<%BANNER%>

Determinants of Behavior Related to Heart Disease Risk among Undergraduate Female Students

Permanent Link: http://ufdc.ufl.edu/UFE0042795/00001

Material Information

Title: Determinants of Behavior Related to Heart Disease Risk among Undergraduate Female Students
Physical Description: 1 online resource (225 p.)
Language: english
Creator: SNEED,SUZANNE M
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2011

Subjects

Subjects / Keywords: FEMALE -- HEART -- RISK -- UNDERGRADUATES
Health Education and Behavior -- Dissertations, Academic -- UF
Genre: Health and Human Performance thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy DETERMINANTS OF BEHAVIOR RELATED TO HEART DISEASE RISK AMONG UNDERGRADUATE FEMALE STUDENTS By Suzanne M. Sneed May 2011 Chair: Jill Varnes Major: Health and Human Performance Heart disease is the leading cause of death among American women. Heart disease is highly preventable through positive lifestyle behavior; therefore intervention methods addressing positive heart health behavior are needed. Prevention is most effective when appropriate behaviors are initiated early in life. There has been little research devoted to young adults, especially women and their risk for heart disease development. Risk management behavior begun early in life may impact disease onset and progression, therefore studies among young adults are needed to assess behavior and focus attention on promotion of health enhancing behaviors among the young adult population. The focus of this study was to examine university female students? perception of heart disease and heart disease risk, assess overall behavior associated with heart disease, and examine the influence of age, ethnicity and perception on behavior. The study also examined age and ethnicity in their relationship to the Health Belief Model constructs of perceived risk, perceived benefits, perceived barriers, cues to action and self-efficacy. The Health Belief Model constructs were assessed relative to behavior associated with heart disease risk. Overall findings indicate that female students engage in behaviors that may lead to heart disease development. Most young college aged women do not perceive themselves to be at risk for heart disease and may therefore engage in behavior that places them at increased risk in the future. The findings from this study suggest that future research investigating students behavioral choices are needed. These results can assist health educators in developing more focused health education interventions targeted specifically to college aged women to facilitate action to encourage long term heart health.
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 SUZANNE M SNEED.
Thesis: Thesis (Ph.D.)--University of Florida, 2011.
Local: Adviser: Varnes, Jill W.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-10-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2011
System ID: UFE0042795:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042795/00001

Material Information

Title: Determinants of Behavior Related to Heart Disease Risk among Undergraduate Female Students
Physical Description: 1 online resource (225 p.)
Language: english
Creator: SNEED,SUZANNE M
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2011

Subjects

Subjects / Keywords: FEMALE -- HEART -- RISK -- UNDERGRADUATES
Health Education and Behavior -- Dissertations, Academic -- UF
Genre: Health and Human Performance thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy DETERMINANTS OF BEHAVIOR RELATED TO HEART DISEASE RISK AMONG UNDERGRADUATE FEMALE STUDENTS By Suzanne M. Sneed May 2011 Chair: Jill Varnes Major: Health and Human Performance Heart disease is the leading cause of death among American women. Heart disease is highly preventable through positive lifestyle behavior; therefore intervention methods addressing positive heart health behavior are needed. Prevention is most effective when appropriate behaviors are initiated early in life. There has been little research devoted to young adults, especially women and their risk for heart disease development. Risk management behavior begun early in life may impact disease onset and progression, therefore studies among young adults are needed to assess behavior and focus attention on promotion of health enhancing behaviors among the young adult population. The focus of this study was to examine university female students? perception of heart disease and heart disease risk, assess overall behavior associated with heart disease, and examine the influence of age, ethnicity and perception on behavior. The study also examined age and ethnicity in their relationship to the Health Belief Model constructs of perceived risk, perceived benefits, perceived barriers, cues to action and self-efficacy. The Health Belief Model constructs were assessed relative to behavior associated with heart disease risk. Overall findings indicate that female students engage in behaviors that may lead to heart disease development. Most young college aged women do not perceive themselves to be at risk for heart disease and may therefore engage in behavior that places them at increased risk in the future. The findings from this study suggest that future research investigating students behavioral choices are needed. These results can assist health educators in developing more focused health education interventions targeted specifically to college aged women to facilitate action to encourage long term heart health.
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 SUZANNE M SNEED.
Thesis: Thesis (Ph.D.)--University of Florida, 2011.
Local: Adviser: Varnes, Jill W.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-10-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2011
System ID: UFE0042795:00001


This item has the following downloads:


Full Text

PAGE 1

1 DETERMINANTS OF BEHAVIOR RELATED TO HEART DISEASE RISK AMONG UNDERGRADUATE FEMALE STUDENTS By SUZANNE M. SNEED A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2011

PAGE 2

2 2011 Suzanne M. Sneed

PAGE 3

3 To the Sneed f amily and Michael Murphy

PAGE 4

4 ACKNOWLEDGMENTS Without the love and support of an amazing family and friends, I would not have attempted, let alone finished, this large task. First, I want to thank the Lord for His continual guidance He truly is an ontime God. I am thankful for His placing me at thi s University and for blessing my life with so many brilliant individuals. I have been fortunate to work with Dr. Jill Varnes as chairperson. She encouraged me to continue by providing both corrective criticism and a listening ear. I also greatly appreciate the guidance and support given by Dr. Kelli McCormack Brown; her enthusiasm for my subject area was often the nudge I needed to push forward. I could not have completed this research without the help and expertise of Dr. David Miller and Dr. Virginia Dodd. A special thank you goes to Dr. Miller for his patience with me when I asked many statistical questions. I am blessed not only professionally, but personally. I am thankful for my parents, Robert and Marjorie Sneed, who taught me to set goals and work t oward them. I am grateful for their consistent prayers, love, support, words of encouragement, and listening ears. I am also grateful to my sister, Amanda, for answering the phone the many times I called and for listening and giving much needed advice. Additionally, I am appreciative of both my brothers, Adam and Daniel, and to my sister in law, Tracy. I am also grateful for the sweetness of my nieces and nephews Kelsey, Alyssa, Colin, Brendan, and Tara. I am truly grateful for the prayers of my extended family as well. I know my A unt Nelbeth, U ncle David, and C ousin Emilie prayed for me daily. I am also thankful for the many years of encouragement my grandmother provided, she was always excited to hear about my research, and I miss her dearly. I am blessed to have a church family who both supported and prayed for me; of this family, I am grateful to

PAGE 5

5 Sherman Carnes for his encouragement and statistical support. Last, but most certainly not least, I am thankful for my soonto be husband, Michael Murphy. I am t ruly indebted to him for his belief in me, steady encouragement, and unwavering love.

PAGE 6

6 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES .......................................................................................................... 10 LIST OF FIGURES ........................................................................................................ 12 ABSTRACT ................................................................................................................... 13 CHAPTER 1 INTRODUCTION .................................................................................................... 15 Heart Disease among Women ................................................................................ 15 Re search Problem .................................................................................................. 16 Purpose of the Study .............................................................................................. 17 Rationale for the Study ........................................................................................... 18 Behavior and Heart Disease ............................................................................. 19 National Health Goal ........................................................................................ 19 Perception of Risk ............................................................................................ 20 Research o n College Students ......................................................................... 21 Research Questions ............................................................................................... 22 Limitations ............................................................................................................... 24 Delimitations ........................................................................................................... 24 Definition of Terms .................................................................................................. 24 Significance of the Study ........................................................................................ 26 Summary ................................................................................................................ 26 2 REVIEW OF THE LITERATURE ............................................................................ 28 Heart Disease and Women ..................................................................................... 28 Women vs. Men Different Risk ............................................................................ 30 Heart Disease and Undergraduates ....................................................................... 31 Theoretical Framework Health Belief Model ....................................................... 33 Health Belief Model and Women ...................................................................... 35 Health Belief Model and Undergraduates ......................................................... 36 Risk Factors for Heart Disease ............................................................................... 36 Smoking .................................................................................................................. 37 Smoking and Women ....................................................................................... 38 Smoking and Undergraduates .......................................................................... 39 Nutrition .................................................................................................................. 40 Nutrition and Women ........................................................................................ 41 Nutrition and Undergraduates .......................................................................... 42 Physical Inactivity ................................................................................................... 43 Physical Inactivity and Women ......................................................................... 45

PAGE 7

7 Physical Inactivity and Undergraduates ........................................................... 46 Obesity .................................................................................................................... 49 Obesity and Women ......................................................................................... 51 Obesity and Undergraduates ............................................................................ 51 Diabetes .................................................................................................................. 53 Diabetes and Women ............................................................................................. 53 Clinical Risk Factors ............................................................................................... 54 Clinical Risk and Women .................................................................................. 55 Clinical Risk and Undergraduates .................................................................... 56 Family History ......................................................................................................... 58 Racial and Eth nic Disparities .................................................................................. 59 Prevention ............................................................................................................... 62 Prevention and Women .................................................................................... 62 Prevention and Undergraduates ....................................................................... 63 Summary ................................................................................................................ 65 3 METHODS .............................................................................................................. 67 Research Design .................................................................................................... 67 Research Variables ................................................................................................ 68 Survey Development and Progression ................................................................... 68 Initial Question Development ............................................................................ 68 Expert Review .................................................................................................. 69 Initial Survey Instrument ................................................................................... 69 Pilot Study One ....................................................................................................... 71 Participants ....................................................................................................... 72 Data Collection ................................................................................................. 73 Data Analysis ................................................................................................... 74 Results ............................................................................................................. 74 Pilot Study Two ....................................................................................................... 75 Participants ....................................................................................................... 77 Data Collection ................................................................................................. 77 Data Analysis ................................................................................................... 79 Results ............................................................................................................. 79 Final Survey Implementation .................................................................................. 80 Participants ....................................................................................................... 81 Data Collection ................................................................................................. 83 Data Analysis ................................................................................................... 86 Summary ................................................................................................................ 87 4 RESULTS ............................................................................................................... 90 Descriptive Analys is of the Sample ......................................................................... 90 Sample Demographics ............................................................................................ 90 Current Behavior ..................................................................................................... 91 Nutrition ............................................................................................................ 91 Physical Activity ................................................................................................ 92

PAGE 8

8 Smoking ........................................................................................................... 92 Family History ................................................................................................... 93 Doctor Visits and Clinical Risk Assessment ..................................................... 94 Research Question Results .................................................................................... 95 Re search Question One Part One ..................................................................... 95 Research Question One Part Two ..................................................................... 96 Nutrition ............................................................................................................ 97 Physical Activity ................................................................................................ 98 Smoking and Hookah ....................................................................................... 98 Doctor Visit Behavior ........................................................................................ 99 Clinical Risk Assessment ................................................................................. 99 Research Question Two ....................................................................................... 100 Health Belief Model and Nutrition ................................................................... 100 Health Belief Model and Physical Activity ....................................................... 101 Health Belief Model and Smoking .................................................................. 102 Health Belief Model and Doctor Visits and Clinical Risk Assessment ............ 102 Summary .............................................................................................................. 103 5 DISCUSSION ....................................................................................................... 121 Current Student Behavior and Family History ....................................................... 123 Nutrition Behavior ........................................................................................... 123 Physical Activity Behavior ............................................................................... 125 Smoking Behavior .......................................................................................... 127 Doctor Visit and Clinical Risk Assessment Behavior ...................................... 130 Family History ................................................................................................. 133 Behavioral Risk Summary .............................................................................. 135 Research Question Discussion ............................................................................. 136 Research Question One Part One ................................................................... 136 Research Question One Part Two ................................................................... 137 Age ................................................................................................................. 138 Ethnicity .......................................................................................................... 139 Perception ...................................................................................................... 141 Summary ........................................................................................................ 142 Research Question Two ....................................................................................... 143 Health Belief Model and Nutrition ................................................................... 144 Health Belief Model and Physical Activity ....................................................... 145 Health Belief Model and Smoking .................................................................. 146 Health Belief Model and Doctor Visit and Clinical Assessment Behavior ....... 147 Summary ........................................................................................................ 149 Strengths .............................................................................................................. 150 Limitations ............................................................................................................. 150 Implications and Future Research ........................................................................ 151 Recommendations ................................................................................................ 153 Conclusions .......................................................................................................... 158

PAGE 9

9 APPENDIX A LIST OF EXPERT PANEL MEMBERS ................................................................. 161 B RELIABILITY AND FACTOR ANALYSIS TABLES ............................................... 162 C INSTITUTIONAL REVIEW BOARD FALL 2008 DOCUMENTS PILOT STUDY ONE ...................................................................................................................... 168 D STATISTICAL REQUEST ..................................................................................... 173 E INSTITUTIONAL REVIEW BOARD SUMMER 2009 DOCUMENTS PILOT STUDY TWO ........................................................................................................ 175 F PRENOTIFICATION EMAIL ............................................................................... 176 G NOTIFICATION E MAIL ........................................................................................ 177 H REMINDER EMAIL ............................................................................................... 179 I FINAL SURVEY INSTRUMENT ........................................................................... 180 J STATISTICAL REQUEST ..................................................................................... 196 K INSTITUTIONAL REVIEW BOARD SPRING 2010 DOCUMENTS FINAL STUDY .................................................................................................................. 197 L PRENOTIFICATION MAILED LETTER ............................................................... 199 M INITIAL E MAIL NOTIFICATION .......................................................................... 200 N REMINDER EMAIL .............................................................................................. 202 O FINAL NO TIFICATION ......................................................................................... 203 P INCENTIVE ........................................................................................................... 204 LIST OF REFERENCES ............................................................................................. 205 BIOGRAPHICAL SKETCH .......................................................................................... 225

PAGE 10

10 LIST OF TABLES Table page 3 1 Initial Survey Instrument Design Table ............................................................... 88 3 2 Pilot Two Survey Instrument Design Table ......................................................... 88 3 3 Final Survey Instrument Design Table ................................................................ 88 3 4 Sample Size ....................................................................................................... 88 3 5 Sample Class Rank and Overall Population Class Rank .................................... 89 3 6 Sample Ethnicity and Overall Population Ethnicity ............................................. 89 4 1 Distribution of Participants by Age, Ethnicity, and Class Rank ......................... 103 4 2 Frequency Analysis Results for Nutrition Behavior ........................................... 104 4 3 Frequency Analysis Results for Physical Activity Behavior .............................. 105 4 4 Frequency Analysis Results for Smoking and Related Behavior ...................... 105 4 5 Frequency Analysis Results for NonCigarette Smoker Hookah Behavior ....... 106 4 6 Frequency Analysis Results for Family History of Heart Disease ..................... 106 4 7 Frequency Analysis Results for Doctor Visits and Clinical Risk Assessment ... 107 4 8 Frequency Analysis Results for Perceived Risk of Heart Disease .................... 109 4 9 Perception Variable Variance ........................................................................... 109 4 10 Ethnicity Frequencies for Regression Analysis ................................................. 109 4 11 Final Survey Item Analysis Results for Behaviors Associated with Increased Risk for Heart Disease ...................................................................................... 109 4 12 Linear Regression Analyses of Age, Ethnicity, and Perception with Behaviors Associated with Heart Disease Risk ................................................................. 110 4 13 Final Survey Item Analysis Results for Health Belief Model Constructs Related to Behavior Associated with Increased Risk for Heart Disease ........... 114 4 14 Linear Regression Analyses of Age and Ethnicity with Health Belief Model Constructs for Nutrition ..................................................................................... 115

PAGE 11

11 4 15 Linear Regression Analyses of Age and Ethnicity with Health Belief Model Constructs for Physical Activity ......................................................................... 116 4 16 Linear Regression Analyses of Age and Ethnicity with Health Belief Model Constructs for Smoking .................................................................................... 117 4 17 Linear Regression Analyses of Age and Ethnicity with Health Belief Model Constructs for Doctor Visit and Clinical Risk Assessment Behavior ................. 119 B 1 Initial Survey Item Analysis Results .................................................................. 162 B 2 Factor Loadings for the Health Belief Model Behavioral Questions Pilot One .. 163 B 3 Pilot Two Item Analysis Results ........................................................................ 164 B 4 Factor Loadings for the Health Bel ief Model Behavioral Questions Pilot Two .. 165 B 5 Final Survey Item Analysis Results .................................................................. 166

PAGE 12

12 LIST OF FIGURES Figure page 2 1 Health Belief Model ............................................................................................ 66 3 1 Transtheoretical Model (Stages of Change) ....................................................... 87 3 3 Adjusted sample size .......................................................................................... 88 5 1 Ecological approach toward heart disease risk reduction ................................. 160 P 1 Final survey incentive ....................................................................................... 204

PAGE 13

13 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy DETERMINANTS OF BEHAVIOR RELATED TO HEART DISEASE RISK AMONG UNDERGRADUATE FEMALE STUDENTS By Suzanne M. Sneed May 2011 Chair: Jill Varnes Major: Health and Human Performance Heart disease is the leading cause of death among American women. Heart disease is highly preventable through positive lifestyle behavior; therefore intervention methods addressing positive heart health behavior are needed. Prevention is most effective when appropriate behaviors are initiated early in life. There has been little re search devoted to young adults, especially women and their risk for heart disease development. Risk management behavior begun early in life may impact disease onset and progression, therefore studies among young adults are needed to assess behavior and foc us attention on promotion of health enhancing behaviors among the young adult population. The focus of this study was to examine university female students perception of heart disease and heart disease risk, assess overall behavior associated with heart disease, and examine the influence of age, ethnicity and perception on behavior. The study also examined age and ethnicity in their relationship to the Health Belief Model constructs of perceived risk, perceived benefits, perceived barriers, cues to action and

PAGE 14

14 self efficacy. The Health Belief Model constructs were assessed relative to behavior associated with heart disease risk. Overall findings indicate that female students engage in behaviors that may lead to heart disease development. Most young college aged women do not perceive themselves to be at risk for heart disease and may therefore engage in behavior that places them at increased risk in the future. The findings from this study suggest that future research investigating students behavioral choice s are needed. These results can assist health educators in developing more focused health education interventions targeted specifically to college aged women to facilitate action to encourage long term heart health.

PAGE 15

1 5 CHAPTER 1 INTRODUCTION Heart disease is the leading cause of death among both American men and women (Kung, Hoyert, & Murphy, 2008). Research indicates that heart disease is largely preventable; early identification and correction of negative lifestyle behavior can help prevent the onset of heart disease (McMahan, Cathorall, & Romero, 2007). Detection and treatment are important, but primary prevention is the best defense and should be initiated in early adulthood (Spencer, 2002). Heart disease risk factors include but are not limited toage, physical inactivity, obesity, high waist circumference, smoking, poor nutrition and diet, high cholesterol, high blood pressure, and diabetes (Ty pe I or Type II) (American Heart Association [AHA], 2009). Overall risk is heightened if the individual has a family history of the disease. Risk management behavior begun early in life may have an impact on overall disease onset and/or progression (Collins, Dantico, Shearer, & Mossman, 2004). For example, becoming aware of ones personal family history, beginning a walking program, or eating more fruits and vegetables can lead to a lifetime of good heart health. Starting as early as ones 20s, indi viduals should be mindful of their heart health. It is important to promote behavior that reduces ones risk for heart disease through appropriate lifestyle choices (Green, Grant, Hill, Brizzolara, & Belmont, 2003). To address this problem, studies need to both examine behavior and promote behavior change among young adults. Heart Disease among Women Heart disease is the leading cause of death among American women, accounting for one in every four deaths ( National Heart Lung and Blood Institute [NHLBI], 2010).

PAGE 16

16 American Heart Association data indicate that heart disease has claimed more female than male lives every year since 1984 (Noori & Anim Nyame, 2005) and therefore is a growing problem among women. Women are more likely to die from heart disease than from all types of cancer combined; yet breast cancer is perceived to be a greater threat (Collins et al., 2004). Women have lower levels of knowledge concerning heart disease compared to men, and perceive themselves less susceptible thereto (Collins et al., 2004). Once heart disease has developed, females have a worse prognosis and greater incidence of heart failure than men (Sibley, Blumenthal, Merz, & Mosca, 2006). Women are in need of increased awareness of heart disease risk and heart disease risk factors It is essential that prevention efforts begin at a young age to reduce the number of women initially diagnosed with the di sease (Hardesty & Trupp, 2005). Research Problem This study determined perception of heart disease risk among the female student population. This study also determined the frequency in which students engage in negative behavior relative to heart disease risk and examined the influence of perception, ethnicity, and age on the specific negative behaviors. Lastly, the study investigated t he influence of age and ethnicity relative to the Health Belief Model constructs on healthy heart behavior among university student females. These constructs included (1) perceived susceptibility, (2) perceived severity, (3) perceived benefits, (4) perceiv ed barriers, (5) cues to action, and (6) self efficacy (National Cancer Institute [NCI], Theory at a Glance, 2005). The investigator coupled the constructs of perceived susceptibility and perceived severity to create the construct of overall perceived risk

PAGE 17

17 Purpose of the Study This study was implemented to fulfill the following purposes: (1) to assess university female students perceptions regarding heart disease and personal heart disease risk; (2) to assess university female behaviors that are associated with an increased risk for heart disease; (3) to examine the influence of age, ethnicity, and perception on behaviors associated with heart disease risk; (4) to examine the influence of age and ethnicity on the Health Belief Model constructs related to s tudent behavior; and (5) to fill the gap in the literature regarding heart disease risk behavior and risk perception among university females. Misperceptions regarding heart disease are common among the university female student population. This mispercept ion is due in part to a lack of risk factor knowledge among young adults. There is an overall misperception that a persons currently engaged in behavior will not have an effect on future health status or quality of life. It is imperative that risk percept ion be accurate to promote change. Therefore, by assessing perception of heart disease risk and perception of behaviors that lead to heart disease, behavior change efforts among this study population may be more successful. Students are exhibiting behavior that places them at increased risk for heart disease. Behaviors exhibited as a young adult often become established behaviors and are therefore continued into adulthood. This continuance of poor behavior elevates ones overall risk for heart disease. It i s proposed that if students become more aware of behaviors that elevate risk for heart disease and recognize that they themselves are exhibiting said behaviors, positive lifestyle change may be established and therefore reduce their personal overall risk.

PAGE 18

18 Continued efforts are needed to test the sustainability of behavior change. This information can be used to more closely tailor interventions among the college student population and assist students in determining and developing behavior change goals by providing information regarding risk factors most in need of change. Research regarding heart disease and the university female student population is not abundant; therefore, a gap exists in the current literature. This research appears to be one of the fi rst studies to use Health Belief Model constructs to study determinants of college aged individuals behavior relative to heart disease risk. This study contributes to the field of literature regarding perception of heart disease risk and reasons why students engage or do not engage in heart healthy lifestyles. To more fully understand risk among the university population, research such as this current study should be both developed and implemented. Rationale for the Study Research on heart disease risk behavior s among college students is important for five main reasons. (1) Behavior s contribute to overall heart disease risk, and college students engage in risky health behavior s; (2) reduction of risk for heart disease is a goal of Healthy People 2010 and Healthy Campus 2010; increasing cardiovascular health in the United States is a proposed Healthy People 2020 objective; (3) understanding why students engage in such behavior and how they perceive heart disease is imperative in the fight against heart disease; (4) college students lack an understanding of heart disease risk; and (5) research related to heart disease among college students is scant

PAGE 19

19 Behavior and Heart Disease Research indicates that college students are likely to engage in behavior that plac es them at risk for heart disease (Spencer, 2002). It is hypothesized that sampled university students engage in behaviors that may lead to heart disease such as smoking, lack of physical activity, and poor nutrition/diet. These behaviors currently have an effect on both the present and future heart health of students. The leading cause of death among Americans is heart disease; heart disease is also the third leading cause of death for individuals aged 2544 years (Spencer, 2002). Behavior contributes to heart disease risk, and activities performed at a young age are often indicative of what will be perpetuated into adulthood (McMahan et al., 2007). Risk factors may begin in adolescence; for that reason, research and prevention efforts are encouraged in young adults (Spencer, 2002). Assessment of behavioral risk factors is important when studying heart disease among college students. Although family history plays a major role in heart disease prognosis, unhealthy lifestyles also promote the onset of the disease. National Health Goal Healthy People 2010 objectives were developed to help the nation achieve two main goals: (1) increase quality and years of life and (2) eliminate health disparities (American College Health Association [ACHA] Healthy Campus 2010, 2002). Healthy People 2010 initiatives encourage investigation of heart disease risk factors among young adults as information in that population is lacking (Koutoubi & Huffman, 2002). Modifications to Healthy People 2010 for use in Healthy People 2020 inc lude increasing overall cardiovascular health in the United States population and reducing the incidence

PAGE 20

20 rates for heart disease and stroke (U S Department of Health and Human Services [USHHS] 2009). It is important to understand the influence that college has on unhealthy behavioral trends (Racette, Deusinger, Strube, Highstein, & Deusinger, 2005). In a study by Green et al. (2003), college students lacked an understanding of some basic causeandeffect relationships between risk factors and heart diseas e. This lack of understanding led to an inaccurate perception of heart disease and indicated that future studies need to address this lack of knowledge. Healthy Campus 2010 narrows the focus of Healthy People 2010 to university and college campuses by establishing objectives for improving the health of students (ACHA, 2009). Healthy Campus 2010 sets objectives to be used when planning and implementing health promotion efforts among college students (ACHA, 2002). The health of the nation can be improved if health promotion efforts among college students are implemented and behavior change is sustained (ACHA, 2002). Three of the ten leading health indicators of Healthy Campus 2010 are also behavioral risk factors for heart disease: (1) physical activity; (2) o verweight and obesity ; and (3) tobacco use. Understanding behavioral trends among university students and increasing researchers knowledge on reasons why students engage in such behavior will aid in the development of future programs that help accomplish national health objectives. Perception of Risk Heart disease is perceived to affect only older adults; younger individuals believe that there is no reason to be concerned about heart disease risk factors (Collins et al., 2004). This misperception is due in part to a lack of risk factor knowledge among young adults. There is an overall misperception that behavior engaged in now will not have an

PAGE 21

21 effect on future health status or quality of life. It is imperative that researchers investigate the origin of this misperception, study behavioral risk factors in the young, and create successful interventions that will promote sustainable behavior change. According to the Health Belief Model, individuals who perceive they are more at risk for a condition are more lik ely to participate in positive lifestyle behavior (NCI, 2005). College students perceive their risk for heart disease to be low; therefore, they lack concern and engage in poor lifestyle behavior (Wendt, 2005). Previous studies indicate that more research is needed both to develop interventions for improved perceptions among college students and to encourage students to act upon this improved perception to reduce overall heart disease morbidity and mortality (Green et al., 2003). There is substantial ignor ance of heart disease and the associated risk factors among college students (Collins et al., 2004). Students between the ages of 18 and 25 are unaware of heart disease risk, and they lack knowledge of behavioral changes that reduce their risk (McMahan et al., 2007). Even those students who acknowledge some risk unfortunately perceive that risk to be minimal and therefore do not engage in preventive measures or behavior (Collins et al., 2004). It is plausible that knowledge regarding heart disease and the r elated risk factors will impact ones decision to take action to reduce their risk of being diagnosed with the disease (Getliffe, Crouch, Gage, Lake, & Wilson, 2000). Research on College Students There is scant research performed among individuals enrolled in college related to heart disease risk (McMahan et al., 2007). The research that does exist shows that college students engage in poor health activities such as fast food diets, smoking, and physical inactivity (Spencer, 2002). The college population is an often forgotten

PAGE 22

22 population when screening for and assessing heart disease behavioral risk factors, yet the college environment encourages behavioral changes in many areas of students lives (Racette et al., 2005). Early adulthood and the transition from high school to college create an increase in responsibility and greater control over everyday decisions that can ultimately lead to negative lifestyle choices and behavior (Jung, Bray, & Ginis, 2008; McGowen, Joffe, Duggan, & McCay, 1994; Sparling, Snow, & Beavers, 1999). During this time of personal responsibility and choice, students may be more sensitive to health related issues and have greater health awareness (Frost, 1992), creating a fertile environment for health education messages to take hold. R esearch regarding heart disease risk perception is not abundant (Green et al., 2003). Although much is understood regarding risk associated with heart disease, little is known regarding the perception of risk, as exhibited by college students. Perceived su sceptibility and perceived severity are two constructs that impact behavior change. This research will attempt to fill the gaps in the literature regarding perception of heart disease risk among college students. College is an ideal time for health educators to reach many young adults and assist them in establishing and maintaining heart healthy behavior (Dinger, 1999; McGowen et al., 1994; Spencer, 2002). The longterm health of the student can be positively affected by promoting primary prevention strategies for disease (Smith, 2008). Awareness and promotion of healthy behavior while in college is likely to produce longterm benefits for the individuals, reducing their overall risk for heart disease in the future (Racette et al., 2005). Research Questions This study addresses t he following research questions:

PAGE 23

23 1. Does the university female student population perceive themselves to be at risk for heart disease? Do age, ethnicity, and/or perception impact ones behavior? Specific Aim 1. This study tested perception of heart disease and heart disease risk factors among university women. It was hypothesized that accurate perception of heart disease risk is minimal among the target population and that by correcting the misperception, behavioral changes are more likely to be established that ultimately reduce risk. According to the Health Belief Model, individuals who perceive they are more at risk for a condition are more likely to participate in positive lifestyle behavior (NCI, 2005). Using Specific Aim 1, the investigator assessed perception of risk for heart disease. Specific Aim 2. O verall behavior that has been documented to lead to heart disease was assessed It was hypothesized that there would be a significant difference in behavior related to age, ethnicity, and perception. The hypothesis was tested by grouping participants according to positive and negative risk perception, ethnicity, and age using survey results of perception and self reported age and ethnicity. The groups were tested for a relationship with behaviors related to increased heart disease risk. 2. Does age and/or ethnicity impact Health Belief Model constructs related to each behavioral risk factor? Specific Aim 3. The research assessed the relationship of age and/or ethnicity to specific Health Belief Model constructs related to each behavioral heart disease risk. It was hypothesized that there would be a significant difference related to the variables of ethnicity and age. This hypothesis was tested by grouping participants ac cording to self -

PAGE 24

24 reported ethnicity and age and assessing determinants of behavior based on the Health Belief Model construct portion of the survey. Limitations The following limitations are present in this study: Students were not required to participate i n this study; participation was voluntary. Ethnic diversity was narrowed to female students choosing to participate in the study. Due to a limited geographic scope, the random sample of students may not have represented national undergraduate female populations. The generalizability of the results is limited to students sampled in this study. Delimitations The following delimitations were identified in this study: Participants were excluded from the study if they did not meet the criteria of the investigators undergraduate female definition. The random sample of students was selected from one university. The study was based only on the theoretical framework of the Health Belief Model. Definition of Terms Body Mass Index (BMI) A ratio of body weight to height; used to help determine if weight falls with a healthy range for height. Cholesterol A fatty material found in tissues throughout the body. Cues to Action Cues that trigger or promote the individual to act. Diabetes A disease in which the body does not manage blood sugar (does not produce enough insulin or use it appropriately). Family History Information about mother or fathers history of heart disease. A positive family history is defined as the biological moth er diagnosed with some form of heart disease before age 55 and a biological father diagnosed with some form of heart disease before age 65. Fast Food Inexpensive food, such as hamburgers and fried chicken, prepared and served quickly.

PAGE 25

25 Heart Checkup A d octors visit when a physician investigates the patient for signs and symptoms of disease. Heart Disease Any sickness or disorder that affects the heart. High Density Lipoprotein (HDL) Termed "good" cholesterol. HDL aids in the removal of cholesterol f rom the bodys tissues. High Fat Foods Foods such as hamburgers, steak, whole milk products, chocolate, ice cream, fried foods, etc. Hookah (Waterpipe tobacco smoking) A pipe used to smoke tobacco; tobacco smoke is passed through water prior to inhalat ion. Hypertension A reading of blood pressure 140/90 mmHg or higher; an above normal blood pressure. Low Density Lipoprotein (LDL) Termed bad cholesterol. LDL carries cholesterol throughout the blood. Low Fat Foods Foods such as skinless chicken, fish, low fat dairy, vegetables, beans, grilled foods, etc. Nutrition The consumption of a diet and how a body processes food. Obesity Overweight, weighing more than 25% of an ideal weight for women. Perceived Susceptibility An individuals belief o f how likely they think they are to develop and be diagnosed with heart disease. Perceived Severity An individuals belief regarding how serious an effect heart disease will have on overall quality of life; belief that the disease has negative consequences. Perceived Benefits Believe taking action will reduce their risk. Perceived Barriers Believe that the benefits of performing the desired positive behavior outweigh the costs; hindrances to taking action. Primary Care Physician A health care provider that one considers their regular doctor. Physical Activity Exercise that improves or maintains ones fitness and health. Second Hand Smoke Cigarette smoke that any nearby individual, including nonsmokers, can breathe into their lungs. Self Efficacy An individuals confidence that they can successfully engage in a specific behavior.

PAGE 26

26 Smoking Inhalation of tobacco, most often in the form of cigarettes. Undergraduate Female A currently enrolled university female student who was 18 to 29 years old and working toward a bachelors degree at the time of survey implementation. Significance of the Study Heart disease is a long developing condition; therefore, there is need for early intervention and research among the young adult population. This research will identify an essential area for health education intervention and highlight the need for hear t disease prevention efforts among female university students. This research will bring awareness of heart disease risk factors to the target population and focus the attention of the health education/promotion profession on heart disease risk among the university population. Awareness among the target population may promote motivation toward positive health behavior change. Focused attention among the profession will establish the importance of targeting this age and gender group in heart disease prevention efforts. Summary College is a time of independence, and students are impressionable; therefore, college is an ideal environment for research and intervention (McMahan et al., 2007). Assessing and understanding the health needs of college students is i mperative to achieve healthy campuses. College students are at risk for heart disease, and unhealthy lifestyles lead to clinical risk factors (Spencer, 2002). Based upon this information, to reduce ones risk, an individual must be aware of her risk factor s and must actively engage in lifestyle behavior to reduce personal risk. Clearer perceptions of heart disease risk are needed to prompt college students to alter risky behavior (Green et al., 2003). Improving the health of college students requires careful examination of

PAGE 27

27 both behavioral and clinical risk factors so that evidence based interventions can be directed toward heart disease risk reduction. Empowering students with the ability to identify their heart disease risks, providing knowledge, and incorporating methods in which they can change behavior, are important strategies that researchers and health educators can use to reduce the rate of heart disease in women (Hardesty & Trupp, 2005). C hapter 1 highlighted the main aspects of the study and disclos e d the research problem, purpose, and rationale for the study. Research questions were listed and explained. Chapter 2 is an overview of current literature on heart disease and heart disease risk factors among women and university students. It provides an overview of the conceptual framework used in this line of research.

PAGE 28

28 CHAPTER 2 REVIEW OF THE LITERATURE C hapter 2 provides an overview of heart disease and heart disease risk factors. The first section of this C hapter 2 presents research regarding heart disease among women and female undergraduates. The next section provides an overview of the conceptual framework used in this study the Health Belief Model. The third section reviews modifiable heart disease risk factors in general, among women, and among undergraduate students. A brief section regarding family history is included to discuss this important nonmodifiable risk factor for heart disease. Ethnic/racial disparities of heart disease (and its risk factors) are discussed following the section regarding family history. The fourth and final portion of C hapter 2 provides an overview of prevention and prevention strategies for use among women and undergraduates. Heart Disease and Women Data suggests that women make up the majority of the adult population in the United States (Tsang, Barnes, Gersh, & Hayes, 2000). Heart disease is the leading cause of death and disability among women in the world (Hemingway, 2007; World Health Organizatio n [WHO], 2003). Researchers approximate that in the United States alone; more than 32 million women have some form of heart disease (Bello & Mosca, 2004). Heart disease should be a concern among women; some form of heart disease is responsible for one out of every four deaths among women (NHLBI, 2010); every minute a women dies from some form of heart disease (Hardesty & Trupp, 2005). It is likely women perceive heart disease as mainly affecting those over the age of 50, yet this perception is incorrect. More than 9000 women younger than age 45 suffer heart attacks each year (Miracle, 2006; Wenger, 2004). However, large portions of

PAGE 29

29 women believe that they are not at risk until menopause (Giardina, 1998) This prevailing misconception among women may contribute to their lack of motivation to engage in behavioral change (Biswas, Calhoun, Bosworth, & Bastian, 2002). Research indicates that women continue to hold to the misperception that heart disease is not their problem (Mosca, Manson, Sutherland, Langer, Manolio, & Barrett Connor, 1997). Women 25 years and older surveyed in the United States indicate that overall, they do not perceive the effects of heart disease to be as severe as what clinical research indicates the morbidity and mortality to be among the female population (Mosca et al., 2000; Pilote & Hlatky, 1995). Heart disease kills more women than all other cancers combined (Malarcher, Casper, Matson, Koffman, Brownstein, Croft et al., 2001). Many w omen are unaware of their personal risk for heart disease and, as a result do not recognize disease warning signs (Giardina, 1998; Mosca, Jones, King, Ouyang, Redberg, & Hill, 2000; Mosca, Edleman, Mochari, Christian, Paultre, & Pollin, 2006; Mosca et al., 2004; Wenger, 2003). A general lack of knowledge regarding heart disease and personal risk may be a factor in the higher incidence of the disease among women (Long, Waldrep, Hernandez, & Strickland, 2005; Meisler, 2001) and may present itself as a major barrier to lifestyle change (Bello & Mosca, 2004). Biswas et al. (2002) found that womens concern increased as their number of known risk factors increased. According to the National Heart Lung and Blood Institute (2010), female risk factors for heart disease which can be modified include the following: cigarette smoking, hypertension (high blood pressure), high cholesterol, overweight, sedentary behavior, and diabetes. Age and family history of heart disease increase ones risk yet cannot be

PAGE 30

30 changed (NHLBI 2010). Statistics indicate that two out of every three women possess at least one major risk factor for heart disease, and as they age their risk increases (Meisler, 2001). Women possessing one risk factor are much more likely to develop heart disease when compared with women presenting no risk factors (NHLBI, 2010). First cardiovascular events have proven fatal among women; therefore, risk factor management should be considered early in order to prevent onset of heart disease (Mosca, Grundy, Judelson, Ki ng, Limacher, & Oparil, 1999). It is essential that women receive education regarding heart disease (Wenger, 2003). Awareness efforts are needed among women to bridge the gap between their perception and the reality of heart disease (Wenger, 2003). If wome n do not fully understand risk factors associated with heart disease, they are not fully equipped to take action to prevent heart disease through behavior change (Oliver McNeil & Artinian, 2002). Alleviation of risk through lifestyle change will not be pos sible until women realize they are susceptible to heart disease and recognize the severity of the disease, including the negative effects (Biswas et al., 2002). Women vs. Men Different Risk Cardiovascular disease has, in the past, been viewed as a diseas e that affects only men (Hart, 2005; Price, 2004; Wenger, 2004); the disease now is being understood to be the leading cause of death among both men and women (Knopp, 2002; Long et al., 2005; Wenger, 2004). Women have died more often from heart disease than men for the past 25 years (Wenger, 2003) Heart disease takes the lives of women more quickly than it does men; approximately 38% of women who have a heart attack compared to 25% of men, will die within a year from suffering the attack ( Kordella, 2005).

