Table 3 - uploaded by Ethylin Wang Jabs
Content may be subject to copyright.
Genetic causal beliefs about obesity and obesity-related diseases by sociodemographic and health-related characteristics of the 205 participants in the ENGAGE structured interview study in New York City, June-Sept. 2010
Source publication
Background:
New genetic associations with obesity are rapidly being discovered. People's causal beliefs about obesity may influence their obesity-related behaviors. Little is known about genetic compared to lifestyle causal beliefs regarding obesity, and obesity-related diseases, among minority populations. This study examined genetic and lifestyl...
Context in source publication
Context 1
... our study, 69% of participants held genetic causal beliefs about obesity (33% and 36% believed genetics in- fluence obesity 'a lot' and 'some', respectively; table 2 ). There were no associations between genetic causal be- liefs about obesity (dichotomized into 'a lot'/'some' vs. 'a little'/'not at all') and any sociodemographic or health- related characteristics ( table 3 ). The mean genetic caus- al belief score for obesity was 2.91 (SD = 0.98, possible range = 1-4). ...
Citations
... We prepared a semi-structured interview to explore mood and food behavior during the study appointments. Researchers reviewed vast literature and selected key themes to create questions in a survey-like format with open or closed-ended questions [29,30]. The selected items were the frequency of unhealthy food decision making, perceived selfefficacy, which emotion triggered patients to eat more, and if patients have had binge episodes. ...
The Genome-based Mexican (GENOMEX) diet is a strategy for preventing and managing obesity. Emotion and eating behavior in the context of a nutrigenetic intervention have not been thoroughly studied. We aimed to explore the influence of the GENOMEX diet on emotions, self-efficacy, and rewarding behaviors in unhealthy eating among subjects with risk factors for obesity-related chronic diseases. Twenty-eight subjects included in the six-month GENOMEX intervention answered questions regarding emotions that influence food consumption. Additionally, the Patient Health Questionnaire (PHQ-9) and the Reward-based eating drive scale (RED) were applied. In the study, minimal, mild, moderate, and severe depression were present in 46.4%, 39.3%, 10.7%, and 3.6%, respectively. RED did not change, but it correlated with a higher intake of fats (r2 = 0.684, β = 2.066, p = 0.003). Mood influenced unhealthy eating in 71.7% of subjects, and 76.9% experienced binge episodes triggered by anxiety. Sugars were the most consumed foods during binge episodes (42.2%). Both low self-efficacy levels and binge episodes were associated with high consumption of unhealthy foods. After the intervention, 10.7% of subjects reported a high level of self-efficacy. In conclusion, a culturally acceptable and genetically compatible regional Mexican food diet reduced negative emotions and unhealthy eating while increasing self-efficacy.
... Our findings are consistent with previous reports that obesity is most often attributed to behaviour 31 and that behavioural attributions are more highly endorsed than genetic. 29,34,35 Combined, our results suggest that beliefs about brain influence are distinct from beliefs about other domains of influence, and that brain factors are considered less influential than proximal behavioural influences on obesity, while also being viewed somewhat similarly to other biological and genetic factors. ...
Neuroimaging studies demonstrate associations of brain structure and function with children's eating behaviour and body weight, and the feasibility of integrating brain measures into obesity risk assessment and intervention is growing. However, little is known about lay perceptions of how the brain influences obesity. We investigated parent perceptions of brain contributions to obesity in three separate studies: 1) a study of mothers of adolescents recruited for neuroimaging research (n = 88), 2) a study of ethnically Chinese parents of 5–13 year olds participating in a parent feeding survey (n = 277), and 3) a study of parents of 3–15 year olds completing an online survey (n = 113). In general, parents believed that brain factors influence obesity, but considered them less influential than behaviours such as diet and exercise. Causal attributions for brain factors were correlated with attributions for genetic factors and biological factors (e.g., metabolism). Parents who perceived their child to be overweight or had a high concern about their child becoming overweight in the future rated brain factors as more important in determining their child's weight and more likely to lessen their child's ability to control their weight. Our results suggest that parents attribute obesity to the brain to a moderate degree, and that education or feedback regarding brain influences on obesity could be a promising obesity intervention component.
