researchers. At the data collection sessions participants also had their height and weight measured by nurses and research

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32 assistants studying medicine, all of whom were trained a nd experienced in taking these measures. Participants had a choice of completing either the English or Spanish version of the AB. Participants were told that they could have a family member or a research assistant read and/or explain it to them. The AB to ok approximately 20 minutes to complete. The duration of the baseline data collection was two months. Participants were given $25 in cash or mailed a $25 money order upon completion and return of the completed AB. Only t he baseline data was used for the pr esent study.

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33 CHAPTER 4 RESULTS First, the results of preliminary analyses to address the general characteristics of the data are presented. Second, the descriptive data for all of the major variables in the present study are reported. Third, the results o f Pearson product moment correlations (hereinafter referred to simply as Pearson correlations) performed to test Hypothesis One are reported Fourth, the results of a bootstrapped path analysis to test Hypothesis Two are reported. Fifth, the results of the Univariate Analyses of Variance (ANOVAs) conducted to address Research Question One are presented. Preliminary Analyses Prior to conducting the analyses to address the hypotheses and exploratory research question, the demographic characteristics (e.g., ag e, sex, race/ethnicity and socioeconomic status) and variables of interest (motivation to engage in health promoting behaviors, self praise of health promoting behaviors, coping through the use of instrumental social support, health responsibility, weight management self efficacy, health promoting behaviors, and BMI) were examined for accuracy of data entry, missing values, and fit between their distributions and the assumptions of the General Linear Model. All variables were trimmed for values exceeding + / 3 standard deviations from the mean. The assumption of normality was met by verifying that skewness and kurtosis statistics were less than 1 or | 1 |, and by producing and inspecting histograms and normal probability plots. Because of their categorical nature, sex, race/ethnicity and income were not normally distributed. Motivation to engage in health promoting behaviors was negatively skewed because several participants had strong agreement that being healthier was a goal for them. This variable was n ot transformed because of the narrow response range (i.e., 1 4); however, using the bootstrapping method normalized this measure. All other variables were fairly normal. Linearity and homoscedasticity were verified by producing and inspecting bivariate sca tterplots. In

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34 addition, inspection of the correlation matrix revealed no bivariate correlations above 0.70 among the variables of interest, indicating that multicollinearity did not exist. The internal reliability of each self sample was calculated for the full scale of the Health Self Praise Ques alpha for the Use of Instrumental Social Support subscale of the Cope Questionnaire was .78 for for this sample were as follows: .87 for the Health Responsibility subscale, .86 for the Physical the Weight Efficacy Lifestyle Questionnaire was .95 for this sample. After detailed inspection of the data, it became clear that the proportion of missing data was problematic. Although there is not a consensus on what is the appropriate cutoff for acceptable percentages of missing data, the acceptable range is somewhere be tween 5 20% or less (Schafer, 1999; Bennett, 2001; Peng et al., 2006). Although the demographic data fall within the acceptable range, the other investigated variables were beyond the acceptable missing data range. Specifically, the percent of missing data for the self reported psychological theory variables ranged from a low of 8.24% to a high of 28.53%, and the percent of missing BMI data was 43.24% (see Table 4 1 ). After inspection of a random subset of the data, it appeared that data seemed to be missin g at random. However, it is important to note that BMI had the highest percentage of missing data. Although participants returned assessment batteries, some did not attend the weight and height measurement portion of the data collection, thus resulting in the high percentage of missing BMI data. There did not seem to be any patterns among the participants who did not give BMI data. Conflicts between the work schedules of participants and the times of the weight and height data collection sessions likely exp lain the high percent age

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35 of missing BMI data. Because participants could mail or simply drop off their self report data (i.e., responses to the questionnaires/inventories to assess the psychology theory variables of interest), these schedule conflicts had less of an impact on the amount of these data collected. Full Information Maximum Likelihood (FIML) and Multiple Imputation (MI) methods are recommended for handling missing data as these methods show the least amount of bias (Scholmer, Bauman, and Card, 2010). The multiple imputation method was used in this study because a full sample was required to implement the bootstrapped estimates of standard errors for the path analysis used in the present study. The Bayesian multiple imputation method was selected same distribution and error with its imputation points. Ten Bayesian multiple imputations were run and the consequential combined data set ( n =3400) was used. Des criptive Statistics for the Major Investigated Variables Descriptive statistics were calculated for both the raw data set and the imputed data set. See Table 4 1 and Table 4 2 to review the descriptive statistics for the raw data set and the imputed data set, respectively. Additionally, the principle investigator examined the percentage of participants who completed the Spanish language version of the Assessment Battery. Thirt y eight (10.4%) participants completed the Spanish version of the assessment battery; all other participants (89.6%) completed the English version of the assessment battery. Due to the small number of participants completing the Spanish version, no analys es were conducted assessing differences in the investigated variables in association with language preference. Results of Pearson Correlations to Test Hypothesis One Pearson correlations were performed to test Hypothesis One, which states that the variable s constituting the HSET will be positively correlated. The results of these correlations when using the raw data set (i.e., the data set before it was imputed) provide only partial support

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36 for Hypothesis One. As presented in Table 4 3 self praise of healt h promoting behaviors was shown to have a significant positive correlation with all of the other HSET variables including motivation to engage in health promoting behaviors ( r = .20, p < .01), coping through the use of instrumental social support ( r = .33, p < .01), health responsibility ( r = .38, p < .01), and weight management self efficacy ( r = .16, p < .05). Additionally, health responsibility was shown to have a significant positive correlation with coping through the use of instrumental social support ( r = .30, p < .01) and weight management self efficacy ( r = .13, p < .05). The other correlations were nonsignificant. The Pearson correlations using the imputed data set produced similar r values as the r values from the Pearson correlations using the r aw data set. This similarity c an be seen by comparing Tables 4 3 and 4 4 The major difference between the Pearson correlation using the raw data set and the Pearson correlation using the imputed data set is that the correlations that were nonsignificant with the raw data were significant with the imputed data, likely because of the large n in the imputed data set The only relationship that was nonsignificant in both correlations is the relationship between motivation to engage in health promoting behavio rs and coping through the use of instrumental social support. Results of Bootstrapped Path Analysis to Test Hypothesis Two Model Specification Hypothesis Two stated that the Health Self Empowerment Theory (HSET) variables will positively predict health promoting behaviors (i.e., eating a healthy diet and engaging in physical activity) and negatively predict BMI; the health promoting behaviors will negatively predict BMI; and the health promoting behaviors will mediate the relationship between the HSET v ariables and BMI (see Figure 4 1 for the model). To test this hypothesis, a bootstrapped path analysis was applied to the relevant imputed data set variables. The HSET variables entered in the path analysis include motivation to engage in health promoting behaviors (which are labeled

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37 praise of health promoting behaviors, coping through the use of instrumental social support, health responsibility, and weight management self efficacy. The health promoting behaviors en tered in the path analysis were engaging in physical activity th analysis that was performed tested all total effects, the direct effects of the HSET variables on health promoting behaviors and on BMI, the direct effects of the health promoting behaviors on BMI, and the indirect effects of the HSET variables on BMI through health promoting behaviors ( i.e., the mediation). The model was fully recursive. Signif icance Tests Significance tests were conducted using bootstrapped estimates of standard errors for direct, indirect, and total effects. Only the imputed data set was used for the bootstrapped path analysis because no missing data is permitted when using th e bootstrapping method. The bootstrapping method produces thousands of subsamples using various data points from the raw data sample to create a normalized sampling distribution; this method can be useful when testing mediation effects (Shrout & Bolger, 20 02). To test Hypothesis Two in the present research, 1000 bootstrapped subsamples were selected. The bootstrapping method was conducted for each of the 10 Bayesian imputation sets and the results from these sets were combined to test the mediation model. T wo statistical packages, AMOS 18.0 and SPSS 17.0, were used to conduct the imputations and bootstrapped mediation To combine the data from the 10 imputed data sets Microsoft Excel 2007 was used. A ll calculations involved were based on standardized values. Table 4 5 shows the resulting standardized direct regression paths, indirect mediation paths and total regression paths The overall model accounted for 32.2% of the variance in engaging in physi cal activity, 32.7% of the variance in eating a healthy diet, and 13.9% of the variance in BMI.

