Abstract
The aims of the present study were to (i) examine the prevalence of perceived behavioural and genetic causal beliefs for four chronic conditions (i.e. obesity, heart disease, diabetes and cancer); (ii) to examine the association between these causal beliefs and attempts at behaviour change (i.e. physical activity, weight management, fruit intake, vegetable intake and soda intake). The data come from the Health Information National Trends Survey, a nationally representative population-based survey of adults (N = 3407). Results indicated that participants held both behavioural and genetic causal beliefs for all four chronic conditions. Multivariate analyses indicated that behavioural causal beliefs were significantly associated with attempts to increase physical activity and vegetable intake and to decrease weight. Genetic causal beliefs for cancer were significantly associated with reported attempts to maintain weight. Behaviour and genetic causal beliefs were not associated with changes in either fruit or soda intake. In conclusion, while behavioural causal beliefs are associated with behavioural change, measurement must capture diseasespecific behavioural causal beliefs as they are associated with different health behaviours.
Keywords: causal beliefs, behaviour change, chronic conditions
Non-communicable, chronic diseases such as diabetes, heart disease and cancer comprise the most common and preventable health problems in the developed and developing world (Partnership to Fight Chronic Disease [PFCD], 2013). In 2010, half of the US population had at least one chronic health condition, with many suffering from multiple conditions (Anderson, 2010). By 2030, that number is projected to reach 171 million. The rising rates of chronic diseases pose a significant and growing public health problem. In the US, chronic diseases account for more than 80% of deaths annually, are the leading cause of disability and contribute to rising health care costs (Anderson, 2010; Hovert & Xu, 2012; US Burden of Disease Collaborators, 2013).
Public perceptions of the roles of genetics and behaviours in chronic diseases
Research provides strong evidence for the association between modifiable risk factors such as tobacco use and exposure, physical inactivity, unhealthy diet and the increasing rates of chronic diseases (Danaei et al., 2009; Kim & Oh, 2013; Leventhal, Huh, & Dunton, 2013; Stein & Colditz, 2004; Yusuf et al., 2004), yet there is low public adherence to national and federal health guidelines and recommendations for physical activity and diet. Recent data from the Centers for Disease Control and Prevention (CDC) indicate that only one in five adults in the US meet recommended guidelines for both aerobic and muscle-strengthening physical activity (CDC, 2013), and 37.7 and 22.6% of US adults consume less than one daily serving of fruits and vegetables, respectively (National Center for Chronic Disease Prevention and Health Promotion, 2013). Behavioural theories such as the Health Belief Model (Rosenstock, Stretcher, & Becker, 1988), the Theory of Planned Behaviour (Ajzen, 1999) and Social Cognitive Theory (Bandura, 1986) attempt to explain behaviour change by acknowledging that (i) the link between health information and behaviour change is mediated by cognitions; (ii) knowledge about the behaviour is only one of the multiple components to produce behavioural change; and (iii) perceptions, motivations and the environment exert influence on behaviour (The National Cancer Institute [NCI], 2005).
A common theme in cognitive–behavioural theories is the tenet that behavior change is influenced by the surrounding health cognitions or beliefs that an individual has about the behaviour and disease. Behavioural causal beliefs refer to the attributions that an individual makes in identifying modifiable risk factors and lifestyle factors (e.g. diet, physical activity or smoking) as the cause of health and disease, while genetic causal beliefs refer to the attributions for genes, the role of heredity and family history of disease. On one hand, behavioural and genetic causal belief attributions may constitute diametrically opposed measures, existing on opposite ends of a polar scale, that is, the endorsement of one set of causal beliefs may diminish the value of the other, and some study findings support this polar relationship (Marteau & Weinman, 2006; Senior, Marteau, & Peters, 1999). However, individuals’ causal beliefs for disease may contain both behavioural and genetic attributions as some research suggests that these are not diametrically opposed constructs as (Condit et al., 2009; Murphy et al., 2005; O’Neill, McBride, Alford, & Kaphingst, 2010; Sanderson et al., 2013; Sanderson, Waller, Humphries, & Wardle, 2011; Wang & Coups, 2010), and that in some cases, holding genetic causal beliefs increases the likelihood that the individual will also endorse behavioural causal beliefs (O’Neill et al., 2010; Sanderson et al., 2013, 2011). In the present study, we argue that the relationship between genetic and behavioural causal beliefs extends beyond a continuum scale as individuals may hold behavioural and genetic causal beliefs in equal regard.
Genetic information and health behaviours
With advances in commercially available genetic screening tests and with more biotechnology and genetic information disseminated to the public, there is concern that that a public focus on genetic causes of chronic diseases may reduce preventive and promotive health practices (Condit, 2001; Sanderson et al., 2011; Ten Eyck, 2005). It is possible that beliefs for genetic causes of disease and heritability may engender beliefs in genetic determinism, invoke fatalistic beliefs and lower self-efficacy in activating behaviour change (Condit et al., 2009; Senior et al., 1999; Senior, Marteau, & Weinman, 2000; Wright et al., 2012). The assignment of genetic causes for disease has been associated with lowered perceived effectiveness of treatment (Marteau et al., 2004; Wright et al., 2012) and with lower engagement in health behaviours (Wang & Coups, 2010). A study by Wright and colleagues (2012) demonstrated that genetic causal beliefs for heart disease reduced the perceived effectiveness of non-pharmacological treatments (i.e. healthy diet and physical activity) but had no effect on perceived medication effectiveness. However, genetic causal beliefs for obesity did not appear to influence perceived effectiveness for either treatment. Thus, rather than discouraging overall behaviour change, genetic risk information may change individuals’ beliefs about the most effective strategies to improve health for a particular health condition.
