Ready to treat patients with obesity? Evaluation of undergraduate students’ body image, disordered eating attitudes & behaviors, and anti-fat attitudes

Abstract

Weight bias exerts an adverse impact on the overall health and well-being. This study examined dieting behaviors, the effect of the BMI chart in one’s body perceptions by using the photographic figure rating scale, and explicit weight bias among university students (N = 192) in different career-focused disciplines such as allied health programs. Participants tended to select larger photographic figures when asked to choose an image that represents a healthy, an overweight, and an obesity figure without viewing the BMI chart. Results showed no significant differences in anti-fat attitudes between different career-focused groups. While gender played a significant role in anti-fat attitudes, weight status influenced participants’ fear of becoming fat. To a similar degree across career paths, weight bias might evenly exist. Using the BMI chart to determine one’s weight status might create inconsistent body perceptions, which may lead to weight stigmatized attitudes toward individuals labeled as overweight or obesity.

Keywords:

• weight bias
• body perceptions
• body image
• BMI

PUBLIC INTEREST STATEMENT

Weight bias, which is a form of discrimination based on one’s body weight, shape or type, may give an adverse impact on the overall health and well-being. To eliminate weight bias among health professionals, we examined dieting behaviors, the effect of the BMI chart in one’s body perceptions by using the photographic figure rating scale, and explicit weight bias among college students in different career-focused disciplines. Our results showed no significant differences in anti-fat attitudes between different career-focused groups. But gender played a significant role in anti-fat attitudes. Also, weight status influenced participants’ fear of becoming fat. Lastly, our results found using the BMI chart to determine one’s weight status might create inconsistent body perceptions, which may lead to weight stigmatized attitudes toward individuals labeled as overweight or obese.

1. Introduction

Weight bias, defined as stigmatizing or discriminatory views based on body weight and appearance, (Forhan & Salas, 2013; Pearl & Puhl, 2016; Phelan et al., 2014; Sutin et al., 2016) has been a risk factor of the weight-related health issues such as obesity (Ehlert et al., 2015; Forhan & Salas, 2013; Phelan et al., 2015; Schwartz et al., 2003). One of the significant concerns related to weight bias is a lack of respect and care toward patients with overweight or obesity among healthcare providers including clinicians specialized in eating disorders (Carels et al., 2013; Garcia et al., 2016; Phelan et al., 2014, 2015; Puhl et al., 2014). Weight bias in the healthcare field is especially detrimental to patient care quality, and it leads to inequalities (Tomiyama et al., 2018). For example, physicians with weight-biased attitudes tend to be more rushed, less thorough, and share few resources with their patients with obesity (Forhan & Salas, 2013). Thus, if patients are treated poorly and belittled by their physicians because of their weight or size, they are more likely to discontinue follow-up visits (Drury & Louis, 2002; Tomiyama et al., 2013). This disconnection between healthcare providers and patients can be one obstacle to prevent and treat any chronic disease conditions such as heart disease and diabetes. Furthermore, there is an undeniable fact that weight bias is linked to the current obesity epidemic, (Forhan & Salas, 2013) and potentially disordered eating attitudes and behaviors, (Puhl & Suh, 2015) which are highly prevalent among college students in the United States (American College Health Association, 2018).

A previous study conducted by Puhl et al. (2017) revealed that over 90% of women with obesity in their study agreed on “high importance” to address weight bias in health care settings, and 79% of those believed that health professionals might play a significant role in reducing weight bias. A study conducted by Phelan et al. (2015) portrayed medical schools’ culture around the nation and revealed that their medical students possessed stigmatizing attitudes toward patients with overweight or obesity. Thus, a few studies have suggested providing more education on weight bias in health professionals, including medical students and public health practitioners (Poustchi, Saks, Piasecki, Hahn, Ferrante et al., 2013a; Puhl et al., 2017; Swift, Choi et al., 2013; Mc Vey et al., 2013). As (Pearl & Phul, 2018) ^ʹs systematic review article points out, although mixed results might exist, there may be some strategies to make an effective shift by emphasizing that weight is an uncontrollable factor. To respond to the obesity epidemic, the medical community has focused on the medical nutrition education reform (Cooke et al., 2017), an evidence-based strategy, and more beneficial to patients with any medical conditions rather than focusing on weight. Nevertheless, this reform itself is less likely to reduce weight bias among medical students if it does not address weight bias directly.

