Emerging Adults with Familial Risk for Type 2 Diabetes. A thesis presented to. the faculty of. the College of Arts and Sciences of Ohio University

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1 Examining Perceived Susceptibility of Illness and Health Protective Behaviors Among Emerging Adults with Familial Risk for Type 2 Diabetes A thesis presented to the faculty of the College of Arts and Sciences of Ohio University In partial fulfillment of the requirements for the degree Master of Science Bonita Sur December Bonita Sur. All Rights Reserved.

2 This thesis titled 2 Examining Perceived Susceptibility of Illness and Health Protective Behaviors Among Emerging Adults with Familial Risk for Type 2 Diabetes by BONITA SUR has been approved for the Department of Psychology and the College of Arts and Sciences by Julie A. Suhr Professor of Psychology Robert Frank Dean, College of Arts and Sciences

3 ABSTRACT 3 SUR, BONITA, M.S., December 2015, Clinical Psychology Examining Perceived Susceptibility of Illness and Health Protective Behaviors Among Emerging Adults with Familial Risk for Type 2 Diabetes Director of Thesis: Julie A. Suhr Type 2 Diabetes Mellitus (T2DM) is a lifelong chronic illness influenced by both genetic and behavioral risk factors. Although children of parents with T2DM are at significantly greater risk for developing the disease than age-matched controls (Valdez, Yoon, Liu, & Khoury, 2007), there is a dearth of research examining the factors that influence their perceptions of their own risk for the illness and their likelihood to engage in health risk and protective behaviors. In this descriptive cross-sectional survey study, we recruited university students (N = 197, 86% Caucasian, 66% female, average 19±1.6 years old) to examine factors associated with perceived susceptibility of T2DM and engagement in health protective behaviors in young adult college students with (N = 66) or without a parent/grandparent (N = 131) diagnosed with T2DM. Consistent with hypotheses, individuals with familial risk for T2DM were more likely to report greater own absolute risk and direct comparative risk of developing T2DM than individuals without familial risk for T2DM. However, contrary to hypotheses, no differences in physical activity, healthy diet, and sugar sweetened beverage consumption were found between the two groups. Further, contrary to expectations, both greater own absolute risk of developing T2DM and greater direct comparative risk of developing T2DM were associated with being less likely to have a healthy diet, less likely to engage in regular physical activity, and more likely to consume sugary drinks. Results may provide a better

4 understanding of poor lifestyle choices associated with increased diabetes risk in 4 individuals with familial risk of diabetes and could inform development of interventions targeted towards young adults.

5 ACKNOWLEDGMENTS 5 Thank you to my advisors, Julie Suhr, Ph.D., and Elizabeth Beverly, Ph.D., my research assistants, Dareon Michael Freeman, Brynne Hahn, Inez Maria McQueen, the Department of Psychology at Ohio University, and the Osteopathic Heritage Foundations Graduate Assistantship Program at Ohio University Heritage College of Osteopathic Medicine.

6 TABLE OF CONTENTS 6 Page Abstract... 3! Acknowledgments... 5! List of Tables... 7! Introduction... 8! Method... 20! Results... 27! Discussion... 34! References... 46! Appendix A: Materials... 54! Appendix B: Additional Supplemental Analyses....82!

7 LIST OF TABLES 7 Page Table 1: Characteristics of Participants, by Familial Risk of Type 2 Diabetes.21 Table 2: Mean Scores on Health Protective Behaviors, Perceived Threat Measures by Familial Risk of Type 2 Diabetes (T2DM) Table 3: Characteristics of Participants, by Gender...82 Table 4: Perceived Susceptibility and Health Protective Behaviors, By Risk Group...86 Table 5: Perceived Susceptibility and Health Protective Behaviors, By BMI Risk Group.86 Table 6: Perceived Susceptibility and Health Protective Behaviors, By Waist Circumference (WC)..87 Table 7: Perceived Susceptibility and Health Protective Behaviors, By Gender..87 Table 8: Internal Health Locus of Control and Health Protective Behaviors, by Familial Risk...89 Table 9: Internal Health Locus of Control and Health Protective Behaviors, by Familial Risk...91

8 INTRODUCTION 8 Type 2 Diabetes Mellitus (T2DM) is a lifelong chronic illness, affecting an estimated 29.1 million people (9.3% of the population) in the United States. If current trends continue, the CDC projects that as many as 1 in 3 adults in the US will be diagnosed with T2DM by 2050 (CDC, 2011). It is imperative for researchers to investigate the factors that may influence at-risk populations likelihood to engage in behaviors that would help prevent T2DM and its associated complications. The heritable nature of T2DM is well confirmed, as evidenced by different prevalence rates in different ethnic groups despite similar environmental factors, familial risk of the disease, twin studies, and a genetic basis for diabetes phenotypes (e.g.,vaag, Henriksen, Madsbad, Holm, & Beck-Nielsen, 1995; Florez, Hirschhorn, & Altshuler, 2003; Das & Elbein, 2006). However, behavior can also play a significant role in the development of T2DM. Lifestyle factors such as weight gain, physical inactivity, poor diet, and current smoking all independently increase the risk of T2DM (Colditz et al., 1995; Chan et al., 1994; Hu et al., 2001b). Many of these lifestyle risk factors for T2DM are learned behaviors (Repetti, Taylor, & Seeman, 2002; Reilly et al., 2005) that are nested within ingrained family beliefs of what is considered healthy and what is not (Etelson, Brand, Patrick, & Shirali, 2003). At present, research is lacking in understanding the likelihood of engaging in poor lifestyle choices associated with increased diabetes risk in individuals with familial risk for diabetes. A better understanding of emerging adults perceived beliefs and attitudes surrounding their personal diabetic risk could inform researchers and clinicians who are

9 developing interventions for poor lifestyle choices at a point in life when changes to 9 behavior might significantly reduce risk for developing the disease and its complications. Familial Risk of T2DM Results from the Framingham Offspring Study, a longitudinal study of 1,303 nuclear families every four years over the course of 20 years, indicate parental transmission of diabetes risk is mostly likely due to both genetic and environmental risk (Meigs, Cupples, & Wilson, 2000), finding the odds ratio of developing T2DM with only a single parent with T2DM (OR = ) nearly doubles if both parents are affected (Meigs et al., 2000). Further evidence of genetic risk is rooted in the identification of genes, such as calpain 10 (CAPN10) and TCF7L2 (Grant et al., 2006; Ali, 2013) that are associated with developing T2DM. At present, roughly 70 susceptibility genes have been identified as being associated with T2DM; however further study is still needed in understanding how genetic susceptibility correlates with disease progression (Sun, Yu, & Hu, 2014). Familial risk may also be non-genetic and related to environmental learning. Many lifestyle risk factors for diabetes, such as poor diet and lack of physical activity, are partially learned behaviors, most often from direct observation of parental behaviors (Baranowski, Nader, Dunn, & Vanderpool, 1982). Other Risk and Protective Factors Associated with T2DM T2DM is associated with many risk factors other than familial risk, including older age, obesity, history of gestational diabetes, impaired glucose metabolism, physical inactivity, diet, psychological risk factors, and race/ethnicity (CDC, 2014a). Health protective behaviors are conceptualized as the antithesis of risk behaviors that are within an individuals control; thus they are behaviors that may delay and possibly prevent the

10 10 onset of the disease. Losing weight, increasing physical activity, and healthier eating in individuals at risk for developing T2DM have all been shown to delay onset of the disease (DPP Research Group, 2002). One of the strongest predictors of T2DM is obesity, as indicated by Body Mass Index (BMI) and waist circumference (Kahn, Hull, Utzschneider, 2006; Hu et al., 2001a; Molarius & Seidell, 1998), with findings suggesting carrying excess body fat is the single most important determinant of T2DM (Hu et al., 2001a; Hu et al., 2001b; Ganz et al., 2014). Another important risk factor in the development of T2DM is physical inactivity. The benefits of physical activity in T2DM prevention extend beyond regulating body weight; physical activity also helps reduce insulin resistance, hypertension, atherogenic dyslipidemia, and inflammation; and helps improve insulin sensitivity, glycemic control, and fibrinolytic and endothelial function (Bassuk & Manson, 2005). Adults who engage in low levels of physical activity are particularly at risk for T2DM, especially when low physical activity is combined with other risk factors such as high BMI, hypertension, or parental history of diabetes (Steyn et al., 2004). However, physically active individuals in the same obesity class as physically inactive individuals have 2/3 less risk for developing T2DM. In addition, there is a decline in T2DM risk as levels of physical activity increase, after adjustment for age, BMI, history of hypertension, and parental history of T2DM (Helmrich, Ragland, Leung, & Paffenbarger, 1991). Exercise training has also been shown to be an effective method to possibly prevent T2DM in youth. In a recent metaanalysis of 24 randomized controlled studies in 1,599 children and adolescents (aged 6-19 years), found a small to moderate effect size for physical activity on fasting insulin

11 11 (effect size = 0.48, p < 001) and reducing insulin resistance (effect size = 0.31, p <.05) (Fedewa, Gist, Evans, & Dishman, 2014). Diet is another of several lifestyle risk factors for T2DM in adults (Hu, Van Dam, & Liu, 2001b; Hu et al., 2001a). A poor diet (low in cereal fiber and polyunsaturated fat and high in saturated and trans fats and glycemic load) has been associated with a significantly increased risk of T2DM, even after controlling for BMI (Hu et al., 2001b). Another important dietary factor is the excessive consumption of soft drinks and fruit punches. Although consumption of sugar sweetened beverages is on the decline in the United States in both youth and adults (Kit, Fakhouri, Park, Nielsen, Ogden, 2013), it is a key culprit for many health risks, including increased weight gain, obesity, and risk for T2DM among both young adults and adults (Malik, Schulze, & Hu, 2006; Schulze et al., 2004; Ebbeling et al., 2003). Factors Associated with Health Protective Behaviors Certain factors have been identified that may be potentially linked to general engagement in health protective behaviors. These factors include family modeling of behavior, health locus of control, and perceived susceptibility of illness. However, there is less research linking these factors specifically to the context of T2DM. While family modeling is a factor associated with engagement in health protective behaviors generally, there is limited literature tying this variable directly to T2DM prevention. However, many of the previously mentioned lifestyle risk factors for diabetes, such as diet, lack of physical activity, and weight gain are partially learned behaviors, most often from direct observation of parental behaviors (Baranowski, Nader, Dunn, & Vanderpool, 1982). A few studies have investigated parent-child pairs in order

12 12 to explore the relationship between parent modeling of health behaviors and the child s likelihood to engage in those behaviors (Rossow & Rise, 1985; Lau et al., 1990). Rossow & Rise (1985) investigated four main health behaviors; smoking, alcohol consumption, fat intake, and exercise between parents and adolescent/young adults (between years of age) in 337 nationally representative Norwegian families and found a positive association between parent and adolescent fat intake, which was significantly greater than the relationship between any other observed health behavior. Adolescent exercise only had a significant relationship with father s frequency of exercise, and no association was found comparing both parents level of exercise compared to one parent s exercise. Using a slightly different paradigm, Lau and colleagues conducted a longitudinal study in the United States on 947 parent-child pairs, exploring stability and changes in health beliefs and behavior regarding alcohol consumption, diet, exercise, and wearing seat belts during the child s years at college (Lau et al., 1990). Direct modeling of behavior (seeing behaviors carried out first hand) by both parents and peers was found to be the strongest influence of behavior overall, compared to transmission of beliefs about behavior (beliefs transmitted unintentionally), or explicit training efforts (beliefs transmitted directly and purposefully). Modeling by parents had strong influence on alcohol consumption, eating habits, and seatbelt use, whereas peers had strong influence on alcohol consumption, eating habits, and exercise. This study suggests there is significant influence from parents in both health protective and risk behaviors, even after the child has left the house. Certain psychological factors have also been linked to engagement in these health protective behaviors generally, with at least some research on their relation to health

