The burden of bias: Patient experiences and providers’ perspectives on weight bias

Abstract

Background

Weight bias is pervasive, occurs in a variety of contexts, and is associated with a range of suboptimal outcomes, including delays or avoidance in seeking health care, misdiagnosis, and denial of services. The aim of this study was to quantitatively describe the relationship between experiences of weight bias and eating behaviors of patients in a lifestyle medicine program and qualitatively describe healthcare providers’ insights about weight bias.

Methods

This study utilized an explanatory sequential design that drew on one-time survey data collected from 109 patients of a Plant-Based Lifestyle Medicine (PBLM) Program in New York City in 2019. Ordinary Least Squares and logistic regression models examined the association between weight bias and healthful plant-based eating and emotional eating after adjusting for sociodemographic characteristics. Qualitative data come from asynchronous interviews with five healthcare providers from the PBLM program completed in 2024 which were analyzed using rapid coding.

Results

Survey participants were on average 53 years old, 67.0% female, 68.6% Black, Indigenous, and other people of color (BIPOC), and 56.2% reported experiencing weight bias. Weight bias was associated with 4.07 fewer points on the healthful plant-based eating measure (95%CI: 6.86, −1.27), and participants had 5.06 times the odds (95%CI: 1.76, 14.59) of emotional eating compared to those not reporting weight bias. Interview themes were: (1) Weight bias negatively influences patient lives, (2) Weight bias is negatively associated with patients’ mental health, (3) Promising weight-inclusive approaches, and (4) Recommendations for providers to address weight bias.

Conclusion

Experiences of weight bias were associated with suboptimal eating behaviors and poor mental health. Providers observed their patients experience harm in connection with weight bias, suggested weight-inclusive approaches, and identified recommendations that would mitigate weight bias in healthcare environments including routinely screening for weight bias, training providers on weight inclusive care, incorporating mental health services into care teams, and creating size inclusive spaces. This study demonstrates that weight bias is linked to deleterious outcomes and the importance of weight-inclusive care delivery and environments.

Graphical abstract

Highlights

• •Our study revealed that weight bias is commonly experienced by patients and is negatively associated with their eating behaviors and mental health.
• •Lifestyle medicine providers identified strategies for programs and providers to implement weight-inclusive practice in healthcare settings, including addressing patient concerns, deemphasizing weight loss in favor of healthful behaviors, focusing on achievable steps/goals, and encouraging self-compassion with patients.
• •Recommendations for reducing weight bias in healthcare settings, including enhanced training for providers on non-biased, patient-centered care; inclusion of mental health professionals in the care team; formal assessment of patients for experiences of weight bias; and creating size-inclusive environments may encourage the adoption of health promoting behaviors among patients.

1. Introduction

Weight bias, negative attitudes, beliefs, stereotypes, and behaviors toward individuals based on their body size and weight, which can be both internalized and externally directed, is pervasive and occurs in a variety of contexts, including healthcare settings. Substantial evidence exists documenting uncomfortable and stigmatizing events that people living in larger bodies experience while seeking care. Many of these experiences are because healthcare settings are not adequately designed and equipped for all patients. They often lack size-inclusive spaces, including chairs without arm rests that larger individuals can comfortably fit in, gowns that fit different body sizes and shapes, and necessary equipment in exam rooms (e.g., appropriately sized blood pressure cuffs) [1]. Patients also routinely report that their health concerns are not taken seriously by healthcare providers, who instead focus on their weight and need for weight loss, rather than addressing the issue for which a patient has sought care. Healthcare providers have been shown to spend less time with some patients in larger bodies [2], and allocate time with patients differently as compared to their straight-sized counterparts [3]. Moreover, there is documented evidence that many healthcare providers, medical students, and medical trainees hold negative opinions about individuals living in larger bodies, believing they are lazy [4] and less likely to adhere to treatment [5]. These types of experiences of weight bias from the healthcare environment are associated with a range of suboptimal outcomes, including: delays or avoidance in seeking health care [6] and misdiagnosis and denial of services [7].