To seek what medical school training and experiences are related to reducing anti-fat attitudes among medical students, (Meadows et al., 2017) monitored the changes in weight bias attitudes among 3,576 students enrolled across 49 medical schools in the United States first four years of their medical school. Results found that prejudice-reduction and empathy-based training effects varied by those medical students’ traits, and more egalitarian and emphatic students may respond to the training designed to increase empathy toward patients with obesity more effectively (Meadows et al., 2017). Simultaneously, the study indicated how complex it was for medical schools to address and reduce weight bias during the busy curriculums.

Allied-health graduate programs must incorporate training curricula to reduce healthcare provider’s weight bias. However, allied-health undergraduate programs may also develop robust curricula for allied-health students to increase empathic and egalitarian attitudes and behaviors, especially toward patients with overweight or obesity, before college students enter allied-health graduate programs. Body dissatisfaction among college students is another concern that may play a significant role in addressing weight bias and stigma. Simply, believing the thin-ideal and anti-weight bias attitudes rarely co-exist for better outcomes (Lipson et al., 2017; Pearl & Phul, 2018; Puhl et al., 2014; Puhl & Suh, 2015).

Lipson et al. (2017) reported significant symptoms of eating disorders or increased weight concerns among roughly one in three college students in their study, who also had not sought any treatment for their eating disorders or weight concerns with trained professionals. Before proposing weight bias training, a first step may be to focus on body image. Furthermore, college students are typically aware that using BMI is not an accurate health measurement. However, many of them still feel dissatisfied with their body because of their weight or body mass index (BMI) Lipson et al. (2017). To provide effective medical consultation, healthcare provides must feel comfortable discussing weight-related issues with positive attitudes. Thus, it is crucial for medical schools allied-health undergraduate programs to provide meaningful curricula to improve students’ body image and address stigmatized attitudes toward people with obesity.

There are no particular curriculum guidelines for allied-health undergraduate programs to address body image and weight bias issues in the United States. To explore teaching strategies and subjects for current allied-health undergraduate curricula, we conducted this study to examine undergraduate students’ perceptions of weight and health and disordered eating attitudes and behaviors and compare current allied-health students’ weight bias attitudes toward people with obesity to non-allied-health students in a university setting. Our first aim was to assess gender comparisons of undergraduate students’ body perceptions, health perceptions, and disordered eating attitudes and behaviors. We expected to see reasonably similar distributions of body weight self-perception and health perception responses between males and females. However, we predicted more disordered eating attitudes and behaviors among female participants. Our second aim was to explore how the BMI chart changed participants’ responses to the photographic images in response to questions related to different body types (e.g., underweight, overweight). We made no a priori predictions about the second aim. Lastly, we examined whether gender, weight status (i.e., BMI), and allied-health programs vs. non-allied health programs predicted levels of anti-fat attitudes. We predicted participants’ BMI and gender would be a predictor of anti-fat attitudes, but we made no a priori predictions about the relationship between participants’ program status (allied-health programs vs. non-allied health) and anti-fat attitudes.

2. Methods

2.1. Participants and procedure

This cross-sectional study recruited undergraduate students ages 18–65 years of male and female and a diverse collection of degrees and professional pursuits at a public university located in the United States’ Rocky Mountain region. Upon receiving our institutional review board approval, we recruited participants via emails sent directly to professors who then redistributed our study recruitment message through online or in-class announcements. All participants were given the informed consent form before their beginning a Qualtrics survey. After completion of the survey, participants had the option to enter their name and email into 20 drawings for $10 gift cards in a link that was unattached to the student’s answers.

2.2. Instruments

Self-reported demographic questions included age, gender, race/ethnicity, BMI calculated based on self-reported height and weight, marital status, college year, and career goals (e.g., allied-health, health education, business, engineer). From the National College Health Assessment (NCHA) survey instrument developed by the American College Health Association (2018), body weight self-perceptions (e.g., “How do you describe your weight?”), weight management goals (e.g., “Are you trying to lose weight?”), health status (e.g., “How would you describe your general health?”), and weight management strategies (e.g., “Within the last 30 days, did you exercise to lose weight?”) questions were added to understand participants’ general health beliefs weight loss behaviors.

