Predictors of Weight Bias in Exercise Science Students and Fitness Professionals: A Scoping Review

Background Although previous studies have reported weight bias among students and professionals in exercise science, physical education, kinesiology, and fitness instruction, predictors of weight bias in these professions have not been extensively reviewed. Aim The purpose of this scoping review was to explore the available literature on predictors of weight bias in exercise science students and fitness professionals to identify key concepts and research gaps. Methods PubMed and ERIC were searched from January 1990 to May 2019. Eighteen studies were included in this review. A thematic analysis was conducted. Findings. Six main themes were drawn from these studies including beliefs in the personal controllability of weight; sex differences; enrollment in a health sciences-related program; psychosocial and personal factors; knowledge of obesity; lack of personal history, family, or friend with obesity. Our scoping review highlighted diverse predictors of weight bias among exercise science students and professionals that warrant further study and intervention.


Introduction
Weight bias is defined as negative beliefs and attitudes toward people living with overweight or obesity [1]. Beliefs and opinions that occur in a conscious and expressive manner are defined as explicit forms of weight bias [2]. Previous studies have documented the existence of explicit weight bias in various settings such as in healthcare among trainees (i.e., nursing, dietetic, and medical students) [2], education (i.e., schoolteachers) [3], and medicine and public health [4][5][6][7][8]. Healthcare providers often associate people with obesity with negative labels and stereotypes such as "lazy", "weak", "lack willpower", "unattractive", or "unintelligent" [9,10]. In fact, patients with obesity have reported low trust, poor communication, lack of training, and disrespectful treatment from their healthcare providers [11]. Experiencing weight bias in healthcare settings is particularly harmful because it can negatively affect patient engagement and utilization of healthcare services [11]. Future health professionals' biases are problematic as they may deter both clients and patients from adopting healthy lifestyle choices [12,13]. For example, individuals with obesity experiencing explicit weight bias can experience physical and emotional tribulations including stress, anxiety, depression, avoidance or lower motivation for exercise, and disordered eating [14] and may not be receiving appropriate care for their health conditions [15].
While there is a lack of clearly defined approaches to reduce weight bias among healthcare professionals, a systematic review of weight bias reduction interventions identified preprofessional educational training in healthcare programs as one potential target [16]. Since many healthcare professionals and health educators working with adults and children with obesity often have educational training backgrounds in physical education, health, and kinesiology, it would be important to understand predictors of weight bias among students and professionals in these fields. Numerous studies have reported weight bias among students in physical education [17], kinesiology, and exercise science programs [18,19] and professionals in these fields (e.g., physical education teachers, fitness instructors) [20,21]. Weight bias has been observed among physical education teachers whereby they have expressed lower expectations in performance and abilities of students with obesity compared to their normal weight peers [20]. Students enrolled in physical education programs who have not addressed their own weight-biased attitudes throughout their training program may express these biases as future physical education teachers toward their own students with obesity [20]. In fact, physical educators' weight bias, lower expectations, and experiences of weight teasing of children with obesity may lead to poorer self-esteem, body image issues, lower physical activity performance and lower motivation if students experience differential treatment because of their body weight [22][23][24].
ere is evidence that suggests physical education students display greater weight bias toward the third year of their program compared to their first year and display higher weight bias compared to other health science students [17].
is further highlights the importance of understanding factors associated with weight bias in the early formative years to avoid the propagation of weight bias in professional practice and its potential to negatively affect the treatment and quality of healthcare of individuals with obesity.
While it is clear that weight bias is pervasive in students and professionals in the field of exercise science, we need a better understanding of the factors that predict weight bias in this field to avoid the propagation of weight bias and its negative consequences on patient adherence, health behaviors, and clinical outcomes. e purpose of this scoping review was to synthesize all available literature pertaining to the predictors of weight bias in students and professionals in exercise sciences. For the purpose of this paper, students in exercise science refer to students enrolled in undergraduate or graduate programs in exercise science, physical education, and kinesiology and professionals in exercise science include physical educators, fitness instructors, exercise physiologists, and exercise specialists. We used a scoping review methodology due to the diverse body of literature on this topic and large range of study designs and methodologies before considering undertaking a systematic review.
is scoping review further aimed to identify knowledge gaps and future research directions in the field of weight bias among exercise and fitness trainees and professionals.

