Authors: Guillermo Escalante1, Rafael Alamilla1, Eric Vogelsang2, Christopher Gentry1, Jason Ng1

1Department of Kinesiology, California State University, San Bernardino, USA; 2Department of Sociology, California State University, San Bernardino, USA

Corresponding Author:
Guillermo Escalante, DSc, MBA, ATC, CSCS, CISSN
California State University- San Bernardino, Department of Kinesiology
5500 University Parkway
San Bernardino, CA 92407
gescalan@csusb.edu
(909) 537-7236

Weight Discrimination among Students from a Diverse Urban University

ABSTRACT

Purpose: To examine the association between university students’ weight discrimination and their academic discipline, gender, ethnicity, body mass index (BMI), body fat percentage, explicit overweight bias, personal body perceptions, and their personal experiences with weight loss. Methods: Sixty-two students (Age: 23.9 ± 4.7 y) from various disciplines completed 1) a 41-question survey that addressed the participant’s explicit overweight bias, prior struggles with body weight, and body perceptions; 2) the Weight-Implicit Association Test (WIAT) to address overweight implicit bias; and 3) measurement of height, weight, and body fat. Chi-Square tests were performed between the participant’s WIAT results and academic discipline, BMI, body fat, explicit bias, personal experience with their body fat, and body perception. Moreover, differences in BMI and body fat percentage were examined with two separate 2 (gender) × 2 (academic discipline) repeated measures ANOVAs. Results: ANOVA results revealed a relationship between an explicit bias and WIAT implicit bias. No relationships were found between the results of the WIAT and academic discipline, BMI classification, body fat classification, personal experience with body fat, or perceptions of their body. Conclusions: An implicit anti-fat bias exists regardless of academic discipline, percent body fat, BMI, explicit anti-fat bias, prior struggles with body fat, or perceptions of their body. These findings support previous literature that suggests individuals have an unconscious negative prejudgment of overweight people. Applications in Sport: Current physical educators, healthcare professionals, fitness professionals, sport coaches, and university faculty preparing students for these professions must begin to take the steps necessary to eliminate weight bias from their environments. The authors recommend that all members of the aforementioned communities develop an understanding of the factors that may lead to weight gain and develop strategies of encouraging overweight individuals to reduce their weight without further perpetuating weight stigma.   

Key words: discrimination, weight-bias, implicit association

INTRODUCTION

Weight discrimination and stigmatization, partly a consequence of the United States’ high overweight/obesity rates, has had a substantial impact on the lives of many Americans. Over the past 30 years, there has been an increase in perceived weight discrimination among Americans. Andreyeva et al. (3) determined that the prevalence of weight discrimination increased from 7.3% in 1995-1996 to 12.2% in 2004-2005. In another study, Sutin et al. (29) determined that weight discrimination has the greatest consequences on people who are already overweight or obese; they also reported that people who experienced weight discrimination during initial testing were three times more likely to remain obese at the follow up. 

Weight bias literature has characterized bias as either explicit or implicit. Explicit bias is any belief or attitude that is expressed at the conscious level—measured using self-report questionnaires. Implicit bias is any belief or attitude that impacts our understanding, actions, and decisions in an unconscious manner. Implicit weight bias has been traditionally measured using the Implicit Association Test, a response-latency task that measures the strength of association between a social attitude (i.e. weight bias) and an attribute (i.e. lazy). Previous literature has shown that implicit and explicit bias are moderately related to each other (12) and are both predictive of bias (10, 12).

Previous investigations have indicated the relationship between descriptive characteristics—such as BMI, ethnicity, college major, etc.—and expression of weight bias. Latner et al. (14) undertook a 356-participant study that assessed the prevalence of weight stigmatization among young adults. Their results demonstrated that weight stigmatization was prevalent among all participants—irrespective of gender, ethnicity, and BMI. Interestingly, investigators found that African Americans and women were less stigmatizing than Caucasians and men, respectively. Similarly, a study measuring the implicit and explicit weight bias of a large medical student population demonstrated that bias is dependent of several demographic factors. Namely, men expressed more bias than women, Caucasians and Hispanics expressed more bias than African Americans, and individuals with a lower BMI expressed greater bias (19).