13 13 protective behaviors in individuals at risk for T2DM. One such psychological variable is health locus of control (HLC), which refers to the degree an individual believes his or her health is due to his or her own behavior, luck or chance, or powerful others (e.g. doctors) (Wallston, et al., 1976). If an individual has a high internal locus of control, he or she is more likely to modify behavior, due to belief that he or she has greater control over possible outcomes. For example, the 1970 British Cohort Study, an ongoing longitudinal study of 17,198 live births, found that individuals with a propensity towards a more internal locus of control at age 10 had a significantly reduced risk of multiple health conditions at age 30, including obesity and being overweight, even after adjusting for possible covariates including education, social class, and current income (Gale, Batty, & Deary, 2008). Steptoe & Wardle (2001) conducted a cross-sectional study of 4,358 female and 2,757 male university students from 18 European countries, examining the relationship between the three orientations of locus of control (internal, powerful others, and chance) and ten health-related behaviors (physical exercise, smoking, alcohol consumption, eating breakfast, tooth-brushing, seat belt use, and consumption of fruit, fat, fiber, and salt), finding the odds of carrying out healthy activities were 40-70% greater in those in the highest quartile of internal health locus of control, compared to those in the lowest quartile, after adjustments for sex, age, health value, and other locus of control scales. Although there have also been several studies finding an internal locus of control is associated with greater adherence to a diabetes regimen in individuals already diagnosed with the illness (Alogna, 1980; Peyrot & Rubin, 1994; Tillotson & Smith, 1996), there has been little research in individuals at risk for the illness. Further studies are necessary to examine how the unique experience of increased risk for T2DM

14 14 among young adults may be related to health locus of control orientation and likelihood to engage in health protective behavior. Health locus of control is also an essential psychological factor within the Health Beliefs Model, a dominant theory of health behavior change. The Health Beliefs Model framework incorporates locus of control and other factors, such as perceived threat of illness, into how and why individuals choose to engage in health protective behaviors. Perceived Threat of Illness The Health Beliefs Model, an influential theory describing factors influencing health behavior change, states perceived threat of disease can influence the degree to which individuals will modify their behavior to lower their own disease risk (Rosenstock, 1974). According to Rosenstock (1974), perceived susceptibility ( the threat posed by illness, comprised of the likelihood of its occurrence ) and perceived severity ( its potential for causing physical harm and interfering with social functioning ) (p. 349) combine to form an individual s perceived threat of illness, which provides the individual with the motivational force to engage in health protective behavior. The evidence of the relationship between perceived threat of an illness and health protective behavior was demonstrated in early review papers and studies on adult populations in the United States in a wide range of health behaviors, including dental check-ups, tuberculosis, influenza, and Tay-Sachs disease (Becker, Haefner, Kasl, Kirscht, Maiman, & Rosenstock, 1977; Cummings, Jette, Brock, & Haefner, 1979). More recently, this relationship has been shown to be weaker than initially thought in a wide range of health protective behaviors and also in diabetes preventative behaviors (Carpenter, 2010; Hivert, Warner, Shrader,

15 Grant, & Meigs, 2009). This may be due in part to inconsistencies in measures of 15 perceived threat. Ranby, Aiken, Gerend, and Erchull (2012) examined the inconsistencies among commonly used measures of perceived threat judgments, including own absolute risk, How likely do you think it is that you will get diabetes in the future? direct comparative risk, What do you believe are your chances of developing diabetes compared with other individuals your age?, and indirect comparative risk, How likely do you think it is that individuals will develop diabetes in the future?. The researchers focused on understanding the differences in these measures with disease risk factors in a myriad of illnesses and found different risk assessments led to different conclusions regarding perceived susceptibility. In an assessment of 123 women (aged years) free of T2DM, absolute risk and indirect comparative risk were significantly associated with familial risk of T2DM, while direct comparative risk and other absolute risk were not. Further, absolute risk, direct comparative risk, and indirect comparative risk were all significantly associated with increased BMI, an indicator for increased risk of T2DM, while direct comparative risk was not (Ranby et al., 2012). Further study is necessary to better understand how to assess the construct of perceived susceptibility, perceived severity, and perceived threat of illness in order to understand their association with health protective behaviors. It is unclear from prior research on perceived threat whether past findings are consistent with the Health Beliefs Model, that is whether individuals who already have a higher than average diabetes risk due to family diabetes history acknowledge their increased susceptibility and the severity of the illness and thus engage in more protective

16 health behaviors. (Forsyth and Goetsch (1997) examined perceived susceptibility of 16 T2DM and engagement in health protective behaviors in a population at risk for diabetes, as determined by familial risk. Adults (mean age = years) with parental history of T2DM reported a greater perceived susceptibility to diabetes (r 2 =.493) and engaged in more health protective behaviors (r 2 =.127), specifically weight-control efforts, than adults with no parental history of T2DM. Adults with a parental history of HTN also experienced greater perceived susceptibility to hypertension and engaged in more health protective behaviors than the control group. However, due to the cross-sectional nature of this study, it is unclear whether perceived susceptibility influenced behavior or behavior influenced perceived susceptibility, or whether this relationship was due to a third variable. Despite group differences in perceived susceptibility and health protective behaviors, perceived susceptibility to illness was not significantly related to engagement in health protective behaviors in either the T2DM risk group r =.25, or the hypertension risk group r = -.35, although notably the effects were in the opposite direction for the two groups and of small effect size. Another limitation of this study was the failure to include a measure of perceived severity, which would have allowed the authors to truly measure perceived threat of illness. More recently, Hivert et al. (2009) conducted a study using the validated Risk Perception Survey for Developing Diabetes (Walker & Wylie-Rosett, 1998) in a sample of 150 patients at differing levels of risk for T2DM. This study only used one item from the Risk Perception Survey for Developing Diabetes to assess perceived threat of illness, which asked the participant to rate their own absolute risk of developing diabetes from 1 (Almost No Risk) to 4 (High Risk). This one item is not sufficient to assess perceived

17 17 threat of diabetes, and in fact is a measure of perceived susceptibility (more specifically, own absolute risk). Using a mean split, their sample was divided into 99 individuals in the low perceived susceptibility of diabetes group, with a mean age 56, and 51 individuals in the high perceived susceptibility of diabetes group, with a mean age of 55. Actual risk for developing diabetes was determined by identified metabolic syndrome risk factors (e.g. BMI, poor metabolic control) in each patient s medical history. The authors found that individuals with a high perceived susceptibility to illness were more likely to have a self-reported familial risk of diabetes (r =.50) and to be at greater actual risk for diabetes (r =.374) than individuals with a low perceived susceptibility to illness. Most of the individuals at high perceived susceptibility and low perceived susceptibility for developing diabetes reported a belief that diet and exercise can prevent onset of T2DM; however the groups were not different in intentions to modify lifestyle behaviors in the upcoming year, in the form of diet, exercise or weight-loss. These findings suggest that individuals who are at risk for T2DM (by metabolic and familial risk) do perceive themselves at risk for the illness. However, individuals with increased perceived risk do not differ in intention to adopt healthier behaviors than individuals without perceived risk of illness. However, given the limitations of a crosssectional study assessing perceived threat by one variable, and the limited sample of predominately white, middle-aged individuals, clear conclusions cannot be drawn. Further, with the increase in childhood obesity and T2DM, it is important to examine the full construct of perceived threat of illness and health behaviors at earlier ages.

18 The Present Study 18 As is evident in the literature, there are many potential precipitating factors for diabetes, and some of these factors are potentially modifiable behaviors within an individuals control; however, many individuals engage in these risk behaviors, despite their potential health consequences. Further study is necessary in order to understand what factors are related to engagement in healthy behaviors, particularly in the context of familial risk for T2DM. The present study is the first to examine the relationship of familial risk, parental modeling, health locus of control, and perceived susceptibility to engagement in protective health behaviors in a young adult sample. The first aim was to understand the relationship of familial risk of T2DM to perceived susceptibility to T2DM in young adult college students. Consistent with results from prior studies, it was hypothesized that individuals at high familial risk for T2DM would perceive a greater susceptibility to T2DM than individuals without familial risk for T2DM. A second aim of the study was to examine the relationship of familial risk of T2DM to engagement in health protective behaviors (diet and physical activity) in young adult college students. It was hypothesized that individuals at high familial risk for T2DM would engage in more health protective behaviors (greater dietary adherence and more physical activity) than individuals without familial risk of T2DM. A third aim of the study was to examine the relationship between perceived susceptibility to T2DM and health protective behaviors (diet and physical activity) in young adult college students. Consistent with predictions from the Health Beliefs Model, it was hypothesized that individuals with a greater perceived susceptibility to T2DM would engage in more health

19 protective behaviors (greater dietary adherence and more physical activity) than 19 individuals with a lower perceived susceptibility to T2DM. Exploratory analyses were also conducted to evaluate whether internal health locus of control was related to familial risk, to measures of perceived susceptibility, or to engagement in health protective behaviors. Further analyses examined tobacco use and alcohol use in relation to familial risk and measures of perceived susceptibility. Finally, exploratory analyses were conducted to examine whether health protective behaviors (diet and physical activity) were related to family modeling of health protective behavior.

20 METHOD 20 Participants Participants were 206 undergraduate students recruited from psychology courses at a four-year university in southern Ohio. Eligible participants were identified using a pre-screen questionnaire, which assessed personal history of T2DM or other diabetes and familial risk of T2DM. Inclusion criteria included (a) being at least 18 years of age, (b) no personal history of diabetes, (c) no parent history of Type 1 Diabetes Mellitus (d) no current psychiatric medication use, and (e) ability to provide informed consent. 9 individuals were screened out of the final participant pool by reporting having a parent with Type 1 Diabetes Mellitus. Of the remaining participant pool of 197 individuals, 86% reported being Caucasian, 129 (66%) were female, and participant age ranged from 17 to 36 years, (M=19.09, SD = 1.64). Average Body Mass Index was 24.4 (SD = 3.97) and average waist circumference was 32.3 inches (SD = 4.16). Of the total sample of 197 participants, 22 (11.2%) reported having at least one biological parent with T2DM, 59 (29.9%) reported having at least one biological grandparent with T2DM, and 15 (7.61%) participants reported having both one or more biological grandparents with T2DM and a biological parent with T2DM. In the final sample of 197 participants, 66 participants were included in the familial risk group (all participants who reported having at least one parent or grandparent with T2DM) and 131 participants were included in the no familial risk group. See Table 1 for complete demographic characteristics, and Table 3 in Appendix B for demographic characteristics by gender.

21 Table 1 21 Characteristics of Participants, by Familial Risk of T2DM Total Familial No Familial Risk N Sample 197 Risk Demographic Characteristics Age mean (SD) 19.1 (1.64) 18.9 (1.01) 19.2 (1.88) Sex (% female) Use of mental health services (%) Ethnic background % white, not Hispanic % black, not Hispanic % white, Hispanic % black, Hispanic Asian American Other Clinical Characteristics Body Mass Index (kg/m 2 ) mean (SD) 24.4 (3.97) 24.7 (4.2) 24.3 (3.87) Waist circumference (inches) mean 32.3 (4.16) 32.6 (4.5) 32.1 (3.99) (SD) Note. No significant differences were found between individuals with familial risk of T2DM and individuals without familial risk of T2DM. Procedure After providing informed consent, participants completed several questionnaires via an online survey in a standardized order. See Appendix A for order of tests. Following completion of the questionnaires, participants had anthropometric measures assessed (weight, height, and waist circumference). Participants then provided their parents mailing address to allow the research team to send a brief letter introducing the study, brief questionnaires for both mother and father to complete, and a postage-paid envelope to the parents. To maintain confidentiality, each parent questionnaire was identified with the participant s numerical code ID only to combine parental responses

22 with the participant s responses. No identifying information (i.e. name, address) 22 was collected with the parent questionnaire responses. We received parent data for 112 (57%) of the participants. Of this sample we were able to confirm the participant s report of negative diabetes status for 86 of the participants parents and participant s report of positive diabetes status for 13 of the participants parents. We also found 13 participants reported their parents as not having diabetes when their parents stated having some form of the illness. However, this was predominately in parents who stated having pre-diabetes and not having fully developed the disease; therefore, we did not consider these inaccurate responses. We found no participants reporting their parent as having diabetes when both parents reported not having the illness. Lastly, we were also able to corroborate the participant s report of negative diabetes status for 55 of the participants grandparents and participant s report of positive diabetes status for 18 of the participants grandparents (parent reports of their parents diabetes status). Given that this data generally suggests accuracy in participant report about familial diabetes risk and that the study was about perceptions of diabetes risk, we conducted the main analyses using the full sample of 197 participants and based on their self-reported familial risk. Measures Copies of all non-copyrighted measures and detailed psychometrics for all instruments appear in Appendix A.