Methods
We conducted a scoping review using the five-stage methodology outlined by Arksey and O'Malley [25]. With this method, all evidence and sources pertaining to our research question were gathered and summarized into overarching themes [25]. As such, scoping reviews are guided by a requirement to identify all relevant literature regardless of the heterogeneity of the body of literature, design, or quality [26].
is methodological approach is effective in presenting a broad overview of the literature on our research topic and is an effective way to identify research gaps [25].
e Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) flow diagram (see Figure 1) and checklist (Table 1) [27] were used to guide the reporting and show the steps taken in the article selection process of this review (see Appendix).

Literature Search.
A literature search was designed and conducted in consultation with a health sciences librarian. PubMed and ERIC databases were searched on December 14, 2017, and again on May 8, 2019, using combinations of keywords and subject terms for weight, bias, health science students, and fitness professionals. Results were limited to articles published after January 1,1990, and written in English or French. e complete search strategy for both databases can be found in the Appendix. Other articles (n � 2) were retrieved from the reference lists of pertinent studies that were identified in the personal libraries of the researchers.

Study Selection.
Research studies that sought to understand the predictors and causes of weight bias in exercise science students and professionals were included in this scoping review. Only articles that included students and professionals in the fields of exercise science, physical education, kinesiology, physical therapy, fitness instruction, and exercise physiology were eligible for inclusion. Among selected articles, only those measuring the predictors or potential causes of weight bias in exercise science students and professionals were included for analysis. Studies that assessed weight bias in practicing health professionals including physicians, nurses, doctors, dietitians, psychologists, and social workers were not included in this review.

Data
Charting. Reviewers (L.Z and T.S.) charted the characteristics of included studies in a table outlining title, authors, date of publication, country, study purpose, participant characteristics, methodology, and main findings. All authors verified the charted data for accuracy and the data are presented in Table 2.

Results
e literature search conducted on May 8, 2019, resulted in 1310 unique articles (after 9 duplicate articles were removed). An additional two articles were identified from the reference lists from the researchers' personal libraries resulting in a total of 1312 articles. ese 1312 articles were screened and assessed for eligibility based on the inclusion criteria. Of the 41 articles that were screened as potentially relevant, 18 studies met the eligibility criteria and were included in the scoping review ( Figure 1). Table 2 shows the characteristics of included studies. More than half of the studies included were conducted in the USA (61.11%, n � 11) and used a combination of implicit and explicit measures to assess weight bias (61.11%, n � 11). e rest of the studies measured weight bias through explicit measures only (27.77%, n � 5) or through other measures (11.11%, n � 2) such as Q-methodology and one-on-one interviews to explore personal constructs of body shape and weight. e majority of the studies (66.67%, n � 12) sampled undergraduate students enrolled in exercise science (n � 3), kinesiology (n � 3), physiology (n � 1), kinesiology, health promotion, and recreation (n � 1), health sciences (n � 1), and health and physical education training programs (n � 3). Four of the 18 studies (22.22%, n � 4) sampled fitness professionals only which included personal trainers and fitness instructors (n � 1), health educators (n � 1), fitness center employees (n � 1), and physical education professors (n � 1). Finally, one study (5.55%, n � 1) sampled both exercise science students and fitness professionals, e.g., physical education and exercise science students and athletes.

3.2.
emes. While conducting the thematic analysis according to Arksey and O'Malley [25], the researchers identified and grouped similar themes from each study' findings. Next, similar group themes were further synthesized into overarching themes. A total of six themes emerged to explain the predictors of weight bias in students and professionals in exercise science: beliefs in the controllability of weight; sex or gender differences; enrollment in a health sciences degree or program; psychosocial and personal factors; knowledge of obesity; lack of personal history, family, or friend with obesity.

