Mental Health, Health Behaviors, Social Support, Self-Efficacy and Disease: An Integrative Model

The utility of modifiable health behaviors for better physical health outcomes is well-established. Because mental illness is a serious public health concern worldwide, an important question pertains to whether health behaviors likewise have benefits for mental health. Although simple research methods indicate positive relations, no investigations have tested models with mediating factors to discern specific pathways between health behaviors and mental health. As such, this study aimed to do so using path analysis to examine the impact of three key health behaviors (i.e., smoking cessation, exercise, and healthy eating) on mental health. In addition, the potential roles of perceived social support, general self-efficacy and physical health within the health behaviors and mental health relationship were investigated. A population-based sample of 427 adults completed survey measures. One important finding was the mediating effect of physical health between exercise/ healthy eating, and mental health functioning. Implications are further discussed. Group comparisons indicated that : 1) Smoking doesn’t appear significantly related to self-efficacy, perceived social support and mental health functioning, 2) engaging in exercise and healthy eating for at least six months is strongly linked to better general self-efficacy and mental health, 3) those who are contemplating engaging in exercise and/or healthy eating in the next six months appear to be particularly different than maintainers in terms of their mental health status, 4) perceived social support is significantly related to physical and mental health functioning, and 5) general self-efficacy is significantly linked to mental health functioning.

v LIST OF TABLES                DIRECT MODEL 77 cardiovascular disease) had a 42% increase in the risk of ever having a psychiatric disorder compared with people without a medical disorder. In the 2003 National Comorbidity Survey Replication study sponsored by the National Institutes of Health, more than 68% of adults with a mental disorder had at least one medical condition, and 29% of those with a medical disorder had a comorbid mental health condition (Alegria, Jackson, Kessler, & Takeuchi, 2003).
Comorbidity is also indicated by the fact that cardiovascular disease is one of the leading causes of mortality in people with schizophrenia (Casey & Hansen, 2003).
Similarly, individuals with depression and anxiety are more likely to develop cardiovascular disease (Suls & Bunde, 2005). Depression has also been shown to increase the risk of mortality in people with diabetes by 30% (World Federation for Mental Health (WFMH), 2010). Conversely, individuals with a physical illness are at increased risk for clinically significant mental illnesses (De Hert, Correll, Bobes, Cetkovich-Bakmas, Cohen, Asai, et al., 2011). For instance, individuals with current or chronic depression are 60% more likely to be obese than those with no history of depression (WFMH, 2010). In sum, the staggering comorbidity rates between mental and physical illness range from mild to severe.

Smoking Cessation and Mental Health.
There is evidence to suggest that being a non-smoker is positively linked to better mental health. Close to 40% of adults with a MI are cigarette smokers compared with only 21% of adults who do not have a MI (CDC, 2013). In other words, those with a MI are about 70% more likely to smoke compared to adults with no MI (CDC, 2013). In a meta-analysis (Taylor, McNeill, Girling, Farley, Lindson-Hawley et al., The DOSE study (Dunn, Trivedi, Kampert, Clark, Chambliss, 2002;Dunn, Chambliss, 2005) found that exercising 30 minutes five or more days a week was the minimum needed to reduce depression. A review (Lawlor & Hopker, 2001) of 14 randomized controlled trials looking at the effectiveness of exercise in the management of depression found antidepressant effects comparable to that of cognitive therapy. A study of 16,483 university undergraduates found that exercise correlated with lower levels of depression (Steptoe, Wardle, Filler, Holte, Justo, Sanderman, 1997).
Similarly, aerobic exercise has been found to have anxiolytic, in other words, calming effects (Salmon, 2000). In a general population sample of 55,000, a selfreported correlation between recreational exercise and better mental health was demonstrated, including fewer symptoms of depression and anxiety (Stephens, 1988).
Aerobic activity was shown to specifically reduce depression in two well-controlled studies of 10-11 weeks of walking and running in two populations selected for exposure to stress or high anxiety (Steptoe et al., 1989;Roth & Holmes., 1987, as cited in Salmon, 2001). Aerobic exercise compared to strength and flexibility training reduced anxious mood in subjects with high anxiety up to three months (Steptoe et al., 1989).

Healthy Eating and Mental Health.
Nutrients. There is evidence of the importance of nutrition for mental health, and the most prominent review of over 160 studies suggests that mental health is linked to diet (Go´mez-Pinilla, 2008). Specifically, individuals who consume a diet akin to the "Mediterranean diet" consisting of fruits, vegetables, nuts, whole grains, fish, and unsaturated fat are approximately 30% less likely to develop depression than those who typically consume a diet that consists of processed food and saturated fats (Sánchez-Villegas, Delgado-Rodríguez, Alonso, Schlatter et al., 2009). Such a diet has been shown to improve cognitive functions and academic performance in children and alleviate affective and schizophrenia spectrum illnesses in adults (Walsh, 2011).
Some researchers strongly encourage the use of food supplements such as particular vitamins, folic acid, and fish oil for improving mental health (Sarris, Schoendorfer, & Kavanagh, 2009). While there are a number of studies advocating for the link between specific nutrients and diet and mental health function, findings remain limited and mixed with regard to nutritional supplementation as a specific treatment for depression (Jacka, Mykletun, & Berk, 2012). Aside from the utility of omega 3fatty acids in severe depression (Appleton, Rogers, & Ness, 2010) and folate as a concomitant treatment , there is little robust evidence for such effectiveness (Jorm, Christensen, Griffiths, Rodgers, 2002).

Given the complex combinations and interactions among nutrients in an
individual's daily diet, the analysis of the impact of a single food or nutrient on health outcomes has its limitations. Therefore, dietary patterns have gained considerable attention. Indeed, people do not consume nutrients or single foods but combinations of foods (Munoz, Fito, Marrugat, Covas et al., 2009). Furthermore, dietary components may interact, making the search for associations between single dietary factors and health outcome more difficult (Munoz et al., 2009). Diet is a multifaceted phenomenon and, hence, attempting to link specific single nutrients or food groups to disease prevalence and symptoms will continue to pose challenges (Quirk, Williams, O'Neill, Pasco et al., 2013). Furthermore, dietary patterns may confound specific diet and illness relationships (Quirk et al., 2013).
One systematic review (Quirk et al., 2013) evaluated the association between overall diet quality and depression in adults across 25 studies from nine countries.
Findings indicated limited evidence to support an association between traditional diets (i.e., Mediterranean -further discussed in a separate section below -or Norwegian diets) and depression. There was also mixed evidence for associations between a traditional Japanese diet and depression, a "healthy" diet and depression, a Western diet and depression, and individuals with depression and the likelihood of eating a less healthy diet (Quirk et al., 2013). A significant amount of variability was observed in relation to the measurement of diet quality and patterns, definitions of a "healthy diet," depression assessment and study samples (Quirk et al., 2013). The majority of studies included in the latter review were cross-sectional precluding any interpretations of directionality of associations. The authors commented that any potential association between diet quality and patterns and mental illness are likely influenced by a large number of confounding variables including demographic (e.g. socioeconomic), behavioral, genetic, environmental and sociocultural characteristics (Quirk et al., 2013). The analyses undertaken in each of the studies included in the review typically controlled for age and gender (Quirk et al., 2013). The association between diet and mental health is likely bi-directional, with depressed individuals to be more likely to eat unhealthy (Kilian, Becker, Kruger, Schmid et al., 2006).