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38 Direct effects The significant direct effects highlight the significant relationships in the model without accounting for any mediating variables. Results indic ated that health responsibility had a significant positive direct effect on physical activity, t (300) = 7.29, p < .001, and on eating a healthy diet, t (300) = 3.94, p < .001. Results also indicated that self praise of health promoting behaviors had a signi ficant positive direct effect on physical activity, t (300) = 7.29, p < .001, and on eating a healthy diet, t (300) = 8.19, p < .001. Motivation to engage in health promoting behaviors (health goals) had a significant positive direct effect on BMI, t (300) = 2.14, p < .05. Finally, eating a healthy diet had a significant negative direct effect on BMI, t (300) = 5.19, p < .001. All other direct effects were not significant. See Figure 4 2 for a graphical depiction of all significant direct effects. Indirect eff ect The test of the indirect effects (i.e., the meditational effects) of the health promoting behaviors on the relationship between the theory variables and BMI revealed only one significant indirect effect. Self praise of health promoting behaviors had a significant negative indirect effect on BMI, t (300) = 2.42, p < .05. B oth health promoting behaviors (i.e., engaging in physical activity and eating a healthy diet) mediated the relationship between self praise and BMI. In other words, self praise lowered BMI by acting through the health promoting behaviors. All other indirect effects were not significant. Total effects The significant total effects highlight the significant relationships in the overall model. Results indicated that health responsibility had a significant positive total effect on physical activity, t (300) = 6.52, p < .001. Additionally, health responsibil ity had a significant positive total effect on eating a healthy diet, t (300) = 3.94, p < .001. Results also indicated that self praise of health promoting behaviors had a significant positive total effect on physical activity, t (300) =

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39 7.29, p < .001, and eating a healthy diet, t (300) = 8.19, p < .001. Finally, there was a significant negative total effect between eating a healthy diet and BMI in the overall model, t (300) = 5.19, p < .001. All other total effects were nonsignificant. See Figure 4 2 for a g raphical depiction of all significant total effects. Results of the ANOVAs to Test Research Question One Research Question One is as follows: Are there differences in levels of the HSET variables, levels of engagement in physical activity and eating a heal thy diet, and levels of BMI in association with sex and race/ethnicity? Only the raw data set was used for the analyses to test this research question because the demographic variables were not included in the multiple imputations. Between subjects, one wa y Analyse s of Variance (ANOVA s ) w ere conducted to explore the sex component of Research Question One. Because the assumption of homogeneity of variance was violated for the weight management self efficacy variable and the BMI variable, the Welch F ratio is reported for those variables. All other variables met the homogeneity of variance assumption. Results of the ANOVA s revealed no significant sex differences for most of the variables, except for health responsibility and motivation to engage in health promoting behaviors. Women ( M = 2.27 SD = 0.61 ) reported significantly higher levels of health responsibility than men ( M = 2.05 SD = 0.60 ), F (1, 279) = 7.89, p < .01. Women ( M = 3.70 SD = 0.45 ) also reported significantly higher levels of motivation to engage in health promoting behaviors than men ( M = 3.57 SD = 0.44 ), F (1, 310) = 4.86, p < .05. See Table 4 6 for the statistics from all of the ot her one way ANOVA s using sex as the independent variable. More between subjects, one way ANOVA s w ere conducted to explore the race/ethnicity component of Research Question One. All variables met the assumption of homogeneity of variance. There were severa l significant race/ethnicity differences among the investigated variables. One of these was a significant difference in self praise of health promoting behaviors, F (4, 255) = 3.38, p < .05 in association with race/ethnicity. A series of post hoc t tests us ing the

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40 Bonferroni correction indicated that Asian Americans ( M = 2.95) reported significantly higher levels of self praise than European Americans ( M = 2.51), t (255) = 3.04, p < .05. It was also found that weight management self efficacy significantly differed by race/ethnicity, F (4, 232) = 4.57, p < .01. A series of post hoc t tests using the Bonferroni correction indicated that Asian Americans ( M = 140.22) reported significantly high er levels of weight management self efficacy than European Americans ( M = 115.72), t (232) = 2.98, p < .05, and African Americans ( M = 138.35) reported significantly higher levels of weight self efficacy than European Americans ( M = 115.72), t (232) = 3.83, p < .01. H ealth responsibility also significantly differed by race/ethnicity, F (4, 272) = 2.66, p < .05; however, the post hoc t tests using the Bonferroni correction revealed no significant specific effects. Additionally, it was found that eating a healt hy diet differed by race/ethnicity, F (4, 274) = 2.53, p < .05. A series of post hoc t tests using the Bonferroni correction indicated that Asian Americans ( M = 2.60) reported significantly more engagement in eating a healthy diet than European Americans ( M = 2.29), t (274) = 2.94, p < .05. Finally, Body Mass Index (BMI) significantly differed by race/ethnicity, F (4, 188) = 11.56, p < .001. A series of post hoc t tests using the Bonferroni correction indicated that Asian Americans ( M = 23.53) had significan tly lower BMIs than European Americans ( M = 32.21), t (188) = 4.76, p < .001; than African Americans ( M = 34.87), t (188) = 6.29, p < .001; and than Hispanics/Latinos ( M = 29.90), t (188) = 3.11, p < .05. Additionally, Hispanics/Latinos ( M = 29.90) had significantly lower BMIs than African Americans ( M = 34.87), t (188) = 3.43, p < .01. See Table 4 7 for the statistics of all of the one way ANOVA s using race/ethnicity as the independent variable.

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41 Table 4 1 Selected d escriptive s tatistics for a ll inv estigated v ariables u sing raw d ata s et Variable % Missing out of 340 M SD Obtained Range Possible Range Obtained Scale Sex ( n = 338) 0.59% ---------------------Race/Ethnicit y ( n = 334) 1.76% ---------------------Motivation ( n = 312) 8.24% 3.66 0.45 1.38 4 1 4 0.85 --Self Praise ( n = 265) 22.06% 2.70 0.69 1 4 1 4 0.92 --Use of Instrumental Social Support ( n = 260) 23.53% 10.92 2.86 4 16 4 16 0.78 0.75 Health Responsibility ( n = 283) 16.76% 2.21 0.61 1 4 1 4 0.87 0.81 Weight Management Self Efficacy ( n = 243) 28.53% 127.9 40.16 26.32 200 20 200 0.95 0.92 Engaging in Physical Activity ( n = 282) 17.06% 2.00 0.65 1 3.75 1 4 0.86 0.81 Eating a Healthy Diet ( n = 285) 16.18% 2.37 0.51 1.13 3.75 1 4 0.73 0.76 Body Mass Index ( n = 193) 43.24% 32.04 7.23 16.88 54.88 ------Note The Body Mass Index was obtained at the baseline data collection sessions.

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42 Table 4 2 S elected descriptive statistics for all i nvestigated variables using imputed data s et Moti va tion Self Praise Use of Social Support Health Respons ibi lity Weight Effi cacy Phys Activity Diet BMI M 3.66 2.69 10.88 2.21 127.31 2.00 2.37 31.8 3 SD 0.45 0.69 2.89 0.61 39.58 0.64 0.51 7.41 Comparative M --11.50 a 2.66 b 116.0 c 2.24 b 2.47 b 26.8 29.2 d Comparative Sample SD --2.88 a 0.57 b 30.5 c 0.70 b 0.54 b 0.1 0.4 d a The COPE comparative sample mean is adapted from Carver, Scheier, & Weintraub (1989). b The HPLP comparative sample mean is adapted from Johnson (2005) using a sample of African American women. c The WEL comparative sample mean is adapted from Clark, Abrams, Niaura, Eaton, & Rossi (1991). d The BMI comparative sample mean is adapted from Ogden, Fryar, Carroll, & F legal (2004).