... 13 14 In structured interviews 82% of the general public believed T2D was caused 'somewhat' and 'a lot' by genetics. 15 Higher genetic causality beliefs were not associated with lifestyle beliefs or socioeconomic status. 15 Genetics of T2D GWAS has contributed evidence of the genetic basis of T2D. ...
... 15 Higher genetic causality beliefs were not associated with lifestyle beliefs or socioeconomic status. 15 Genetics of T2D GWAS has contributed evidence of the genetic basis of T2D. 16 Population, family and twinbased studies estimate a 20-80% heritability rate. ...
... Relevant studies have found that when participants are asked about T2D, they tend to make causal attributions that include both genetic and behavioral factors, rather than choosing one or the other. [19][20][21] However, in one instance when unaffected participants were asked to compare the causes of T1D and T2D, they were more likely to attribute behavioral factors to T2D and genetic factors to T1D. 22 It is often assumed that behavioral risk factors, like diet and physical activity levels, are perceived to be under the control of the individual and thus associated with high controllability beliefs about disease onset and that genetic explanations are associated with low controllability beliefs. However, this assumption is rarely tested and has never been assessed in the context of diabetes. ...
Objective
The present study aims to describe and compare causal attributions for type 1 diabetes (T1D) and type 2 diabetes (T2D) among affected and unaffected individuals and to investigate the relationships among attributions, attitudes, and beliefs.
Research design and methods
Adults with no diabetes (N=458), T1D (N=192), or T2D (N=207) completed an online survey. Measures assessed diabetes conceptual knowledge, causal attributions for T1D and T2D, perceived control over diabetes onset, and favorability judgements of individuals affected by each type.
Results
Results indicate general agreement on causal attributions for T1D and T2D among all respondent groups, with some divergences by disease status. All respondents attributed both T1D and T2D to genetics, and genetic attributions were positively associated with favorability judgements of individuals with T2D, but not those with T1D.
Conclusions
This report sets the stage for investigations into how and why attributions for T1D and T2D differ and the implications of these differences including stigmatization of individuals with diabetes and diabetes-related self-concept. Additionally, this work can inform efforts towards clinical and public health education to prevent and optimize treatment of T1D and T2D.
... For example, members of melanoma-prone families who receive genetic counseling with explanations of how sun protection can reduce melanoma risk report either no change or increases in their beliefs that they can con- trol their risk through sun protection ( Aspinwall et al., 2015). Further, people who endorse genetic causes of disease are sometimes more likely to endorse behavioral causes for conditions with gene-behavior links that are easily recog- nized and understood (Lippa & Sanderson, 2012;Sanderson et al., 2013;Wang & Coups, 2010). With obesity, for example, the considerable media attention on the integrated roles of genes, low metabolism, and high-caloric diets as causal factors could foster understanding of how dietary changes could reduce the genetic risk of obesity. ...
... Understanding the multifactorial nature of disease etiology is a critical component of genomic health literacy, which has been defined as "the capacity to obtain, process, understand, and use genomic information for health-related decision making" (Hurle et al., 2013). Yet, many laypeople do not endorse the multifactorial model of disease causation (Ashida et al., 2011;Claassen et al., 2011;O'Neill, McBride, Alford, & Kaphingst, 2010;Parrott, Silk, & Condit, 2003;Sanderson et al., 2013;Sanderson, Waller, Humphries, & Wardle, 2011;Wang, Miller, Egleston, Hay, & Weinberg, 2010;Wold, Byers, Crane, & Ahnen, 2005). Examination of population-level data indicates that 21-36% of people in the U.S. do not hold multifactorial causal beliefs for many common health conditions (Waters, Muff, & Hamilton, 2014). ...