The Mediterranean Diet (MD) and Social Support.
MD is characterized by high consumption of green foods, modest consumption of alcohol and low consumption of meat (Munoz, Fito, Marrugat, Covas et al., 2009).
The reported health effects of MD have substantial biochemical underpinnings (Serra-Majem, Roman, & Estruch, 2006). In addition, this particular diet is embedded in a social context that has suggested increasing personal well-being (Munoz et al., 2009).
One assumption is that that consuming a Mediterranean diet may reflect the living of the "Mediterranean lifestyle" and practicing the corresponding behaviors, including eating at home, spending time cooking, sharing lunchtime with other people and going to the market to buy foods (Munoz et al., 2009). Eating within the context of social gatherings may improve self-perceived quality of life (Munoz et al., 2009). Social and family support can promote health by providing persons with positive experiences, pro-social behaviors and roles and better ways of coping with stressors (Seeman, 2000).
Perceived social support from family members and significant others has received increasing attention as a factor contributing to health outcomes (Korkiakangas, Taanila, & Keinänen-Kiukaanniemi, 2011). A report from the Centers for Disease Control and Prevention (2008) suggested an association between perceived social support and health-related quality in older individuals. In addition, perceived social support has been the target of lifestyle modification interventions for persons with a variety of medical conditions, including obesity, cardiovascular disease, and type 2 diabetes (Kumanyika & Economos, 2011;Van Dyck et al., 2011).
Perceived health has been shown to be a predictor of mortality at long term (Wannamethee & Shaper, 1991). There is growing evidence supporting the effectiveness of social support approaches enhancing diet and increasing physical activity compared to interventions that focus solely on an individual (Gorin, Phelan, Tate, Sherwood, Jeffery, & Wing, 2005). A positive environment may contribute to having a better self-perceived health-related quality of life related to diet (Munoz et al., 2009).
Social support has been shown to be an important predictor of having fruit at the home (Baranowski, Watson, Missaghian, Broadfoot et al., 2008). Support from friends, family, and co-workers helped participants exercise more and eat less fat in an organizational health promotion program (Zimmerman & Conor, 1989). In a metaanalytic review across 148 prospective studies (308,849 participants followed for an average 7.5 years), the random effects weighted average effect size was OR = 1.50 (95%CI) of survival for participants with stronger social relationships (Holt-Lunstad, Smith, & Layton, 2010) suggesting a comparable effect to prominent risk factors of morbidity and mortality such as physical inactivity and obesity (Holt-Lunstad et al., 2010).
In this realm, one cross-sectional study (N=158) specifically looked at overweight participants with severe mental illness and characteristics of social support around healthy eating and exercise (Aschbrenner, Mueser, Bartels, & Pratt, 2013).
Regular social contact was defined as at least twice monthly face-to-face contact with either family members or friends (Aschbrenner et al., 2013). Findings indicated a correlation between increased frequency of family contact and unhealthy family eating environments (e.g. buying or eating unhealthy foods in front of the participants), as well as increased friend contact and unhealthy eating environments (Aschbrenner et al., 2013). Females compared to males were more likely to be exposed to unhealthy environments (Aschbrenner et al., 2013). Yet, females compared to males were more likely to receive encouragement for healthy eating behaviors from friends (t(93) = -2.44, p < .05 (Aschbrenner et al., 2013). Of note, participants who reported greater readiness to change their typical portion size were significantly more likely to receive encouragement from friends for healthy eating behaviors (r(92) = .291, p < .01) (Aschbrenner et al., 2013). And, higher level of symptoms was a significant predictor of more unhealthy family eating environments (r(77) = .35, p < .001) (Aschbrenner et al., 2013). This latter finding adds to the understanding of the complex bi-directional relationship between diet and mental health (Aschbrenner et al., 2013). One plausible assumption is that overweight and obese individuals attempting to make a lifestyle change may feel discouraged by unhealthy eating behaviors of family members and, thus, experience anxiety, frustration, and a sense of hopelessness in their efforts to change their own behaviors (Aschbrenner et al., 2013). Another assumption is that those with depression are potentially more likely to perceive the unhealthy behaviors of others as intentionally discouraging which could create a barrier to healthy eating (Aschbrenner et al., 2013).
In a 10-year longitudinal population-based study conducted in the U.S., data were obtained from 2,379 girls assessed annually from ages 9-19 (Franko, Thompson, Affenito, Barton, & Striegel-Moore, 2008). The mediational analysis sought to evaluate whether the frequency of family meals in childhood was related to health outcomes by examining the mediating links of family cohesion and coping skills, after adjusting for baseline demographics and previous levels of the outcome variables (Franko et al., 2008). More frequent family meals in the first 3 years predicted greater family cohesion and coping in years 7 and 8 in to the study (Franko et al., 2008).
Family cohesion mediated family meals and risk of smoking in Year 10 (Franko et al., 2008). Coping mediated family meals and stress in Year 10 (Franko et al., 2008). The authors concluded that eating together as a family during childhood may have benefits in adulthood (Franko et al., 2008).