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43 Table 4 3. Pearson c orrelations of H ealth S elf Empowerment Theory variables u sing r aw data s et Variable Motivation Self Praise Use of Social Support Health Responsibility Weight Efficacy Motivation ( n = 312) 1 Self Praise ( n = 265) .20** 1 Use of Instrumental Social Support ( n = 260) .01 .33** 1 Health Responsibility ( n = 283) .12 .38** .30** 1 Weight Management Self Efficacy ( n = 243) .11 .16* .08 .13* 1 p < .05. ** p < .01. Table 4 4. Pearson c orrelations of Health Self Em powerment Theory variables using imputed d ata s et Variable Motivation Self Praise Use of Social Support Health Responsibility Weight Efficacy Motivation ( n = 3400) 1 Self Praise ( n = 3400) .20** 1 Use of Instrumental Social Support ( n = 3400) .00 .35** 1 Health Responsibility ( n = 3400) .12** .39** .31** 1 Weight Management Self Efficacy ( n = 3400) .11** .16** .08** .14** 1 ** p < .01.

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44 Table 4 5. Direct, indirect and total effects in the path m odel Predictors Motivation Self Praise Use of Social Support Health Responsibility Weight Efficacy Physical Activity Healthy Diet Dependent Variable: Physical Activity Total 0.010 0.392 *** 0.065 0.342 *** 0.028 --(0.049) (0.054) (0.049) (0.053) (0.051) Direct 0.010 0.392 *** 0.065 0.342 *** 0.028 --(0.049) (0.054) (0.049) (0.053) (0.051) Indirect -------Eating a Healthy Diet Total 0.084 0.440 *** 0.022 0.224 *** 0.078 --(0.053) (0.054) (0.049) (0.057) (0.053) Direct 0.084 0.440 *** 0.022 0.224 *** 0.078 --(0.053) (0.054) (0.049) (0.057) (0.053) Indirect -------BMI Total 0.116 0.169 0.022 0.117 0.096 0.176 0.403 *** (0.073) (0.090) (0.086) (0.073) (0.068) (0.091) (0.078) Direct 0.150 0.061 0.019 0.148 0.070 0.176 0.403 *** (0.070) (0.096) (0.083) (0.086) (0.071) (0.091) (0.078) Indirect 0.034 0.108 0.002 0.031 0.026 --(0.020) (0.045) (0.020) (0.038) (0.021) p < .05, ** p < .01, *** p < .001 Note Values represent standardized effect estimates for total, direct, and indirect effects of each predictor. The values in parentheses represent standard errors, which were empirically estimated with 1,000 bootstrapped samples.

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45 Table 4 6. Statistics of ANOVA s using sex as the independent v ariable Self Praise Use of Social Support Weight Efficacy Physical Activity Healthy Diet BMI Female n 191 187 176 207 208 144 Male n 72 71 65 73 75 49 Female M 2.72 10.95 127.01 2.01 2.41 32.13 SD 0.70 2.92 42.06 0.67 0.52 7.72 Male M 2.61 10.86 129.15 2.00 2.29 31.77 SD 0.65 2.74 34.73 0.59 0.46 5.60 Df 1, 261 1, 256 1, 137.39 1, 278 1, 281 1, 113.99 F 1.26 0.05 0.16 a 0.00 3.14 0.12 a Sig. 0.26 0.83 0.69 0.98 0.08 0.73 a F.

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46 Table 4 7. Statistics of ANOVA s using race/ethnicity as the independent v ariable Motivation Self Praise Use of Social Supp. Health Responsibl. Weig. Eff. Phys.Act. Diet BMI Asian American n 33 29 29 30 30 29 30 16 African American n 111 89 83 96 85 97 97 75 European American n 99 90 89 95 90 94 95 66 Hispanic/ Latino n 52 40 41 43 20 43 44 28 Other n 16 12 13 13 12 13 13 8 Asian American M 3.52 2.95 11.76 2.01 140.22 2.12 2.60 23.53 SD 0.44 0.58 2.52 0.55 33.73 0.62 0.49 4.30 African American M 3.68 2.77 10.71 2.34 138.35 2.08 2.36 34.87 SD 0.47 0.65 2.87 0.62 39.69 0.66 0.49 6.97 European American M 3.64 2.51 10.91 2.20 115.72 1.92 2.29 32.21 SD 0.48 0.75 2.95 0.59 40.25 0.67 0.55 6.43 Hispanic/ Latino M 3.75 2.69 10.44 2.14 123.42 2.02 2.41 29.90 SD 0.37 0.65 2.98 0.68 44.22 0.62 0.44 6.33 Other M 3.80 2.92 11.54 1.96 123.25 1.87 2.52 28.68 SD 0 .26 0. 56 2. 50 0. 49 24.13 0. 43 0. 3 9 7.60 Df 4, 306 4, 255 4, 250 4, 272 4, 232 4, 271 4, 274 4, 188 F 1.85 3.38 1.17 2.66 4.57 1.20 2.53 11.56 Sig. 0.12 0.01 0.32 0.03 0.001 0.31 0.04 0.00

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47 Figure 4 1. Full path model. Figure 4 2. Path model showing significant total and direct effects.

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48 CHAPTER 5 DISCUSSION First, a summary and interpretation of the results are presented. Limitations and future research directions are then discussed. Finally, conclusions from th e study are presented. Summary of Results Hypothesis One Hypothesis One stated that the variables in the HSET will be positively correlated. This hypothesis was mostly supported. The Pearson correlation analysis applied to the raw data set to test this hypothesis revealed significant positive correlations among the HSET variables with two exceptions. First, weight management self efficacy was not significantly correlated with coping through the use of instrumental social support. Second, motivation to e ngage in health promoting variables was only significantly correlated with self praise, but was not significantly correlated with any other HSET variables. The explanation for the lack of correlation between weight management self efficacy and use of soci al support is unclear. While it could be argued that the two variables assess two very different and unrelated constructs, use of social support and weight management self efficacy were both correlated with all of the other HSET variables which all assess different constructs. A more likely explanation is that more planning or action oriented coping styles would be more highly correlated with weight management self efficacy than social support focused coping styles. It is possible that the lack of correlat ion between motivation to engage in health promoting behaviors and the other HSET variables other than self praise, may have something to do with the measure of motivation used in this study. First of all, although the Health Behaviors Goal Agreement (HBG A) Rating Form used to assess motivation to engage in health promoting behaviors in this study resulted in reliable scores for this sample, it did not have established validity or reliability prior to use in the present study. Furthermore, the HBGA may

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49 no t be a valid measure of motivation to engage in health promoting behaviors. With a different measure of such motivation in future similar studies to the present study, it may be found that motivation to engage in health promoting behaviors is significantly correlated not only with self praise of health promoting behaviors but also with other HSET variables. Overall, the results from testing Hypothesis One suggest that the HSET has much potential for use as a unified theory in health promotion research; howe ver, more research is needed to examine the correlations among its variables using measures that have evidenced reliability and validity. Hypothesis Two Hypothesis Two stated that the Health Self Empowerment Theory (HSET) variables will positively predi ct health promoting behaviors (i.e., eating a healthy diet and engaging in physical activity) and negatively predict BMI; the health promoting behaviors will negatively predict BMI; and the health promoting behaviors will mediate the relationship between t he HSET variables and BMI. This hypothesis was only partially supported; however, the results from testing it provided a great deal of information on how the investigated variables are interrelated. The model did account for a large amount of variance in h ealth promoting behaviors (32.2% for physical activity and 32.7% for eating a healthy diet) and a m oderate amount of variance in BMI (13.9%). Direct and total effects The results from testing Hypothesis Two showed that self praise of health promoting beha viors and health responsibility were the dominant predictor variables in the path model used to test this hypothesis. Self praise and health responsibility each had a significant positive direct effect on eating a healthy diet and engaging in physical acti vity. However, i t is important to note that the significant relationship between health responsibility and the health promoting behaviors may be a result of both constructs being subscales of the same measure.

