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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGMENTS
  8. DISCLOSURE
  9. References
  10. Supporting Information

Advances in genomic technologies are rapidly leading to new understandings of the roles that genetic variations play in obesity. Increasing public dissemination of information regarding the role of genetics in obesity could have beneficial, harmful, or neutral effects on the stigmatization of obese individuals. This study used an online survey and experimental design to examine the impact of genetic versus non-genetic information on obesity stigma among self-perceived non-overweight individuals. Participants (n = 396) were randomly assigned to read either genetic, non-genetic (environment), or gene—environment interaction obesity causal information. A total of 48% of participants were female; mean age was 42.7 years (range = 18–86 years); 75% were white; 45.2% had an annual household income of less than $40,000; mean BMI was 23.4 kg/m2. Obesity stigma was measured using the Fat Phobia Scale — short form (FPS-S). After reading the experimental information, participants in the genetic and gene—environment conditions were more likely to believe that genetics increase obesity risk than participants in the non-genetic condition (both P < 0.05), but did not differ on obesity stigma. Obesity stigma was higher among whites and Asians than Hispanics and African Americans (P = 0.029), and associated with low self-esteem (P = 0.036). Obesity stigma was also negatively associated with holding 'germ or virus' (P = 0.033) and 'overwork' (P = 0.016) causal beliefs about obesity, and positively associated with 'diet or eating habits' (P = 0.001) and 'lack of exercise' (P = 0.004) causal beliefs. Dissemination of brief information about the role of genetics in obesity may have neither a beneficial nor a harmful impact on obesity stigmatization compared with non-genetic information among self-perceived non-overweight individuals.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGMENTS
  8. DISCLOSURE
  9. References
  10. Supporting Information

Obesity continues to be a major public health issue, with over 60% of the US population being either overweight or obese (1). It is not yet clear whether or how new discoveries in obesity genomics research might aid public health initiatives to decrease the prevalence of obesity, as well as its health and social consequences. In addition to the dire health implications of obesity itself, the stigmatization of obese individuals in society is an important problem that has serious consequences, and which is increasing in prevalence (2). Obese individuals are highly stigmatized and encounter many forms of prejudice and discrimination because of their weight. Negative attitudes toward those who are overweight causes discrimination in employment settings, health-care facilities, and educational institutions (2). Obesity stigma can have significant negative effects on the emotional and physical wellbeing of obese individuals. Individuals who experience weight stigma are at an increased risk of negative psychological effects such as depression and body dissatisfaction, as well as lower self-esteem (3). Experiences with weight stigma are also associated with negative health behaviors such as avoidance of dieting and exercise (4). Obesity stigma is therefore an important public health issue.

Recent advances in obesity genomics research (5) could potentially have major implications for obesity stigma-reducing efforts. Attribution Theory (6) suggests that weight stigmatization emerges from attributions about the causes of obesity, where perceptions that body weight is within personal control worsen attitudes toward obese persons. Increasing public dissemination of new research findings illustrating that obesity has a significant genetic component could increase awareness of an important uncontrollable element of obesity, thereby potentially reducing obesity stigma.

There have been only few efforts to develop obesity stigma-reducing strategies (7), a handful of which have specifically examined whether weight stigmatization can be reduced by providing individuals with information about the genetic causes of obesity (6,8,9,10,11). These studies have produced mixed results. Three studies found positive effects (6,9,11) and two found no effect of genetic information on obesity stigma reduction (8,10). The three studies that found positive effects of genetic information on obesity stigma reduction were all conducted with student populations, and so were not representative of, or generalizable to, the general population. In addition, one of these studies (11) used long 3–4 h tutorials to explicitly persuade participants that obesity is caused primarily by uncontrollable factors; this arguably has limited relevance to public health initiatives given that the length and intensity of the intervention used in the study would not be realistic on a public health scale.