Beliefs in the Personal Controllability of Weight.
Eight studies [18,21,23,33,[36][37][38][39] showed that exercise science students and professionals generally believe that weight is personally controllable. Common statements included "everyone has control over their weight" [39]; "eating right and exercising puts you on the right path for a long healthy life" [36]; and "physical activity is very important in the treatment of obesity" [33].
Exercise science students and professionals endorsing strong beliefs in weight controllability tended to explicitly associate people with obesity with "bad" attributes [21], "lazy" stereotypes and held higher explicit weight bias on social/character disparagement and weight control/blame attributes [18].

Sex Differences.
Five studies reported sex or gender differences in the perception of weight toward individuals with obesity [18,21,29,34,35]. Two out of five studies found that men displayed higher weight bias attitudes than women [34,35]. Indeed, according to Langdon et al., Exercise Science/Health students (ESHS) who were male tended to hold stronger explicit weight bias beliefs on weight control/ blame, social/character disparagement, and physical/romantic subscales than ESHS who were female [34]. Alternatively, two studies found that women had stronger implicit weight bias toward individuals with obesity [18,21] and were more likely to implicitly describe them as "bad" Full-text articles excluded, with reasons (n = 23) (i) Predictors of weight bias were not explored (n = 11) (ii) Participants were not exercise science students or fitness professionals (n = 7) (iii) Both predictors of weight bias were not explored, and participants were not exercise science students or fitness professionals (n = 5) Studies included in the review (n = 18)  Provide an explicit statement of the questions and objectives being addressed with reference to their key elements (e.g., population or participants, concepts, and context) or other relevant key elements used to conceptualize the review questions and/or objectives.

Methods
Protocol and registration 5 Indicate whether a review protocol exists; state if and where it can be accessed (e.g., a Web address); and if available, provide registration information, including the registration number.

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Eligibility criteria 6 Specify characteristics of the sources of evidence used as eligibility criteria (e.g., years considered, language, and publication status), and provide a rationale.
Pages 5-6 Information sources * 7 Describe all information sources in the search (e.g., databases with dates of coverage and contact with authors to identify additional sources), as well as the date the most recent search was executed. Selection of sources of evidence † 9 State the process for selecting sources of evidence (i.e., screening and eligibility) included in the scoping review. Pages 5-6 Data charting process ‡ 10 Describe the methods of charting data from the included sources of evidence (e.g., calibrated forms or forms that have been tested by the team before their use, and whether data charting was done independently or in duplicate) and any processes for obtaining and confirming data from investigators.

Data items 11
List and define all variables for which data were sought and any assumptions and simplifications made. Pages 20-33 Critical appraisal of individual sources of evidence § 12 If done, provide a rationale for conducting a critical appraisal of included sources of evidence; describe the methods used and how this information was used in any data synthesis (if appropriate).

Synthesis of results 13
Describe the methods of handling and summarizing the data that were charted. Pages 6, 20-33

Selection of sources of evidence 14
Give numbers of sources of evidence screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally using a flow diagram. Summarize the main results (including an overview of concepts, themes, and types of evidence available), link to the review questions and objectives, and consider the relevance to key groups. Journal of Obesity [18,21] and "lazy" [21]. Two studies found no statistically significant differences in antifat bias scores between males and females [28,29]. Although there is a relationship between sex and gender differences and weight bias, the studies have shown opposing findings in predicting the direction of the relationship with sex and gender.