23 Demographics and Medical History 23 We administered a demographic and medical history questionnaire to assess age, gender, race/ethnicity, education, and socioeconomic data. We also used this questionnaire to assess physical and mental health history, including medical history, family medical history, and past or current use of mental health services, including type of treatment and medications for mental health issues. This questionnaire also assessed family history of T2DM, which was used to assign participants to the familial risk of T2DM and no familial risk of T2DM groups. Health Protective Behaviors Several measures were used to assess level of engagement in three health protective behaviors: physical activity, healthy eating, and sugar sweetened beverage consumption. Participants completed parts of The Summary of Diabetes Self-Care Activities Measure pertaining to 1) physical activity (2 items) 2) healthy eating, composed of fruit and vegetable consumption (2 items), high fat foods (1 item), and following a healthful eating plan (3 items). They also completed items from the Behavioral Risk Factor Surveillance System including the sugary drinks subscale (2 items) and the alcohol use subscale (1 item). All items were assessed using a number of days scale (from 0-7 days) in which they engaged in the specific behavior over the past week. A summary physical activity variable was created by averaging the two items in the Summary of Diabetes Self-Care Activities Measure. A summary healthy eating variable was created by averaging the five items from the Summary of Diabetes Self- Care Activities Measure. A summary sugar sweetened beverage consumption variable was created by averaging the two items from the Behavioral Risk Factor Surveillance

24 System sugary drinks subscale. Higher scores on the physical activity and healthy diet 24 measures indicate greater engagement in health protective behavior. Lower scores on the sugar sweetened beverage consumption and alcohol use measures indicate greater engagement in health protective behavior. Lastly, participants also completed one item from the Behavioral Risk Factor Surveillance System tobacco use subscale, asking do you smoke every day, some days or not at all, which was coded as a dichotomous variable (yes = every day or some days, no = not at all). Perceived Threat of T2DM To assess perceived threat of developing diabetes of T2DM, we used items reported by Ranby et al. (2010). Due to the lack of consistent findings on measures of perceived susceptibility, own absolute risk of illness (2 items) and direct comparative risk of illness (1 item) were used to separately assess perceived susceptibility. Own absolute risk of illness was assessed by a self-report rating of two items on a 5-point Likert scale ranging from 1 (lowest susceptibility) to 5 (highest susceptibility). These two items were averaged to form the participant s own absolute risk measure. Direct comparative risk was assessed by a self-report rating of one item on a 5-point Likert scale ranging from 1 to 5. These two constructs, own absolute risk of illness and direct comparative risk of illness, have a high inter-item correlation (r =.62 to.73) (Ranby et al., 2010). In addition, in the present sample, the two items forming the own absolute risk of illness scale had a high internal consistency (α =.90). Perceived severity of diabetes was used in exploratory analyses and was assessed by a self-report rating of one item on a 5-point Likert scale ranging from 1 (not severe at all) to 5 (highest level of severity).

25 Health Locus of Control 25 The Multidimensional Health Locus of Control (MHLC) Scales, consisting of three 6 item subscales; internal, chance, and powerful others, were given to assess health locus of control beliefs (Wallston et al., 1976). Each item was assessed by a self-report rating on a 6-point Likert scale, ranging from strongly disagree to strongly agree. The MHLC scales have been found to have moderate internal consistency (α = ), and test-retest reliability ranges from for retest after a four-month interval (Wallston, 2005) and.69 for retest after a four week interval (McCusker & Morrow, 1979). The internal health locus of control scale was used in exploratory analyses, and the chance and powerful other scales were also reported for descriptive purposes. Parental Health History and Family Modeling Participants were asked for permission to mail a brief health history form to their biological parents. This health history form was used to assess height and weight (to calculate BMI), whether or not the parent had a test for high blood sugar in the past three years, any past diabetes-related diagnoses (Type 1, Type 2, borderline/pre-diabetes) and age at diagnosis, other medical co-morbidities, familial risk of T2DM, and diet and physical activity. Because we were unable to locate a validated measure to assess perceived family modeling, we used the same diet and physical activity items from the SDSCA as the participant. Parents were also given additional questions if they indicated a past diagnosis of T2DM. These questions pertained to current insulin usage, current HbA1c level, diabetes-related complications, and blood sugar testing behavior. The letter to parents and parental health history forms can be found in Appendix A. The purpose of the health history form was to confirm parental diabetes status. The supplemental

26 questions concerning parental health behaviors were used in exploratory analyses of 26 family modeling of health behavior. Body Mass Index and Waist Circumference Each participant had three anthropometric measurements taken in the lab. Height (in inches) and weight (in pounds) was assessed using a Defecto stadiometer, and waist circumference (in inches) was assessed using a tape measure. BMI was calculated using the following formula: weight (lb) / [height (in)] 2 x 703 (CDC, 2014c). BMI and waist circumference demonstrated were highly correlated in the present sample (r =.86).

27 RESULTS 27 There were no significant differences between the familial risk group and the no familial risk group in age, t(195) =.924, p =.36, gender, X 2 (2, N = 197) =.062, p =.80, minority/nonminority status, X 2 (2, N = 197) =.175, p =.68, percent using mental health services currently or in the past, X 2 (2, N = 197) = 2.06, p =.15, BMI, t(195) = -.601, p =.55, or waist circumference, t(195) = -.704, p =.48. See Table 1. Thus, none of these variables were used as covariates in the subsequent analyses. Familial Risk and Perceived Threat In order to test the first hypothesis, we conducted independent samples t-tests to determine whether there were group differences (familial risk, no familial risk) for own absolute risk of T2DM, direct comparative risk of T2DM and perceived severity of T2DM. Consistent with the hypothesis, the familial risk group reported significantly more own absolute risk of T2DM, t(195) = -6.48, p <.001, and significantly more direct comparative risk of T2DM, t(105) = -2.25, p =.026, relative to the no familial risk group. However, results indicated no group differences for perceived severity, t(195) =.164, p =.87. See Table 2. Familial Risk and Health Protective Behaviors In order to test the second hypothesis, we conducted independent samples t-tests to determine whether there were group differences (familial risk, no familial risk) for physical activity, healthy diet, and sugar sweetened beverage consumption. Contrary to predictions, results indicated no group differences for physical activity, t(193) = -.24, p =.82, healthy diet, t(195) =.94, p =.35, or sugar sweetened beverage consumption, t(194) =.31, p =.76. See Table 2.

28 Perceived Threat and Health Protective Behaviors 28 In order to test the third hypothesis, simple correlations between measures of perceived threat and health protective behaviors were calculated for the entire sample. Opposite to predictions, individuals who reported greater own absolute risk of developing T2DM were found to be significantly less likely to have a healthy diet, r = -.28, p <.001, were less likely to engage in regular physical activity, r = -.25, p <.001, and were more likely to consume sugar sweetened beverages, r =.20, p =.006. Similarly, individuals who reported greater direct comparative risk of developing T2DM were also found to be significantly less likely to have a healthy diet, r = -.21, p =.004, less likely to engage in regular physical activity, r = -.16, p =.025, and more likely to consume sugar sweetened beverages, r =.22, p =.002. Perceived severity of T2DM showed no relationship to having a healthy diet, r =.072, p =.32, engaging in physical activity, r =.046, p =.52, or sugar sweetened beverage consumption, r = -.033, p =.65.

29 29 Table 2 Mean Scores on Health Protective Behaviors, Perceived Threat Measures by Familial Risk of Type 2 Diabetes (T2DM) Familial Risk (n = 66) No Familial Risk (n = 131) Effect Size Variable M SD M SD d Health Protective Behaviors Physical Activity Healthy Diet Sugar Sweetened Beverages Alcohol Use % Smoke 10.8% % Perceived Threat Direct Comparative Risk* Own Absolute Risk** Perceived Severity Health Locus of Control Internal* Powerful Others

30 30 Table 2: continued Luck or Chance Note. * T-test is significant at the 0.05 level (2-tailed). ** T-test is significant at the 0.01 level (2-tailed).

31 Exploratory Analyses 31 An independent samples t-test was conducted to determine whether there were group differences (familial risk, no familial risk) in internal health locus of control. Individuals with familial risk for T2DM reported lower internal health locus of control than individuals without familial risk of the illness, t(186) = 2.07, p =.040. See Table 2 for mean scores. Simple correlations between measures of perceived threat and internal health locus of control were calculated for the entire sample. Individuals who reported greater own absolute risk of developing T2DM were less likely to have an internal health locus of control, r = -.16, p =.028, and individuals who reported greater direct comparative risk of developing T2DM were less likely to have an internal health locus of control, r = -.20, p =.005. Perceived severity of diabetes was not related to internal health locus of control, r =.084, p =.25. Simple correlations also showed that high internal health locus of control was associated increased physical activity, r =.20, p =.006, but was not related to sugar sweetened beverage consumption, r =.002, p =.79, or with having a healthy diet, r =.11, p =.006, With regard to examination of parental modeling, no associations were found between parent diet and child diet, r = -.005, p =.96, or parent physical activity and child physical activity, r =.064, p =.51. We also found no group differences between the familial risk group and no familial risk group for alcohol consumption, t(192) =.58, p =.56, or for smoking, X 2 (2, N = 196) =.463, p =.59. Own absolute risk was not associated with smoking, r =.045, p =.53, or with alcohol use, r =.013, p =.86. Direct comparative risk was not associated with smoking, r =.044, p =.55, or with alcohol use, r = -.071, p =.33. Perceived

32 32 severity was also not associated with smoking, r =.051, p =.48, or with alcohol use, r = -.072, p =.32. Supplemental Analyses Details regarding supplemental analyses are presented in Appendix B. Given the unexpected findings regarding perceived susceptibility and health protective behavior, supplemental analyses were conducted to further explore the relationship between measures of perceived susceptibility and health protective behavior separately by familial risk group, BMI, waist circumference, and gender. Fisher r-to-z transformation showed that generally correlations between measures of perceived susceptibility and health protective behaviors were equivalent regardless of familial risk group, BMI, waist circumference, or gender, with a few exceptions. The correlation between direct comparative risk and sugar sweetened beverage consumption was significantly greater for women compared to men, Z = -2.6, p =.009. The only other significant difference we found was that the correlation between direct comparative risk and physical activity was significantly greater for the healthy BMI group compared to the elevated BMI group, Z = 2.74, p =.006. In order to examine whether familial risk influenced the relationship between internal locus of control and other study variables, simple correlations between internal locus of control and health protective behaviors were calculated separately by familial risk group. Using the Fisher r-to-z transformation, we tested for differences between familial risk groups on correlations between internal health locus of control and health protective behaviors, finding no significant differences. Using the Fisher r-to-z transformation, we also tested for differences between the familial risk group and the no

33 33 familial risk group in correlations between internal health locus of control and perceived susceptibility measures. The inverse correlation between internal health locus of control and direct comparative risk was significantly greater for the familial risk group compared to the no familial risk group, Z = 2.00, p =.046; however the relationship between internal health locus of control and own absolute risk did not differ by familial risk group. Finally, in order to examine whether the relationship of parent and child health protective behaviors would vary by familial risk, we calculated simple correlations for family modeling of health protective behaviors separately by familial risk group. Using the Fisher r-to-z transformation, we tested for differences between familial risk group based on correlations between parent diet and physical activity and child diet and physical activity. We found parent diet and child diet were significantly and positively correlated in the familial risk group, although there was no association between parent and child diet in the no familial risk group Z = -2.45, p =.014.