The Photographic Figure Rating Scale (PFRS) Swami et al. (2008) was used because of its accuracy in depicting morphological body changes in persons with different BMI categories. The scale features ten female photographed figures aligned with a BMI chart, ranging from BMI 12.51 (underweight) to BMI 41.23 (obesity). All of the images were captured at a standard distance, in a set pose, clothed in gray leotards and leggings, with all facial images obscured to avoid distraction by facial cues. An initial examination of the PFRS revealed significant test-retest values after three weeks in the following variables: current self-ratings, r = .90, p < .001; ideal body size ratings, r = .88, p < .001; and body dissatisfaction scores, r = .85, p < .001 21 Swami et al., 2008). In addition, (Gardner & Brown, 2010) has shown the highest validity and reliability for the PFRS through their study investigating perceptual female body image through figural drawing scales.

Explicit weight bias was assessed with the 13-item Crandall’s Anti-Fat Attitudes Scale (AFA) Crandall (1994) to determine participants’ attitudes toward individuals in the overweight or obesity category. The AFA scale with a 10-likert scale (0; strongly agree to 9; strongly disagree) examines one’s views toward individuals with obesity in three different categories: Fear of Fat (e.g., “I worry about becoming fat.”), Willpower (e.g., lack of power, “Some people are fat because they have no willpower.”), and Dislike (e.g., “I really don’t like fat people much”). Cronbach’s alphas for subscales in the current study were .86, .72, and .82, respectively.

2.3. Data analysis

All demographic variables, BMI categories, body weight perceptions, weight management goals, health status, weight management strategies, the PFRS scores, and the AFA subscale scores were tabulated in frequencies. Paired-samples t-tests were used to determine whether there were any statistically significant differences between participants’ perceptual body image when they responded to the PFRS scale with and without the BMI chart. To examine how gender, career goals (allied-health, health education, other), and BMI associated with different types of explicit weight bias, a series of separate linear regression analyses were also performed on the AFA subscales. All statistical analyses were conducted in SPSS version 20.0. We assessed statistical significances at a p level of less than 0.05.

3. Results

There were 199 participants, and seven incomplete surveys were excluded (3.5%). Table 1 illustrates participants’ demographic information. A majority of the participants were white (86%). Approximately 80 % were either third or fourth year of college students, and 69% of the participants were women. There were seven career paths available to choose from on the survey. For data analysis, we sorted out those options into three categories: patient care (n = 77), community health education (n = 74), and other (n = 41). One unique demographic factor found in our study population was that 38% of participants were married. Using suggested cut-off points for defining weight status from the (Centers for Disease Control and Prevention, 2016) the weight distribution of the participants included 5% who were underweight, 58% who were normal weight, 21% participants with overweight, and 15% participants with obesity.

Table 1. Demographic information

Variables	Male (n = 59)	Female (n = 133)
Age, M (SD)	24.9 (6.47)	23.3 (5.93)
Race/ethnicity, n (%)
White	49 (84.5)	116 (87.2)
School Year, n (%)
1^st year	6 (10.2)	13 (10.0)
2^nd year	9 (15.3)	23 (17.2)
3^rd year	16 (27.1)	38 (28.5)
4^th year	28 (47.4)	59 (44.3)
Marital status, n (%)
Married	26 (44.1)	46 (34.6)
Single, never married	33 (55.9)	83 (62.4)
Single, divorced	0	4 (3)
Career Goals, n (%)
Allied Health	24 (40.7)	53 (39.8)
Health Education	17 (28.8)	57 (42.9)
Other	18 (30.5)	23 (17.3)
Body mass Index, M (SD)	24.92 (6.47)	23.26 (5.93)
Weight Status, n (%)
Underweight (< 18.5)	1 (2.0)	9 (6.8)
Normal (18.5–24.9)	33 (56.9)	79 (59.9)
Overweight (25–29.9)	14 (24.1)	26 (19.7)
Obese (> 30)	10 (17.0)	18 (13.6)

Body weight self-perception, weight management goal, health status, and weight management strategy questions helped us understand participants’ views on self-perceived weight and appearance (see, Table 2). Participants were asked to describe how they felt about their weight and if they felt that they were “very/slightly underweight” (7.8%), “about the right weight” (60.9%), or “very/slightly overweight” (31.3%). Participants’ responses to the question, “Which of the following are you trying to do about your weight?”, were as follows: 46.9% reported that they were trying to lose weight; 9.9% indicated that they were trying to gain weight; 22.4% that they were trying to stay the same weight, and 20.8% were not trying to do anything about their weight. While more female participants reported statistically higher desire to lose weight (53.4%), male participants (25.4%) showed more desire to gain weight compared to female participants (3.0%). When asked how they would rate their health, approximately 92% of participants reported their health was either “excellent,” “very good” or “good.”