Enrollment in a Health Sciences Degree or Program.
Four studies compared weight bias in students majoring in health and exercise science to students enrolled in nonhealth disciplines, e.g., business, psychology, and other nonhealth majors [10,17,32]. ree studies showed that physical education and kinesiology students have higher weight bias compared to students enrolled in nonhealth degree programs [23]. O'Brien et al. showed that physical education students displayed higher levels of implicit and explicit weight bias as compared to psychology students [17]. Lynagh et al. found that enrollment in the health and physical education (HPE) specialist degree was a significant predictor of implicit weight bias and that HPE students held higher levels of weight bias compared to students in the nonspecialist teaching degree program [23]. Greenleaf et al. showed that students enrolled in Kinesiology, Health Promotion and Recreation (KHPR) had higher levels of explicit weight bias and endorsed stereotypes for children with obesity such as "likely to be teased", "bad to be", and "lazy" compared to non-KHPR majors [32]. However, one study by Robinson et al. found similar levels of both implicit and explicit weight bias in health science students enrolled in medicine, medical science, nursing/midwifery, pharmacy, dietetics, public health, exercise science, physiotherapy, etc., compared to nonhealth majors in business programs [10]. Interestingly, differences in levels of implicit weight bias were also found between year one and year three physical education students whereby third year students displayed higher levels of weight bias than first year physical education students [17,19].
Enrollment in a health and exercise science program is a potential predictor of weight bias, although more research is needed to determine if weight bias increases in students enrolled in kinesiology or exercise science throughout the duration of their undergraduate degrees.

Psychosocial and Personal Factors.
Six studies associated psychosocial factors, professional philosophies, and perceptions of self with weight bias in exercise science students and professionals [17,29,31,[33][34][35]. Psychosocial factors such as having ego-oriented goals and a tendency to internalize the athletic body ideal were measured in exercise science students. Students with ego-oriented goals "may avoid challenging tasks and feel discouraged when their performance is perceived as inferior to others" [34]. In this study, exercise science students were also likely to exhibit high internalization of the athletic body ideal and were "particularly susceptible to media messaging that idealizes the athletic body, portrayed as competent, competitive, and healthy" [34]. High internalization of the athletic body type ideal among exercise science students was found to be a predictor of fat phobia and weight control blame [34].
Social dominance orientation was cited as a predictor for explicit and implicit measures of weight bias whereby physical education students "see their own group as superior to and dominant over other relevant groups" [17]. Physical education students with high social dominance orientation displayed weight bias on explicit measures such as "dislike", "fear of fat", and "lack of willpower" and the implicit measures "bad" and "lazy" [17].
In fitness professionals, three studies have associated professional philosophies and perception of self with weight bias [17,29,35]. One study looked at different professional philosophies such as "behavior change", "cognitive-based", "decision-making", "freeing/functioning", and "social change" [29]. Among all professional philosophy measures, it was found that the "behavior change" education philosophy (i.e., emphasizing behavior modification as key in managing obesity) was associated with higher explicit weight bias in health educators [29]. In another study by Martinez-Lopez et al., 2010, self-efficacy expectations were measured to assess weight bias in physical education trainees toward youth with obesity. Self-efficacy in physical education trainees was defined as "the perceptions about their own Describe sources of funding for the included sources of evidence, as well as sources of funding for the scoping review. Describe the role of the funders of the scoping review.

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JBI � Joanna Briggs Institute; PRISMA-ScR � Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. * Where sources of evidence (see second footnote) are compiled from, such as bibliographic databases, social media platforms, and Web sites. † A more inclusive/ heterogeneous term used to account for the different types of evidence or data sources (e.g., quantitative and/or qualitative research, expert opinion, and policy documents) that may be eligible in a scoping review as opposed to only studies. is is not to be confused with information sources (see first footnote). ‡ e frameworks by Arksey and O'Malley (6) and Levac and colleagues (7) and the JBI guidance (4,5) refer to the process of data extraction in a scoping review as data charting. § e process of systematically examining research evidence to assess its validity, results, and relevance before using it to inform a decision. is term is used for items 12 and 19 instead of "risk of bias" (which is more applicable to systematic reviews of interventions) to include and acknowledge the various sources of evidence that may be used in a scoping review (e.g., quantitative and/or qualitative research, expert opinion, and policy document).  Journal of Obesity 7  Trainees who scored high on their perceived self-efficacy in the assessment of their own teaching methods and in the progress witnessed in students with overweight had more favorable attitudes toward these students.
Journal of Obesity 9 postcourse Q sorts were compared in exercise students before and after exposure to the exercise and weight course.
Following the course, "assimilator learners" showed more acceptance of obesity condition in accordance with comprehensive course content. "Askew learners" did not accept course content due to their own focus remaining on diet restrictions and energy expenditure while believing that obesity is completely preventable and controllable. 10 Journal of Obesity Journal of Obesity 11   Journal of Obesity 13 capabilities to foster student's learning and engagement" [35]. Results showed that physical education trainees with higher levels of perceived self-efficacy displayed more favorable attitudes toward the educational treatment of children and youth obesity [35]. Another study sampled professors of physical education majors who consistently believed that "physical education teachers should not be obese, since they are role models for their students" [31]. A similar perception was also found in an earlier study conducted by Hare et al., 2000, whereby a sample of health fitness instructors, exercise test technologists, and exercise specialists believed that they should maintain normal weight to be role models for their clients/patients [33]. According to this study, most of the information on weight control was derived from textbooks, college courses, and scientific data [33].