Clinical practice has traditionally taken a weight-normative approach, emphasizing weight as an indicator of health status and weight management for improved health [8]. Contrary to weight-normative approaches, weight-inclusive approaches focus on health and well-being independent of weight and size [8]. Models like the Health at Every Size (HAES) framework promote size inclusivity, respectful care, eating for well-being and pleasure, and engaging in physical activity for personal enjoyment [9]. Registered dietitians who were aligned with the HAES framework hold lower weight blaming attitudes [10], suggesting adoption of a weight-inclusive approach may reduce weight bias. Despite recommendations for medical schools to integrate teachings on how to minimize bias and provide respectful care to patients living in larger bodies, most family medicine residents do not receive prior education on the biopsychosocial management of obesity during medical school [11]. Internal medicine residency programs are inconsistent with their coverage of obesity-related competencies in their curricula, with weight bias and discrimination, etiologic aspects of obesity, and pharmacologic treatment being least covered [12].

Addressing weight bias and promoting weight-inclusive approaches are critical to ensuring that patients in larger bodies receive optimal care. In an online study, participants who were exposed to cues that physicians believe in the HAES movement reported lower weight bias concerns [13]. Even more promising, emerging evidence suggest that programs/interventions that incorporate weight-neutral or weight-inclusive approaches may be associated with improvements in health-promoting behaviors and health outcomes. A pilot virtual weight-inclusive intuitive eating-based workplace wellness program demonstrated increased intuitive eating, decreased internalized weight bias (i.e., applying negative attitudes and stereotypes about weight and size to oneself), and decreased disordered eating symptoms at follow-up [14]. However, there is limited literature on the inclusion of weight-inclusive approaches in lifestyle medicine programs implemented in healthcare settings.

The current explanatory sequential mixed method study is part of the larger evaluation of the pilot Plant-Based Lifestyle Medicine (PBLM) program [[15], [16], [17]], and assesses the association between PBLM patients' experiences of weight bias and eating behaviors. Further, this study qualitatively explores providers' perspectives on the negative influence of weight bias on patients of the program, documents examples of delivering weight-inclusive care, and identifies best practices for addressing weight bias within the healthcare setting. To our knowledge, this is the first study to publish providers’ insights on these topics from a lifestyle medicine intervention implemented in a safety-net healthcare setting.

2. Methods

This study utilized an explanatory sequential design with a quantitative component followed by a qualitative component [18]. Because experiences of weight bias were common among patients in the study, and were associated with suboptimal eating behaviors, the research team sought to understand providers' perspectives on how weight bias may patients’ lives, how the PBLM program delivered weight-inclusive care, and ways in which weight bias within the healthcare setting could be mitigated. Research team members jointly developed qualitative measures, reviewed the quantitative and qualitative findings, and contributed to the joint interpretation. The New York University Grossman School of Medicine IRB and the Office of Research and Administration for Implementation at NYC Health + Hospitals/Bellevue approved all study procedures and all participants provided informed consent (i18-01319).

2.1. The program

A detailed description of the PBLM program has been published previously [15]. The program was piloted in the largest public healthcare system in the United States to reduce patients’ cardiometabolic risk through adopting a plant-predominant eating pattern along with other evidence-based lifestyle behaviors (i.e., increased physical activity, improved sleep, improved stress management, increased social connection, and avoidance of substance use). Adults 18 and over with type 2 diabetes, prediabetes, heart disease, high blood pressure, high cholesterol, and/or excess weight (BMI ≥25) were eligible. Patients met individually with providers with expertise in plant-based nutrition and lifestyle medicine, including a physician, a registered dietitian, and a health coach, for behavior-change counseling and goal setting. The program explicitly applied weight-inclusive approaches to reduce bias.

2.2. Quantitative

2.2.1. Sample

PBLM team members informed patients (n = 173) of the evaluation and solicited consent to share their contact information with the NYU evaluation team. Of those, 131 agreed to be contacted by the study team, 111 provided verbal consent (85%), and 109 unique individuals completed a single, one-time baseline survey (98%). Study team members administered this one-time baseline survey between January and July 2019, prior to an individual patient's participation in the PBLM program. The survey took approximately 25 min to complete and respondents received a $10 gift card. No additional survey waves or repeated survey administrations were used in this analysis.

2.2.2. Measures

Quantitative outcomes of interest were baseline healthful plant-based eating and frequency of emotional eating. To determine baseline healthful plant-based eating, patients were asked 16 questions adapted from the PrimeScreen dietary screening tool about their food and beverage consumption the previous week [19]. For example, “In the last 7 days, how often did you eat legumes such as beans, lentils, chickpeas, etc.?” As described elsewhere [17], the study team used responses to these questions to calculate a healthful plant-based eating composite score based on Satija et al.’s Healthful Plant-Based Index (hPDI) [20] with viable scores ranging between 0 and 60. Higher scores suggest a healthier overall dietary pattern consisting of greater healthful plant-based food intake and lower intake of animal and less healthful plant-based foods. Emotional eating was measured with one question, “How often do your emotions such as feeling anxious, stressed, depressed, sad, lonely, helpless, etc. cause you to eat?” Response options were on a four-point Likert scale from “Never” to “Always”. For analyses, responses were collapsed to “Never/Sometimes” vs. “Often/Always”.