How individuals perceive others who are overweight or obese might be affected by their own struggles with their weight. Previous investigations have shown that perceptions of obese individuals can change positively after they lose weight (8). However, other studies have shown that—regardless of the manner in which weight is lost—individuals are still subject to negative weight stigmatization (15). As such, clarification as to how prior weight struggles impact an individual’s weight bias needs to be addressed.

Properly reaching out to overweight individuals is important if they are to succeed in losing body fat. One of the many tasks for healthcare and fitness professionals is to help guide overweight individuals in losing excess body fat to improve their health. Therefore, it is important to recognize if an anti-fat bias might affect their ability to help overweight or obese individuals effectively. Unfortunately, researchers have reported that healthcare specialists have strong negative associations toward obese persons (26). It has also been reported that weight stigma in healthcare settings leads to poor quality of care for overweight patients (30). A study on implicit weight bias among health professionals found that they held negative implicit attitudes about weight as well as implicit anti-fat attitudes associating “fat” with bad, lazy, stupid, and worthless (20, 27).  Hence, improving self-awareness of biases may help improve the communication between health professionals and overweight clients to achieve better outcomes.   

Kinesiology majors—students seeking to enter a fitness, coaching, physical educator, or healthcare career path— could play an important role in reducing anti-fat bias in their future careers. Previous studies have shown the presence of implicit anti-fat bias in pre-service physical education teachers (1, 18, 24). As a result, it would seem necessary to discuss the possibility of such feelings within future physical educators to help curtail the impact it may have in the actual kindergarten through twelfth grade (K-12) setting. A negative body image can have a detrimental impact on K-12 students in the physical education environment (13), so it is important to consider the views of future physical educators who will be an influencing factor on the general population and future kinesiology majors.  

In a study investigating implicit anti-fat bias among fitness professionals and regular exercisers, investigators reported that both fitness professionals and regular exercisers had a strong anti-fat bias. Furthermore, the authors stated that the bias was more pronounced for fitness professionals who had never been overweight themselves and who believed that personal control dictated body weight (23). In another study investigating the efficacy of a multi-component intervention to reduce kinesiology pre-professionals’ implicit and explicit anti-fat bias, authors reported that the participants’ strong implicit anti-fat bias remained unchanged after the intervention despite the reduction in the explicit bias (25). A similar strong implicit anti-fat bias was also reported by Sabin et al. (26) among physicians and non-physicians alike. Investigators reported that obese physicians as classified by BMI only had a moderate implicit anti-fat bias while physicians that were overweight, normal weight, or underweight had a strong implicit anti-fat bias; these results were also similar among non-physicians (26).

Since several studies have reported that an implicit anti-fat bias exists among the general population, healthcare professionals, fitness and education professionals, and kinesiology pre-professionals, the objective of this study was to examine the results of the Weight Implicit Association Test (WIAT) among students from a diverse urban university that is considered a Hispanic Serving Institution. Specifically, the relationships between the results of the WIAT were examined among kinesiology and non-kinesiology majors, gender, ethnicity, and race. Furthermore, the relationship between the results of the WIAT and the participants’ BMI, percentage of body fat, explicit feelings toward thin people, explicit feelings toward overweight people, their personal experiences with their body fat, and the perceptions of their body were examined. 

Since kinesiology students generally like to exercise and are potentially in better physical condition than other college students, it was hypothesized that kinesiology majors would have a stronger implicit anti-fat bias than non-kinesiology majors. Furthermore, it was hypothesized that those students with a lower percentage of body fat, a lower BMI, positive explicit feelings toward thin people, negative explicit feelings toward overweight people, positive experiences with controlling their body fat, and positive perceptions about their body would have a stronger anti-fat bias than those students with a higher percentage of body fat, a higher BMI, negative explicit feelings toward thin people, positive explicit feelings toward overweight people, difficult experiences with controlling their body fat, and negative perceptions about their body. No differences were hypothesized in anti-fat bias among gender, ethnicity, or race.  