Knowledge of Obesity.
ree studies measured exercise science students' and professionals' knowledge of obesity in relation to weight bias [10,19,29]. One study examined perceived obesity education and found that health students who poorly rated their knowledge regarding the genetic causes of obesity had higher explicit weight bias on the "blame" subscale [10]. Kinesiology students enrolled in a nontraditional curriculum intervention emphasizing uncontrollable causes of weight (i.e., genetics) decreased explicit weight bias on the "blame" subscale compared to the control group of students who were learning the traditional curriculum focused on the role of exercise and diet in weight management [19]. One out of the three studies did not find a significant association between health educators' knowledge of obesity and weight bias [29].

Lack of Personal History, Family, or Friend with Obesity.
A lack of personal history of overweight predicted high implicit weight bias measures of "bad" and "lazy" among a sample of fitness professionals and regular exercisers [21]. Chambliss et al., 2004, showed that a lack of family history of obesity and a lack of friends with obesity were associated with higher explicit weight bias [18]. However, DeBarr and Pettit reported that there were no statistical differences in weight bias between health educators who were overweight compared to their normal weight peers [29].

Discussion
In this scoping review, 18 studies were reviewed to identify predictors of weight bias among exercise science students and professionals. In the following section, we identify gaps from each of the six themes, discuss future research directions, and outline the strengths and limitations of this scoping review.