While previous studies have explored behavioural and genetic causal beliefs of chronic diseases, they have mainly focused on clinical samples with previously diagnosed conditions in which changes in risk perception and perceived effectiveness of treatment have been studied (Marteau et al., 2004; Wright et al., 2012). However, a recently published paper by Waters et al. (2014) utilising population-based data from the Health Information National Trends Survey (HINTS) examined the associations of behavioural and genetic causal beliefs with current health behaviours such as current smoking status, weekly physical activity, dietary intake, cancer screening adherence and obtaining primary care visits. While their findings indicated that endorsing both behavioural and genetic causal beliefs was associated with increased cancer screening, causal beliefs were not found to be significantly associated with the other measures of current health behaviours. We believe that this may be due to the way in which current engagement in preventive health behaviours (e.g. physical activity and dieting) is driven by a myriad of factors that include but also extend beyond social-cognitive factors. These factors may include socio-economic status (Carlson et al., 2014; Irala-Estevez et al., 2000), peer and societal influence (Salvy, de la Haye, Bowker, & Hermans, 2012; St George & Wilson, 2012), structural barriers and factors relating to access (McIsaac, Fuller-Thomson, & Talbot, 2001; Pahor, 2011; Riis, Grason, Strobino, Ahmed, & Minkovitz, 2012), other predisposing factors such as age and gender (Milanovic et al., 2013; Ottenbacher et al., 2014; Rolls, Dimeo, & Shide, 1995) in addition to the socialcognitive factors that include knowledge, attitudes, motivations and beliefs (Hagger & Chatzisarantis, 2009; Plotnikoff, Costigan, Karunamuni, & Lubans, 2013). As a result, causal beliefs may be unable to account for enough explanatory variance of engagement of current health behaviours.
On the other hand, while changes in health behaviours are susceptible to the same factors that we mention above, we believe that social-cognitive factors such as the causal beliefs surrounding chronic conditions are more relevant in determining whether or not an individual engages in healthy behavioural changes in attempts to mitigate risk for illness or disease. For example, one’s beliefs about what causes chronic illness are likely to induce health behaviour change, particularly if the perceived cause is an under- lying health behaviour or modifiable risk factor. To our knowledge, this is the first population-based study in the US that examines genetic and behavioural causal beliefs for the development of multiple chronic conditions and their associations with changes in health behaviour.
It is not known whether behavioural causal beliefs and genetic causal beliefs constitute constructs that exists on polar ends of a scale as some research suggests that the endorsement of one may diminish the value of the other (Marteau & Weinman, 2006; Senior et al., 1999) or if behavioural and genetic causal beliefs can exist independently as some research suggests that the endorsement of one may not affect the relative contributions of the other (Condit et al., 2009; Murphy et al., 2005; O’Neill et al., 2010; Sanderson, Waller, Humphries, & Wardle, 2011; Sanderson et al., 2013; Wang & Coups, 2010), and that in some cases, holding genetic causal beliefs increases the likelihood that the individual will also endorse behavioural causal beliefs (O’Neill et al., 2010; Sanderson et al., 2011; 2013). The present study will attempt to elucidate the roles of both genetic and behavioural causal beliefs on attempted behaviour change by employing a unique set of items assessing these constructs.
In addition, it may also be possible that individuals who highly endorse behavioural causal beliefs but not genetic causal beliefs are more likely to engage in preventive behaviours than those who highly endorse genetic causal beliefs but not behavioural causal beliefs. However, it is unclear whether individuals who endorse both behavioural and genetic causal beliefs are more or less likely to engage in preventive behaviours. It is possible that behavioural causal beliefs may act as a protective factor and buffer the negative effect of genetic causal beliefs on preventive behaviour. To test these relationships, the present study will examine the interaction between behavioural causal beliefs and genetic causal beliefs on attempted behavioural change.
Objectives
The aims of the present study are to (i) examine the prevalence of perceived behavioural and genetic causal beliefs for chronic conditions (i.e. obesity, heart disease, diabetes and cancer); (ii) to examine the association between these causal beliefs and attempts at behaviour change (i.e. physical activity, weight management, fruit and vegetable intake and soda intake).
Design
The data come from the HINTS. The HINTS is a probability-based, nationally representative survey of the US non-institutionalised adult population, aged 18 or older, and follows trends in health-related information and communication and health-related behaviours, perceptions and knowledge (Finney Rutten et al., 2012). The data are crosssectional. The HINTS has fielded three national data collections (HINTS, 2003, 2005 and 2007) with the fourth iteration (HINTS 4) currently ongoing. HINTS 4 is comprised of four separate data collection cycles that began in 2011 and will extend into 2014, using self-administered mail questionnaires that randomly sample from a frame of non-vacant US residential addresses. All analyses in the present paper are conducted on the HINTS 4 (Cycle 2) data which were collected from July 2012 to October 2012. The overall response rate was 40%. More detailed sampling and methodological information is available elsewhere (Finney Rutten et al., 2012).
Measures
Main outcome measures
Attempted behaviour change. Self-report measures of attempted behaviour change included whether respondents made intentional changes/maintenance in five domains: physical activity, weight management, vegetable intake, fruit intake and soda intake which are associated with chronic disease prevention. To assess change, participants read items that asked ‘At any time in the past year, have you intentionally tried to …’ and chose responses relevant to the specific domain. Attempted behavioural change was assessed for physical activity (increase the amount of exercise you get in a typical week/maintain the amount of exercise you get in a typical week/you haven’t really paid much attention to the amount of exercise you get), weight management (lose weight/ maintain weight/gain weight/you haven’t really paid attention), fruit intake (increase the amount of fruit you eat or drink/maintain the same amount of fruit you eat or drink/you haven’t really paid attention to the amount of fruit you eat or drink each day), vegetable intake (increase the amount of vegetables you eat or drink/maintain the same amount of vegetables you eat or drink/you haven’t really paid attention to the amount of vegetables you eat or drink each day) and soda intake (decrease the amount of regular soda or pop you usually drink in a week/maintain the same amount of regular soda or pop you usually drink a week/you haven’t really paid attention to the amount of regular soda or pop you usually drink in a week).
Covariates
Analyses also included known covariates associated with intentions to change health behaviours, notably demographic variables and current health behaviour. Demographic variables included age (measured in years), body mass index (BMI), gender, race/ethnicity and educational attainment. Current reported daily fruit and vegetable intake was captured by two items, one each for fruit and vegetable intake: ‘About how many cups of fruit [vegetables] (including 100% pure fruit[vegetable] juice) do you eat or drink each day?’ Weekly soda intake was assessed with one item, ‘Not counting any diet soda or pop, about how often do you drink regular soda or pop in a typical week?’ Current reported physical activity was assessed as a summary value comprised of two items, ‘In a typical week, how many days do you do any physical activity or exercise of at least moderate intensity?’ and ‘On the days that you do any physical activity or exercise of at least moderate intensity, how long do you typically do these activities?’.