METHODS

Data Collection

A total of 62 students of various disciplines from a diverse Hispanic serving university volunteered and completed this study; Table 1 displays their characteristics. Students were recruited to participate in this study for a period of 12 weeks via fliers posted around campus and via word of mouth. Volunteers went to the Human Performance Laboratory where they met a member of the research team for their appointment. Each participant read and signed an informed consent document approved by the university’s institutional review board. Next, participants were provided a computer station where they completed a questionnaire on Qualtrics.com that included items about themselves, their explicit feelings toward thin and overweight people, their personal experiences with gaining/losing weight, and their perceptions of their body. The survey was 41 questions in length, but after answering the first 30 items of the survey they were asked to move to another computer where they completed the WIAT. Upon completing the WIAT, the participants’ height, weight, BMI (as calculated as weight in kg ÷ m2), and percentage of body fat were measured by a member of the research team. Finally, the participants returned to the original survey computer station and answered the final 11 questions.   

Table 1. Participant Characteristics (N = 62)
  Kinesiology Majors Non-Kinesiology Majors Total
N 44 18 62
Age (y) 24.5 (4.67) 22.5 (4.54) 23.9 (4.7)
Race/Ethnicity
Hispanic 75.0% 66.7% 72.6%
White (Non-Hispanic) 9.1% 11.1% 9.7%
Multi-Race 2.3% 5.6% 3.2%
Asian 6.8% 5.6% 6.4%
Black 4.6% 5.6% 4.8%
Other 2.3% 5.6% 3.2%
Gender = Female 59.1% 55.6% 56.1%
Male BMI (kg·m-2) 25.8 (3.0) 26.8 (2.3) 26.1 (2.8)
Female BMI (kg·m-2) 24.5 (4.6) 25.2 (4.3) 24.3 (4.7)
BMI Categories
Normal (18.5 – 24.9 kg·m-2) 45.4% 50.0% 46.8%
Overweight/Obese (25.0 – 29.9/>30 kg·m-2) 54.6% 50.0% 53.2%
Male Body Fat (%) 16.8 (5.4) 21.5 (5.8) 18.3 (5.9)
Female Body Fat (%) 27.5 (7.1) 27.3 (7.5) 27.5 (7.1)
Body Fat Categories
Very lean/Excellent 20.5% 27.8% 22.6%
Good/Fair 11.4% 5.6% 9.7%
Poor/Very poor 68.2% 66.7% 67.7%
Implicit Bias: Strong Anti-Fat Bias 50.0% 33.3% 45.2%
Explicit Bias
None 43.2% 33.3% 40.3%
Slight Preference: Thin People 22.7% 22.2% 22.6%
Moderate Preference: Thin People 18.2% 22.2% 19.4%
Strong Preference: Thin People 9.1% 16.7% 11.3%
Preference: Overweight People 6.8% 5.6% 6.4%
Prior Struggle: Body Fat
Never Struggled 34.9% 33.3% 34.4%
Never Struggled because effort 20.9% 5.6% 16.4%
Struggled Since High School 14.0% 16.7% 14.8%
Struggled Since College 9.3% 16.7% 11.5%
Struggled Since (Younger age) 7.0% 16.7% 9.8%
Used to struggle 14.0% 11.1% 13.1%
Data reported as mean (SD) where applicable, BMI: body mass index

Implicit bias: The implicit association test 

The Implicit Association Test (IAT) is a widely used tool of implicit social cognition that measures relative association strengths between two pairs of concepts (9) that was first reported in the literature in 2001 by Teachman et al. (30). The IAT is a validated method of measuring automatic and subconscious attitudes and has satisfactory test-retest reliability (10, 12, 28). The IAT has been shown to capture evaluations that are related but different from self-report (12), have good reliability in comparison with other implicit methods (6, 17), and are relatively robust with repeated measures for pre-post evaluation (17). The web-based WIAT is a form of the original IAT that is designed to measure an individual’s attitudes and beliefs they may be unwilling or unable to report about thin versus overweight people.  Test takers are required to organize pictures of overweight and thin people and value laden words as they appear on a computer screen by pressing one of two computer keys. In one condition, the participants categorize “good” words with thin people and “bad” words with overweight people. In the second condition, the participants are asked to categorize “bad” words with thin people and “good” words with overweight people. The difference in the average response time between the two groups is an indicator of the relative association bias toward one group rather than the other. The possible results of the WIAT that an individual can receive after completing it are: 1) strong preference for fat people, 2) moderate preference for fat people, 3) slight preference for fat people, 4) No preference for thin or fat people, 5) slight preference for thin people, 6) moderate preference for thin people, 7) strong preference for thin people.  A frequency table outlining the results of the test are found in Table 2.    