PAGE 31

31 Risk factors predict different effects in women than in men; for example, womens risk is more affected more by cholesterol and diabetes (Simontacchi & FitzGerald, 2004). When compared with men, women have shown lower levels of heart disease knowledge, and many times they do not correctly identify heart disease as their greatest health threat (Collins et al., 2004; Lefler, 2004; Meischke, Kuniyuki, Yasui, Bowen, Anderson, & Urban, 2002; Mosca et al., 2000). Women do not perceive their risk to be as high as a mans risk (Lefler, 2004; Richards, Reid, & Watt, 2002). Unfortunately, this common misperception may lead a woman to have unrealistic optimism regarding their risk for heart disease and therefore prevent such women from making positive changes and seek ing intervention to reduce their risk (Hart, 2005). Women still are highly underrepresented in research studies regarding heart disease (Mikhail, 2005; Rosenfeld, 2006). Heart disease risk, symptoms, and progression disparities exist among men and women ( Rosenfeld, 2006) providing a compelling case to include women in clinical studies (Wenger, 2004). Further research is needed utilizing samples of women to fully understand the differences among men and women regarding heart disease. Heart Disease and Under graduates Young adults are not immune to heart disease risk. Heart disease is the fifth leading cause of death among Americans aged 1524 years (National Center for Health Statistics [NCHS], 2008 as cited in Smith, 2008). Traditional college students fall within this risk category; however, awareness among college students between ages 1825 is minimal, and students do not fully understand lifestyle strategies to reduce risk for heart disease (McMahan et al., 2007). Low levels of knowledge regarding heart disease and heart disease risk have been noted among the university student population (Collins et

PAGE 32

32 al., 2004). College students do not have correct risk perception regarding heart disease and their individual risk (Collins et al., 2004; Green et al., 2003; Schroetter & Peck, 2008; Vanhecke, Miller, Franklin, Weber, & McCullough, 2006; Wendt, 2005). Data indicate that collegeaged individuals, both male and female, misjudge their heart disease risk and do not select heart disease as a womans greatest cause of death (Collins et al., 2004; Green et al., 2003; Jensen & Moser, 2008); in fact students perceive cancer as their greatest health risk (Collins et al., 2004). In a study by Green et al. (2003), university students indicated that they did not understand c ause and effect relationships between behavior and heart disease risk; as a result, they did not have an accurate perception of risk. It is thought that students are less concerned about heart disease because they believe that the onset of the disease is in the distant future, and they are more concerned about their current health risks (Collins et al., 2004). Although heart disease does not appear until mid to late adulthood, college students show indicators of cardiac risk (Spencer, 2002). Many of the behavioral risk factors for heart disease may begin during adolescence or young adulthood (Smith, 2008). Heart disease risk factors present in adolescence smoking tobacco, decreased physical activity, high blood pressure, high cholesterol, high fat diets, and obesity are predictors for future heart disease (Hemingway, 2007; Smith, 2008). Few studies regarding heart disease have been conducted among young adults. However, existing research indicates that college students engage in behaviors that place them at risk for heart disease; examples include poor diet (Brevard & Ricketts, 1996 as cited in Spencer, 2002; Frost, 1992; Glore et al., 1993 as cited in Spencer,

PAGE 33

33 2002; Larouche, 1998 as cited in Spencer, 2002; Makrides et al., 1988 as cited in Spencer, 2002; Oleckno et al., 1990 as cited in Spencer, 2002; Spencer, 2002; Troyer et al., 1990 as cited in Spencer, 2002), tobacco use (Emmons, Wechsler, Dowdall, & Abraham 1998; Spencer, 2002; Fiore et al., 1993 as cited in Schorling et al., 1994; F rost, 1992; Sax, 1997; Schorling, Gutgesell, Klas, Smith, & Keller, 1994), and physical inactivity (Frost, 1992; Makrides et al., 1988 as cited in Spencer, 2002; Oleckno et al., 1990 as cited in Spencer, 2002). College is a time when students shape behavi or s that will continue into adulthood (Desai, Miller, Staples, & Bravender, 2008; Racette et al., 2005). This period is also a time when individuals start becoming accountable for their own behavioral choices ; choices that ultimately affect their health (Lenz, 2004). College offers an excellent opportunity to study young adult students and their risk for heart disease (Smith, 2008). Theoretical Framework Health Belief Model One of the strongest health related theoretical models is the Health Belief Model (HBM) it has been used extensively in research to explain or analyze prevention, illness, and behavior (Becker, Maiman, Kirscht, Haefner, & Drachman, 1977; Mirotznik, Feldman, & Stein, 1995). The Health Belief Model is used to help determine if an indi vidual will engage in preventive behavior and health promoting actions. The Health Belief Model applies when studying behaviors or conditions that create health concerns among the general population; it works well when studying lifestyles for the purpose of developing behavioral change interventions (NCI, 2005). The HBM is made up of three basic tenets (Becker et al., 1977): (1) motivating factors ; (2) illness threat perception; and (3) behavior perception (Mirotznik et al., 1995).

PAGE 34

34 According to the HBM health promoting behavior may be related to perception of both benefits and barriers to positive health behavior maintenance (Thanavaro, Moore, Anthony, Narsavage, & Delicath, 2006) and ones personal beliefs about an illness (Mirotznik et al., 1995). The mod el assumes that perceived susceptibility is imperative to inspire behavioral change by influencing ones readiness to act (Avis, Smith, & McKinlay, 1989). Therefore it can be stated that, according to the Health Belief Model, motivation to change ones behavior stems from the individuals perception of disease risk. Perception of risk is based upon the individuals belief that they themselves are susceptible to said disease (Mirotznik et al., 1995). The Health Belief Model focuses on individual beliefs and behavior and uses six constructs to measure what encourages or discourages individuals from participating in a desired behavior (Figure 21). The social psychologists who developed the theoretical model argued that individuals are ready to act if they (1) believe they are susceptible to the disease perceived susceptibility; (2) believe the disease has negative consequences perceived severity; (3) believe taking action will reduce their risk perceived benefits; (4) believe that the benefits of performing the desired behavior outweigh the costs perceived barriers; (5) have been exposed to items that trigger or promote actioncues to action; and (6) have the confidence that they can successfully perform the activity self efficacy (NCI, 2005). Perceived suscept ibility has been operationalized as an individuals belief of how likely they are to develop and be diagnosed with heart disease (Mirotznik et al., 1995). Perceived susceptibility is an indicator of why individuals choose whether or not to participate in heart disease risk reduction lifestyle behavior (Collins et al., 2004; Silagy,

PAGE 35

35 Muir, Coulter, Thorogood, & Roe, 1993). Perceived severity has been operationalized as an individuals belief regarding how serious an effect heart disease will have on their overall quality of life (Mirotznik et al., 1995). Perceived risk (threat) includes susceptibility and severity of the disease directly related to ones personal risk (Ali, 2002). Individuals are less likely to engage in preventive measures and heart healthy b ehaviors if they do not perceive to be at risk for disease (Frost, 1992; Green et al., 2003; McMahan et al., 2007; Oliver McNeil & Artinian, 2002). Health Belief Model and Women Perception of risk should be seen as an important area to address to increase womens understanding of heart healthy behavior (Lefler, 2004). Perception of heart disease risk may play an important role in prevention among women (Schroetter & Peck, 2008). According to previous research, women are more likely to engage in positive li festyle behavior when they have perception of an increased personal risk for heart attack occurrence (Lefler, 2004; Meischke, Yasui, Kuniyuki, Bowen, Anderson, & Urban, 1999). According to Murdaugh and Verran (1987), it is not fully understood why women d ont engage in a health promoting lifestyle; however, perceived benefits are said to motivate women to action, and perceived barriers are said to inhibit women from taking the necessary steps to prevent disease onset. Thanavaro et al. (2006) conducted a st udy to determine the best predictors of health promoting behavior among a sample of women with no prior history of heart disease; the study assessed behavior regarding coronary heart disease risk factors related to the constructs of perceived barriers and benefits of the Health Belief Model. Thanavaro et al. (2006) found that the women in their study (aged 35 60 with no prior history of heart disease) had low levels of heart

PAGE 36

36 disease knowledge and risk perception, indicating that women do not completely co mprehend heart disease. Women who indicated a better understanding of heart disease and heart disease risk more often reported that they engaged in health promoting activity (Thanavaro et al., 2006). Knowledge about a disease and understanding risk factors associated with the disease should motivate individuals to engage in behaviors and activities that reduce their overall susceptibility to the disease (Getliffe et al., 2000). Further information is needed regarding womens perception of heart disease risk to better plan and implement programs to address perceptions and motivate individuals to act in a positive manner. Health Belief Model and Undergraduates Young adults have little knowledge and an inaccurate perception of heart disease risk; without a perc eived risk of developing the disease, preventive measures will not be successful (McMahan et al., 2007). Research regarding risk perception of heart disease among young adults is scarce (Green et al., 2003). Determinants of perception of heart disease need to be known to fully understand how to address risk reduction and develop successful interventions among the collegeaged population (Collins et al., 2004). More research is needed to improve perceptions among collegeaged students, thereby motivating them to take action to reduce their risk of heart disease through behavioral change (Green et al., 2003). Risk Factors for Heart Disease Heart disease is widespread yet highly preventable (Mosca, Ferris, Fabunmi, & Robertson, 2004; Stoddard, Palombo, Troped, Sorensen, & Will, 2004). For the majority of individuals in the United States at risk for heart disease, risk can be attributed to lifestyle behavior and environment (Haskell, 2003). Poor lifestyle behavior is a main

PAGE 37

37 contributor to poor health and places women at risk for heart disease (King & Arthur, 2003). Risk factors for heart disease include but are not limited to the following: smoking tobacco, high blood pressure, high total cholesterol, diabetes, positive family history, obesity, physical inactivit y, and a poor diet (Bello & Mosca, 2004; Fleury, Keller, & Murdaugh, 2000; Mosca et al., 1997). An alternative risk factor present among women is elevated abdominal fat apple shaped women have greater risk than pear shaped women (Simontacchi & FitzGerald, 2004). Research indicates that women who have one or more risk factors may be at even higher risk for heart disease (Damlo, 2007). Smoking Smoking is an established risk factor for heart disease development (Bowman, Gaziano, Buring, & Sesso, 2007); hear t disease is one of many diseases directly related to smoking ( Moran, Glazier, & Armstrong 2003; McCauley, 2007; USHHS, 2004). Research indicates that approximately 30% of cardiovascular morbidity/mortality is related to smoking tobacco ( Krummel, Koffman, Bronner, Davis, Greenlund, Tessaro et al., 2001); however, smoking tobacco is the most manageable risk factor for heart disease ( Kuehn, McMahon, & Creekmore 1999). Healthy People 2010 refers to smoking as the most preventable cause of disease and death (Hardesty & Trupp, 2005; McCauley, 2007; USHHS, Healthy People 2010 ;), and the American Heart Association (2007) lists smoking as one of the first major ris k factors for heart disease. Risk for heart disease is quickly reduced when smoking is ceased (Daviglus, LloydJones, & Pirzada, 2006; Rea, Heckbert, Kaplan, Smith, Lemaitre, & Psaty, 2002; USHHS, 1990 as seen in Daviglus et al., 2002). Water pipe tobacco smoking (hookah) prevalence, an alternative to traditional tobacco smoking in the form of cigarettes, is increasing in the United States (Cobb,

PAGE 38

38 Ward, Maziak, Shihadeh, & Eissenberg, 2010). Hookah uses a pipe to smoke tobacco; tobacco smoke is passed through water prior to inhalation. Research regarding hookah is limited but forthcoming. According to recent reports, this alternative to cigarette smoking may continue to grow in prevalence and popularity (Cobb et al., 2010). American adults have yet to reach the 12% smoking rate goal set forth by Healthy People 2010 (Nehl, Blanchard, Peng, Rhodes, Kupperman, Sparling et al 2009, USHHS, Healthy People 2010); the Centers for Disease Control and Prevention (2008) reports that an estimated 21% of American adult s currently smoke. Smoking negatively affects every organ in the body, causing disease (Sanem, Berg, An, Kirch, & Lust, 2009; USHHS, 2004). Smoking drastically affects a persons health and is of major interest for public health professionals; therefore, s moking cessation efforts should be a key health promotion focus (Gemmell & DiClemente, 2009). Smoking and Women Smoking among young adult women has been described as a full blown epidemic (Gaffney, Wichaikhum, & Dawson, 2002, pg. 506); the CDC (2004) reported that in the year 2004, 18.5% of U.S. women indicated they were current smokers (CDC, 2004 as cited in Bowman et al., 2007). Women who smoke place themselves at an increased risk for a myriad of diseases, including heart disease and lung cancer (Gaffn ey et al., 2002). A woman who smokes increases her risk for heart disease up to six times that of a nonsmoking female, and women who are exposed to second hand smoke also increase their personal risk for heart disease (Kawachi, Colditz, Speizer, Manson, S tampfer, Willett et al. 1997; Tsang et al., 2000). The Nurses Health Study found that smoking even a few cigarettes a day increases womens risk for heart disease/heart attack ( Cheek, Sherrod, & Tester, 2008; Moran et al., 2003; Rosenberg,

PAGE 39

39 Miller, Kaufma n, Helmrich, Van de Carr, Stolley et al., 1983). Women who smoke regularly die approximately 14.5 years earlier than women who do not smoke cigarettes (CDC, 2009 as cited in Evangelista & McLaughlin, 2009). Women should be advised not to smoke and to avoid second hand smoke (Damlo, 2007; Mosca et al., 2004). Smoking and Undergraduates A goal of Healthy Campus 2010 states that reducing disease related to tobacco use among adults ages 18 and older and reducing exposure to secondhand smoke should be a health priority (ACHA, 2002). College is a critical time for students and tobacco use (Lenz, 2004). Research indicates that of the college students who smoke, approximately 11 1 4% smoked their first cigarette after high school (Costa, Jessor, & Turbin, 2007; Ever ett, Husten, Kann, Warren, Sharp, & Crossett, 1999; Naquin & Gilbert, 1996 as cited in Everett et al., 1999; Weshler et al., 1998 as cited in Everett et al., 1999). In a study by Lenz (2004), one out of three college students in their first or second year reported they had used tobacco within the past month. Although occurrence of smoking declined overall during the past twenty years, smoking among the college student population has not. The highest prevalence of smoking (24.4%) is among adults aged 1824 ( Nehl et al., 2009). Determinants of college student smoking are unknown due to a lack of research regarding why students smoke (Costa et al., 2007; Emmons et al., 1998). Reports indicate that hookah smoking is becoming increasingly more common in the Unit ed States among the college population (Cobb et al., 2010; Primack, Sidani, Shadel, & Eissenberg, 2008). College towns appear to be prime locations for business owners to establish hookah cafs; approximately 200300 cafs opened around college campuses between the years 19992004 ( Smokeshop Magazine, 2004 as seen in

PAGE 40

40 Primack et al., 2008). Perception of low risk may be a predictor of hookah use. A study among 647 university students indicated that a percentage of participants (33%) believed hookah to be les s harmful than cigarette smoking and less addictive (52.1%) (Primack et al., 2008). Young adult populations should be targeted for smoking prevention/cessation interventions college presents researchers with a fitting opportunity to reach a large number of young smokers (Snyder et al., 2009 as cited in Sanem et al., 2009). Nutrition Healthy diet is one of the first steps toward a healthy lifestyle. Diet and lifestyle improvement are major methods of the American Heart Associations plan to prevent heart dis ease development among the population in the United States (Lichtenstein, Appel, Brands, Carnethon, Daniels, Franch et al., 2006). Proper nutrition/diet plays a major role in decreasing ones risk for heart disease (Haberman & Luffy, 1998; Haskell, 2003; Toft, Kristoffersen, Lau, Borch Johnsen, & Jorgensen, 2006). Various dietary components effect heart disease development and progression; therefore, to reduce risk of heart disease, individuals need to improve their total diet (Lichtenstein et al., 2006). N utrition guidelines for a healthy diet include eating a variety of fruits and vegetables, fish, and whole grains (AHA, 2010; McCauley, 2007). Diets high in fruit and vegetable content are associated with a decreased risk of heart disease and stroke (Albert 2005; Damlo, 2007; Daucher, Amouyel, Hercberg, & Dallongeville, 2006; Pereira, OReilly, Augustsson, Fraser, Goldbourt, & Heitmann et al., 2004; Simmontacchi & FitzGerald, 2004). Individuals who eat more fruits and vegetables have a lower risk for heart disease than those who eat mostly meat in their diet (Haskell,

PAGE 41

41 2003; Hu, Rimm, Stampfer, Asherio, Spiegelman, & Willet, 2000). Research indicates that heart disease risk is reduced up to 20% by eating three servings of fruits/vegetables daily (Joshipura, H u, Manson, Stampfer, Rimm, Speizer et al., 2001; McCauley, 2007). Individuals should limit their salt and sugar intake and reduce saturated fat and processed meat consumed (AHA, 2010). Diets rich in wholegrain foods and fiber have been linked to lower heart disease risk (Hu & Willet, 2002; Lichtenstein et al., 2006). Diets low in saturated fats and cholesterol also reduce ones risk for heart disease development and progression (Lichtenstein et al., 2006). According to recent reports, there is a direct relationship between salt and a noted risk factor for heart disease. As salt intake increases, blood pressure increases (Johnson, Nguyen & Davis, 2001; Lichtenstein et al., 2006; Sacks, Svetkey, Vollmer, Appel, Bray, Harsha et al., 2001). Nutrition and Women The overall health of a womans heart is linked to the food she eats (USHHS, 2007). Heart disease among women can be predicted by assessing ones dietary behavior ( Akesson, Weismayer, Newby, & Wolk 2007; Fung, Willett, Stampfer, Manson, & Hu, 2001; Hu et al., 2000). Results of nutrition studies indicate that a healthy diet is imperative to good heart health and can lower ones risk for development and progression of heart disease. A study investigating the effects of the Dietary Approaches to Stop Hypertension Diet (DASH) found that women in their study population who consumed foods from the DASH diet had lower risk of heart disease (Fung, Chiuve, McCullough, Rexrode, Logroscino, & Hu, 2008). The study also recorded lower blood pressure and low er cholesterol levels among the women whose diets adhered to DASH guidelines (Fung et al., 2008). This study reinforced previous

PAGE 42

42 findings that women who consume fruits and vegetables, whole grains, low fat dairy and limit their consumption of meat have low ered risk of heart disease development (Fung et al., 2008). To promote good health, womens diets should include vegetables, fruits, fish and lean meats (USHHS, 2007). Few women in the United States currently adhere to nutrition guidelines that promote heart health (Krummel et al., 2002). Diet can also a ffect other heart disease risk factors such as cholesterol, blood pressure, and proper weight maintenance (AHA, 2010). Clearly, among many women, dietary changes are needed to alleviate heart disease risk f actors (Hayman & Hughes, 2006; Howard, Van Horn, Hsia, Manson, Stefanick, Wassertheil Smoller et al., 2006). Nutrition and Undergraduates Health educators and health professionals should be concerned about college students eating behaviors. Students in c ollege are transitioning to a time of primary decisionmaking regarding diet ( Smith, Taylor, & Stephen, 2000). Unhealthy e ating habits established while in college are likely to continue into adulthood and can negatively affect both the student as an adult and their future generations (Brown, Dresen, Eggett, 2005; Ha & Caine Bish, 2009). In a longitudinal study by Lau, Quadrel, and Hartman (1990) a sample of college students (N=947) was found to have very poor eating habits after leaving home. Lau et al. ( 1990) also reported that students in their first year of college had diets lacking proper nutrition, and their eating habits degenerated as their time away from home increased. College students are engaging in unhealthy dietary behavior; data suggests that students do not eat the recommended amount of fiber and do not consume five fruits/vegetables daily ( Huang, Harris, Lee, Nazir, Born, & Kaur 2003). Students

PAGE 43

43 consume a diet lacking recommended vitamin and mineral content ( Irazusta, Hoyos, Irazusta, Ruiz, Diaz, & Gil, 2007; SerraMajem et al., 2001 as cited in Irazusta et al., 2007). Research indicates that college students skip meals (Huang, Song, Schemmel, & Hoerr, 1994) and often eat low nutrient snacks (Skinner, Salvetti, & Penfield, 1984 as cited in H a & Caine Bish, 2009). In fact, college students consume many of their daily allotment of calories in the form of snacks (NCEP, 1991 as cited in McGowan et al., 1994). In a study performed by Brevard and Ricketts (1996), researchers discovered that a sam ple of college students (N=104) surveyed ate more than the recommended amounts of fat (both saturated and unsaturated), cholesterol, and salt and did not include enough fruits and vegetables in their diets. In a more recent study, Ha and Caine Bish (2009) found that their sample of college students (N=80) fell short of proper fruit and vegetable consumption. Individuals in college are not fully aware of fruit and vegetable consumption benefits; risk of poor diet is also not fully recognized among this popul ation (Chung, 2006; Ha & CaineBish, 2009). College is an ideal time to promote behavior change for nutrition including lowering dietary fats and increasing amount of fiber in the diet ( Brunt, Rhee, & Zhong, 2008; McGowan et al., 1994). It is important that nutrition education and interventions be established among college students as diet behaviors begun in college may continue into adulthood and place individuals at increased risk for heart disease (Haberman & Luffy, 1998). Physical Inactivity The American Heart Association currently recommends that all adults ages 18 to 65 engage in moderate physical activity for 30 minutes a day for five days a week or vigorous physical activity for 20 minutes on three days a week (Haskell, Lee, Pate,

PAGE 44

44 Powell, Blair, & Franklin, 2007). Approximately 60% of men and women do not engage in regular physical activity (Mosca et al., 1997). Many individuals who live an inactive lifestyle also engage in other unhealthy behaviors that, if left unchanged, increase ones risk for h eart disease (Marcus et al., 2006). Inactivity is associated with heart disease, cancer, overweight and obesity, and other health problems (Haase, Steptoe, Sallis, & Wardle, 2004). Physical inactivity is an independent risk factor for heart disease dev elop ment (Tsang et al, 2000; Wel ler & Corey, 1998) and is listed as one of the leading contributors of death among adults (Marcus, Williams, Dubbert, Sallis, King, Yancey et al., 2006; Mokdad, Giles, Bowman, Mensah, Ford, Smith et al., 2004). Risk of heart dis ease increases in inactive individuals, 1.5 times to 2.4 times respectively (C rane & Wallace, 2007). Physical activity is necessary for achieving good health (Hardesty & Trupp, 2005; Keating, Guan, Pinero, & Bridges, 2005); activity alleviates risk factor s of high blood pressure, high cholesterol, obesity, and diabetes (Simontacchi & FitzGerald, 2004). Research indicates that physical activity is helpful in the primary prevention of heart disease (Mirotznik et al., 1995). Physical activity has been associated with a 15% 50% decrease in heart disease risk (Blair, Kohl, Paffenbarger, Clark, Cooper, & Gibbons, 1989; Sherman, DAgostino, Cobb, & Kann, 1994; Wenger, 2003). Studies report that more physically active men and women, when compared with less active i ndividuals, have lower risk for heart disease (Haskell, 2003; Manson, Greenland, LaCroix, Stefanick, Mouton, Oberman et al., 2002; Myers, Prakash, Froelicher, Do, Partington, & Atwood, 2002; Tanasescu, Leitzmann, Rimm, Willett, Stampfer, & Hu, 2002). Benefits

PAGE 45

45 will only be experienced throughout their lifespan if individuals engage in regular physical activity (Mirotznik et al., 1995). Physical Inactivity and Women A sedentary lifestyle is the most common risk factor for heart disease in women (Bedinghaus et al., 2001). Women need to engage in moderate physical activity for 30 minutes on most days of the week ( AHA, 2011), yet research indicates that women fall short of achieving the recommended levels of daily physical activity. Inactivity has contributed to approximately 30% of all heart disease events in women of middle age (Manson, Hu, RichEdwards, Colditz, Stampfer, Willet et al., 1999; Perry & Bennett, 2006). Studies involving large female samples have shown a significant inverse relationship between physical activity and heart disease (Hong, Friedman, & Alt, 2003; Kushi, Fee, Folsom, Mink, Anderson, & Sellers 1997; Lee, Rexrode, Cook, Manson, & Buring, 2001; Manson et al., 1999; Manson et al., 2002; Perry & Bennett, 2006; Sesso, Pappenbarger, Ha et al 1999). Research indicates that women who are active have a lower risk for heart disease when compared with women who are inactive (Jneid & Thacker, 2001; Price, 2004). Mora, Cook, Buring, Ridker, & Lee (2007) assessed women older than 45 (N= 27,055) to te st the relationship between physical activity and heart disease risk. This longitudinal study resulted in the finding that women who were more active had a healthier lifestyle, better weight management, and lower risk than women who were inactive (Mora et al., 2007). Evidence suggests that physical inactivity and obesity may be a bigger predictor of heart disease in women than in men (Eaker, Chesebro, Sacks, Wenger, Whisnant, & Winston, 1993). Women tend to be less active than men (Bild, Jacobs, Sidney, Has kell, Anderssen, & Oberman, 1993; Crane & Wallace, 2007). Exercise reduces ones risk for

PAGE 46

46 heart disease by maintaining proper cholesterol and decreasing blood glucose levels exercise can in fact reduce a wom a ns risk by up to 50% (Cheek et al., 2008). Studies suggest that women are not reaping the benefits of physical activity because they are not meeting the recommended guidelines. Further research is needed to explore reasons why women are not meeting national guidelines of 30 minutes of physical activit y on most days of the week Physical Inactivity and Undergraduates Healthy Campus 2010 goals include improving health of college students through physical activity; it is recommended that students engage in moderate physical activity for at least 30 minut es on at least three days of the week (ACHA, 2002). The American Heart Association promotes 150 minutes of moderate activity per week, or 75 minutes of vigorous activity per week for all adults (AHA, 2011). The CDC has noted that college students are not engaging in physical activity at the appropriate and recommended levels (Behrens & Dinger, 2003; CDC, 1995 as cited in Behrens & Dinger, 2003; Dinger, 1999; Pinto & Marcus, 1995). The lack of physical activity among college students is of concern to public health researchers (Jackson & Howton, 2008); physical inactivity is widespread among the university student population (Corbin, 2002; Keating et al., 2005). Approximately 6166% of students enrolled in college do not currently meet recommended physical act ivity levels (Douglas et al., 1997 as cited in Bray & Born, 2004). Physical activity declines during adolescence and continues to decline with age (Buckworth & Nigg, 2004). College students exercise less than high school students; there is not only a reduction in amount of physical activity, but also a reduction of intensity (Baranowski et al., 1997 as cited in Bray & Born, 2004). The 1995 National

PAGE 47

47 College Health Risk Behavior Survey found that 42.2% of undergraduates had not exercised in the week prior to the survey (Douglas & Collins, 1995; Suminski, Petosa, Utter, & Zhang, 2002). In a study by Irwin (2007), university students were surveyed regarding their physical activity levels, and 65% of the sample was not meeting healthy lifestyle recommendations. More than half of all college students are female, and it is well documented that female college students are not meeting physical activity recommendations (Suminski et al., 2002). Huang et al. (2003) found that more male students engaged in aerobic physical activity on more days of the week when compared to female college students. Suminski et al. (2002) conducted a survey among approximately 2900 university students and found that 53% of the women had not participated in vigorous physical activity d uring the prior month. A national survey of college students resulted in the finding that men were more likely to engage in physical activity than women, and female students exercise less each time they exercise compared to male students (Lowry, Galuska, Fulton, Wechsler, Kann, & Collins, 2000; Suminski et al., 2002). Studies among young individuals also indicate that physical activity declines more in female individuals than male individuals as age increases (ACHA, 2002; Hardesty & Trupp, 2005; Sallis, 2000). Moving from high school to college is a time of change and adjustment in a number of behavioral issues (Bray & Born, 2004; Lafreniere et al., 1997 as cited in Bray & Born, 2004; Terenzini et al., 1994 as cited in Bray & Born, 1994). The transitional ti me of beginning college appears to negatively affect physical activity levels among the female population, and the amount of physical activity students engage in while in

PAGE 48

48 college is not enough to have a positive impact on ones health (Jung et al., 2008; K ilpatrick, Hebert, & Bartholomew 2005; Kwan, Bray, & Ginis, 2009; Marietta, 1999 as cited in Jung et al., 2008). Physical activity not only decreases when students enter college, activity levels also decrease after students graduate (Jackson & Howton, 2008; Kasparek, Corwin, Valois, Sargent, & Morris 2008; Leslie, Fotheringham, Owen, & Bauman, 2001). According to Sparling and Snow (2002), physical activity among seniors in college was one of the most powerful determinants for maintenance of physical activity throughout adulthood (Jackson & Howton, 2008). It has been determined that many health behaviors are established while in adolescence and young adulthood (Buckworth & Nigg, 2004); therefore, if college students are not engaging in physical activity, they may not engage in physical activity as they grow older, placing them at risk for a myriad of diseases (Fish & Nies, 1996; Keating et al., 2005; Sparling & Snow, 2002 as cited in Keating et al., 2005) including heart disease. Sullivan, Keating, Chen, Guan, Delzeit McIntyre, and Bridges (2008) performed a study among minority community college students (N=291) and found that approximately 66% of students were at risk for heart issues such as hypertension, due to high levels of physical inactivity and over weight. Little research has been performed among this population to understand why activity levels decrease among individuals during the transitional period (Baranowski et al., 1997; Malina et al., 2001 as cited in Kwan et al., 2009). To increase physical activity levels among college students, an understanding of participation patterns and gender differences related to physical activity is needed (Behrens & Dinger, 2003). Understanding causes of college student physical inactivity is crucial when attempti ng to

PAGE 49

49 change negative patterns exhibited by the population (Keating et al., 2005). College students who do engage in physical activity counter negative behaviors and this positive behavior may also continue into adulthood. If determinants regarding what motivates or hinders students to engage in physical activity are known, promotion efforts can be tailored to ensure success. The university setting is an excellent location for researchers to implement physical activity promotion efforts to fight inactive behavior (Keating et al., 2005). Promoting exercise and helping students establish physical activity patterns while in college may aid students in the maintenance of physical activity throughout the remainder of their lives and combat the onset of heart disease. Obesity Obesity is considered an independent risk factor for heart disease (Eckel & Krauss, 1998; Sharma, 2003). Research indicates a strong correlation between overweight status and heart disease (Fox, Coady, Sorlie, DAgostino, Pencina, Vasan et al ., 2007). Approximately 4060% of populations around the world (Sharma, 2003) and approximately 67% of adults living in the United States are considered overweight/obese (Olshansky, Passaro, Hershow, Layden, Carnes, Brody et al., (2005). There is a potenti al risk for the reduction of life expectancy within the United States (Olshansky et al., 2005), stemming directly from the increasing obesity epidemic and its contributing effects on heart disease development and progression (Burke, Bertoni, Shea, Tracy, W atson, Blumenthal et al., 2008; Flegal, Graubard, Williamson, & Gail, 2005). Weight gain has unfavorable effects related to heart disease (Willet et al., 1999 as cited in Hu, 2003). Burke et al., (2008) conducted a study among men and women to study frequency and progression of heart disease. The study resulted in the findings

PAGE 50

50 that a higher BMI was associated with higher blood pressure, higher cholesterol and higher glucose levels all known risk factors for heart disease (Burke et al., 2008). Obesity sta tus was strongly linked to known risk factors for heart disease (Burke et al., 2008). As obesity percentages continue to rise in the United States, prevalence of heart disease is expected to increase (Burke, Bertoni, Shea, Tracy, Watson, Blumenthal et al., 2008). Previous research indicates that both diet and exercise are needed to reduce ones weight and improve ones level of physical fitness, thereby reducing overall risk for heart disease (Daviglus et al., 2006; Del Negro, 2003; Hu, 2003). A reduction in weight can lead to lower blood pressure and decreased cholesterol and glucose levels (Lichtenstein et al., 2006). The benefits of weight loss include reversing ones risk of the disease (Hu, 2003; Must, Spadano, Coakley, Field, Colditz, & Dietz, 1999; P i Sunyer, 1993; Sharma, 2003; Stamler, Stamler, Riedlinger, Algera, & Roberts, 1976). Achieving and maintaining a healthy weight can influence and alleviate additional heart disease risk factors (Sharma, 2003). In fact, obesity prevention alone may lead to a significant reduction in overall risk for heart disease (Evangelista & McLaughlin, 2009). To best promote a healthy lifestyle, early identification of individuals who are overweight or obese along with health promotion efforts at a young age are needed (Del Negro, 2003). Obesity prevention should be a top priority in public health. Unless public health interventions promoting the reduction of obesity are implemented, the impact of obesity on heart disease risk may be sizeable (Daviglus et al., 2006). Ef fective interventions targeting obesity reduction need to be developed and implemented to reduce the

PAGE 51

51 negative effect of obesity prevalence and secure long life expectancy for youth and young adults (Olshansky et al., 2005). Obesity and Women Obesity percentages among women in the United States are rising. Obesity among women from the years 1960 to 2000 increased by 18%, bringing the total percentage of women considered obese in the year 2000 to approximately 34% (Burke, Bertoni, Shea, Tracy, Watson, Blumenthal et al., 2008). A healthy weight is essential for a long life. Obesity is damaging to a womans health (Evangelista & McLaughlin, 2009; Hu, 2003). If overweight, women are more likely to get heart disease even when they do not possess any other known r isk factors ( USHHS, 2007). In a study by Li, Rana, Manson, Willett, Stampfer, Colditz et al., (2006), researchers found that overweight women, when compared to women of normal weight, had an increased relative risk of heart disease. The more overweight a w om a n the greater her risk for heart disease (USHHS, 2007). Obesity and Undergraduates Obesity rates are climbing in the United States, and the collegeage population is not immune to risk. Overweight and obesity rates appear to have the greatest increase among individuals age 18 to 29 years of age (Crombie, Ilich, Dutton, Panton, & Abood, 2 009; Mokdad, Serdula, Dietz, Bowman, Marks, & Koplan, 1999; Racette et al., 2005) and there is an association between weight gain from the ages of 18 to middle adulthood and heart disease risk (Fox et al., 2007). In the year 1991, obesity affected only 12% (Brunt et al., 2008; Mokdad et al., 1999) of the college population; in 2004 obesity among college students had increased to 36% (Ogden et al., 2006 as cited in Brunt et al., 2008). Healthy Campus 2010 objectives indicate that college students must

PAGE 52

52 achiev e and maintain a healthy weight to improve the overall health of the college population (ACHA, 2002). College students are at risk for obesity and maintaining overweight status into later adulthood (Desai et al., 2008; GordonLarsen, Adair, Nelson, & Popki n, 2004; Racette et al., 2005). Racette et al. (2005) found that approximately 70% of their survey population gained weight during the first two years of college, and weight gain was attributed to unhealthy diets and less than optimal amounts of physical activity. Clement, Schmidt, Bernaix, Covington and Carr (2004) also noted that a lack of physical activity and poor diet contribute to obesity among the college population. Desai et al. (2008) found that students who were overweight and obese were more likely to self report physical inactivity when compared with normal weight students. Kasparek et al. (2008) found that freshmen gained weight approximately seven times more than what is considered normal adult weight gain for that age range. When students enter their freshman year of college, they increase their caloric intake and decrease their physical activity, thereby increasing their personal risk for obesity (Hoffman, Policastro, Quick, & Lee, 2006). Research proposes that this increase in weight is due i n part to the consumption of snack food and alcohol that adds unneeded calories to their diet (Levitsky et al., 2004 and Magruder, 2005 as cited in Jung et al., 2008). To prevent obesity, proper nutrition and physical activity recommendations need to be met. Opportunities to encourage change in the areas of diet, healthy weight maintenance, and physical activity levels are prevalent among this population (Lowry et al., 2000). Therefore, physical activity and nutrition interventions are needed among

PAGE 53

53 college students to instruct them in ways to properly manage and maintain a healthy weight to reduce the rise in obesity among this population. Diabetes Diabetes is a major risk factor for heart disease ( USHHS, 2007). The leading cause of death among individuals with diabetes is heart disease (Welty, 2004). Heart disease cases attributable to diabetes have increased over the past fifty years (Fox, Coady, Sorlie, DAgostino, Pencina, Vasan et al., 2007). Individuals with diabetes are two to four times more at risk for dying from heart disease than individuals without diabetes (Haffner, Lehto, Ronnemas, Pyorala, Laakso, 1998 as cited in Balkau et al., 2007). Diabetes is the third leading cause of death in the United States (Simontacchi & FitzGerald, 2004) and prevalence is increasing (Balkau, Deanfield, Despres, Bassand, Fox, Smith et al., 2007). Research indicates that behavior change can work to prevent or hinder diabetes onset even if the individual is considered high risk (USHHS, 2007). It is perhaps for this re ason that the Healthy People 2020 proposed objectives include the objective of increasing the number of people with risk factors for diabetes who engage in diabetes preventative behaviors (USHHS, 2009). Diabetes and Women One of the most important risk f actors for heart disease among women is diabetes (Kuhn & Rackley, 1993; Price, 2004). Prevalence of Type II Diabetes has increased (Cooper et al., 2000) and incidence of Type II diabetes is ever increasing among women in the United States (Friedlander, Ar bogast, Schwartz, Marcovina, Austin, Rosendaal et al., 2001). Approximately six million women in the United States have diabetes ( USHHS, 2007). Lifetime risk of diabetes for women is approximately 38%

PAGE 54

54 (Balkau et al., 2007). Diabetic women have a three to s even times increased risk of heart disease when compared to nondiabetic women (King & Mosca, 2000; Lerner & Kannel, 1986; Murabito, 1995; Newton, 2004; Price, 2004). Women with diabetes also have a higher risk of dying from heart disease (Welty, 2004). Cl inical Risk Factors Hypertension. Hypertension (high blood pressure) has a causal relationship with heart disease (Hong et al., 2003); even a slight increase in blood pressure increases risk for heart disease ( USHHS, 2007). At least 65 million people in the United States are presently living with high blood pressure (Field, Burt, Cutler, Hughes, Rocella, & Sorlie, 2004 as cited in Bowman et al., 2007), this equates to approximately one out of every four American adults (Tsang et al., 2000). Research associ ates high blood pressure with excess weight and unhealthy eating behavior (Getliffe et al., 2000). Treating high blood pressure and lowering pressure to an optimal level reduces the number of strokes and cardiovascular events (Kuehn et al., 1999). High blood pressure is possibly best prevented and/or lowered by behavioral changes; if one has high blood pressure it can be managed by coupling behavioral change with drug therapy (Giardina, 1998). Lifestyle change in the areas of exercise, weight, and diet is t he best recommendation for prevention of high blood pressure and or reaching optimal pressure of 120/80 mmHg (Price, 2004; Tsang et al., 2000; Wenger, 2003; Whelton, He, Appel, Cutler, Havas, Kotchen et al., 2002). Cholesterol. Elevated total cholesterol has a direct relationship with risk for heart disease (McGowan et al., 1994), and is considered a modifiable risk factor (Sparling et al., 1999). Individuals diagnosed with high cholesterol should be counseled to make changes in their behavior, the first of which should be diet (Giardina, 1998).