Social Support and Mental Health.
A concept that has been found to be linked to health just as strongly as health risk behaviors such as smoking and sedentary life is perceived social support (Cacioppo, Hawkley, Norman, & Berntson, 2011;Fratiglioni et al., 2004). Social support has been extensively studied, and the subject of medical and behavioral research for approximately 40 years, with the general conclusion that social support has therapeutic value in mental and physical health (Hogan, Linden, & Najarian, 2002;Uchino, 2009;Wallston et al., 1983) and is a key buffer to the effects of psychosocial stressors. Specifically, social support may influence health outcomes by encouraging individual behavior modification (Cohen, 1988) or adherence to medical recommendations (Wallston et al., 1983).
Social support has been specifically linked to depression and anxiety, recovery from chronic illnesses, greater life satisfaction, enhanced ability to cope with life stressors, and with an overall ability to perform instrumental activities of daily living (Cohen & Wils, 1985;Cohen, Underwood, & Gottlieb, 2000).
There are several pathways by which social relationships may affect health, one of which may be in the provision of social support (Golden, Conray, Bruce, Denihan, Greene, Kirby et al., 2009). Deficits in social support have been associated with a variety of adverse health outcomes, ranging from physical health to depression and self-harm (Dennis, Wakefield, Molloy, Andrews, & Friedman, 2005). Perceptions of support may reduce stress by altering one's evaluation of the stressors, by changing one's coping patterns, by affecting perceptions of one's self-efficacy, or by altering problem-solving behaviors (Rogers, Anthony, Lyass, 2004). Indicators of perceived social support have been found to have the strongest links with indicators of reduced stress and psychological distress, as well as indicators of improved well-being (e.g., Gjesfjeld, Greeno, Kim, & Anderson, 2010). Although a positive relationship between social support and health have been demonstrated in the general population, less is known about the effect of perceived social support on mental health functioning.

Self-Efficacy and Mental Health.
Self-efficacy has been defined as the belief that one is capable of performing in a certain manner to attain a certain set of goals (Miller & Dollard, 1941). Health behavior and functioning is also influenced by the belief in one's own self-efficacy (Bandura, 1977). Perceived self-efficacy is related to subsequent behavior and, consequently, is relevant for clinical practice and behavior change (Schwarzer & Jerusalem, 1995). This core belief affects each of the basic stages of change, whether people even consider changing their health behaviors and how well they maintain the behavior changes they have achieved (Bandura, 2004). Self-efficacy has a valuable role in different aspects of life and health (Maddux , 2002;Dweck & Leggett, 1988) and a main role in individuals' thinking modes, their decision-making, the quality of their encounter with problems, and their depression and anxiety status (Kim, 2003).
Researchers have shown that general self-efficacy is negatively related to depression and anxiety, as two main components of mental health, and positive self-efficacy beliefs have an effective role in the treatment of mental diseases.

PURPOSE OF THE PRESENT STUDY
From infections to cancer, the most effective way of contributing to physical health has been through prevention efforts rather than treatment (Jacka, Mykletun, & Berk, 2012). In contrast, although mental health issues are regarded high on the public health agenda in the U.S. and worldwide, the prevention of mental health issues has received less consideration compared to direct treatment (Jacka et al., 2012). As such, there is a need for emphasis on the development of preventive approaches to mental health issues at a population level (Cuijpers, Beekman, & Reynolds, 2012).
An important challenge of developing prevention approaches and models to mental health is the vast number of interacting factors contributing (Jacka et al., 2012).
While other potentially modifiable factors such as socioeconomic variables and childhood maltreatment contribute to mental health issues, prevention efforts should capitalize on variables of utmost modifiability (Jacka et al., 2012). One such universal modifiable variable is "lifestyle," (Jacka et al., 2012;Walsh, 2010) which encompasses health behaviors such as smoking cessation, exercise, and healthy eating.
As discussed, in the literature review, these health behaviors are shown to not only be linked to chronic medical disease but also mental health issues.
In summary, growing evidence indicates that exercise has been shown to be effective in treatment studies (Stathopoulous, Powers, Berry, Smits, & Otto, 2006).
There is some evidence for smoking also independently increasing the risk for common mental health issues (e.g. Breslau, Novak, & Kessler, 2004;Mykletun, Overland, Aaro, Liabo, & Stweart, 2008;Pasco, Williams, Jacka, Ng, Henry, Nicholson, et al., 2008). Diet/healthy eating is the most recent area of interest in the lifestyle-mental health research field (Jacka et al., 2012). Diet-mental health related research has focused on nutritional supplementation to treat depression resulting in inconsistent and limited findings (Jacka et al., 2012).
Taking in to consideration these findings, there appears to be a dearth of research in the areas of delineating the specific pathways between health behaviors and mental health. As such, it is important to start moving away from looking at simple associations between such variables to utilizing more sophisticated multivariate tools and modeling.
Specifically, structural modeling and mediational analysis can help determine the relative role of each variable in a proposed relationship between variables (Lockwood, DeFrancesco, Elliot, Beresford, & Toobert, 2010). For example, in a review of 40 studies (Lockwood, et al., 2010), the authors presented all the observational studies and randomized intervention trials published in nutritional research utilizing mediational analysis. Of those, none of the studies looked at healthy eating/diet or proxy measures of healthy eating/diet as the independent variable or any mental health variable as the dependent variable.
Similarly, a different study on the applicability of a mediational model to study how physical activity influences mental health (Cerin, 2010) pointed to the shortage of such analyses in the exercise arena as well. Moreover, the author stated that even though there have been several biophysical and psychosocial factors identified as potential mechanisms responsible for the association between physical activity and favorable mental health outcomes, by and large it is still fundamentally unknown how physical activity predicts positive mental health outcomes (Cerin, 2010). This is a startling fact given that physical activity is one of the most studied behaviors in health research, and further reflects the limitations within the diet and smoking cessation research fields.

Stage of Change.
The Transtheoretical Model of Behavior Change (TTM) is a comprehensive model which lays out a blueprint for intentional behavior change (Prochaska & DiClemente, 1983;Prochaska, DiClemente, & Norcross, 1992;Prochaska & Velicer, 1997). Stage of Change, one of the core constructs of the TTM, provides a useful approach to conceptualizing readiness to change any particular healthy behavior (Prochaska & Velicer, 1997). In the Precontemplation stage (PC), individuals are intending to take action to change a given behavior in the next six months. Their reluctance may be due to unawareness, misinformation, or resistance to change. In the next stage, Contemplation (C), individuals tend to be ambivalent about change but at the same time are intending to take action in their behavior in the next six months. In Preparation (PR), individuals have a clear intention of changing their behavior in the next 30 days and may have even started taking steps towards behavior change. In the action stage (A), individuals are in the process of changing their behavior for at least 24 hours but have done so for less than six months. In the Maintenance (M) stage, individuals work on maintaining the acquired healthy behavior which they have managed for at least 6 months whilst also focusing on curtailing setbacks. In the Termination (T) stage (for smoking cessation), individuals have not smoked for at least 5 years.