Table 2. Detailed Frequency of Weight Implicit Association Test Results (N = 62)
  Kinesiology Majors Non-Kinesiology Majors Total %
Strong preference: overweight people 0 0 0 –
Moderate preference: overweight people 0 1 1 1.6%
Slight preference: overweight people 2 4 6 9.7%
No preference 5 2 7 11.3%
Slight preference: thin people 5 3 8 12.9%
Moderate preference: thin people 10 2 12 19.4%
Strong preference: thin people 22 6 27 45.2%

BMI and body fat

Each participant’s height, weight, and body composition were measured by a trained research assistant. Height was measured with a stadiometer and weight was measured with an electronic scale. Fat weight and fat free weight were estimated with a handheld bioelectrical impedance analysis device (Omron, Hoffman Estates, IL). After the research assistant entered the participant’s age, height, weight, and sex into the device, the participant was instructed to grip the testing handles of the device and hold it in front of the body while the device estimated body composition. ​BMI was calculated with the measurements gathered from height and weight. The categories for BMI were as follows: 18.5-24.9 kg·m-2 = Normal, 25.0-29.9 kg·m-2 = Overweight, ≥ 30.0 kg·m-2 = Obese (2). Since few students fell into the obese category (n=5), they were combined with the overweight category for the purpose of these analyses. The categories for body fat percentage were as follows for men ages 20-29 y: < 10.5% = Very lean/Excellent, 10.6 – 18.6% = Good/Fair, > 18.6% = Poor/Very Poor (2).  The categories for body fat percentage were as follows for women ages 20-29: < 16.8% = Very lean/Excellent, 16.9 – 23.4% = Good/Fair, > 23.4% = Poor/Very poor (2).

Explicit Bias

The participant’s explicit feelings toward thin and overweight people were measured by asking explicit questions about thin and overweight people in the survey. Specifically, participants were asked if they prefer thin people or fat people. The available responses were: 1) I strongly prefer thin people to fat people, 2) I moderately prefer thin people to fat people, 3) I slightly prefer thin people to fat people, 4) I do not prefer thin people more than fat people, 5) I slightly prefer fat people to thin people, 6) I moderately prefer fat people to thin people, 7) I strongly prefer fat people to thin people.  For the logistic regression, those that reported a “strongly prefer thin people to fat people” (42% of respondents) were coded as a “1”.   

Prior struggles with body fat

The participants’ experiences with their body fat was assessed by asking if they have ever struggled with their body fat in the survey. The available responses were: 1) I have never struggled with my body fat, 2) I have never struggled with my body fat only because I have always worked on it, 3) I have struggled with my body fat since high school and still struggle, 4) I have struggled with my body fat since college and still struggle, 5) I have struggled with my body fat since I was (specific younger age) and still struggle, 6) I used to struggle with my body fat but I have learned to manage it.   

Body perceptions

The participant’s perception of their body was assessed by asking, in two separate items, if they felt they could or should gain or lose weight and whether they thought other people would say they need to gain or lose weight in the survey. The available responses for the first question were: 1) I feel I could/should gain some body fat, 2) I feel no need to gain or lose body fat, 3) I feel I could/should lose 5 or less pounds of body fat, 4) I feel I could/should lose 6-15 pounds of body fat, 5) I feel I could/should lose 16-25 pounds of body fat, 7) I feel I could/should lose 26-49 pounds of body fat, 8) I feel I could/should lose 50+ pounds of body fat.  Similarly, the available answers to the second question were: 1) Others would say I could/should gain some body fat, 2) Others would say I have no need to gain or lose body fat, 3) Others would say I could/should lose 5 or less pounds of body fat, 4) Others would say I could/should lose 6-15 pounds of body fat, 5) Others would say I could/should lose 16-25 pounds of body fat, 7) Others would say I could/should lose 26-49 pounds of body fat, 8) Others would say I could/should lose 50+ pounds of body fat.    