PAGE 55

55 Clinical Risk and Women Results from a study performed by Crane and Wallace (2007) indicate that young to middleaged women have clinical risk for heart disease: more than 30% had high cholesterol, and 60% had been informed they had high blood pressure. Therefore, it is important that women have their blood pressure and cholesterol checked regularly and early. Hypertension. High blood pressure is a significant risk factor for the development of premature heart disease and is prevalent among women (Evangelista & McLaughlin, 2009; McCauley, 2007). High blood pressure factors into more deaths in women than any other changeable heart disease risk factor (Lowe, Greenland, Ruth, Dyer, Stamler, & Stamler, 1998). The Chicago Heart Association Detection Project which screened approximately 40,000 men and women older than 18 during the years 19671973 found that the most common risk fac tor for heart disease was high blood pressure present in 53% of the women (Lowe et al., 1998). Women tend not to pay attention to their blood pressure because high blood pressure usually has no noticeable symptoms (USHHS, 2007), yet it is a major risk f actor for heart disease development. Women with high blood pressure have an increased risk of up to four times that of women without high blood pressure (Kitler, 1992; Newton, 2004; Welty, 2004), and up to 50% of women may have high blood pressure before m enopause placing them at increased risk for heart disease (Cheek et al., 2008). Cholesterol. To reduce risk of heart disease development and progression, women need to be aware of their cholesterol numbers and work to keep them at the best possible leve l (USHHS, 2007). Women of any age are placed at an elevated risk

PAGE 56

56 for heart disease when total blood cholesterol is high (Manolio, Pearson, Wenger, Barrett Connor, Payne, & Harlan, 1992; Newton, 2004; Wilson, 1990). Between the ages of 20 and 34, and after menopause, women have higher total cholesterol than men (Simontacchi & FitzGerald, 2004); and overall, women have higher high density lipoprotein (HDL) cholesterol levels than men (Mosca et al., 1999). However, a woman does not have to be menopausal or pos t menopausal to experience high cholesterol. Lowe et al. (1998) reported that high cholesterol was observed in approximately 30% of the women studied during the Chicago Association Detection Project. According to the National Health and Nutrition Examinati on Survey III, approximately 8.0% of women who were premenopausal had elevated low density lipoprotein levels (LDL) (Cleeman & Grundy, 1997). Clinical Risk and Undergraduates Hypertension. High blood pressure among middle to older adults is a recognized r isk factor for heart disease; however, high blood pressure causes damage to vessels over time (Pletcher, Bibbins Domingo, Lewis, Wei, Sidney, & Carr, 2008). Little is known regarding how high blood pressure as a young adult affects the life of the individual, but young adulthood is a time when elevated pressure could cause harmful lifetime effects to the heart health of the individual (Pletcher et al., 2008). The Coronary Artery Risk Development in Young Adults (CARDIA) study observed effects and tested blood pressure of 5115 young adults aged 1830 (Pletcher et al., 2008). Reported results from the 20 year follow up indicated that approximately 20% of the sample population had developed prehypertension before the age of 35 and the elevated blood pressure was associated with coronary calcium (hardening of arteries) in their later lives (Pletcher et al., 2008).

PAGE 57

57 Students perceive high blood pressure to be the greatest risk factor for heart disease development (Collins et al., 2004), yet they rarely have their blood pressure measured by a licensed diagnostician. Healthy Campus 2010 objectives indicate that the number of students with controlled blood pressure needs to increase, and the number of students who have their blood pressure and cholesterol measured needs to increase to improve health on college campuses (ACHA, 2002). Educational interventions are needed among this population to prompt individuals to have their blood pressure measured to prevent harmful effects later in life. Cholesterol. High total cholesterol is a risk factor for atherosclerosis (Spencer, 2002) which has been noted to begin in ones youth (Berenson et al., 1998; Cleeman, 1997; Green et al., 2003; Sparling et al., 1999; Strong, Malcom, McMahan et al., 1999). The Bogalusa Heart Study indic ated that atherosclerosis early in life has a direct relationship to an individuals heart disease risk factors (Berenson, Srinivasan, Bao, Newman, Tracy, & Wattingney, 1998, Spencer, 2002). High total cholesterol as a young adult increases ones risk for heart disease when older (Cleeman & Grundy, 1997). In a study by Sparling et al. (1999), 11.1 % of a sample of college students (N=1088) had elevated total cholesterol. College aged individuals need to visit their doctor for clinical tests that will aid in their understanding of the importance of personal health promoting behavior. The National Cholesterol Education Program (NCEP) recommends adults 20 years and older have their cholesterol checked (Cleeman & Grundy, 1997) and every five years following their initial cholesterol screening (Spencer, 2002). An individuals heart disease risk (for their next 3040 years) can be predicted though cholesterol

PAGE 58

58 measurements in one s twenties specifically age 22 (Klag et al., 1993 as cited in Cleeman & Grundy, 1997). If cholesterol numbers are known, early intervention and prevention efforts including promoting physical activity, proper nutrition, and proper weight management can begin and may reduce overall long term risk of heart disease (Cleeman & Grundy, 1997). Family History Family history is a confirmed risk factor for heart disease ( Barrett Connor & Khaw, 1984; Juonala, Viikari, Rsnen, Helenius, Pietikinen, & Raitakari, 2006). Individuals with a positive family history of heart disease have a greater risk of personal heart disease development compared to individuals without a family history of the disease (Hawe, Talmud, Miller, & Humphries, 2003; Jousilahti, Puska, Vurtiai nen, Pekkunen, & Tuomilehto, 1996; Leander, Hallqvist, Reuterwall, & Ahlbom, 2001; Thompson, Pell, Anderson, Chow, & Pell, 2010). Data from Danish t win studies and Finland family studies show that a positive family history of heart disease is a greater ris k factor for women than men (Leander et al., 2001). Family history of heart disease can predict risk of future heart disease among men and women even when controlling for all other behavioral and clinical risk factors (Hopkins, Williams, Kuida, Stults, Hu nt, Barlow et al., 1988; Jousilahti, Tuomilehto, Vartianinen, Pekkanen, & Puska., 1996; Tsang et al 2000). LloydJones, Nam, DAgostino, Levy, Murabito, Wang et al. (2004), studied whether or not parental heart disease was a risk factor for childrens heart disease. Results of the study indicated that parental heart disease was an independent risk factor for disease onset and progression in their children (LloydJones et al., 2004). The presence of heart disease in

PAGE 59

59 at least one parent increased risk for both men and women even when all other known risk factors were controlled (LloydJones, 2004). When assessing risk for heart disease, if family history is ignored, risk is underestimated (Crouch & Gramling, 2005). If a first degree relative (parent, brother, or sister) has heart disease, risk increases (Kordella, 2005). Individuals with a positive family history of heart disease are more susceptible to behavioral risk factors and possibly more susceptible to clinical risk when compared to individuals wit hout family history (Juonala et al., 2006). Individuals with a positive family history also have an increased threat of possessing other risk factors (Juonala et al., 2006). The clustering of heart disease in families is due in part to heredity and also fr om children inheriting poor lifestyle behavior from their parents (Thompson et al., 2010). Crouch and Gramling (2005) found that when individuals have knowledge of their positive family history for heart disease, they are motivated to engage in behavior s to reduce their risk. Juonala et al. (2006) found that knowledge of family history affects dietary behavior. It appears that knowledge of family history of heart disease may prompt an individual to engage in more positive lifestyle behaviors; therefore, it is imperative that individuals know their personal family history. Racial and Ethnic Disparities Ethnic differences in incidence and prevalence of heart disease (and its risk factors) are considerable in the United States (Kurian & Cardarelli, 2007). Mortality rates of heart disease differ among racial/ethnic groups (Cooper, Cutler, DesvigneNickens, Fortmann, Friedman, Havlik et al., 2000) with mortality and incidence being greater among minority populations (Winkleby Kraemer, Ann, & Varady, 1998). Ethnic minority women present with risk factors more often than white women (Winkleby et al., 1998);

PAGE 60

60 thus reinforcing that heart disease is a greater burden among ethnic minority populations. Research has shown that minority populations exhibit both clinical and lifestyle risk factors for heart disease. Winkleby et al. (1998), following a study of ethnic differences in heart disease risk factors, reported that African Americans and Mexican Americans had higher body mass indices higher blood pressure, engaged in less physical activity, and had greater prevalence of Type II diabetes when compared to whites. Obesity and overweight are more prevalent among African Americans and Mexican Americans than white Americans (Cooper et al., 2000). Minority status is associ ated with diabetes prevalence (Kurian & Cardarelli, 2007). African American and Mexican American women have a higher prevalence of Type II diabetes than white women (Kurian & Cardarelli, 2007). African Americans have an increased risk of heart disease mortality compared to whites (Ferdinand, 2006); mortality rates are higher among African American women than white women (Jain, Peschock, McGuire, Willett, Yu, Vega et al., 2004). The increased risk of heart disease documented among African Americans may be due in part to a clustering of risk factors prevalent among this ethnic population (Ferdinand, 2006). Several studies have been performed looking at heart disease differences between African Americans and whites. Investigators of the Dallas Heart Study looked at a multi ethnic population (over sampled African Americans) to compare coronary calcium deposits due to heart disease risk factors among African Americans and whites (Jain et al., 2004). Results of the study showed that BMI was greater, blood pressur e means were elevated, and glucose levels were higher in African American women

PAGE 61

61 compared to white women (Jain et al., 2004). For many years, researchers have reported that African Americans have the highest prevalence of high blood pressure (Cooper et al., 2000; Kurian & Cardarelli, 2007). Obesity rates are reported to be higher among African American women as well with Olshansky et al. (2005) reporting approximately 50% of nonHispanic black women are considered obese. Research indicates that African Ameri cans are more likely to smoke, have higher blood pressure, and have high rates of diabetes when compared to whites (Ferdinand, 2006). Other studies indicate that African American women have higher blood pressure, are more overweight, have higher rates of diabetes, and exercise less than white women (Winkleby et al., 1998). Wei, Mitchell, Haffner, and Stern (1996) reported that results of the San Antonio Heart Study, research looking at heart disease ethnic disparities, indicated Mexican Americans are at an increased risk for heart disease due to the high prevalence of diabetes and obesity among this population. Diabetes is a proven predictor of heart disease mortality among Mexican Americans (Wei et al., 1996). The study also showed that Mexican Americans have a higher probability for heart disease compared to whites (Kurian & Cardarelli, 2007). Studies indicate that ethnic disparities in heart disease are a result of environmental factors, lifestyle behavior, and lack of health care availability for prevent ion services (Cooper et al., 2000). Differences in risk factors among ethnic populations and causes of the differences need to be understood to develop programs that are culturally sensitive (Kurian et al., 2007) and that address risk factors and provide optimal care (Ferdinand, 2006).

PAGE 62

62 Prevention Heart disease can be avoided if preventive measures are practiced (Simontacchi & FitzGerald, 2004); heart disease prevention guidelines focus first on lifestyle intervention and behavioral change (Haskell, 2003; Mo sca, McGillen, & Rubenfire 1998; Noori & Anim Nyame, 2005; Wenger, 2006). Positive behavioral change should be a main objective for public health (Cheek et al., 2008; John, Meyer, Schumann, Ulbricht, Freyer, & Hapke, 2006) and researchers must be proactiv e in the fight against this disease and focus on prevention (Hardesty & Trupp, 2005). Behaviors that should be promoted include the following: smoking cessation, promoting physical activity, eating fruits and vegetables, and working toward a healthy weight (Mosca et al., 1997; Stampfer, Hu, Manson, Rimm, & Willet, 2000). A multifactorial approach may be the best way to prevent heart disease; therefore, health promotion should not focus on only one behavioral risk factor and hope to prevent the onset of hear t disease. Multiple risk factors can and should be addressed when interventions are planned to fight heart disease risk and onset. Prevention and Women Prevention and early identification of heart disease risk factors are imperative toward reducing incidence and morbidity/mortality of the disease among women (Hardesty & Trupp, 2005); women under the age of 30 should be important targets for prevention efforts (Turner, Vader, & Walters, 2008). Efforts can be effective in the prevention and controlling of ri sk factors of heart disease (Eaker et al., 1993); early efforts may have strong positive effects among women (Hardesty & Trupp, 2005). A large amount of heart disease cases can be prevented if women quit smoking, eat and maintain a healthy diet, engage in regular physical activity, and maintain a healthy

PAGE 63

63 weight (Erkkila, Lichtenstein, Mazaffarian, & Herrington, 2004; Folta, Goldberg, Lichtenstein, Seguin, Reed, & Nelson, 2008; Hu et al., 2000; Hu, Bronner, Willet, & Stampfer, 2002; Joshipura et al., 2001; Lapointe, Balk, & Lichtenstein, 2006; Lichtenstein, 2003; McCauley, 2007; Schaefer, 2002).There is a need for primary prevention due to the astounding number of women with heart disease or heart disease risk (Rosenfeld, 2006). Recognition of personal risk and initiating and maintaining behavioral change will help a woman sustain a healthy heart. Being aware of risk is an important step in the prevention of heart disease, and a healthy lifestyle should be a priority (Mosca et al., 2004). Awareness, education, and early interventions discussing behavior change are needed to turn the tide on this disease among women (Mikhail, 2005). Educating women and empowering them regarding heart disease risk factors and providing the information needed to change behavioral risk factors may lead to positive strides in the prevention of heart disease (Hardesty & Trupp, 2005; Kuehn et al., 1999). Most heart disease in women is preventable and positive lifestyle behaviors are imperative to reduce ones risk; therefore, initial efforts should concentrate on ones lifestyle to reduce risk among women (Bello & Mosca, 2004). Prevention and Undergraduates Heart disease prevention efforts must begin early to reduce risk factors present in young adult females and reduce the prevalence of heart disease among older adult females. Prevention efforts initiated between the ages of 20 and 30 or before are most effective (Grant, Jacobs, & Clancy, 2004; Mitka, 2007). It is true that heart disease most often appears in older adulthood, but the disease originates much earlier; therefore, risk reduction efforts among young adults may prove to have an impact on

PAGE 64

64 the onset of signs and symptoms of disease (Collins et al., 2004; Eaker et al., 1993). Lifestyle behaviors that quicken the development of disease are present in young children and young adults, therefore, early life behavioral changes are necessary to impact heart disease development (McMahan et al., 2007). Early efforts have the greatest benefit to an individuals health and to reduce risk (Collins et al., 2004; Folta et al., 2008; Jensen & Moser, 2008). Heart disease develops slowly; early efforts of prevention and promotion of behavioral change may help to slow progression (Green et al., 2003; McMahan et al., 2007; Navas Nacher, Colangelo, Beam, & Greenland, 2001; Price, 2004). Young adulthood is an opportune time to begin primary prevention efforts for heart disease (McCarron, Smith, Okasha, & McEwen, 2000; Spencer, 2002). Researchers argue that the earlier risk factors are identified and reduced, the more likely it is that heart disease will be prevented (Frost, 1992; Green et al., 2003; McMahan et al., 2007; Strong et al., 1988). The college environment offers a distinct opportunity to reach young adults and influence health behavior (O kchowski, Graham Beverly, & Dupkanick, 2009). Risk reduction information can reach a large number of young adults if prevention efforts are incorporated among college students (Spencer, 2002). Because lifestyle behavior patterns may be established while in dividuals are of college age, college may be one of the final opportunities for researchers to promote a healthy lifestyle to a sizeable assemblage of young adults through education and empowerment training (Sullivan et al., 2008). College is a time of change, a time when students are becoming more mature and more independent, and a time of making ones own behavioral choices (Sparling et al.,

PAGE 65

65 1999). It is thought that during this transition, recognition of health may be enhanced (Fr ost, 1992). Students are impressionable during college, therefore, it is a convenient time period and population in which to intervene and try to promote knowledge of heart disease and instill healthy behaviors that reduce overall risk for the disease (McM ahan et al., 2007). Summary Chapter 2 provided an overview of heart disease, the Health Belief Model, heart disease risk factors and prevention for the general population, women, and undergraduate students. Although heart disease research is abundant, lit tle is known about the determinants of heart healthy behavior related to Health Belief Model constructs and the undergraduate population. Pediatric and geriatric health and health programs are plentiful; however a period of underemphasized importance is young adulthood. The nature of college health is affected by daily routines, socialization, academic rigors, and athletic endeavors. College health represents an all important crossroads of health the building block upon which all future health is establi shed. Therefore, college is a fertile ground for uniquely meaningful interventions Assessments of diet, physical activity, obesity, and other heart disease risk factors need to be understood in order to combat risk. The assessment of risk will aid in comm unication and intervention to make campus prevention efforts more effective. The study of college health, therefore, will benefit from research that assesses determinants of healthy behavior especially for the heart.

PAGE 66

66 Figure 21. Health Belief Model

PAGE 67

67 CHAPTER 3 METHODS The overall purpose of this study was to determine heart health risk behaviors present in the undergraduate female university population and their perception of heart disease and heart disease risk factors. Constructs of the Healt h Belief Model were used to assess perception determinants of university heart healthy behavior. Additionally, this study explored whether age, ethnicity and/or perception were related to students heart healthy behavior. The study also explored whether age or ethnicity related to Health Belief Model constructs for each risk behavior assessed. C hapter 3 provides an overview of methods used in the study and is organized in the following sections: (1) r esearch d esign; (2) research variables ; (3) survey design and development ; (4) pilot study one implementation; (5) pilot study two implementation; and (6) final survey implementation. Research Design This quantitative study used a nonexperimental cross sectional survey distributed among university undergraduate females. Participants enrolled in a large southeastern university completed an online survey regarding personal activities that may increase ones risk for heart disease and perceptions of risk behavior related to adult onset of heart disease. Students were also asked questions related to Health Belief Model constructs grouped according to each assessed heart disease behavioral risk factor. Contact information for a random sample of university undergraduate females was obtained from the university regist rar.

PAGE 68

68 Research Variables The outcome or dependent variable for research question one was defined as behavior exhibited among university undergraduate females related to heart disease risk. The predictor or independent variables included the following: (1) a ge; (2) ethnicity ; and (3) perception. The outcome or dependent variables for research question two included the following Health Belief Model Construct components: (1) perceived susceptibility + perceived severity = perceived threat (risk) ; (2) perceived benefits ; (3) perceived barriers ; (4) cues to action; and (5) self efficacy (NCI, 2005). The predictor or independent variables were (1) age and (2) ethnicity. Survey Development and Progression The investigator developed the survey instrument specifical ly for use in this study. The initial survey titled An Assessment of University Students Heart Healthy Behavior contained 131 questions and was designed to provide the investigator with descriptive and frequency information that would aid in health prom otion efforts among the college population. The steps to developing the survey are described in the following sections. Initial Question Development An extensive review of the literature and further review of previous/current surveys implemented among the college population was performed. Health Belief Model questions and Transtheoretical Model of Change questions were formed by the investigator for each behavior assessed. The questions were constructed to measure the dependent variable. Initial questions w ere then categorized into the following sections: (1) b ehavioral self report; (2) general health (included weight and height); (3) Transtheoretical Model for physical activity, nutrition, smoking, heart health checkups,

PAGE 69

69 and weight management; (4) Health Belief Constructs for each behavioral assessment; and (4) demographics. Expert Review Two faculty members in health education, who were considered experts in survey development, college health, and heart disease behavioral risk factors, reviewed the survey drafts. They were asked to recommend additional items, delete any items they deemed irrelevant, and make further suggestions for instrumentation edits. This procedure was completed three times with edited survey drafts until the principal investigator and expert reviewers agreed upon a final instrument. Appendix A depicts the names of the faculty members who comprised the expert panel. Initial Survey Instrument The 131item survey instrument incorporated questions from each tenet of the Transtheoretical Model of Change (NCI, 2005) and from each of the six basic constructs of the Health Belief Model (NCI, 2005; See Fig. 21). Based on suggestion from the expert panel the investigator chose to merge two of the constructs from the Health Belief Model perceived susceptibility and perceived severity. The construct was termed perceived threat (risk) and therefore reduced the Health Belief Model constructs to five. The Transtheoretical Model (Fig. 31) states that individuals move through five stages when deciding whether or not to initiate behavior change (NCI, 2005). The five stages are thought to play a role in whether or not an individual will adopt and/or maintain a healthy behavior or discontinue participating in an unhealthy behavior. The current study incl uded questions pertaining to the following stages of change: precontemplation (lack of intention to change) ; contemplation (considering a change),

PAGE 70

70 preparation (preparing to change) ; action (currently engaging in healthy behavior) ; and maintenance (prolonged and maintained behavior change). Multiple choice answer responses were used for each stage of the Transtheoretical Model. Questions related to Model constructs utilized a fiveitem Likert type response scale ranging from strongly agree to strongly disagree. The investigator measured perceived threat (susceptibility and severity) to determine if university females perceived themselves susceptible to and whether they understood the consequences of heart disease behavioral risk factors. To assess the construct of perceived benefits, students were asked questions regarding possible advantages of changing behavior. The investigator asked questions related to hindrances/obstacles to lifestyle change (monet ary and nonmonetary costs) to assess the construct of perceived barriers. Several questions related to readiness to act were incorporated into the instrument to measure cues to action, and questions related to confidence in ones ability to change their own behavior were used to measure the construct of self efficacy. Multiple choice or fill in the blank response options were used for the remainder of the questions and demographic questions concerning ethnicity, age, and class rank were included in the survey instrument. The initial question on the survey asked the respondent to report their current age. This question was used to provide data regarding inclusion of the survey; only individuals 18 and older were permitted to complete the survey. Behavioral questions regarding diet/nutrition, physical activity, smoking status, and health checkups were asked to analyze current behavior of college students. Questions regarding family history of heart disease were used to establish hereditary risk. For the pur pose of the initial survey, a positive family history was defined as a

PAGE 71

71 blood relative with incidence of heart disease or stroke. Participants were asked to self report height and weight. Table 31 lists the type of variable measured and the number of items used to assess the variable. T able 3 1 does not include demographic questions or current height and weight measurement responses. Reliability assessment was performed on the Health Belief Model construct groupings for each assessed behavior using Cronbac hs measure of internal consistency. The unstandardized coefficient for each construct for nutrition assessment ranged from .623 to .875. Reliability coefficients for Health Belief Model physical activity assessment ranged from .632 to .883. Coefficients for smoking ranged from .098 to .979. Coefficients for heart checkup assessment ranged from .540 to .922. The final reliability assessment for weight ranged from .733 to .933. Appendix B, Table B 1 depicts the reliability coefficients for each Health Belief Model construct behavioral assessment and lists the number of item s used to assess each construct for its respective behavior. Pilot Study One The investigator developed the survey used in this research; therefore, previous studies did not test the reliability or validity of the instrument. Two pilot studies were conduc ted to aid in establishing reliability and validity of the survey instrument. An initial exploratory pilot study was conducted using a convenience sample of University of Florida male and female students during the fall semester of 2008 to aid in determini ng need for heart disease prevention efforts among the college population. Students were recruited through various undergraduate courses offered through the College of Health and Human Performance. Professors chose to award extra credit to students who

PAGE 72

72 agr eed to complete the survey. It should be noted that this nonmonetary incentive was not planned nor solicited by the principal investigator. A total of 308 students completed the survey. It is important to note that the classes chosen for implementation represent general education courses offered through the College of Health and Human Performance; therefore, classes consisted of both male and female students of varying ethnicity and varying academic majors. The paper based survey was created using Verity TeleForm optical scanner software software used to create, capture, and process documents. Initially, health behaviors were assessed using the Health Belief Model and Transtheoretical Model of health behavior change constructs. As explained earlier in this C hapter 3 the instrument was developed based on a review of existing literature, and faculty recommendations and edits. Participants Females comprised 82.9% (n=252) of the sample with 17.1% (n=52) of the participants being male. The principal investigator was most interested in the female college population; therefore, males were removed prior to data analysis and evaluation of results. The majority of the female sample (n=252) were sophomores (56.0%, n=141) followed by 18.3% (n=46) juniors, 13.5% (n=34) seniors, and 12.3% (n=31) freshmen. The largest ethnic group was White (64.3%, n=157) followed by Black/African American (13.1%, n=32). There was a 3.2% nonresponse rate for ethnicity. When asked if Hispanic/Latino, 81.0% (n=204) reported they were not Hispanic/Latino. The age ranged from 18 24 years and the mean age for the participants was 19.42 years (SD 1.03). Two individuals chose not to report their age and were excluded from the analysis

PAGE 73

73 Data Collection The University of Floridas Institutional Review Board (IRB) approved the research protocol (Appendix C). The principal investigator personally distributed surveys to participants at the beginning or end of each class permitting survey completion. The participants each received an informed consent form and survey document. Before completion of the survey, the principal investigator read the informed consent letter explaining the study to the students. It was stressed that participation was both voluntary and anonymous. Participants used blue/black ink or pencil to record their responses directly onto the survey document and were encouraged to pen comments near any items they deemed inappropriate or not applicable. To ensure anonymity, IRB members required that participants place their completed sur vey into boxes located at the front of the classroom. The participants completed the paper based questionnaire during one of the selected courses, and most completed the survey in 30 minutes. Exit interviews were conducted by the primary investigator to ask students if there were any questions that needed to be edited or removed from the survey and why. Per instructed, participants also penned many comments on the actual form regarding questions that were deemed inappropriate or too difficult to answer. The investigator made edits to the survey based on suggestions from the target population and factor analysis results. When creating this pilot study, the investigator was interested in studying both Models of Behavior Change; however, following implementat ion of pilot study one and prior to data analysis, the Transtheoretical components were pulled from the instrument due to the researcher wanting to focus more on testing Health Belief Model behavioral responses and less on testing two theories. Data analys is was not performed on the Transtheoretical Model question responses.

PAGE 74

74 Data Analysis Completed surveys were scanned using the Teleform optical scanner, and data was exported to SPSS version 16.0. Due to the nature and purpose of this study, only descriptiv e and frequency statistics were used to analyze results of the study. Principal component factor analysis was conducted on the Health Belief Model construct questions to determine which items most closely related to the target population. In doing so, items were deleted and/or retained for use in a secondary pilot study utilizing a webbased approach. The standard rule is to keep items that are greater than or equal to .60; however, the investigator chose to retain items that were greater than or equal to .55 in order to increase the probability that an item would load. Results of the factor analysis are depicted in Appendix B, Table B 2. Table B 2 reports only factors which loaded above or equal to the .55 level set by the principal investigator. The investigator chose to keep many of the items that did not load during factor analysis due to investigator interest. Results Behavior s reported by the sample, such as poor nutrition and low levels of physical activity, may place the sampled students at risk for future heart disease development. A large percentage of participants (71.0%, n=179) reported they did not eat three servings of fruits/vegetables daily, and 30.9% (n=78) reported they ate vegetables one time a week or less. Approximately 30% (n=76) repor ted they consumed fast food two or more times per week. Over half of the sample (58.3%, n=147) exercised three times per week or less, well below the current recommended levels for physical activity. It is encouraging to note that a large majority of this sample

PAGE 75

75 did not smoke, with 86.8% (n=217) reporting they had never smoked a cigarette. Questions related to Hookah were not included in the pilot study. Research indicates that individuals should begin having cholesterol checks during young adult hood, speci fically beginning at age 20 (Cleeman & Grundy, 1997). Approximately 38% (n=96) of the sample had never had their blood cholesterol measured. The most alarming results were that of family history of heart disease. Seventy two percent (n=173) and 62.1% (n=141) reported that their mother or father had been diagnosed with heart disease respectively Results of pilot study one indicated the need for future study among the college population to garner further information related to behavioral risk to plan and implement effective health promotion interventions. Pilot Study Two A second pilot study was conducted using a webbased survey distributed to a random sample of university females. The revised instrument from pilot study one was implemented in this study. The 71item survey, An Assessment of University Students Heart Health Behavior, was organized in the following order: (1) b ehavior questions ; (2) Healt h Belief Model nutrition; (3) Health Belief Model physical activity ; (4) Health Belief Model heart healt h checkups ; and (5) demographic questions. Health Belief Model questions related to smoking were removed from the initial survey due to the finding in pilot study one that 86.8% (n = 217) of the female university sample were nonsmokers. Female students al so indicated that they are never to rarely exposed to second hand smoke (25.9%, n = 65 and 56.6%, n = 142 respectively). Health Belief Model questions regarding weight management were removed based on exit interviews and comments written directly on the s urvey documents indicating that the participants were unhappy or unwilling to answer questions regarding

PAGE 76

76 their weight. To more closely focus on Health Belief Model constructs and reduce the overall length of the survey, the investigator also removed the Tr anstheoretical Model construct items. The Likert scale response options were reduced from five to four, incorporating only strongly agree, agree, disagree, and strongly disagree. Table 32 lists the type of variable measured and the number of items used to assess that variable for pilot study two. T able 3 2 does not include demographic, weight, or height questions. Reliability assessment was performed on the Health Belief Model construct groupings for each assessed behavior using Cronbachs measure of internal consistency. Unstandardized reliability coefficients for each construct for nutrition ranged from .721 to .895. Reliability coefficients for Health Belief Model physical activity assessment ranged from .706 to .913, and coefficients for heart checkup assessment ranged from .693 to .921. Appendix B, Table B 3 depicts the reliability coefficients for each Health Belief Model construct behavioral assessment and lists the number of items used to assess each construct for its respective behavior. The investigator conducted the study from initiation to completion, following a process of administration similar to what would be used for the primary research study. The second pilot study did not use a mixed method of notification and did not include an incentive. The 71item survey instrument was converted from a Microsoft Word document format to webbased survey format by inputting the survey into Survey Monkey survey template software. Survey Monkey allowed the investigator to create a link to incorporate within email notifications. The notification and reminder emails both contained the link to complete the survey The survey could only be completed one time per student.

PAGE 77

77 Participants A statistical request (Appendix D) form was sent to the university registrars office requesting a random sample (n=1000) of university female students email addresses. A total of 1 61 students completed the survey yielding a 16.2% response rate. The investigator chose to include only university females for the following reasons: (1). Females tend to underestimate their risk for heart disease and overestimate their risk for breast can cer (Wendt, 2005). (2). Females are often unaware that heart disease is the leading cause of death among women (Mosca et al., 2004). Although the survey was sent to females only, 98.7% of the participants were female (n=151), and 1.3% indicated they were male (n=2). It is hypothesized that the two participants chose the incorrect radio button and were in fact female. Eight participants chose not to provide their gender. Both students were included in the analysis. Respondents included 34.9% seniors (n=53), 29.6% juniors (n=45), 17.1% sophomores (n=26), and 18.4% freshmen (n=28). Nine participants chose not to provide their class rank. The largest ethnic group was white (65.4%, n= 100) followed by Hispanic or Latino (19.0%, n=29). Eight respondents did not indicate their ethnicity yielding a 1.0% nonresponse rate for that question. The age ranged from 1847 years and the mean age for the participants was 20.4 years (SD 3.39). The majority of participants placed themselves in the weight ranges of 100120 lbs (34.4%, n=55) and 121140 lbs (35.0%, n=56). Data Collection Data collection procedures for pilot study two were replicated in the final survey implementation. Narrative regarding the step by step procedure is located in the

PAGE 78

78 discussion of the final survey methodology. Therefore, pilot two data collection procedures will serve as an overview of methodology only. The University of Florida s Institutional Review Board (IRB) approved the webbased research study (Appendix E). Following Dillman et.al (2009) guidelines for online surveys, a pre notification email (Appendix F ) was sent informing the potential participants to expect an email co ntaining a survey link to complete the research study within the week. Four emails were returned to the investigator, reducing the total number of potential participants to n=996. E mails were most often returned due to full mailboxes or incomplete address information. E mails that were returned were excluded from the study. Four days later the notification email was sent which also served as the informed consent document. This email (Appendix G ) contained the link to the survey that if clicked and completed, implied consent. One week later a reminder/thank you email was sent (Appendix H ). This email was sent a second time three weeks after the pre notification email transmission. Four weeks after initial communication was made, a final e mail was s ent to all potential participants reminding them to complete the survey if not completed, and thanking them if completed. This email also included the hyperlink needed to complete the survey. The average time students needed to complete the survey was 25 m inutes. The World Wide Web presents researchers with new opportunities for online research and web based data collection (Rhodes, Bowie, & Hergenrather, 2003). Webbased survey technology allows researchers to adjust the survey when needed (Rhodes et al., 2003). Data can be collected quickly from many participants; therefore, overall time of survey distribution and data collection is reduced. E mail was determined to be

PAGE 79

79 the most appropriate mode for participant recruitment and a webbased survey the most suitable mode of data collection for the following reasons: (1) All university students have email addresses and are informed upon acceptance to the university that they are to check their university account regularly. Therefore, it can be understood that university students have access to a computer with Internet capabilities. (2) Web based surveys provide anonymity to all participants. Therefore, it was anticipated that individuals answered questions openly and honestly. (3) Web based surveys require less time for implementation and can be therefore carried out from start to finish in a short amount of time. (4) In a society that is increasingly conscious of protecting the environment, moving from a paper to a webbased survey will contribute to widespread efforts that attempt to eliminate the unnecessary use of printing on paper. Data Analysis Once data collection was completed, results were downloaded from the survey site to an excel sheet and analyzed using SPSS version 16.0. Factor analysis was co nducted on the Health Belief Model construct questions to determine which items most closely related to the target population. In doing so, it was found that few items needed to be deleted to strengthen the instrument. Results of the factor analysis are depicted in Appendix B, Table B 4. Factor analysis criteria is typically set at .70, yet some reports indicate that when research is performed for assessment/exploratory purposes only, a lower level such as .40 to .60 can be used. The investigator and expert panel determined .55 to be an appropriate criterion for the study. T able B 4 reports only factors which loaded above, or equal to the .55 level set by the principal investigator. Results Pilot study one indicated that female students were exhibiting beha vior that may lead to future onset of heart disease if that behavior is not altered. Pilot study two results served to reinforce findings from the previous study. Thirty nine percent (n=63) of respondents indicated they ate a serving of green vegetables one time per week or

PAGE 80

80 less with an alarming 16.1% (n=26) reporting they never ate greenleafy vegetables. A large percentage of participants (62.1%, n=100) reported they did not eat three servings of fruits/vegetables daily. Twenty seven percent (n=44) reported they consumed fast food two or more times per week. As seen in pilot study one, again, over half of the sample (59.6%, n=96) exercised three times per week or less. The majority of the sample (77.0%, n=124) reported they had never smoked a cigarette and 40.4% (n=65) limited their second hand smoke exposure to little or none. Approximately 45% (n=72) of individuals had never had their cholesterol checked, and 60.2% (n=97) had never been tested for diabetes. The results from pilot study two mirrored thos e found in pilot study one, indicating the need for behavioral assessment and future behavioral interventions among this population to reduce their risk of future onset of heart disease. Final Survey Implementation The cross sectional survey An Assessment of University Females Heart Health Behaviors (Appendix I ) was developed by the investigator based on questions identified in the literature, Health Belief Model constructs, and analysis of pilot study data. The 94 item revised survey instrument was organized in the following order: (1) b ehavior questions ; (2) H ealth Belief Model nutrition; (3) Health Belief Model physical activity ; (4) Health Belief Model smoking; (5) Health Belief Model heart health checkups ; and (6) demographic questions. Health Belief Model questions related to smoking were added back to the survey instrument based upon expert suggestion. Questions related to hookah were also added based upon recent research suggesting the smoking of hookah as a growing trend among university students. Table 33 lists the type of variable measured and the number of items used to assess that variable for the final study. T able 3 3 does not include demographic, weight, or height questions.