Most of the people in the general population will learn about new genomic discoveries, including those regarding obesity, via mass media sources such as television, newspapers, or online news articles (12). Thus, it is relevant to public health to explore the potential impact on obesity stigma of information about obesity genomics that is presented as, for example, a news article. To our knowledge, only one study has assessed the impact of reading news article-style information about obesity genomics on obesity stigma among a non-student population (8). This study by Teachman and colleagues (8) was published in 2003 before major milestones in obesity genomics research such as the discovery of the FTO gene (13) were accomplished, and so both expert and lay understandings of obesity genomics may have evolved since that time. In addition, the information that was provided to participants in that study (8) was very short in length (101–103 words) and contained very little detail about obesity genetics, e.g., participants were informed primarily that genetics “can account for 80% of obesity” and that the “bottom line appears to be that genetics are to blame for our nation's battle with obesity” (original text kindly provided by Bethany Teachman, personal communication). No effect of this very brief, nondetailed genetic information on obesity stigma was found. The potential effects of the increasing public dissemination of genomic information about obesity on obesity stigma therefore remain to be seen.

For the present study, we developed written information about obesity genetics that was more detailed than that used in previous research (8), but which was still written broadly in the style of a brief news article. A general population sample of individuals was then recruited to complete an online survey, and respondents were randomly assigned to either receive the information about genetic causes of obesity, or to receive information about non-genetic causes of obesity. We also included a third condition, which included information about both genetic and non-genetic causes, given this more accurately reflects the complex multifactorial, gene—environment interactions almost certainly involved in the etiology of obesity (14).

The primary aim of this study was to examine whether providing individuals drawn from the general population with information about the genetic causes of obesity leads to lower obesity stigma than providing them with information about non-genetic causes. In the present analyses, we focused only on individuals who believed that they were not overweight; this was because individuals who believed themselves that they were overweight were administered a different measure of “internalized” weight bias (stigma) specifically developed for overweight/obese individuals. Our hypotheses were based on Attribution Theory (6). Our primary hypotheses were that individuals in the genetic condition would report stronger genetic causal beliefs, and lower obesity stigma, than those in the non-genetic condition, and that the association between genetic information and lower obesity stigma would be mediated by the association with genetic causal beliefs. Our secondary hypothesis was that genetic causal beliefs and obesity stigma scores among individuals in the third condition (genetic and non-genetic, or 'gene—environment interaction,' causes) would fall between those of the individuals in the genetic and non-genetic conditions. We also explored whether there were modifying effects of providing behavioral advice about how to reduce obesity risk alongside the causal information.

Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGMENTS
  8. DISCLOSURE
  9. References
  10. Supporting Information

Study design

An Internet-based survey of individuals in the general population was conducted using www.SurveyMonkey.com (a commercial online survey tool ref. 15). In addition to being randomized to receive one of the three different types of information about the causes of obesity, participants were also randomized to either receive or not receive behavioral advice regarding weight management (e.g., eat more healthily, increase physical activity levels) so that the effects of genetic information both with and without this type of behavioral recommendation could be assessed. Thus, this study used a 3×2 experimental design: obesity causal information type (genetic vs. non-genetic (environmental) vs. genetic—environment interaction) × behavioral advice (Yes vs. No). However, the behavioral advice component of the experiment was not a primary focus of the present analysis. The study was reviewed, and determined to be exempt from Institutional Review Board (IRB) approval, by the IRB at the Mount Sinai School of Medicine, New York.

Participants and recruitment

Participants were recruited through an online marketing research group, Survey Sampling International (SSI ref. 16). In order to recruit survey participants to their participant panels in general, SSI uses pop-up windows and banner ads on various websites, including their affiliate partners' websites, social media websites, and online communities. Interested individuals are invited to be on the SSI panel of participants; they then complete a series of online multiple choice questions so that they can be characterized in terms of various demographic and other descriptive variables. SSI panel participants are then randomly assigned to be invited to complete online surveys for which they are eligible. SSI uses a confidential identification number (which they do not share with investigators) to provide respondents with an incentive (a quarterly drawing for $25,000) to complete a survey. Every respondent who completes at least one survey during a given quarter is entered into the quarterly contest. In addition, a second incentive is provided to participants aged between 18 and 23 years because of their relatively low response rate: this age group is offered 300 points (equivalent to $3) to complete the survey.

The eligibility criterion for completing the survey in the present study was only that participants were required to be over the age of 18 years, there were no selection criteria based on any weight-related variables. However, we used two different measures of fat phobia based on whether participants identified themselves overweight or not. Within the present survey, once participants had self-reported their weight status, only those who identified themselves as very underweight, slightly underweight, or a healthy weight were asked to complete the Fat Phobia Scale — short form (FPS-S ref. 17). Individuals who self-identified as slightly or very overweight completed the Weight Bias Internalization Scale(18) and were part of another study examining the impact of genetic vs. non-genetic information on Weight Bias Internalization Scale scores among self-identified overweight individuals. Thus, only those participants who self-reported themselves to be underweight or healthy weight were included in the present analyses.