Analytic strategy

Several statistical tests were performed to examine the relationships between the results of the WIAT and kinesiology versus non-kinesiology majors, and among gender, ethnicity, race, the participants’ BMI, percentage of body fat, explicit feelings toward thin people, explicit feelings toward overweight people, their personal experiences with their body fat, and the perceptions of their body. First, nine Chi-Square tests of independence between each of the following pairs of categorical variables were performed:  1. Academic major (kinesiology vs non-kinesiology) and the participant’s WIAT results, 2. Gender and the participant’s WIAT results, 3. Ethnicity and the participant’s WIAT results, 4. Race and the participant’s WIAT results, 5. BMI category (normal, overweight, obese) and the participant’s WIAT results, 6. Body fat percentage (Very lean/Excellent, Good/Fair, Poor/Very poor) and the participant’s WIAT results, 7. Explicit bias (for thin or fat people) and the participant’s WIAT results, 8. Personal experience with their body fat and the participant’s WIAT results, and 9. Perceptions of their body and the participant’s WIAT results.  Furthermore, two separate factorial ANOVAs were conducted to compare the main effects of gender and academic discipline and the interaction between gender and academic discipline on BMI and on body fat percentage. Finally, a logistic regression was estimated with “An implicit strong anti-fat bias” as the dependent variable.  For this regression, a strong anti-fat bias (45%) was dichotomized versus all other responses (see Table 2). In this regression model, gender, Hispanic ethnicity, BMI, explicit bias, past struggles with body fat, and whether or not the student was a Kinesiology major were controlled. All data were analyzed using SPSS version 24 (IBM, Chicago, IL). 

RESULTS AND DISCUSSION

No significant main effects or interaction between gender and academic discipline on BMI were found for the ANOVA’s. Regarding body composition, there was a significant main effect (F(1,3) = 19.80, p < 0.001) for gender where males had a lower percentage of body fat than females (18.3 ± 5.9% and 27.5 ± 7.1%, respectively). There was no significant main effect for academic discipline and body composition or interaction between gender and academic discipline on body composition. There was a significant relationship between the participants’ explicit bias and WIAT implicit bias. No significant relationships were found between the results of the participant’s WIAT and academic discipline, gender, ethnicity, race, BMI rank, body fat percentage rank, personal experience with their body fat, or the perceptions of their body. Table 2 displays the frequency of the results of the WIAT for kinesiology and non-kinesiology majors. 

Results from the logistic regression can be found in Table 3.  Results from this analysis did not find statistically significant relationships between a strong implicit anti-fat bias and either (a) demographic characteristics, (b) explicit bias, or (c) prior struggle with body fat.  After controlling for other independent variables, students that were overweight/obese (OR=2.69) or Kinesiology majors (OR=2.38) were estimated to have greater odds of a strong implicit bias, but these results were only significant at p < 0.20. 

Table 3. Odds Ratios of Having Strong Anti-Fat Bias (N = 62)
  OR (SE)
Female (ref: Male) 1.88 (1.53)
Hispanic (ref: Non-Hispanic) 0.90 (0.60)
BMI Overweight/Obese (ref: Normal) 2.69 (1.83)
Kinesiology Major (ref: Non-Kinesiology Major) 2.38 (1.53)
Explicit Bias (ref: None)    
Slight Preference: Thin 0.77 (0.56)
Moderate Preference: Thin 2.77 (2.19)
Strong Preference: Thin 1.19 (1.15)
Prefer Fat 0.18 (0.24)
Prior Struggle with Body Fat    
No Struggle (Reference) 1.00 (0.00)
Used to Struggle but worked on it 0.46 (0.33)
Has been and still is a struggle 0.87 (0.66)
AIC 96.8
N 62

The results of this study demonstrate that kinesiology and non-kinesiology majors alike have an anti-fat bias. Much like previous studies (27, 30), participants within the current study exhibited negative implicit anti-fat attitudes. Our findings are similar to results from larger samples of the general public who voluntarily complete the WIAT at the Project Implicit® website (26).  In addition, results of multiple studies examining bias in university students support the findings of the current study (18, 25). Specifically, O’Brien et al. (18) explored the implicit bias of 340 university students and found that physical education majors demonstrated higher levels of implicit anti-fat bias than psychology students and other health professionals.