The independent variable was perceived experiences of weight bias, which was measured with one question, “People have judged you because of your weight” using a four-point “Strongly Disagree” to “Strongly Agree” Likert response option scale. For analyses, responses were collapsed to “Strongly Disagree/Disagree” vs. “Strongly Agree/Agree”.

Sociodemographic measures included gender identity (man, woman, transgender man, transgender woman, non-binary/gender queer, other), age (in years), marital status (single, married/living with partner, separated/divorced, widowed), race/ethnicity (BIPOC vs. White), and education (high school or less, some college or more). While the survey allowed respondents to select from multiple gender identities, all respondents in the analytic sample identified as either women or men. For analyses, response options were dichotomized to woman vs. man.

2.2.3. Analyses

Descriptive statistics were run on study variables for the full sample and also stratified by experiences of weight bias. To examine the association between weight bias and healthful plant-based eating, we first ran a simple linear regression model with experiences of weight bias as the independent variable and healthful plant-based eating as the dependent variable. Next, we added sociodemographic characteristics (i.e., gender, age, marital status, race/ethnicity, and education) to the model. We then repeated this process using logistic regression to examine the association between experiences of weight bias and emotional eating by first running an unadjusted model and then an adjusted model that included sociodemographic characteristics. IBM SPSS Statistics, Version 29 was used for statistical analyses.

2.3. Qualitative

2.3.1. Sample

Qualitative data come from asynchronous email interviews with current PBLM program providers, conducted between March and September 2024 [21]. Six PBLM program providers were invited to participate via email; one declined due to leaving the institution. The final sample of five included three physicians, one health coach, and one registered dietitian. The study team obtained written informed consent from those who agreed to participate, provided each person with six written questions via email, and participants sent written responses via secure email. Participants were initially given one month to respond, but the data collection period was extended due to delayed responses. A study team member followed up monthly by email. After six months, a final email reminder was sent and data collection was closed. Interview questions focused on providers’ reactions to, and perceptions about, the influence of weight bias on PBLM patients; how providers assess weight bias in the program; and weight-inclusive practices in the PBLM program and clinical settings more broadly.

2.3.2. Analyses

Two study team members used a rapid coding process [22] to analyze interview responses. Each independently read the responses and identified themes using a hybrid deductive/inductive approach, applying themes from interview questions and generating new themes through transcript review. They then met to compare themes and revise codes. After multiple rounds of discussion and revision, they created a final coding scheme focused on: (1) assessment of weight bias, (2) sources of weight bias, (3) perceived influence of weight bias on patient lives, (4) weight-inclusive approaches, and (5) modification for future practice. A summary template was created to apply the coding scheme systematically and extract main points and illustrative quotes. They used the summary template to independently code responses and met to resolve coding discrepancies. Finally, one member synthesized the data across the summarized transcripts to organize results. We determined that thematic saturation was reached when similar points were repeatedly raised across interview responses and no new themes related to weight bias, patient experiences, or weight-inclusive practices emerged in the final interviews.

3. Results

3.1. Quantitative

3.1.1. Sample

As Table 1 illustrates, the majority of participants were female (67.0%), were on average 53 years old, almost half were married (43.9%), more than two thirds were BIPOC (68.6%), and the vast majority completed at least some college (88.8%). More than half the sample reported experiencing weight bias (56.2%), the mean healthful plant-based eating score was 37.3 out of 60, and more than a third reported that they “often” or “always” engaged in emotional eating (39.6%).

Table 1.

Sample characteristics and eating behaviors by weight bias experience at baseline (N = 109).