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50 Additionally, results suggested that motivat ion to engage in health promoting behaviors had a significant positive direct effect on BMI. In other words, higher levels of motivation to engage in health promoting behaviors were associated with higher levels of BMI. This finding was in the unexpected d irection. As mentioned before, the measure used to assess motivation to engage in health promoting behaviors has questionable validity and as such might explain the unexpected and surprising relationship between motivation to engage in health promoting beh aviors and BMI. Finally, eating a healthy diet had a significant negative direct effect on BMI. Conceptually, it seems clear that eating a healthy diet was predictive of BMI and physical activity was not. To have a significant influence on BMI, one must engage in physical activity very frequently and intensely because of the difficulty of burning fat and calories during physical activity. In addition, physical activity can result in possible fluctuation in weight (and thus BMI) due to transformation of fa t into muscle. On the other hand, slight changes in diet (e.g., The total effects (i.e., the significant relationships in the overall model) were very similar to the direct effects in that self praise, health responsibility, and eating a healthy diet were all significant predictors in the model. However, motivation to engage in health promoting behaviors did not have a significant total effect on BMI while motivation to engage in health pro moting behaviors did have a significant direct effect on BMI. These results provide partial support for Hypothesis Two in that certain variables in the HSET were significant predictors of health promoting behaviors and BMI. Indirect effects Testing the indirect effects in the path model was the true test of Hypothesis Two because the indirect effects indicate whether the hypothesized mediation effects exist. It was found that engaging in physical activity and eating a healthy diet mediated the relationsh ip between self

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51 praise and BMI. In other words, self praise resulted in lower BMI through both health promoting behaviors (i.e., physical activity and healthy diet). This result demonstrates the power that self Theoretical con siderations Overall, the results of the path model suggest that self praise of health promoting behaviors was the primary predictor variable in the model in that it showed a significant direct effect on both of the health promoting behaviors investigated in this study (i.e., engaging in physical activity and eating a healthy diet) and an indirect effect through both health promoting behaviors on BMI. Health responsibility also had a significant positive effect on the health promoting behaviors. Both self p raise of health promoting behaviors and health responsibility were the least studied of the investigated psychological predictors of health behaviors reported in the existing health promotion literature. The present study thus is a contribution to the heal th promotion literature. Interestingly, the other HSET variables were, for the most part, not significant predictors of health promoting behaviors and BMI Specifically, coping through the use of instrumental social support, weight management self effica cy, and motivation to engage in health promoting behaviors were not significant predictors of health promoting behaviors. What is particularly surprising is that these psychological variables have substantial research showing their link to health promoting behaviors. Possible reasons for the finding that motivation to engage in health promoting behaviors did not predict these behaviors in the present study have been provided. A possible explanation for coping thr ough the use of social support not predicting the investigated health promoting behaviors is that it is not a form of active coping the type of coping that has been empirically linked to health behaviors in previous health promotion research (Bianchi, Zea, Poppen, Reisen, & Echeverry, 2004; Tucker, Butler, Loyuk, Desmond, & Surrency, 2009; Watson, Logan, & Tomar, 2008). In terms of weight management self efficacy, it may be that

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52 more specific forms of self efficacy (e.g., exercising efficacy) impact health promoting behaviors, particularly among sam ples with an overrepresentation of racial/ethnic minorities and individuals with low household incomes. Research Question Research Question One asked whether levels of each of the investigated variables differed by sex or race/ethnicity. Before summarizin g the results related to this research question, it is important to note that race/ethnicity and sex groups were compared only to determine if there is support for possibly studying these groups separately in the future in accordance with the difference mo del research approach, which examines cultural groups separately without comparison. Readers must be careful not to interpret the results of the present study using a cultural deficit approach, which compares cultural groups and use s the performance/scores of the majority group as the standard for comparing all other groups (see Baca Zinn, 1989; Inniss & Feagin, 1990; Mincy, Sawhill, & Wolf, 1990). The concern is that comparing racial/ethnic groups or sex groups often inadvertently leads to negative stereot yping of racial/ ethnic minority groups and women as a result of findings in which they perform at lower levels compared to non Hispanic White Americans and males, respectively, as often occurs in the commonly used deficit model research. (The sub samples o f each racial/ethnic group and of males and females were too small to test the hypotheses with each group separately.) Because health disparities typically impact each racial/ethnic group and sex differently, it may indeed be the case that within each grou p there are different predictors of health behaviors and outcomes such as BMI. Examination of Research Question One revealed that women reported higher levels of motivation to engage in health promoting behaviors and health responsibility than men. The se finding s are not surprising given existing research indicating that women, relative to men, place more importance on health overall (Wardle, Haase, Steptoe, Nillapun, Jonwutiwes, & Bellisle, to stay thin. According to

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53 Objectification Theory, women internalize societal standards of beauty to the point where they view themselves from the perspective of the observer and strive to meet external standards of beauty, which leads to body shame and an xiety about their appearance (Moradi, 2010). This theory may be used cross culturally (Moradi, 2010). Furthermore, in general, women are less satisfied with their bodies than men ( lgars, M., Santtila, P., Varjonen, M., Witting, K., Johansson, A., Jern, P ., et al., 2009). This lack of satisfaction with their bodies may lead women to be much more motivated to engage in health related behaviors, including health promoting behaviors that will lead to weight loss. Th e finding that women reported higher levels of health responsibility than men is consistent with other research findings with African Americans (Johnson, 2005) and with blue collar, skilled trade, and white collar workers (Lusk, Kerr, & Ronis, 1995). This finding is also consistent with research fi ndings that show that women utilize healthcare services more than men (Bertakis, Azari, Helms, Callahan, & Robbins, 2000; Tom Xu & Borders, 2003). A possible explanation of this sex difference is the socialization of gender roles. Men are often socialized to seeking behaviors that are considered health responsible ( Mller Leimkhler, 2002). Women instead are often socialized to assume a caretaking role which can result in the m attending to their own health and the health of others (Ratner, Bottorff, Johnson, & Hayduk, 1994). Additionally, the examination of Research Question One revealed several race/ethnicity differences in the investigated variables; however, most of these differences had to do with differences between the Asian American group and the other racial/ethnic groups in this study. Asian Americans reported higher levels of self praise of health promoting behaviors, weight management self efficacy, and eating a hea lthy diet than European Americans. Asian Americans also had a lower mean Body Mass Index (BMI) than European Americans, African Americans, and Hispanic/Latinos. These results may highlight why the prevalence of risk factors associated

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54 with obesity related diseases is lower for Asian Americans than other racial/ethnic groups (Hayes, Denny, Keenan, Croft, Sundaram, & Greenlund, 2006); however, Asian Americans tend to not be represented well in health related research and were not highly represented in this st udy. Future studies similar to the present study should focus on Asian Americans or include large samples of Asian Americans, particularly given that they do experience disproportionately high incidences of a range of cancers (Chen, 2005), experience lowe r access to healthcare than European Americans (Lee, Martinez, Ma, Hsu, Robinson, Bawa, & Juon, 2010), and receive fewer routine health prevention procedures than European Americans such as influenza vaccines and pap smears (Collins, Hall, & Neuhaus, 1999) Additionally, it was found in the present study that African Americans reported higher levels of weight management self efficacy than European Americans. This result was unexpected given that African Americans had the highest BMI. If this finding is supp orted in future similar research, it may support existing research indicating that a focus on weight is not useful in interventions to reduce BMI and/or increase health promoting behaviors among African Americans (Kumanyika, Morssink, Agurs, 1992). Finally from the examination of Research Question One, it was found that Hispanic/Latinos had a lower mean BMI than African Americans. This is consistent with findings from previous studies examining racial/ethnic differences in BMI among women the sex group m ostly represented in the present study (National Heart, Lung and Blood Institute, 2009; Sobal, 1991). However, it has also been found that Hispanic/Latino men tend to have higher BMIs than African American men (National Heart, Lung and Blood Institute, 200 9). It is income, whereas in the majority of other research, the samples consist of individuals from higher income backgrounds.