Our data suggest that the presence of implicit and explicit weight bias is present irrespective of academic discipline, gender, ethnicity, race, BMI, body fat percentage, personal experience with their body fat, or the perceptions of their body. Miller et al. (16) reported similar results for gender and race in a large cohort of medical students. Sabin et al. (26) assessed the implicit and explicit weight bias of medical doctors (N = 2,284) across a five-year span. Their results demonstrated a strong implicit bias towards thin individuals regardless of gender and BMI—except for those who were classified as obese. Caucasian and Hispanic medical doctors demonstrated a significant weight bias toward overweight individuals, but African Americans and Asians did not. Although this disagrees with our results, it might be due to the smaller sample size and lack of heterogeneity of our population. Others demonstrated that weight stigmatization was prevalent among all participants irrespective of gender, ethnicity, and BMI (14). Of note, we are the first group to document the weight bias of a primarily Hispanic study cohort. The aforementioned investigations were composed primarily of non-Hispanic White and African American participants, with only a small percentage of the study population being composed of Hispanics. Considering the high rates of obesity among the Hispanic population, more studies should investigate the weight bias of this group.

These results may point to weaknesses in the educational system in that it might neglect to challenge such biases. Many kinesiology students continue on career paths to become fitness professionals, healthcare providers, coaches, or physical educators; many are also currently regular exercisers. The anti-fat bias that Robertson et al. (23) reported among fitness professionals and regular exercisers is comparable to the participants of this study. This may point towards many of these kinesiology students maintaining their biases into their professional careers unless some intervention is successfully implemented.

Explicit and implicit bias extends beyond the university setting, which would seem to suggest that such bias may be deeply rooted, especially for those whom one would assume that many years of education would help to minimize its prevalence. A large sample of medical doctors (N = 2,284) and individuals from the lay population (N = 359,261) voluntarily accessed Project Implicit® to complete the WIAT. Results uncovered a strong implicit anti-fat bias among medical doctors (Cohen’s d = 0.93) and the lay population (Cohen’s d = 1.0) alike (26). In addition, all test takers preferred thin individuals over fat people, which indicates an explicit anti-fat bias (26). These results are similar to that of Teachman et al. (30) who reported that healthcare specialists have negative associations toward obese people. Sabin et al. (26) found that the more negative associations came from the population of doctors that were not obese according to their BMI. 

Both explicit and implicit anti-fat bias need to be challenged within universities. In particular, universities with kinesiology programs need to be aware of anti-fat bias since many graduates will be required to work with individuals who are overweight or obese. In programs such as physical education teacher education, not only is it necessary to educate future teachers about their potential implicit and explicit bias, but it is also valuable to discuss the impact of perceived or explicit bias among primary school students because of the role they play in attitudes toward overweight individuals (21).  Such attitudes must be challenged because physical education should provide an accepting environment that encourages all to be active without fear of teacher or peer judgement. While Rukavina et al. (25) suggests that implicit weight bias is difficult to change, multiple studies have explored weight bias and have produced some evidence to suggest that purposeful education on the various causes of obesity—such as genetics, hormones, and socioeconomic status, among other reasons, may reduce anti-fat bias (5,  7, 11, 22).

Future research should focus on interventions designed to reduce weight bias among university students, especially those who enter careers that focus on promoting physical activity, healthcare, and education. Additional information on the impact of metacognition in having students challenge their own biases and consciousness-raising of obesity bias—i.e., making students aware of the impact of obesity bias (4)—may also provide valuable information for university faculty. Future research may also encompass interventions that require students to examine their own biases. Education on the various reasons for overweight and obesity should be addressed in all programs. In addition, communication strategies should be discussed to educate kinesiology students on how to support individuals (clients, primary school students, patients, etc.) who struggle with exercise and physical activity settings due to their own negative body image issues. 

Although this study is the first that the researchers are aware of that (a) examines implicit weight bias among university students from a Hispanic Serving Institution of various majors and (b) explores the implications of implicit anti-fat bias among kinesiology students, this project has four important limitations. The first limitation is the small sample size. Although this study was conducted over a 12-week period and was advertised across the university campus, only 62 students volunteered to participate in this investigation; this might be indicative of the lack of interest in this subject matter among university students. The second limitation is that the study only examined implicit attitudes at a diverse university — a regional, mostly-commuter, university that has the demographics of a Hispanic Serving Institution (predominantly first-generation college students). The third limitation is the use of a volunteer sample accompanied by self-selection bias despite the fliers for the study being displayed equitably across the university campus. For example, the demographics characteristics clearly indicate that Kinesiology students (70% of the participants) are much more likely to volunteer for studies about BMI, body fat, and implicit weight bias when compared to non-Kinesiology students. Regardless, this selection bias was at least partially ameliorated by the fact that the percentage of female students (58%) and Hispanic students (72%) are similar to those at the general university population (61% and 64%, respectively).  Lastly, the use of the WIAT test is accompanied by the limitation that the test does not provide information about actual behavior toward overweight individuals. Hence, it cannot be concluded that an implicit anti-fat bias will lead to poor treatment of overweight individuals.