34 DISCUSSION 34 As was hypothesized, individuals with familial risk of T2DM were more likely to report greater own absolute risk (high likelihood of getting diabetes in the future) and direct comparative risk (higher chances of developing diabetes compared with other same-age peers) than individuals without familial risk for T2DM. This finding for own absolute risk was supported in previous literature, specifically for T2DM, in older adult women (mean age = 67.2) (Ranby et al., 2012), older primary care patients (mean age = 55.8 years) (Hivert et al., 2009), as well as in a sample of middle-aged adults (mean age = 32.3 years) (Forsyth & Goetsch, 1997). Direct comparative risk was only assessed previously in the study by Ranby et al., (2012) and, in contrast to the present study, they found no difference between individuals with familial risk for T2DM and without on direct comparative risk. Given that the sample in the present study was a younger sample with relatively fewer health complications, it is conceivable that direct comparative risk would be stronger in individuals with a parental history when comparing this risk of developing diabetes to other same age peers who are healthy and less likely to have health complications. In contrast, older adults may view developing diabetes as more common, especially as risk of developing T2DM increases with age (CDC, 2014a). The present results corroborate previous findings that not all measures of perceived susceptibility are equal or interchangeable (Ranby et al., 2012), while also suggesting T2DM risk perceptions may be influenced by developmental factors, such as age. Contrary to what the Health Beliefs Model predicts, no group differences in familial risk were found for perceived severity of T2DM. This construct also showed no correlation with reported health protective behaviors. Even in supplemental analyses, the

35 35 relationship between perceived severity and health protective behaviors did not differ by gender, BMI status, waist circumference, or familial risk status. In a review of 29 Health Beliefs Model related studies, Janz & Becker (1984) acknowledged perceived severity as having the weakest association with health protective behavior in the model. The present findings also supports the Health Beliefs Model and previous literature, finding that perceived severity is independent from measures of perceived susceptibility (Rosenstock, 1974; Harrison, Mullen, & Green, 1992). Although according to Rosenstock (1974), the Health Beliefs Model posits perceived susceptibility and perceived severity combine to form an individual s perceived threat of illness, which provides the individual with the impetus to engage in a health protective behavior, previous studies have predominately examined each construct in perceived threat separately (Janz & Becker 1984; Harrison et al., 1992; Ranby et al., 2012). In addition, inconsistencies in the assessment of each construct composing perceived threat has hindered the ability to generalize and apply these factors to health prevention. Future studies should aim to increase the validity of the constructs measuring perceived threat, and attempt to understand how they may interact, either independently or in tandem. Although it was hypothesized that individuals at familial risk for T2DM would engage in more health protective behaviors, such as following a healthy diet, consuming fewer sugary drinks, and engaging in more physical activity than individuals without familial risk for T2DM, the present results showed no difference in physical activity, healthy diet, and sugar-sweetened beverage consumption between the two groups. This finding is somewhat consistent with findings in a sample of middle-aged adults (mean age = 32.3 years) (Forsyth & Goetsch, 1997), where those with a parent with T2DM

36 engaged in more weight control efforts, but did not differ on diet and physical activity 36 behaviors, from individuals with healthy parents. One possible explanation for the lack of findings may be that young adults might not understand the relationship between health protective behaviors and likelihood to develop diabetes. Another explanation may be that both groups demonstrated generally healthy behavior, engaging in physical activity for at least 30 minutes on average 5 days a week, eating healthy 4.7 days a week, and having a sugary drink 2 days a week. In the future, a more varied sample may provide a more accurate picture that is more representative of the US population. The third hypothesis, individuals with a greater perceived susceptibility to T2DM would engage in more health protective behaviors (greater dietary adherence, more physical activity, less sugar sweetened beverages) than individuals with a lower perceived susceptibility to T2DM, was not supported; in fact, the findings were in the opposite direction than predicted. In the present sample, individuals with a greater own absolute risk of developing T2DM reported fewer healthy behaviors, such that they were less likely to have a healthy diet, less likely to engage in regular physical activity, and more likely to consume sugary drinks. Similarly, individuals who reported a greater direct comparative risk of developing T2DM were less likely to have a healthy diet, less likely to engage in regular physical activity, and more likely to consume sugary drinks. These findings differ from what was expected based on the Health Beliefs Model, as well as from previous studies exploring the relation between perceived susceptibility of illness and health protective behaviors. Early meta-analyses of cross-sectional and longitudinal studies demonstrated that increased perceived susceptibility to various illnesses was related to increased engagement in health protective behaviors (Becker,

37 37 Haefner, Kasl, Kirscht, Maiman, & Rosenstock, 1977; Janz & Becker, 1984; Harrison et al., 1992). However, a more recent meta-analysis using more stringent inclusion criteria and only including longitudinal studies found this association to be weaker (Carpenter, 2010). In addition, cross-sectional studies in the context of diabetes-related perceptions of susceptibility have also found a weaker association between perceived susceptibility and engagement in health protective behaviors (Hivert, et al., 2009; Forsyth & Goetsch, 1997). Forsyth & Goetsch (1997) found that despite group differences between individuals with or without familial risk for T2DM in perceived susceptibility and some health protective behaviors, overall perceived susceptibility showed no correlation with health protective behaviors. Hivert et al., (2009) found similar results in a sample of older primary care patients (mean age = 55.8 years), finding no association between perceived susceptibility and diet, physical activity or weight management efforts in the past year or coming year. In fact, Hivert et al., only found perceived susceptibility to be associated with a stronger belief that Doing regular exercise and following a diet takes a lot of effort. Curiously, both individuals at high perceived susceptibility and low perceived susceptibility for developing diabetes reported believing that diet and exercise can prevent onset of T2DM, however, neither group reported intentions to modify lifestyle behaviors in the upcoming year, in the form of diet, exercise or weight-loss. Given the unexpected findings regarding perceived susceptibility and health protective behavior, supplemental analyses were also conducted to further explore the relationship between measures of perceived susceptibility and health protective behavior separately by a few key constructs. When this relationship was explored separately in individuals with familial risk for T2DM and those without, the relationship remained the

38 38 same. The finding suggests the relationship between perceived susceptibility and health protective behavior did not interact with familial risk and therefore, perception of risk may have a greater link to engagement in healthy behaviors than actual risk. In further analyses, the correlations between measures of perceived susceptibility and health protective behaviors were generally equivalent regardless of BMI, waist circumference and gender. However, the correlation between direct comparative risk and sugar sweetened beverage consumption was significantly greater for women compared to men. The other significant difference we found was in the correlation between direct comparative risk and physical activity, which was significantly greater for the healthy BMI group compared to the elevated BMI group. These findings suggest anthropometric measures and gender may act as unique third variables between measures of perceived susceptibility and health protective behaviors. Further study is necessary to better understand these possible interactions. These findings also support the original proposition by Ranby et al., (2010) that not all measures of perceived susceptibility are equal, even in the context of health protective behaviors. Lastly, exploratory analyses revealed some interesting findings regarding internal health locus of control. In the original Health Beliefs Model, health locus of control was considered as a Cues to Action variable, the internal or external stimulus, that initiates the decision-making process, such that it would directly influence perception of disease risk (Becker, 1974). Although these relationships have not been previously explored in the literature, the present findings do appear consistent with the Health Beliefs Model, purporting perceptions of increased risk is inversely related to believing your health is due to your own behavior. Individuals who reported greater own absolute risk of

39 developing T2DM and individuals who reported greater direct comparative risk of 39 developing T2DM were less likely to have an internal health locus of control. Perceived severity of T2DM was not related to internal health locus of control. Another exploratory analysis revealed individuals with familial risk of T2DM reported lower internal health locus of control than individuals without familial risk of the illness. This finding is consistent with the theoretical framework of health locus control (Wallston, Wallston, & DeVellis, 1978), as individuals exhibiting a high internal locus of control are more likely to believe that the outcome of a situation is within their own personal control more than it is in the control of external factors (e.g. familial risk). Thus it is conceivable that individuals with familial history of an illness would be less likely to believe their own health is within one s control following past experiences perceiving family members unable to control their own health. Supplemental analyses also showed that the correlation between internal health locus of control and direct comparative risk was significantly greater for the familial risk group compared to the no familial risk group, however internal health locus of control and own absolute risk did not differ by familial risk group. This finding suggests internal health locus of control and perception of T2DM susceptibility, although related, may be influenced by a third variable, familial risk. Further study between the interactions of these three variables may provide greater insight into how perception of disease risk is related to actual disease risk. Consistent with prior studies (Gale, Batty, & Deary, 2008; Steptoe & Wardle, 2001) higher internal health locus of control was also associated with increased physical activity; however, it was not related to sugar sweetened beverage consumption or having a healthy diet. Supplemental analyses showed that this pattern of results did not vary by

40 familial risk. Although the present results did not find a relationship between internal 40 health locus of control and diet, it is possible that individuals are less likely to view diet as an important behavior in need of modification, as compared to diet and physical activity. In addition, on average, the present sample reported consuming relatively few sugar-sweetened beverages and eating health five days out of the week on average, and therefore may have already modified this behavior; restriction of range may have also affected the findings. Overall, given the present findings, it is important to note that further study on internal health locus of control is warranted. Although the association between internal health locus of control and general engagement in health protective behaviors has been studied extensively, less is known about its relationship to health protective behaviors in individuals at risk for illnesses such as T2DM (Gale, et al., 2008; Steptoe & Wardle, 2001); thus, it will be important in the future to form a better understanding of how internal health locus of control interacts with perceived and actual disease risk. This finding that individuals with familial risk for T2DM have lower internal health locus of control and that internal locus of control is related to engagement in health protective behaviors raises important suggestions for future interventions. If future researchers are able to increase internal health locus of control in these individuals at risk, this action may also bring an increase in health protective behaviors. Finally, family modeling of health behavior was also explored, using data returned from the participants parents. Results indicated no relationships existed between parent diet and child diet, or between parent physical activity and child physical activity, when examining the sample as a whole. However, parent diet and child diet were

41 significantly and positively correlated in the familial risk group, while they were not 41 associated in the no familial risk group, suggesting in the context of illness, parent diet may have a stronger and more lasting influence on child food preferences. Within the Health Beliefs Model, family modeling would likely be considered a Cues to Action variable, aiding in the decision-making to make healthier choices. Especially considering T2DM management requires careful monitoring of food intake, it is reasonable that families with a member with T2DM may modify their overall family eating patterns, and such dietary preferences may be passed on to the child. To our knowledge, only one other study (Forsyth & Goetsch, 1997) has investigated family modeling in the context of T2DM, finding no significant associations between individuals self report of parent health behaviors and their own health behaviors, however this study used participants recollection of parent behavior instead of collecting data from the actual parent, which would have been more reliable. A longitudinal study looking at the interaction of parent and child diet preferences in the context of illness over the course of the child s development would be an interesting future study to consider. In addition, it would be beneficial to learn how having a parent with T2DM with poor diabetes control manifested by poor dietary patterns would impact the child s food choices. Given that the present sample had been away at college on average for one full semester, it is also conceivable they had adopted their own independent health behaviors during this time, possibly from peer, school, or other influence. A prior study on parent and peer influence on health behaviors during the first three years of college found peers had a strong impact on the amount of change on alcohol consumption, eating habits, and exercise (Lau et al., 1990). Certain colleges also provide greater incentives towards

42 maintaining a healthy diet or exercise routine, which may have accounted for some 42 influence over health behavior. In the future, it would be interesting to assess family modeling of health behavior in the first semester of college, and then again later in the college career, in order to verify changes in family modeling as a direct result of college influence. Limitations There are several limitations to the present study that are of note. First, the sample was predominately Caucasian college students from the Midwest and is not representative of the general emerging adult population. Overall the sample was welleducated and from middle to high SES families, with mean combined family income between $50,000 and $74,999. These factors generally contribute to healthy behaviors (Department of Health and Human Services, 2000) and are likely associated with this sample s relatively healthy lifestyle. The participants also had free access to gym and exercise equipment provided from the university, as well as healthy food options. In addition, students self-selected to participate in this study, with prior knowledge that their height, weight, and waist circumference were to be assessed. Therefore, it is possible that students with less health risks opted to participate in this study, and students who engaged in less healthy and more risky health behaviors or who had more risk factors for diabetes may have self-selected out. Unfortunately, the present study design did not allow us to recruit from other universities or from community samples. Another important limitation to consider was the use of brief self-report questionnaires to assess diet, physical activity, and sugar sweetened beverage consumption. Over and underreporting in the measurement of health behavior is therefore