Stage of Change for Healthy Eating assessed by Calorie and Fat
Intake (CALFAT, Johnson et al., 2008). Participants were categorized in one of the five stages of change of the Transtheoretical Model of Behavior Change that was used to assess an individual's readiness to eat the number of calories that allows an individual to reach and maintain a healthy weight and eating a diet low in saturated and trans fats. Healthy eating was measured by assigning a numeric value to each of the stages of change (i.e. 1=Precontemplation, 2=Contemplation, 3=Preparation, 4=Action, and 5=Maintenance) (see Appendix).
The General Self-Efficacy Scale (GSE, Schwarzer & Jerusalem, 1995). This is a 10-item one-dimensional assessment tool assessing general sense of perceived self-efficacy with the aim in mind to predict coping with daily hassles, as well as adaptation after experiencing all kinds of stressful life events. Participants rated statements with response options ranging from 1 = Not at all True to 4 = Exactly True.
GSE was calculated by averaging the items. Higher scores indicated a higher level of general self-efficacy. In samples from 23 nations, Cronbach's alphas ranged from .76 to .90, with the majority in the high .80s. The Cronbach's alpha for this sample was .92, indicating a high level of internal consistency (see Appendix).
The Multidimensional Scale of Perceived Social Support (PSS, Zimet, Dahlem, Zimet, & Farley, 1988). This measure is widely-used across a range of cultures, clinical populations and age with a fourth grade reading-level. It consists of 12 items and three subscales: Support from Family (α=.87), Support from Friends (α=.85) and Support from Significant Others (α=.91). Cronbach's alpha for the total scale was .88. High levels of perceived social support were associated with low levels of depression and anxiety symptomatology (Zimet et al., 1988). The Cronbach's alpha for this sample was .95, indicating a high level of internal consistency. For the purpose of this study, only the total score of the measure was utilized in the analyses (see Appendix). Ware, & Raczek, 1993). The SF-36 is a self-report questionnaire that is a reliable and valid measure of physical and mental health-related functioning. It measures health on eight multi-item dimensions, covering functional status, wellbeing and overall evaluation of health (Brazier et al., 1992). For each dimension, item scores are coded, summed, and transformed on a scale from 0 (worst health) to 100 (best health). The SF-36 is a generic measure, one that does not target a specific age group or disease, but instead, can be used with diverse populations. It was designed for use in surveys of general and specific populations, health policy evaluations, clinical practice and/or research.

The Medical Outcome Study Short Form (McHorney,
The SF-36 has been widely used in clinical studies and has demonstrated adequate psychometric integrity (McHorney, Ware, & Raczek, 1993;Ware & Sherbourne, 1992). Factor analytic studies confirm that the eight scales assessing eight different health concepts make up two distinct factors: mental health and physical health, and that these factors account for 80-85% of the reliable variance in the eight scales in the U.S general population (Ware, Kosinski & Keller, 1994  Finally, it is important to note that three of the model variables (SMK, EX CALFAT) are categorical variables with five or more levels/groups. Bentler and Chou (1987) indicate that when the number of categories are large (i.e. four or more categories) as is in the present study, treating categorical variables as continuous variables is appropriate as the chi-square statistic is less amenable to influence as the number of categories increases (Green, Akey, Fleming, Hershberger, & Marquis, 1997). As such, the aforementioned categorical variables (i.e SMK, EX, and CALFAT) will be treated as continuous variables for the purposes of the path analyses.
Maximum likelihood (ML) estimation was used to estimate model parameters.
Models were compared to one another in terms of their empirical fit with the data, using practical fit indices. Such indices consisted of the chi-square goodness-of-fit index (GFI), the comparative fit index (CFI; Bentler, 1990), the root mean square error of approximation (RMSEA; Steiger & Lind, 1998), R 2 values for estimating effect size, and standardized regression path coefficients for each model (Harlow, 2005).
More specifically, the CFI is a normed fit index that adjusts for degrees of freedom.
CFI greater than 0.90, and RMSEA of less than .10 are indicative of good fitting models (Bentler & Wu, 1995). Insignificant chi-squares are ideal, however, due to the large sample size, it is expected that the chi-squares may be significant for the main analyses. If the macro fit indices for the model were deemed adequate, then the microfit indices were examined. These included the number of significant paths, their significance levels, and the strength of each relationship. For all analyses, a cut-off value for significance was set at p= 0.05.

RESULTS
Preliminary Findings.

Demographics Characteristics.
As expected, the sample had demographic characteristics (race and sexual orientation) similar to that of the general U.S. population (U.S. Census Bureau, 2013).
The largest group of participants (28.3%) fell in the 45-54 age range. A little more than half of the sample (54.1%) was married or in a domestic partnership/civil union.  Table 1 for full demographic characteristics.

Clinical Characteristics.
Forty-three percent of participants indicated ever having (past or current) been diagnosed with a mental illness. The most common diagnosed mental illness was depression (15.4%) followed by an "other" illness (4.9%) and anxiety (4.4% indicated currently undergoing medical treatment for an illness. Sixty-one percent of the total sample was overweight or obese. See Table 2 for full clinical characteristics.

Health Behaviors.
See Table 10 for correlations between health behaviors.

Smoking Cessation.
The stage of change distribution for the sample was as follows: Precontemplation cigarettes, 27.4% less than 9, 12.6% 20-29 and 8.4% indicated smoking more than 30 cigarettes a day. See Table 3.

Healthy Eating Measured by Stage of Change for Calorie and Fat
Intake.

Non-Health Behavior Continuous Model Variables.
Means, standard deviations and observed ranges for mental health functioning (MCS), physical health functioning (PCS), general self-efficacy (GSE) and perceived social support (PSS) are presented in Table 5. A follow-up ANOVA, contrary to the hypothesis, indicated that SMK did not have a statistically significant relationship with MCS, F(6, 420) = 1.32, p = .25, partial η 2 = .02. See Table 6a.