Behavioural causal beliefs for chronic conditions
Participants were asked to respond to items regarding their beliefs about the role of health behaviour in developing common chronic diseases. Participants were asked ‘How much do you think health behaviors like diet, exercise and smoking determine whether or not a person will develop each of the following conditions?’ Response options included: not at all, a little, somewhat and a lot. Respondents answered these items with respect to four conditions: obesity, heart disease, diabetes and cancer.
Genetic causal beliefs for chronic conditions
Participants were asked to respond to items regarding their beliefs about the role of genetics in developing common chronic diseases. Participants were asked ‘How much do you think genetics, that is characteristics passed from one generation to the next, determine whether or not a person will develop each of the following conditions?’ Response options included: not at all, a little, somewhat and a lot. Respondents answered these items with respect to four conditions: obesity, heart disease, diabetes and cancer.
Interaction effects between genetic causal beliefs and genetic causal beliefs for chronic conditions
Higher order effects were created to examine the interaction between behavioural causal beliefs and genetic causal beliefs (behavioural causal beliefs X genetic causal beliefs) with respect to four conditions: obesity, heart disease, diabetes and cancer.
Analyses
All analyses were conducted using SAS 9.3 and SAS-callable SUDAAN 10.0 statistical software to account for the complex sampling design of HINTS. Survey weights (final survey weights and replicate weights) were used to obtain population-level point estimates and accurate variance estimates. All p-values reported are for two-tailed tests and a value of < .05 was considered statistically significant. Chi-square goodness-of-fit tests were conducted to test whether observed proportions for responses in behavioural causal belief items differed significantly from proportions for responses in genetic causal beliefs.
Weighted multinomial logistic regression models were conducted to examine the associations of behavioural and genetic causal beliefs across four separate medical conditions/chronic diseases (i.e. obesity, heart disease, diabetes and cancer) on self-reported behaviour change. We also examined the interaction between behavioural and genetic causal beliefs on behaviour change. The response ‘haven’t paid attention …’ served as the reference for all outcome variables as indicated in Tables 3–6. Although we test for all possible contrast effects among the different categories, we chose to display the ref- erence group that offered the most information in the Tables for ease of reading. We report all other significant group contrasts that are not found in the Tables in the text of the Results section. Odds ratios and the respective 95% confidence intervals are reported for each of the four levels of perceptions of the role of behaviour and genetics for each medical condition. All analyses were weighted to obtain population estimates. We only display results for behavioural changes in weight management, physical activity and vegetable intake as findings for fruit intake and soda consumption were non-significant.
Table 3.
Causal beliefs for obesity and attempted behavioural change.
| Weight managementa REF = Haven’t paid much attention (n = 1638) |
||||||
|---|---|---|---|---|---|---|
| Decrease (n = 1773) |
Maintain (n = 816) |
|||||
| OR | 95% CI OR | OR | 95% CI OR | p-value | F | |
| 68.37** | ||||||
| Behaviour beliefs | .41 | |||||
| Not at all | .82 | .25, 2.70 | .74 | .14, 3.84 | ||
| A little | .58 | .22, 1.56 | .73 | .23, 2.29 | ||
| Somewhat | .53 | .29, .97 | .56 | .32, .99 | ||
| A lot (REF) | 1.00 | - | 1.00 | - | ||
| Genetic beliefs | .33 | |||||
| Not at all (REF) | 2.22 | .90, 5.43 | 1.39 | .57, 3.33 | ||
| A little | 1.24 | .76, 2.04 | 1.43 | .81, 2.52 | ||
| Somewhat | 1.29 | .84, 1.99 | 1.06 | .67, 1.67 | ||
| A lot (REF) | 1.00 | - | 1.00 | - | ||
| Physical activitya REF=Haven’t paid much attention (n=963) |
||||||
| Increase (n=1773) |
Maintain (n=763) |
|||||
| OR | 95% CI OR | OR | 95% CI OR | p-value | F | |
| 65.49** | ||||||
| Behaviour beliefs | ≤. 001 | |||||
| Not at all | 1.33 | .38, 4.68 | 2.44 | .44, 13.51 | ||
| A little | .18 | .08, .41 | .49 | .12, 2.02 | ||
| Somewhat | .47 | .31, .72 | .72 | .45, 1.14 | ||
| A lot (REF) | 1.00 | - | 1.00 | - | ||
| Genetic beliefs | .77 | |||||
| Not at all | 1.36 | .72, 2.59 | 1.29 | .56, 2.99 | ||
| A little | 1.41 | .93, 2.13 | 1.09 | .60, 2.00 | ||
| Somewhat | 1.25 | .79, 1.96 | 1.08 | .63, 1.84 | ||
| A lot (REF) | 1.00 | - | 1.00 | - | ||
| Vegetable intakea REF=Haven’t paid much attention (n=1081) |
||||||
| Increase (n=1402) |
Maintain (n=989) |
|||||
| OR | 95% CI OR | OR | 95% CI OR | p-value | F | |
| 60.44** | ||||||
| Behaviour beliefs | .02 | |||||
| Not at all | .51 | .15, 1.79 | 4.16 | .64, 27.03 | ||
| A little | .33 | .11, .97 | .95 | .48, 1.89 | ||
| Somewhat | .60 | .60, .41 | 1.04 | .64, 1.70 | ||
| A lot (REF) | 1.00 | - | 1.00 | - | ||
| Genetic beliefs | .60 | |||||
| Not at all | 1.00 | .45, 2.23 | .62 | .25, 1.56 | ||
| A little | 0.97 | .65, 1.45 | .81 | .54, 1.23 | ||
| Somewhat | 1.00 | .70, 1.42 | .72 | .48, 1.10 | ||
| A lot (REF) | 1.00 | - | 1.00 | - | ||
controlling for age, BMI, gender, education, race and weekly soda intake.
Significant at p ≤ .01.
Significant at p ≤ .05.
Table 6.