Characteristic	Full Sample (n = 109)		Did Not Experience Weight Bias (n = 46)		Experienced Weight Bias (n = 59)
	N	Percent or Mean (SD)	N	Percent or Mean (SD)	N	Percent or Mean (SD)
Gender	106		45		59
Man		33.0%		40.0%		28.8%
Woman		67.0%		60.0%		71.2%
Age	106	53.0 (12.0)	46	58.2 (10.3)	58	49.0 (12.0)
Marital Status	107		46		59
Single/Divorced/Separated/Widowed		56.1%		45.7%		64.4%
Married		43.9%		54.3%		35.6%
Race/Ethnicity	105		45		58
Black or African American		39.0%		35.6%		41.4%
White		31.4%		35.6%		29.3%
Hispanic or Latino		17.1%		15.6%		19.0%
Asian		5.7%		8.9%		3.4%
American Indian or Alaska Native		1.0%		2.2%		.0%
Two or more		2.9%		.0%		3.4%
Other		2.9%		2.2%		3.4%
BIPOC	105	68.6%	45	64.4%	58	70.7%
Education	107		46		59
High school or less		11.2%		15.2%		8.5%
Some college or more		88.8%		84.8%		91.5%
Experienced Weight Bias	105
No		43.8%	46	100.0%
Yes		56.2%			59	100.0%
Eating Behaviors
Healthful plant-based eating score (0–60)	109	37.3 (7.05)	46	39.7 (7.3)	59	35.5 (6.1)
Emotional Eating	106		45		59
Never/Sometimes		60.4%		82.2%		42.4%
Often/Always		39.6%		17.8%		57.6%

Notes:

Ns may vary due to missing data.

BIPOC stands for Black, Indigenous, and People of Color.

Healthful plant-based eating score is a composite measure where higher scores suggest a diet consisting of greater healthful plant-based food intake.

3.1.2. Healthful plant-based eating & emotional eating

In unadjusted analyses, experiencing weight bias was associated with 4.24 fewer points on the healthful plant-based eating score at the time patients enrolled in the program (95% CI -6.83 to −1.65; Table 2 Model 1). In adjusted analyses, for those who experienced weight bias, the healthful plant-based eating score was 4.07 points lower than for those who did not report experiencing weight bias (95% CI -6.86 to −1.27; Model 2).

Table 2.

OLS and logistic regression models predicting healthful plant-based eating and emotional eating from weight bias at baseline (N = 104).

	Model 1 Unadjusted Healthful Plant-Based Eatinga			Model 2 Adjusted Healthful Plant-Based Eatinga			Model 3 Unadjusted Emotional Eatingb		Model 4 Adjusted Emotional Eatingb
	b	SE	95% CI	b	SE	95% CI	OR	95% CI	AOR	95% CI
Weight Bias (yes)	−4.24	1.31	−6.83–-1.65	−4.07	1.41	−6.86–-1.27	6.29	2.50–15.82	5.06	1.76–14.59
Woman				0.04	1.37	−2.67–2.76			2.35	0.84–6.57
Age (years)				0.02	0.06	−0.09–0.14			0.95	0.91–0.99
Married				−0.47	1.29	−3.04–2.09			2.11	0.80–5.60
BIPOC				−5.20	1.40	−7.97–-2.43			0.62	0.22–1.76
Some college or more				−0.60	2.01	−4.59–3.38			1.26	0.24–6.68
Constant	39.72	0.98	37.77–41.66	42.43	4.18	34.14–50.73
Model Statistics			p-value			p-value
F	10.51		0.002	4.92		<0.001
df	103			95
R2	0.09			0.24

Notes:

b = beta coefficient; SE = standard error;

OR = odds ratio, AOR = adjusted odds ratio, CI = confidence interval.

Statistically significant values are shown in bold.

Reference categories: weight bias = no; gender = man; married = single, divorced/separated, widowed; BIPOC = white; education = less than high school or high school.

BIPOC stands for Black, Indigenous, and People of Color.

Healthful plant-based eating score is a composite measure where higher scores suggest a diet consisting of greater healthful plant-based food intake.

^aOLS Regression.

^bLogistic Regression.

In unadjusted analyses, experiencing weight bias was associated with increased odds of “often” or “always” engaging in emotional eating at program entry (OR 6.29; 95% CI 2.50–15.82; Model 3). In adjusted analyses, those who experienced weight bias had 5.06 times the odds of “often” or “always” engaging in emotional eating as compared to those who did not experience weight bias (95% CI 1.76–14.59; Model 4).

3.2. Qualitative

Qualitative results are organized by four themes. The first two relate to PBLM provider perceptions about the influence of weight bias on individual patient experience while the last two relate to provider perspectives of weight-inclusive practice. Analysis of interview responses revealed a strong pattern of similarity across participating providers, indicating saturation was reached. See Table 3, Table 4 for illustrative quotes related to each theme.

Table 3.