For the present study, SSI randomly selected 1,477 adult males and females from its participant panel, sent them a link to our online survey, and invited them to take part. Of these, 1,475 individuals began the survey, and 1,207 (82%) completed it.

Experimental conditions

After entering the online survey, participants answered some initial questions regarding eating attitudes, height, and weight (described fully below) and were then randomly assigned to one of the written information experimental conditions (each comprising a total of 226–330 words). All participants received identical information about the risk and consequences of obesity (Table 1). One-third of them then received genetic information, while another third received non-genetic (environment) information, and the final third received gene—environment interaction information about the causes of obesity. Half of the participants also received behavioral advice about how to reduce obesity risk, but as noted above this was not the primary focus of the present analysis.

Table 1.  Content of the experimental information provided to participants
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In brief, the genetic information contained information about twin studies, heredity and genetic transmission of obesity relevant genes. The non-genetic (environment) information contained information about large portion sizes, easy access to nutrient poor and high calorie foods, and conditions that favor sedentary behavior. The gene—environment interaction information contained information from both the genetic and environmental information sheets and how genes interact with the environment. Finally, the behavioral advice contained information about eating healthier and exercising more frequently. Table 1 shows the full wording of all components of the written experimental information used in the study. Although there was a minor error in the text describing the benefits of physical activity (the statement “About 60 min a day may be needed to prevent weight gain” should have read “About 60 min a day may be needed to prevent weight gain after initial weight loss”) there is no reason to suspect that this would have influenced outcomes relating to the study hypotheses. Information for the content of the experimental conditions was compiled from the scientific literature (13,19,20,21), a CDC website about obesity (22), and an online news article from Newsweek about obesity and genetics (23). The experimental information was not pilot tested before use in the current study. Participants were then asked to complete the rest of the questionnaire.

Measures

Measures assessed premanipulation (i.e., before participants were randomized to the experimental conditions) included maladaptive eating attitudes, self-reported height, and self-reported weight. Measures assessed postmanipulation (i.e., after participants were randomized to the experimental conditions) included evaluation of the information they had just read, perceived weight status, obesity stigma, self-esteem, causal beliefs about obesity, family history of obesity, and demographics.

Demographic and weight-related variables. Demographic characteristics were assessed by asking participants to report their age, gender, the highest level of education completed, annual household income, ethnicity, and relationship status. BMI (kg/m2) was calculated from participant self-reports of their height and weight, and BMI categories were assigned based on CDC guidelines (22). Participants were excluded if their calculated BMI was unrealistically low (<16 kg/m2) or unrealistically high (>55 kg/m2). Participants were also asked to indicate whether they perceived themselves to be 'very underweight', 'slightly underweight', 'healthy weight', 'slightly overweight', or 'very overweight'. Reporting that they were 'slightly' or 'very' overweight was an exclusion criterion for the present analysis. Family history was assessed by asking participants whether they thought any of their first degree relatives (mother, father, siblings, and children) were overweight. The total number of first degree relatives that were overweight was calculated for each participant.

Evaluation of the information. Participants were asked to rate the information in the experimental conditions regarding whether the information they had just read was (i): “easy to read”, (ii): “relevant to me”, (iii3): “useful to me”, and (iv) “provided me with new information”. The five response options for each statement were “strongly agree”, “agree”, “neither agree nor disagree”, “disagree”, and “strongly disagree”.

Psychological variables. Self-esteem was assessed using a single-item measure (24), “I have high self-esteem” with respondent options ranging from one (not very true of me) to five (very true of me). This measure has shown strong convergent validity for men and women, for different ethnic groups and for both college students and community members, with the Rosenberg Self-Esteem Scale (25).