Table 2. Percentage of participants’ body weight perception, health perception, weight management goals, and weight management strategies, by sex

	Total	Male	Female
Body weight self-perception: n (%)
Very/slightly underweight	15 (7.8)	8 (13.5)	7 (5.3)
About the right weight	117 (60.9)	35 (59.3)	82 (61.7)
Very/slightly overweight	60 (31.3)	16 (27.2)	44 (33.0)
Weight management goals: n (%)*
Desire to lose weight	90 (46.9)	19 (32.2)	71 (53.4)
Desire to gain weight	19 (9.9)	15 (25.4)	4 (3.0)
Desire to maintain the current weight	43 (22.4)	12 (20.3)	31 (23.3)
No desire to do anything about weight	40 (20.8)	13 (22.0)	27 (20.3)
Health Status: n (%)
Excellent/very good/good	176 (91.7)	57 (96.6)	119 (91.7)
Fair	14 (7.3)	2 (3.4)	12 (7.3)
Poor	2 (1)	0 (0)	2 (1)
Weight management strategies: n (%)
Exercise to control weight*	122 (63.9)	27 (45.8)	95 (72.0)
Restricting food intake	105 (54.7)	28 (47.5)	77 (57.9)
Fasting > 24 hours	9 (4.7)	5 (8.5)	4 (3.1)
Using diet pills, powders, or liquids to control weight	8 (4.2)	5 (8.5)	3 (2.3)
Vomiting or taking laxatives to control weight	0 (0)	2 (1.5)	2 (1.0)

* = p < .001

A series of weight management strategy questions asked participants about their dieting, exercise, and weight loss behaviors in the past 30 days. The results showed 63.9% of participants had exercised to lose weight or keep from gaining weight, and female participants (72%) reported statistically higher rates than male participants (45.8%). Furthermore, 54.7% had restricted calories, consumed fewer calories, or avoided foods high in fat to avoid gaining weight. Fewer than 0.1% of participants had either gone 24 hours without eating to avoid weight gain, taken diet pills, powders, or other liquids without a doctor’s advice to lose weight or keep from gaining weight or vomited or taken laxatives to lose weight or keep from gaining weight.

To explore whether the BMI chart affects participants’ perceptions toward figures, our participants were asked to view ten photographed female figures and rate the most representative images in response to the following five questions twice; 1) a healthy figure, 2) a figure preferred by the opposite gender, 3) an underweight figure, 4) an overweight figure and 5) an obesity figure. First, they responded to the five questions without viewing the BMI chart (T1). Then, we showed the participants the same photographed female figures having the corresponding BMI chart again (T2). We used paired sample t-tests to assess a discrepancy in participants’ responses between T1 and T2 (Table 3). Paired t-tests for questions 2) a figure preferred by the opposite gender and 3) an underweight figure showed no significant difference. All of the other questions, 1) a healthy figure (t = 4.87, df = 190, p < 0.001, Cohen’s d = .36), 4) an overweight figure (t = 7.71, df = 191, p < 0.001, Cohen’s d = .56), and 5) an obesity figure (t = 5.70, df = 190, p < 0.001, Cohen’s d = .31) were significantly different.

Table 3. Mean comparisons (paired samples t-tests) of the PFRS rate^a with & without the BMI table

PFRS
	W/O BMI Chart M (SD)	With BMI Chart M (SD	t	Df
Q1. Which figure best represents a healthy figure?	4.67 (1.01)	4.35 (.77)	4.87***	190
Q2. Which figure would be preferred by the opposite gender?	3.55 (1.07)	3.61 (.97)	−1.04	191
Q3. Which figures associate with underweight?	1.66 (.43)	1.67 (.53)	−1.33	191
Q4. Which figures associate with overweight?	8.47 (.86)	7.92 (1.09)	7.71***	191
Q5. Which figures associate with obesity?	9.48 (.63)	9.27 (.70)	5.70***	190

* = p < .05, ** = p < .01, *** = p < .001

^aFigure scale rate 1 (BMI12-14), 2 (BMI 15–17), 3 (BMI 18–20), 4 (BMI 21–23), 5 (BMI 24–26), 6 (BMI 27–29), 7 (BMI 30–32), 8 (BMI 33–35), BMI (36–38).