Future Research and Recommendations.
is scoping review identified studies in undergraduate students in physical education, exercise science, and kinesiology and two studies assessed professionals in fitness instruction health education. To our knowledge, we could not find studies that assessed predictors of weight bias among practicing kinesiologists, physiotherapists, and athletic therapists although one study assessed a mixed sample of exercise professionals including sports physiologists [30]. Future research is also warranted to examine predictors of weight bias in other health sectors and settings (e.g., public health). While studies have shown that weight bias from primary care providers negatively affects quality of care and healthcare utilization of patients with obesity [11], impacts on the behaviors, treatment, and quality of care of individuals with obesity have yet to be assessed systematically in exercise science and physical education practice settings.
Only three studies measured knowledge of obesity in relation to weight bias in exercise science students and professionals in this scoping review [10,19,29]. To better understand exercise science students and professionals' behaviors toward overweight and obesity, future research should seek to examine the contents of exercise science course curricula that may foster and potentially sustain weight bias in exercise science students. Because it has been shown that weight bias increases in physical education students as they progress through their educational programs [17], it should also be determined if weight bias increases from the start to the completion of exercise science programs as well. Future weight bias reduction interventions should be designed to address these potential predictors of weight bias and evaluate their impact throughout students' educational training in exercise science programs.
Eight of the studies included in this scoping review identified beliefs of controllability of weight as a predictor of weight bias [18,21,23,33,[36][37][38][39]. Crandall (1994) coined the phrase "ideology of blame" to define the dominant social belief that individuals are personally responsible for their weight. is social belief may explain exercise science students and professionals' weight bias. Studies also show that exposure to simulated courses emphasizing the controllable aspect of weight and the rigid concepts of "eating less and moving more" may lead to higher weight bias [40]. One study showed that exercise science students believed obesity to be preventable and controllable through diet restriction and energy expenditure highly valuing diet and exercise for weight management and weight loss [39]. e study suggested a lack of knowledge on other therapeutic interventions including bariatric surgery and that there is still resistance on understanding obesity as a complex condition [39]. is paper highlights the need to increase awareness of the complexity of obesity in the curriculum offered to exercise science students and more research to understand the causes of students' resistance to adopt and learn new concepts about obesity.
Few studies exist to explain sex differences in weight bias attitudes and have shown mixed results [18,21,29,34,35]. It is unclear how sex or gender plays a role in weight bias [34]. Although it has been proposed that women may be more sensitive to weight bias due to their higher vulnerability to the "thin ideal" [21,41], further research studies should be designed to be adequately powered to examine potential sex and gender differences and in exercise science students and professionals. It would also be important to determine causes of differences in weight bias between sexes and genders in this field.
Other areas that warrant further study are the potential influences of ethnicity and setting on the development of weight bias (i.e., how are individuals with obesity seen when observed in a neutral setting versus being seen in an exercise facility/setting). Two articles showed mixed results in ethnicity as a potential predictor of weight bias in exercise science students [18,28]; one stated no differences in explicit weight bias between American and Mexican athletes [28] while another found that exercise science students of Caucasian ethnicity living in a rural environment had higher levels of weight bias compared to those of other ethnicities [18]. With regard to setting, one study found that fitness center employees exhibited moderately strong implicit weight biases regardless of the setting in which they found themselves in (i.e., both neutral and exercise settings) [30]. e context in which the weight bias judgments were made did not affect the strength of implicit weight bias [30], suggesting weight bias still exists regardless of context in this aforementioned study. More research is needed to determine whether setting may act as a predictor of weight bias in fitness trainees and professionals.
One study by Fontana et al. evaluated the attitudes of professors in physical education departments toward individuals with obesity [31]. e sample of professors teaching physical education in this study held high implicit weight bias and disapproved physical education teachers with obesity as role models to students. is study demonstrated that greater explicit weight bias was associated with stronger disapproval of physical educators who have obesity as roles models and accepting physical education student majors living with obesity [31]. An earlier study by Hare et al. also showed that exercise professionals thought they should maintain normal weight to be role models for their clients [33]. ese findings suggest a potential relationship between the importance exercise science students and professionals place on appearance and body weight (appearance orientation and body image preoccupation) with their current or future career as exercise science professionals.
is suggests that more research is warranted on internalized weight bias ("the belief that negative stereotypes about weight apply to the self" [42]) in exercise science students and professionals to better understand underlying root causes of these internalized beliefs about weight, body image, and being role models in their field.

Strengths and Limitations.
e present study is the first, to our knowledge, to gather the existing literature on predictors of weight bias in exercise science students and professionals. is scoping review provides a comprehensive summary of the overarching themes that emerged from the published studies that explored this topic.
is comprehensive review helps identify predictors of weight bias that can serve as potential targets to address with curriculum changes and interventions aimed to improve training on the complexity of obesity and reduce weight bias in the early formative years before students become professionals in the field. However, since our scoping review focused on predictors of weight bias in exercise science students and professionals, suggestions for future research and interventions drawn from this paper can only be made about students and professionals in exercise science, kinesiology, and physical education fields.

Conclusion
is scoping review identified many overarching themes that predict weight bias in exercise science students and professionals. Belief in the personal controllability of weight was found to be the most researched and consistent predictor of weight bias in our population of interest. ere appeared to be sex differences in weight bias that warrants further study; enrollment in a health sciences-related degree or program; psychosocial and personal factors relating to philosophy and personalities; traditional knowledge of obesity focusing mostly on diet and exercise; and lastly, a lack of personal history, family, or friend with obesity. Future research studies are needed to better understand predictors of weight bias in other health and exercise science-related fields, understand the impact of curricula that is heavily based on lifestyle factors only such as diet and exercise on weight bias, and evaluate the impact of weight bias reduction interventions in undergraduate students and professionals in the field of exercise science, kinesiology, and physical education.