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55 In sum, the race/ethnicity and sex rel ated findings suggest that interventions to increase health promoting behaviors should carefully asses s the psychological empowerment variables that influence these behaviors Interventionists should avoid assuming that these psychological empowerment vari ables will have the same effect ac ross all sexe s and race s /ethnicities. Interventionists could possibly use any sex and race/ ethnicit y differences in the psychological empowerment variables to customiz e health promotion interventions to be responsive to these differences. Limitations and Future Directions Sample Limitations It is important to note that the majority of the sample consisted of women and the sex groups were unbalanced in the analyses. This sample bias raises questions about the generalizability of the results of the present study across sex e s. Additionally, the majority of the sample was overweight or obese and from a low income background, whic h reduces generalizability of the results to normal and underweight groups and to groups from other income level backgrounds. It is problematic that the sample is predominantly overweight and obese; however, there is a considerable amount of variance amo ng even the overweight and obese BMI scores and some representation of individuals with normal BMI scores. Despite this, the results of the present study could change using a group with more variance in BMI scores. Another issue is that household income of the adult participants in this study was assessed without accounting for number of family members living in the household, making it difficult to determine true socioeconomic status of the sample in the present study. Measure Limitations Two of the measu res used in this study did not have previously obtained supporting reliability and validity data. These measures are the Health Behaviors Goal Agreement Rating

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56 Form and the Health Self Praise Questionnaire. It is the case, however, that these measures did produce reliable data when used with the sample in the present study. Another important limitation of the present study is that the measures all involved self report responses, with the exception of BMI. The self report responses may be somewhat biased and may not indicate true levels of utilization of the psychological variables or true levels of engagement in the health promoting behaviors. Limitations in Study Design Because this study used a cross sectional design, it is not known whether the Health Sel f Empowerment (HSET) variables are predictive of long term engagement in health promoting behaviors or changes in BMI Future research to assess the long term impact of the HSET variables on health promoting behaviors and on BMI is needed. Future Direction s Future studies similar to the present study should include large representative samples that will allow testing the hypotheses and research questions separately by race/ethnicity and by sex in accordance with the difference model research approach. Futu re studies should also use measures that are reliable and valid to test the HSET. Additionally, researchers may consider using a different measure of motivation to engage in health promoting behaviors. Recently, a measure of motivation of and barriers to h ealth promoting behaviors (i.e., the Motivators of and Barriers to Health Smart Behaviors; MB HSBI) was developed and validated using culturally diverse groups (see Tucker, Rice, Hou, Kaye, Nolan, Grandoit, et al., 2010). The MB HSBI can be used as a more direct measure of motivation in future studies to test the HSET. Researc hers may also consider finding separate measures for assessing health responsibility and health promoting behaviors rather than measuring these variables with different subscales of th e same measure as done in the present study.

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57 Because the results from Hypothesis Two suggested that self praise and health responsibility along with eating a healthy diet seemed to be the primary predictor variables of BMI, support is provided for includ ing self praise, health responsibility, and eating a healthy diet as predictors of BMI in future studies involving culturally diverse individuals who differ by race/ethnic ity, sex and socioeconomic status. Future studies could also examine the efficacy of interventions that emphasize promoting self praise and health responsibility to increase health promoting behaviors among low income, culturally diverse individuals who are overweight or obese. Conclusions The purpose of this study was to examine both the theoretical integrity of the Health Self Empowerment Theory (HSET) and its usefulness in predicting health promoting behaviors and BMI. The HSET consists of five psychological variables (i.e., motivation to engage in health promoting behaviors, self prais e of health promoting behaviors, adaptive coping styles, health responsibility, and weight management self efficacy) that were in vestigated as predictors of health promoting behaviors and BMI. F urthermore, two health promoting behaviors (i.e., engaging in physical activity and eating a healthy diet) were investigated as mediators of the relationships between the HSET variables and BMI. This study provided partial support regarding the theoretical integrity of the HSET, showing that most of the psychologica l variables within the HSET were correlated. This study and BMI because only two of the psychological variables (i.e., self praise and health responsibility) wer e predictive of health promoting behaviors and BMI. However, given the sample and measurement limitations in this study, these conclusions must be viewed with caution. Support is provided for future similar research to evaluate the HSET and its usefulness

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58 in understanding the occurrence of health promoting behaviors and health outcomes such as BMI. This study adds to the literature on how psychological factors impact health promoting behaviors and health outcomes. The results provide support for creating psychologically informed intervention programs to increase consistent engagement in eating a healthy diet and improve BMI among racial/ethnic minority adults and adults with low household incomes who are overweight or obese or are at risk for obesity relat ed health problems. Such psychologically based intervention programs may empower racial/ethnic minority individuals, individuals with low household incomes, and other individuals who are obese or at risk for obesity with the psychological tools (i.e., self praise and health responsibility) needed to take charge of their health behaviors regardless of their circumstances. It is such research that holds the potential for reducing health disparities through evidenced based psycho logical interventions.

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59 APPENDIX MEASURES Demographic Data Questionnaire (DDQ) Family Health Self Empowerment Project Adult Information Questionnaire Directions : Please fill in the blanks and answer the questions in this questionnaire. For questions that have bubbles ( O ), completely fill in the bubble beside the information that best describes you. Filled in bubbles should look like this: Please PRINT your name: __________________________________________________ Please PRINT your address: _____________________________ ________ _____________________________________ _____________________________________ Home telephone: _____________________ Work telephone: _____________________ Other telephone: _____________________ Please give us the following information about a relative or friend whom we may contact if we are not able to contact you: Name: ____________________________ Telephone(s): _________________________________________________________ Relationship to you: _____________________________________ Do you consider yourself to be Hispanic/Latino? O Yes O No

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60 What is your race? (Bubble in all that apply) ( Note : Even if you consider yourself to be Hispanic/Latino, you may also consider yourself to be one or more of the following races.) O American Indian or Alaska Native O Asian American O African American / Black O Caucasian / White / European American O Native Hawaiian or other Pacific Islander O Other _________________________________ (Please write in your race if it is not listed.) What is your sex? O Female O Male What is your current relationship status? O I am living with my spouse or partner O I am not living with my spouse or partner O I do not have a spouse or partner How many children currently live in your home? O none O five O one O six O two O seven O three O eight O four O other: _______ How many adults currently live in your home (including yourself)? O none O five O one O six O two O seven O three O eight O four O other: _______

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62 If you have any children, indicate below who among those living in your home helps care for these children? (Bubble in all that apply.) O mother or mother in law O father or father in law O step mother O grandmother or grandmother in law O grandfather or grandfather in law O step father O _____________________________________________________________________ What is your employment status? O I work full time O I work part time O I do not work What is the highest level of education that you have completed ? O a grade b efore the end of elementary school O elementary school O junior high/middle school O some college O completed 4 year college/university O some professional/graduate school O high school or GED O completed professional/graduate school O post high school (e.g. trade, technical) What is your annual household income level? O Below $10,000 O $10,000 to $14,999 O $15,000 to $19,999 O $20,000 to $24,999 O $25,000 to $29,999 O $30,000 to $34,999 O $35,000 to $39,999 O $40,000 O Above $40,000 What is your height? ________ feet and _________ inches What is your weight? _________ pounds What is your age? _________

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62 When we mail you letters and other documents, what language would you like them to be written in? O English O Spanish Do you presently have health insurance for yourself? O Yes O No Do you presently have health insurance for your children? O Yes O No O I do not have children Are you on a special diet because of a health condition such as diabetes or high blood pressure? O Yes O No Are you on a diet or trying to lose w eight? O Yes O No Thank you for helping us with this research!

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63 Health Behaviors Goal Agreement (HBGA) Rating Form GOALS Please tell us how much you agree that each of the behaviors below is a goal for you (something you would like to do). Tell us how much you agree or disagree by filling in the circle under one of the four agreement choices like this: A goal for me (something I would like to do) Strongly Disagree Somewhat Disagree Somewhat Agree Strongly Agree 1. Eating a healthy breakfast each day. O O O O 2. Eating fruits each day. O O O O 3. Eating vegetables each day. O O O O 4. Eating whole grains (like brown or wild rice, whole wheat bread, oatmeal, or whole grain cereal) each day. O O O O 5. Drinking water and other healthy drinks each day. O O O O 6. Eating lower calorie, healthy foods and snacks each day. O O O O 7. Doing more physical activities each day (like walking or exercising). O O O O 8. Watching less TV and videos each day. O O O O

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64 Health Self Praise Questionnaire (HSPQ)

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65 Coping Questionnaire (COPE)

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70 Health Promoting Lifestyle Profile (HPLP)

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71

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72 Weight Efficacy Lifestyle Questionnaire (WEL)