Provider perspectives on the influence of weight bias on individual patient experience.

Theme	Codes	Quotes
Theme 1: Experiences of weight bias are common for patients in the program and influences their lives in profound ways	Patients cite weight loss as primary goal, motivated by internalized weight bias	“Asking … if [weight loss] is their primary reason for joining our program, is usually the question I find that will lead to patients … referring to themselves in a negative way ('I hate myself for being fat'or'I hate being big') and prompting me to ask if they have experienced stigma …” (Provider 3) “Most of the time, patients will discuss their desire to lose weight as their'why'or action plan. When we start to discuss this further, patients share the shame and guilt they feel about being overweight, and have been told, by many medical providers (not in our clinic) that they must lose weight. Most often, they aren't given tools or even a discussion, about the underlying issues.” (Provider 4)
	Patients experience weight bias from wide range of sources including friends, family, medical providers, and in public	“… friends/neighbors always tease her about her weight and suggest that her weight is due to laziness or eating lots of unhealthy foods …” (Provider 3) “… patients are scarred from years and years of their medical providers telling them to “lose weight with healthy eating and exercise” without providing support or structure …” (Provider 2) “[Patients] have problems with transportation- they have a hard time with the subway stairs or they take up more than 1 seat on the subway/bus and get stares. Some patients avoid social situations and unfamiliar places …” (Provider 2)
Theme 2: Weight bias is taking a significant toll on the mental health of patients	Weight bias is negatively associated with patients' mental health	“Many patients have concomitant anxiety, depression and stress and emotional eating is oftentimes a symptom of the underlying mental health consequences from years and years of judgment about their weight.” (Provider 2)
	Poor mental health is a barrier to healthy lifestyle changes	“Weight-related stigma and related trauma can reduce a person's feelings of self-efficacy, self-esteem and motivation, such that they are less likely to feel capable of making lifestyle changes and practicing self-care, including adopting nutritious eating patterns.” (Provider 1)

Table 4.

Provider perspectives on weight-inclusive practice and practice change suggestions.

Theme	Codes	Quotes
Theme 3: Promising weight-inclusive approaches from an exemplar lifestyle medicine program	Acknowledge and listen to patients' concerns	“Taking patients' symptoms and concerns seriously and performing necessary evaluations rather than assuming these symptoms are due to their weight.” (Provider 1)
	Deemphasize weight/weight loss in favor of focus on healthy habits	“I find myself spending time at the initial visit and subsequent visits to not be as focused on the weight as much as the things that are under their control which are the lifestyle habits we work on- sleep, stress, diet and exercises.” (Provider 3) “We concentrate on the way they feel and what they want to accomplish in their lives, more energy, less pain, better lab results, instead of a number on the scale.” (Provider 4)
	Focus on small steps and building confidence	“Putting patients at ease that this experience in the Lifestyle Medicine Programs will be unlike any other they've had before in health care—because here, they won't have to talk about weight or the scale. They won't be shamed. They will be heard, understood, and have constant cheerleaders in each of the team members, celebrating every small step they take.” (Provider 5)
	Encourage self-compassion and nullify willpower	“I discussed with her that obesity and overweight is a complicated medical condition, like diabetes, high blood pressure and there was nothing she has done wrong." (Provider 2) “… ‘willpower’ is not the reason she has been unable to lose weight. Also discussing with her that most diets don't work, especially ones that are overly restrictive. I encouraged her to show herself compassion, which triggered her to start crying.” (Provider 2)
Theme 4: Recommendations for lifestyle medicine programs or other providers to address weight bias in healthcare settings	Formalize assessment of weight bias in clinic	“Develop standard screening processes to evaluate whether patients have had experiences of weight bias/stigma/discrimination and if so, understand how these experiences have affected them physically and psychologically.” (Provider 1) “It would be important to screen for this in our patients during the initial interview so that we may address the effects the stigma has had on them. We can screen for whether they may benefit from more counseling to address the stress this stigma may have led to and any other effects this may have had.”(Provider 3)
	Integrate mental health	“We are also establishing a partnership with behavioral health – specifically, having a dedicated psychologist for each of our lifestyle medicine programs – to ensure that all program patients have access to support and treatment for weight stigma and its consequences.” (Provider 1) “I believe the addition of psychologists into our clinic teams will be very instrumental to help facilitate conversations around these topics of discrimination and weight stigma.” (Provider 5)
	Require care team training on weight inclusivity	“Ensure all members of the health care team, including medical providers, medical assistants, and others, are familiar with the concept of weight inclusivity and ways to support it – including a focus on health behaviors rather than weight loss.” (Provider 1) “I think that all members of the team should be trained and have ongoing training in making sure we identify our own biases and do not exacerbate weight stigma and are sensitive to all the repercussions it may have on our patients.” (Provider 3)
	Create size inclusive spaces for patients	“Ensure clinical settings have appropriate-sized chairs, blood pressure cuffs, and other equipment.” (Provider 1) “From a pure processes standpoint- making the clinic itself more accessible and not weighing patients is a good start.” (Provider 2)