The AFA subscales were used to examine participants’ explicit weight bias toward individuals with obesity. Table 4 summaries all of the mean scores on the subscales in male and female participants. Although there were no statistically significances between males’ and females’ scores on the subscales, mean scores for the Fear of Fat subscale on the AFA were higher among female participants (M = 6.97, SD = 2.31) compared to males (M = 4.76, SD = 2.56). On the other hand, male participants’ mean scores for other two subscales, Willpower (M = 6.79, SD = 1.69) and Dislike (M = 2.50, SD = .96), were higher than female participants’ scores (M = 6.41, SD = 1.66; M = 2.23, SD = .90, respectively). To examine how gender, career paths, and weight status predict the subscales of anti-fat attitudes, we performed a series of separate linear regression analyses (see, Table 5). Models for Fear of Fat and Dislike subscales were statistically significant (F (3, 180) = 19.590, p < .001, Adjusted R² = .234; F (3, 164) = 2.89, p < .05, Adjusted R² = .03, respectively). For the Fear of Fat subscale, gender and weight status added statistically significant to the prediction, p < .001. Gender (p < .05) was the only significant predictor for the Dislike subscale.

Table 4. Means and standard deviations from the anti-fat attitudes scale

AFA	Male M (SD)	Female M (SD)	Total M (SD)
Fear of Fat Willpower Dislike	4.76 (2.56) 6.79 (1.69) 2.50 (.96)	6.97 (2.31) 6.41 (1.66) 2.23 (.90)	6.30 (2.59) 6.52 (1.67) 2.31 (.92)

Table 5. Summary of linear regressions predicting anti-fat attitudes

AFA Fear of Fat (n = 180)	B	SE B	β	p
BMI	.16	.04	.30	<.001
Gender	2.50	.37	.44	<.001
Allied-health vs. Non-allied-health	.06	.06	.06	.34
Adjusted R^2		0.234
F		19.590***
AFA Will Power (n = 179)	B	SE B	β	p
BMI	.01	.03	.02	.76
Gender	−.54	.26	−.15	< .05
Allied-health vs. Non-allied-health	−.02	.04	−0.31	.65
Adjusted R^2		.009
F		1.577
AFA Dislike (n = 164)	B	SE B	β	p
BMI	−.02	.01	−.10	.21
Gender	−.37	.14	−.20	< .05
Allied-health vs. Non-allied-health	−.02	.02	−.08	.32
Adjusted R^2		.03*
F		2.89

* = p < .05, ** = p < .01, *** = p < .001

4. Discussion

This study examined disordered attitudes and behaviors, health and weight perceptions, the BMI chart’s effect on participants’ body perceptions toward different body types, and explicit weight bias among college students in different career disciplines. Overall, the weight status distribution illustrated by self-reported BMI and body perception scores among participants indicated that most of them were in a “normal” or “healthy” weight range based on the BMI chart. Ninety-nine percent of the participants also reported their health status as fair, good, very good, or excellent. However, our results showed clear evidence of body dissatisfaction and misconception of the association between weight and health and disordered eating attitudes among the participants. Also, our study participants tended to identify larger photographic figures when asked to choose the most representative of body types for healthy, overweight, and obesity without viewing the BMI chart. Since healthcare workers often make negative assumptions toward patients based on weight status or BMI data, this finding may suggest healthcare workers should intentionally avoid looking at patient’s BMI data before they enter an exam room. Also, this routine may remind healthcare workers to reduce weight bias. Lastly, our results indicated there were no differences in explicit weight bias in the three different career goals. While gender played a significant role in explicit weight bias (Hayran et al., 2013), weight status predicted participants’ fear of becoming fat (Phul et al., 2017).

To echo previous studies, (American College Health Association 2018; Schaumberg et al., 2014) participants in our study tended to view their weight negatively. Although approximately 99% of the participants perceived their health status as fair, good, very good, or excellent, 46% reported that they were trying to lose weight. As predicted, female participants (53.4%) had a higher desire to lose weight than males (32.2%). On the other hand, male participants (25.4%) showed more desire to gain weight than female participants (3%). These findings show that the cultural norms of female’s thin-ideal and muscularity for males are consistent with previous studies (Grossbard et al., 2011; Hayran et al., 2013).