Mean Comparisons between Health Behaviors (SMK, EX, HE) and Non-Health
1b. Participants who endorse being further along the Stages of Change for Exercise (EX) will report higher levels of mental health functioning (MCS). Based on follow-up ANOVAs, SMK did not have a statistically significant relationship with GSE, F(6, 420) = .88; p = .51; partial η 2 = .01) ( Table 6). In contrast, EX did have a statistically significant positive relationship with GSE, F(4, 422) = 7.49; p = .000; partial η 2 = .07). Table 7b  2b. Participants who endorse higher general self-efficacy (GSE) will report higher levels of mental health functioning (MCS).
There was a moderate, positive correlation between MCS and GSE (GSE), r = .33, p <.01. See Table 9. 3b. Participants who endorse higher levels of perceived social support (PSS) will report higher levels of physical health functioning (PCS).
Contrary to the hypothesis, there was no significant correlation between PSS and PCS, r = .09. See Table 9.
3c. Participants who endorse higher levels of perceived social support (PSS) will be associated with higher levels of general self-efficacy (GSE).
Confirming the hypothesis, there was a moderate, positive correlation between PSS and GSE, r = .31, p <.01. See Table 9.
3d. Participants who endorse higher levels of perceived social support (PSS) will be associated with higher levels of mental health functioning (MCS).
Confirming the hypothesis, there was a moderate, positive correlation between PSS and MCS, r = .31, p <.01. See Table 9.

Hypothesis 4. Physical Health Functioning, General Self-Efficacy, and Mental
Health Functioning.
Participants who endorse higher levels of physical health functioning (PCS) will have: 4a. higher levels of general self-efficacy (GSE).
Contrary to the hypothesis, there was no significant correlation between PCS and GSE, r = .09. See Table 9.
Confirming the hypothesis, there was a small, positive correlation between PCS and MCS, r = .16, p <.01. See Table 9.
Please see Tables 6b, 7b,  Standardized parameter estimates are shown in Figure 1. However, because this model was not a good fit, estimates of individual parameters were unreliable and hence cannot be interpreted.
The Wald test suggested dropping the following parameters to improve fit: 1) Calorie and fat intake to physical health 2) perceived social support to calorie and fat intake, 3) calorie and fat intake to general self-efficacy, and 4) smoking cessation to general self-efficacy. The Lagrange Multiplier test suggested adding the following parameters to improve fit: 1) Perceived social support to general self-efficacy, 2) perceived social support to mental health, and 3) perceived social support to physical health.

Model 2: Full Model.
Results indicated that the full model did provide a good fit to the data at the The Wald test suggested dropping the following parameter to improve fit , 1) perceived social support to calorie and fat intake. The Lagrange Multiplier test indicated that no parameters should be added to the model to improve fit.

Model 3: Direct Model
Results indicated that the Direct model did not provide a good fit to the data at the macro and micro levels (χ 2 (2, N= 427) = 94.67, p = .000; CFI=.66. RMSEA=0.13, 90% CI = [.10, .15]). Analysis of the largest standardized residuals indicated values exceeding |0.20|, which shows that this model did not adequately explain the relationships between variables. Standardized parameter estimates are shown in Figure   3. However, because this model was not a good fit, estimate of individual parameters were unreliable and hence cannot be interpreted.
The Wald test suggested dropping the following parameters to improve fit: 1) SMK to mental health, 2) CALFAT to mental health, and 3) EX to mental health. The Lagrange Multiplier test suggested adding 12 parameters to improve fit.
Overall, of the three models tested, the Full Model, the model in which the relationship between various relationships between the model variables and mental health is the most appropriate model for the data based on the criteria of empirical fit described earlier. SMK and EX, had an indirect effect on mental health (i.e., mediated by physical health). The Mediational Model and the Direct Model each did not provide an adequate fit for the data.

Exploratory Analyses Findings.
To capture a more comprehensive snapshot and richer understanding of the study sample, additional descriptive analyses were conducted; some of the findings are presented below.

Demographic Characteristics by Presence of Mental Illness.
Chi-square testes indicated that there was no overall statistically significant difference between presence of mental illness across demographics characteristics including age group, gender, race, sexual orientation, marital status, education level, health insurance status, and region of residence. See Table 11.

Physical Health Characteristics by Presence of Mental Illness.
Chi-square tests indicated that there was an overall statistically significant difference between presence of mental illness across physical health characteristics including body mass index (χ² (3) = 10.34, p = .02) and currently undergoing any kind of medical treatment (e.g. medication) (χ² (1) = 27.72, p = .000). See Table 12.
For post-hoc comparisons, Delucchi (1993) recommends identifying the cells with the largest residuals. A residual is the difference between the observed and expected values for a cell. The larger the residual, the greater the contribution of the cell to the magnitude of the resulting chi-square obtained value. Based on the latter assertion, post-hoc tests indicated that overweight and obese (standardized residual = 1.7) individuals were more strongly associated with being diagnosed with mental illness compared to those who were underweight or have normal weight (standardized residual = -1.7).
In addition, those who had been diagnosed with a mental illness, reported being currently under medical treatment more than would be expected (standardized residual = 3.4, larger than the critical value of 1.96).

Fruit and Vegetable Intake By Presence of Mental Illness.
Chi-square test indicated that there wasn't an overall statistically significant difference between presence of mental illness across stages of change for fruit and vegetable intake (χ² (4) = 6.44, p = .17). See Table 14.

Emotional Eating By Presence of Mental Illness.
Chi-square test indicated that there was an overall statistically significant difference between presence of mental illness across stages of change for emotional eating (χ² (5) = 21.78, p = .001). See Table 14.
Post-hoc tests indicated that Never Eaters, in other words, those who had never eaten food in response to emotional distress (standardized residual = -2.0 < -1.96) were significantly less likely to be diagnosed with mental illness than expected.

Facebook Utilization.
Of the total sample, 85% indicated currently having a Facebook account. The majority of the latter group (53.7%) indicated that they did not use Facebook as a way to receive social support, while 36.1% indicated that they did use Facebook as a way to receive social support and 10.2% were unsure about whether they used Facebook as a way to receive social support. See Table 15.

Facebook Utilization by Presence of Mental Illness.
Chi-square tests indicated that there was no overall statistically significant difference between presence of mental illness and having Facebook account or not (χ² (1) = 2.58, p = .11). Similarly, there was no overall statistically significant difference between presence of mental illness and whether an individual uses Facebook as a means to receive social support or not (χ² (2) = 2.19, p = .33) and amount of time spent on Facebook (χ² (5) = 1.32, p = .93). However, there was an overall statistically significant difference between presence of mental illness and whether an individual's experiences on Facebook make them feel better, no different, or worse (χ² (2) = 7.87, p = .02). However, it should be noted that a very small sample was evident for those who felt worse when using Facebook; those with (n=8) and without (n=3) a diagnosed mental illness. See Table 16.