43 43 conceivable. Although more thorough methods of assessing diet and physical activity are available, (e.g. Actigraphy, or daily diary) these methods were not feasible in the present study. A future study using these methods would be helpful in clarifying the findings in the present study. In addition, our measure of sugar-sweetened beverage only included soda, sugar-sweetened fruit drinks (such as Kool-Aid and lemonade), sweet tea, and sports or energy drinks. College age students may also generate significant sugar intake from other beverages, including coffee drinks and alcoholic mixed drinks, which were not included in this measure. Additionally, the method for classifying diabetes familial risk group was a limitation. Due to sample size consideration, we combined the familial risk group to include both parents and grandparents with T2DM. In future studies, dividing these two groups may be important. In addition, we did not have data concerning if the participant experienced living with the parent or grandparent with T2DM. These would be important considerations in a future study, especially to discern what effect increased exposure to the family member s illness experience may have on the individual. In order to assess familial risk, we used data reported by the participant of their family history of T2DM. Unfortunately, we were unable to corroborate these findings with a physician or medical record. However, we did send a letter to parents in an effort to confirm parental diabetes status. We received at least one parent s data form from 115 of the 197 total participants. We were able to confirm 31 participants parents had diabetes from parental report. Further, we were able to confirm 86 participants parents did not have diabetes from parental report. For those parents we were able to confirm diabetes status, we were unable to determine how long they have had the illness, or any complications of the

44 illness. We were also unable to regulate the order in which the parent answered the 44 questions, or whether one parent was filling out the survey on behalf of both parents. In future studies, these would be important variables to consider. Due to the cross-sectional nature of this study, it is impossible to determine causality between perceived susceptibility measures and familial risk or between perceived susceptibility and health protective behaviors, or whether this relationship is related to a third variable or variables. Although the Health Beliefs Model predicts increased perceived threat of illness would precede and influence the extent to which individuals will adopt healthy behaviors to lessen their risk of developing the illness, the present study is unable to confirm this causal relationship. A longitudinal study design with multiple timepoints would be able to accurately substantiate this claim. Lastly, our measures of perceived susceptibility are newly developed with few established psychometrics. Further research is necessary to critically examine these measures and whether they are actually assessing their intended constructs. Implications The present study improves current knowledge of young adult college students perceptions of susceptibility to developing T2DM, in relation to their actual familial risk and their current associated health behaviors. This study also adds to our broad understanding of the differences within perceived susceptibility measures and their relationships to actual disease risk factors (e.g. familial risk) and psychological risk factors (e.g. internal health locus of control). Further research is necessary to understand how individuals perceive and weigh risks, especially as they relate to decisions regarding health behavior. This continued area of research is vital for healthcare practitioners to

45 facilitate their patients adopting and maintaining healthier lifestyles. By better 45 understanding young adults perceived beliefs and current behaviors surrounding their personal diabetes risk, research would be able to better inform how to ultimately reduce poor lifestyle choices early enough in development when adoption of a healthy lifestyle may reduce significant risk for developing the disease and associated complications.

46 REFERENCES 46 Ali, O. (2013). Genetics of type 2 diabetes. World Journal of Diabetes, 4(4), 114. Alogna, M. (1980). Perception of severity of disease and health locus of control in compliant and noncompliant diabetic patients. Diabetes Care, 3(4), Baranowski, T., Nader, P., Dunn, K., & Vanderpool, N. (1982). Family Self -Help: Promoting Changes in Health Behavior. Journal of Communication, 32(3), Bassuk, S. S., & Manson, J. E. (2005). Epidemiological evidence for the role of physical activity in reducing risk of type 2 diabetes and cardiovascular disease. Journal of Applied Physiology, 99(3), Becker, M. H. (1974). The Health Belief Model and Sick Role Be=havior. Health Education & Behavior, 2(4), Becker, M. H., & Janz, N. K. (1985). The health belief model applied to understanding diabetes regimen compliance. The Diabetes Educator, 11(1), Becker, M. H., Haefner, D. P., Kasl, S. V., Kirscht, J. P., Maiman, L. A., & Rosenstock, I. M.(1977). Selected psychosocial models and correlates of individual healthrelated behaviors. Medical Care, 15 (Supplement), Carpenter, C. J. (2010). A meta-analysis of the effectiveness of health belief model variables in predicting behavior. Health Communication, 25(8), Centers for Disease Control and Prevention, Division of Adolescent and School Health. (2011). Trends in the prevalence of obesity, dietary behaviors, and weight control practices national YRBS: Centers for Disease Control and Prevention. (2011). National diabetes fact sheet:

47 national estimates and general information on diabetes and pre-diabetes in the 47 United States, Atlanta, GA: US Department of Health and Human Services. Centers for Disease Control and Prevention. (2014a). National diabetes statistics report: estimates of diabetes and its burden in the United States, Atlanta, GA: U.S. Department of Health and Human Services. Centers for Disease Control and Prevention (2014b). The Behavioral Risk Factor Surveillance System History, Atlanta, GA: U.S. Department of Health and Human Services. Centers for Disease Control and Prevention. (2014c). Adult Obesity, Atlanta, GA: U.S. Department of Health and Human Services. Chan, J. M., Rimm, E. B., Colditz, G. A., Stampfer, M. J., & Willett, W. C. (1994). Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men. Diabetes Care, 17(9), Colditz, G. A., Willett, W. C., Rotnitzky, A., & Manson, J. E. (1995). Weight gain as a risk factor for clinical diabetes mellitus in women. Annals of Internal Medicine, 122(7), Cummings, K. M., Jette, A. M., Brock, B. M., & Haefner, D. P. (1979). Psychosocial determinants of immunization behavior in a Swine Influenza campaign. Medical Care, 17, Das, S. K., & Elbein, S. C. (2006). The genetic basis of type 2 diabetes. Cellscience, 2(4), 100. Diabetes Prevention Program (DPP) Research Group. (2002). The Diabetes Prevention

48 48 Program (DPP) description of lifestyle intervention. Diabetes Care, 25(12), Diabetes Prevention Program Research Group. (2009). 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. The Lancet, 374(9702), Ebbeling, C. B., Leidig, M. M., Sinclair, K. B., Hangen, J. P., & Ludwig, D. S. (2003). A reduced glycemic load diet in the treatment of adolescent obesity. Archives of Pediatrics and Adolescent Medicine, 157(8), Etelson, D., Brand, D. A., Patrick, P. A., & Shirali, A. (2003). Childhood obesity: do parents recognize this health risk?. Obesity Research, 11(11), Fedewa, M. V., Gist, N. H., Evans, E. M., & Dishman, R. K. (2014). Exercise and Insulin Resistance in Youth: A Meta-Analysis. Pediatrics, 133(1), e163-e174. Florez, J. C., Hirschhorn, J., & Altshuler, D. (2003). The inherited basis of diabetes mellitus: implications for the genetic analysis of complex traits. Annual Review of Genomics and Human Genetics, 4(1), Forsyth, L. H., & Goetsch, V. L. (1997). Perceived threat of illness and health protective behaviors in offspring of adults with non-insulin-dependent diabetes mellitus. Behavioral Medicine, 23(3), Gale, C. R., Batty, G. D., & Deary, I. J. (2008). Locus of control at age 10 years and health outcomes and behaviors at age 30 years: the 1970 British Cohort Study. Psychosomatic Medicine, 70(4), Ganz, M. L., Wintfeld, N., Li, Q., Alas, V., Langer, J., & Hammer, M. (2014). The

49 association of body mass index with the risk of type 2 diabetes: a case control 49 study nested in an electronic health records system in the United States. Diabetology & Metabolic Syndrome, 6(1), 50. Grant SF, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, Sainz J, Helgason A, Stefansson H, Emilsson V, Helgadottir A, Styrkars-dottir U, Magnusson KP, Walters GB, Palsdottir E, Jonsdottir T, Gud- mundsdottir T, Gylfason A, Saemundsdottir J, Wilensky RL, Reilly MP, Rader DJ, Bagger Y, Christiansen C, Gudnason V, Sigurdsson G, Thorsteinsdottir U, Gulcher JR, Kong A, Stefansson K: Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nature Genetics 38: , 2006 Harrison, J. A., Mullen, P. D., & Green, L. W. (1992). A meta-analysis of studies of the health belief model with adults. Health Education Research, 7(1), Helmrich, S. P., Ragland, D. R., Leung, R. W., & Paffenbarger Jr, R. S. (1991). Physical activity and reduced occurrence of non-insulin-dependent diabetes mellitus. New England Journal of Medicine, 325(3), Hivert, M. F., Warner, A. S., Shrader, P., Grant, R. W., & Meigs, J. B. (2009). Diabetes Risk Perception and Intention to Adopt Healthy Lifest yles Among Primary Care Patients. Diabetes Care, 32(10), Hu, F. B., Manson, J. E., Stampfer, M. J., Colditz, G., Liu, S., Solomon, C. G., & Willett, W. C. (2001). Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. New England Journal of Medicine, 345(11), Hu, F. B., Van Dam, R. M., & Liu, S. (2001). Diet and risk of type II diabetes: the role of types of fat and carbohydrate. Diabetologia, 44(7),

50 Janz, N. K., & Becker, M. H. (1984). The health belief model: A decade later. Health 50 Education & Behavior, 11(1), Kahn, S. E., Hull, R. L., & Utzschneider, K. M. (2006). Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature, 444(7121), Kit, B. K., Fakhouri, T. H., Park, S., Nielsen, S. J., & Ogden, C. L. (2013). Trends in sugar-sweetened beverage consumption among youth and adults in the United States: The American Journal of Clinical Nutrition. 98, Levenson, H. (1973). Multidimensional locus of control in psychiatric patients. Journal of Consulting and Clinical Psychology, 41, Malik, V. S., Schulze, M. B., & Hu, F. B. (2006). Intake of sugar-sweetened beverages and weight gain: a systematic review. The American Journal of Clinical Nutrition, 84(2), McCusker, J., & Morrow, G. (1979). The relationship of health locus of control to preventive health behaviors and health beliefs. Patient counselling and health education, 1(4), Meigs, J. B., Cupples, L. A., & Wilson, P. W. (2000). Parental transmission of type 2 diabetes: the Framingham Offspring Study. Diabetes, 49(12), Molarius, A., Seidell, J.C. (1998). Selection of anthropometric indicators for classification of abdominal fatness a critical review. International Journal Obesity Related Metabolic Disorders, 22, Peyrot, M., & Rubin, R. R. (1994). Structure and correlates of diabetes-specific locus of control. Diabetes Care, 17(9), Ranby, K. W., Aiken, L. S., Gerend, M. A., & Erchull, M. J. (2010). Perceived

51 51 susceptibility measures are not interchangeable: absolute, direct comparative, and indirect comparative risk. Health Psychology, 29(1), 20. Reilly, J. J., Armstrong, J., Dorosty, A. R., Emmett, P. M., Ness, A., Rogers, I.,... & Sherriff, A. (2005). Early life risk factors for obesity in childhood: cohort study. British Medical Journal, 330(7504), Repetti, R. L., Taylor, S. E., & Seeman, T. E. (2002). Risky families: family social environments and the mental and physical health of offspring. Psychological Bulletin, 128(2), 330. Rosenstock, I. M. (1974). Historical origins of the health belief model. Health Education & Behavior, 2(4), Rosenstock, I. M., Strecher, V. J., & Becker, M. H. (1988). Social learning theory and the health belief model. Health Education & Behavior, 15(2), Schulze, M. B., Manson, J. E., Ludwig, D. S., Colditz, G. A., cstampfer, M. J., Willett, W. C., & Hu, F. B. (2004). Sugar-sweetened beverages, weight gain, and incidence of type 2 diabetes in young and middle-aged women. JAMA, 292(8), Steptoe, A., & Wardle, J. (2001). Locus of control and health behaviour revisited: a multivariate analysis of young adults from 18 countries. British Journal of Psychology, 92(4), Sun, X., Yu, W., & Hu, C. (2014). Genetics of type 2 diabetes: insights into the pathogenesis and its clinical application. BioMed Research International, Tillotson, L. M., & Smith, M. S. (1996). Locus of control, social support, and adherence to the diabetes regimen. The Diabetes Educator, 22(2),