Furthermore, 63.5% of the participants in our study used exercise to control their weight (e.g., lose weight or avoid weight gain), and 54.7% restricted calories, consumed fewer calories, or eliminated foods high in fat to avoid gaining weight. These typical dieting behaviors seem to be more socially acceptable in American culture. In particular, more people, including our participants, may misuse exercise to control weight for better appearance (Schaumberg et al., 2014). Because our culture typically views exercise as a healthy behavior, people may receive compliments even when they engage in compulsive or excessive exercise. Moreover, a recent article indicated that 31% to 81% of patients with eating disorders (e.g., anorexia nervosa, bulimia nervosa) showed dysfunctional exercise as a part of their eating disorder behaviors (Schlegl et al., 2018). It is crucial to address compulsive or excessive exercise behavior in general populations as a prevention effort.

Interestingly, while participants seemed to understand the numerical cutoffs for categorizing BMI ranges, they tended to choose slightly larger photographic figures when asked to select images representing healthy, overweight, and obesity body types without the BMI chart. These results illustrated that the BMI chart played a unique role in changing participants’ body perceptions toward photographed images for the healthy, overweight, and obesity body types. As publicly discussed in recent years, there have been many controversial arguments over BMI’s role as a health measurement tool to define one’s health (Tomiyama et al., 2016). From a different viewpoint, our findings also raised some concerns about using the BMI chart to label individuals in various weight categories among health professionals. Therefore, our results suggested that health professionals should discuss patients’ health status without focusing on their weight status to reduce weight bias.

As previous studies showed weight bias existed among health professionals in healthcare, (Garcia et al., 2016; Phelan et al., 2014, 2015; Puhl et al., 2014) our study participants also illustrated weight bias attitudes. Although there were no different levels of explicit weight bias between the three career goal groups (i.e., allied-health, health education, other), levels of explicit weight bias among our participants were undoubtedly concerning. Female participants notably demonstrated higher levels of fear of being fat. On the other hand, although our results indicated that more male participants assumed a lack of willpower was the primary problem among individuals with obesity, female participants scored higher on willpower. From these results, regardless of educational backgrounds/career focuses or gender, it was apparent that undergraduate students need more education on body image and weight bias.

This study had several limitations to be addressed. First, predominately female white participants, recruitment at a single institution only, and the cross-sectional nature of the measurements limited the generalizability of the conclusions. Further, self-reported answers for sensitive survey questions (e.g., BMI, dieting behaviors, explicit weight bias) may be considered potential biases. For example, we must note that BMI scores obtained in this study were self-reported. Therefore, the use of BMI might not represent an accurate weight status for the study population. The other potential limitation was asking participants about their dieting behaviors. As (Harring et al., 2010) pointed out, participants might not share the same definition of “dieting.” In addition, although the PFRS was a validated measure and the features were in black and white to eliminate some racial biases, the images were limited to portray body diversity. We also lacked images of males due to the unavailability of a male version of the PFRS. Because of these conditions associated with the PFRS, results from the PFRS in this study represented data on manipulation effects toward female body types only. Lastly, our study focused on weight bias toward individuals with larger body sizes only. Future studies may examine weight bias toward other body types such as underweight to address associations between weight bias and eating disorders (Drury & Louis, 2002).

5. Conclusion

Our results illustrated that weight bias might evenly exist among participants, regardless of career goals, at the study site. Using the BMI chart to determine one’s weight status may create undesirable body perceptions, leading to weight stigmatized attitudes toward individuals labeled as overweight or obesity. Additional studies that seek to address these misperceptions and false notions through education, (Poustchi, Saks, Piasecki, Hahn, Ferrante et al., 2013b; Swift, Tischler et al., 2013; Mc Vey et al., 2013), self-monitoring, and promoting a culture of equality among all students would greatly benefit universities. Furthermore, we strongly suggest that health and allied-health undergraduate programs (e.g., pre-health, health education, nutrition science) should incorporate evidence-based intervention programs (e.g., The Body Project) and anti-dieting paradigm (e.g., Health at Every Size®) into current course topics so that undergraduate students are prepared to start their future professions as a health professional and provide more compassionate care for community members.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Maya Miyairi

Maya Miyairi was an Associate Professor of Health Promotion and Education in the Department of Kinesiology and Health Science at Utah State University - Brigham City campus. She received her master’s degree in Exercise and Sport Science and her doctoral degree in Health Promotion and Education at the University of Utah. Since she started her faculty position at USU in 2013, she has taught undergraduate courses for community health and health science programs through interactive video conference (IVC) and online. Her research interests include 1) Weight stigma and bias (e.g., Weight-based Bullying), 2) Innovative health promotion practices (e.g., Health at Every Size [HAES]®), and 3) social justice issues (e.g., Clery Act, Aging).