To assess maladaptive eating attitudes, we used items from the Eating Disorder Examination Questionnaire (26). This questionnaire examines four subscales: restraint, weight concern, shape concern, and eating concern. To our knowledge, the Eating Disorder Examination Questionnaire has not previously been used in a general population sample, and we excluded some of the questions that seemed inappropriate to ask in a survey of a nonclinical population (e.g., the item “Over the past 28 days, how many times have you made yourself sick (vomit) as a means of controlling your shape or weight?”). This led to the exclusion of roughly half of the questions in the restraint, weight concern, and shape concern subscales, and all but one of the eating concern subscale, leaving questions 1, 3, 4, 8, 12, 20, 22, 23, 25, 26, 27, and 28. Items were rejected based on discussion and consensus between the study investigators. Because half of the questions from the restraint, weight concern, and shape concern subscales were included in the survey, a mean score for these subscales could be calculated. According to Fairburn et al. (27), “if ratings are only available on some items, a score may nevertheless be obtained by dividing the resulting total by the number of rated items so long as more than half the items have been rated” (pages 4 and 5). Alpha reliability scores were 0.86 for the restraint subscale, 0.90 for the shape concern subscale, and 0.85 for the weight concern subscale. Eating concern was assessed using question 20 on the Eating Disorder Examination Questionnaire, this item assesses guilt about eating because of its effect on shape and weight.

Causal beliefs about obesity were assessed using 10 items adapted from the revised Illness Perception Questionnaire (IPQ-R ref. 28). Participants were asked on a 5-point Likert scale how much they agreed or disagreed that each of the following causes obesity: “stress or worry”, “a germ or virus”, “diet or eating habits”, “chance or bad luck”, “hereditary—it runs in families”, “overwork”, “ageing”, “the environment”, “lack of exercise”, and “a person's genes”. The five response options for each item were strongly agree, agree, neither agree nor disagree, disagree, and strongly disagree.

Obesity stigma. The FPS-S was used to assess obesity stigma among survey respondents who did not perceive themselves to be overweight (17). The FPS-S is a semantic differential scale containing 14 items that assess attitudes about obese people (lazy—industrious, no will power—has will power, attractive—unattractive, good self-control—poor self-control, fast—slow, having endurance—having no endurance, active—inactive, weak—strong, self-indulgent—self-sacrificing, dislikes food—likes food, shapeless—shapely, undereats—overeats, insecure—secure, and low self-esteem—high self-esteem). In each word pair, the adjectives are placed at opposite ends of a scale that ranges from 1 to 5. Scores below 3 indicate positive attitudes toward obese people (i.e., no obesity stigma), and scores above 3 indicate negative attitudes toward obese people (i.e., obesity stigma). The alpha reliability in the present study was 0.93. We chose this scale over other possibilities (e.g., the universal measure of bias—fat version, ref. 29) in part because it is relatively straightforward for participants to complete, comprising pairs of words rather than whole sentences.

Statistical analyses

Demographic and weight-related characteristics (gender, age, education, income, ethnicity, BMI, family history, relationship status, and perceived weight status) of the sample were described using frequencies, means, and s.d., and compared between the experimental groups using ANOVAs to check for potential confounders. The effects of the experimental manipulations on obesity stigma were examined using ANOVA. In order to do this, two new categorical variables were created representing (i) obesity cause information type (genetic/environmental/gene—environment interaction) and (ii) behavioral advice (yes/no), and then entered simultaneously into an ANOVA. The main effects of each of these as well as the interaction between the two were examined. We also examined associations between obesity stigma, demographic, and weight-related characteristics and psychological variables (self-esteem, maladaptive eating attitudes, and obesity causal beliefs): unadjusted ANOVAs and χ2 were used for continuous and categorical independent variables, respectively. We then used ANCOVA to examine the associations between obesity stigma and any variables that were significantly associated with obesity stigma in the prior unadjusted models. P values of less than 0.05 were considered significant in all statistical analyses. All statistical analyses were performed using IBM SPSS statistics 19 (Chicago, IL).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGMENTS
  8. DISCLOSURE
  9. References
  10. Supporting Information

Characteristics of participants

Demographic characteristics. In total, 1,207 participants fully completed the survey. Of these, 416 self-reported that they were not overweight and so completed the FPS-S and were potentially eligible for this study. Twenty individuals were subsequently excluded because of unrealistic BMIs that were either below 16 kg/m2 or above 55 kg/m2. Thus, the final sample size for this study was 396 individuals. Within this sample, 152 individuals were randomly assigned to the genetic condition (76 received genetic information only, and 76 received genetic information plus behavioral advice); 128 were randomly assigned to the non-genetic (environment) condition (55 did not and 73 did receive additional behavioral advice); and 116 were assigned to the gene—environment condition (63 did not and 53 did receive behavioral advice).