Understanding that cancer is caused by both genetic and behavioral risk factors is an important component of genomic literacy. However, a considerable percentage of people in the United States do not endorse such multifactorial beliefs. Using nationally representative cross-sectional data from the U.S. Health Information National Trends Survey (N = 2,529), we examined how information seeking, information scanning, and key information-processing characteristics were associated with endorsing a multifactorial model of cancer causation. Multifactorial beliefs about cancer were more common among respondents who engaged in cancer information scanning (p = .001), were motivated to process health information (p = .005), and reported a family history of cancer (p = .0002). Respondents who reported having previous negative information-seeking experiences had lower odds of endorsing multifactorial beliefs (p = .01). Multifactorial beliefs were not associated with cancer information seeking, trusting cancer information obtained from the Internet, trusting cancer information from a physician, self-efficacy for obtaining cancer information, numeracy, or being aware of direct-to-consumer genetic testing (ps > .05). Gaining additional understanding of how people access, process, and use health information will be critical for the continued development and dissemination of effective health communication interventions and for the further translation of genomics research to public health and clinical practice.
... Bottom: the Overview, Design concepts and Details (ODD) protocol (from [21]). The selected articles come from different continents: America (43), Europe (26), Australia (11) and Asia (6). Some papers are pure reviews or in silico simulations. ...
... The selected articles come from different continents: America (43), Europe (26), Australia (11) and Asia (6). Some papers are pure reviews or in silico simulations. ...
... This distribution gives an idea about the perception of obesity based on the geographic location and on the social awareness in the spread of this epidemic. The diagram in Figure 4 (top) classes the factors of obesity into three categories [20]: the first describes environmental determinants, including social and physical factors; the second includes psychological and behavioral factors; and the third consists of biological factors [22], notably the genetic background of the concerned individual [23][24][25][26][27]. A summary diagram ( Figure 5) is carried out on the basis of [20] to illustrate the successive stages of the extraction of 106 studies selecting many factors on three important levels, namely: (i) environmental; (ii) psychological and behavioral; and (iii) biological. ...
1) Background: The aim of this paper is to show that e-health tools like smart homes allow the personalization of the surveillance and preventive education of chronic patients, such as obese persons, in order to maintain a comfortable and preventive lifestyle at home. (2) Technologies and methods: Several types of sensors allow coaching the patient at home, e.g., the sensors recording the activity and monitoring the physiology of the person. All of this information serves to personalize serious games dedicated to preventive education, for example in nutrition and vision. (3) Results: We built a system of personalized preventive education at home based on serious games, derived from the feedback information they provide through a monitoring system. Therefore, it is possible to define (after clustering and personalized calibration) from the at home surveillance of chronic patients different comfort zones where their behavior can be estimated as normal or abnormal and, then, to adapt both alarm levels for surveillance and education programs for prevention, the chosen example of application being obesity.
... 10 Among patients with "unexplained physical symptoms," comorbidity with depression and anxiety was associated with more psychological, as opposed to genetic, attributions. 11 For obesity, findings regarding the impact of genetic causal attributions have varied, with some studies showing no effect, [12][13][14] and others showing an association of genetic attributions with reduced control over eating. 15,16 In this paper, we investigated the relations of depression, the most frequent psychiatric comorbidity in the epilepsies, 17,18 to the perception that epilepsy has a genetic cause. ...
Objectives:
Rapid advances in genetic research and increased use of genetic testing have increased the emphasis on genetic causes of epilepsy in patient encounters. Research in other disorders suggests that genetic causal attributions can influence patients' psychological responses and coping strategies, but little is known about how epilepsy patients and their relatives will respond to genetic attributions of epilepsy. We investigated the possibility that among members of families containing multiple individuals with epilepsy, depression, the most frequent psychiatric comorbidity in the epilepsies, might be related to the perception that epilepsy has a genetic cause.
Methods:
A self-administered survey was completed by 417 individuals in 104 families averaging 4 individuals with epilepsy per family. Current depression was measured with the Patient Health Questionnaire. Genetic causal attribution was assessed by three questions addressing the following: perceived likelihood of having an epilepsy-related mutation, perceived role of genetics in causing epilepsy in the family, and (in individuals with epilepsy) perceived influence of genetics in causing the individual's epilepsy. Relatives without epilepsy were asked about their perceived chance of developing epilepsy in the future, compared with the average person.