CONCLUSION

Results from this investigation demonstrate an implicit anti-fat bias exists among the college students surveyed regardless of academic discipline, gender, ethnicity, race, body fat levels, BMI, explicit anti-fat bias, prior struggles with body fat, or perceptions of their body. In agreement with other studies, society in general appears to have an implicit anti-fat bias. Although it is difficult to draw a direct relationship between having an implicit anti-fat bias and treating overweight people poorly, studies have suggested that people with a strong implicit anti-fat bias may treat overweight people differently than they do thin people. Since research indicates that negative treatment toward overweight people negatively affects their self-esteem and their ability to lose excess body fat, it is imperative that people working with the overweight population become aware of their potential implicit and explicit anti-fat biases. If they can be conscientious of their biases, they may try to treat overweight people in the same manner that they treat thin people.        

APPLICATION IN SPORT

This study has a direct impact on all members of the higher education, healthcare, fitness, and physical activity communities. This study demonstrates that weight bias is prevalent amongst a diverse population and may be indicative of a greater societal problem yet to be addressed. Future research should expand the population sample size and include more students from different ethnicities and/or nationalities. Moreover, future studies could include students from different class standings (e.g. undergraduate and graduate)—comparing the rates of implicit and explicit bias among them.  Current physical educators, healthcare professionals, fitness professionals, sport coaches, and university faculty preparing students for these professions must begin to take the steps necessary to eliminate weight bias from their environments. The authors recommend that all members of the aforementioned communities develop an understanding of the factors that may lead to weight gain and develop strategies of encouraging overweight individuals to reduce their weight without further perpetuating weight stigma.