52 52 Toobert, D. J., Hampson, S. E., & Glasgow, A. R. (2000). The summary of diabetes self- care activities measure: results from 7 studies and a revised scale. Diabetes Care, 23(7), US Department of Health and Human Services. (2000). Healthy People 2010: Understanding and improving health and objectives for improving health. Washington, DC: US Department of Health and Human Services. Vaag, A., Henriksen, J. E., Madsbad, S., Holm, N., & Beck-Nielsen, H. (1995). Insulin secretion, insulin action, and hepatic glucose production in identical twins discordant for non-insulin-dependent diabetes mellitus. Journal of Clinical Investigation, 95(2), 690. Valdez, R., Yoon, P. W., Liu, T., & Khoury, M. J. (2007). Family History and Prevalence of Diabetes in the US Population The 6-year results from the National Health and Nutrition Examination Survey ( ). Diabetes Care, 30(10), Walker E. A., Wylie-Rosett J. (1998). Evaluating risk perception of developing diabetes as a multi-dimensional construct. Diabetes 47(Suppl. 1), A5. Wallston, K. A. (2005). The validity of the multidimensional health locus of control scales. Journal of Health Psychology, 10(5), Wallston, K. A., & Wallston, B. S. (1981). Health locus of control scales. Research with the locus of control construct, 1, Wallston, K. A., Wallston, B. S. & DeVellis, R. (1978). Development of the multidimensional health locus of control (MHLC) scales. Health Education Monographs, 6, Wallston, B. S., Wallston, K. A., Kaplan, G. D., & Maides, S. A. (1976). Development

53 53 and validation of the health locus of control (HLC) scale. Journal of Consulting and Clinical Psychology, 44(4), 580. Wdowik, M. J., Kendall, P. A., & Harris, M. A. (1997). College students with diabetes: using focus groups and interviews to determine psychosocial issues and barriers to control. The Diabetes Educator, 23(5),

54 APPENDIX A: STUDY MATERIALS 54 Psychometrics Health Protective Behavior Several measures were be used to assess level of health protective behavior. In the present study, participants completed parts of The Summary of Diabetes Self-Care Activities Measure pertaining to physical activity, fruits and vegetable consumption, high fat foods and following a healthful eating plan, and the sugary drinks subscale from the Behavioral Risk Factor Surveillance System. Each behavior was assessed separately. Lower scores on each measure indicate less engagement in the health protective behavior. Diet Questions from the Behavioral Risk Factor Surveillance System (BRFSS) pertaining to sugar-sweetened beverage consumption were administered to assess dietary behavior. The BRFSS, first conducted in 1984, is an annual multi-modal survey conducted by state health departments in conjunction with the CDC, which provides estimates of health-related risk behaviors that relate to the leading causes of premature morbidity and mortality among adults in the United States (CDC, 2014b). In the present study, participants completed the 2 item sugary drinks subscale to assess sugar-sweetened beverage consumption, which demonstrated moderate internal consistency in the present study (α =.57). The questions were modified to a number of days scale (from 0-7) to mirror the other questions pertaining to health protective behavior. Questions from The Summary of Diabetes Self-Care Activities Measure (SDSCA) pertaining to fruits and vegetables, high fat foods and following a healthful eating plan were used to also assess diet. The SDSCA s inter-item correlations ranged from r = 0.20 to r = 0.76 for four

55 55 SDSCA subscales; 6-month test-retest reliability ranged from r = 0.00 to r = 0.58 across three studies (Toobert, Hampson, & Glasgow, 2000). In the present sample, the five items used from the SDSCA demonstrated high internal consistency (α =.71). Physical Activity Two questions from The Summary of Diabetes Self-Care Activities Measure pertaining to physical activity were administered to assess exercise, which are summed together for analysis. Inter-item correlation of the exercise subscale ranged from and 3 month test-retest reliability ranged from (Toobert et al., 2000). Exercise was also correlated to exercise minutes per day (Stanford 7-day Recall) (r =.20), exercise self-monitoring data (r =.58), and attendance at an exercise class (r =.22) (Toobert et al., 2000). In the present sample, the two items used from the SDSCA demonstrated high internal consistency (α =.81). Perceived Threat of Diabetes Own absolute risk of illness (2 items) and direct comparative risk of illness (1 item) were used to assess perceived susceptibility. These two constructs have a high inter-item correlation (r =.62 to.73) (Ranby et al., 2010). Own absolute risk of illness was assessed by a self-report rating of two items on a 5-point Likert scale ranging from 1 (lowest susceptibility) to 5 (highest susceptibility). These two items were averaged to form the participant s own absolute risk measure. Direct comparative risk was assessed by a self-report rating of one item on a 5-point Likert scale ranging from 1 to 6. Due to the lack of consistent findings on measures of perceived susceptibility, own absolute risk of illness (2 items) and direct comparative risk of illness (1 item) were used to separately assess perceived susceptibility. These three items demonstrated high internal consistency,

56 56 (α =.85). Perceived severity of diabetes was assessed by a self-report rating of one item on a 5-point Likert scale ranging from 1 (not severe at all) to 5 (highest level of severity). Health Locus of Control The Multidimensional Health Locus of Control Scales consist of three 6 item subscales; internal, chance, and powerful others dimensions of health locus of control (HLC) beliefs, were given to assess locus of control (Wallston et al., 1976). Each item is assessed by a self-report rating on a 6-point Likert scale ranging from strongly disagree to strongly agree. These scales measure the degree the participant believes his or her health is due to his or her own behavior, luck or chance, or due to others. The MHLC scales have been found to have moderate internal consistency (Cronbach s α= ), and testretest reliability ranges from for retest after a four-month interval (Wallston, 2005) and.69 for retest after a four week interval (McCusker & Morrow, 1979). The internal HLC subscale (Cronbach s α=.77), was used in analyses in the present study and has been found to have moderate to high reliability (Wallston, Wallston, & DeVellis 1978). In the present study, the internal health locus of control scale had high internal consistency, (α =.88). Each of the MHLC subscales also correlates significantly and positively with its corresponding subscale of Levenson s IPC locus of control scale (1973), the scale the MHLC was modeled after with an added health focus (Wallston, 2005). The internal HLC subscale correlated.57 with Levenson s internal scale, but only -.12 with Levenson s powerful others scale, and -.14 with Levenson s chance scal

57 57 1. What is your current height? PsychPool Prescreen Questions 2. What is your current weight (lbs)? 3. Have you ever been told by a doctor or other health professional that you have diabetes or sugar? YES (TYPE 1) YES (TYPE 2) OR SUGAR DIABETES BORDERLINE OR PRE-DIABETES (blood sugar is higher than normal but not high enough to be called diabetes or sugar diabetes) NO DON T KNOW 4. Have either of your biological parents been told by a doctor that they had type 2 diabetes? YES NO DON T KNOW

58 58 Measures Q1 ID Number: Q2 Age: Q44 Gender:! Male (1)! Female (2) Q3 What year in school are you?! Freshman (1)! Sophomore (2)! Junior (3)! Senior (4)! 5th or above (5) Q4 Approximately what is your combined total family income! Less than $5,000 (1)! $5,000 through $11,999 (2)! $12,000 through $15,999 (3)! 16,000 through $24,999 (4)! $25,000 through $34,999 (5)! $35,000 through $49,999 (6)! $50,000 through $74,999 (7)! $75,000 through $99,999 (8)! $100,000 and greater (9) Q5 Which of the following most closely represents your ethnic background?! White (Anglo/Caucasian/European) background, NOT Hispanic (1)! Black (African) American, NOT Hispanic (2)! White, Hispanic (3)! Black, Hispanic (4)! Mixed race, Hispanic (5)! Caribbean Islander (NOT Hispanic) (6)! Asian/Asian American (7)! Native American (8)! Other (specify) (9)

59 59 Q6 What is your religious affiliation?! Christian (1)! Catholic (2)! Jewish (3)! Muslim (4)! Hindu (5)! Buddhist (6)! No religious affiliation(atheist,agnostic) (7)! Other (specify) (8) Q7 In which of the following communities did you grow up in?! Major Metropolitan Area (over a million people) (1)! Metropolitan Area (500,001-1,000,000 people) (2)! City (100, ,000 people) (3)! Small City (50, ,000 people) (4)! Town (2,500-50,000 people) (5)! Rural Area (fewer than 2500 people) (6) Q8 Did you grow up in the state of Ohio?! Yes (specify which county) (1)! No (2) Q9 Have you been receiving any treatment or taking medication for mental health issues such as anxiety, depression, stress, or other conditions?! No (1)! Yes, currently (2)! Yes, only in the past (3) If#No#Is#Selected,#Then#Skip#To#End#of#Block# Q10 For what condition (please select ALL that apply): " depression (1) " anxiety (2) " other (specify) (3) Q11 What type of treatment are/were you receiving?! medication only (1)! counseling only (e.g., psychologist, social worker, group therapy) (2)! both medication and counseling (3)! other (specify) (4)

60 60 Q12 What medications (for mental health issues) are you currently taking? Q14 PERSONAL HISTORY Q16 HAVE YOU EVER EXPERIENCED ANY OF THE FOLLOWING: (Please check all that apply) # YES#(1)# NO#(2)# BLEEDING#PROBLEMS#(1)#! #! # HEART#PROBLEMS#(2)#! #! # KIDNEY#PROBLEMS#(3)#! #! # MUSCLE/BONE#INJURIES# (4)#! #! # SEIZURES#(5)#! #! # VISION#PROBLEMS#(6)#! #! # CANCER#(7)#! #! # HEPATITIS#(8)#! #! # LIVER#PROBLEMS#(9)#! #! # NEUROLOGICAL# PROBLEMS#(10)# STOMACH#PROBLEMS# (11)# HIGH#BLOOD#PRESSURE# (12)#! #! #! #! #! #! # LUNG#PROBLEMS#(13)#! #! # STROKE#(14)#! #! # THYROID#PROBLEMS#(15)#! #! # Q18 Do you have Diabetes?! Yes, Type 1 (1)! Yes, Type 2 (2)! No (3) If#No#Is#Selected,#Then#Skip#To#What#is#your#current#height?#

61 61 Q20 When were you diagnosed?! At birth (1)! Pre-school age (2)! Elementary school age (3)! Junior High (4)! High School (5)! In college (6) Q22 What is your current height? Feet (1) Inches (2) Q24 What is your current weight (lbs)? Q26 FAMILY HISTORY Q28 HAVE YOUR PARENTS EVER EXPERIENCED ANY OF THE FOLLOWING: (Please check all that apply) # YES#(1)# NO#(2)# HEART#DISEASE#(1)#! #! # KIDNEY#DISEASE#(2)#! #! # HIGH#BLOOD#PRESSURE# (3)#! #! # STROKE#(4)#! #! # LIVER#DISEASE#(5)#! #! # HEART#ATTACK#(6)#! #! # SEIZURES#(7)#! #! # CANCER#(8)#! #! # MENTAL#HEALTH#(9)#! #! # OBESITY#(10)#! #! # TYPE#1#DIABETES#(11)#! #! # TYPE#2#DIABETES#(12)#! #! #

62 Q30 If you checked yes for Type 2 diabetes, which parent(s) was diagnosed? " Mother (How many years have they been diagnosed?) (1) " Father (How many years have they been diagnosed?) (2) " Step-mother (How many years have they been diagnosed?) (3) " Step-father (How many years have they been diagnosed?) (4) Q31 To your knowledge, has anyone else in your family been diagnosed with Type 2 diabetes? " No (1) " Maternal Grandfather (2) " Maternal Grandmother (3) " Paternal Grandfather (4) " Paternal Grandmother (5) " Maternal Aunt (6) " Maternal Uncle (7) " Paternal Aunt (8) " Paternal Uncle (9) " Cousin (10) " Brother (11) " Sister (12) " Other (please specify) (13) Q32 The questions below ask about your self-care activities during the past 7 days. If you were sick during the past seven days please think back to the last seven days when you were not sick. 62

63 Q33 The questions below ask about your eating habits during the past 7 days. If you were sick during the past seven days please think back to the last seven days when you were not sick. # 0#days# (1)# 1.#On#average,# over#the#past# month,#how# many#days#per# week#have#you# followed#your# eating#plan?# (1)# 2.#On#how# many#of#the# last#seven# days#did#you# eat#five#or# more#servings# of#fruits#and# vegetables?# (2)# 3.#On#how# many#of#the# last#seven# days#did#you# eat#high#fat# foods#such#as# red#meat#or# full_fat#dairy# products?#(3)# 4.#On#how# many#of#the# last#seven# days#did#you# space# carbohydrates# evenly# through#the# day?#(4)# 1#day# (2)# 2#days# (3)# 3#days# (4)# 4#days# (5)# 5#days# (6)# 6#days# (7)# 7#days# (8)#! #! #! #! #! #! #! #! # 63

64 64 5.#On#how# many#of#the# last#seven# days#have#you# followed#a# healthful# eating#plan?# (5)#! #! #