Causal beliefs for cancer and attempted behavioural change.
| Weight managementa REF = Haven’t paid much attention (n = 1638) |
||||||
|---|---|---|---|---|---|---|
| Decrease (n = 1773) |
Maintain (n = 816) |
|||||
| OR | 95% CI OR | OR | 95% CI OR | p-value | F | |
| 82.22** | ||||||
| Behaviour beliefs | .56 | |||||
| Not at all (REF) | 1.00 | - | 1.00 | - | ||
| A little | .75 | .20, 2.76 | 1.12 | .19, 6.64 | ||
| Somewhat | .65 | .21, 2.07 | 1.10 | .19, 6.29 | ||
| A lot | .93 | .28, 3.08 | 1.41 | .26, 7.58 | ||
| Genetic beliefs | .05 | |||||
| Not at all | 1.05 | .36, 3.05 | 1.26 | .38, 4.17 | ||
| A little | 1.20 | .76, 1.89 | 2.25 | 1.25, 4.07 | ||
| Somewhat (REF) | 1.00 | - | 1.00 | - | ||
| A lot | 1.16 | .78, 1.71 | 1.56 | 1.05, 2.32 | ||
| Physical activitya REF=Haven’t paid much attention (n=963) |
||||||
| Increase (n=1773) |
Maintain (n=763) |
|||||
| OR | 95% CI OR | OR | 95% CI OR | p-value | F | |
| 83.30** | ||||||
| Behaviour beliefs | .13 | |||||
| Not at all (REF) | 1.00 | - | 1.00 | - | ||
| A little | .61 | .24, 1.54 | .30 | .09, .94 | ||
| Somewhat | .66 | .28, 1.56 | .26 | .09, .79 | ||
| A lot | .91 | .39, 2.16 | .32 | .11, .96 | ||
| Genetic beliefs | .84 | |||||
| Not at all (REF) | 1.00 | - | 1.00 | - | ||
| A little | .77 | .33, 1.78 | 1.21 | .40, 3.64 | ||
| Somewhat | .80 | .39, 1.62 | 1.43 | .57, 3.59 | ||
| A lot | .91 | .47, 1.74 | 1.55 | .63, 3.84 | ||
| Vegetable intakea REF=Haven’t paid much attention (n=1081) |
||||||
| Increase (n=1402) |
Maintain (n=989) |
|||||
| OR | 95% CI OR | OR | 95% CI OR | p-value | F | |
| 23.39** | ||||||
| Behaviour beliefs | .43 | |||||
| Not at all (REF) | 1.00 | - | 1.00 | - | ||
| A little | 1.27 | .52, 3.11 | .60 | .12, 3.06 | ||
| Somewhat | 1.44 | .60, 3.45 | .74 | .16, 3.34 | ||
| A lot | 1.73 | .75, 3.98 | .64 | .14, 2.97 | ||
| Genetic beliefs | .18 | |||||
| Not at all (REF) | 1.00 | - | 1.00 | - | ||
| A little | 1.80 | .61, 5.36 | .77 | .26, 2.29 | ||
| Somewhat | 1.45 | .60, 3.46 | .68 | .27, 1.75 | ||
| A lot | 1.90 | .79, 4.58 | .96 | .35, 2.61 | ||
controlling for age, BMI, gender, education, race and weekly soda intake.
Significant at p ≤ .01.
Significant at p ≤ .05.
We had originally stratified all analyses for previous diagnosis of chronic conditions (e.g. cancer, diabetes, heart disease and obesity). Due to a limited sample of those with a diagnosis, the analyses were underpowered. As a result, we collapsed across previous diagnosis. We then controlled for previous diagnosis of chronic conditions though all failed to be significantly associated with behavioural change with the exception of obesity status. As a result, we control for BMI in all analyses.
Results
The HINTS 4 adult sample1 was comprised of 1384 (50%) males and 2023 (50%) females. With regard to race/ethnicity, there were 2184 (68%) non-Hispanic Whites, 614 (13%) non-Hispanic Blacks, 132 (5%) Asians, 27 (1%) American Indians or Alaskan Natives and 456 (12%) Hispanic respondents. Refer to Table 1 for other respondent characteristics.
Table 1.
Demographic traits of the HINTS 4 adult sample.
| Trait | N (%)* |
|---|---|
| Gender | |
| Male | 1384 (50.16%) |
| Female | 2023 (49.83%) |
| Race/ethnicity | |
| Non-Hispanic White | 2183 (68.42%) |
| Black | 614 (12.75%) |
| Asian | (132 (5.40%) |
| American Indian or Alaskan Native | 27 (1.02%) |
| Hispanics | 456 (12.42%) |
| Educational attainment | |
| Less than high school | 329 (13.51%) |
| High school diploma/GED equivalent | 775 (20.29%) |
| Some college | 1057 (37.57%) |
| College degree + | 1380 (28.64%) |
| Age (years) | |
| 18–24 | 105 (12.91%) |
| 25–34 | 424 (17.63%) |
| 35–49 | 845 (26.44%) |
| 50+ | 2138 (43.01%) |
| Marital status | |
| Married/living with partner | 1857 (57.2%) |
| Divorced/separated/widowed | 1043 (15.7%) |
| Never married/single | 628 (27.2%) |
| Annual household income | |
| <$15,000 | 536 (16.38%) |
| $15,000–34,999 | 705 (20.39%) |
| $35,000–74,999 | 983 (32.25%) |
| $75,000–99,999 | 376 (12.13%) |
| $100,000–199,999 | 427 (15.00%) |
| $200,000+ | 123 (3.84%) |
| Health insurance | |
| Yes | 2999 (82.18%) |
| No | 584 (17.82%) |
Counts are unweighted while proportions are weighted.
Prevalence of behavioural and genetic causal beliefs for chronic disease
Results indicated that participants held both behavioural and genetic causal beliefs for all four chronic conditions (refer to Figure 1). Overall, participants rated behaviours as being more important than genetics in determining chronic conditions (refer to Table 2).
Figure 1.
Prevalence of casual beliefs for chronic conditions.
Table 2.
Causal beliefs across chronic conditions.
| Behaviours (%) | Genetics (%) | p-value* | |
|---|---|---|---|
| Causal beliefs for diabetes | |||
| Not at all | 2.29 | 2.27 | <.001 |
| A little | 6.32 | 11.78 | |
| Somewhat | 22.18 | 43.25 | |
| A lot | 69.22 | 42.70 | |
| Causal beliefs for obesity | |||
| Not at all | 2.70 | 6.78 | <.001 |
| A little | 4.33 | 22.91 | |
| Somewhat | 15.59 | 41.59 | |
| A lot | 77.38 | 28.72 | |
| Causal beliefs for heart disease | |||
| Not at all | 2.21 | 1.98 | <.001 |
| A little | 7.05 | 10.93 | |
| Somewhat | 26.51 | 44.16 | |
| A lot | 64.23 | 42.93 | |
| Causal beliefs for cancer | |||
| Not at all | 4.47 | 3.93 | <.001 |
| A little | 16.99 | 14.91 | |
| Somewhat | 34.22 | 40.89 | |
| A lot | 44.33 | 40.27 |
Tests for differences in observed proportions.