PAGE 81

81 The survey instrument was converted from a Microsoft Word document format to webbased survey format by inputting the survey into Survey Monkey software. Survey Monkey allowed the investigator to create a link to incorporate into both notification and reminder emails. The sample was then asked to follow a link and complete the survey. The investigator separated the survey into pages within Survey Monkey to make it easier for students to complete. All total, the survey was 10 pages in length. Survey Monkey software provided information to the investigator regarding the amount of time that each participant used to complete the survey ; t he average time to complete the online survey was 20 minutes. Reliability assessment was performed on the Health Belief Model construct groupings for each assessed behavior using Cronbachs measure of internal consistency. Unstandardized reliability coefficients for each construct for nutrition ranged from .497 to .904. Reliability coefficients for Health Belief Model physical activity assessment ranged from .564 to .904. Coefficients for smoking ranged from .105 to .932, and coefficients for heart checkup assessment ranged from .535 to .922. Appendix B, Table B 5 depicts the reliability coefficients for each Health Belief Model construct behavioral assessment and lists the number of items used to assess each construct for its respective behavior. Participants A statistical request form (Appendix J) was sent to the university registrar office requesting a random sample (n=1500) of university female email addresses and campus (local) addresses. The sample represented a wide variety of ethniciti es and academic majors from within the university system and therefore a sufficient number of students were in each category The sample therefore did not need to be stratified.

PAGE 82

82 Table 34 represents the sample size variables used to determine overall sampl e size needed for the investigation. The investigator used the sample size formula represented in Figure 32. The formula assumes an infinite population from which to select a random sample and is used to determine the sample size needed to ensure a 95.0% confidence interval. According to the formula, three hundred and ninety two responses were needed to ensure proper confidence intervals. At that time the investigator used the formula represented in Figure 33 to determine how large of a sample was needed to achieve the possibility of 392 usable responses with an estimated 40.0 % response rate. The investigator chose to use an overall sample size of 1500 to ensure a usable number of responses and account for attrition and email bounce backs. A total of 587 female students completed the survey yielding a 39.1% response rate. Respondents included 32. 9% seniors (n=183), 25.0% juniors (n=139), 22. 7 % sophomores (n=126), and 19. 4% freshmen (n=108). Thirty one participants chose not to report their class rank. T able 35 displays class rank of the sample compared to class rank of the overall population at the University of Florida during the fall 2010 semester ( Office of Institutional Planning and Research, 2010). Class rank was representative of overall class rank at the University of Florida. The largest ethnic group was White (60. 4 %, n=337 ) followed by Hispanic or Latino (15. 1 %, n=8 4 ) and African American (13. 3 %, n=74). Thirty respondents did not indicate their ethnicity yielding a .05% nonresponse rate for that question. Table 36 reports that ethnicity of the study sample was representative of overall ethnicity at the University of Florida at the time of survey implementation ( Presidents Council on Diversity 2010). The age ranged from 1829

PAGE 83

83 years with the majority of students reporting they fell within the 1820age range (27.9%, n= 340) and the 2123age range (38.7%, n=227). Over a third of individuals placed themselves in the 121140 pound weight class (37.2%, n=213). Data Collection The University of Floridas Institutional Review Board (IRB) approved the adjusted web based research study (Appendix K ). Once IRB approval was obtained, the principal investigator faxed the statistical request to the University registrar requesting a random sample listing of undergraduate females local mailing addresses and matching university email addresses. The statistical request was approved and the database for the sample was sent to the principal investigator via email. This database was used as the sampling frame for the research study. The survey was implemented in March 2010. March is the midpoint of the semester and students are busy preparing for midterm exams, etc. Time of survey implementation may have impacted survey response rate. Pre Notification Letter. A mixed mode methodology of notification and participation request was determined to be the best way to notify the sample. This mixed mode method of notification has proven to be most effective when implementing online surveys and providing incentives upo n initial request. Contacting potential participants via a United States P ostal S ervice (U S P S ) mailed letter and asking students to respond to an online survey has been shown to improve response rates and reduce nonresponse error (Dillman et al., 2009). This method of mail and email contact has resulted in response rates of up to 59% in former surveys (Dillman et al., 2009). Therefore, by using mixed methodology of notification, it was hoped that response rate would be improved from a pilot study response rate.

PAGE 84

84 The initial notification, a mailed letter (Appendix L ), was personalized. The prenotification letter informed potential participants that they would receive an email containing a survey link within the next two weeks. Notification letters also served to inform participants that completion of the survey was anonymous. This prenotification letter included a gator lapel pin incentive for participation in the study. The letter stated the purpose and importance of the research and explained that the survey was completely voluntary. Each individual letter was customized to include the potential respondents name in the salutation. This was completed for each member of the sample. The text was placed on Department of Health Education and Behavior letterhead and all letters were then printed. The principal investigator personally signed each letter with blue ink. Business envelopes were labeled with local addresses and stuffed with both the prenotification letter and incentive. The data base was missing 497 local addresses. It is hypothesized that the students with missing local addresses had a privacy flag associated with their student account; therefore, addresses could not be disclosed. The investigator chose to mail 1003 prenotification letters. Over the next several weeks, f orty four letters were returned to the sender due to insufficient address or invalid address reducing the total number of mailed letters to 959. An incentive was included in the final survey methodology to promote increased response rate and in turn, improve the quality of data collected (D illman et.al., 2009). The incentive used in this study was a gator lapel pin (Appendix P ). The university mascot is a gator; school loyalty and pride is evident among this population of students. This incentive was chosen to better tailor the promotion to the target population.

PAGE 85

85 E mail Notification. Nine days after the mailing of the prenotification letters, e mails (Appendix M ) containing the link to the online survey w ere sent to all 1500 prospective participants university webmail accounts. E mail noti fications were sent to those receiving and not receiving the prenotification letter. The investigator was prepared to send the notification letter with the incentive should a student request one. The email included text referring to the mailed letter and incentive they should have received the week prior. Instructions were given regarding the survey link located at the end of the survey. The notification explained that upon clicking on the survey link, completing and submitting the survey, informed consent was implied. The email also reminded the potential participants that the survey was anonymous and voluntary. At this time, the principal investigator activated the survey via S urvey M onkey making the survey available for participants. One week followi ng the transmission of the notification e mail, a reminder/thank you email (Appendix N ) was sent. The reminder email was sent to all potential participants. Survey Monkey does not link responses to personal identifiers and therefore there was no way to determine who had completed the survey. This email thanked the individuals who had already completed the survey and urged those who had yet to complete the survey to please click the link and do so. The email also reminded, once again, that they should have received a gator lapel pin and mailed notification. A second reminder/thank you was sent approximately two weeks after the initial notification e mail. The principal investigator was urged to send one final reminder (Appendix O ) three weeks after activ ating the survey. In total, three reminders were sent. A week following the final reminder, the survey link was deactivated and the

PAGE 86

86 survey closed. No further responses were allowed. The survey was open for a total of four weeks. Following each bulk email transmission, bounce back emails were sent to the principal investigators inbox. Five to seven emails were returned each time a notification was sent. Only two individuals of the 497 who did not receive an initial U S P S mailed notification contacted t he principal investigator asking for their gator lapel pin, stating they had not yet received a letter. A letter and incentive was sent to both students immediately upon request. Data Analysis Completed survey data was downloaded from the survey softwar e site to a Microsoft Excel spreadsheet. Survey Monkey is programmed to automatically code the survey responses according to the codes entered by the principal investigator upon initial survey input. The data were then uploaded to SPSS version 16.0 for data analysis. Descriptive analyses were used to obtain percentages of demographic variables of ethnicity, age, percentages of family history risk, and smoking status of the overall population. Correlation analysis was used to analyze relationships between variables and to determine the predictive value of each group of questions for each construct of the Health Belief Model compared with overall risk for heart disease. A series of regression analyses were used to examine the predictive relationship betwee n the dependent variable of behavior and the independent variables of age, ethnicity, and perception. A series of regression analyses were also used to determine the relationship between the Health Belief Model variables and the independent variables of age and ethnicity. Results are reported in the next C hapter 4

PAGE 87

87 Summary C hapter 3 detailed the research design, research variables, survey design and development, two pilot studies, and final study implementation methodology. Data were collected among a random sample of university females. The survey instrument used in the study was developed by the investigator to assess behavior that may elevate future heart disease risk among young adult females. The survey was validated using an expert panel and tested for reliability. The cross sectional survey An Assessment of University Females Heart Health Behaviors was based on an extensive review of the literature and Health Belief Model constructs. Fi gure 31. Transtheoretical Model (Stages of Change) N = N = 19285 = 391.87 = 392 1 + N (e)2 1 + 19285 (.05)2 Figure 3 2. Adjusted s ample size formula

PAGE 88

88 Sample = Desired Sample Size = 392 = 980 Estimate d Response Rate .40 Figure 33. Adjusted sample size Table 31. Initial Survey Instrument Design Table Variable Item Number Nutrition Behavior 6 Physical Activity Behavior 2 Family History 2 Smoking Behavior (Skip pattern included) 8 Medical Care Access/Clinical Risk 11 Transtheoretical Model 5 Health Belief Model Nutrition 16 Health Belief Model Physical Activity 22 Health Belief Model Smoking 18 Health Belief Model Medical Visits 13 Health Belief Model Personal Weight Perceptions 18 Note: One question with 6 response options for each behavior assessed. Table 32. Pilot Two Survey Instrument Design Table Variable Item Number Nutrition Behavior 6 Physical Activity Behavior 1 Family History 3 Smoking Behavior 2 Medical Care Access/Clinical Risk 11 Health Belief Model Nutrition 14 Health Belief Model Physical Activity 14 Health Belief Model Medical Visits 13 Table 33. Final Survey Instrument Design Table Variable Item Number Nutrition Behavior 6 Physical Activity Behavior 1 Family History 3 Smoking Behavior *Including Hookah 7 Medical Care Access/Clinical Risk 11 Health Belief Model Nutrition 16 Health Belief Model Physical Activity 15 Health Belief Model Smoking 15 Health Belief Model Medical Visits 13 Table 34. Sample Size Sample Size Variables Numeric Values

PAGE 89

89 Table 34. Continued Sample Size Variables Numeric Values N (total population) 19,285 University Females E .05 P .05 Confidence interval 95% Table 35 Sample Class Rank and Overall Population Class Rank Class Rank Study Sample Fall 2010 Female Undergraduate Population Freshman 10 8 (19. 4 %) 2,437 ( 13.8 %) Sophomore 126 (22. 7 %) 3,718 ( 21.1 %) Junior 1 39 (25. 0 %) 5,148 ( 29.1 %) Senior 183 (32. 9 %) 6,363 ( 36.0 %) Total *556 **17,666 Note: Thirty one study participants chose not to report class rank. **Total slightly less than total female population in fall 2009. Table 36. Sample Ethnicity and Overall Population Ethnicity Ethnicity Study Sample Fall 2009 Female Undergraduate Population White 337 (60.4%) 11,223 (59.0%) Hispanic 84 (15.1%) 3,000 (15.8%) Black /African American 74 (13.3%) 2,291 (12.1%) Asian 37 (6.7%) 1,613 (8.4%) Other 25 (4.5%) 885 (4.7%) Total *557 19,012 *Note: Thirty study participants chose not to report ethnicity.

PAGE 90

90 CHAPTER 4 RESULTS The purpose of this study was to determine perception of heart disease among undergraduate females, identify behaviors exhibited among undergraduate females associated with risk for heart disease, and determine whether Health Belief Model constructs influenced behavior associated with heart disease risk. C hapter 4 presents the results of this study and is organized into three sections: (1) d escriptive analyses of the sample; (2) behavior frequencies reported by the sample; (3) results for the two research questions. Descriptive Analysis of the Sample Sample Demographics This section provides an overview of sample demographics for participants in this study. Request for participation notifications were sent to a random sample of 1500 female undergraduate students. A total of 598 female students from the University of Florida participated. After the inclusion criteria for age (1829) was applied, 587 usable surveys remained, yielding a 98.0% inclusion rate. Table 41 displays a summary of study participants according to their age range, ethnicity, and class rank. Over half of the sample (57.8%, n=339) reported their age as being between the ages of 1820 years. Nearly one out of every three participants (32.9%, n=183) were seniors, and approximately 61.0% (n= 337) of sample participants were white /nonHispanic The sample is representative of the female university population at the time of survey implementation (Table 35 and Table 36 )

PAGE 91

91 Current Behavior One purpose of this study was to assess behavior associat ed with risk exhibited among the university female population. This section describes reported participant behavior associated with increased risk of heart disease. Poor nutrition, physical inactivity, and smoking increase ones risk for heart disease. Fam ily history of heart disease, doctor visits, blood pressure, cholesterol checks, and diabetes frequencies are also reported. In this study, participants responded to multiple choice questions when reporting their current behavior. Nutrition Students were asked questions related to fast food, leafy greens, fish, fruit and vegetable, and grain consumption. The fruit and vegetable and grain questions asked individuals to respond yes or no to whether or not they consumed at least five servings of vegetables and whether they consumed three servings of grains daily. Students were also asked a question related to the foods they most often eat and were given four response options: (1) m ostly high fat foods ; (2) mostly low fat foods ; (3) vegan or vegetarian; and (4) a mixture of low fat/high fat. Table 42 presents the results from the questions related to nutrition. The results of the descriptive analysis indicate that students are not fully following nutrition guidelines set forth by the American Heart Associat ion (2010) yet one cannot be certain that the population of interest has knowledge of guidelines Although the majority of the students indicated they consumed fast food (defined by the researcher as inexpensive food, such as hamburgers and fried chicken, prepared and served quickly e.g. Burger King, Taco Bell, McDonalds etc ) very little, students (69.1% ; n=398) also reported they did not eat three servings of fruits/vegetables daily. The most

PAGE 92

92 alarming nutrition statistic was the fact that over half of t he sample of students (51.0% ; n=295) reported they ate fish never to very little. Fish is imperative to a womans health, especially the health of their hearts (USHHS, 2007). Physical Activity Table 43 shows how often students engage in at least 30 minutes of moderate to vigorous physical activity. The American Heart Association heart healthy guidelines state that all adults aged 18 to 65 should engage in moderate physical activity for at least 30 minutes for five days per week or vigorous activity for 20 minutes on three days per week (Haskell et al., 2007). The results of this study indicate that students fall short of meeting these guidelines with over half (64.6 %, n=372) of the participants indicating they exercised three days per week or less. Smoking Table 44 presents the results of the questions regarding smoking behavior related to current smoking, smoking within the past 30 days, and how often participants are exposed to second hand smoke. Hookah questions were included because, as explained in C hapter 2 hookah is increasing in both prevalence and incidence among college students. Table 44 also includes results from hookah related responses. It is important to point out that smoking percentage reports are consistent with pilot one results with 81.5% (n=466) of participants indicating they had never smoked a cigarette. However, a large number of students (46.6% ; n=266) indicated they had smoked hookah. It is interesting to note that when frequency analysis was performed on nonsmokers only those who reported never smoking a cigarette (n=466) findings indicated that 179 (38.4%) of non cigarette smokers had in fact smoked hookah, and

PAGE 93

93 182 (39.1%) of noncigarette smokers report ed they had visited a hookah bar. Results from this analysis are reported in Table 45 Family History Although many risk factors for heart disease can be prevented and/or avoided, risk associated with a positive family history cannot be altered. Students were asked whether or not both their mother and father had been diagnosed with some form of heart disease to help determine hereditary risk. It is encouraging to note that only a small portion of participants responded that they were unsure if their parents had been diagnosed or had some form of heart disease (6.3%, n= 36 mother and 12.3%, n=71 father) It is important for students to be aware of family history of heart disease to better prevent onset of personal heart disease. Students then were asked how likel y they thought they were to be diagnosed with heart disease based upon their response to the questions regarding their parental family risk for heart disease. The investigator performed descriptive analysis on only participants indicating a positive family history to determine what percentage of those students believed themselves at risk for heart disease due to positive history. Table 46 presents the results of a self reported knowledge of family history risk and also displays results related to knowledge of family history and perception of increased risk. Students reported awareness of their mothers or fathers history of heart disease with very few reporting they did not know or were not sure of their family history. According to the results of the anal ysis of those with a positive family history, it appears that overall, students surveyed believed themselves somewhat susceptible to heart disease based upon their positive family history (71.4%, n=90 family history mother;

PAGE 94

94 68.6%, n=153 family history father). This statistic is encouraging in that it indicates students are aware that a positive family history increases their personal risk. Doctor Visits and Clinical Risk Assessment Risk behavior related to heart disease includes failing to get routine check s on clinical measures such as high blood pressure, cholesterol, and glucose. Research indicates that individuals age 20 and older should routinely have their clinical risk assessed (Cleeman & Grundy, 1997). Awareness of a possible heightened risk for hear t disease allows individuals the opportunity to change behavior and/or use drug therapy to reduce clinical risk. Students were asked questions regarding whether they had a primary care physician, the last time they visited a doctor for a checkup, if they had had blood pressure measures assessed, and if they had ever had their cholesterol checked. Students were then asked, if they had their cholesterol checked, were they aware of the measurement. Descriptive statistics are also reported for participants who indicated they had had their cholesterol measures checked. Reported statistics indicate whether or not, of those indicating measurement, the individuals remembered their results. Table 47 represents the results of questions regarding doctor visits and cli nical risk assessment. It is interesting to note that students who answered they had not visited their doctor recently or that they rarely go to the doctor gave reasons of I only go when I am sick, I only go when I am asked to go for work/school, I dont go because I dont have insurance, and I dont go because I dont have any money Clearly students need to be more aware and educated regarding the importance of regular doctor visits. While it is encouraging that 288 students reported having their cholesterol checked, it is discouraging that only 100 of those reported remembrance of their test

PAGE 95

95 results. Having ones cholesterol checked is imperative to heart health, but awareness of measurement is crucial to making lifestyle changes to reduce possible risk of future heart disease. Research Question Results This section presents the results of the multivariate linear regression analyses conducted to answer each of the two research questions. Descriptive and reliability analysis results are reported w hen applicable. Research Question One Part One An alternate purpose to this study was to assess university female perception of personal heart disease risk. Research question one was formulated to aid in the assessment of this critical piece of information. Research question one was made up of two parts. Part One: Does the university female student population perceive themselves to be at risk for heart disease? Descriptive analysis was performed to determine whether the random sample of univers ity females considered themselves at risk for heart disease development. Students were asked to rate their level of risk by giving them the following dichotomous response option: low risk or high risk. If the individual indicated that they considered thems elves to be at low risk for heart disease, they were placed into the no risk category. If the individual responded that they were at high risk for heart disease they were placed into the risk category. The majority of the female sample believed themsel ves to be at low risk for future heart disease development (80.4%, n=472). Table 48 presents the results of the personal perception of risk questionnaire item Table 49 displays the variance for the perception item. Variance was limited in the

PAGE 96

96 perception variables and therefore carries forward with statistics regarding the perception variable. Research Question One Part Two Part Two: Does age, ethnicity, and/or perception impact ones behavior? The first goal of the research was to describe behavior exhibited among the university female population that may increase their risk for heart disease development. Age groups used in the regression analysis were the same as those listed as response options on the questionnaire. Table 41 shows the frequencies for each age group represented in the study. The age group 27 29 was selected as the baseline group. Ethnicity was combined into the following categories prior to regression analysis: Asian, Hispanic, African American, White/Caucasian, and Ot her. The other ethnicity category included Native Hawaiian and Pacific Islanders, American Indian or Alaska native, Middle Eastern and all written in descriptions for ethnicity. Whites were chosen as the baseline group for ethnicity. Table 410 displays the frequencies of each group for ethnicity. Reliability analysis was performed on groupings of survey items of behavior to determine if grouped variables, for each total behavior, could be created. The results of the analysis are located in T able 411. A s noted by Table 411, the grouped variables of nutrition, doctor visits, and clinical measures did not correlate well; therefore, regression analyses were performed on individual items for those variables. Behavioral research seldom finds one main reason for an outcome effect. For example, the variables of age, ethnicity, and perception may all influence a particular behavior assessed in this study or the three independent variables may not influence behavior. Assuming that no one variable took precedenc e over the others, the Enter

PAGE 97

97 method was used when placing age, ethnicity, and perception into the regression model. For each regression, the independent variables were placed into the model for the following behaviors: i ndividual items of nutrition (consum ption of fast food, leafy green vegetables, fish, fruits and vegetables, grains, and foods most often eaten); physical activity; grouped smoking variable (current smoking status, cigarettes smoked entire life, cigarettes smoked the past 30 days, exposure t o second hand smoke); grouped hookah variable (hookah bar attendance, hookah smoked for past 30 days, smoking of hookah); individual items of medical visits (have a primary physician, last doctor visit, last checkup, how often visit doctor for checkup); individual items of clinical measures (diabetes testing, cholesterol testing). Regression is a good analysis technique to use when assessing more than two independent variables. Regression coefficients indicate the direction and strength of the association between the independent variable(s) and dependent variable. The coefficient can be interpreted as the increase in the dependent variable for each unit increase in the explanatory variable. In this study, there were three independent variables, so the regre ssion coefficient indicates the unit association between each individual independent variable when all other independent variables are held constant. For all regression analyses, t he baseline group for age was group 2729, and the baseline group for ethnicity was whites. Unstandardized betas are reported for the description of the results Table 412 shows the results from the regression analyses and reports Nutrition No difference was found in age related to any of the i ndividual food consumption items (fast food, leafy greens, fish, fruit/vegetables, grains, or foods most often

PAGE 98

98 consumed). However, findings indicated that compared to whites, African Americans were more likely to eat fast food (B=.617, p=<.001) and less li kely to consume leafy green vegetables (B= .403, p=.001). Also, when compared to whites, African Americans were more likely to consume fish (B=.269, p=.007) and less likely to consume grains (B= .144, p=.012). No difference was found in ethnicity related to fruit and vegetable consumption or foods most often eaten (mostly high fat, mostly low fat, mixture of both, or vegan/vegetarian options). Also, individuals who reported greater perception of risk (perceived risk of heart disease development) were more likely to consume fast food (B=.491, p=<.001) and less likely to consume leafy green vegetables (B= .203, p=.038). No difference in perception related to fish, fruit and vegetable consumption, foods most often eaten, or grains was found. It is important to note that for the analysis conducted on individual survey items for nutrition, the results are only generalizable to each specific item. Physical Activity No difference in age was found related to physical activity. However, regression findings indicated that compared to whites, Asians and African Americans were less likely to engage in physical activity (B= 1.101, p=.001; B= .648, p=.007). Also, perception did not have a statistical relationship to physical activity Smoking and Hookah Compared to individuals age 2729, those age 1820 and 2123 were less likely to engage in smoking related behavior (B= 3.642, p=.014; B= 3.413, p=.022). However, those in the age group 2123 were more likely to engage in hookah related behavior (B=.981, p=.020). Al so, compared to whites, Asians and African Americans were less likely to engage in smoking related behavior (B= 1.327, p=.033; B= 1.047, p=.022).

PAGE 99

99 African Americans were also less likely to engage in hookah related behavior (B= .749, p=<.001). Perception of risk was not significantly related to smoking or hookah behavior. Doctor Visit Behavior No difference was found in age related to whether the individual had a primary doctor or how often the individual reported they went to the doctor for checkups. However, when compared to individuals in the age group 2729, more time had passed since a checkup visit for those in the age group 2426 (B= 1.546, .033). Also, more time had passed for age groups of 1820, 2123, and 2426 since their last doctor visit (B = 1.059, p = .002; B= 1.111, p=.001; B= 1.315, p=.001). Compared to whites, Asians and Hispanics were less likely to have a primary doctor (B= .214, p=.001; B= .155, p=.020). This finding was not surprising due to the large amount of literature repor ting limited access to care among minority populations (Ginzberg, 1991; Winkleby et al. 1998). Less time had passed since a last doctor visit for both Asians and African Americans (B=.475, p=.001; B=.257, p=.012). Compared to whites, no difference was found in ethnicity related to time since their last checkup or how often one gets checkups. Perception was not statistically significantly related to any variables associated with doctor visit behavior. It is important to note that for the analysis conducted on individual survey items related to doctor visits, etc., the results are only generalizable to those specific items. Clinical Risk Assessment No difference was found in age related to diabetes or cholesterol checks. Compared to whites, no difference was found in ethnicity and clinical variables. Also, no difference was found relating perception to clinical variables.

PAGE 100

100 Research Question Two Do age and/or ethnicity impact Health Belief Model constructs related to each behavioral risk factor? R eliability an alysis was performed on each construct of the Health Belief Model (perceived risk, perceived benefits, perceived barriers, cues to action, and self efficacy) for each behavior related to increased risk of heart disease. Constructs that had a reliability co efficient of a rounded > 0.60 were grouped together for use in the regression analyses. Cues to action for nutrition, self efficacy items for smoking, and perceived barriers to medical visits did not have sufficient reliability coefficients; therefore, regr ession analysis was performed on individual items for those construct measures. The results of the item analysis are summarized in Table 4.13 Assuming that no one variable took precedence over the others, the Enter method was used when placing age and et hnicity into the regression model for each of the Health Belief Model construct variables. For each regression the independent variables were placed into the model for the Health Belief Model constructs of perceived risk, perceived benefits, perceived barr iers, cues to action, and self efficacy for the following behavioral items relating to the constructs: n utrition, p hysical a ctivity, smoking, and m edical visits. It is important to note that the regression was testing for perception, not whether perception related to increased behavior. Unstandardized betas are reported for the description of the results. Health Belief Model and Nutrition No difference was found in age related to nutrition perceived risk, perceived benefits, perceived barriers, the cue of f riends eating healthy, or the cue of needing to lose weight. When compared to the age group 2729, the age group 2426 was more

PAGE 101

101 likely to relate to the cue to nutrition taking a nutrition course (B=.801, p=.035). No difference in age was found related t o the Health Belief Model construct of nutrition self efficacy. No difference was found in ethnicity related to nutrition perceived risk or perceived barriers. Findings indicated that compared to whites, African Americans were more likely to perceive benef its to physical activity (B=.401, p=.026). Also, when compared to whites, African Americans were more likely to perceive when friends eat healthy as a cue to action to nutrition (B=.291, p=.001). Findings indicated that Asians, African Americans, and others were more likely to perceive needing or wanting to lose weight as a cue to nutrition (B=.293, p=.036; B=.491, p=<.001; B=.528, p=.008). It is important to note that for the analysis conducted on individual survey items related to the Health Belief M odel, constructs related to nutrition are only generalizable to those specific items. Results of the regression analysis are located in T able 41 4 Health Belief Model and Physical Activity No difference was found in age related to physical activity percei ved risk, perceived benefits, cues to action, or self efficacy. However, results indicated that, when compared to age group 2729, individuals in age group 2123 were more likely to perceive barriers to physical activity (B=1.506, p=.044) Findings indicat ed that when compared to whites, individuals in the other ethnic group were more likely to perceive risks to physical activity (B=1.622, p=.006) and barriers to physical activity (B=1.188, p=.007). Also, Asians and African Americans were more likely to perceive benefits related to physical activity (B=.499, p=.043; B=.465, p=.011). African Americans were also more likely to perceive cues toward physical activity (B=.526, p=.035). Findings also indicated that when compared to whites, Asians and African Americans perceived

PAGE 102

102 self efficacy toward engaging in physical activity behavior (B=.734, p=.032; B=.629, p=.014). Results of the regression analysis are located in T able 41 5 Health Belief Model and Smoking Findings indicate there was no difference in age related to any of the Health Belief Model constructs related to smoking. However, when compared to whites, African Americans were less likely to perceive barriers to smoking (B= 1.591, p=.031). No difference was found in ethnicity related to the remainder of the Health Belief Model constructs related to smoking. It is assumed this is due in part to the small volume of cigarette smokers in the sample, and nonsmokers responses would have been skewed or missing. Results from the regression analysis are located in T able 416. Health Belief Model and Doctor Visits and Clinical Risk Assessment No difference was found in age related to the Health Belief Model constructs of perceived risk of medical visits, perceived benefits, the barrier of cost of appointment, cues to action, and self efficacy. However, findings indicated that when compared to the age group of 2729, individuals in the age groups 1820 and 2426 were more likely to perceive inconvenience as a barrier to going to the doctor (B=.746, p=.018; B= .798, p=.035). Also, individuals in the 2426 year old category were more likely perceive fear of finding somet hing is wrong with their health as a barrier to visiting their doctor (B=.778, p=.037). No difference was found in ethnicity related to perceived risk of medical appointments, perceived benefits, the barrier of cost of an appointment, and cues to action. However, results indicated that African American s were more likely to perceive inconvenience as a barrier to visiting their doctor (B=.317, p=.002) and less likely to perceive fear of finding something is wrong with their health as a barrier to visiting their doctor (B= .286, p=.004). Individuals in the other ethnic group, when

PAGE 103

103 compared to whites, were less likely to perceive self efficacy to engaging in doctor visit/clinical risk behavior (B= 1.194, p=.006). It is important to note that for the analysis conducted on individual survey items related to the Health Belief Model, constructs related to doctor visits and clinical risk assessment are only generalizable to those specific items. Results of the regression analysis are located in T able 417. Summary C hapter 4 presented descri ptive results of overall behavior reported by the sample of female university students who participated in this study. Findings for each of the research questions were also provided. Regression analysis was conducted on the three explanatory variables of age, ethnicity, and perception, with all behaviors assessed in this study Age and ethnicity were placed into regression models for Health Belief Model constructs related to behavior associated with heart disease. A discussion of the results is presented in C hapter 5 Table 41. Distribution of Participants by Age, Ethnicity, and Class Rank Item f % Age 18 20 339 57.8 21 23 227 38.7 24 26 14 2.4 27 29 6 1.0 Total 586 Ethnicity Asian 37 6.6 Black or African American 74 13.3 Hispanic or Latino 84 15.1 Native Hawaiian or Pacific Islander 1 .2 American Indian or Alaska native 1 .2 White or Caucasian 337 60.5 Middle Eastern 4 .7 Other 19 3.4 Total 557 Class Rank

PAGE 104

104 Table 41. Continued Item f % Freshman 108 19.4 Sophomore 126 22.7 Junior 139 25.0 Senior 183 32.9 Total 556 Note: Age was missing for one participant. Ethnicity was missing for 30 participants and class rank was missing for 31 participants. Valid percentages were reported. Table 42. Frequency Analysis Results for Nutrition Behavior Item f % Fast Food Never to very little 254 44.1 One time/week 167 29.0 Two to four times/week 145 25.2 Five or more times/week 10 1.7 Total 576 Leafy Green Vegetables Never to very little 77 13.3 One time/week 139 24.0 Two to four times/week 260 45.0 Five or more times/week 102 17.6 Total 578 Fish Never to very little 295 51.0 One time/week 188 32.5 Two to four times/week 88 15.2 Five or more times/week 7 1.2 Total 578 Fruits and Vegetables No 398 69.1 Yes 178 30.9 Total 576 Grains No 158 27.5 Yes 417 72.5 Total 575 Foods Most Often Eaten Mostly high fat foods 60 10.4 Mostly low fat foods 201 34.8

PAGE 105

105 Table 42. Continued Item f % Vegan or Vegetarian 42 7.3 Mixture of high/low fat foods 274 47.5 Total 577 Note: Reported fast food consumption and fruit/vegetable consumption was missing for 11 participants, leafy green and fish reports were missing from nine, and grain consumption reports were missing from 12 participants. Ten participants chose not to report what type of foods they most often eat. Valid percentages are reported. Table 43. Frequency Analysis Results for Physical Activity Behavior Number of Days f % Zero 69 12.0 One 80 13.9 Two 92 16.0 Three 131 22.7 Four 74 12.8 Five 84 14.6 Six 21 3.6 Seven 25 4.3 Total 576 Note: Eleven students did not report their physical activity frequency. Valid percentages are reported. Table 44. Frequency Analysis Results for Smoking and Related Behavior Item f % Smoking Status No, I have never smoked 466 81.5 No, I quit within the last six months 7 1.2 No, I quit more than six months ago 10 1.7 No, I have tried cigarettes once or twice but stopped 67 11.7 Yes, I currently smoke 22 3.8 Total 572 Smoked within Past 30 Days Never (0 days) 538 94.6 Almost never (1 2 days) 14 2.5 Occasionally (3 4 days) 7 1.2 Regularly (5 7 days) 10 1.8 Total 569 Smoke Exposure Past 30 Days Never (0 days) 270 47.4 Almost never (1 2 days) 237 41.6 Occasionally (3 4 days) 41 7.2 Regularly (5 7 days) 22 3.9

PAGE 106

106 Table 44. Continued Item f % Total 570 Visited Hookah Bar No 314 55.0 Yes 257 45.0 Total 571 Ever Smoked Hookah No 305 53.4 Yes 266 46.6 Total 571 Hookah Past 30 Days Never (0 days) 535 93.7 Almost never (1 2 days) 34 6.0 Occasionally (3 4 days) 2 .4 Total 571 Table 45. Frequency Analysis Results for NonCigarette Smoker Hookah Behavior Item f % Never Smoked Cigarette/Hookah Bar No 282 60.8 Yes 182 39.2 Total 464 Never Smoked Cigarette/Hookah smoke No 285 61.4 Yes 179 38.6 Total 464 Never Smoked Cigarette/Hookah 30 days Never (0 days) 439 94.2 Almost never (1 2 days) 26 5.6 Occasionally (3 4 days) 1 .2 Total 466 Note: Fifteen participants did not report smoking status and 16 chose not to report how many cigarettes they had smoked their entire life. Eighteen did not report their 30day smoking habits, 17 did not report their second hand smoke exposure, and 16 chose not to answer any of the three hookah related questions. Valid percentages are reported. Analysis for nonsmokers related to hookah each had two missing responses. Table 46 Frequency Analysis Res ults for Family History of Heart Disease Item f % Mom Heart Disease

PAGE 107

107 Table 46 Continued Item f % No 411 71.7 Yes 126 22.0 Don't know/not sure 36 6.3 Total 573 Dad Heart Disease No 280 48.7 Yes 224 39.0 Don't know/not sure 71 12.3 Total 575 Personal Risk Not at all likely 274 47.8 Somewhat likely 271 47.3 Very likely 28 4.9 Total 573 Positive Risk Mom/Personal Risk Not at all likely 16 12.7 Somewhat likely 90 71.4 Very likely 20 15.9 Total 126 Positive Risk Dad/Personal Risk Not at all likely 50 22.4 Somewhat likely 153 68.6 Very likely 20 9.0 Total 223 Note: Fourteen students did not answer the question related to their mothers history of heart disease, and 12 did not respond to the question regarding family history for their father. Fourteen did not respond to the personal risk question. Valid percentages are reported. Table 47 Frequency Analysis Results for Doctor Visits and Clinical Risk Assessment Item f % Have primary physician No 169 29.8 Yes 375 66.0 Not sure 24 4.2 Total 568 Last Checkup Within the past year 352 61.8 Within the past two years 120 21.1

PAGE 108

108 Table 47 Continued Item f % Within the past three years 39 6.8 Within the past four years 17 3.0 Five or more years ago 13 2.3 Never 4 .7 Dont remember 25 4.4 Total 570 Last Doctor Visit 436 76.8 Within the past year 83 14.6 Within the past two years 30 5.3 Within the past three years 8 1.4 Within the past four years 11 1.9 Five or more years ago 568 How Often Get Check Ups One time per year 266 46.7 Two times per year 72 12.7 One time every other year 136 23.9 One time every five years 45 7.9 Other 50 8.8 Total 569 Current High Blood Pressure No 541 94.9 Yes 29 5.1 Total 570 Cholesterol Checked No 191 33.5 Yes 288 50.5 Not sure/don't remember 91 16.0 Total 570 Known Cholesterol Measures No 205 36.3 Yes 102 18.1 Not sure 133 23.5 Cholesterol never checked 125 22.1 Total 565 Cholesterol checked/Numbers known No 123 43 Yes 100 35 Not sure 63 22

PAGE 109

109 Table 47 Continued Item f % Total 286 Note: Nineteen participants did not report whether they had a primary doctor, and 17 did not report the last time they had a checkup. Nineteen did not report time since last doctor visit and 18 did not report how often they get checkups. Seventeen individuals also chose not to report whether they had high blood pressure or if they had had their cholesterol checked. Sixteen did not report if they had been tested for diabetes, and 22 did not respond to the question related to known cholesterol numbers. Valid percentages are reported. Table 48 Frequency Analysis Results for Perceived Risk of Heart Disease Perception f % Low Risk/No Risk 472 80.4 High Risk/Yes Risk 115 19.6 Total 587 Table 49 Perception Variable Variance Item SD Variance Perception .397 .158 Total 587 Note: Perception was measured by a dichotomous response variable. Table 410. Ethnicity Frequencies for Regression Analysis Ethnicity f % Asian 37 6.7 Hispanic 84 15.1 African American 74 13.3 White 336 60.4 Other 25 4.5 Note: Thirty one individuals chose not to report their racial/ethnic affiliation Table 41 1 Final Survey Item Analysis Results for Behaviors Associated with Increased Risk for Heart Disease Behavior Item Number Reliability Coefficient Standardized Coefficient Nutrition 6 .084 .117 Physical Activity 1 NA NA Smoking 4 .695 .768 Hookah 3 .704 .679 Doctor Visit 4 .329 .150 Clinical 2 .452 .455 *Items correlate at the .60 level.