65 Q36 The questions below ask about your exercise during the past 7 days. If you were sick during the past seven days please think back to the last seven days when you were not sick. 1.#On#how# many#of# the#last# # 0#days# (1)# seven#days# did#you# 1#day# (2)# 2#days# (3)# 3#days# (4)# 4#days# (5)# 5#days# (6)# 6#days# (7)# 7#days# (8)# 65 participate# in#at#least#! #! # 30#minutes# of#physical# activity?# (1)# 2.#On#how# many#of# the#last# seven#days# did#you# participate# in#a#! #! # specific# exercise# session# (such#as# such#

66 66 swimming,# walking,# biking)# other#than# what#you# do#around# the#house# or#as#part# of#your# work?#(2)# 1.#On# average,# how#often# did#you# drink# regular# soda#or# pop#that#! #! # contains# sugar?#(do# not#include# diet#soda# or#diet# pop).#(3)# 2.#On#! #! #

67 67 average,# how#often# did#you# drink# sugar_ sweetened# fruit# drinks# (such#as# Kool_aid# and# lemonade),# sweet#tea,# and#sports# or#energy# drinks# (such#as# Gatorade# and#red# Bull)?#(Do# not#include# 100%#fruit# juice,#diet# drinks,#or# artificially# sweetened#

68 68 drinks)#(4)# During#the#past#30#days,#how#many#days#per#week#or#per#month#did#you#have#at#least#one#drink#of# any#alcoholic#beverage#such#as#beer,#wine,#a#malt#beverage#or#liquor?## #Days#per#week## # Do#you#now#smoke#cigarettes#every#day,#some#days,#or#not#at#all?## 1#Every#day## 2#Some#days## 3#Not#at#all#!# Q63 This survey will provide important information about how people feel about the risk of getting a chronic disease, like diabetes. There are no right or wrong answers. We are interested in your opinions and attitudes. Please answer each question as best as you can from a scale of 1 (lowest susceptibility) to 5 (highest susceptibility). # 1#(lowest# susceptibility)# (1)# What#do#you# believe#is# the#chance# that#you#will# develop# type#2# diabetes#in# your# lifetime?#(1)# How# susceptible# do#you#feel# you#are#to# type#2# diabetes#in# your# lifetime?#(2)# 2#(2)# 3#(3)# 4#(4)# 5#(highest# susceptibility)# (5)#! #! #! #! #! #! #! #! #! #! #

69 Q64 Now I would like you to think of others your age and all of the factors involved in type 2 diabetes. Please answer this question as best as you can from a scale of 1 (not likely at all) to 5 (highest level of likelihood)? # 1#(not#likely# at#all)#(1)# What#do#you# believe#are# your# chances#of# developing# type#2# diabetes# compared# with#others# your#age?# (1)# 2#(2)# 3#(3)# 4#(4)# 5#(highest# level#of# likelihood)#(5)#! #! #! #! #! # 69 Q65 Please answer this question as best as you can from a scale of 1 (not severe at all) to 5 (highest level of severity)? # 1#(not#severe# at#all)#(1)# How#severe# is#type#2# diabetes# (what#is#its# potential#for# causing# physical# harm#and# interfering# with#day#to# day# functioning)# (1)# 2#(2)# 3#(3)# 4#(4)# 5#(highest# level#of# severity)#(5)#! #! #! #! #! #

70 Q72 Instructions: Each item below is a belief statement about your medical state with which you may agree or disagree. Beside each statement is a scale which ranges from strongly disagree (1) to strongly agree (6). Please make sure that you answer EVERY ITEM and that you circle ONLY ONE number per item. This is a measure of your personal beliefs; there are no right or wrong answers. # Strongly# Disagree# (1)# If#I#get#sick,#it# is#my#own# behavior# which# determines# how#soon#i# get#well# again.#(1)# No#matter# what#i#do,#if#i# am#going#to# get#sick,#i# will#get#sick.# (2)# Having# regular# contact#with# my#physician# is#the#best# way#for#me# to#avoid# illness.#(3)# Most#things# that#affect# my#health# happen#to# me#by# accident.#(4)# Whenever#I# don't#feel# well,#i#should# consult#a# medically# trained# Moderately# Disagree# (2)# Slightly# Disagree# (3)# Slightly# Agree#(4)# Moderately# Agree#(5)# Strongly# Agree#(6)# 70

71 71 professional.# (5)# I#am#in# control#of#my# health.#(6)# My#family# has#a#lot#to# do#with#my# becoming# sick#or# staying# healthy.#(7)# When#I#get# sick,#i#am#to# blame.#(8)# Luck#plays#a# big#part#in# determining# how#soon#i# will#recover# from#an# illness.#(9)# Health# professionals# control#my# health.#(10)# My#good# health#is# largely#a# matter#of# good# fortune.#(11)# The#main# thing#which# affects#my# health#is# what#i# myself#do.# (12)# If#I#take#care# of#myself,#i#

72 72 can#avoid# illness.#(13)# Whenever#I# recover#from# an#illness,#it's# usually# because# other#people# (for#example,# doctors,# nurses,# family,# friends)#have# been#taking# good#care#of# me.#(14)# No#matter# what#i#do,#i# 'm#likely#to# get#sick.#(15)# If#it's#meant# to#be,#i#will# stay#healthy.# (16)# If#I#take#the# right#actions,# I#can#stay# healthy.#(17)# Regarding# my#health,#i# can#only#do# what#my# doctor#tells# me#to#do.# (18)#

73 73 # Strongly# Disagree# (1)# If#I#become# sick,#i#have# the#power#to# make#myself# well#again.# (1)# Often#I#feel# that#no# matter#what#i# do,#if#i#am# going#to#get# sick,#i#will#get# sick.#(2)# If#I#see#an# excellent# doctor# regularly,#i# am#less#likely# to#have# health# problems.#(3)# It#seems#that# my#health#is# greatly# influenced#by# accidental# happenings# (4)# I#can#only# maintain#my# health#by# consulting# health# professionals.# (5)# I#am#directly# responsible# for#my# Moderately# Disagree# (2)# Slightly# Disagree# (3)# Slightly# Agree#(4)# Moderately# Agree#(5)# Strongly# Agree#(6)#

74 74 health.#(6)# Other#people# play#a#big# part#in# whether#i# stay#healthy# or#become# sick.#(7)# Whatever# goes#wrong# with#my# health#is#my# own#fault.#(8)# When#I#am# sick,#i#just# have#to#let# nature#run#its# course.#(9)# Health# professionals# keep#me# healthy.#(10)# When#I#stay# healthy,#i'm# just#plain# lucky.#(11)# My#physical# well_being# depends#on# how#well#i# take#care#of# myself.#(12)# When#I#feel# ill,#i#know#it# is#because#i# have#not# been#taking# care#of# myself# properly.# (13)#

75 75 The#type#of# care#i#receive# from#other# people#is# what#is# responsible# for#how#well#i# recover#from# an#illness.# (14)# Even#when#I# take#care#of# myself,#it's# easy#to#get# sick.#(15)# When#I# become#ill,# it's#a#matter# of#fate.#(16)# I#can#pretty# much#stay# healthy#by# taking#good# care#of# myself.#(17)# Following# doctor's# orders#to#the# letter#is#the# best#way#for# me#to#stay# healthy.#(18)#

76 76 # Strongly# Disagree# (1)# If#my# condition# worsens,#it#is# my#own# behavior# which# determines# how#soon#i# will#feel# better#again.# (1)# As#to#my# condition,# what#will#be# will#be.#(2)# If#I#see#my# doctor# regularly,#i# am#less#likely# to#have# problems# with#my# condition.#(3)# Most#things# that#affect# my#condition# happen#to# me#by# chance.#(4)# Whenever# my#condition# worsens,#i# should# consult#a# medically# trained# professional.# (5)# Moderately# Disagree# (2)# Slightly# Disagree# (3)# Slightly# Agree#(4)# Moderately# Agree#(5)# Strongly# Agree#(6)#

77 77 I#am#directly# responsible# for#my# condition# getting#better# or#worse.#(6)# Other#people# play#a#big# role#in# whether#my# condition# improves,# stays#the# same,#or#gets# worse.#(7)# Whatever# goes#wrong# with#my# condition#is# my#own#fault.# (8)# Luck#plays#a# big#part#in# determining# how#my# condition# improves.#(9)# In#order#for# my#condition# to#improve,#it# is#up#to#other# people#to#see# that#the#right# things# happen.#(10)# Whatever# improvement# occurs#with# my#condition# is#largely#a# matter#of# good#fortune.#

78 78 (11)# The#main# thing#which# affects#my# condition#is# what#i#myself# do.#(12)# I#deserve#the# credit#when# my#condition# improves# and#the# blame#when# it#gets#worse.# (13)# Following# doctor's# orders#to#the# letter#is#the# best#way#to# keep#my# condition# from#getting# any#worse.# (14)# If#my# condition# worsens,#it's# a#matter#of# fate.#(15)# If#I#am#lucky,# my#condition# will#get# better.#(16)# If#my# condition# takes#a#turn# for#the# worse,#it#is# because#i# have#not#

79 79 been#taking# proper#care# of#myself.# (17)# The#type#of# help#i#receive# from#other# people# determines# how#soon#my# condition# improves.# (18)# Q75 Thank you! You have now finished the survey portion. Please tell the researcher you are finished. Q76 Height: Q77 Weight: Q78 Waist Circumference:

80 80 ID NUMBER: Parent Forms Because this study is concerned with the effects of a genetic history of diabetes, this form should be completed by the biological mother/father only. Please DO NOT put any identifying information (i.e. name, address) on the questionnaire. Circle one: Mother Father 1. What is your age? 2. What is your height? 3. What is your current weight? lbs. 4. Have you had a blood test for high blood sugar or diabetes within the past three years? YES NO DON T KNOW 5. Other than during pregnancy have you ever been told by a doctor or other health professional that you have diabetes or sugar? YES (TYPE 1)! If yes, How old were you when you were diagnosed? YES (TYPE 2) OR SUGAR DIABETES! If yes, How old were you when you were diagnosed? BORDERLINE OR PRE-DIABETES (blood sugar is higher than normal but not high enough to be called diabetes or sugar diabetes) NO DON T KNOW! If yes, How old were you when you were diagnosed? 6. HAVE YOU HAD OR DO YOU HAVE ANY OF THE FOLLOWING: (Please check all that apply) BLEEDING PROBLEMS CANCER HEART PROBLEMS HIGH BLOOD PRESSURE HEART ATTACK KIDNEY PROBLEMS LIVER PROBLEMS LUNG PROBLEMS NEUROLOGICAL PROBLEMS STOMACH PROBLEMS THYROID PROBLEMS STROKE OTHER PAST MEDICAL OR SURGICAL HISTORY: 7. Please circle any of your biological relatives who were told by a doctor that they had type 2 diabetes: Your Mother Your Father Your Sister(s) Your Brother(s) The questions below ask about your self-care activities during the past 7 days. If you were sick during the past seven days please think back to the last seven days when you were not sick.