Multinomial logistic regression
Neither behaviour nor genetic causal beliefs were significantly associated with changes in soda or fruit consumption. As a result, we do not provide further detail on those models in Tables 3–6. In addition, higher order effects that examined the interaction between behavioural and genetic causal beliefs did not significantly improve overall model variance, as a result, we omit interaction effects and only present results of the models with lower order effects.
Behavioural and genetic causal beliefs for obesity
Behavioural causal beliefs for obesity were significantly associated with reported attempts to increase exercise and to increase vegetable intake. Those who answered ‘a little’ were less likely than those who answered ‘somewhat’ to have intentionally increased exercise. Those who answered ‘a little’ and ‘somewhat’ were less likely to have intentionally increased exercise and vegetable intake in comparison to those who answered ‘a lot’. Overall, genetic causal beliefs for obesity were not significantly associated with attempted behaviour change (refer to Table 3).
Behavioural and genetic causal beliefs for heart disease
Behavioural causal beliefs for heart disease was significantly associated with reported attempts to increase exercise. Those who answered ‘a little’ were less likely to have intentionally increased exercise in comparison to those who answered ‘a lot’. Behavioural causal belief for heart disease was significantly associated with reported attempts to increase vegetable intake. Those who answered ‘somewhat’ were less likely to have intentionally increase vegetable intake in comparison to those who answered ‘a lot’. Overall, genetic causal belief for heart disease was not significantly associated with attempted behaviour change (refer to Table 4).
Table 4.
Causal beliefs for heart disease and attempted behavioural change.
| Weight managementa REF = Haven’t paid much attention (n = 1638) |
||||||
|---|---|---|---|---|---|---|
| Decrease (n = 1773) |
Maintain (n = 816) |
|||||
| OR | 95% CI OR | OR | 95% CI OR | p-value | F | |
| 64.24** | ||||||
| Behaviour beliefs | .23 | |||||
| Not at all (REF) | 1.00 | - | 1.00 | - | ||
| A little | .79 | 0.6,10.59 | 1.26 | 0.6,26.40 | ||
| Somewhat | 0.94 | .07, 11.74 | 2.32 | .10, 51.93 | ||
| A lot | 1.52 | .12, 19.29 | 2.99 | .14, 64.46 | ||
| Genetic beliefs | .54 | |||||
| Not at all (REF) | 1.00 | - | 1.00 | - | ||
| A little | .36 | .04,3.15 | .023 | .03,1.70 | ||
| Somewhat | .39 | .04,3.41 | .18 | .02,1.60 | ||
| A lot | .32 | .04,2.84 | .15 | .02,1.37 | ||
| Physical activitya REF=Haven’t paid much attention (n=963) |
||||||
| Increase (n=1773) |
Maintain (n=763) |
|||||
| OR | 95% CI OR | OR | 95% CI OR | p-value | F | |
| 99.08** | ||||||
| Behaviour beliefs | .02 | |||||
| Not at all | .70 | .11,.4.40 | 3.23 | .46,22.95 | ||
| A little | .36 | .16,.80 | .78 | .33,1.83 | ||
| Somewhat | .72 | .49,1.06 | .64 | .42, .96 | ||
| A lot (REF) | 1.00 | - | 1.00 | - | ||
| Genetic beliefs | .95 | |||||
| Not at all | 1.61 | .57,4.56 | .90 | .20, 3.97 | ||
| A little | .91 | .56,1.49 | 1.05 | .60, 1.84 | ||
| Somewhat | 1.08 | .74,1.57 | 1.12 | .73, 1.71 | ||
| A lot (REF) | 1.00 | - | 1.00 | - | ||
| Vegetable intakea REF=Haven’t paid much attention (n=1081) |
||||||
| Increase (n=1402) |
Maintain (n=989) |
|||||
| OR | 95% CI OR | OR | 95% CI OR | p-value | F | |
| 11.84** | ||||||
| Behaviour beliefs | .02 | |||||
| Not at all | .45 | .09, 2.32 | 3.06 | .07, 133.52 | ||
| A little | .54 | .25,1.14 | .85 | .42, 1.71 | ||
| Somewhat | .56 | .42, .76 | .80 | .59,1.08 | ||
| A lot (REF) | 1.00 | - | 1.00 | - | ||
| Genetic beliefs | .95 | |||||
| Not at all | .99 | .15, 6.54 | 1.13 | .11, 11.95 | ||
| A little | 1.03 | .61, 1.73 | 1.00 | .48, 2.08 | ||
| Somewhat | 1.13 | .84, 1.53 | .92 | .64, 1.31 | ||
| A lot (REF) | 1.00 | - | 1.00 | - | ||
controlling for age, BMI, gender, education, race and weekly soda intake.
Significant at p ≤ .01.
Significant at p ≤ .05.
Behavioural and genetic causal beliefs for diabetes
Behavioural causal beliefs for diabetes were significantly associated with reported attempts to lose weight. Specifically, those who answered that behaviour ‘somewhat’ contributed to diabetes were less likely to report having intentionally tried to lose weight in the past year compared to those who answered that behaviour contributes ‘a lot’ to diabetes. Behavioural causal beliefs for diabetes were significantly associated with reported attempts to increase exercise and to increase vegetable intake. Those who answered ‘a little’ and ‘somewhat’ were less likely to have intentionally increased exercise and vegetable intake in comparison to those who answered ‘a lot’. Overall, genetic causal beliefs for diabetes were not significantly associated with attempted behaviour change (refer to Table 5).
Table 5.