As Table 2 shows, the mean age of the eligible 396 participants was 42.7 years (range 18–86 years); less than half (44%) were female; only 33% had a Bachelors or higher degree; 45% had an annual household income of less than $40,000; and 75% were white. There were no differences between experimental groups on any of the demographic variables (data not shown).

Table 2.  Demographic and weight-related characteristics of participants
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Perceived weight status and BMI. By definition all participants included in the present analyses reported that they did not believe themselves to be overweight: 78% believed they were a healthy weight and 22% believed they were underweight. However, when BMIs were calculated from self-reported height and weight and categorized according to CDC criteria, the mean BMI of the sample was 23.4 kg/m2 (indicating healthy weight) and the minimum was 16.1 kg/m2 (indicating underweight), but the maximum BMI calculated was 51.6 kg/m2, which indicates obesity. Overall, 112 (28%) participants had BMIs indicating that they were overweight or obese (Table 2). There were no differences in perceived weight status or BMI between experimental groups (data not shown).

Family history of obesity. A total of 55% of participants reported that they had at least one first degree relative (parent, sibling, or child) who was overweight (Table 2). There were no differences in family history between experimental groups (data not shown).

Evaluation of the information. Evaluation of the information responses were dichotomized into “yes” (i.e., strongly agree or agree responses) vs. “no” (i.e., neither agree nor disagree, disagree, and strongly disagree responses). Overall, using this categorization, 78% of participants reported that the information they had just read was easy to read, 48% felt it provided them with new information, 40% felt it was useful to them, and 29% felt that it was relevant to them. When compared between the genetic, non-genetic (environment), and gene—environment interaction conditions, there were no significant differences.

Self-esteem. The mean self-esteem score in the sample overall was 3.57 (s.d. = 1.16, range = 1–5). Response options were then recategorized into low (i.e., response score of 1 or 2), medium (response score of 3), and high (response score of 4 or 5). Using this categorization, 65 (16.4%) respondents had low self-esteem, 94 (23.7%) had medium self-esteem, and 237 (59.8%) had high self-esteem. There were no significant differences in self-esteem between the three main experimental conditions.

Maladaptive eating attitudes. As Table 2 shows, scores on the measures of maladaptive eating attitudes subscales were 1.23 (s.d. = 1.83) for restraint, 1.09 (s.d. = 1.53) for weight concern, 1.27 (s.d. = 1.55) for shape concern, and 1.01 (s.d. = 1.53) for eating concern. Possible ranges were 0–6 for all four subscales, with 0 indicating low and 6 indicating high restraint/weight concern/shape concern/eating concern; thus, these scores indicate generally low levels of restraint, weight concern, shape concern, and eating concern. The mean scores were slightly lower than previously published norms in nonclinical populations comprising young females (30,31), young adolescents (32), women under the age of 45 years (30), and women under the age of 30 years (33). Means were similar for restraint and weight concern, but slightly lower for shape concern, compared with undergraduate males (34).

Genetic causal beliefs

Absolute levels. When asked whether they agreed or disagreed that “a person's genes” were a cause of obesity (i.e., genetic causal beliefs) after reading the information about causes of obesity, 70 (17.7%) of the sample overall strongly agreed, 170 (42.9%) agreed, 107 (27.0%) neither agreed nor disagreed, 32 (8.1%) disagreed, and 17 (4.3%) strongly disagreed; the mean score was 3.62 (s.d. = 1.01, range = 1–5). See Figure 1 for genetic as well as non-genetic causal beliefs (proportions shown are people who agreed or strongly agreed that the potential risk factor influences obesity risk).

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Figure 1. Causal beliefs about obesity.

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Comparison between genetic and non-genetic conditions. Figure 2 shows the mean genetic (“A person's genes”) causal belief scores for each of the genetic, non-genetic, and gene—environment conditions. As expected, genetic causal beliefs were significantly higher in the genetic condition than in the non-genetic condition (P = 0.047). The mean genetic causal belief score was also significantly higher in the gene—environment than in the non-genetic condition (P = 0.036), but did not differ between the gene—environment and genetic conditions (P = not significant).

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Figure 2. Genetic causal beliefs by experimental condition.

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Obesity stigma

Absolute levels. The mean obesity stigma score as measured by the FPS-S was 3.16 (s.d. = 0.85, range = 1.2–5.0). This indicates that obesity stigma in this sample was present, but slightly lower than the FPS means previously reported in studies that have included overweight individuals (17,35).