PAGE 110

110 Table 41 2 Linear Regression Analyses of Age, Ethnicity, and Perception with Behaviors Associated with Heart Disease Risk Variable Unstandardized Beta SE Standardized Beta t Sig Nutrition Fast Food Age 18 20 .152 .343 .087 .443 .658 Age 21 23 .213 .345 .120 .617 .538 Age 24 26 .151 .406 .027 .371 .710 Asian .212 .142 .061 1.489 .137 Hispanic .045 .100 .019 .452 .651 African American .617 .105 .243 5.862 <.001 Other .175 .197 .037 .889 .374 Perception .491 .088 .226 5.559 <.001 Leafy Green Age 18 20 .331 .379 .178 .875 .382 Age 21 23 .209 .381 .111 .549 .583 Age 24 26 .450 .449 .077 1.002 .317 Asian .234 .157 .063 1.486 .138 Hispanic .054 .111 .021 .492 .623 African American .403 .116 .149 3.466 .001 Other .120 .217 .024 .555 .579 Perception .203 .098 .088 2.082 .038 Fish Age 18 20 .003 .324 .002 .010 .992 Age 21 23 .064 .326 .040 .198 .843 Age 24 26 .336 .384 .068 .874 .382 Asian .212 .135 .068 1.577 .115 Hispanic .056 .095 .026 .586 .558 African American .269 .100 .117 2.700 .007 Other .150 .186 .035 .808 .419 Perception .054 .084 .027 .641 .522 Fruit/Vegetable Age 1820 .171 .194 .183 .882 .378 Age 21 23 .132 .195 .139 .674 .501 Age 24 26 .256 .230 .087 1.112 .267

PAGE 111

111 Table 41 2 Continued Variable Unstandardized Beta SE Standardized Beta t Sig Asian .010 .081 .006 .128 .898 Hispanic .055 .057 .043 .967 .334 African American .079 .060 .058 1.315 .189 Other .083 .111 .033 .747 .456 Perception .004 .050 .003 .077 .939 Grains Age 18 20 .213 .187 .236 1.140 .255 Age 21 23 .187 .188 .204 .994 .321 Age 2426 .177 .221 .062 .801 .424 Asian .039 .078 .022 .508 .611 Hispanic .023 .055 .019 .422 .673 African American .144 .057 .110 2.510 .012 Other .154 .107 .063 1.434 .152 Perception .017 .049 .015 .353 .725 Foods Most often Eat Age 18 20 .130 .465 .058 .281 .779 Age 21 23 .236 .467 .104 .506 .613 Age 24 26 .512 .550 .072 .930 .353 Asian .206 .193 .046 1.071 .284 Hispanic .013 .136 .004 .098 .922 African American .159 .142 .049 1.115 .265 Other .331 .266 .054 1.243 .214 Perception .239 .120 .085 1.995 .047 Physical Activity Age 18 20 .292 .778 .077 .375 .707 Age 21 23 .308 .782 .080 .394 .693 Age 2426 .517 .921 .043 .561 .575 Asian 1.101 .322 .146 3.416 .001 Hispanic .178 .227 .034 .785 .433 African American .648 .240 .116 2.701 .007 Other .394 .446 .038 .884 .377 Perception .351 .200 .074 1.753 .080 Smoking

PAGE 112

112 Table 41 2 Continued Variable Unstandardized Beta SE Standardized Beta t Sig Age 18 20 3.642 1.480 .498 2.461 .014 Age 21 23 3.413 1.489 .460 2.292 .022 Age 24 26 .234 1.752 .010 .134 .894 Asian 1.327 .622 .091 2.135 .033 Hispanic .338 .437 .033 .774 .439 African American 1.047 .457 .098 2.292 .022 Other 1.500 .848 .076 1.768 .078 Perception .405 .382 .045 1.060 .290 Hookah Age 18 20 .713 .417 .341 1.708 .088 Age 21 23 .981 .420 .464 2.339 .020 Age 24 26 .629 .499 .092 1.260 .208 Asian .197 .173 .048 1.139 .255 Hispanic .031 .124 .011 .249 .804 African American .749 .128 .247 5.855 .000 Other .302 .239 .053 1.264 .207 Perception .063 .108 .024 .588 .557 Medical Visits/Clinical Risk Primary Doctor Age 18 20 .303 .215 .286 1.408 .160 Age 21 23 .176 .216 .164 .815 .415 Age 24 26 .010 .258 .003 .039 .969 Asian .121 .089 .058 1.361 .174 Hispanic .214 .063 .147 3.402 .001 African American .155 .066 .100 2.337 .020 Other .006 .123 .002 .052 .958 Perception .107 .056 .081 1.924 .055 Last Check Up Age 18 20 1.027 .612 .347 1.678 .094 Age 21 23 .891 .616 .297 1.447 .148 Age 24 26 1.546 .725 .166 2.133 .033 Asian .470 .254 .080 1.851 .065 Hispanic .010 .179 .002 .057 .955

PAGE 113

113 Table 41 2 Continued Variable Unstandardized Beta SE Standardized Beta t Sig African American .018 .188 .004 .095 .925 Other .038 .351 .005 .108 .914 Perception .072 .158 .020 .457 .648 Last Doctor Visit Age 18 20 1.059 .334 .644 3.173 .002 Age 21 23 1.111 .336 .667 3.312 .001 Age 24 26 1.315 .395 .254 3.330 .001 Asian .475 .140 .144 3.393 .001 Hispanic .142 .098 .063 1.458 .145 African American .257 .102 .108 2.510 .012 Other .125 .191 .028 .656 .512 Perception .063 .087 .031 .728 .467 How Often Get Checkups Age 18 20 .304 .427 .148 .713 .476 Age 21 23 .093 .429 .045 .217 .829 Age 24 26 .217 .505 .034 .430 .668 Asian .172 .177 .042 .974 .330 Hispanic .025 .125 .009 .203 .839 African American .044 .132 .015 .334 .738 Other .035 .245 .006 .142 .887 Perception .018 .110 .007 .164 .870 Tested for Diabetes Age 18 20 .208 .321 .134 .649 .517 Age 21 23 .334 .323 .212 1.034 .302 Age 24 26 .207 .380 .042 .545 .586 Asian .021 .133 .007 .158 .874 Hispanic .101 .094 .047 1.079 .281 African American .146 .098 .065 1.487 .138 Other .254 .184 .060 1.379 .169 Perception .133 .083 .069 1.604 .109 Cholesterol Checked Age 18 20 .153 .284 .111 .538 .591

PAGE 114

114 Table 41 2 Continued Variable Unstandardized Beta SE Standardized Beta t Sig Age 21 23 .017 .286 .012 .059 .953 Age 24 26 .117 .336 .027 .349 .727 Asian .163 .118 .060 1.387 .166 Hispanic .137 .083 .072 1.645 .101 African American .012 .087 .006 .139 .890 Other .212 .163 .057 1.303 .193 Perception .018 .073 .010 .243 .808 *Significant at the p= <.05 level. Table 41 3 Final Survey Item Analysis Results for Health Belief Model Constructs Related to Behavior Associated with Increased Risk for Heart Disease Behavior Health Belief Model Construct Item Number Reliability Coeff icient Standardized Coefficient Nutrition Perceived Risk 3 .903 .910 Perceived Benefits 3 .782 .785 Perceived Barriers 3 .674 .675 Cues to Action 3 .497 .499 Self Efficacy 4 .788 .794 Physical Activity Perceived Risk 3 .903 .906 Perceived Benefits 3 .892 .891 Perceived Barriers 3 .557 .554 Cues to Action 3 .749 .748 Self Efficacy 3 .821 .823 Smoking Perceived Risk 3 .931 .932 Perceived Benefits 4 .855 .862 Perceived Barriers 3 .836 .833 Cues to Action 3 .864 .865 Self Efficacy 2 ** .104 .104 Medical Visits Perceived Risk 3 .921 .921 Perceived Benefits 2 .875 .875 Perceived Barriers 3 .536 .535 Cues to Action 2 .555 .556 Self Efficacy 3 .808 .814 *Items correlate at the .60 level. **It is hypothesized that this items coefficient is low due to the high number of missing responses (484 did not respond).

PAGE 115

115 Table 41 4 Linear Regression Analyses of Age and Ethnicity with Health Belief Model Constructs for Nutrition Variable Unstandardized Beta SE Standard ized Beta t Sig Nutrition Perceived Risk .264 .863 .063 .305 .760 Age 18 20 .315 .868 .075 .362 .717 Age 21 23 .728 1.033 .054 .705 .481 Age 24 26 .313 .358 .038 .875 .382 Asian .070 .252 .012 .276 .782 Hispanic .195 .266 .032 .734 .463 African American .793 .494 .070 1.604 .109 Other .264 .863 .063 .305 .760 Perceived Benefits Age 18 20 1.015 .581 .361 1.749 .081 Age 21 23 .810 .584 .284 1.387 .166 Age 24 26 .989 .687 .112 1.439 .151 Asian .217 .241 .039 .903 .367 Hispanic .061 .169 .016 .363 .717 African American .401 .179 .098 2.238 .026 Other .153 .333 .020 .461 .645 Perceived Barriers Age 18 20 .946 .828 .238 1.142 .254 Age 21 23 .894 .832 .222 1.074 .283 Age 24 26 1.439 .979 .116 1.470 .142 Asian .359 .357 .044 1.007 .315 Hispanic .283 .244 .051 1.156 .248 African American .298 .259 .051 1.149 .251 Other .596 .486 .054 1.226 .221 Cues to Action Friends Eat Healthy Age 18 20 .422 .287 .302 1.472 .142 Age 21 23 .512 .288 .362 1.776 .076 Age 24 26 .152 .339 .035 .448 .654 Asian .040 .119 .015 .340 .734 Hispanic .024 .084 .013 .291 .771 African American .291 .088 .143 3.292 .001 Other .085 .164 .022 .515 .607 Need/Want to Lose Weight Age 18 20 .355 .336 .214 1.056 .292 Age 21 23 .390 .338 .232 1.153 .249 Age 24 26 .739 .398 .142 1.858 .064

PAGE 116

116 Table 41 4 Continued Variable Unstandardized Beta SE Standardized Beta t Sig Asian .293 .139 .089 2.101 .036 Hispanic .092 .098 .040 .935 .350 African American .491 .104 .202 4.715 <.001 Other .528 .198 .114 2.670 .008 Taking a Nutrition Course Age 18 20 .435 .321 .282 1.357 .175 Age 21 23 .428 .322 .274 1.329 .184 Age 24 26 .801 .379 .165 2.112 .035 Asian .041 .133 .013 .309 .758 Hispanic .122 .094 .057 1.297 .195 African American .041 .099 .018 .409 .683 Other .192 .188 .045 1.021 .308 Self Efficacy Age 18 20 .188 .877 .044 .214 .831 Age 21 23 .101 .882 .023 .114 .909 Age 24 26 .592 1.038 .044 .570 .569 Asian .201 .364 .024 .552 .581 Hispanic .225 .257 .038 .877 .381 African American .465 .271 .075 1.719 .086 Other .989 .503 .086 1.967 .050 *Significant at the p= <.05 level. Table 41 5 Linear Regression Analyses of Age and Ethnicity with Health Belief Model Constructs for Physical Activity Variable Unstandardized Beta SE Standardized Beta t Sig Physical Activity Perceived Risk Age 18 20 .422 .978 .089 .431 .667 Age 21 23 .231 .985 .048 .235 .814 Age 24 26 .622 1.158 .042 .538 .591 Asian .088 .405 .009 .217 .828 Hispanic .016 .287 .002 .056 .955 African American .175 .303 .025 .577 .564 Other 1.622 .591 .120 2.743 .006 Perceived Benefits Age 18 20 .549 .595 .191 .924 .356 Age 21 23 .350 .598 .120 .585 .559 Age 24 26 .369 .703 .041 .524 .600 Asian .499 .246 .088 2.025 .043

PAGE 117

117 Table 415. Continued Variable Unstandardized Beta SE Standardized Beta t Sig Hispanic .007 .173 .002 .039 .969 African American .465 .183 .111 2.539 .011 Other .043 .349 .005 .123 .902 Perceived Barriers Age 18 20 1.202 .741 .335 1.622 .105 Age 21 23 1.506 .745 .414 2.021 .044 Age 24 26 1.090 .877 .097 1.243 .214 Asian .433 .311 .060 1.392 .164 Hispanic 8.875E 5 .216 .000 .000 1.000 African American .058 .231 .011 .250 .803 Other 1.188 .435 .119 2.727 .007 Cues to Action Age 18 20 .711 .798 .184 .891 .373 Age 21 23 .878 .802 .224 1.094 .274 Age 24 26 .119 .944 .010 .126 .900 Asian .376 .331 .049 1.137 .256 Hispanic .316 .233 .059 1.356 .176 African American .526 .249 .092 2.114 .035 Other .760 .469 .071 1.621 .106 Self Efficacy Age 18 20 .547 .825 .137 .663 .508 Age 21 23 .737 .830 .182 .888 .375 Age 24 26 .325 .976 .026 .333 .739 Asian .734 .342 .093 2.147 .032 Hispanic .168 .240 .030 .698 .485 African American .629 .254 .108 2.473 .014 Other .649 .485 .058 1.338 .182 *Significant at the p= <.05 level. Table 416. Linear Regression Analyses of Age and Ethnicity with Health Belief Model Constructs for Smoking Variable Unstandardized Beta SE Standardized Beta t Sig Smoking Perceived Risk Age 18 20 .750 1.999 .206 .375 .708 Age 21 23 .176 2.034 .047 .087 .931 Age 24 26 .056 2.169 .006 .026 .979 Asian .299 .716 .040 .417 .678 Hispanic .624 .430 .143 1.450 .150

PAGE 118

118 Table 41 6 Continued Variable Unstandardized Beta SE Standardized Beta t Sig African American 1.055 .583 .182 1.810 .073 Other 1.019 .939 .118 1.085 .280 Perceived Benefits Age 18 20 1.500 2.659 .319 .564 .574 Age 21 23 1.079 2.706 .224 .399 .691 Age 24 26 1.641 2.886 .146 .569 .571 Asian .850 .953 .089 .892 .375 Hispanic .555 .574 .098 .967 .336 African American .275 .737 .039 .373 .710 Other .447 1.252 .040 .357 .722 Perceived Barriers Age 18 20 3.250 2.638 .671 1.232 .221 Age 21 23 2.934 2.685 .591 1.093 .277 Age 24 26 2.442 2.863 .212 .853 .396 Asian .393 .946 .040 .416 .678 Hispanic .544 .577 .092 .944 .347 African American 1.591 .726 .221 2.190 .031 Other .533 1.240 .046 .430 .668 Cues to Action Age 18 20 4.500 2.789 .899 1.614 .110 Age 21 23 4.050 2.839 .794 1.427 .157 Age 24 26 6.147 3.028 .519 2.030 .045 Asian .705 1.000 .070 .705 .482 Hispanic .096 .621 .016 .154 .878 African American .653 .794 .085 .822 .413 Other .966 1.314 .082 .735 .464 Self Efficacy Quit Assist Program Age 18 20 .667 1.087 .348 .613 .541 Age 21 23 .615 1.105 .314 .556 .579 Age 24 26 .871 1.174 .193 .742 .460 Asian .176 .377 .046 .466 .642 Hispanic .236 .238 .098 .992 .324 African American .559 .291 .198 1.921 .058 Other .682 .565 .136 1.206 .230 No Assist Program Age 18 20 .667 1.107 .352 .602 .548 Age 21 23 .466 1.126 .242 .414 .680

PAGE 119

119 Table 41 6 Continued Variable Unstandardized Beta SE Standardized Beta t Sig Age 24 26 .246 1.196 .055 .206 .837 Asian .237 .384 .062 .618 .538 Hispanic .229 .238 .098 .960 .339 African American .123 .296 .044 .416 .679 Other .399 .575 .080 .694 .489 *Significant at the p= <.05 level. Table 41 7 Linear Regression Analyses of Age and Ethnicity with Health Belief Model Constructs for Doctor Visit and Clinical Risk Assessment Behavior Variable Unstandardized Beta SE Standardized Beta t Sig Doctor Visits/Clinical Risk Perceived Risk Age 18 20 .696 .805 .180 .865 .387 Age 21 23 .706 .809 .180 .873 .383 Age 24 26 1.599 .951 .133 1.680 .093 Asian .264 .338 .035 .783 .434 Hispanic .219 .237 .041 .924 .356 African American .157 .254 .027 .618 .537 Other .590 .486 .054 1.214 .225 Perceived Benefits Age 18 20 .334 .425 .164 .787 .432 Age 21 23 .357 .428 .173 .835 .404 Age 24 26 .237 .502 .037 .471 .638 Asian .038 .176 .010 .218 .828 Hispanic .236 .125 .084 1.888 .060 African American .175 .134 .058 1.310 .191 Other .099 .264 .017 .375 .708 Perceived Barriers Cost of Appointment/Tests Age 18 20 .393 .362 .224 1.086 .278 Age 21 23 .175 .364 .098 .481 .631 Age 24 26 .476 .428 .087 1.112 .267 Asian .300 .150 .087 2.001 .046 Hispanic .128 .106 .053 1.208 .228 African American .007 .113 .003 .061 .951 Other .229 .219 .046 1.048 .295 Inconvenient Age 18 20 .746 .315 .486 2.363 .018 Age 21 23 .629 .317 .405 1.982 .048

PAGE 120

120 Table 41 7 Continued Variable Unstandardized Beta SE Standardized Beta t Sig Age 24 26 .798 .377 .161 2.115 035 Asian .011 .131 .004 .084 .933 Hispanic .019 .092 .009 .212 .832 African American .317 .099 .139 3.186 .002 Other .086 .190 .020 .452 .652 May Learn I am not Well Age 18 20 .324 .315 .212 1.029 .304 Age 21 23 .328 .317 .211 1.034 .302 Age 24 26 .778 .373 .162 2.086 .037 Asian .215 .130 .071 1.644 .101 Hispanic .065 .092 .031 .709 .479 African American .286 .099 .126 2.880 .004 Other .017 .190 .004 .092 .927 Cues to Action Age 18 20 .084 .482 .036 .175 .861 Age 21 23 .127 .484 .054 .262 .793 Age 24 26 .770 .570 .106 1.352 .177 Asian .005 .202 .001 .026 .979 Hispanic .226 .141 .071 1.607 .109 African American .035 .151 .010 .234 .815 Other .129 .299 .019 .433 .665 Self Efficacy Age 18 20 .655 .693 .196 .945 .345 Age 21 23 .632 .697 .187 .907 .365 Age 24 26 .852 .820 .082 1.040 .299 Asian .027 .287 .004 .093 .926 Hispanic .155 .201 .034 .770 .442 African American .426 .221 .085 1.929 .054 Other 1.194 .431 .122 2.774 .006 *Significant at the p= <.05 level.

PAGE 121

121 CHAPTER 5 DISCUSSION This study used a cross sectional webbased survey design to determine perception of heart disease risk among the female undergraduate student population and determined the frequency in which students engage in negative behavior relative to heart disease risk. The investigator also examined the relationship of age, ethnicity, and perception to specified behaviors. Lastly, the study investigated the influence of age and ethnicity relative to Health Belief Model constructs on assessed healthy heart behavior. Findings from this study disclosed behaviors students engage in that place them at risk for heart disease, and revealed whether age, ethnicity, and/or perception influenced said behavior. This study also revealed whether the variables of age and ethnicity related to constructs of the Health Belief Model in relation to behavior. As yet, few studies have investigated the three explanatory variables related to behavior and this study appears to be the first to addr ess the influence of age and ethnicity on Health Belief Model constructs related to heart disease risk behaviors among a simple random sample of female college students. This study included two pilot studies and the final administration of a webbased surv ey. The pilot studies were used to test whether further study assessing health risk behavior was needed among this population. Pilot test responses were used to refine and improve the survey instrument for use in the final implementation. The following res earch questions were formulated for use in this study: 1. Does the university female student population perceive themselves to be at risk for heart disease? Does age, ethnicity, or perception impact ones behavior?

PAGE 122

122 2. Does age and/or ethnicity impact Health Belief Model constructs related to each behavioral risk factor? Data analysis was guided by the research questions. For the first research question, frequency and descriptive analysis was performed to determine whether the student population perceived themselves at risk for heart disease. Also, multivariate linear regression analysis was used to test for a difference in age, ethnicity, or perception related to heart disease risk behavior. Multivariate linear regression was also used to analyze the influence and relationship of age and/or ethnicity on Health Belief Model constructs related to heart disease risk behavior. The study used a simple random sample from a large southeastern university and the study was implemented during the spring semester o f 2010. Students were asked to complete a webbased survey and received a gator lapel pin prior to the request as an incentive for completion. A total of 598 randomly selected female undergraduate students from the female population at the university compl eted the survey. Following inclusion criteria implementation for age (1829 years ), 587 usable surveys remained. Over half the sample indicated they were between the ages of 1820 and approximately 61.0% of the sample reported they were white. The sample ethnic diversity is considered a close representation of ethnicity at the sampled institution (Table 36) The main purpose of C hapter 5 is to present the results for the current study. Results from current student behavior assessment and research question findings are included. C hapter 5 also presents strengths and limitations of the study and provides future research recommendations and s tudy conclusions.

PAGE 123

123 Current Student Behavior and Family History It was hypothesized that female university students engage in behaviors that place them at risk for heart disease, practicing behaviors such as smoking, physical inactivity, and poor nutrition/diet. Behavioral risk factors were assessed to better report overall risk for heart disease among the female college student population. Nutrition Behavior One of the first steps toward a heart healthy lifestyle is a healthy diet. A persons risk for hear t disease is reduced if proper nutrition/diet is practiced (Haberman & Luffy, 1998; Haskell, 2003; Toft et al., 2006). Several aspects of ones diet have an effect on heart disease development; therefore, individuals need to improve their total diet or mai ntain a healthy diet to reduce their risk for heart disease (Lichtenstein et al., 2006). This study determined the frequency that students consumed specific food items on average per week. The survey was unique in that it included nutritional items direct ly associated with an increased risk for heart disease when not consumed according to recommended guidelines These data provide a more precise description of nutritional behavior among collegeage females and are useful to health practitioners who deliver dietary interventions. Eating a healthy diet should begin when individuals are young, as habits established prior to and during the college years may continue into full adulthood. Failure to engage in and maintain healthy nutrition is likely to negativel y affect the overall health of the individual (Brown et al., 2005; Ha & CaineBish, 2009). Nutrition behavior was assessed to acquire greater knowledge of dietary behavior among the population of interest. Nutrition behavior was measured by asking participants specific questions related to dietary behavior. The items were based on an extensive literature

PAGE 124

124 review indicating that heart disease among women can be predicted through an assessment of dietary behavior (Akesson et al., 2007; Fung et al., 2001; Hu et al., 2000). The assessed behavior was associated with increased risk of heart disease and nutrition guidelines for a healthy diet (AHA, 2010). Findings from this study indicated that students do not consume the proper amounts of fruit/vegetables and engage in poor diets including regular consumption of high fat foods. A large percentage (69.1%) of students reported they consumed less than the daily recommended amount of fruits/vegetables. This finding was consistent with findings from previous research (H a & Caine Bish, 2009; Huang et al., 2003). This study also provided support for previous studies reporting that a portion of college students include an elevated level of fatty foods in their diet (Brevard & Ricketts, 1996). Approximately 27% reported they consumed fast food two or more times per week, and 10.4% reported they ate mostly high fat foods. These seemingly small percentages in no way belittle the fact that students are consuming more than the recommended amounts of fat in their daily diet. Risk for heart disease increases when the intake of fat is coupled with the high percentage of sampled students not eating fruits and vegetables. Unhealthy dietary behavior was also noted in areas of reported consumption of small amounts of leafy greens and limited to zero consumption of fish per week both nutritional items necessary for heart disease prevention (AHA, 2010). It is encouraging that students reported they consumed the recommended amount of grains daily (72.5%, n=417). This finding is inconsistent with literature that reports students do not consume proper amounts of dietary fiber in the form of whole grains (Huang et al., 2003).

PAGE 125

125 Collectively, the findings on the frequency analysis of student nutritional behavior suggest that female students do not eat the recommended amount of heart healthy food items ( USHHS, 2007) placing them at increased risk for heart disease. The findings reinforce information stating that college students may not be fully aware of the benefits related to fruit and vegetabl e consumption and a healthy diet (Brown et al., 2005; Ha & Caine Bish, 2009). Therefore, nutrition intervention efforts on college campuses should focus on promoting awareness regarding the benefits of proper consumption of heart healthy food items such as fish, fruits, and vegetables. Healthy food items are available through campus dining and some restaurants on campus provide healthy menu options. Consequently, college health practitioners should find innovative ways to promote beneficial nutrition and st ress the importance of selecting healthy food items and consuming a healthy diet for the prevention of heart disease. Physical Activity Behavior Physical inactivity is an independent risk factor for heart disease development (Tsang et al, 2000; Wel ler & Corey, 1998) and is the most common risk factor for heart disease in women (Bedinghaus et al., 2001). Physical activity is effective in the primary prevention of heart disease (Mirotznik et al., 1995), but benefits will only be experienced if individuals engage in regular physical activity (Mirotznik et al., 1995). The transitional time of beginning college appears to have a negative impact on physical activity participation among the female student population (Jung et al., 2008; Kilpatrick et al., 2005), and research indicates that physical inactivity is widespread among the university student population (Corbin, 2002; Keating et al., 2005). To increase physical activity levels among college students, researchers need to understand participation patterns r elated to physical activity (Behrens & Dinger, 2003).

PAGE 126

126 To aid in the endeavor toward understanding participation patterns, physical activity was assessed by asking students to report how many days per week they engaged in moderateto vigorous physical acti vity for at least 30 minutes. Specific physical activity b ehavior s, such as walking to class were not included in the physical activity definition. The time/intensity of the exercise used was based upon American Heart Association guidelines for physical activity (Haskell et al., 2007; Schroetter & Peck, 2008). Physical activity frequency assessment in this study served to reinforce previous studies indicating that female students fall short of recommended activity guidelines (Suminski et al., 2002). Over 40% of the sample reported they exercised two days per week or less, and a disturbing 12.0% stated they did not exercise at all (zero days per week). The current studys percentage of littleto no exercise is less than that of previous studies which found t hat approximately 6166% of students were not achieving the recommended amounts of physical activity (Douglas et al., 1997 as cited in Bray & Born, 2004). Previous studies assessed both male and female physical activity behavior as a whole; therefore, this current research may be comparable if previous analyses had been performed using female reported behavior only. Another plausible explanation for the contradiction of previous findings could be the influence of the college environment somewhat unique to t he university sampled. The student population at the University of Florida ( used for sa mpling for this study ) is considered a very active community and therefore students may have reported what was expected rather than their actual amount of physical activ ity due to the influence of the perception of an active university community.

PAGE 127

127 The lack of reported physical activity among the sample in this study places students in the category of increased risk for heart disease (Sullivan et al., 2008). Physical act ivity is an imperative part of prevention of heart disease (Simontacchi & FitzGerald, 2004; Mirotznik et al., 1995). Therefore, intervention efforts on college campuses should focus on increasing the number of students adhering to recommended guidelines. The university setting is a prime location to implement physical activity promotion efforts (Keating et al., 2005). Consequently, college health practitioners should continue to find innovative strategies to help students implement physical activity into th eir daily and weekly schedule and promote the many benefits activity provides for ones heart health. Promoting increased physical activity may help students establish patterns that will continue into adulthood and, in so doing, combat heart disease development. Smoking Behavior Smoking is an established risk factor for heart disease development (Bowman et al., 2007), but it is also the most manageable ( Kuehn et al., 1999). Smoking significantly affects a persons health (Gemmell & DiClemente, 2009). A wom an who smokes increases her risk of heart disease up to six times that of a nonsmoking female (Kawachi et al., 1997; Tsang et al., 2000). Exposure to secondhand smoke also increases a womans risk for heart disease. A stated goal of Healthy Campus 2010 was to reduce diseases related to tobacco use among adults ages 18 and older and reduce exposure to secondhand smoke (ACHA, 2002). This is also a goal of Healthy People 2020 to reduc e the initiation of tobacco use among young adults aged 1825 years ( USHHS, 2009)

PAGE 128

128 S moking was defined as inhalation of tobacco, most often in the form of cigarettes. Questions related to current smoking behavior were based on previous studies and an extensive review of literature. Previous studies indicated that approximately 33.0% of co llege students smoked tobacco within the previous thirty days of the study (Lenz, 2004). Studies also reported that smoking among young adult women had reached epidemic proportions (Gaffney et al., 2002, pg. 506). Findings from the current study were in st ark disagreement with previous work looking at smoking among both the young adult female and undergraduate populations. A large percentage (81.5%) of the sample reported they had never smoked a cigarette and 94.6% reported they had not smoked during the pr evious 30 days. The investigator hypothesizes that current college students are members of a generation who, when compared to previous generations, are more aware of the many health consequences related to smoking. Because of this, health professionals/educators may see a continual decline in the number of student smokers on college campuses. Florida state policy prohibits smoking indoors and the University of Florida recently instituted a campus nosmoking policy Both Floridas Clean Indoor Air Act (FCIAA) and the campus nosmoking policy may have impacted smoking behavior among the sample of students surveyed ( Florida Department of Health [FDH], 2010a) A large number of students at the University of Florida are Florida natives and have therefore participated in the Students Work Against Tobacco (SWAT) program. According to the Florida Department of Health, SWAT is a statewide organization used to educate and encourage school aged children to work against tobacco and thereby promote a non-

PAGE 129

129 smoking future for the children ( 2010b) Students who have participated in SWAT may be less likely to engage in smoking behavior and therefore smoking status among university female students in Florida may be impacted. However, it can also be hypothesized that this sample is not the nor m for smoking behavior and results should not be generalized to the greater population pending further research with a more diverse sample. College students should be a targeted population for smoking prevention/cessation interventions (Snyder et al., 2009 as cited in Sanem et al., 2009). Continued programs related to smoking cessation are needed to facilitate the decline of this practice. Cigarette smoking on college campuses may be on the decline, but an alternative form of smoking is on the rise and poses a serious risk to college females heart health. Water pipe tobacco smoking (hookah) is increasing in the United States and is becoming more popular among college students (Cobb et al., 2010). For the purpose of this study hookah smoking was defined as a pipe used to smoke tobacco; tobacco smoke is passed through water prior to inhalation. Findings from the hookah portion of the analysis served to reinforce results from previous studies that indicated the practice of smoking hookah was becoming more common among the collegeage population (Cobb et al., 2010; Primack et al., 2008). Almost half of the current sample reported they had visited a hookah bar and/or smoked hookah. Hookah bars are seen as social ly facilitating environments ; therefore, students may be frequenting the establishments in order to enjoy the social aspect of the hookah bar in addition to actually smoking hookah. Hookah may be perceived as sophisticated and socially accepted; therefore, hookah norms are reversed from cigarette norms. It is

PAGE 130

130 hypothesized that social facilitation may have impacted increased hookah related behavior. Recent literature also reports that students believe hookah to be less harmful than smoking cigarettes (Primack et al., 2008). Students who reported they had never smoked (as defined by the investigator) also reported that they had in fact visited a hookah bar and/or smoked hookah. The investigator speculates that the sample of students believed that smoking cigarettes is more harmful to ones health when compared with hookah, a hypothesis that is supported by previous study results ( Primack et al., 2008) The lack of awareness regarding the risk that hookah places on health is cause for great concern. Research efforts should target this critical population to better determine the source of the misperception that hookah smoking is not as detrimental to personal health as cigarette smoking. Suitable programs that appeal to those students frequenting hookah bars and smoking hookah need to be implemented to promote awareness concerning the health dangers related to smoking hookah. Health educators should use qualitative techniques of study such as focus groups to determine what promotes visiting hookah bars/smoking hookah. Results should be used to better plan and implement programs aimed at reducing hookah smoking behavior Health educators need to identify strategies that will be effective in reducing the number of students engaging in this practice that poses a heart health risk to the individual. Doctor Visit and Clinical Risk Assessment Behavior The presence of diabetes, elevated blood pressure, and/or elevated total cholesterol serves to increase an individuals risk for heart disease development. Diabetes is listed as a major risk factor for heart disease (USHHS, 2007). Individuals

PAGE 131

131 with diabetes are at a two to four time greater risk of dying from heart disease than individuals without diabetes (Haffner et al., 1998 as cited in Balkau et al., 2007). Promotion of diabetes assessment and behavior change was a goal of Healthy People 2010 and is a current goal of Healthy People 2020 (USHHS, Healthy People 2010; USHHS, 2009). An increase in blood pressure, even if it appears to be insignificant, increases risk for heart disease ( USHHS, 2007). High blood pressure is a significant risk factor for early heart disease development and is widespread among women (Evangelista & McLaughlin, 2009; McCauley, 2007). High cholesterol early in life is directly related to increased heart disease risk (Berenson et al., 1998; Spencer, 2002); to control high cholesterol and reduce its effect on overall heart health, individuals must have their cholesterol checked and understand how to interpret their cholesterol measurements. Questions related to diabetes, high blood pressure, and cholesterol checks were asked to assess clinical risk factors related to heart disease development. Failure to obtain routine checks on clinical measures such as glucose, blood pressure, and cholesterol, contributes to risk of heart disease; individuals age 20 and older are encouraged to have routine checks (Cleeman & Grundy, 1997). Results of this study indicate these s tudents are not adhering to recommended guidelines for health assessments. A small percentage indicated they had been told that they have diabetes, yet alarmingly the majority of the sample report ed never having their blood glucose levels measured. High blood pressure was present in a small percentage (5.1%) of the sample. The investigator hypothesized that the percent of high blood pressure reported

PAGE 132

132 in this study may not be a true representation of high blood pressure present in the sample. This hypothesis was formulated based on the consideration that students may not have had their blood pressure measured recently. Therefore, it is thought that students responding to the survey may not have known whether or not they have high blood pressure due t o lack of clinical assessment. L iterature stresses the importance of ones awareness of their cholesterol numbers (USHHS, 2007). However, approximately one out of every three students surveyed in this study had never had their cholesterol checked; and, of those who indicated they had their cholesterol tested, many did not know their cholesterol measures. Ignorance of clinical risk may be related to student medical visits and reasons why they visit or do not visit their doctor. A large portion (66.0%) of the current sample reported they had a primary care physician, and they had visited their doctor within the past year (61.8%). It is interesting to note that although a large portion of the sample reported having a current physician and had visited their doc tor recently, they also reported that cholesterol or glucose had not been measured. The investigator also hypothesized that students, when visiting their doctor, do not ask the right questions or do not request specific heart healthy clinical tests. Effort s promoting regular doctor visits need to be implemented to promote prevention among this population. Educational interventions are needed that encourage individuals to have their glucose, blood pressure, and cholesterol checked. Interventions must stress that although being tested is a step toward improved health, knowing the results of the analysis, interpreting them correctly, and directing the interpretation toward better behavior is of most importance.

PAGE 133

133 Being proactive and asking for information relativ e to the results of the clinical assessments may be another area of educational intervention. Glucose, blood pressure, and cholesterol must be measured and understood for interventions encouraging students to begin heart disease prevention efforts related to clinical risk to be effective. All three clinical risk factors can be controlled, possibly even prevented, if behaviors such as physical activity and improved eating habits are adopted (Cleeman & Grundy, 1997). The American College Health Association reported on the importance of increased efforts on college campuses focused on increasing the number of students having their cholesterol and blood pressure checked (2002). According to the results of this study, efforts among this population need to increas e. Health practitioners and health educators should promote awareness and behavior change related to clinical assessment. Family History Family history is a longestablished risk factor for heart disease ( Barrett Connor & Khaw, 1984; Juonala et al., 2006). The presence of heart disease in at least one parent increases heart disease risk for both men and women (LloydJones, 2004). Students were asked to report whether or not their biological mother, before the age 65, and whether or not their biological father, before the age 55, had some form of heart disease. The following examples of heart disease were provided on the survey: heart attack, poor blood flow, high cholesterol, high blood pressure, irregular heartbeat, or heart failure. Family history of hear t disease was assessed for the reason that, if ignored, risk for heart disease is not estimated correctly (Crouch & Gramling, 2005). Positive family history was noted in a large number of individuals. O f those reporting only one parent

PAGE 134

134 with a history of heart disease, a small portion judged themselves to be very likely at risk for heart disease development. Further descriptive/frequency analyses resulted in the finding that 64 respondents had both parents with a positive family history of heart disease, placing them at even higher risk for heart disease development (Hawe et al., 2003; Jousilahti et al., 1996; Leander et al., 2001; Thompson et al., 2010). The students that reported both parents having some form of heart disease believed themselves to be somewhat likely more at risk for heart disease development. An interesting finding from the study is the percentage of students who reported they were not likely to develop heart disease based upon their family history. Approximately 8.0% reported they were not at all likely to develop heart disease despite the fact that they reported having family history of heart disease in both biological parents. This finding suggests that students are unaware of the direct link and increased risk that a positive family history has with heart disease development. Although the majority of the students sampled were able to report whether or not their mother and/or father had some form of heart disease, there appears to be a lack of knowledge regarding the direct relationship between family history of heart disease and personal risk. Educators and practitioners on college cam puses must work to find methods that enhance awareness related to increased risk when a positive family history is present. Previous research reported that individuals who had knowledge of their positive family history were motivated to engage in behaviors to reduce their risk (Crouch & Gramling, 2005). Hence, increased information efforts coupled with behavior change strategies to better equip students in their fight against heart disease need to be planned and implemented.