Causal beliefs for diabetes and attempted behavioural change.
| Weight managementa REF = Haven’t paid much attention (n = 1638) |
||||||
|---|---|---|---|---|---|---|
| Decrease (n = 1773) | Maintain (n = 816) | |||||
| OR | 95% CI OR | OR | 95% CI OR | p-value | F | |
| 59.54** | ||||||
| Behaviour beliefs | .05 | |||||
| Not at all (REF) | .44 | .07, 2.91 | .61 | .08, 4.47 | ||
| A little | .42 | .17, 1.00 | .59 | .23, 1.48 | ||
| Somewhat | .50 | .33, .77 | .56 | .29, 1.06 | ||
| A lot | 1.00 | - | 1.00 | - | ||
| Genetic beliefs | .50 | |||||
| Not at all (REF) | 2.11 | .55, 8.03 | 2.46 | .67, 9.09 | ||
| A little | 1.56 | .83, 2.94 | 1.64 | .85, 3.15 | ||
| Somewhat | 1.16 | .86, 1.59 | .97 | .68, 1.37 | ||
| A lot | 1.00 | - | 1.00 | - | ||
| Physical activitya REF=Haven’t paid much attention (n=963) |
||||||
| Increase (n=1773) |
Maintain (n=763) |
|||||
| OR | 95% CI OR | OR | 95% CI OR | p-value | F | |
| 74.33** | ||||||
| Behaviour beliefs | .002 | |||||
| Not at all | 1.22 | .31, 4.83 | 4.54 | .90, 22.81 | ||
| A little | .38 | .19, .77 | .70 | .30, 1.65 | ||
| Somewhat | .50 | .34, .74 | .58 | .38, .91 | ||
| A lot (REF) | 1.00 | - | 1.00 | - | ||
| Genetic beliefs | .57 | |||||
| Not at all | 1.05 | .39, 2.83 | .68 | .24, 1.98 | ||
| A little | 1.21 | .73, 2.00 | 1.12 | .60, 2.11 | ||
| Somewhat | 1.24 | .92, 1.67 | 1.21 | .84, 1.76 | ||
| A lot (REF) | 1.00 | - | 1.00 | - | ||
| Vegetable intakea REF=Haven’t paid much attention (n=1081) |
||||||
| Increase (n=1402) |
Maintain (n=989) |
|||||
| OR | 95% CI OR | OR | 95% CI OR | p-value | F | |
| 34.10** | ||||||
| Behaviour beliefs | .002 | |||||
| Not at all | .81 | .19, 3.52 | 3.53 | .19, 64.01 | ||
| A little | .29 | .13, .63 | .80 | .37, 1.71 | ||
| Somewhat | .49 | .34, .72 | .68 | .48, .96 | ||
| A lot (REF) | 1.00 | - | 1.00 | - | ||
| Genetic beliefs | .15 | |||||
| Not at all | .89 | .33, 2.39 | 1.15 | .29, 4.54 | ||
| A little | 2.08 | 1.10, 3.94 | 1.52 | .85, 2.72 | ||
| Somewhat | 1.25 | .95, 1.64 | 1.01 | .72, 1.43 | ||
| A lot (REF) | 1.00 | - | 1.00 | - | ||
controlling for age, BMI, gender, education, race and weekly soda intake.
Significant at p ≤ .01.
Significant at p ≤ .05.
Behavioural and genetic causal beliefs for cancer
Genetic causal beliefs for cancer were significantly associated with reported attempts to maintain weight. Those who answered genetics contribute ‘a little’ and ‘a lot’ to cancer were more likely to have attempted to intentionally maintain weight in the past year compared to those who answered that genetics contributes ‘somewhat’ to cancer. Overall, behavioural causal beliefs for cancer were not significantly associated with attempted behaviour change (refer to Table 6).
Discussion
The aims of the present study were twofold. We were interested in examining the prevalence of perceived behavioural and genetic causal beliefs for chronic conditions across different chronic conditions (i.e. obesity, heart disease, diabetes and cancer). In addition, we were interested in studying the association between these causal beliefs and attempts at behaviour change in across multiple domains (i.e. physical activity, weight management, fruit and vegetable intake and soda intake). In addressing the first aim, our findings indicated that participants held both behavioural and genetic causal beliefs for chronic diseases. Our findings are in agreement with other research that suggests that the public has a nuanced understanding of health and illness and possesses multifactorial theories of the etiology of chronic illness (Condit et al., 2009; Murphy et al., 2005; O’Neill et al., 2010; Waters et al., 2014). Previous studies indicate that individuals who endorse genetic causal beliefs for chronic diseases were likely to also endorse behavioural causal beliefs for cancer (Sanderson et al., 2011) and for obesity (Sanderson et al., 2013). With rapid development in genomics research, there is concern that belief in genetic causes of disease may lead to diminished perceived importance of lifestyle and behaviours (Marteau & Weinman, 2006). Our findings provide evidence that behavioural and genetic causal beliefs do not constitute opposite ends of a continuum, suggesting that the public has a more complex and nuanced understanding of the etiology of chronic diseases examined in this study than previously assumed.
In addressing the second aim of the study, our findings provide support that behavioural causal beliefs are associated with behavioural change, confirming a study by Wang and Coups (2010) that found that within the 2007 adult HINTS sample, endorsement of behavioural causal beliefs were associated with reported levels of physical activity. However, our findings extend current understanding of the role of health cognitions by shedding light on how disease-specific behavioural causal beliefs are associated with different health behaviours. For example, behavioural causal beliefs for obesity, heart disease and diabetes were related to behavioural changes in exercise and vegetable intake; however, we failed to reproduce the same association for behavioural causal beliefs for cancer. These findings highlight the importance of specificity in the measure- ment of health cognitions and risk perception as our findings suggest that respondents were more likely to associate vegetable intake (but not soda intake) with heart disease (but not cancer).
Our study findings indicated that behavioural causal beliefs for obesity, heart disease and diabetes were related to some behavioural outcomes (i.e. exercise and vegetable intake) but not others (i.e. fruit or soda consumption). In addition, only genetic causal beliefs for cancer were associated with health behaviour. These findings suggest that causal beliefs are important to consider when communicating messages about disease prevention and risk factors as causal beliefs appear to vary across chronic diseases. While less than a quarter of respondents in one study believed that genetics were important in determining obesity (Wang & Coups, 2010), more than 75% of women in another study believed that genetics were important in determining breast and colorectal cancer (Wang, Miller, Egleston, Hay, & Weinberg, 2010). Causal attributions about behavioural and genetic factors are disease-specific and may have important implications for how genetic risk information affects how individuals process and react to the information (Wang & Coups, 2010). Those who endorse genetic causal beliefs for cancer may be less likely to engage in smoking cessation behaviours as demonstrated by one study found that smokers who believed that genes are the main cause of lung cancer were significantly more likely to be current smokers (Kaphingst, Lachance, & Condit, 2009).