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74 LIST OF REFERENCES lgars, M., Santtila, P., Varjonen, M., Witting, K., Johansson, A., Jern, P., et al. (2009). The adult body: How age, gender, and body mass index are related to body image. Journal of Aging and Health 21 (8), 1112 1132. doi:10.1177/0898264309348023. Anderson, E.S., Wi nett, R.A., & Wojcik, J.R. (2007). Self regulation, self efficacy, outcome expectations, and social support: Social cognitive theory and nutrition behavior. Annals of Behavioral Medicine, 34 (3), 304 312. doi: 10.1007/BF02874555. Anderson, E. S., Winett, R. A., Wojcik, J. R., Winett, S. G., & Bowden, T. (2001). A computerized social cognitive intervention for nutrition behavior: Direct and mediated effects on fat, fiber, fruits and vegetables self efficacy and outcome expectations among food shoppers. Annals of Behavioral Medicine, 23 (2), 88 100 doi: 10.1207/S15324796ABM2302_3. Baca Zinn, M. (1989). Family, race, and poverty in the eighties. Signs: Journal of Women in Culture and Society 14 856 874. Retrieved October 17, 2010 from http://www.jstor.o rg/stable/3174687 Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall. Bandura, A. (1998). Health promotion from the perspective of social cognitive theory. Psychology & Health 13 (4), 623 649. doi:10.1080/08870449808407422. Bandura, A. (2004). Health promotion by social cognitive means. Health Education & Behavior, 31 143 164. doi :10.1177/1090198104263660. Bas, M. & Donmez, S. (2009). Self efficacy and restrained eating in relatio n to weight loss among overweight men and women in Turkey. Appetite, 52 (1), 209 216. doi:10.1016/j.appet.2008.09.017. Batsis, J., Clark, M., Grothe, K., Lopez Jimenez, F., Collazo Clavell, M., Somers, V., et al. (2009). Self efficacy after bariatric sur gery for obesity. A population based cohort study. Appetite 52 (3), 637 645. doi:10.1016/j.appet.2009.02.017. Bennett, D. A. (2001). How can I deal with missing data in my study? Australian and New Zealand Journal of Public Health, 25, 464 469. doi: 10.111 1/j.1467 842X.2001.tb00294.x. Bertakis, K., Azari, R., Helms, J., Callahan, E., & Robbins, J. (2000). Gender differences in the utilization of health care services. The Journal of Family Practice 49 (2), 147 152. Retrieved from PsycINFO database. Bianchi, F., Zea, M., Poppen, P ., Reisen, C., & Echeverry, J. (2004). Coping as a mediator of the impact of sociocultural factors on health behavior among HIV positive Latino gay men. Psychology and Health, 19 (1), 89 101. doi:10.1080/08870440410001655340

PAGE 75

75 Byrne, S. M. (2002). Psychological aspects of weight maintenance and relapse in obesity Journal of Psychosomatic Research, 53 (5), 1029 1036. doi:10.1016/S0022 3999(02)00487 7. Carver, C., Scheier, M., & Weintraub, J. (1989). Assessing coping strategies: A theoreti cally based approach. Journal of Personality and Social Psychology, 56 267 283. doi:10.1037/0022 3514.56.2.267. Centers for Disease Control and Prevention. (2007). Eliminating racial and ethnic health disparities Retrieved February 16, 2009, from http:// www.cdc.gov/omhd/About/disparities.htm Center for Disease Control and Prevention (2008). Overweight and obesity Retrieved December 5, 2008 from http://www.cdc.gov/nccdphp/dnpa/obesity/index.htm Chang CT. (2007). Applicability of the stages of change and Weight Efficacy Lifestyle Questionnaire with natives of Sarawak, Malaysia. Rural and Remote Health, 7 (online), 864. Available from: http://www.rrh.org.au Chen, M.S. (2005). Cancer health disparities among Asian Americans. Cancer, 104 (S12), 2895 2902. doi: 10.1002/cncr.21501. Clark, M., Abrams, D., Niaura, R., Eaton, C.A., & Rossi, J.S. (1991). Self efficacy in weight management. Journal of Consulting and Clinical Psychology, 59 (5) 739 744. doi:10.1037/0022 006X.59.5.739. Collins, S.C., Hall, A., & Neuhaus, C. (1999). US Minority Health: A Chartbook. New York, NY: The Commonwealth Fund. Conroy, D., Hyde, A., Doerksen, S., & Ribeiro, N. (2010). Implicit attitudes and explicit motivation prospectively predict physical activity. Annals of Behavioral Me dicine 39 (2), 112 118. doi:10.1007/s12160 010 9161 0. of healthy older adults related to modifying the onset of vascular dementia. Archives of Gerontology and Geriatrics 39 (1), 43 58. doi:10.1016/j.archger.2003.12.006. Croy M., & Marquart, L. (2005). Factors influencing whole grain intake by health club members. Topics in Clinical Nutrition 20 166 176. doi: 00008486 200504000 00010. Culver, J.L, Arena, P.L. Antoni, M.H., & Carver, C.S. (2002). Coping and distress among women under treatment for early stage breast cancer: Comparing African Americans, Hispanics and non Hispanic whites. Psycho Oncology, 11, 495 504. doi: 10.1002/pon.615.

PAGE 76

76 Delahanty, L.M., Meigs J.B., Hayden, D., Williamson, D.A., Nathan, D.M., & DPP Research Group (2002). Psychological and behavioral correlates of baseline BMI in the Diabetes Prevention Program (DPP). Diabetes Care, 25 (11), 1992 1998. doi: 10.2337/diacare.25.11.1992. DeNavas Wa lt, C., Proctor, B.D., & Smith, J.C. (2007). Income, poverty, and health insurance coverage in the United States: 2007 Retrieved February 16, 2009 from http://www.census.gov/prod/2008pubs/p60 235.pdf Duffy, M. (1988). Determinants of health promotion in midlife women. Nursing Research 37 (6), 358 362. doi:10.1097/00006199 198811000 00009. Dutton, G.R., Davis Martin, P., Rhode, P.C., & Brantley, P.J. (2004). Use of Weight Efficacy Lifestyle Questionnaire with African American women: Validation and extension of previous findings. Eating Behaviors, 5 (4), 375 384. doi: 10.1016/j.eatbeh.2004.04.2005. Epton, T. & Harris, P. R. (2008). Self a ffirmation promotes health behavior change. Health Psychology 27 (6), 746 752. doi:10.1037/0278 6133.27.6.746. Fontaine, K.R. & Cheskin, L.J. (1997). Self efficacy, attendance, and weight loss in obesity treatment. Addictive Behaviors, 22 (4), 567 570. do i:10.1016/S0306 4603(96)00068 8. Freeman, L., & Gil, K. (2004). Daily stress, coping, and dietary restraint in binge eating. International Journal of Eating Disorders 36 (2), 204 212. doi:10.1002/eat.20012. Glynn, S. M., & Ruderman, A. J. (1986). The development and validation of an Eating Self Efficacy Scale. Cognitive Therapy and Research,10, 403 420. doi:10.1007/BF01173294 Guillaumie, L., Godin, G., & Vzina Im, L. (2010). Psychosocial determinant of fruit and vegetable intake in adult population: A systematic review. International Journal of Behavior Nutrition and Physical Activity, 7 (12). doi :10.1186/1479 5868 7 12. Harris, P., & Epton, T. (2009). The impact of self affirmation on health cognition, health behaviour and other health related responses: A narrative review. Social and Personality Psychology Compass 3 (6), 962 978. doi:10.1111/j.1751 9004.2009.00233.x. Harris, P., Mayle, K., Mabbott, L., & Napper, L. (2007). Self affirmation reduces smokers' defensiveness to graphic on pack cigarette warning labels. Health Psychology 26 (4), 437 446. doi:10.1037/0278 6133.26.4.437. Harris, P., & Napper, L. (2005). Self Affirmation and the Biased Processing of Threatening Health Risk Information. Personality and Social Psychol ogy Bulletin 31 (9), 1250 1263. doi:10.1177/0146167205274694.