PAGE 135

135 Behavioral Risk Summary Overall findings from this study support the hypothesis that university students engage in behaviors that may lead to heart disease such as smoking, lack of physical activity, and poor nutrition and diet. Although this sample should not be considered cigarette sm okers, they do smoke hookah, which poses a great threat to the heart health of the individual. Student behavioral identity is shaped in college (Desai et al., 2008; Racette et al., 2005); behavior contributes to heart disease risk, and behavior performed as a young adult may continue into adulthood (McMahan et al., 2007). College is a time when students become more accountable for their behavioral choices, choices that could negatively affect their health (Lenz, 2004). Therefore, college is a prime opportunity for development and implementation of educational interventions regarding behavior that promotes increased overall health and reduces risk for heart disease development. Assessment of behavior exhibited among college students is necessary when evaluat ing heart disease risk among this population. The American College Health Association reported that the nations health can be improved if health promotion efforts are implemented and behavior change is sustained among the college population (2002). Under standing trends of behavior among university students and increasing researcher knowledge regarding said behavior will aid in the development of programs that may improve the overall health of the college population, and in turn the health of the nation.

PAGE 136

136 Research Question Discussion Research Question One Part One Research question one part one states, Does the university female student population perceive themselves to be at risk for heart disease? Students are most concerned about health risks that currently present themselves; heart disease is perceived as less of a risk to their health because it presents itself in the future (Collins et al., 2004). College students do not have correct risk perception regarding heart disease and their individual r isk (Collins et al., 2004; Green et al., 2003; Schroetter & Peck, 2008; Vanhecke et al., 2006; Wendt, 2005) ; young adults do not perceive risk (Wendt, 2005). Perception of risk was assessed using a dichotomous response variable asking the respondents whet her they perceived themselves to be high risk or low risk for heart disease development. Frequency analysis on the item was performed to determine if students perceived themselves to be at risk for heart disease. It was hypothesized that accurate perception of risk would not be present in the current sample of university students, a hypothesis supported by the findings from the frequency analysis on the perception question item. Although the majority of the sample believed themselves to be low risk for hear t disease, they engaged in behavior that placed them at an increased risk. Findings from this study indicated that students engaged in poor diets of little fruit and vegetable consumption and over consumption of fast food. Students also reported an alarmin gly low frequency of physical activity and indicated they frequented hookah bars and smoked hookah. Also, positive family history of heart disease was noted in a large number of students in the sample.

PAGE 137

137 The investigator also hypothesized that if the misperception of risk is corrected among this population, behavioral changes are more likely to be established to reduce risk. This hypothesis was not tested in this study, but is of interest to the investigator. This study reported that students do not perceiv e themselves to be at risk for heart disease; still, further research is needed to determine the source of this misperception. Research such as this is important in view of the high volume of students believing themselves to be at low risk for heart diseas e. A continued study of behavioral risk factors and determination of why students believe themselves to be at low risk for heart disease will aid health professionals in the development of successful interventions promoting behavior change. Implementation of programs addressing the misperception of risk and encouraging students to act on the improved perception are necessary if health researchers wish to aid in the reduction of heart disease risk among this population (Green et al., 2003). Research Question One Part Two Research question part two states, Does age, ethnicity, or perception impact ones behavior? The three explanatory variables of age, ethnicity, and perception assessed in this research study were chosen based on empirical support demonstrating an association with behavior among females and the collegeaged population. The variables were also selected in part due to the investigators hypothesis of their relationship with heart health risk behavior. Multivariate linear regr ession analysis was used to answer the research question. It was hypothesized that regression analysis would indicate significant differences when compared to the independent variables.

PAGE 138

138 Age A limited number of studies provided evidence linking age to heart disease risk behavior. However, research provided evidence linking the transitional time of entering college, traditionally a time when students are between the ages of 1820, to poor nutrition/diet (Lau et al., 1990). Previous research also linked inc reasing age with a deterioration of eating habits while in college (Lau et al., 1990). Research provides documented evidence of an inverse relationship between physical activity and age. Modest research has been performed on age related to smoking, doctor visit behavior, and clinical assessment behavior. Although empirical findings have listed age as a possible determinant of poor nutrition behavior and physical activity, the results from this study indicated no difference in age related to any of the food consumption items or physical activity behavior. Also, findings indicated that age was not a significant factor in doctor visit behavior or clinical measurement behavior. The empirical support linking age to poor behavior related to heart disease comes pr edominantly from studies including both male and female students. Thus one explanation for a lack of difference in age related to behavior in this study is the narrowing of focus to only female participants. Also, a limited amount of research linked age to heart disease behavior; therefore, the finding of nonsignificance promotes the need for further study to support or refute this result. Conversely, a difference in age was noted related to variables of smoking behavior, hookah behavior, time since last c heck up, and time since last doctor visit. Students in the 1820 and 2123 age groups were less likely to engage in smoking behavior but students in the 2123 age group were more likely to engage in smoking hookah behavior. Also, individuals in the age group 2426 allowed more time to pass

PAGE 139

139 between checkups, and all other age groups, allowed longer amounts of time to pass before visiting their doctor. These findings indicate that age may factor into smoking, hookah, and doctor visit behavior; therefore, research is needed to better determine why specific age groups are more prone to certain behavior and less prone to engage in other behavior. In learning this, prevention efforts can better address age groups at risk for heart disease and provide specific strategies targeted to those individuals. Ethnicity Ethnicity had the strongest empirical support among the three explanatory variables r elating ethnicity to behavior associated with heart disease. Studies provide strong evidence linking ethnicity to hear t disease risk behaviors. Ethnic minority women possess more risk factors for heart disease than white women (Winkleby et al., 1998). Research indicates that minority populations exhibit both clinical and lifestyle risk factors for heart di sease (Winkleby et al., 1998). Research indicated that African Americans and Mexican Americans had higher body mass indexes, higher blood pressure, engaged in less physical activity, and had greater prevalence of Type II diabetes when compared to whites (Winkleby et al., 1998). Findings from the multivariate linear regression analysis indicated a difference in ethnicity related to African American consumption of fast food, leafy green vegetables, fish consumption, and grain consumption when compared to whites. African Americans were more likely to consume fast food and fish, but less likely to cons ume leafy greens and grains. Also, Asians and African Americans were less likely to engage in physical activity. Due to strong empirical support for the influence of ethnicity on nutrition and physical activity, these findings were not unexpected. The findings from this study reinforce the fact that nutrition and physical activity promotion efforts need to be

PAGE 140

140 implemented targeting minority populations within the college population. No difference was found related to fruit/vegetable consumption or types of food most often eaten. This finding is somewhat bewildering due to literature linking ones ethnic ity with poor eating habits. Because of the contradiction to previous research, further investigation is recommended. Asians and African Americans were less l ikely to engage in smoking related behavior compared to whites. African Americans were also less likel y to engage in hookah smoking. This finding is contrary to previous research linking African Americans to increased smoking activity (Ferdinand, 2006). A plausible explanation for this contradiction is that the majority of the sample indicated they did not smoke; therefore, there may not have been enough variability in the survey item response to make an accurate evaluation. Research utilizing a multi colle ge population is needed to moreeffectively investigate the relationship between ethnicity and smoking. Ethnicity was not related to time since last check up or how often one gets checkups but was related to time since last doctor visit and having a primary doctor. It is interesting to note that no difference in ethnicity was found related to clinical risk behavior for heart disease. This too is in contradiction to previous findings. Further investigation is needed to corroborate the finding of a disassoci ation between ethnicity and clinical risk factors of high blood pressure and diabetes. One plausible explanation is that previous studies have incorporated samples of minority women of all ages, not just the young adult population. Further research, using only young adult females, will aid in supporting or refuting this finding.

PAGE 141

141 Studies indicate that ethnic disparities in heart disease are a result of environmental factors, lifestyle behavior, and lack of health care availability (Cooper et al., 2000). Risk factor differences among ethnic populations and origins of those dissimilarities need to be understood to develop programs that are culturally sensitive (Kurian et al., 2007). Findings confirm that ethnicity is related to behaviors associated with heart disease, and programs addressing positive lifestyle behavior need to be implemented among minority populations on college campuses. Perception Little research has been performed regarding heart disease risk perception among the college age population (Gree n et al., 2003). Information regarding perception of risk, as exhibited by college students, is not abundant. Still, available research provides evidence that college students perceive their risk for heart disease to be low (Wendt, 2005). Misperceptions regarding heart disease are common among the university female student population, creating a lack of concern toward heart disease development and in turn leading to negative health behaviors. Perception was not significantly related to fish, grain, or fr uit and vegetable consumption. Also, no difference was found in perception related to foods most often eaten. Regression findings indicated an inverse relationship in perception related to consumption of fast food and leafy green vegetables. This finding w as in direct contrast to previous research. The majority of the sample perceived themselves to be at low risk for heart disease development. The low risk perception finding may have biased the results of the regression analysis; therefore, further study is needed to validate these findings contradicting former research. Findings from the regression analysis also indicate that perception of risk was not significantly related to physical activity behavior,

PAGE 142

142 smoking, or hookah behavior. No difference in percept ion was found related to doctor visit behavior or clinical risk behavior. The empirical support linking perception of risk to heart disease risk behavior is limited, yet theoretical support is strong. The Health Belief Model is built upon constructs direc tly related to perception (risk, barriers, and benefits) influencing human behavior. Because strong theoretical support exists regarding perception and behavior, the lack of significance relating perception of risk to behavior in this study is bewildering. A possible explanation for this finding is that the majority of the students believed their risk for heart disease to be low limiting overall variance ; and, therefore, perception of risk did not play a role in their behavioral choices. Another explanation is that because of their youth and overall perceived good health status, they believe themselves to be invincible. Students who perceive themselves invincible may engage in behaviors that place them at a greater risk for heart disease. Therefore, further investigation is warranted regarding the concept of invincibility in relation to perception of heart disease risk. Summary The significance found in research question one indicates that ethnicity can be linked with some behaviors related to heart disease risk. Knowing what most influences said behavior can lead to programs that target variables that most explain behavior and populations that are most at risk for engaging in risk behavior. Overall, the findings from the analysis solidify the need for further and broader study assessing age, ethnicity, and perception. Further testing is needed to expand information garnered from this particular study prior to making any definitive conclusions

PAGE 143

143 Research Question Two Research question two states, Does age and /or ethnicity impact Health Belief Model constructs related to each behavioral risk factor? The two explanatory variables of age and ethnicity were assessed in their relationship to Health Belief Model constructs of perceived risk, perceived benefits, per ceived barriers, cues to action, and self efficacy. Multivariate linear regression analysis was used to answer the research question. It was hypothesized that regression analysis would indicate significant differences related to the variables of ethnicity and age. The Health Belief Model has been used in research to explain or analyze prevention, illness, and behavior (Becker et al., 1977; Mirotznik, Feldman, & Stein, 1995). According to the Health Belief Model, motivation to change ones behavior stems from the individuals perception of disease risk. Healthpromoting behavior participation may be related to perception of both benefits and barriers to positive behavior maintenance (Thanavaro et al., 2006). Young age may be related to overall perception. Re search indicates that individuals between the ages of 25 and 34 years do not perceive heart disease to be their greatest health threat (Mosca et al., 2000). Misperception of risk is present in young adults, and without an accurate perception of risk, preve ntive measures will not be successful (McMahan et al., 2007). Research using the Health Belief Model to assess cause for behavior among the university population is scant. Therefore, one of the purposes for this research question was to provide a theoretic al basis for behavior and fill a gap in the literature regarding heart disease risk behavior and the college population.

PAGE 144

144 Health Belief Model and Nutrition Findings from the multivariate linear regression analysis indicated no difference in age for any of the Health Belief Model constructs save one of the individual cue to action items. Age was related to the perceived cue to action of taking a nutrition course. A possible explanation for this finding is that perhaps the age group of 2426 years who wer e more likely to perceive taking a class as a cue to nutrition, had recently completed a course on nutrition. Also, this finding stresses the importance that health educators utilize their time spent with students to promote a healthy diet. A plausible exp lanation for lack of significance of age related to the remainder of the Health Belief Model constructs is survey error. It is hypothesized that the items used to assess construct variables were not fully applicable to the young adult population. Further r esearch should incorporate qualitative techniques prior to survey development to ensure relevance to the target population. Little to no research has been performed on the relationship of ethnicity to Health Belief Model constructs for heart health risk behaviors. Findings from this study indicated that ethnicity was related to perceived benefits and cues to action items for nutrition. African Americans were more likely to perceive benefits toward nutrition and perceived friends eating healthy and wanti ng or needing to lose weight as a cue to a healthy diet. Also, Asians and the other ethnic group perceived wanting or needing to lose weight as a cue to a healthy diet. Based on the findings from this study, health professionals need to incorporate both benefits and cues to action when developing nutrition interventions for use among the college population. No difference was found in ethnicity related to the remaining constructs of the Health Belief Model.

PAGE 145

145 T hese findings support the need for further research regarding ethnicity and reasons why individuals chose to engage in or not to engage in a healthy diet. Ethnicity appears to value perception of benefits and specific cues toward nutrition behavior. Future res earch should further investigate items related to benefits and cues among ethnic populations in college to better understand causes for action and thereby create more tailored interventions for this population. Health Belief Model and Physical Activity Age was not related to the constructs of perceived risk, perceived benefits, and cues to action or self efficacy. Empirical support relating age and ethnicity to Health Belief Model constructs for physical activity is nearly non existent. With limited evidenc e linking age to Health Belief Model constructs, it is difficult to determine whether this finding of no significance is reliable, and it is believed that further research is needed to support the findings from this study. There are many reasons why the fi ndings might be different, but much more research is needed to determine the reality of the relationship. Age was significantly related to perceived barriers to physical activity. The 2123 year age group was likely to perceive barriers to physical activ ity. This finding raises questions related to young adult females and their perception of barriers Physical activity is a necessary component to prevention of heart disease and therefore should be established while young. Based on results from this analys is, young adults perceive barriers to physical activity; health educators should consequently seek to reduce perception of barriers and increase perception of benefits. The cost analysis must be in favor of benefits for physical activity interventions to be successful among this population.

PAGE 146

146 Findings from the regression analysis with ethnicity and Health Belief Model constructs were interesting. Significant findings related to ethnicity were recorded for all the constructs of the Health Belief Model. Asians and African Americans, when compared to whites, were more likely to perceive benefits and self efficacy to physical activity. African Americans were also more responsive to cues toward physical activity. The other ethnic group was significant in both perceived risk and perceived barriers t o physical activity. Findings from this study promote further research regarding ethnicity and motivation for physical activity. Ethnicity appears to be responsive to Health Belief Model constructs; however, further investigation is needed to corroborate t hese findings. Health Belief Model and Smoking There is a lack of evidence in the literature supporting age and ethnicity as explanatory variables of Health Belief Model constructs related to smoking behavior. Age was not related to Health Belief Model constructs for heart risk behavior. The majority of the sample of all age groups reported they were not cigarette smokers and it is hypothesized that hookah smokers may not perceive themselves to be smokers ; therefore, responses to this item were not based on true perception. If the individual considers herself a nonsmoker, s he cannot truly perceive risk, barriers, or benefits to the practice of smoking. This may be a possible explanation for the lack of significance of findings. It is believed that further investigation utilizing a more diverse sample, including cigarette smokers, is needed to better understand the relationship of age on Health Belief Model constructs of smoking behavior. Also, future surveys should include more specific questions related t o smoking to ensure that smoking is assessed correctly.

PAGE 147

147 Questions related to smoking should distinguish between cigarette smoking, hookah smoking, marijuana smoking, cigars, etc. The investigator could then better report and classify smoking related behavi ors. The only difference in ethnicity related to smoking behavior was noted in perceived barriers. Findings indicated that African Americans, when compared to whites, were less likely to perceive barriers to smoking. Perception of barriers can be a strong deterrent for negative lifestyle behavior; this finding indicates that the collegeage sample does not have an accurate perception of barriers associated with smoking behavior. Another explanation for this finding could be that the barriers composing this construct did not translate to the sample population because the majority of the sample were self reported nonsmokers. However, further research is needed to more closely research the effect of ethnicity on constructs related to the Health Belief Model to support or refute these findings. An understanding of ethnicity and perception is a critical component to planning and implementing smoking cessation or smoking prevention programs on college campuses. Health Belief Model and Doctor Visit and Clinical A ssessment Behavior Little to no recent research has been performed testing the relationship of age and ethnicity on the Health Belief Model constructs related to doctor visit or clinical assessment behavior. Age was related to only two perceived barrier it ems of the Health Belief Model. A difference was noted in the age groups 1820 and 2426; both groups were more likely to perceive inconvenience as a barrier to doctor visits/clinical assessment. Also, the age group 2426 perceived fear of learning something was wrong with health as a barrier. It is important to note that students may fear going to the doctor because they do not have health insurance; if visiting the doctor results in the

PAGE 148

148 discovery of a condition, the student potentially would be hi ndered from acquiring health insurance as many insurance providers do not have policies that cover preexisting conditions of a patient. According to the results from the regression analysis age influences perception of barriers related to going to the doc tor. Further research on a larger sample spanning more than one university may indicate that age is related to several constructs of the Health Belief Model related to doctor visit/clinical assessment behavior. Findings from the multivariate analysis indi cated a significant difference in ethnicity related to the perceived barriers of inconvenience and fear to doctor visits. African Americans perceived inconvenience as a barrier to doctor visits/clinical assessments but were less likely to perceive f ear of finding out something is wrong as a barrier. Perceived barriers appear to be applicable to the minority population on college campuses. Based on the results from this finding, interventions should incorporate strategies to reduce perception of barr iers to doctor visits/clinical assessment among this population. However, future research incorporating qualitative analysis results prior to survey development would aid in validity of the findings. Also, the other ethnic group was significant related t o self efficacy and medical visit behavior reporting they were less likely to perceive confidence in visiting a doctor This finding is significant in that no other ethnic group perceived self efficacy to medical visit behavior. This lack of significance m ay be related to relevance of self efficacy items to this population but may also be due in part to individuals lack of confidence in medical visit/clinical assessment behavior. Doctor visits and clinical risk assessment is necessary if heart disease is t o be prevented. Self efficacy is a strong motivator for behavior; therefore, programs must

PAGE 149

149 incorporate items that serve to increase participant confidence in their ability to perform a particular behavior. Again, further research is needed to confirm the f indings from this study. Summary The support linking Health Belief Model constructs to behavior is expansive, yet research related to the Health Belief Model and the female collegeage population related to heart disease risk factors has yet to be reported. This is one of the first, if not the first study to look at the relation of age and ethnicity to Health Belief Model constructs related to heart disease risk factor behavior among a sample of female college students. Overall, minimal difference in age and ethnicity was noted related to the constructs of the Health Belief Model. Therefore, due to overall lack of significant findings, full interpretation cannot be performed. The investigators hypothesis that there would be a significant difference related to the variables of ethnicity and age was not supported. The finding of little difference is in itself a significant one. Results indicate that the Health Belief Model may not be the best model to use when assessing age and ethnicity related to perception or motivation toward action. Future research should incorporate health behavior model testing to determine what model most translates to this population of study. Another plausible explanation is that due to the limited empirical support for age and ethnic ity related to the Health Belief Model among the college population, further research is needed to better understand if the independent variables are in fact related to this area. Further information is needed regarding womens perception of heart disease risk to better plan and implement programs to address perceptions and motivate individuals to act in a positive manner. Determinants of

PAGE 150

150 perception of heart disease need to be known to fully understand how to address risk reduction and develop successful interventions among the collegeaged population (Collins et al., 2004). Strengths It is important to note that the study had a high response rate (39.1%) and a large sample size (598 respondents; 587 usable surveys). The study provided support for use of webbased surveys among the college population. The study incorporated a mixed mode notification process, using both a mailed notification and email reminders. The use of a modest incentive sent with the initial response request that had direct meaning to t he sample studied, is thought to have improved the response rate. This warrants further investigation to determine what modes of contact are optimal and what incentives produce the best results among this population. Also, the study supplied further inform ation regarding behavior exhibited among female college students and addressed a gap in the knowledge regarding age, ethnicity, and perception related to risk behavior. Limitations The study is not void of limitations, and the findings from this study must be examined in the context of those limitations. First, the study analysis was based on self report data only. Therefore, response bias may have been present. Because the study used self report only, student responses could have been a reflection of what they thought the investigator wanted to have reported. This may alter their true responses and provide inaccurate information/data. Reported behavior accuracy is dependent on the truthfulness of the sample. Future studies including more objective measures of behavior and true measurement of clinical risk factors may strengthen studies of this

PAGE 151

151 type. Second, the narrow focus related to the definition of age may have been a limitation because the research focuses on a larger age group. Future research should i ncorporate a broader definition for age. The relationship of age to heart disease risk and risk factors can then be better assessed. Third, given the age of respondents, family history may not have been accurately assessed. Parents of the sample may be too young to have experienced some form of heart disease. Future surveys may need to incorporate questions regarding grandparent history of heart disease to acquire a more accurate report of family history risk Fourth, due to a lack of correlation of certain items with each other, individual items were used as outcome variables in a number of the regression models. The results from those regression analyses are limited in their generalizability as the findings can only be generalized to those items specifically. And finally, the small geographic scope of the sample limits generalizability to the students sampled in this study. A more diverse population, perhaps a multi campus study, would help generate greater generaliz ability of the results of this study. Implications and Future Research Findings from this study indicate a great need for future research investigating why individuals choose to participate in or refrain from heart disease prevention behaviors. Although the study may not have findings supporting all hypotheses, the research findings can help guide future study development. Further research regarding heart health is needed to determine what theoretical model is the best fit for use among the college population. Based on the findings from this study, the Health Belief Model may not be the model to best provide information needed to better understand motivating factors for behavior among students on campus. Therefore, future research should test

PAGE 152

152 and utilize other health behavior models that may better suit the population and variables of interest. It is critical that research continue exploration of risk behavior exhibited by the college female population. Knowledge of behavioral risk factors present in the university population will allow for targeted behavioral objectives in program development. It is imperative that researchers understand in what behaviors students are participating, in order to focus further research into what motivates or causes students to select certain behaviors, even those they know to be detrimental to their long term health status. The more information gained, the better health educators are equipped to plan and implement successful programs aimed at addressing risk behavior. Also, previous research has associated ethnicity with behavior related to heart disease. The findings from this study confirm a relationship of ethnicity to behavior. Future research assessing risk behavior among ethnic populations most at risk for heart disease may be beneficial in the understanding of behaviors associated with those populations. Prevention programs can then be established that target individuals and risk activities most in need of change. Empirical evidence relating age and perception to behavi or is lacking. Future research is needed to fully support findings from this study related to age or perceptions relationship to behavior. The explanatory variables need to be examined among a broader population to investigate if a link exists between the independent variables of age and perception with outcome behavior variables. Also, longitudinal studies looking at age, ethnicity, and perception across the span of time when students enter college to when students graduate from college, would help solidi fy previous and current findings. Following students over a period of time will allow

PAGE 153

153 researchers to assess and record changes in their health behavior and facilitate the development of programs more targeted to the behavior change needs of the students. Recommendations Health educators on college campuses are well positioned to effectively address the needs of the collegeaged population. This study found important information that should be noted when developing interventions to promote behavior change t hat reduces risk for heart disease, especially in college aged females The period of time when young adults attend college is an ideal time for health educators to implement programs that may result in changes being made to establish behaviors that are heart healthy. (Dinger, 1999; McGowen et al., 1994; Spencer, 2002). Interventions addressing the misperception of students regarding their risk for heart disease are needed; effectiveness can be greatly enhanced if coupled with nutrition and physical activit y interventions. Information related to age, ethnicity, and perception can be used to tailor interventions to groups most susceptible and prone to engage in risk behavior. Also, considering risk for heart disease is more often seen in ethnic populations, program development addressing this variable in relation to behavior is strongly encouraged (Ferdinand, 2006; Kurian et al., 2007) Based on the findings from this study, it is also important that interventions be tailored to diverse populations of student s, namely, African American and Asian populations. Health messages should be created linking family history to increased risk for heart disease and need to stress that heart disease can be prevented through behavior change. Also, it is proposed that health educators work closely with student health care centers and provide free screening services of blood pressure, cholesterol, and glucose, as these measures are greatly needed among the population. Heart disease risk can be predicted through

PAGE 154

154 cholesterol ass essment performed when an individual is in their 20s (Klag et al., 1993 as cited in Cleeman & Grundy, 1997). If students are aware of their cholesterol numbers, early intervention and prevention efforts promoting behavior for reduction of clinical risk ca n begin and may reduce overall long term risk of heart disease (Cleeman & Grundy, 1997). A womans heart disease risk is not only more affected by cholesterol but also by diabetes (Simontacchi & FitzGerald, 2004). Glucose screening is needed to make the individual aware of their risk for diabetes; educational interventions can then be implemented promoting behavior change objectives to prevent or control diabetes. Clinical risk assessments are necessary to help young women recognize their level of risk for heart disease. Screening services should be coupled with programs stressing the need for physical activity and proper nutrition to keep clinical measures at recommended levels The need for educational and behavioral interventions was noted throughout Chapter 5. It would be very easy to victim blame, but there are strategies using different levels of intervention to promote optimal heart health among the college population. An ecological approach to behavior promotion is an effective method to promote behav ior change (NCI, 2005). This approach promotes the idea that a persons behavior is partially determined by his or her social environment there is a relationship between the individual and their environment (NCI, 2005). Changing negative lifestyle behavior to positive lifestyle behavior can aid in the reduction of heart disease risk among the college female population. The following paragraphs propose changes that can be made at different levels using an ecological approach to promote behavior change related to heart disease risk among college students (Figure 51).

PAGE 155

155 The most effective intervention efforts use a multilevel approach toward health promotion, beginning with the individual (NCI, 2005) Individuals are responsible for beginning and maintaining behavioral change. To promote behavior change at the individual level, health educators need to focus on increasing knowledge and providing individuals with the tools necessary to engage, continue, or discontinue in a behavior (NCI, 2005). Promotion efforts need to target nutrition, increased physical activity, clinical risk assessment, and decreased smoking behavior (including hookah behavior) to combat heart disease development H ealth educators could work with the student health care center to create a health assessment web page. The web page which should be made available for all students could allow the students to input physical activity behavior, nutritional behavior, smoking behavior, and clinical risk. The web page could then generate tailored lifestyle advice based on inputted behaviors. The option to chat live with a personal counselor could also be provided. The following items may also be made available on this web page to assist the student in positive behavior change: links to local heal thy restaurants, programs tracking physical activity levels, clinical risk measure importance, and lists of the dangers of smoking cigarettes and hookah. Students could sign up for bi monthly e mail alerts that provide information and reminders regarding e vents on campus promoting nutrition, physical activity, and decreased smoking. Health classes could be used to intervene at the individual level. During these classes, this health promotion web page could be introduced, along with demonstrations and instructions for using the site. The educator could also utilize time with the student

PAGE 156

156 to promote and educate listing and providing examples of specific strategies to increase physical activity, eat healthy, and reduce smoking. Individuals also respond to their interpersonal environment which includes family and friends (NCI, 2005). A college students group of friends and social network is one of importance. Therefore, this area may be one to utilize when promoting behavior change. Peer led programs toward healthier eating, increased physical activity, and decreased smoking could work to promote behavior change. A n healthy eating club could be established where peers educate students regarding healthy nutrition/diet in the campus environment. Topics such as choosing healthy food options at restaurants, how to eat healthy using a campus dining card, and how to cook healthy meals with a small income could be used to promote good nutritional behavior. Walking, running, swimming, skating, and biking groups could be established on campus to promote the sociable aspect of physical activity. Students could lead other students in games and presentations related to the dangers of smoking including discussion on hookah dangers and misperceptions. Peer relationships c ould also be used to promote clinical risk assessments. Friend check reminders could be promoted on the student health care center web page and in personal and family health classes. One option may be for the student health care center to offer free screenings during one week out of the year; another option would be to offer free screenings as an incentive to students who bring a friend with them to get screened. The interpersonal component should focus on peer involvement to promote behavior change. Insti tutional factors such as regulation and policy may also influence behavior (NCI, 2005). Health educators could work with campus dining to promote proper

PAGE 157

157 nutrition. P osting signs in campus restaurants promoting good nutrition may encourage students to choose wisely when selecting their meals. University policy could institute standards that must be met when planning meals for students Campus restaurants or those clos to campus could be required to include healthy food options on their menu. The healthy options should also be highlighted to promote selection. Vending machines could offer healthy food options. Since students enter classroom buildings daily, signs promoting use of stairs instead of elevators may be a practical way to promote daily physical activity. Initiatives could be established to promote walk to class day, perhaps disallowing buses to drop students off right in front of their buildings. Campuses nationwide could follow the example of the University of Florida and institute policy prohibiting smoking on campus. Although smoking may still occur, the rates of smoking would be affected. The university community could work to promote nutrition, increased physical activity, and decreased smoking. A monthly farmers market could be planned and implemented. The market should be located in a central location on campus and allow students to sample foods made from healthy ingredients. Programs related to increased bike paths, sidewalks, or parks need to be established and funded. The community could promote awareness of these opportunities for physical activity. Student park days could be planned to introduce college students to the many benefits of parks surrounding the university. The university community could establish hookah and smoking awareness campaigns. Policy can also aid in behavior change (NCI, 2005). Currently students must present a vaccination record; clinical measures could also be required. College

PAGE 158

158 campuses could institute policy requiring a health risk appraisal when entering as f reshman. Assessment should include cholesterol, glucose, and blood pressure measurements. If required, students would be taking a step toward good heart health through clinical risk assessment. The student could then speak with a behavior al counselor regar ding assessment results and determine a program of action. The university could also require that all entering freshmen take a personal health course focused on teaching the benefits of physical activity, proper nutrition, clinical risk assessment and doct or visit behavior, and decreased smoking behavior. The course could also give practical strategies for the students to engage in such behavior. To curb the problem of hookah smoking, it may be necessary to work with community and university leaders to establish policies that inhibit hookah bars to be built within a certain distance from college campuses. Policy could require hookah bar owners to prominently display warnings related to the dangers of smoking hookah. Although individuals are responsible for beginning and maintaining behavioral change, the social environment plays a role in overall health. If health behaviorists can intervene through the social environment, promoting healthy behaviors among at the university level, then college students will have a stronger chance of having an overall healthy lifestyle and reduced risk of heart disease. Conclusions In conclusion, this study was conducted to assess overall behavior reported among the university female population that may place them at an incr eased risk for heart disease and investigated the role age, ethnicity, and perception has in promoting or hindering said behavior. Also, an investigation of age and ethnicity related to Health Belief Model constructs for heart disease risk behavior was per formed. The findings

PAGE 159

159 suggest that students are engaging in activities that place them at an increased risk for heart disease development. Findings also suggest that although age and perception are not greatly associated with behavior, ethnicity is related to behavior associated with heart disease. Finally, the study showed that a difference in age or ethnicity was noticed very few times in relation to Health Belief Model constructs related to heart disease risk behavior. Implications of the findings from this study are important for health educators and researchers on college campuses. The findings from this study contribute to the fact that interventions need to be implemented while individuals are young. Because heart disease is the leading cause of deat h among women, but can be prevented through behavior change (Kung, Hoyert, & Murphy, 2008, McMahan et al., 2007), interventions for improving health enhancing behaviors among college students are essential (McCarron et al., 2000; Spencer, 2002). Heart disease risk assessment and prevention efforts are needed to help college aged women recognize their risk and promote behavior change to reduce their risk. The earlier risk is identified and minimized, the more likely heart disease will be prevented (Frost, 1992; Green et al., 2003; McMahan et al., 2007; Strong et al., 1988). Awareness and promotion of healthy behavior among college students is likely to reduce their overall risk for heart disease development in the future (Racette et al., 2005). Results from t his study can aid in more tailored and targeted health behavior and heart disease prevention programs to facilitate student action toward positive health.

PAGE 160

160 Figure 51. Ecological approach toward heart disease risk reduction Good nutritional habits activity hookah behavior assessment Individual Health assessment webpage Tailored lifestyle counseling promotion Interpersonal Peer led programs Nutritional clubs Physical activity clubs Smo king danger peer led demonstration Institutional Physical activity initiatives Nutrition initiatives o smoking policies Community Increased areas for physical activity Smoking/hookah awareness days Policy Health risk appraisal Personal health course Prohibit smoking Increase distance of hookah bars to campus Adapted from Theory at a Glance (NCI, 2005)

PAGE 161

161 APPENDIX A LIST OF EXPERT PANEL MEMBERS 1. Dr. Jill Varnes. Ed.D. Professor, Department of Health Science Education University of Florida PO Box 118210 Gainesville, FL 326118210 2. Dr. Steven Pokorny Ph.D. Director of Health Promotion Alachua County Health Department 224 Southeast 24th Street Gainesville, FL 326413699 Bus: (352) 3347980 Fax: (352)9552126 Email: Steven_Pok orny@doh.state.fl.us

PAGE 162

162 APPENDIX B RELIABILITY AND FACT OR ANALYSIS TABLES Table B 1. Initial Survey Item Analysis Results Behavior Health Belief Model Construct Item Numbers Reliability Coefficient Standardized Coefficient Nutrition Perceived Risk 3 .875 .887 Perceived Benefits 4 .719 .746 Perceived Barriers 4 .623 .620 Cues to Action 1 NA NA Self Efficacy 4 .676 .694 Physical Activity Perceived Risk 4 .883 .885 Perceived Benefits 5 .847 .860 Perceived Barriers 5 .632 .634 Cues to Action 4 .780 .781 Self Efficacy 4 .733 .753 Smoking Perceived Risk 4 .979 .981 Perceived Benefits 6 .837 .878 Perceived Barriers 3 .807 .824 Cues to Action 3 .642 .643 Self Efficacy 2 ** .098 .106 Medical Visits Perceived Risk 3 .864 .865 Perceived Benefits 2 .922 .922 Perceived Barriers 3 .635 .634 Cues to Action 2 .540 .541 Self Efficacy 3 .797 .801 Personal Weight Perceived Risk 4 .790 .856 Perceived Benefits 5 .933 .933 Perceived Barriers 3 .784 .785 Cues to Action 3 .733 .739 Self Efficacy 3 .850 .857 *Items correlate at the .60 level. **Value is negative due to a negative average covariance among them. Item codes were evaluated and deemed appropriate. Only 22 individuals answered these questions, which may have led to negative number. It is hypothesized that the true population covariance is positive, but sampling error produced a negative average.

PAGE 163

163 Table B 2. Factor Loadings for the Health Belief Model Behavioral Questions Pilot One Behavior Model Construct Item Numbers Item Factor Loading Nutrition Perceived Risk 2 Risk of Diabetes .674 Risk of Heart Disease .795 Perceived Benefits 1 Heart Disease Risk Reduced .732 Perceived Barriers 1 Taste .615 Cues to Action 0 NA NA Self Efficacy 0 NA NA Physical Activity Perceived Risk 2 Risk of Obesity .587 Risk of Heart Disease .555 Perceived Benefits 3 Increased Strength and Muscle Tone .632 Feel Better about Oneself .752 Improved Physical Appearance .654 Perceived Barriers 0 NA NA Cues to Action 2 See Others who Look Fit in a Bathing Suit .643 Friends Stress the Need for Exercise .619 Self Efficacy 1 Able to Exercise at Least 3x Per Week .613 Smoking Perceived Risk 2 Risk of Premature Death .564 Risk of Heart Disease .566 Perceived Benefits 5 Live Longer .694 Increased Lung Capacity .711 Fresher Clothes/Breath .591 Increased Energy .576 Reduce the Risk of Chronic Disease .730 Perceived Barriers 1 Gain Weight .572 Cues to Action 0 NA NA Self Efficacy 0 NA NA Medical Visits Perceived Risk 0 NA NA Perceived Benefits 1 Decrease Risk of Heart Disease .596 Perceived Barriers 2 Inconvenient .725 Fear of Learning I am Ill .595

PAGE 164

164 Table B 2. Continued Behavior Model Construct Item Numbers Item Factor Loading Cues to Action 1 When my Friend has a Checkup .615 Self Efficacy 2 Able to Record and Understand Blood Pressure/Cholesterol Numbers .788 Able to Talk With Doctor Regarding Results .744 Personal Weight Perceived Risk 2 Risk of Obesity .710 Risk of Low Energy .594 Perceived Benefits 4 Increased Confidence .724 Overall Health will Improve .683 Clothes will Fit Better .563 Will Receive Compliments Regarding my Appearance .564 Perceived Barriers 2 Will Not See Immediate Results .580 Will Not Have the Willpower to Maintain Weight Loss .607 Cues to Action 0 NA NA Self Efficacy 1 Able to Maintain a Healthy Lifestyle for Weight Management .573 Table B 3. Pilot Two Item Analysis Results Behavior Health Belief Model Construct Item Numbers Reliability Coefficient Standardized Coefficient Nutrition Perceived Risk 3 .895 .904 Perceived Benefits 3 .775 .777 Perceived Barriers 3 .721 .721 Cues to Action 1 NA NA Self Efficacy 4 .744 .750 Physical Activity Perceived Risk 2 .913 .913 Perceived Benefits 3 .897 .898 Perceived Barriers 3 .706 .705 Cues to Action 3 .816 .814 Self Efficacy 3 .892 .892

PAGE 165

165 Table B 3. Continued Behavior Health Belief Model Construct Item Numbers Reliability Coefficient Standardized Coefficient Medical Visits Perceived Risk 3 .921 .922 Perceived Benefits 2 .897 .897 Perceived Barriers 3 .693 .695 Cues to Action 2 .701 .701 Self Efficacy 3 .872 .875 *Items correlate at the .60 level. Table B 4. Factor Loadings for the Health Belief Model Behavioral Questions Pilot Two Behavior Model Construct Item Numbers Item Factor Loading Nutrition Perceived Risk 1 Risk of Diabetes .580 Perceived Benefits 0 NA NA Perceived Barriers 0 NA NA Cues to Action 0 NA NA Self Efficacy 0 NA NA Physical Activity Perceived Risk 2 Risk of Diabetes .687 2 Risk of Heart Disease .688 Perceived Benefits 3 Increased Strength and Muscle Tone .657 3 Increase Self Esteem .699 Improved Physical Appearance .653 Perceived Barriers 1 Fear of Being Attacked .563 Cues to Action 2 See Others who Look Fit in a Bathing Suit .595 See Myself in a Bathing Suit .605 Self Efficacy 3 Able to Exercise at Least 3x Per Week .576 Able to Exercise When Friends Cannot .553 Able to Exercise in Inclement Weather .604 Medical Visits Perceived Risk 3 Risk of Heart Disease .725 Risk of Diabetes .631 Risk of Dying Young .685 Perceived Benefits 2 Will Become More Aware of Health .734

PAGE 166

166 Table B 4. Continued Behavior Model Construct Item Numbers Item Factor Loading May Aid in the Prevention of Illness .726 Perceived Barriers 1 Fear that May Learn there is Something Wrong with my Health .564 Cues to Action 2 When my Friend has a Checkup .591 During the Month of February when the Need for Heart Checkups is Stressed .587 Self Efficacy 3 Able to Schedule an Appointment .633 Able to Record and Understand Blood Pressure/Cholesterol Numbers .665 Able to Talk With Doctor Regarding Results .651 Table B 5. Final Survey Item Analysis Results Behavior Health Belief Model Construct Item Number Reliability Coefficient Standardized Coefficient Nutrition Perceived Risk 3 .904 .910 Perceived Benefits 3 .784 .787 Perceived Barriers 3 .671 .672 Cues to Action 3 .497 .499 Self Efficacy 4 .789 .795 Physical Activity Perceived Risk 3 .904 .907 Perceived Benefits 3 .892 .892 Perceived Barriers 3 .564 .561 Cues to Action 3 .756 .754 Self Efficacy 3 .820 .821 Smoking Perceived Risk 3 .932 .933 Perceived Benefits 4 .857 .862 Perceived Barriers 3 .837 .834 Cues to Action 3 .862 .863

PAGE 167

167 Table B 5. Continued Behavior Health Belief Model Construct Item Number Reliability Coefficient Standardized Coefficient Self Efficacy 2 ** .105 .105 Medical Visits Perceived Risk 3 .922 .922 Perceived Benefits 2 .875 .875 Perceived Barriers 3 .535 .535 Cues to Action 2 .569 .570 Self Efficacy 3 .809 .814 *Items correlate at the .60 level. **It is hypothesized that this items coefficient is low due to the high number of missing responses (484 did not respond).