Unlike our models for diabetes, obesity and heart disease, behavioural causal beliefs for cancer were not significantly associated with behaviour change. We offer caution in over-interpreting these results. In HINTS 4 (Cycle 2), causal beliefs were assessed for general cancer than for specific cancer sites. Because cancer is a heterogeneous disease with many different subtypes, it is possible that this particular item activated thoughts about different subtypes of cancer for different respondents. Findings from the HINTS 2005 showed that beliefs about the role of lifestyle and behaviour varied across cancer sites as respondents were less likely to endorse behavioural causal beliefs for colorectal cancer as to other cancers sites that included skin or lung, suggesting that some modifiable risk factors (e.g. sun protection or smoking) are perceived to be more influential for cancer etiology. Also, significant differences in causal attributions between cancer sites were noted by Wang et al. (2010) as respondents were more likely to endorse heredity and pollution as a cause of breast cancer while endorsing diet, aging, alcohol and lack of exercise as a cause of colorectal cancer. It is possible that the existence of various cancer subtypes may be masking differences among these relationships and there may be too much noise to detect meaningful effects. Future research should follow up disaggregated analyses for specific cancer subtypes.
Our study failed to find significant interactions between behavioural causal beliefs and genetic causal beliefs. We believe this may have been likely due to issues relating to power. In examining cross-tabulations of behavioural and genetic causal beliefs, the majority of respondents highly endorsed both behavioural and genetic causal beliefs, while fewer endorsed either the role of behaviour or the role genetics, and even fewer endorsed neither behavioural or genetic causal beliefs. In addition, our study focused on outcomes of primary prevention (e.g. diet and exercise). Future research should also examine the role of causal beliefs on other types of health behaviours such as cancer screening, where focus may shed light on the public’s understanding of secondary prevention and disease risk. Additional research is also needed to explore potential additive effects endorsing multiple behavioural causal beliefs. It is possible that possessing behavioural causal beliefs across multiple chronic conditions would lead to greater behavioural changes.
Lastly, future studies should also examine the role of other health cognitions such as the perceived risk or fatalism as effect modifiers on the relationship between causal beliefs and health behaviours. It is possible that for individuals who perceive themselves to be at high risk for a chronic illness such as cancer, high endorsement of behavioural causal beliefs for cancer may be associated more strongly with preventive health behaviours than in comparison to individuals who perceive themselves to be at low risk.
This study had several limitations. The HINTS is cross-sectional in design and restricts our ability to make causal inferences. Also, single-item measures were used to assess both behaviour and genetic causal beliefs for chronic conditions, limiting the reliability for assessing causal beliefs domains. In addition, all behavioural change measures relied on self-report measures which may not be accurate estimates of actual behaviour due to error in recall and biased reporting. However, using a self-administered survey – where answering in socially desirable ways will be less salient– suggests that this issue may not be as important here. Future research should incorporate the use of longitudinal data in order to clarify the causal pathways between behavioural and genetic attributions and behavioural change. In addition, the population-based data are weighted to reflect the US adult population. As a result, we draw from a large population which may capture statistically significant but small effect sizes.
Conclusion
The public possess a multifactorial understanding of the etiology of chronic illness. Behavioural causal beliefs are associated with behavioural change; however, measurement must capture disease-specific behavioural causal beliefs as they are associated with different health behaviours. Additional research is needed to examine the potential interaction between behavioural and genetic causal beliefs and how they may impact preventive behaviours.
Footnotes
Note
1.Counts are unweighted, while proportions are weighted.
References
- Ajzen I (1999). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. [Google Scholar]
- Anderson G (2010). Chronic care: Making the case for ongoing care Princeton, NJ: Robert Wood Johnson Foundation. [Google Scholar]
- Bandura A (1986). Social foundations of thought and action: A social cognitive theory Englewood Cliffs, NJ: Prentice-Hall. [Google Scholar]
- Carlson JA, Mignano AM, Norman GJ, McKenzie TL, Kerr J, Arredondo EM, … Sallis JF (2014). Socioeconomic disparities in elementary school practices and children’s physical activity during school. American Journal of Health Promotion, 28, S47–S53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- CDC. (2013). Adult participation in aerobic and muscle-strengthening physical activites – United States, 2011 (Rep. No. 62) Atlanta, GA. [PMC free article] [PubMed] [Google Scholar]
- Condit C (2001). Science and society: What is ‘public opinion’ about genetics? Nature Reviews Genetics, 2, 811–815. [DOI] [PubMed] [Google Scholar]
- Condit CM, Gronnvoll M, Landau J, Shen L, Wright L, & Harris TM (2009). Believing in both genetic determinism and behavioral action: A materialist framework and implications. Public Understanding of Science, 18, 730–746. [Google Scholar]
- Danaei G, Ding EL, Mozaffarian D, Taylor B, Rehm J, Murray CJ, & Ezzati M (2009). The preventable causes of death in the United States: Comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Medicine, 6, e1000058. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Finney Rutten LJ, Davis T, Beckjord EB, Blake K, Moser RP, & Hesse BW (2012). Picking up the pace: Changes in method and frame for the health information national trends survey (2011–2014). Journal of Health Communication, 17, 979–989. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hagger MS, & Chatzisarantis NL (2009). Integrating the theory of planned behaviour and self-determination theory in health behaviour: A meta-analysis. British Journal of Health Psychology, 14, 275–302. [DOI] [PubMed] [Google Scholar]
- Hovert DL, & Xu J (2012). Deaths: Preliminary data for 2011 (Rep. No. 61(6)). Hyattsville, MD: National Center for Health Statistics. [PubMed] [Google Scholar]
- Irala-Estevez JD, Groth M, Johansson L, Oltersdorf U, Prattala R, & Martinez-Gonzalez MA (2000). A systematic review of socio-economic differences in food habits in Europe: Consumption of fruit and vegetables. European Journal of Clinical Nutrition, 54, 706–714. [DOI] [PubMed] [Google Scholar]