PAGE 77

77 Haughton McNeill, L., Wyrwich, K.W., Brownson, R.C., Clark, E.M., & Kreuter, M.W. (2006). Individual, social environmental, and physical environmental influences on physical activity among black and white adults: A structural equation analysis. Annals of Behavioral Medicine, 31 (1), 36 44. doi: 10.1207/s15324796abm3101_7. Hayes, D., Denny, C., Keenan, N., Croft, J., Sundaram, A., & Greenlund, K. (2006). Racial/ethnic and socioeconomic differences in multiple risk factors for heart disease and stroke in women: Behavioral risk factor surveillance system, 2003. Journal of Women's Health 15 (9), 1000 1008. doi:10.1089/jwh.2006.15.1000. Hearty, ., McCarthy, S., Kearney, J., & Gibney, M. (20 07). Relationship between attitudes towards healthy eating and dietary behaviour, lifestyle and demographic factors in a representative sample of Irish adults. Appetite 48 (1), 1 11. doi:10.1016/j.appet.2006.03.329. Hermstad, A.K., Swan, D.W., Kegler, M.C. Barnette, J.K., & Glanz, K. (2010). Individual and environmental correlates of dietary fat intake in rural communities: A structural equation model analysis. Social Science & Medicine, 71 (1) 93 101. doi: 10.1016/j.socscimed.2010.03.028. Holdsworth, C. (2009). Factors associated with parents' intention to follow pediatric recommendations for their child's weight loss. Dissertation Abstracts International 70 Retrieved from PsycINFO database. Hong, J., & Li, Z. (2008). Health promoting life style and per ceived health self efficacy among nursing students during clinical practice. Chinese Mental Health Journal 22 (3), 210 213. Retrieved from PsycINFO database. Inniss, L., & Feagin, J. (1989). The Black 'underclass' ideology in race relations analysis. Socia l Justice, 16, 13 34. Jayanti R. K., & Burns A. C. (1998). The antecedents of preventive health care behavior: An empirical study. Journal of the Academy of Marketing Science, 26, 6 15. doi:10.1177/0092070398261002 Jessop, D., Simmonds, L., & Sparks, P. (2009). Motivational and behavioural consequences of self affirmation interventions: A study of sunscreen use among women. Psychology & Health 24 (5), 529 544. doi:10.1080/08870440801930320. Johnson, R. (2005). Gender Differences in Health Promoting Lifest yles of African Americans. Public Health Nursing 22 (2), 130 137. doi:10.1111/j.0737 1209.2005.220206.x. Kant, A.K., Andon, M.B., Angelopoulos, T.J., & Rippe, J.M. (2008). Association of breakfast energy density with diet quality and body mass index in Ame rican adults: National Health and Nutrition Examination Surveys, 1999 2004. American Journal of Clinical Nutrition, 88 1396 1404. Retrieved from Google Scholar database.

PAGE 78

78 Keller, C., Fleury, J., Gregor Holt, N., & Thompson, T. (1999). Predictive ability of Social Cognitive Theory in exercise research: An integrated literature review. The Online Journal of Knowledge Synthesis for Nursing, E6 (1) 19 31. doi: 10.1111/j.1524 475X.1999.00019.x Kim, J., Bramlett, M. H., Wright, L. K., & Poon, L. W. (1998). Raci al differences in health status and health behaviors of older adults. Nursing Research, 47 (4), 243 250. doi: 00006199 199807000 00010 Kruger, J., & Miles, I. (2007). Prevalence of regular physical activity among adults United States, 2001 and 2005. MMWR, 56 1209 1212. Kruger, J., Yore, M., Solera, M., & Moeti, R. (2007). Prevalence of fruit and vegetable consumption and physical activity. MMWR, 56 301 304. Kumanyika, S.K., Morssink, C., Agurs, T. (1992). Models for dietary and weight change in African Am erican women: Identifying cultural components. Ethnicity and Disease, 2, 166 175. Retrieved from PubMed database. Lee, S., Martinez, G., Ma, G.X., Hsu, C.E., Robinson, E.S., Bawa, J., & Juon, H. (2010). Barriers to health care access in 13 Asian American c ommunities. American Journal of Health Behavior, 34(1), 21 30. Retrieved from PsycInfo database. Linde, J. A., Rothman, A. J., Baldwin, A. S., & Jeffery R. W. (2006). The impact of self efficacy on behavior change and weight change among overweight partici pants in a weight loss trial. Health Psychology, 25, 282 291. doi:10.1037/0278 6133.25.3.282 Lucas, J., Orshan, S., & Cook, F. (2000). Determinants of health promoting behavior among women ages 65 and above living in the community. Scholarly Inquiry for Nu rsing Practice 14 (1), 77 100. Retrieved from PsycINFO database. Lusk, S. L., Kerr, M. J., & Ronis, D. L. (1995). Health promoting lifestyles of blue collar, skilled trade, and white collar workers. Nursing Research, 44 (1), 20 24. doi: 10.1097/00006199 199501000 00005 Luszczynska, A., & Haynes, C. (2009). Changing nutrition, physical activity and body weight among student nurses and midwives: Effects of a planning intervention and self efficacy beliefs Journal of Health Psychology, 14 (8), 1075 1084. doi: 10.1177/1359105309342290 Marshall, S.J, Jones, D.A., Ainsworth, B.E., Reis, J.P., Levy, S.S., & Macera, C.A. (2007). Race/ethnicity, social class, and leisure time physical inactivity. Medicine & Science in Sports & Exercise, 39 (1), 44 51. doi: 10.1249/01mss.0000239401.16381.37. Mata, J., Silva, M., Vieira, P., Carraa, E., Andrade, A., Coutinho, S., et al. (2009). Motivational determination and exercise intrinsic motivation predict eat ing self regulation. Health Psychology 28 (6), 709 716. doi:10.1037/a0016764.

PAGE 79

79 Mincy, R., Sawhill, I., & Wolf, D. (1990). The underclass: Definition and measurement. Science, 248, 450 453. doi:10.1126/science.248.4954.450. Mller Leimkhler, A. (2002). Barr iers to help seeking by men: A review of sociocultural and clinical literature with particular reference to depression. Journal of Affective Disorders 71 (1 3), 1 9. doi:10.1016/S0165 0327(01)00379 2. Moorman C., & Matulich E. (1993). A model of consumers preventive health behaviors: The role of health motivation and health ability. Journal of Consumer Research 20, 208 228. Retrieved October 17, 2010 from http://www.jstor.org/stable/2489270 Moradi, B. (2010). Addressing gender and cultural diversity in body image: Objectification theory as a framework for integrating theories and grounding research. Sex Roles 63 (1 2), 138 148. doi:10.1007/s11199 010 9824 0. National Center for Health Statistics (2009). Health, United States, 2008 with chartbook. Hyattsv ille, MD. National Heart, Lung and Blood Institute (2009). Diseases and Conditions Index. Retrieved November, 27, 2009, from http://www.nhlbi.nih.gov/health/dci/index.html National Heart, Lung, and Blood Institute (2010). Calculate Your Body Mass Index. Re trieved September 24, 2010, from http://www.nhlbisupport.com/bmi/ National Institutes of Health (2008). Health Disparities Retrieved February 16, 2009, from http://www.nlm.nih.gov/medlineplus/healthdisparities.html Ogden, C.L., Fryar, C.D., Carroll, M.D., & Flegal, K.M. (2004). Mean body weight, height, and body mass index, United States 1960 2002. Advanced Data from Vital and Health Statistics, 347. Hyattsville, MD: National Center for Health Statistics. Peng, C.Y. J., Harwell, M., Liou, S. M., & Ehman, L. H. (2006). Advances in missing data methods and implications for educational research. In S. Sawilowsky (Ed.), Real data analysis (pp. 31 78). Greenwich, CT: Information Age. Perkins, J.M., Multhaup, K.S., Perkins, H.W., & Barton, C. (2008). Self efficacy and participation in physical and social activity among older adults in Spain and the United States. The Gerontologist, 48 (1), 51 58. Ratner, P., Bottorff, J., Johnson, J., & Hayduk, L. (1994). The interaction eff ects of gender within the Health Promotion Model. Research in Nursing & Health 17 (5), 341 350. doi:10.1002/nur.4770170505. Rawsthorne, L.J. & Elliott, A.J. (1999). Achievement goals and intrinsic motivation: A meta analytic review. Personality and Social Psychology Review, 3 (4), 326 344. doi: 10.1207/s15327957pspr0304_3