PAGE 168

168 APPENDIX C I NSTITUTIONAL REVIEW BOARD FALL 2008 DOCU MENTS PILOT STUDY ONE

PAGE 169

169

PAGE 170

170

PAGE 171

171

PAGE 172

172

PAGE 173

173 APPENDIX D STATISTICAL REQUEST

PAGE 174

174

PAGE 175

175 APPENDIX E INSTITUTIONAL REVIEW BOARD SUMMER 2009 DO CUMENTS PILOT STUDY TWO

PAGE 176

176 APPENDIX F PRENOTIFICATION EMAIL Dear University of Florida Student, Within the next couple of days, you will receive an email from Suzanne Sneed, a doctoral student in the Department of Health Education and Behavior from the University of Florida asking for your participation in a research survey about healthy heart behav ior. The purpose of the survey is to collect information and assess University students perceptions and attitudes about heart disease and behaviors associated with risk for heart disease. I am writing in advance because many people like to know ahead of t ime that they will be contacted. Id like to ask that you consider participating in this survey because it is an important project that will aid in the overall understanding of college student heart health behavior. Sincerely, Suzanne Sneed Room 200 FLG PO Box 118210 Gainesville, Florida 32611

PAGE 177

177 APPENDIX G NOTIFICATION E MAIL Dear University of Florida student: I am writing to ask you to participate in a survey about your current health behavior entitled University students heart disease perceptions and knowledge regarding risk and behaviors associated with elevated risk. The purpose of this study is to assess University students perceptions and attitudes about heart disease and behaviors associated with risk for heart disease. The survey results will aid in the development of better programs that more effectively target the college student population. If you choose to participate in the study, you will be asked to answer questions about your current behaviors which can be linked to heart health. Y our answers are completely anonymous and will only be reported as a part of group summaries. Email/IP addresses will not be collected or linked to your responses so the survey is anonymous. You cannot be linked to any of your answers because no personal identifiers will be attached to your data. However, you do not have to answer any question that you do not wish to answer. You have the right to stop taking the survey at anytime without consequence. Please complete the survey individua lly in order to keep your responses confidential. There are no anticipated benefits or risks associated with the completion of this survey. You will not receive any compensation for your participation. To participate in this important survey, please click on the link provided at the end of this email message. If you are under the age of 18, please do not complete this survey. The survey will take approximately 20 minutes to complete. Consent is implied by clicking the submit button following completion of the survey. Please accept my thanks for your participation. Please use the following link to access and complete the survey: http://www.surveymonkey.com/s.aspx?sm=9lMVhs4_2fRDMUK39o5Sz3iQ_3d_3d Sincerely, Principle Investigator: Suzanne Sneed, M.S., Doctoral student, Room 200 FLG PO Box 118210, Gainesville, Florida 32611

PAGE 178

178 Supervisor: Jill Varnes, Ph.D., Assistant Professor, FLG 242D PO Box 118210, Gainesville, FL 32611 (You may wish to print this form for your records) If you have any questions or comments regarding this survey, please contact: Suzanne Sneed @ 3523920578 ext. 1327 Whom to contact about your rights as a research participant in the study: IRB02 Office Box 112250, University of Florida, Gainesville, FL 326112250; phone 3920433. IRB02 Office, Box 112250, University of Florida, Gainesville, FL 326112250; phone 3920433.

PAGE 179

179 APPENDIX H REMINDER EMAIL Dear University of Florida student, A couple of weeks ago we asked you to participate in a survey about university student heart health behavior. If you have completed the survey, we thank you for your participation. On behalf of the Principal Investigator and her committee, we appreciate th e service that you have provided for the continuation of this important area of research best wishes on your academic endeavors. If not, we hope that you choose to complete the survey soon. This is a reminder that the survey is voluntary and completely anonymous. If you choose to participate in this survey, your answers are completely confidential and will only be reported as part of group summaries. To participate in this survey, please click on the link provided below: http://www.surveymonkey.com/s.aspx?sm=9lMVhs4_2fRDMUK39o5Sz3iQ_3d_3d This survey should not take more than 20 minutes to complete. Thank you very much for assisting with this important project. Sincerely, Suzanne Sneed Room 200 FLG PO Box 118210 Gainesville, Florida 32611

PAGE 180

180 APPENDIX I FINAL SURVEY INSTRUMENT An assessment of University females heart health behaviors The purpose of this survey is to collect information about UF students perceptions, knowledge and behaviors related to heart disease. Participation is voluntary. You may skip any question that you are not comfortable answering. Remember, all of your responses are completely anonymous. If under the age of 18, please do not participate in this survey. 1. Please select the range in which your age falls. o 1820 o 2123 o 2426 o 2729 o Other 2. Please rate your level of risk for heart disease. o Low risk o High risk Choose the answer that most closely applies to your behavior and select the corresponding response. Questions about your Nutrition 3. How often do you eat fast food? (By fast food, we mean inexpensive food, such as hamburgers and fried chicken, prepared and served quickly e.g. Burger King, Taco Bell, McDonalds etc. ). o Never to very little o One time/week

PAGE 181

181 o Two to four times/week o Five or more times/week 4. How often do you eat a serving (about 1 cup) of dark leafy green vegetables (e.g. spinach, broccoli, collard greens, etc )? o Never to very little o One time/week o Two to four times/week o Five or more times/week 5. How often do you eat a serving (a serving is 3 ounces, which would be about the size of a checkbook or a deck of cards) of fish or shellfish? (Fish and shellfish including shrimp, lake trout, tuna and salmon). o Never to very little o One time/week o Two to four times/week o Five or more times/week 6. Do you eat 5 or more servings of fruits/vegetables each day ? A serving is one medium apple, banana or orange, 1 cup of raw leafy vegetables (like spinach or lettuce), cu p of cooked beans or peas, cup of chopped, cooked or canned fruit/vegetables or cup of fruit/vegetable juice. o No o Yes 7. Do you eat 3 or more servings of whole grains each day (wheat bread, whole grain pasta, brown rice, oatmeal etc. )? A serving is 1 s lice of bread, 1 ounce of breakfast cereal or cup of cooked cereal, pasta or rice. o No o Yes 8. Indicate what kinds of foods you most often eat. A Hamburgers, steak, whole milk products, chocolate, ice cream, fried foods, etc

PAGE 182

182 B Skinless chicken, fish, low fat dairy, vegetables, beans, grilled foods, etc C Vegan or vegetarian options only o Mostly A o Mostly B o C o A mixture of both A and B. Questions about your Physical Activity 9. On average, how many days per week do you engage in moderate to vigorous physical activity for at least 30 minutes (i.e. jogging, swimming, active sports, brisk walking)? o 0 days per week o 1 day per week o 2 days per week o 3 days per week o 4 days per week o 5 days per week o 6 days per week o 7 days per week Questions about your Family History 10. Did your biological mother experience heart problems (e.g. heart attack, poor blood flow, high cholesterol, high blood pressure, irregular heart beat or heart failure) before the age of 65? o No o Yes o Dont know/not sure 11. Did your biological father experience heart problems (e.g. heart attack, poor blood flow, high cholesterol, high blood pressure, irregular heart beat or heart failure) before the age of 55?

PAGE 183

183 o No o Yes o Dont know/not sure 12. Based upon your responses to numbers 9 and 10, how likely do you think you are to be diagnosed with heart disease? o Not at all likely o Somewhat likely o Very likely Questions about your Smoking 13. Are you currently a cigarette smoker? o No, I have never smoked o No, I quit within the last 6 months (not smoking at all anymore) o No, I quit more than 6 months ago (not smoking at all anymore) o No, I have tried cigarettes once or twice but stopped o Yes, I currently smoke cigarettes 14. About how many cigarettes have you smoked in your entire life? o None o A few puffs, but never a whole cigarette o 1 cigarette o 2 to 5 cigarettes o 6 to 15 cigarettes o 16 to 25 cigarettes o 26 to 99 cigarettes o 100 or more cigarettes 15. During the past 30 days, on how many days did you smoke cigarettes?

PAGE 184

184 o Never (0 days per week) o Almost never (1 to 2 days per week) o Occasionally (3 to 4 days per week) o Regularly (5 to 7 days per week) 16. During the past 30 days, about how often were you usually exposed to other peoples smoke at home, at work, in a car, at recreational locations (e.g., parks, local pool halls, bowling alleys etc )? o Never (0 days per week) o Almost never (1 to 2 days per week) o Occasionally (3 to 4 days per week) o Regularly (5 to 7 days per week) 17. Have y ou ever visited a hookah bar? o No o Yes 18. Have you ever smoked hookah? o No o Yes 19. How many days out of the PAST 30 days have you smoked hookah? o Never (0 days per week) o Almost never (1 to 2 days per week) o Occasionally (3 to 4 days per week) o Regularly (5 to 7 days per week) Questions about your Medical Care 20. Do you currently have a primary care physician? A health care provider that you would consider your regular doctor (e.g. knows your medical history, etc )? o No

PAGE 185

185 o Yes o Not Sure 21. When was your last physical examination or check up? o Within the past year o Within the past two years o Within the past three years o Within the past four years o Five or more years ago o Never o Dont remember 22. When was your last visit to a doctor (excluding immunization visits)? o Within the past year o Within the past two years o Within the past three years o Within the past four years o Five or more years ago 23. How often do you visit your doctor for a checkup? o Two times per year o One time per year o One time every other year o One time every five years o Other: 24. Have you ever been tested for diabetes? o No

PAGE 186

186 o Yes o Not sure 25. Has a doctor every told you that you have diabetes? o No o Yes o Not sure 26. Do you currently have diabetes? o No o Yes o Not sure 27. Has anyone in your family (blood related) been diagnosed with diabetes? o No o Yes o Not sure 28. Have you ever been told you have high blood pressure? (High blood pressure is defined as a reading of 140/90 mmHg or higher). o No o Yes 29. Have you ever had your cholesterol checked? o No o Yes o Not sure/dont remember 30. If you have had your cholesterol checked, do you know your cholesterol numbers (e.g. LDL, HDL, Total cholesterol)? o No o Yes

PAGE 187

187 o Not sure o Never had cholesterol checked 31. What is your current height? Your best estimate is fine. inche s Feet Inches Total Inches 4 0 48 4 1 49 4 2 50 4 3 51 4 4 52 4 5 53 4 6 54 4 7 55 4 8 56 4 9 57 4 10 58 4 11 59 5 0 60 5 1 61 5 2 62 5 3 63 5 4 64 5 5 65 5 6 66 5 7 67 5 8 68 5 9 69 5 10 70 5 11 71 6 0 72 6 1 73 6 2 74 6 3 75 6 4 76 6 5 77 6 6 78 6 7 79 6 8 80 6 9 81 6 10 82 32. Into what range does your current weight fall? o Less than 100 pounds

PAGE 188

188 o 100120 pounds o 121140 pounds o 141160 pounds o 161180 pounds o 181 200 pounds o 201220 pounds o 221240 pounds o 241260 pounds o 261280 pounds o 281300 pounds o More than 300 pounds Questions about your Nutrition 33. If I do NOT maintain a healthy diet, I am at risk for obesity. Strongly Agree Agree Disagree Strongly Disagree 34. If I do NOT main tain a healthy diet, I am at risk for diabetes. Strongly Agree Agree Disagree Strongly Disagree 35. If I do NOT maintain a healthy diet, I am at risk for heart disease. Strongly Agree Agree Disagree Strongly Disagree A potential benefit for me to have a healthy diet is: 36. My risk of heart disease will be reduced. Strongly Agree Agree Disagree Strongly Disagree 37. My energy level will be increased. Strongly Agr ee Agree Disagree Strongly Disagree 38. I may be able to better manage my weight. Strongly Agree Agree Disagree Strongly Disagree

PAGE 189

189 A potential barrier for me to have a healthy diet is: 39. My grocery cost is increased when I purchase healthy foods such as fruits and vegetables. Strongly Agree Agree Disagree Strongly Disagree 40. Preparing nutritious meals takes too much of my time. Strongly Agree Agree Disagree Strongly Disagree 41. It is difficult for me to access healthier foods such as fresh fruits and vegetables. Strongly Agree Agree Disagree Strongly Disagree I am more likely to eat nutritious foods/meals when: 42. I see my friends eating healthy foods such as salads, fruits and vegetables. Strongly Agree Agree Disagree Strongly Disagree 43. I feel the need to lose weight. Strongly Agree Agree Di sagree Strongly Disagree 44. I take a nutrition course and learn the benefits of a healthy diet. Strongly Agree Agree Disagree Strongly Disagree I am confident in my ability to: 45. Eat 5 servings of fruits/vegetables every day, including green leafy vegetables. Strongly Agree Agree Disagree Strongly Disagree 46. Choose healthy food items (low fat and nutritious) at restaurants. Strongly Agree Agree Disagree Strongly Disagree 47. Cook healthy meals (low fat and nutritious). Strongly Agree Agree Disagree Strongly Disagree 48. Identify and purchase healthy items such as fruits and vegetables at the grocery store.

PAGE 190

190 Strongly Agree Agree Disagree Strongly Disagree Questions about your Physical Activity If I do NOT increase how much I exercise I may face the following health outcome: 49. I am at risk for diabetes. Strongly Agree Agree Disagree Strongly Disagree 50. I am at risk for heart disease. Strongly Agree Agree Disagree Strongly Disagree 51. I am at risk for obesity. Strongly Agree Agree Disagree Strongly Disagree A potential benefit for me to increase the amount I exercise is: 52. I will have increased strength and improved muscle tone. Strongly Agree Agree Disagree Strongly Disagree 53. I will feel better about myself. Strongly Agree Agree Disagree Strongly Disagree 54. My physical appearance will be improved. Strongly Agree Agree Disagree Strongly Disagree A potential barrier for me to increase the am ount I exercise is: 55. I may feel physically inadequate compared to others who are already exercising. Strongly Agree Agree Disagree Strongly Disagree 56. I may find it difficult to schedule exercise into my daily routine. Strongly Agree Agree Disagree Strongly Disagree 57. I may be in an isolated setting and fear being attacked. Strongly Agree Agree Disagree Strongly Disagree

PAGE 191

191 I am more likely to exercise when: 58. I see other students exercising. Strongly Agree Agree Disagree Strongly Disagree 59. I see other students who look fit in bathing suits. Strongly Agree Agree Disagree Strongly Disagree 60. I see myself in a bathing suit. Strongly Agree Agree Disagree Strongly Disagree I am confident in my ability to exercise: 61. At least 3 times per week for at least 30 minutes each time. Strongly Agree Agree Disagree Strongly Disagree 62. Even when my friends cannot. Strongly Agree Agree Disagree Strongly Disagree 63. When weather conditions are less than ideal. Strongly Agree Agree Disagree Strongly Disagree Questions about your Smoking *For this grouping of questions, please use the following definition of smoking: Inhalation of tobacco in the form of cigarettes, cigars, or a hookah pipe. 64. If I do NOT quit smoking I am at risk for lung cancer (If you never smoke, please skip to question # 79). Strongly Agree Agree Disagree Strongly Disagree 65. If I do NOT quit smoking I am at risk of dying prematurely. Strongly Agree Agree Disagree Strongly Disagree 66. If I do NOT quit smoking I am at risk for heart disease. Strongly Agree Agree Disagree Strongly Disagree

PAGE 192

192 A potential benefit for me to quit smoking is: 67. I will live longer. Strongly Agree Agree Disagree Strongly Disagree 68. I will experience increased energy to perform daily tasks. Strongly Agree Agree Disagree Strongly Disagree 69. I may reduce my ris k for many chronic diseases. Strongly Agree Agree Disagree Strongly Disagree 70. I will save money. Strongly Agree Agree Disagree Strongly Disagree A potential barrier for me to quitting smoking is: 71. I may be pushed out of my smoking (hookah) social circle. Strongly Agree Agree Disagree Strongly Disagree 72. I may experience an increase in stress. Strongly Agree Agree Disagree Strongl y Disagree 73. I may gain weight. Strongly Agree Agree Disagree Strongly Disagree I am more likely to quit smoking when: 74. I learn or know about other people like me who were able to quit. Strongly Agree Agree Disagree Strongly Disagree 75. I hear others coughing in my presence when I smoke. Strongly Agree Agree Disagree Strongly Disagree 76. I see individuals suffering from a smoking related illness. Strongly Agree Agree Disagree Strongly Disagree

PAGE 193

193 I am confident in my ability to quit smoking: 77. Using a quit assist program or methods (e.g. cessation programs, nicotine replacement the patch or gum, pharmacological aids Zban, Chantrex, etc ). Strongly Agree Agree Disagree Strongly Disagree 78. Cold turkey (using no outside programs or quit assist aids). Strongly Agree Agree Disagree Strongly Disagree Start here if you reported you never smoke. Questions about heart health checkups including cholesterol, glucose, and blood pressure checks 79. If I do NOT have a heart health check up I am at increased risk for heart disease. Strongly Agree Agree Disagree Strongly Disagree 80. If I do NOT have a heart health check up I am at increased risk for diabetes. Strongly Agree Agree Disagree Strongly Disagree 81. If I do NOT have a heart health check up I am at increased risk of dyi ng young. Strongly Agree Agree Disagree Strongly Disagree A potential benefit for me to having a heart health check up is: 82. I will experience increased heart health awareness. Strongly Agree Agree Disagree Strongly Disagree 83. I will be initiating early prevention measures that could decrease my risk for developing heart disease. Strongly Agree Agree Disagree Strongly Disagree A potential barrier for me to having a heart health check up is: 84. I may have to pay for the doctor appointment and blood tests. Strongly Agree Agree Disagree Strongly Disagree 85. It may be inconvenient for me.

PAGE 194

194 Strongly Agree Agree Disagree Strongly Disagree 86. I may find out that there is something wrong with my health. Strongly Agree Agree Disagree Strongly Disagree I am more likely to have a heart check up when: 87. One of my friends has his/her heart health checked. Strongly Agree Agree Disagree Strongly Disagree 88. I am reminded in February due to ads and increased media attention indicating that February is heart health month or I see a reminder of its importance on television. Strongly Agree Agree Disagree Strongly Disagree I am confident in my ability to: 89. Schedule an appointment for a heart health checkup. Strongly Agree Agree Disagree Strongly Disagree 90. Record and understand my cholesterol glucose, and blood pressure numbers. Strongly Agree Agree Disagree Strongly Disagree 91. Talk to my doctor about the results from the heart health checkup. Strongly Agree Agree Disagree Strongly Disagree Demographic Questions 92. What is your class rank? o Freshman o Sophomore o Junior o Senior 93. What is your anticipated major? (e.g. health science, journalism, engineering, etc )

PAGE 195

195 94. How do you describe yourself? Please mark all that apply. o Asian o Black or African American o Hispanic or Latino o Native Hawaiian or other Pacific Islander o American Indian or Alaska native o White or Caucasian o Middle Eastern o Other: Thank you for completing this survey. If you want to learn more about how to protect and maintain your heart health please visit http://www.americanheart.org

PAGE 196

196 APPENDIX J STATISTICAL REQUEST

PAGE 197

197 APPENDIX K INSTITUTIONAL REVIEW BOARD SPRING 2010 DOCUMENTS FINAL STUDY

PAGE 198

198

PAGE 199

199 APPENDIX L PRENOTIFICATION MAILED LETTER Dear [Name], Within the next two weeks, you will receive an email from Suzanne Sneed, a doctoral student in the Department of Health Education and Behavior from the University of Florida asking for your participation in a research survey about healthy heart behavior. The purpose of the survey is to collect information and assess female university students perceptions and attitudes about heart disease and behaviors associated with risk for heart disease. To thank you in advance for completing the survey, e nclosed is a gator lapel pin as a token of my appreciation. I am writing in advance because many people like to know ahead of time that they will be contacted. Limited information is available regarding university females and heart disease more information is needed. Id like to ask that you consider participating in this survey because it is an important project that will aid in the overall understanding of female college student heart health behavior and the findings will benefit other women in the future. If you choose to participate in the study, you will be asked to answer questions about your current behaviors which can be linked to heart health. Y our answers are completely anonymous and will only be reported as a part of group summaries. Email/IP addresses will not be collected or linked to your responses so the survey is anonymous. You cannot be linked to any of your answers because no personal identifiers will be attached to your data. Sincerely, [Signature] Suzanne Sneed Room 106K PO Box 118210 Gainesville, Florida 32611

PAGE 200

200 APPENDIX M INITIAL E MAIL NOTIFICATION Dear University of Florida student: Recently you were sent a mailed letter indicating that you would be contacted via email and asked to participate in a study concerning heart disease. To thank you in advance for participation, the letter contained a gator lapel pin. I am writing to ask you to participate in a survey about your current health behavior entitled Assessment of University Females Heart Health Behaviors The purpose of this study is to assess University students perceptions and attitudes about heart disease and behaviors associated with risk for heart disease. The survey results will aid in the development of better programs that more effectively target the college student population. If you choose to participate in the study, you will be asked to answer questions about your current behaviors which can be linked to heart health. Y our answers are completely anonymous and will only be reported as a part of group summaries. Email/IP addresses will not be collected or linked to your responses so the survey is anonymous. You cannot be linked to any of your answers because no personal identifiers will be attached to your data. You do not have to answer any question that you do not wish to answer. You have the right to stop taking the survey at anytime without consequence. Please complete the survey individually in order to keep your responses confidential. There are no anticipated benefits or risks associated with the completion of this survey. You will not receive any compensation for your participation. To participate in this important survey, please click on the link provided at the end of this email message. If you are under the age of 18, please do not complete th is survey. The survey will take approximately 20 minutes to complete. Consent is implied by clicking done when the survey is complete. Please accept my thanks for your participation. Please use the following link to access and complete the survey: http://www.surveymonkey.com/s/9FDJ3PH Sincerely,

PAGE 201

201 Principle Investigator: Suzanne Sneed, M.S., Doctoral student, Room 106K FLG, PO Box 118210, Gainesville, Florida 32611, 352 3920578 ext. 1293 Supervisor: Jill Varnes, Ph.D., Assistant Professor, FLG 242D, PO Box 118210, Gainesville, FL 32611, (352) 3920583 ext. 1230 (You may wish to print this form for your records) If you have any questions or comments regarding this survey, please contact: S uzanne Sneed @ 3523920578 ext. 1327. Whom to contact about your rights as a research participant in the study: IRB02 Office, Box 112250, University of Florida, Gainesville, FL 326112250; phone 3920433.

PAGE 202

202 APPENDIX N REMINDER EMAIL Dear University of Florida Student, About a week ago you were asked to participate in a survey about university student heart health behavior. To thank you in advance for participation, you received a mailed letter containing a gator lapel pin. If you have completed the survey, we thank you for your participation. On behalf of the Principal Investigator and her committee, we appreciate the service that you have provided for the continuation of this important area of research best wishes on your academic endeav ors. If not, we hope that you choose to complete the survey soon. This is a reminder that the survey is voluntary and completely anonymous. If you choose to participate in this survey, your answers are completely confidential and will only be reported as part of group summaries. To participate in this survey, please click on the link provided below: http://www.surveymonkey.com/s/9FDJ3PH This survey should not take more than 20 minutes to complete. Thank you very much for assisting with this important project. Sincerely, Suzanne Sneed Room 106K FLG PO Box 118210 Gainesville, Florida 32611

PAGE 203

203 APPENDIX O FINAL NOTIFICATION Dear University of Florida Student, I hope the spring semester ended successfully for all of you! For those of you who graduatedCongratulations! I wanted to take this time to thank those of you who have already completed the survey. I appreciate the service that you have provided for the continuation of this important area of research best wishes on your academic endeavors. If you have not had the chance to do so, please consider completing the survey. This is the final reminder that the survey is voluntary and completely anonymous. If you choose to participate in this s urvey, your answers are completely confidential and will only be reported as part of group summaries. To participate in this survey, please click on the link provided below: http://www.surveymonkey.com/s/9FDJ3PH This survey should not take more than 20 minutes to complete. Thank you very much for assisting with this important project. I hope you all have a wonderful summer! Sincerely, Suzanne Sneed Room 106K FLG PO Box 118210 Gainesville, Florida 32611

PAGE 204

204 APPENDIX P INCENTIVE Figure P 1. Final survey incentive

PAGE 205

205 LIST OF REFERENCES American College Health Association. (2009). Healthy Campus 2010. Retrieved from http://www.acha.org/Topics/HC2010.cfm American Heart Association. (2009). Risk factors and coronary heart disease. Retrieved from http://www.americanheart.org/presenter.jhtml?identifier=4726 American Heart Association (2010). Nutrition center healthy diet goals Retrieved from http://www.americanheart.org/presenter.jhtml?identifier=3071616 American H eart Association (2011). Physical Activity American Heart Association Guidelines Retrieved from http ://www.heart.org/HEARTORG/GettingHealthy/PhysicalActivity/GettingActive/A mericanHeart Association Guidelines_UCM_307976_Article.jsp Albert, N.M. (2005). We are what we eat: women and diet for cardiovascular health. Journal of Cardiovascular Nursing, 20, 451460. Ali, N.S. (2002). Prediction of coronary heart disease preventive behaviors in women: a test of the Health Belief Model. Women & Health, 35, 8396. Akesson, A., Weismayer, C., Newby, P.K., & Wolk, A. (2007). Combined effect of low risk dietary and lifestyle behaviors in primary prevention of myocardial infarction in women. Archives of Internal Medicine, 167, 21222127. American College Health Association. (2002). Healthy Campus 2010. Baltimore, Md: U.S. Department of Health. Avis, N.E., Smith, K.W., & McKinlay, J.B. (1989). Accuracy and perceptions of heart attack risk: what influences perceptions and can they be changed? American Journal of Public Health, 79, 16081612. Balkau, B., Deanfield, J.E., Despres, J.P., Bassand, J.P., Fox, K.A.A., Smith, S.C., et al. (2007). International day for the evaluation of abdominal obesity (IDEA): A study of waist circumference, cardiovascular disease, and diabetes mellitus in 168, 000 primary care patients in 63 countries. Circulation 116, 19421951. BarrettConnor, E., & Khaw, K. (1984). Family history of heart attack as an independent predictor of death due to cardiovascular disease. Circulation 69, 10651069. Becker, M.H., Maiman, L.A., Kirscht, J.P., Haefner, D.P., & Drachman, R.H. (1977). The Health Belief Model and prediction of dietary compliance: A field experiment. Journal of Health and Social Behavior 18, 348366. Bedinghaus, J., Leshan, L., & Diehr, S. (2001). Coronary artery disease prevention: Whats different for women? American Family Physician 63, 13921401.

PAGE 206

206 Behrens, T.K. & Dinger, M.K. (2003). A preliminary investigation of college students physical activity patterns. American Journal of Health Studies 18, 169172. Bello, N. & Mosca, L. (2004). Epidemiology of coronary heart disease in women. Progress in Cardiovascular Diseases 46, 287295. Berenson, G.S., Srinivasan, S.R., Bao, W., Newman, W.P., Tracy, R.E., & Wattingney, W.A. (1998). Association between multiple c ardiovascular risk factors and atherosclerosis in children and young adults. The New England Journal of Medicine, 338, 16501656. Bild, D.E., Jacobs, D.R., Sidney, S., Haskell, W.L., Anderssen, N., & Oberman, A. (1993). Physical activity in young black and white women. The Cardia Study. Annals of Epidemiology 3, 636644. Biswas, M.S., Calhoun, P.S., Bosworth, H.B., & Bastian, L.A. (2002). Are women worrying about heart disease? Womens Health Issues 12, 204211. Blair, S.N., Kohl, H.W. III, Paffenbarger, R.S. Jr., Clark, D.G., Cooper, K.H., & Gibbons, L.W. (1989). Physical fitness and all cause mortality: a prospective study of healthy men and women. Journal of the American Medical Association, 262, 23952401. Bowman, T.S., Gaziano, J.M., Buring, J.E., & S esso, H.D. (2007). A prospective study of cigarette smoking and risk of incident hypertension in women. Journal of the American College of Cardiology 50, 20852092. Bray, S.R. & Born, H.A. (2004). Transition to university and vigorous physical activity: I mplications for health and psychological well being. Journal of American College Health 52, 181188. Brown, L.B., Dresen, R.K., & Eggett, D.L. (2005). College students can benefit by participating in a prepaid meal plan. Journal of the American Dietetic Association, 105, 445448. Brunt, A., Rhee, Y., & Zhong, L. (2008). Differences in dietary patterns among college students according to body mass index. Journal of American College Health, 56, 629634. Buckworth, J. (2001). Exercise adherence in college st udents: Issues and preliminary results. Quest 53, 335345. Buckworth, J. & Nigg, C. (2004). Physical activity, exercise, and sedentary behavior in college students. Journal of American College Health, 53, 2834. Burke, G.L., Bertoni, A.G., Shea, S., Tracy R., Watson, K.E., Blumenthal, R.S., et al. (2008). The impact of obesity on cardiovascular disease risk factors and subclinical vascular disease. Archives of Internal Medicine, 168, 928935.

PAGE 207

207 Brevard, P.B. & Ricketts, C.D. (1996). Residence of college students affects dietary intake, physical activity, and serum lipid levels. Journal of the American Dietetic Association 96, 3538. Centers for Disease Control and Prevention. (2008, November 14). Cigarette smoking among adults United States, 2007. [Electr onic Version]. Morbidity and Mortality Weekly Report, 57, 12211226. Retrieved from http://www.cdc.gov Cheek, D., Sherrod, M., & Tester, J. (2008). Women and heart disease: Whats new? Nursing. [Electronic Version] Retri eved from www.nursing2008.com 3741. Cleeman, J.I. & Grundy, S.M. (1997). National cholesterol education program recommendations for cholesterol testing in young adults. Circulation 95, 16461650. Clement, J.M., Schmidt, C.A., Bernaix, L.W., Covington, N.K., & Carr, T.R. (2004). Obesity and physical activity in college women: Implications for clinical practice. Journal of the American Academy of Nurse Practitioners 16, 291299. Cobb, C., Ward, K.D., Maziak, W., Shihadeh, A.L. & Eissenberg, T. (2010). Waterpipe tobacco smoking: An emerging health crisis in the United States. American Journal of Health Behavior 34, 275285. Collins, K.M., Dantico, M., Shearer, N.B.C., & Mossman, K.L. (2004). Heart disease awarenes s among college students. Journal of Community Health, 29, 405420. Cooper, R., Cutler, J., DesvigneNickens, P., Fortmann, S.P., Friedman, L., Havlik, R., et al. (2000). Trends and disparities in coronary heart disease, stroke, and other cardiovascular di seases in the United States. Circulation 102, 31373147. Corbin, C.B. (2002). Physical education as an agent of change. Quest 54, 182195. Costa, F.M., Jessor, R., & Turbin, M.S. (2007). College student involvement in cigarette smoking: The role of psychosocial and behavioral protection and risk. Nicotine & Tobacco Research, 9, 213224. Crane, P.B. & Wallace, D.C. (2007). Cardiovascular risks and physical activity in middle aged and elderly African American women. Journal of Cardiovascular Nursing 22(4), 297303. Crombie, A.P., Ilich, J.Z., Dutton, G.R., Panton, L.B., & Abood, D.A. (2009). The freshman weight gain phenomenon revisited. Nutrition Reviews 67, 8394. Crouch, M.A. & Gramling, R. (2005). Family history of coronary heart disease: Evidenc e based applications. Primary Care: Clinics in Office Practice 32, 9951010.

PAGE 208

208 Damlo, S. (2007). AHA published guidelines on CVD prevention in women. American Family Physician 75(7), 10961101. Daucher, L., Amouyel, P., Hercberg,S, & Dallongeville, J. (2006). Fruit and vegetable consumption and risk of coronary heart disease: A metaanalysis of cohort studies. The Journal of Nutrition, 136, 25882593. Daviglus, M.L., LloydJones, D.M., & Pirzada, A. (2006). Preventing cardiovascular disease in the 21st Cent ury. American Journal of Cardiovascular Drugs 6(2), 87101. Del Negro, A. (2003). Take weight off your heart and a load off your mind. Medscape Cardiology, 7(1). Desai, M.N., Miller, W.C., Staples, B., & Bravender, T. (2008). Risk factors associated with overweight and obesity in college students. Journal of American College Health 57(1), 109114. Dillman, D.A., Smyth, J.D., & Christian, L.M. (2009). Internet, mail, and mixedmode surveys: The tailored design method (3rd ed.). Hoboken, New Jersey: John Wi ley & Sons, Inc. Dinger, M.K. (1999). Physical activity and dietary intake among college students. American Journal of Health Studies 15, 139148. Douglas, K.A., Collins, J.L., Warren, C., et al. (1995). Results from the 1995 National College Health Risk Behavior Survey. Journal of American College Health, 46, 5566. Eaker, E.D., Chesebro, J.H., Sacks, F.M., Wenger, N.K., Whisnant, J.P., & Winston, M. (1993). Cardiovascular disease in women. Circulation 88, 19992009. Eckel, R.H. & Krauss, R.M. (1998). American Heart Association call to action: Obesity as a major risk factor for coronary heart disease. AHA Nutrition Committee, Circulation 97, 20992100. Emmons, K.M., Wechsler, H., Dowdall, G., & Abraham, M. (1998). Predictors of smoking among U.S. college students. American Journal of Public Health, 88, 104107. Erkkila, A.T., Lichtenstein, A.H., Mazaffarian, D., Herrington, D.M. (2004). Fish intake is associated with a reduced progression of coronary artery atherosclerosis in diabetic women with coronary disease. American Journal of Clinical Nutrition 80, 626632. Evangelista, O. & McLaughlin, M.A. (2009). Review of cardiovascular risk factors in women. Gender Medicine, 6, 1736.