- Kaphingst KA, Lachance CR, & Condit CM (2009). Beliefs about heritability of cancer and health information seeking and preventive behaviors. Journal of Cancer Education, 24, 351–356. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kim HC, & Oh SM (2013). Noncommunicable diseases: Current status of major modifiable risk factors in Korea. Journal of Preventive Medicine & Public Health, 46, 165–172. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Leventhal AM, Huh J, & Dunton GF (2013). Clustering of modifiable biobehavioral risk factors for chronic disease in US adults: A latent class analysis. Perspect in Public Health, 134, 331–338. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Marteau T, Senior V, Humphries SE, Bobrow M, Cranston T, Crook MA, … Genetic Risk Assessment for FH Trial Study Group. (2004). Psychological impact of genetic testing for familial hypercholesterolemia within a previously aware population: A randomized con- trolled trial. American Journal of Medical Genetics, 128A, 285–293. [DOI] [PubMed] [Google Scholar]
- Marteau TM, & Weinman J (2006). Self-regulation and the behavioural response to DNA risk information: A theoretical analysis and framework for future research. Social Science and Medicine, 62, 1360–1368. [DOI] [PubMed] [Google Scholar]
- McIsaac WJ, Fuller-Thomson E, & Talbot Y (2001). Does having regular care by a family physician improve preventive care? Canadian Family Physician, 47, 70–76. [PMC free article] [PubMed] [Google Scholar]
- Milanovic Z, Pantelic S, Trajkovic N, Sporis G, Kostic R, & James N (2013). Age-related decrease in physical activity and functional fitness among elderly men and women. Clinical Interventions in Aging, 8, 549–556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Murphy B, Worcester M, Higgins R, Le GM, Larritt P, & Goble A (2005). Causal attributions for coronary heart disease among female cardiac patients. Journal of Cardiopulmonary Rehabilitation, 25, 135–143. [DOI] [PubMed] [Google Scholar]
- National Center for Chronic Disease Prevention and Health Promotion. (2013). State indicator report on fruits and vegetables, 2013 Atlanta, GA: Centers for Disease Control and Prevention. [DOI] [PMC free article] [PubMed] [Google Scholar]
- O’Neill SC, McBride CM, Alford SH, & Kaphingst KA (2010). Preferences for genetic and behavioral health information: The impact of risk factors and disease attributions. Annals of Behavioral Medicine, 40, 127–137. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ottenbacher A, Yu M, Moser RP, Phillips SM, Alfano C, & Perna FM (2014). Population estimates of meeting strength training and aerobic guidelines, by gender and cancer survivorship status: Findings from the Health Information National Trends Survey (HINTS). Journal of Physical Activity & Health [DOI] [PubMed] [Google Scholar]
- Pahor M (2011). Consideration of insurance reimbursement for physical activity and exercise programs for patients with diabetes. The Journal of the American Medical Association, 305, 1808–1809. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Partnership to Fight Chronic Disease. (2013). An overview of chronic disease in America Almanac of Chronic Disease. [Google Scholar]
- Plotnikoff RC, Costigan SA, Karunamuni N, & Lubans DR (2013). Social cognitive theories used to explain physical activity behavior in adolescents: A systematic review and meta-analysis. Preventive Medicine, 56, 245–253. [DOI] [PubMed] [Google Scholar]
- Riis J, Grason H, Strobino D, Ahmed S, & Minkovitz C (2012). State school policies and youth obesity. Maternal and Child Health Journal, 16, 111–118. [DOI] [PubMed] [Google Scholar]
- Rolls BJ, Dimeo KA, & Shide DJ (1995). Age-related impairments in the regulation of food intake. American Journal of Clinical Nutrition, 62, 923–931. [DOI] [PubMed] [Google Scholar]
- Rosenstock IM, Stretcher VJ, & Becker MH (1988). Social Learning Theory and the Health Belief Model. Health Education & Behavior, 15, 175–183. [DOI] [PubMed] [Google Scholar]
- Salvy SJ, de la Haye K, Bowker JC, & Hermans RC (2012). Influence of peers and friends on children’s and adolescents’ eating and activity behaviors. Physiology & Behavior, 106, 369–378. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sanderson SC, Diefenbach MA, Streicher SA, Jabs EW, Smirnoff M, Horowitz CR, … Richardson LD (2013). Genetic and lifestyle causal beliefs about obesity and associated diseases among ethnically diverse patients: A structured interview study. Public Health Genomics, 16, 83–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sanderson SC, Waller J, Humphries SE, & Wardle J (2011). Public awareness of genetic influence on chronic disease risk: Are genetic and lifestyle causal beliefs compatible? Public Health Genomics, 14, 290–297. [DOI] [PubMed] [Google Scholar]
- Senior V, Marteau TM, & Peters TJ (1999). Will genetic testing for predisposition for disease result in fatalism? A qualitative study of parents responses to neonatal screening for familial hypercholesterolaemia. Social Science and Medicine, 48, 1857–1860. [DOI] [PubMed] [Google Scholar]
- Senior V, Marteau TM, & Weinman J (2000). Impact of genetic testing on causal models of heart disease and arthritis: An analogue study. Psychology & Health, 14, 1077–1088. [DOI] [PubMed] [Google Scholar]
- St George SM, & Wilson DK (2012). A qualitative study for understanding family and peer influences on obesity-related health behaviors in low-income African-American adolescents. Childhood Obesity, 8, 466–476. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Stein CJ, & Colditz GA (2004). Modifiable risk factors for cancer. British Journal of Cancer, 90, 299–303. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ten Eyck TA (2005). The media and public opinion on genetics and biotechnology: Mirrors, windows, or walls? Public Understanding of Science, 14, 305–316. [DOI] [PubMed] [Google Scholar]
- The National Cancer Institute. (2005). Theory at a glance: A guide for health promotion and practice The National Institutes of Health/US Department of Health and Human Services. [Google Scholar]
- US Burden of Disease Collaborators. (2013). The state of US health, 1990–2010: Burden of diseases, injuries, and risk factors. The Journal of the American Medical Association, 310, 591–608. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang C, & Coups EJ (2010). Causal beliefs about obesity and associated health behaviors: Results from a population-based survey. International Journal of Behavioral Nutrition and Physical Activity, 7, 19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang C, Miller SM, Egleston BL, Hay JL, & Weinberg DS (2010). Beliefs about the causes of breast and colorectal cancer among women in the general population. Cancer Causes and Control, 21, 99–107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Waters EA, Muff J, & Hamilton JG (2014). Multifactorial beliefs about the role of genetics and behavior in common health conditions: Prevalence and associations with participant characteristics and engagement in health behaviors. Genetics in Medicine doi:10.1038/gim.2014.49 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wright AJ, Sutton SR, Hankins M, Whitwell SC, Macfarlane A, & Marteau TM (2012). Why does genetic causal information alter perceived treatment effectiveness? An analogue study. British Journal of Health Psychology, 17, 294–313. [DOI] [PubMed] [Google Scholar]
- Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, … INTERHEART Study Investigators. (2004). Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): Case-control study. The Lancet, 364, 937–952. [DOI] [PubMed] [Google Scholar]