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80 Rose, N., Hosig, K., Davy, B., Serrano, E., & Davis, L. (2007) Whole grain intake is associated with body mass index in college students. Journal of Nutrition Education and Behavior, 39 90 94. doi:10.1016/jeb.neb.2006.11.001 Rudnicki, S.R., Graham, J.L., Habboushe, D.F., & Ross, R.D. (2001). Social support and avoidant coping: Correlates of depressed mood during pregnancy in minority women. Women & Health, 34 (3), 19 34. doi: 10.1300/J013v34 n03_02. Schafer, J. L. (1999). Multiple imputation: A primer. Statistical Methods in Medical Research, 8, 3 15. doi:10.1177/096228029900800102 Scheier, M.F., & Carver, C.S. (1992). Effects of optimism on psychological and physical well being: Theoretical o verview and empirical update. Cognitive Therapy and Research, 16 (2), 201 228. doi: 10.1007/BF01173489. Schlomer, G.L, Bauman, S., & Card, N.A. (2010). Best practices for missing data management in counseling psychology. Journal of Counseling Psychology, 57, 1 10. doi:10.1037/a0018082 Schwarzer, R., Luszczynska, A., Ziegelmann, J.P., Scholz, U., & Lippke, S. (2008). Social cognitive predictors of physical exercise adherence: Three longitudinal studies in rehabilitation. He alth Psychology, 27 (1), S54 S63. doi: 10.1037/0278 6133.27.1(Suppl.).S54. Sherman, D., Nelson, L., & Steele, C. (2000). Do messages about health risks threaten the self? Increasing the acceptance of threatening health messages via self affirmation. Persona lity and Social Psychology Bulletin 26 (9), 1046 1058. doi:10.1177/01461672002611003. Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7, 422 445. doi:10.1 037//1082 989x.7.4.22 Silva, M., Vieira, P., Coutinho, S., Minderico, C., Matos, M., Sardinha, L., et al. (2010). Using self determination theory to promote physical activity and weight control: A randomized controlled trial in women. Journal of Behavioral Medicine 33 (2), 110 122. doi:10.1007/s10865 009 9239 y. Sobal, J. (1991). Obesity and socioeconomic status: A framework for examining relationships between physical and social variables. Medical Anthropology 13 231 247. Retrieved from PsycInfo database. Speake, D., Cowart, M., & Stephens, R. (1991). Healthy lifestyle practices of rural and urban elderly. Health Values: Health Behavior, Education & Promotion 15 (1), 45 51. Retrieved from PsycINFO database. Stark, M., & Brinkley, R. (2007). The relationship between perceived stress and health promoting behaviors in high risk pregnancy. The Journal of Perinatal & Neonatal Nursing 21 (4), 307 314. doi:10.1097/01.JPN.0000299788.01420.6e

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81 Strachen, S.M., & Brawley, L.R. (2009). Prospective view health eater identity and self efficacy predict health eating behavior: A prospective view. Journal of Health Psychology, 14 684 695. doi: 10.1177/1359105309104915. Tom Xu, K. & Borders, T.F. (2003). Gender, health, and physician visits among adults in the Unit ed States. Research and Practice, 93 (7), 1076 1079. Retrieved from Google Scholar. Thoolen, B., de Ridder, D., Bensing, J., Gorter, K., & Rutten, G. (2009). Beyond good intentions: The role of proactive coping in achieving sustained behavioural change in t he context of diabetes management. Psychology & Health 24 (3), 237 254. doi:10.1080/08870440701864504. Tucker, C.M., Butler, A.M., Kaye, L.B., Grandoit, D.J., Marsiske, M., Bragg, M., & Hoover, E. (2010). Impact of the health self empowerment workshop seri es on health promoting behaviors and health status of culturally diverse overweight or obese adults. Journal of Counseling Psychology. Manuscript submitted for publication. Tucker, C. M., Butler, A. M., Loyuk, I. S., Desmond, F. F., & Surrency, S. L. (2009). Predictors of a health promoting lifestyle and behaviors among low income African American mothers and White mothers of chronically ill children. Journal of the National Medical Association, 101 103 110. Tucker, C. M., Rice, K. G. Hou, W., Kaye, L. B., Nolan, S. E., Grandoit, D. J., Gonzales, L., Smith, M. B., & Desmond, F. F. (2010). Development of the Motivators of and Barriers to Health Smart Behaviors Inventory. Psychological Assessment, in press. van Koningsbruggen, G., & Das E. (2009). Don't derogate this message! Self affirmation promotes online type 2 diabetes risk test taking. Psychology & Health 24 (6), 635 649. doi:10.1080/08870440802340156. van Koningsbruggen, G., Das, E., & Roskos Ewoldsen, D. (2009). How self affirma tion reduces defensive processing of threatening health information: Evidence at the implicit level. Health Psychology 28 (5), 563 568. doi:10.1037/a0015610. Walker, S., Sechrist, K., & Pender, N. (1987). The Health Promoting Lifestyle Profile: Development and psychometric characteristics. Nursing Research, 36 76 81. Walker, S., Sechrist, K., & Pender, N. (n.d .). Published research using the Health Promoting Lifestyle Profile Retrieved November 19, 2010 from http://www.unmc.edu/nursing/docs/HPLP_HPLPII_Bibliography Wang, Y. & Beydoun, M.A. (2007). The obesity epidemic in the United States gender, age, socioeconomic, racial/ethnic, and geographic characteristics: A systematic review and meta regression analysi s. Epidemiologic Reviews, 29 (1), 6 28. doi:10.1093/epirev/mxm007

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82 Wardle, J., Haase, A.M., Steptoe, A.S., Nillapun, M., Jonwutiwes, K., & Bellisle, F. (2004). Gender differences in food choice: The contribution of health beliefs and dieting. Annals of Behav ioral Medicine, 27 (2), 107 116. doi: 10.1207/s15324796abm2702_5. Warziski, M.T., Sereika, S.M., Styn, M.A., Music, E., & Burke, L.E. (2008). Changes in self efficacy and dietary adherence: The impact on weight loss in the PREFER study. Journal of Behaviora l Medicine, 31 (1), 81 92. doi: 10.1007/s10865 007 9135 2. Watson, J.M., Logan, H.L., Tomar, S.L., (2008). The influence of active coping and perceived stress on health disparities in a multi ethnic low income sample. BMC Public Health, 8 41. doi:10.1186/1 471 2458 8 41. year predictors of physical activity decline among adults in low income communities: A prospective study. International Journal of Behavioral Nutrition and Physical Acti vity, 4 (2), doi: 10.1186/1479 5868 4 2. Winett, R.A., Anderson, E.S., Wojcik, J.R., Winett, S.G., & Bowden, T. (2007). Guide to health: Nutrition and physical activity outcomes of a group randomized trial of an internet based intervention in churches. Anna ls of Behavioral Medicine, 33 (3), 251 261. doi: 10.1007/BF02879907. Whitt Glover, M.C., Taylor, W.C., & Heath, G.W. (2007). Self reported physical activity among Blacks: Estimates from national surveys. American Journal of Preventive Medicine, 33(5), 412 417 doi:10.1016/j.amepre.2007.07.024 Wolff, G.E. & Clark, M.E. (2001). Changes in eating self efficacy and body image following cognitive behavior group therapy for binge eating disorder: A clinical study. Eating Behaviors, 2 (2), 97 104. doi: 10.1016/ S1471 0153(01)00021 6. Yang, X., Telama, R., Viikari, J., & Raitakari, O.T. (2006). Risk of obesity in relation to physical activity tracking from youth to adulthood. Medicine & Science in Sports & Exercise, 38, 919 925. doi:10.1249/01.mss.0000218121.19703 .f7

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83 BIOGRAPHICAL SKETCH Delphia (Flenar) Grandoit was born in Denver, Colorado in 1986. At the age of 4, Delphia moved to the small Midwestern town of Dunkirk, Indiana and spent her childhood and adolescence with her large family in Indiana during the sc hool year and Colorado during the summer. Delphia attended Butler University in Indianapolis, Indiana and graduated Cum Laude with a Bachelor of Arts in Psychology with a minor in Gender Studies. I mmediately after obtaining her b oved to Gainesville, Florida with her supportive partner to pursue her Doctor of Philosophy in p sychology at the University of Florida. Delphia is currently in her third year in the Counseling Psychology program and a Director of Research on Dr. Carolyn M. research interests include empowerment of marginalized groups, addressing health disparities, and multicultural issues in health.