81 followed your eating plan? 2. On how many of the last seven days did you eat five or more servings of fruits and vegetables? 3. On how many of the last seven days did you eat high fat foods such as red meat or full-fat dairy products? 4. On how many of the last seven days did you space carbohydrates evenly through the day? 5. On how many of the last seven days have you followed a healthful eating plan? Exercise Number of days 1. On how many of the last seven days did you participate in at least minutes of physical activity? 2. On how many of the last seven days did you participate in a specific exercise session (such as such swimming, walking, biking) other than what you do around the house or as part of your work? Complete the following ONLY if you have been diagnosed with type 2 diabetes: Blood Sugar Testing Number of days 1. On how many of the last seven days did you test your blood sugar? On how many of the last seven days did you test your blood sugar the number of times recommended by your health care provider? Are you now taking insulin? (Insulin: A chemical used in the treatment of diabetes. Typically, insulin is administered with a syringe by the patient) YES NO DON T KNOW # If yes, For how long have you been taking insulin? 2. If you know, what is your typical A1C level now? 3. HAVE YOU HAD OR DO YOU HAVE ANY OF THE FOLLOWING: (Please check all that apply) Diabetic Ketoacidosis (DKA) Cardiovascular Complications: Heart disease, peripheral vascular disease, stroke Eye Complications: Diabetic retinopathy, cataracts, glaucoma Nerve Damage: Neuropathy Kidney Damage: Nephropathy Hyperglycemic Hyperosmolar Non-Ketotic Syndrome (HHNS) Other Diabetes-Related Complications: THANK YOU! Please return this survey in the enclosed postage-paid envelope. DO NOT provide a return address on the envelope. 81

82 82 APPENDIX B: SUPPLEMENTAL ANALYSES Table 3 Characteristics of Participants, by Gender Total Sample Male Female N Clinical Characteristics Body Mass Index (kg/m 2 ) mean (SD) 24.4 (3.97) 25.1 (3.35) 24.1 (4.23) % BMI greater than normal (>25) Waist circumference (inches) mean (SD) % Waist circumference greater than normal (>35 females; >40 males) 32.3 (4.16) 34.2 (3.77) 31.3 (4.00) Perceived Susceptibility and Health Protective Behaviors, By Risk Group Simple correlations between measures of perceived susceptibility and health protective behaviors were calculated separately for the familial risk group, and the no familial risk group. See Table 4. Individuals with no familial risk of T2DM who reported greater own absolute risk of developing T2DM were found to be significantly less likely to have a healthy diet, r = -.30, p <.001, less likely to engage in regular physical activity, r = , p <.001, and more likely to consume sugar sweetened beverages, r =.22, p =.014. Individuals with no familial risk for T2DM who reported greater direct risk of developing T2DM were also found to be significantly less likely to have a healthy diet, r = -.25, p =.041, less likely to engage in regular physical activity, r = , p =.007, and more likely to consume sugar sweetened beverages, r =.20, p =.023. Individuals with familial risk for T2DM who reported greater own absolute risk of developing T2DM were found to be significantly more likely to consume sugar

83 83 sweetened beverages, r =.25, p =.043; however no significant relationship was found with likelihood to engage in regular physical activity, r = -.15, p =.23, or have a healthy diet, r = -.24, p =.54. Furthermore, individuals with familial risk for T2DM who reported greater direct risk of developing T2DM were found to be significantly more likely to consume sugar sweetened beverages, r =.27, p =.0128; however no significant relationship was found with likelihood to engage in regular physical activity, r = -.017, p =.89, or having a healthy diet, r = -.091, p =.747. Using the Fisher r-to-z transformation, we tested for differences between the familial risk group, and the no familial risk group based on correlations between measures of perceived susceptibility and health protective behaviors. We found no significant differences. Simple correlations between measures of perceived susceptibility and health protective behaviors were calculated separately for a healthy BMI group (BMI < 25) and an elevated BMI group (BMI > 25). See Table 5. Individuals with a BMI in the healthy range who reported greater own absolute risk of developing T2DM were found to be significantly less likely to have a healthy diet, r = -.30, p <.001, and more likely to consume sugar sweetened beverages, r =.25, p =.005, however no significant relationship was found with likelihood to engage in regular physical activity, r = -.17 p =.06. Individuals with a healthy BMI who reported greater direct risk of developing T2DM were significantly less likely to have a healthy diet, r = -.18, p =.039, and more likely to consume sugar sweetened beverages, r =.30, p <.001; however no significant

84 84 relationship was found with likelihood to engage in regular physical activity, r = p =.96. Individuals with an elevated BMI who reported greater own absolute risk of developing T2DM were significantly less likely to engage in regular physical activity, r = , p <.001, and less likely to have a healthy diet, r = -.26, p =.03; however no relationship was found for likelihood to consume sugar sweetened beverages r =.11, p =.36. Individuals with an elevated BMI who reported greater direct comparative risk of developing diabetes were less likely to engage in regular physical activity r = , p <.001, and less likely to have a healthy diet, r = -.24, p =.05; however no difference was found in the likelihood to consume sugar sweetened beverages, r =.093, p =.45. Using the Fisher r-to-z transformation, we tested for differences between the healthy BMI group and the elevated BMI group based on correlations between measures of perceived susceptibility and health protective behaviors. The correlation between direct comparative risk and physical activity was significantly greater for the healthy BMI group compared to the elevated BMI group, Z = 2.74, p =.006. All other comparisons were not significant. Simple correlations between measures of perceived susceptibility and health protective behaviors were calculated separately for a healthy waist circumference group (waist circumference < 35 for women, waist circumference < 40 for men) and an elevated waist circumference group (waist circumference > 35 for women, waist circumference > 40 for men). See Table 6. Individuals with a waist circumference in the healthy range who reported greater own absolute risk of developing T2DM were

85 85 significantly less likely to have a healthy diet, r = -.24, p <.001, less likely to engage in regular physical activity, r = , p =.005, and more likely to consume sugar sweetened beverages, r =.19, p =.011. Individuals with a healthy waist circumference who reported greater direct risk of developing T2DM were significantly less likely to have a healthy diet, r = -.17, p =.03,more likely to consume sugar sweetened beverages, r =.27, p <.001; however no more or less likely to engage in regular physical activity, r = -.15, p =.056. Individuals with an elevated waist circumference who reported greater own absolute risk of developing T2DM were significantly less likely to have a healthy diet, r = -.50, p =.015, and less likely to engage in regular physical activity, r = , p =.021; however, no increased or decreased likelihood to consume sugar sweetened beverages was found, r =.14, p =.52. Individuals with an elevated BMI who reported greater direct comparative risk of developing diabetes were no more or less likely to engage in regular physical activity, r = , p =.30, to have a healthy diet, r = -.31, p =.15, or to consume sugar sweetened beverages, r = -.13, p =.55. Using the Fisher r-to-z transformation, we tested for differences between the healthy waist circumference group and the elevated waist circumference group based on correlations between measures of perceived susceptibility and health protective behaviors. The correlation between perceived susceptibility measures and health protective behaviors did not differ by waist circumference.

86 86 Table 4 Perceived Susceptibility and Health Protective Behaviors, By Risk Group Familial Risk (n = 66) No Familial Risk (n = 131) r to Z values Own Absolute Risk Direct Comparative Risk Own Absolute Risk Direct Comparative Risk Own Absolute Risk Direct Comparative Risk Healthy Diet ** -.25** Physical Activity ** -.24** Sugar Sweetened Beverages.25*.27*.22*.20* Note. ** Significant at the 0.01 level (2-tailed). * Significant at the 0.05 level (2-tailed). Table 5 Perceived Susceptibility and Health Protective Behaviors, By BMI Risk Group Healthy BMI (n = 129) Elevated BMI (n = 68) r to Z values Own Absolute Risk Direct Comparative Risk Own Absolute Risk Direct Comparative Risk Own Absolute Risk Direct Comparative Risk Healthy Diet -.30** -.18* -.26* -.24* Physical Activity ** -.40** ** Sugar Sweetened Beverages.25**.30** Note. ** Significant at the 0.01 level (2-tailed). * Significant at the 0.05 level (2-tailed).

87 87 Table 6 Perceived Susceptibility and Health Protective Behaviors, By Waist Circumference (WC) Healthy WC (n = 174) Elevated WC (n = 23) r to Z values Own Absolute Risk Direct Comparative Risk Own Absolute Risk Direct Comparative Risk Own Absolute Risk Direct Comparative Risk Healthy Diet -.24** -.17* -.50* Physical Activity -.22** * Sugar Sweetened Beverages.19*.27** Note. ** Significant at the 0.01 level (2-tailed). * Significant at the 0.05 level (2-tailed). Table 7 Perceived Susceptibility and Health Protective Behaviors, By Gender Male (n = 68) Female (n = 129) r to Z values Own Absolute Risk Direct Comparative Risk Own Absolute Risk Direct Comparative Risk Own Absolute Risk Direct Comparative Risk Healthy Diet ** -.26** Physical Activity -.30* ** -.21* Sugar Sweetened Beverages **.29** ** Note. ** Significant at the 0.01 level (2-tailed). * Significant at the 0.05 level (2-tailed).

88 88 Simple correlations between measures of perceived susceptibility and health protective behaviors were calculated separately for men and women. See Table 7. Men who reported greater own absolute risk of developing T2DM were significantly less likely to engage in regular physical activity, r = , p =.013; however no effect was found for likelihood to have a healthy diet, r = -.22, p =.078, or to consume sugar sweetened beverages, r =.056 p =.65. Among men, direct comparative risk had no significant association with any health protective behavior; physical activity, r = , p =.43, healthy diet r = -.084, p =.50, and sugar sweetened beverage consumption, r =.098, p =.43. Women who reported greater own absolute risk of developing T2DM were significantly less likely to engage in regular physical activity, r = , p =.009, less likely to have a healthy diet, r = -.32, p <.001, and more likely to consume sugar sweetened beverages, r =.29, p <.001. In addition, women who reported greater direct comparative risk of developing T2DM were significantly less likely to engage in regular physical activity, r = , p =.016, less likely to have a healthy diet, r = -.26, p =.003, and more likely to consume sugar sweetened beverages, r =.29, p <.001. Using the Fisher r-to-z transformation, we tested for differences between men and women based on correlations between measures of perceived susceptibility and health protective behaviors. The correlation between direct comparative risk and sugar sweetened beverage was significantly greater for women compared to men, Z = -2.6, p =.009. We found no other significant differences.

89 89 Internal Health Locus of Control and Health Protective Behaviors, by Familial Risk Simple correlations between internal locus of control and health protective behaviors were calculated separately by familial risk group. See Table 8. For individuals with no familial risk, having an internal health locus of control was significantly associated with physical activity, r =.23, p =.009, and with having a healthy diet, r =.25, p =.003; however no relationship was found for sugar sweetened beverage consumption, r = , p =.48. Among individuals with familial risk for T2DM, internal health locus of control had no significant association with any health protective behavior: physical activity, r = -.016, p =.90, healthy diet r =.033, p =.79, and sugar sweetened beverage consumption, r =.12, p =.35. Using the Fisher r-to-z transformation, we tested for differences between familial risk group based on correlations between internal health locus of control and health protective behaviors, finding no significant differences. Table 8 Internal Health Locus of Control and Health Protective Behaviors, by Familial Risk Familial Risk (n = 66) No Familial Risk (n = 131) R to Z values Internal Health Locus of Control Internal Health Locus of Control Internal Health Locus of Control Healthy Diet ** Physical Activity ** Sugar Sweetened Beverages Note. ** Significant at the 0.01 level (2-tailed). * Significant at the 0.05 level (2-tailed).

90 90 Internal Health Locus of Control and Perceived Susceptibility, by Familial Risk Simple correlations between internal health locus of control and perceived susceptibility measures were calculated separately for the familial risk group, and the no familial risk group. See Table 9. For individuals with no familial risk, having a high internal health locus of control was inversely related to own absolute risk of T2DM, r = -.21, p =.018, and to direct comparative risk of T2DM, r = -.25, p =.004. Among individuals with familial risk for T2DM, internal health locus of control had no significant association with own absolute risk of T2DM, r =.052, p =.68, or to direct comparative risk of T2DM, r =.052, p =.68. Using the Fisher r-to-z transformation, we tested for differences between the familial risk group, and the no familial risk group based on correlations between internal health locus of control and perceived susceptibility measures. The correlation between internal health locus of control and direct comparative risk was significantly greater for the familial risk group compared to the no familial risk group, Z = 2.00, p =.046; however internal health locus of control and own absolute risk risk did not differ by familial risk group, Z = 1.72, p =.085.

91 91 Table 9 Internal Health Locus of Control and Health Protective Behaviors, by Familial Risk Familial Risk (n = 66) Internal Health Locus of Control No Familial Risk (n = 131) Internal Health Locus of Control R to Z values Internal Health Locus of Control Own Absolute Risk * 1.72 Direct Comparative Risk ** 2.00* Note. ** Significant at the 0.01 level (2-tailed). * Significant at the 0.05 level (2-tailed). Family Modeling and Health Protective Behaviors, by Familial Risk Simple correlations for family modeling of health protective behaviors were calculated separately by familial risk group. Using the Fisher r-to-z transformation, we tested for differences between familial risk group based on correlations between parent diet and physical activity and child diet and physical activity. Parent diet and child diet were significantly and positively correlated in the familial risk group but were not associated in the no familial risk group, Z = -2.58, p <.001. The relationship between parent physical activity and child physical activity did not differ by familial risk group, Z = -1.82, p =.069.

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