Results:
Prevalence of current depression was 14.8% in 182 individuals with epilepsy, 6.5% in 184 biologic relatives without epilepsy, and 3.9% in 51 individuals married into the families. Among individuals with epilepsy, depression was unrelated to genetic attribution. Among biologic relatives without epilepsy, however, prevalence of depression increased with increasing perceived chance of having an epilepsy-related mutation (p = 0.02). This association was not mediated by perceived future epilepsy risk among relatives without epilepsy.
Significance:
Depression is associated with perceived likelihood of carrying an epilepsy-related mutation among individuals without epilepsy in families containing multiple affected individuals. This association should be considered when addressing mental health issues in such families.
... 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 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., 2013Sanderson et al., , 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. ...
... 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 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., 2013Sanderson et al., , 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. ...
... contributions of the other 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. ...
Abstract The aims of the present study were to (1) examine the prevalence of perceived behavioral and genetic causal beliefs for four chronic conditions (i.e., obesity, heart disease, diabetes, and cancer); (2) to examine the association between these causal beliefs and attempts at behavior change (i.e., physical activity, weight management, fruit intake, vegetable intake, and soda intake). The data come from the Health Information National Trends Survey (HINTS), a nationally representative population-based survey of adults (N = 3,407). Results indicated that participants held both behavioral and genetic causal beliefs for all four chronic conditions. Multivariate analyses indicated that behavioral 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. Behavior and genetic causal beliefs were not associated with changes in either fruit or soda intake. In conclusion, while behavioral causal beliefs are associated with behavioral change, measurement must capture disease-specific behavioral causal beliefs as they are associated with different health behaviors.
... 3,4,6 Yet, the extent to which the general public holds such multifactorial causal beliefs is unknown. Some research suggests that multifactorial beliefs may be relatively common, [10][11][12][13] but these beliefs are challenging to examine systematically because laypeople have difficulty articulating the basic concept. 14 Several studies have approached the problem by assessing genetic and behavioral causal beliefs as separate constructs. ...
... Analyzing endorsement of "singular" beliefs revealed that participants endorse genetics as a causal factor for obesity, diabetes, heart disease, and several different cancers, but the strength of that endorsement and the degree to which they also endorse behavioral factors varies widely. [10][11][12][15][16][17][18][19] Past work has also identified demographic and health history correlates of genetic and behavioral causal beliefs of common diseases. 4,8,[10][11][12]15,16,18,20,21 However, the direction and significance of these relationships have also been inconsistent across studies. ...
... [10][11][12][15][16][17][18][19] Past work has also identified demographic and health history correlates of genetic and behavioral causal beliefs of common diseases. 4,8,[10][11][12]15,16,18,20,21 However, the direction and significance of these relationships have also been inconsistent across studies. For example, older adults were more likely to endorse genetics as a risk factor for cancer in one study, 15 but older women were less likely to endorse heredity as a cause of breast and colorectal cancer in another. ...
Purpose:
Many common health conditions arise due to a combination of genetic factors and lifestyle-related behaviors. People's understanding of the multifactorial nature of health conditions has implications for their receptivity to health messages regarding genomics and medicine, and may be related to their adoption of protective health behaviors. Although past work has investigated aspects of either genetic or behavioral causal beliefs, multifactorial beliefs have not been evaluated systematically.
Methods:
Utilizing nationally representative cross-sectional data from the Health Information National Trends Survey, we examined the prevalence of multifactorial beliefs regarding the etiology of cancer, obesity, diabetes, heart disease, and hypertension, as well as associations between such beliefs and demographic, health history, and health behavior variables in the US population.
Results:
Among 3,630 participants, the vast majority (64.2-78.6%) endorsed multifactorial beliefs. The number of statistically significant associations was limited. Trends suggest that endorsement of multifactorial beliefs may differ by demographic and health history characteristics. Beliefs about the multifactorial etiology of cancer were associated with cancer screening behaviors. Multifactorial beliefs about other common health conditions were associated with few health promotion behaviors.
Conclusion:
These findings and recommendations for future research provide preliminary guidance for developing and targeting genomics-related health messages and communications.