Comparison between genetic and non-genetic conditions. As Table 3 shows, there was no difference in obesity stigma between the genetic and non-genetic conditions (mean FPS-S scores: 3.18 vs. 3.14, respectively, P = 0.71). There was also no difference between the gene—environment condition (mean = 3.17) and either the genetic condition (P = 0.10), or the non-genetic condition (P = 0.73) (Figure 3). Thus, the hypothesis that genetic information would lead to lower obesity stigma than non-genetic information was not supported. In addition, we had hypothesized that effects of the genetic information on obesity stigma would be mediated by causal beliefs; however, because no main effect of information type on obesity stigma was detected, mediation analyses were not conducted.

Table 3.  Fat Phobia Scale—short form (FPS-S) scores by experimental group
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Figure 3. Obesity stigma (FPS-S) by experimental condition. FPS-S; Fat Phobia Scale — short form.

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Correlates of obesity stigma. In unadjusted analyses, obesity stigma was significantly associated with ethnicity (P = 0.029): mean FPS-S scores were 2.74 among those who identified themselves as Hispanic or Latino and 2.83 among those who identified as black or African American, compared with 3.09 among those who identified as Asian, 3.22 among those who identified as white, and 3.26 among those who identified as “other” or more than one race. Obesity stigma was significantly higher among individuals with lower self-esteem (P = 0.036), and higher among those who did not believe the information they had just read to be useful (P = 0.028). There were also associations between obesity stigma and some causal beliefs about obesity: believing that “diet or eating habits” and a “lack of exercise” contributed to obesity were associated with higher fat phobia, while believing that “a germ or virus” and “overwork” contributed to obesity were associated with lower fat phobia (all P < 0.05; Table 4).

Table 4.  Correlates of the Fat Phobia Scale—short form (FPS-SF)
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In adjusted analyses, obesity stigma remained significantly associated with low self-esteem, as well as with participants having a lower belief that the information they had just read was useful, and having a lower belief that obesity is caused by overwork (Table 5).

Table 5.  Adjusted model of correlates with FPS-SF
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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGMENTS
  8. DISCLOSURE
  9. References
  10. Supporting Information

This study examined the effects on obesity stigma of providing self-reported non-overweight individuals with genetic vs. non-genetic information about the causes of obesity. Results indicated that the causal manipulation was successful in that genetic causal beliefs were higher in the genetic and gene—environment conditions than in the non-genetic experimental condition. Thus, it appears that the experimental manipulation accurately communicated the desired content; however, this information did not differentially influence obesity stigma. Therefore, our primary hypothesis was not supported. These results tentatively suggest that increasing dissemination of knowledge about the role of genetics in obesity may be neither harmful nor beneficial in terms of obesity stigma reduction.

The experimental information also did not influence respondent's views about diet and exercise as contributing to the development of obesity: overall, more than 80% of participants either agreed or strongly agreed that lack of exercise and diet or eating habits contributed to the development of obesity, and this did not differ between groups. This is important because improving nutrition and physical activity are clearly important behavioral modifications needed to lose weight, even among those who are genetically susceptible to weight gain, and there have previously been concerns that providing people with information about genetic influences on obesity may reduce people's focus on the importance of diet and exercise. A recently published article by Persky and Eccleston (36) found that although providing medical students with genetic causal information about obesity resulted in lower negative stereotyping of obese individuals, it also led to the students making less health behavior recommendations to an obese virtual patient than controls, suggesting some degree of fatalism on the students' behalves. However, our findings do not support the concern that focusing people's attention on the genetic aspects of obesity reduces their belief that lifestyles also play a role.

Our finding that lower self-esteem is associated with higher fat phobia has, to our knowledge, only been previously reported in one study (37). In a review of antifat prejudice reduction research and theory, Danielsdottir and colleagues (7) highlight work premised on Social Identity Theory (38), which suggests that antifat prejudice is linked to feelings about one's own appearance. Social Identity Theory (38) suggests that comparing oneself to, and possibly mocking, someone perceived to be less attractive than oneself is a way of feeling better about one's own appearance (7,38). Our novel finding in the present study that there was no association between maladaptive eating attitudes and obesity stigma suggests that the previously identified association between having low self-esteem and the stigmatization of obese individuals is independent of personal disordered attitudes toward eating and beliefs about personal shape and weight.