CONCLUSION
The present study's primary goal was to explore the role of three key health behaviors (SMK, EX, and CALFAT) in relation to mental health among a populationbased adult sample. In addition, the study explored the same three health behaviors and physical health functioning as mediators of the relationship between other modifiable variables including general self-efficacy and perceived social support, and adult mental health functioning. To our knowledge, this study is the first to explore the direct and indirect effects of multiple health behaviors on mental health functioning among a population-based sample.
Importantly, the present study participant demographic characteristics (Table   1) are fairly consistent with U.S. Census statistics (2014) across race, sexual orientation, marital status, and health insurance status prior to the launch of the Affordable Healthcare Act (January 2015). However, education level was higher than that of the general population. In terms of clinical characteristics (Table 2) and health behaviors (Table 3), for example, a little over 20% of participants were current smokers, higher than the national rate see among adults (17.8%) (CDC, 2015). Among the total sample, approximately 58% of participants were engaging in recommended regular exercise which is higher than the national trend at 49% (CDC, 2013), while 31% were engaging in healthy eating behaviors. Furthermore, 61% percent of the total sample was overweight or obese, lower than the rate observed in the general U.S. population (69%) (National Center for Health Statistics, 2014), yet still indicating a substantial proportion of adults. Seventeen percent of participants were currently suffering from one of the four "lifestyle" chronic physical illnesses assessed (i.e. cardiovascular disease, cancer, cerebrovascular disease, and diabetes).
It must be noted that the rates of overweight/obesity, healthy eating and adequate exercise were simultaneously high among this sample. There may be several explanations for the latter finding in which unhealthy weight was coexisting with high levels of engagement in health behaviors typically utilized for weight management.
One explanation is that adequate exercise and healthy eating measured by calorie and fat intake may not be enough to curb weight issues. A study published in 2014 (Ng, Slining, & Popkin) "found a small but steady decline" in the consumption of calories between 2003 and 2011 (not due to the economic recession at the time) among American youth and adults indicative of public attitudes shifting (i.e. anti-soda messages). However, their findings also indicated that, on average, Americans spend only approximately 25 minutes every day preparing food. A separate very recent study (Poti, Mendez, Ng, & Popkin, 2015) indicated that the majority of the U.S. diet is based on consuming "ready to eat" or "ready to heat" products typically highly processed and high in sugar, salt and saturated fat. Furthermore, Poti and colleagues (2015) found that, between 2000 and 2013, the moderately (e.g. flavored pasta, cheese, flavored fruit and vegetable juice, potato chips) and highly processed foods (e.g. soda, sports drinks, alcohol, hot dogs, bagels, frozen pizza, ice cream, candy), with high saturated fat, sugar and salt, comprised more than 75% of people's energy intake. In other words, over 50% of calories consumed by youth and adults are from refined carbohydrates, desserts, fast food, and savory snacks (Poti et al., 2015). This shows that the U.S. diet is still not necessarily a healthier one. While there has been a reduction in the amount of food/calories consumed over the past decade or so, the quality and nutritional aspects of food consumed have not improved (Poti et al., 2015).
In terms of exercise, more than 27% of adults engaged in regular exercise have graduated from college (CDC, 2013c). Those who had received less than a high school diploma had the lowest rates (12%) of exercise (CDC, 2013c). Population-based selfreport data indicate that rates of exercise have plateaued, with those with higher income more likely to engage in exercise and eating more fruit and vegetables (Boseley, 2015). The present study sample did indicate engaging in high levels of fruit and vegetable intake (47%). Diet and nutrition is an area that is complex and national guidelines are consistently updated (about every 5 years) to reflect our current scientific knowledge of the role of different types of nutrition such as sugar, fat, and calories in our health and well-being.
In terms of mental health, 43% had been diagnosed with a mental illness at one point in time; close to the national rate at 50% and, of the total sample, 38% had been prescribed medication for a mental health issue. These findings portray the veracity of this population-based sample and also the dominance and pervasiveness of physical and mental health issues faced by the present sample, consistent with the general US adult population findings.

Hypotheses 1-4 -Group Mean Differences and Correlations.
Contrary to what was hypothesized, findings indicated that SMK did not have a significant relationship with mental health, general self-efficacy, or perceived social support. As hypothesized, EX did have a significant relationship with mental health and general self-efficacy with moderate to large effect sizes (partial η 2 = .07).
Particularly, engaging in regular exercise for at least six months compared to contemplating engaging in exercise, preparing to engage in exercise, and actually actively engaging in regular exercise for less than six months was strongly linked to better mental health. This may suggest that in order to feel the positive effects of exercise on mental health, exercise has to become routine and longer-term.
Maintainers did not differ from Precontemplators perhaps since the latter group of individuals isn't even considering engaging in exercise and is content with their "status quo," and hence may not be feeling any distress about how they are leading their life or potentially engaging in a new behavior that may come with its added challenges. With respect to general self-efficacy, the sense of confidence in one's own ability to overcome challenges in everyday life, those who had been engaging in regular exercise for more than six months differed in their level of confidence compared to those not interested in engaging in EX, those considering engaging and those actively engaging in EX for less than six months. These findings suggest that engaging in longer-term exercise can potentially help build confidence.
As hypothesized, CALFAT had a significant relationship with general selfefficacy and mental health. Again, maintainers were significantly different than Precontemplators with respect to their level of confidence. One argument for this is the fact that contemplators can be quite ambivalent towards engaging in a new behavior, especially healthy eating, given the substantial lifestyle change it may require to practice additional behaviors such as buying healthier foods, reading food labels, and reorganizing daily schedules to buy healthier foods. In addition, contemplators may have some awareness of the benefits of engaging in healthy eating as well as the negative effects of unhealthy eating, and as a result may experience more distress and less confidence about all of the challenges around this new behavior compared to those who are moving towards healthy eating, and even those who are not interested in healthy eating at all. With respect to mental health, those who had been eating healthy for at least six months (maintainers) were particularly different from those who were contemplating engaging in healthy eating in the next 6 months.
As expected, there was a significant relationship between general self-efficacy and mental health, indicating that higher levels of self-efficacy were associated with higher levels of mental health. As hypothesized, perceived social support and physical health were significantly related, indicating that higher levels of perceived social support were associated with higher levels of general self-efficacy. Similarly, there was a significant relationship between perceived social support and mental health, again, indicating that higher levels of perceived social support were associated with higher levels of mental health.
Contrary to what was expected, physical health did not significantly relate to general self-efficacy. And finally, as expected, there was a significant relationship between physical health and mental health, indicating that higher levels of physical health functioning were associated with higher levels of mental health functioning.
When looking at group comparisons, these finding can be summarized as: 1) Smoking doesn't appear significantly related to self-efficacy, perceived social support and mental health functioning, 2) engaging in exercise and healthy eating for at least six months is strongly linked to better general self-efficacy and mental health, 3) those who are contemplating engaging in exercise and/or healthy eating in the next six months appear to be particularly different than maintainers in terms of their mental health status, 4) perceived social support is significantly related to physical and mental health functioning, and 5) general self-efficacy is significantly linked to mental health functioning.