ACKNOWLEDGEMENTS

None

REFERENCES

  1. Alameda, M. W., & Whitehead, J. R. (2015). Comparing levels of anti-fat bias between American and Mexican athletes and undergraduate physical education and exercise science students. The Physical Educator, 72, 1-22.
  2. American College of Sports Medicine. (2009). ACSM Guidelines for Exercise Testing and Prescription (8th ed., pp. 71-72). Philadelphia, MA: Lippincott Williams Wilkins.
  3. Andreyeva, T., Puhl,  R. M., & Brownell, K. D. (2004). Changes in perceived weight discrimination among Americans, 1995-1996 through 2004-2006. Obesity, 16, 1129-1134. 
  4. Chambliss, H. O., Finley, C. E., & Blair, S. N. (2004). Attitudes toward obese individuals among exercise science students. Medicine and Science in Sports and Exercise, 36, 468-474.
  5. Crandall, D. P. (2004). Knowing human moral knowledge to be true: An essay on intellectual conviction. Journal of the Royal Anthropological Institute,10, 307-326.
  6. Cunningham, W. A., Preacher, K. J., & Banaji, M. H. (2001). Implicit attitude measures: Consistency, stability, and convergent validity. Psychological Science, 12, 163-170. 
  7. Diedricks, P. C., & Barlow, F. K. (2011). How to lose weight bias fast! Evaluating a brief anti weight bias intervention. British Journal of Health Psychology, 16, 846-861.
  8. Fardouly, J., & Vartanian, L.R. (2012). Changes in weight bias following weight loss: The impact of weight-loss method. International Journal of Obesity, 36, 314–319.
  9. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. (1998) Measuring individual differences in Implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74, 1464-1480. 
  10. Greenwald, A.G., Poehlman, T.A., Uhlmann, E.L., & Banaji, M.R. (2009). Understanding and using the implicit association test: III. Meta‐analysis of predictive validity. J Pers Soc Psychol, 97,17‐41.
  11. Hague, A. L., & White, A. A. (2005). Web-based intervention for changing attitudes of obesity among current and future teachers. Journal of Nutrition Education and Behavior, 37, 58-66.
  12. Hofmann, W., Gawronski, B., Gschwender, T., Le, H., & Schmitt, M. (2005). A meta-analysis on the correlation between the implicit association test and explicit self-report measures Personal and Social Psychology Bulletin, 31, 1369-1385. 
  13. Kerner, C., Haerens, L., & Kirk, D. (2017). Understanding body image in physical education: Current knowledge and future directions. European Physical Education Review. 24, 255-265.
  14. Latner, J. D., Stunkard, A. J. and Wilson, G. T. (2005), Stigmatized Students: Age, Sex, and Ethnicity Effects in the Stigmatization of Obesity. Obesity Research, 13: 1226-1231. doi:10.1038/oby.2005.145
  15. Mattingly, B. A., Stambush, M. A. and Hill, A. E. (2009). Shedding the Pounds but not the Stigma: Negative Attributions as a Function of a Target’s Method of Weight Loss. Journal of Applied Biobehavioral Research, 14, 128-144. doi:10.1111/j.1751-9861.2009.00045.x.
  16. Miller, D. P., Jr, Spangler, J. G., Vitolins, M. Z., Davis, S. W., Ip, E. H., Marion, G. S., & Crandall, S. J. (2013). Are medical students aware of their anti-obesity bias? Academic medicine: journal of the Association of American Medical Colleges, 88(7), 978–982. doi:10.1097/ACM.0b013e318294f817
  17. Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2007). The implicit association test at age 7: A methodical and conceptual review. In: J. A. Bargh (Eds.), Automatic processes in social thinking and behavior (pp. 265-292). New York: Psychology Press.
  18. O’Brien, K. S., Hunter, J. A., & Banks, M. (2007). Implicit anti-fat bias in physical educators: Physical attributes, ideology and socialization. International Journal of Obesity, 31, 308-314.
  19. Phelan, S. M., Dovidio, J. F., Puhl, R. M., Burgess, D. J., Nelson, D. B., Yeazel, M. W., Hardeman, R. , Perry, S. and Ryn, M. (2014), Implicit and explicit weight bias in a national sample of 4,732 medical students: The medical student CHANGES study. Obesity, 22, 1201-1208. doi:10.1002/oby.20687
  20. Puhl, R. M., & Heuer, C. A. (2010). Obesity stigma: Important considerations for public health. American Journal of Public Health, 100, 1019–1028. 
  21. Puhl, R. M., & Heuer, C, A. (2011). Public opinion about laws to prohibit weight discrimination in the United States. Obesity, 19, 74-82. 
  22. Puhl, R. M., Schwartz, M. B., & Brownell, K. D. (2005). Impact of perceived consensus on Stereotypes about obese people: A new approach for reducing bias. Health Psychology, 24, 517-525.
  23. Robertson, N., & Vohora, R. (2008). Fitness vs. fatness: Implicit bias towards obesity among fitness professionals and regular exercisers. Psychology of Sport and Exercise, 9, 547-557.
  24. Readdy, T., & Wallhead, T. L. (2016). Manifestation of anti-fat bias in preservice physical education teachers. The Physical Educator, 73, 450-470.
  25. Rukavina, P. B., Li, W., Shen, B., & Sun, H. (2010). A service learning based project to change implicit and explicit bias toward obese individuals in kinesiology pre-professionals. Obesity Facts, 3, 117-126.
  26. Sabin, J. A., Maddalena, M., & Nosek, B. A. (2012). Implicit and explicit anti-fat bias among a large sample of medical doctors by BMI, race/ethnicity, and gender. PLOS One, 7(11),      https://doi.org/10.1371/journal.pone.0048448
  27. Schwartz, M.B., Chambliss, H.O., Brownell, K.D., Blair, S.N., & Billington, C. (2003). Weight bias among health professionals specializing in obesity. Obesity Research, 11(9), 1033-9.
  28. Schmukle, S. C., & Egloff, B. (2005). A latent state-trait analysis of implicit and explicit personality measures. European Journal of Psychological Assessment, 21(2), 100–107.
  29. Sutin, A. R., & Terracciano, A. (2013). Perceived weight discrimination and obesity. PLoS One, 8(7), https://doi.org/10.1371/journal.pone.0070048.
  30. Teachman, B. A., & Brownell, K. D. (2001). Implicit anti-fat bias among health professionals: Is anyone immune? International Journal of Obesity, 25(10), 1525-1531. 
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