Compared with past research using the FPS-S to assess obesity stigma (17,35), the mean FPS-S score was lower in our sample. One possible explanation for this is that we may have lowered obesity stigma throughout all experimental conditions since all experimental materials outlined uncontrollable causes of obesity.

Limitations of this study include that we did not use a pretest posttest design, and that there was no no-information control group. This was due in part to resource and time restrictions on the number of questions and participants it was feasible for us to include in the survey. We also chose not to administer the obesity stigma questionnaire pretest because we did not want to prime the respondents in their responses, and potentially dilute any between-group differences that might have been detected on the posttest administration of the measure. Having a no-information control group, however, would have helped determine the effects of providing the information on obesity stigma overall regardless of its specific content. Another option would have been to include a control group that received information about a health condition unrelated to obesity. Similarly, as noted above, having a pretest measure of obesity stigma would have allowed us to determine whether all three interventions reduced (or increased) obesity stigma. These study design features would be worth including in future efforts to replicate and expand on the present study.

A second limitation is arguably the use of a short vignette as the experimental manipulation. Previous research studies that have used lengthy tutorials and messages about the uncontrollable aspects of obesity have been shown to decrease obesity stigma in the general population (6,11). However, studies that have used short vignettes, similar to the present study, have had mixed results (8,9). Thus, it is possible that more lengthy information may have resulted in greater effects on obesity stigma (although as noted previously, this may have less relevance in the public health arena).

Relatedly, we did not include photos in the experimental information presented to the participants. A recent experimental study found that participants randomly assigned to view negative photos of obese people expressed stronger antifat attitudes on the FPS than those who viewed positive photos (39). It would be interesting to explore whether reactions to genetic information about obesity varies in the presence or absence of accompanying images of obese individual in future work.

It is also possible that comparing two “uncontrollable” causes of obesity, i.e., genes (which are internal and uncontrollable) and environments (which are external and uncontrollable), with one another reduced the likelihood that we would detect significant between-group differences. Highlighting the “controllable” aspects of obesity, such as dieting and exercising, is believed to result in the view that obese people are responsible for their weight. According to Crandall (6), the more people believe that obesity is within an individual's own personal control, e.g., caused by their eating and exercise habits, the more those individuals will be stigmatized. However, we did provide behavioral advice to half our participants and found that there was no difference in stigma between the groups. Thus, obesity stigma did not appear to be either increased or decreased by providing individuals with information about controllable, diet and exercise, influences on obesity, in the present study.

Finally, asking participants to complete questions about their eating attitudes, height, and weight before reading the experimental information may have led them to respond differently to the experimental information than if they had not been asked for this information at the start of the survey.

However, these limitations should be weighed against the strengths of the present study, which include the relatively large sample size, the experimental design to test hypotheses regarding obesity stigma reduction, and the non-student population. The demographic characteristics of our sample were quite representative of the general population. For example, in terms of education, 33% of our sample had completed a Bachelors degree or higher, which is similar to the 27% of the adult population in the 2010 census (40). The proportion of individuals who had completed high school or less was lower in our sample than in the general population (29% vs. 45%, respectively ref. 40). However, figures were similar for income: 48% (our sample) and 42% (2010 census) had an annual household income of less than $40,000 per year (40). In addition, our sample was remarkably similar to the 2010 census data regarding race. For example, 75, 9, and 5% of our sample were white, black/African American, or Asian, respectively; in the 2010 census data, these figures were 72, 13, and 5%, respectively (40).

In conclusion, our findings suggest that providing individuals with brief information about the role of genetics in obesity does not influence the stigmatization of obese individuals by those who believe themselves not to be overweight any more or less than brief information about non-genetic, environmental factors. Dissemination of brief information about the role of genetics in obesity may therefore have neither a beneficial nor harmful impact on prevalent obesity stigma.

ACKNOWLEDGMENTS

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGMENTS
  8. DISCLOSURE
  9. References
  10. Supporting Information

The authors thank Tom Hildebrandt for his input to the design of the study, and Rebecca Puhl, for her helpful comments on an early version of the manuscript.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGMENTS
  8. DISCLOSURE
  9. References
  10. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGMENTS
  8. DISCLOSURE
  9. References
  10. Supporting Information

Supporting Information

FilenameFormatSizeDescription
oby_2781_sm_oby2012144_coi.pdf1020KSupporting info item

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