Hypothesis 5 -Model Testing.
An examination of the fully saturated model (Figure 2), predicting mental health from general self-efficacy, perceived social support, and physical health functioning revealed good model fit, accounting for a small amount of variance (R 2 = .17). This finding supported hypotheses and previous research that higher levels of general self-efficacy (e.g. Kim, 2003), perceived social support (e.g. Cacioppo et al., 2011;Fratiglioni et al., 2004) and physical health (e.g. Walsh, 2010) are associated with higher levels of mental health.
In contrast, the three health behaviors did not directly predict or have an effect on mental health. Interestingly, though, exercise and healthy eating had an indirect effect on mental health functioning through physical health. In other words, this may mean that if an individual is engaging in risky health behaviors, for example, not engaging in regular exercise or is a chronic smoker and as a result suffers from physical health issues, then they are likely to experience worse mental health. This is a nuanced finding supporting the significant levels of mental illness comorbidity seen among individuals with physical health issues and vice versa. As such, then, engaging in and maintaining health behaviors is strongly linked to improved physical health which in turn can be linked to improved mental health. This finding is particularly relevant given the recent focus on the integration of behavioral health care in to primary care and medical settings. In such settings, mental and behavioral issues like sedentary lifestyle can be targeted as a way to not only improve more chronic physical health outcomes but also mental health issues.
Interestingly, exercise mediated the relationship between general self-efficacy and mental health. Previous research has found that mediators of physical activity and mental health have included self-efficacy and social interaction (e.g. Peluso, & Guerra de Andrade, 2005). Both findings could be indicative of the dual or bi-directional relationships between the variables.
Perceived social support predicted smoking behavior. This kind of social support refers to the perceived availability of people who can be supportive when one has problems, through behaviors such as empathic listening, providing tangible aid, and/or informational aid for the provision of supportive behaviors specific to health behavior change (Westmaas, Bontemps-Jones, & Bauer, 2010). These support functions can be "abstinence specific" in that they pertain to specifically support the quit attempt or they can be more general such as providing emotional support contributing to a more "stress free" environment that can help with the challenges that come with quitting (Mermelstein et al., 1986). An alternative is that specific supports like a partner or friend can deliberately engage an individual to distract him or her from withdrawal symptoms of smoking (Westmaas et al., 2010).

Exploratory Findings.
Fruit and vegetable intake were discussed previously. In terms of emotional eating, consistent with previous findings, those who do not engage in emotional eating tend to have had a mental illness diagnosis compared to those who have engaged in emotional eating.
With regard to Facebook utilization, those who tended to feel worse (importantly a small sample size) about Facebook experiences were inclined to have had a mental illness diagnosis. It would be important to further explore the presence of specific mental illness given that negative appraisals of experiences can be an inherent feature of certain mental health issues.

Limitations.
First, one important limitation is that this is a cross-sectional study with mediational analyses. Definitive statements about causality can be made only with prospective and longitudinal design. Maxwell and Cole (2007) state that the use of mediation in cross-sectional analyses is unlikely to accurately reflect longitudinal effects. The present results serve as exploratory models to engender ideas for further research. Ideally, the present results need to be replicated with longitudinal data with at least two to three time points (Maxwell & Cole, 2007). Furthermore, there is likely many other factors that play a role in mental health outcomes (e.g. genetic factors, early life exposures, and other situational factors) that are antecedent to and the present study only captured a small part of these complex relationships. In addition, a bi-directional relationship between all of the model variables is plausible.
Second, there was an assessment related limitation. Given that it wasn't within the scope of the present study to assess different forms of perceived social support (PSS), the path analyses combined all forms of PSS (i.e. social support received from family, significant other, and friend) into one composite score that has been established by the authors of that particular measure. By doing so, analyses were not conducted on how a specific form of perceived social support may or may not contribute to mental health. It can be expected that different forms of perceived social support are qualitatively and quantitatively different and may or may not predict different outcomes. Further studies can apply latent variable model testing to include different types of perceived social support as potential predictors of mental health outcomes.
Third, for the purpose of the study in looking at mental health on a population level, the relationship between health behaviors and current (past 4 weeks) mental health functioning was assessed. It would be important to assess past mental health functioning as well to compare it to present functioning alongside the potential change in health risk behaviors to better understand the relationship between health behaviors and mental health functioning over time.

Future Recommendations
Based on the current study's findings and limitations, one major recommendation made is to further improve on the body of literature delineating the relationships between health behaviors and mental health outcomes. First, it is imperative that studies continue to explore direct outcomes health behaviors may have on mental health across cross-sectional and longitudinal studies as well as multivariate and model testing. Specifically, two recent studies have shown bi-directional relationships between depression and obesity (Kontinnen et al., 2014) and mental health and physical activity in older adults (Steinmo, Hagger-Johnson, & Shahab, 2014). It will be important to examine how the model variables relate to each other within longitudinal bi-directional models given the emerging evidence in this realm.
Second, although it is important to continue to assess overall mental health functioning in the general population for public health purposes, it is also important to continue to look at the relative impact of health behaviors on the presence and severity of specific mental illnesses to further our understanding of potential nonpharmacologic prevention and intervention efforts for mental illness.

Description of the Project:
The purpose of this research is to test a statistical model that looks at the relationships between three health behaviors, social support, selfefficacy, and physical health and their relationship to mental health functioning among adults.
2. What will be done: You are one of 400 people who will be asked to complete a survey that asks about mental health, health behaviors, social support, selfefficacy, physical health, and demographics. To participate, you must be at least 18 years-old and able to read and speak English. The survey is administered online and should take approximately 20 minutes.

Risks or Discomforts:
You might experience some discomfort discussing your mental and physical health. There are no other known risks associated with participating in this study.

Expected Benefits of the Study:
You may not receive any direct benefit from taking part in this study. Taking part in the study, however, may help others like you in the future. Some people may find participation in this research informative and personally beneficial.