PAGE 209

209 Everett, S.A., Husten, C.G., Kann, L., Warren, C.W., Sharp, D., & Crossett, L. (1999). Smoking initiation and smoking patterns among U.S. college students. Journal of American College Health, 48, 5560. Ferdinand, K.C. (2006). Coronary artery disease in minority racial and ethnic groups in the United States. American Journal of Cardiology 97, 12A 19A. Fish, C. & Nies, M. (1996). Health promotion needs of students in a college environment. Public Health Nursing, 13, 104111. Flegal, K.M., Graubard, B.I., Williamson, D.F., & Gail, M.H. (2005). Excess deaths associated with underweig ht, overweight, and obesity. Journal of the American Medical Association, 293, 18611867. Fleury, J., Keller, C., & Murdaugh, C. (2000). Social and contextual etiology of coronary heart disease in women. Journal of Womens Health and Gender Based Medicine, 9, 967978. Flint, A. J., Rexrode, K. M., Hu, F. B., Glynn, R. J., Caspard, H., Manson, J. E., Rimm, E. B. (2010). Body mass index, waist circumference, and risk of coronary heart disease: A prospective study among men and women. Obesity Research & Clinical Practice, 4 (3), e171e181. Florida Department of Health (2010a). Florida Clean Indoor Air Act (FCIAA) Retrieved from: http://www.doh.state.fl.us/tobacco/FCIAA.html Florida Department of Health (2010b). Students Working Against Tobacco: Floridas Anti Tobacco Youth Advocacy Organization. Retrieved from: http://www.doh.state.fl.us/tobacco/SWAT.html Folta, S.C., Goldberg, J.P., Lichtenstein, A.H., Seguin, R., Reed, P.N., & Nelson, M.E. (2008). Factors related to cardiovascular disease risk reduction in midlife and older women: A qualitative study. Preventing Chronic Disease, 5, 1 9. Fox, C.S., Coady, S., Sorlie, P.D., DAgostino, R.B., Pencina, M.J., Vasan, R.S., et al. (2007). Increasing cardiovascular disease burden due to Diabetes Mellitus: The Framingham Heart Study. Circulation 115, 15441550. Friedlander, Y., Arbogast, P., Schwartz, S.M., Marcovina, S.M., Austin, M.A., Ros endaal, F.R. et al. (2001). Family history as a risk factor for early onset myocardial infarction in young women. Atherosclerosis 156, 201207. Frost, R. (1992). Cardiovascular risk modification in the college student: Knowledge, attitudes and behaviors. Journal of General Internal Medicine, 7, 317320. Fung, T.T., Chiuve, S.E., McCullough, M.L., Rexrode, K.M., Logroscino, G., & Hu, F.B. (2008). Adherence to a DASH style diet and risk of coronary heart disease and stroke in women. Archives of Internal Medi cine, 168, 713720.

PAGE 210

210 Fung, T.T., Willett, W.C., Stampfer, M.J., Manson, J.E., & Hu, F.B. (2001). Dietary patterns and the risk of coronary heart disease in women. Archives of Internal Medicine, 161, 18571862. Gaffney, K.F., Wichaikhum, O., & Dawson, E.M. (2002). Smoking among female college students: A time for change. Journal of Obstetric, Gynecologic, & Neonatal Nursing 31, 502507. Gemmell, L. & DiClemente, C.C. (2009). Styles of physician advice about smoking cessation in college students. Journal of A merican College Health, 58, 113119. Getliffe, K.A., Crouch, R., Gage, H., Lake, F., & Wilson, SL. (2000). Hypertension awareness, detection and treatment in a university community: Results of a worksite screening. Public Health 114, 361366. Giardina, E. V. (1998). Call to action: cardiovascular disease in women. Journal of Womens Health, 7, 3743. Ginzberg, E. (1991). Access to health care for Hispanics. Journal of the American Medical Association, 265, 238341. GordonLarsen, P., Adair, L.S., Nelson, M.C., Popkin, B.M. (2004). Fiveyear obesity incidence in the transition period between adolescence and adulthood: the National Longitudinal Study of Adolescent Health. American Journal of Clinical Nutrition 80, 569575. Grant, A.O., Jacobs, A.K., & Clancy, C. (2004). Cardiovascular disease in women: Are there solutions? Circulation 109, 561. Green, J.S., Grant, M., Hill, K.L., Brizzolara, J., & Belmont, B. (2003). Heart disease risk perception in college men and women. Journal of American College Health, 5 1, 207211. Haase, A., Steptoe, A., Sallis, J.F., & Wardle, J. (2004). Leisuretime physical activity in university students from 23 countries: Associations with health beliefs, risk awareness, and national economic development. Preventive Medicine, 39, 182 190. Haberman, S. & Luffy, D. (1998). Weighing in college students diet and exercise behaviors. Journal of American College Health, 46, 189191. Hardesty, P. & Trupp, R.J. (2005). The key to reducing cardiovascular disease risk in women. Journal of Cardiovascular Nursing, 20, 433441. Hart, P.L. (2005). Womens perceptions of coronary heart disease. Journal of Cardiovascular Nursing, 20, 170176.

PAGE 211

211 Haskell, W.L. (2003). Cardiovascular disease prevention and lifestyle interventions. Journal of Cardiovascular Nursing 18, 245255. Haskell, W.L., Lee, I.M., Pate, R.R., Powell, K.E., Blair, S.N., Franklin, B.A., et al. (2007). Physical activity and public health. Updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation 116, 10811093. Ha, E.J. & Caine Bish, N. (2009). Effect of nutrition intervention using a general nutrition course for promoting fruit and vegetable consumption among college students. Journal of Nutrition Education and Behavi or 41, 103109. Hawe, E., Talmud, P.J., Miller, G.J., & Humphries, S.E. (2003). Family history is a coronary heart disease risk factor in the second Northwick Park Heart Study. Annals of Human Genetics 67, 97106. Hayman, L.L. & Hughes, S. (2006). Dietary modification and cardiovascular disease prevention: Lessons learned from the womens health initiative. Journal of Cardiovascular Nursing, 21, 249250. Hemingway, A. (2007). Determinants of coronary heart disease risk for women on a low income: Literat ure review. Journal of Advanced Nursing, 60, 359367. Hoffman, D.J., Policastro, P., Quick, V., & Lee, S. (2006). Changes in body weight and fat mass of men and women in the first year of college: a study of the freshman 15. Journal of American College Health 55, 4145. Hong, S., Friedman, J., & Alt. S. (2003). Modifiable risk factors for the primary prevention of heart disease in women. Journal of the American Medical Womens Association 58, 278284. Hopkins, P.N., Williams, R.R., Kuida, H., Stults, B.M., Hunt, S.C., Barlow, G.K., et al. (1988). Family history as an independent risk factor for incident coronary artery disease in a high risk cohort in Utah. The American Journal of Cardiology 62, 703707. Howard, B.V., Van Horn, L., Hsia, J., Manson, J .E., Stefanick, M., Wassertheil Smoller, S., et al. (2006). Low fat dietary pattern and risk of cardiovascular disease. The Womens Health Initiative Randomized Controlled Dietary Modification Trial. Journal of the American Medical Association 295, 655666. Hu, F.B., Rimm, E.B., Stampfer, J., Asherio, A., Spiegelman, D., Willet, W.C. (2000). Prospective study of major dietary patterns and risk of coronary heart disease in men. American Journal of Clinical Nutrition, 72, 912921. Hu, F.B., Bronner, L., Will et, W.C., Stampfer, M.J. (2002). Fish and omega3 fatty acid intake and risk of coronary heart disease in women. Journal of the American Medical Association, 287, 18151821.

PAGE 212

212 Hu, F.B. (2003). Overweight and obesity in women: Health risks and consequences. Journal of Womens Health, 12, 163172. Huang, T.T.K., Harris, K.J., Lee, R.E., Nazir, N., Born, W., & Kaur, H. (2003). Assessing overweight, obesity, diet, and physical activity in college students. Journal of American College Health, 52, 8386. Huang, Y. L., Song, W.O., Schemmel, R.A., & Hoerr, S.M. (1994). What do college students eat? Food selection and meal pattern. Nutrition Research, 14, 11431153. Irazusta, A., Hoyos, I., Irazusta,J., Ruiz, F., Diaz, E., & Gil, J. (2007). Increased cardiovascular ris k associated with poor nutritional habits in first year university students. Nutrition Research, 27, 387394. Irwin, J.D. (2007). The prevalence of physical activity maintenance in a sample of University students: A longitudinal study. Journal of American College Health, 56, 3741. Jackson, E.M. & Howton, A. (2008). Increasing walking in college students using a pedometer intervention: Differences according to body mass index. Journal of American College Health, 57, 159164. Jain, T., Peschock, R., McGuire D.K., Willett, D. Yu, Z., Vega, G.L., et al. (2004). African Americans and Caucasians have a similar prevalence of coronary calcium in the Dallas Heart Study. Journal of the American College of Cardiology 44, 10111017. Jensen, L.A. & Moser, D.K. (2008) Gender differences in knowledge, attitudes, and beliefs about heart disease. Nursing Clinics in North America 43, 77104. John, U., Meyer, C., Schumann, A., Ulbricht, S., Freyer, J., Hapke, U., et al. (2006). Supporting the intention to change health ri sk behaviors. Journal of Public Health, 14, 377383. Johnson, A.G., Nguyen, T.V., & Davis, D. (2001). Blood pressure is linked to salt intake and modulated by the angiotensinogen gene in normotensive and hypertensive elderly subjects. Journal of Hypertensi on, 19, 10531060. Jousilahti, P., Puska, P., Vurtiainen, E., Pekkunen, J., & Tuomilehto, J. (1996). Parental history of premature coronary heart disease: An independent risk factor of myocardial infarction. Journal of Clinical Epidemiology 49, 497503. Jousilahti, P., Tuomilehto, J., Vartianinen, E., Pekkanen, J., Puska, P. (1996). Body weight, cardiovascular risk factors, and coronary mortality: 15 year follow up of middle aged men and women in eastern Finland. Circulation 93, 13721379.

PAGE 213

213 Joshipura, K. Hu, F., Manson, J.A., Stampfer, M., Rimm, E., Speizer, F., et al. (2001). The effect of fruit and vegetable intake on risk for coronary heart disease. Annals of Internal Medicine, 134, 11061114. Jneid, H. & Thacker, H.L. (2001). Coronary artery disease in women: Different, often undertreated. Cleveland Clinic Journal of Medicine 68, 441448. Juonala, M., Viikari, J.S.A., Rsnen, L., Helenius, H., Pietikinen, M., & Raitakari, O.T. (2006). Young adults with family history of coronary heart disease have increased arterial vulnerability to metabolic risk factors: The cardiovascular risk in young Finns study. Arteriosclerosis, Thrombosis, and Vascular Biology 26, 13761382. Jung, M.E., Bray, S.R., & Ginis, K.A.M. (2008). Behavior change and the freshman 15: Tracking physical activity and dietary patterns in 1st year university women. Journal of American College Health, 56, 523530. Kasparek, D.G., Corwin, S.J., Valois, R.F., Sargent, R.G., & Morris, R.L. (2008). Selected health behaviors that influence coll ege freshman weight change. Journal of American College Health, 56, 437444. Kate, L.P., Boman, H., Daiger, S.P., & Motulsky, A.G. (1982). Familial aggregation of coronary heart disease and its relation to known genetic risk factors. The American Journal of Cardiology 50, 945953. Kawachi, I., Colditz, G.A., Speizer, F.E., Manson, J.E., Stampfer, M.J., Willett, W.C., et al. (1997). A prospective study of passive smoking and coronary heart disease. Circulation 95, 23742379. Keating, X.D., Guan, J., Pinero, J.C. & Bridges, D.M. (2005). A meta analysis of college students physical activity behaviors. Journal of American College Health, 54, 116125. Keys, A., Chirst, A., Blackburn, H., Van Buchem, F.S.P., Buzina, R., Djordjevic, B.S., et al. (1972). Probabi lity of middle aged men developing coronary heart disease in five years. Circulation 45, 815828. Kilpatrick, M., Hebert, E., & Bartholomew, J. (2005). College students motivation for physical activity: Differentiating mens and womens motives for sport participation and exercise. Journal of American College Health, 54, 8794. King, K.B. & Mosca, L. (2000). Prevention of heart disease in women: Recommendations for management of risk factors. Progress in Cardiovascular Nursing 15, 3642. King, K.M. & Art hur, H.M. (2003). Coronary heart disease prevention views on womens gender based perceptions and meanings. Journal of Cardiovascular Nursing, 18, 274281.

PAGE 214

2 14 Kitler, M.E. (1992). Differences in men and women in coronary artery disease, systemic hypertension and their treatment. American Journal of Cardiology 70, 10771080. Knopp, R.H. (2002). Risk factors for coronary artery disease in women. The American Journal of Cardiology 89, 28E 34E. Kordella, T. (2005). The heart of a woman. Diabetes Forecast July, 4246. Koutoubi, S. & Huffman, F.G. (2002). Coronary heart disease risk factors among tri ethnic college students. Internet Journal of Cardiovascular Research, 1, 1 13. Krummel, D.A., Koffman, D.M., Bronner, Y., Davis, J., Greenlund, K., Tessaro, I., et al. (2001). Cardiovascular health interventions in women: What works? Journal of Womens Health and Gender Based Medicine, 10, 117136. Krummel, D.A., Humphries, D., & T essaro, I. (2002). Focus groups on cardiovascular health in rural women: Implications for practice. Journal of Nutrition Education and Behavior 34, 3846. Kuehn, J., McMahon, P., & Creekmore, S. (1999). Stopping a silent killer, preventing heart disease i n women. AWHONN Lifelines 3, 3135. Kuhn, F.E. & Rackley, C.E. (1993). Coronary artery disease in women: Risk factors, evaluation, treatment, and prevention. Archives of Internal Medicine 153, 26262636. Kung, H.C., Hoyert, D.L., Xu, J., & Murphy, S.L. (2008). Deaths: Final data for 2005. National Vital Statistics Reports, 56. Kurian, A.K. & Cardarelli, K.M. (2007). Racial and ethnic differences in cardiovascular disease risk factors: A systematic review. Ethnic Disparities 17, 143152. Kushi, L.H., Fee, R.M., Folsom, A.R., Mink, P.J., Anderson, K.E., & Sellers, T.A. (1997). Physical activity and mortality in postmenopausal women. Journal of the American Medical Association, 277, 12871292. Kwan, M.Y.W., Bray, S.R., & Ginis, K.A.M. (2009). Predicting phys ical activity of first year university students: An application of the Theory of Planned Behavior. Journal of American College Health, 58, 4552. Lapointe, A., Balk, E.M., & Lichtenstein, A.H. (2006). Gender differences in the plasma lipid response to diet ary fat. Nutrition Review, 64, 234249. Lau, R.R., Quadrel, M.J., & Hartman, K.A. (1990). Development and change of young adults preventive health beliefs and behavior: Influence from parents and peers. Journal of Health and Social Behavior 31, 240259.

PAGE 215

215 Leander, K., Hallqvist, J., Reuterwall, C.,& Ahlbom, A. (2001). Family history of coronary heart disease, a strong risk factor for myocardial infarction interacting with other cardiovascular risk factors: Results from the Stockholm Heart Epidemiology Prog ram (SHEEP). Epidemiolo gy, 12, 215221. Lee, I.M., Rexrode, K.M., Cook, N.R., Manson, J.E., & Buring, J.E. (2001). Physical activity and coronary heart disease in women, is No pain, no gain pass? Journal of the American Medical Association, 28, 14471454. Lefler, L.L. (2004). Perceived risk of heart attack: A function of gender? Nursing Forum 39, 1826. Lenz, B.K. (2004). Tobacco, depression and lifestyle choices in the pivotal early college years. Journal of American College Health, 52, 213219. Lerner, D.J. & Kannel, W.B. (1986). Patterns of coronary heart disease morbidity and mortality in the sexes: A 26year follow up of the Framingham population. American Heart Journal 111, 383390. Leslie, E., Fotheringham, M.J., Owen, N., & Bauman, A. (2001). A gerelated differences in physical activity level of young adults. Medical Science Sports Exercise 33, 255258. Li, T.Y., Rana, J.S., Manson, J.E., Willett, W.C., Stampfer, M.J., Colditz, G.A. et al. (2006). Obesity as compared with physical activity in predicting risk of coronary heart disease in women. Circulation 113, 499506. Lichtenstein, A.H. (2003). Dietary fat and cardiovascular disease risk: Quantity or quality? Journal of Womens Health, 12, 109114. Lichtenstein, A.H., Appel, L.J., Brands, M., Carnethon, M., Daniels, S., Franch, H.A., et al. (2006). Diet and lifestyle recommendations revision 2006: A scientific statement from the American Heart Association nutrition committee. Circulation 114, 8296. LloydJones, D.M., Nam, B., DAgostino, R.B. Levy, D., Murabito, J.M., Wang, T.J., et al. (2004). Parental cardiovascular disease as a risk factor for cardiovascular disease in middleaged adults. Journal of the American Medical Association, 291, 22042211. Long, D., Waldrep, S., Hernandez, B., & S trickland, G. (2005). Cardiovascular disease risks in women. American Journal of Health Studies 20, 143148. Lowe, L.P., Greenland, P., Ruth, K.J., Dyer, A.R., Stamler, R., & Stamler, J. (1998). Impact of major cardiovascular disease risk factors, particularly in combination, on 22year mortality in women and men. Archives of Internal Medicine, 158, 20072014.

PAGE 216

216 Lowry, R., Galuska, D.A., Fulton, J.E., Wechsler, H., Kann, L., & Collins, J.L. (2000). Physical activity, food choice, and weight management goals and practices among U.S. college students. American Journal of Preventive Medicine, 18, 1827. Malarcher, A.M., Casper, M.L., Matson Koffman, D.M., Brownstein, J.N., Croft, J., & Mensah, G.A. (2001). Women and cardiovascular disease: Addressing disparities through prevention research and a national comprehensive statebased program. Journal of Womens Health & Gender Based Medicine, 10, 717724. Manolio, T.A., Pearson, T.A., Wenger, N.K., Barrett Connor, E., Payne, G.H., & Harlan, W. R. (1992). Cholesterol and heart disease in older persons and women. Review of an NHLBI workshop. Annals of Epidemiology 2, 161176. Manson, J.E., Greenland, P., LaCroix, A., Stefanick, M., Mouton, C., Oberman, A., et al. (2002). Walking compared with vigorous exercise for the prevention of cardiovascular events in women. New England Journal of Medicine 347, 716725. Manson, J.E., Hu, F.B., Rich Edwards, J.W., Colditz, G.A., Stampfer, M.J., Willet, W.C., et al. (1999). A prospective study of walking as compared with vigorous exercise in the prevention of coronary heart disease in women. New England Journal of Medicine, 341, 650658. Marcus, B.H., Williams, D.M., Dubbert, P.M., Sallis, J.F., King, A.C., Yancey, A.K., et al. (2006). Physical activity intervention studies: what w e know and what we need to know: A scientific statement from the American Heart Association Council on nutrition, physical activity, and metabolism(subcommittee on physical activity); Council on cardiovascular disease in the young; and the Interdisciplinar y Working Group on quality of care and outcomes research. Circulation 114, 27392752. McCarron, P., Smith, G.D., Okasha, M., & McEwen, J. (2000). Blood pressure in young adulthood and mortality from cardiovascular disease. The Lancet 355, 14301431. McCa uley, K.M. (2007). Modifying womens risk for cardiovascular disease. Journal of Obstetric, Gynecologic, & Neonatal Nursing, 36, 116124. McGowen, M.P., Joffe, A., Duggan, A.K., & McCay, P. (1994). Intervention in hypercholesterolemic college students: a pilot study. Journal of Adolescent Health, 15, 155162. McMahan, S., Cathorall, M., & Romero, D.R. (2007). Cardiovascular disease riskperception and knowledge: A comparison of Hispanic and White col lege students in a Hispanic serving institution. Journal of Hispanic Higher Education, 6, 5 18. Meischke, H., Yasui, Y., Kuniyuki, A., Bowen, D.J., Anderson, R., & Urban, N. (1999). How women label and respond to symptoms of acute myocardial infarction: Re sponses to hypothetical symptom scenarios. Heart and Lung, 28, 261269.

PAGE 217

217 Meischke, H., Kuniyuki, A., Yasui, Y., Bowen, D.J., Anderson, R., Urban, N. (2002). Information women receive about heart attacks and how it affects their knowledge beliefs, and intent ions to act in a cardiac emergency. Health Care for Women International 23, 149162. Meisler, J.G. (2001). Toward optimal health: The experts discuss heart disease in women. Journal of Womens Health and Gender Based Medicine, 10, 1725. Mikhail, G.W. (20 05). Coronary heart disease in women. British Medical Journal 331, 467468. Miller, C.L. (2002). A review of symptoms of coronary artery disease in women. Journal of Advanced Nursing, 39, 1723. Miracle, V.A. (2006). Coronary Artery Disease in Women. Dime nsions of Critical Care Nursing 25, 209215. Mirotznik, J., Feldman, L., & Stein, R. (1995). The Health Belief Model and adherence with a community center based, supervised coronary heart disease exercise program. Journal of Community Health, 20, 233247. Mitka, M. (2007). A change of heart guidelines for women. Journal of the American Medical Association, 297, 14211422. Mokdad, A.H., Serdula, M.K., Dietz, W.H., Bowman, B.A., Marks, J.S., & Koplan, J.P. (1999). The spread of the obesity epidemic in the United States, 19911998. Journal of the American Medical Association 282, 15191522. Mokdad, A.H., Giles, W.H., Bowman, B.A., Mensah, G.A., Ford, E.S., Smith, S.M., & Marks, J.S. (2004). Changes in health behaviors among older Americans, 19902000. Public Health Reports 119, 356361. Mora, S., Cook, N., Buring, J.E., Ridker, P.M., & Lee, I.M. (2007). Physical activity and reduced risk of cardiovascular events: Potential mediating mechanisms. Circulation 116, 21102118. Moran, S., Glazier, G., & Armstrong, K. (2003). Women smokers perceptions of smokingrelated health risks. Journal of Womens Health, 12, 363371. Mosca, L., Edleman, D., Mochari, H., Christian, A.H., Paultre, F., & Pollin, I. (2006). Waist circumference predicts cardiometabolic and global Framingham risk among women screened during national womans heart day. Journal of Womens Health, 15, 2434. Mosca, L., Ferris, A., Fabunmi, R., & Robertson, R.M. (2004). Tracking womens awareness of heart disease: An American Heart Association national study. Circulation 109, 573579.

PAGE 218

218 Mosca, L., Grundy, S.M., Judelson, D., King, K., Limacher, M., Oparil, S., et al. (1999). Guide to preventive cardiology for women. Circulation 99, 24802484. Mosca, L., Jones, W.K., King, K.B., Ouyang, P., Redberg, R.F., & Hill, M.N. (2000). Awareness, perception, and knowledge of heart disease risk and prevention among women in the United States. Archives of Family Medicine, 9, 506515. Mosca, L., Manson, J.E., Sutherland, S.E., Langer, R.D., Manolio, T., & Barrett Connor, E. (1997). Cardiovascular disease in women. Circulation 96, 24682482. Mosca, L., McGillen, C., & Rubenfire, M. (1998). Gender differences in barriers to lifestyle change for cardiovascular disease prevention. Journal of Womens Health, 7, 711715. Mur abito, J.M. (1995). Women and cardiovascular disease: Contributions from the Framingham Heart Study. Journal of the American Womens Association, 50, 3539. Murdaugh, C.L. & Verran, J.A. (1987). Theoretical modeling to predict physiology of cardiac prevent ive behaviors. Nursing Research, 36, 284291. Must, A., Spadano, J., Coakley, E.H., Field, A.E., Colditz, G., & Dietz, W.H. (1999) The disease burden associated with overweight and obesity. Journal of the American Medical Association, 282, 15231529. Myers, J., Prakash, M, Froelicher, V., Do, D., Partington, S., & Atwood, J.E. (2002). Exercise capacity and mortality among men referred for exercise testing. New England Journal of Medicine, 346, 793801. National Heart Lung and Blood Institute. (2010). D iseases and conditions index: Heart disease in women. Retrieved from http://www.nhlbi.nih.gov/health/dci/Diseases/hdw/hdw_whatis.html Navas Nacher, E.L., Colangelo, L., Beam, C., & Greenland, P. (2001). Risk factors for coronary heart disease in men 1839 years of age. Annals of Internal Medicine, 134, 433439. National Cancer Institute. (2005). Theory at a Glance: A Guide for Health Promotion Practice (2nd ed.). Washington, DC: National Institute of Health, U. S. Department of Health and Human Services. Nehl, E.J., Blanchard, C.M., Peng, C.Y., Rhodes, R.E., Kupperman, J., Sparling, P.B. et al. (2009). Understanding nonsmoking in African American and Caucasian col lege students: An application of the Theory of Planned Behavior. Behavioral Medicine, 35, 2329. Newton, K.M. (2004). Risk factors for coronary heart disease in women. The Nursing Clinics of North America 39, 145163.

PAGE 219

219 Noori, M. & Anim Nyame, N. (2005). Long term cardiovascular risk in women. Vascular Disease Prevention, 2, 261265. Office of Institutional Planning and Research (2010). University of Florida Fact Book Academic Programs. Retrieved from http://www.ir.ufl.edu/oirapps/factbooktest/academic_programs/academic_default.a spx Okchowski, A.E., Graham J.W., Beverly, E.A., & Dupkanick, C.W. (2009). Cigarette smoking, physical activity and the health status of college students. Journal of Applied Social Psychology 39, 683706. Oliver McNeil, S., & Artinian, N.T. (2002). Womens perceptions of personal cardiovascular risk and their risk reducing behaviors. American Journal of Critical Care 11, 221227. Ols hansky, S.J., Passaro, D.J., Hershow, R.C., Layden, J., Carnes, B.A., Brody, J., et al. (2005). A potential decline in life expectancy in the United States in the 21st century. The New England Journal of Medicine, 352, 11381145. Pereira, M.A., OReilly, E., Augustsson, K., Fraser, G.E., Goldbourt, U., Heitmann, B.L., et al. (2004). Dietary fiber and risk of coronary heart disease: A pooled analysis of cohort studies. Archives of Internal Medicine 164, 370376. Perry, C.K. & Bennett, J.A. (2006). Heart di sease prevention in women: Promoting exercise. Journal of the American Academy of Nurse Practioners 18, 568573. Pilote, L. & Hlatky, M.A. (1995). Attitudes of women toward hormone therapy and prevention of heart disease. The American Heart Journal 129, 12371238. Pinto, B.M. & Marcus, B.H. (1995). A Stages of Change approach to understanding college students physical activity. Journal of American College Health, 44, 2731. PiSunyer, F.X. (1993). Medical hazards of obesity. Annals of Internal Medicine, 119, 655. Pletcher, M.J., Bibbins Domingo, K., Lewis, C.E., Wei, G.S., Sidney, S., Carr, J.J. (2008). Prehypertension during young adulthood and coronary calcium later in life. Annals of Internal Medicine, 149, 9199. Presidents Council on Diversity (2010). Diversity at the University of Florida. Retrieved from http://www.hr.ufl.edu/eeo/diversity.htm Price, J.A.D. (2004). Management and prevention of cardiovascular disease in women. Nursing Clinics of North America, 39, 873884.

PAGE 220

220 Primack, B.A., Sidani, J., Shadel, W.G. & Eissenberg, T. (2008). Prevalence of and associations with waterpipe tobacco smoking among U.S. university students. Annals of Behavioral Medicine, 36, 8186. Prochaska, J.O. & DiCle mente, C.C. (1982). Transtheoretical therapy: Toward a more integrative model of change. Psychotherapy: Theory, Research and Practice, 19, 276288. Racette, S.B., Deusinger, S.S., Strube, M.J., Highstein, G.R., & Deusinger, R.H. (2005). Weight changes, exercise, and dietary patterns during freshman and sophomore years of college. Journal of American College Health, 53, 245251. Rea, T.D., Heckbert, S.R., Kaplan, R.C., Smith, N.L., Lemaitre, R.N., & Psaty, B.M. (2002). Smoking status and risk for recurrent c oronary events after myocardial infarction. Annals of Internal Medicine, 137, 494500. Rhodes, S.D., Bowie, D.A., & Hergenrather, K.C. (2003). Collecting behavioral data using the world wide web: considerations for researchers. Journal Epidemiology Commun ity Health 57, 6873. Richards, H.M., Reid, M.E., & Watt, G.M. (2002). Why do men and women respond differently to chest pain? A qualitative study. Journal of the American Medical Womens Association, 57, 7981. Rosenberg, L., Miller, D.R., Kaufman, D.W., Helmrich, M.S., Van de Carr, S., Stolley, P.D., et al. (1983). Myocardial infarction in women under 50 years of age. Journal of the American Medical Association. 250, 28012806. Rosenfeld, A.G. (2006). State of the heart: Building science to improve women s cardiovascular health. American Journal of Critical Care, 15, 556566. stepping stones, milestones and obstructing boulders. Nature Clinical Practice: Cardiovascular Medicine, 3, 194202. Sacks, F.M., Svetkey, L.P., Vollmer, W.M., Appel, L.J., Bray, G.A., Harsha, D., et al. (2001). Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension (DASH) diet. New England Journal of Medicine, 344, 310. Sallis, J.F. (2000). Agerelated decline in physical activity: A s ynthesis of human and animal studies. Medicine and Science in Sports & Exercise, 32, 15981600. Sanem, J.R., Berg, C.J., An, L.C., Kirch, M.A., & Lust, K.A.(2009). Differences in tobacco use among two year and four year college students in Minnesota. Jour nal of American College Health, 58, 151159. Sax, L.J. (1997). Health trends among college freshmen. Journal of American College Health 45, 252262.

PAGE 221

221 Schaefer, E.J. (2002). Lipoproteins, nutrition, and heart disease. American Journal of Clinical Nutrition 75, 191212. Schorling, J.B., Gutgesell, M., Klas, P., Smith, D., & Keller, A. (1994). Tobacco, alcohol and other drug use among college students. Journal of Substance Abuse, 6, 105115. Schroetter, S.A. & Peck, S.D. (2008). Womens risk of heart disease: Promoting awareness and prevention a primary care approach. MEDSURG Nursing 17, 107113. Sesso, H.D. Pappenbarger, R.S., Ha, T., et al. (1999). Physical activity and cardiovascular disease risk in middle aged and older women. American Journal of Epide miology 150, 408416. Sharma, A.M. (2003). Obesity and cardiovascular risk. Growth Hormone & IGF Research, 13, S10S17. Sherman, S.E., DAgostino, R.B., Cobb, J.L., Kannel, W.B. (1994). Physical activity and mortality in women in the Framingham Heart Study. American Heart Journal 128, 879884. Sibley, C., Blumenthal, R.S., Merz, C.N., & Mosca, L. (2006). Limitations of current cardiovascular disease risk assessment strategies in women. Journal of Womens Health 15, 5456. Silagy, C., Muir, J., Coulter, A ., Thorogood, M., & Roe, I. (1993). Cardiovascular risk and attitudes to lifestyle: What do patients think? British Medical Journal 306, 16571660. Simontacchi, C, & Fitzgerald, F.E. (2004). A Woman's guide to a healthy heart Chicago: McGraw Hill. Smith L.E. (2008). Heart Ed 101. Journal of American College Health, 56, 698700. Smith, S.C., Taylor, J.G., & Stephen, A.M. (2000). Use of food labels and beliefs about diet disease relationships among university students. Public Health Nutrition 3, 15182. Smith, D.G., McCarron, P., Okasha, M., & McEwen, J. (2001). Social circumstances in childhood and cardiovascular disease mortality: Prospective observational study of Glasgow University students. Journal of Epidemiology and Community Health, 55, 340341. S parling, P.B., Snow, T.K., & Beavers, B.D. (1999). Serum cholesterol levels in college students: opportunities for education and intervention. Journal of American College Health, 48, 123127.

PAGE 222

222 Spencer, L. (2002). Results of a heart disease risk factor scree ning among traditional college students. Journal of American College Health, 50, 291296. Stamler, J., Stamler, R., Riedlinger, W.F., Algera, G., & Roberts, R.H. (1976). Hypertension screening of 1 million Americans. Community Hypertension Evaluation Clini c (CHEC) program, 19731975. Journal of the American Medical Association 235, 22992306. Stampfer, M.J., Hu, F.B., Manson, J.E., Rimm, E.B., & Willet, W.C. (2000). Primary prevention of coronary heart disease in women through diet and lifestyle. The New E ngland Journal of Medicine, 343, 1622. Stoddard, A.M., Palombo, R., Troped, P.J., Sorensen, G., & Will, J.C. (2004). Cardiovascular disease risk reduction: the Massachusetts WISEWOMAN Project. Journal of Womens Health, 13, 539546. Strong, J.P., Malcom, G.T., McMahan, C.A., et al. (1999). Prevalence and extent of atherosclerosis in adolescents and young adults. Journal of the American Medical Association 281, 727735. Sullivan, S.L., Keating, X.D., Chen, L., Guan, J., Delzeit McIntyre, L., & Bridges, D. (2008). Physical education and general health courses and minority community college student risk levels for poor health and leisuretime exercise patterns. College Student Journal 42, 132152. Suminski, R.R., Petosa, R., Utter, A.C., & Zhang, J.J. (2002). Physical activity among ethnically diverse college students. Journal of American College Health, 51, 7580. Tanasescu, M., Leitzmann, M.F., Rimm, E.B., Willett, W.C., Stampfer, M.J., & Hu, F.B. (2002). Exercise type and intensity in relation to coronary heart disease. Journal of the American Medical Association 288, 19942000. Thanavaro, J.L., Moore, S.M., Anthony, M., Narsavage, G., & Delicath, T. (2006). Predictors of health promotion behavior in women without prior history of heart disease. Applied Nursing Research, 19, 148155. Thompson, H.J., Pell, A.C.H., Anderson, J., Chow, C.K., & Pell, J.P. (2010). Screening families of patients with premature coronary heart disease to identify avoidable cardiovascular risk: A cross sectional study of family members and a general population comparison group. BMC Research Notes 3, 132136. Toft, U., Kristoffersen, L.H., Lau, C., Borch Johnsen, K., & Jorgensen, T. (2006). The Dietary Quality Score: Validation and association with cardiovascular risk factors: the Inter99 study. European Journal of Clinical Nutrition, 61, 270278. Tsang, T.S.M., Barnes, M.E., Gersh, B.J., & Hayes, S.N. (2000). Risks of coronary heart disease in women: Current understanding and evolving concepts. Mayo Clinic Proceedings 75, 12891303.

PAGE 223

223 Turner, M.B., Vader, A.M., & Walters, S.T. (2008). An analysis of cardiovascular health information in popular young womens magazines: What messages are women receiving? American Journal of Health Promotion, 22, 183186. U.S. Department of Health and Human Services (2004). The Health Consequences of Smoking: A Report of the Surgeon General [Electronic version]. Retrieved from http://www.surgeongeneral.gov/library/smokingconsequences/ U.S. Department of Health and Human Services. HealthyPeople 2010, Volume 1: 7 Educational and Community Based Programs. Retrieved from http://www.healthypeople.gov/2010/Document/HTML/Volume1/07Ed.htm U.S. Department of Health and Human Services (2007). Healthy Heart Handbook for Women National Institutes of Health; National Heart, Lung, and Blood Institute. U.S. Department of Health and Human Services (2009). Healthy People 2020: The Road Ahead. Proposed HP 2020 Objectives: Heart Disease and Stroke. Retrieved from http://www.healthypeople.gov/2020/topicsobjectives2020/default.aspx Vanhecke, T.E., Miller, W.M., Franklin, B.A., Weber, J.E., & McCullough, P.A. (2006). Awareness, knowledge, and perception of heart disease among adolescents. European Journal of Cardiovascular Prevention and Rehabilitation, 13, 718723. Wei, M., Mitchell, B.D., Haffner, S.M., & Stern, M.P. (1996). Effects of cigarette smoking, diabetes, high cholesterol, and hypertension on all cause mortality and cardiovascular disease mortality in Mexican Americans: The San Antonio Heart Study. American Journal of Epidemiology 144, 10581065. Weller, I. & Corey, P. (1998). The impact of excluding nonleisure energy expendi ture on the relation between physical activity and mortality in women. Epidemiology 9, 632635. Welty, F.K. (2004). Preventing clinically evident coronary heart disease in the postmenopausal woman. Menopause: The Journal of the North American Menopause Society 11, 484494. Wendt, S.J. (2005). Perception of future risk of breast cancer and coronary heart disease in female undergraduates. Psychology, Health & Medicine 10, 253262. Wenger, N.K. (2003). Coronary heart disease: The female heart is vulnerable. Progress in Cardiovascular Diseases 46, 199229. Wenger, N.K. (2004). Youve come a long way, baby: Cardiovascular health and disease in women: Problems and prospects. Circulation 109, 558560. Wenger, N.K. (2006). Coronary heart disease in women: Highl ights of the past 2 years stepping s tones, milestones and obstructing boulders. Nature Clinical Practice Cardiovascular Medicine, 3, 194202.

PAGE 224

224 Whelton, P.K., He, J., Appel, L.J., Cutler, J.A., Havas, S., Kotchen, T.A. et al. (2002). For the National High Bl ood Pressure Education Program Coordinating Committee. Primary prevention of hypertension: Clinical and public health advisory from the National High Blood Pressure Education Program. Journal of the American Medical Association, 288, 18821888. Winkleby, M .A., Kraemer, H.C., Ann, D.K., & Varady, A.N. (1998). Ethnic and socioeconomic differences in cardiovascular disease risk factors: Findings for women from the third national health and nutrition examination survey, 19881994. Journal of the American Medical Assocation 280, 356362. World Health Organization (2003) MONICA Monograph. WHO,Geneva. Wilson, P.W. (1990). High density lipoprotein, low density lipoprotein and coronary artery disease. American Journal of Cardiology 66, 7A 10A. Yawn, B.P., Mabry, I. R., & Ko, S. (2003). The greatest threat to womens health. The Lancet 62, 1165.

PAGE 225

225 BIOGRAPHICAL SKETCH Originally from Jacksonville, Florida, Suzanne M. Sneed has also enjoyed living in North Carolina, Maryland, and West Virginia. Suzanne earned her undergraduate degree in biology in 1999 from West Virginia University. She returned to Middleburg, Florida, t o teach high school science at a local private school. Following six full years of teaching, she decided to continue her education in pursuit of a masters degree. She moved to Gainesville, Florida, to work toward a masters degree in health science educat ion with a specialization in adapted physical activity from the University of Florida, graduating in 2007. Suzanne then decided to return to the University of Floridas College of Health and Human Performance as a doctoral student. She received a Ph.D. in health and human performance from the University of Florida in May 2011. Her research interests include heart disease risk prevention in the university female student population though behavior assessment and creation of behavior objectives that promote good heart health. Suzanne plans to continue working in academia and continue research in the area of heart disease risk prevention.