Confidentiality:
Participation in this project is completely confidential and anonymous. Your information will not be shared with anyone except study personnel working for the Cancer Prevention Research Center. Survey responses to assessment questions will be stored by the secure database of the survey company server (SurveyMonkey). We will not collect or store IP addresses. SurveyMonkey makes no effort to identify individual responders by IP address and their privacy practices are reviewed for compliance by TRUSTe. SurveyMonkey databases are protected by passwords and database and network firewalls to protect survey information. After online data collection is complete, the data will be transferred to a secure server at URI which is firewall protected with restricted access to study personnel through a virtual private network (VPN). These data will be destroyed within ten years of the collection date.

Exercise
Regular exercise means doing: Moderate-intensity aerobic or "cardio" activity that increases your breathing rate and causes you to break a light sweat (such as brisk walking) for at least 150 minutes (2 hours and 30 minutes) each week OR Vigorous-intensity aerobic or "cardio" activity that causes big increases in your breathing and heart rate and makes conversation difficult (such as jogging or running) for at least 75 minutes (1 hour and 15 minutes) each week OR A mix of moderate and vigorous aerobic activity that is equal to at least 150 minutes of moderate activity, such as 90 minutes of moderate activity and 30 minutes of vigorous activity each week. (Keep in mind that 1 minute of vigorous activity equals about 2 minutes of moderate activity.) Do you engage in regular exercise according to any of the previous definitions?
$ No, and I do not intend to in the next 6 months. $ No, but I intend to in the next 6 months. $ No, but I intend to in the next 30 days. $ Yes, I have been, but for less than 6 months. $ Yes, I have been for more than 6 months.

Godin Leisure-Time Exercise Questionnaire
1) During a typical 7-Day period (a week), how many times on the average do you do the following kinds of exercise for more than 15 minutes during your free time (write on each line the appropriate number)? a) Strenuous exercise (heart beats rapidly) (e.g. running, jogging, hockey, football, soccer, squash, basketball, cross country skiing, judo, roller skating, vigorous swimming, vigorous long distance bicycling) Times per Week _____ b) Moderate exercise (not exhausting) (e.g., fast walking, baseball, tennis, easy bicycling, volleyball, badminton, easy swimming, alpine skiing, popular and folk dancing) Times per Week _____ c) Mild exercise (minimal effort) (e.g., yoga, archery, fishing from river bank, bowling, horseshoes, golf, snowmobiling, easy walking) Times per Week _____ 2) During a typical 7-Day period (a week), in your leisure time, how often do you engage in any regular activity long enough to work up a sweat (heart beats rapidly)?
 Often  Sometimes  Never/Rarely

Healthy eating means doing both of the following:
Eating the number of calories that allows you to reach and maintain a healthy weight Eating a diet that is low in saturated and trans fats Eating the number of calories that allows you to reach and maintain a healthy weight means doing things like: Eating small portions Paying attention to serving sizes Eating more vegetables and fruits Limiting unhealthy snacks Telling yourself that calories count Do you eat the number of calories that allows you to reach and maintain a healthy weight?

Eating a diet low in saturated and trans fats means doing things like:
Eating lean meat, such as chicken without the skin or extra lean ground beef Eating low-fat cheeses and other low-fat dairy products Eating fruits and vegetables as snacks Using olive oil in place of butter Limiting processed foods, particularly snack foods make with hydrogenated oils (trans fats) Do you eat a diet that is low in saturated and trans fats?
Are you planning to change what you eat so you can answer YES to questions 10 and 11?
$ No, and I do not intend to in the next 6 months. (Go to Question 14) $ Yes, and I intend to in the next 6 months. (Go to Question 14) $ Yes, and I intend to in the next 30 days. (Go to Question 14) $ I did answer yes to questions 10 and 11. (Go to Question 13) How long have you been doing these two things?
$ For less than 6 months $ For more than 6 months Do you eat at least 4½ cups of fruits and vegetables per day? (A cup is equal to 1 cup 100% fruit or vegetable juice, 1 cup cooked vegetables, 2 cups raw leafy vegetables, 1 piece of fruit, or ½ cup dried fruit.) $ No, and I do not intend to in the next 6 months. $ No, but I intend to in the next 6 months. $ No, but I intend to in the next 30 days. $ Yes, I have been, but for less than 6 months. $ Yes, I have been for more than 6 months.

One cup of fruit is equal to:
 8 large strawberries  1 cup of 100% fruit juice  2 canned peach halves  a piece of fruit (such as an apple, orange, banana, or peach)  a handful (½ cup) of dried fruit like raisins or apricots How many cups of fruits do you eat in a typical day?
 10  11  12  13  14  15 and more One cup of cooked or raw vegetables is equal to:  12 baby carrots  1 cup of 100% vegetable juice  1 large sweet potato  1 large ear of corn  a large plate of raw, leafy greens such as spinach or lettuce How many cups of vegetables do you eat in a typical day?

Emotional Distress
Everyone experiences emotional distress from time to time. Emotional distress includes boredom, stress, loneliness, frustration, disappointment, unhappiness, failure, and feeling unappreciated, deprived, hassled, or worried. People use a variety of strategies to cope with emotional distress, including strategies that have negative long term consequences, such as eating, smoking, drinking, or using drugs. We are most interested in people who eat in response to emotional distress. When people eat to cope with distress, they are eating not because they are hungry, but to manage their emotions. They eat to feel better, reward themselves, relax, forget their worries, induce sleep, reduce stress, or escape.

Emotional Distress: Your Eating Habits
Some people may not be aware that they eat to cope with distress. The next section lists some signs of eating in response to emotional distress.

Your Health and Well-Being
This survey asks for your views about your health. This information will help keep track of how you feel and how well you are able to do your usual activities. Thank you for completing this survey! For each of the following questions, please mark an in the one box that best describes your answer.

1.
In general, would you say your health is: 3. The following questions are about activities you might do during a typical day. Does your health now limit you in these activities? If so, how much?

Physical Health Questions
Do you currently suffer from any of the following medical conditions? (You may select more than one)  Cancer (e.g. breast, lung, colon etc.)  Cerebrovascular disease (e.g. stroke)  Cardiovascular disease (e.g. coronary heart disease, high blood pressure or hypertension, peripheral heart disease)  Type 1 or 2 diabetes  No Do you receive medical treatment (e.g. medication) for any of the medical conditions you previously indicated?
What is your height? (feet and inches) What is your weight? (pounds)