3.2.1. Theme 1: Prior experiences of weight bias are common for patients in the program and influences their lives in profound ways

Providers reported they do not formally screen for weight bias among patients but noted that patients frequently share experiences of both internalized and external weight bias without specific prompting. Providers reported they follow up with additional questions and discussion once this information is volunteered. Experiences of bias were commonly discussed during initial intake conversations designed to learn why patients joined the program and wanted to change their body size. Providers described the pervasiveness of weight bias in their patients’ lives, noting they experience it in everyday life. Specifically, they noted family and friends often perpetrate stigmatizing behaviors. These experiences discourage some patients from engaging in social activities, going to new places, and using public transportation. Further, providers shared some PBLM patients report experiencing bias by healthcare providers, which was especially concerning to respondents as patients have endured years of being told to lose weight by medical providers who did not truly listen to their concerns or provide adequate support.

3.2.2. Theme 2: Weight bias is taking a significant toll on the mental health of patients

All providers described how weight bias negatively influences their patients' mental health. Providers explained they have observed internalized weight bias leading to, or being associated with, anxiety, depression, stress, emotional eating, and sleep disruption. Providers also commented on how patients’ mental health challenges can be a barrier to making healthy lifestyle changes, contributing to a recursive feedback loop. For example, providers noted that emotional eating tends to favor “comfort foods” – typically calorie-dense, nutrient-poor foods high in added sugars, sodium, and/or fat. One provider opined that mental health issues caused by weight bias negatively impacts sleep quality, which in turn leads to increased cravings for unhealthy foods. Finally, providers shared that patients may suffer from low self-efficacy, self-esteem, and lack of motivation due to experiences of weight bias, which interferes with their ability to adopt healthy lifestyle changes and self-care.

3.2.3. Theme 3: Promising weight-inclusive approaches from an exemplar lifestyle medicine program

Providers identified four valuable weight-inclusive practices utilized successfully in the PBLM program. First, acknowledging and listening to patients' concerns was paramount. Multiple providers reported intentionally acknowledging patients' feelings and experiences with weight bias and listening to their concerns. They underscored that this approach differs from many patients' past experiences where they have felt stigmatized and their concerns were dismissed or assumed to be caused by weight without further inquiry. This shift reportedly made a significant difference in building trust and rapport. Second, PBLM providers deemphasized weight and weight loss in favor of focusing on healthy behaviors. This included not necessarily discussing weight during visits, honoring requests not be weighed, and setting goals not tied to weight loss but focused on implementing healthy lifestyle behaviors. Third, the program focused on helping patients take small, sustainable steps towards healthful lifestyle behaviors in order to build confidence rather than promoting an all-or-nothing approach. One provider explained this mindset is key to striving for “optimal health and longevity” and building confidence over time, while avoiding negative feelings of shame or guilt that can be counterproductive to patients’ progress. Fourth, providers encouraged self-compassion amongst their patients and worked to nullify the perception that willpower is the primary determinant of body size. By acknowledging this, providers shared that it is common for their patients to express emotions caused by years of internalized weight bias.

3.2.4. Theme 4: Recommendations for lifestyle medicine programs or other providers to address weight bias in healthcare settings

Providers were asked to recommend formal structures and processes for lifestyle medicine programs or healthcare settings to address weight bias and improve patient outcomes. Four recommendations emerged. First, providers recommended formalizing weight bias assessment in clinic workflows. Given its prevalence and the negative relationship between weight bias and mental and physical health, they underscored the importance of standardized screening. Second, nearly all providers mentioned the need to integrate mental health into the care team to treat the perceived consequences of weight bias. Integration of mental health providers and/or referral to services would support patients in addressing internalized and external bias, developing coping mechanisms, and creating an effective treatment environment to support lifestyle change adherence. Third, providers felt all care team members should receive formal training on weight-inclusive practice, to ensure everyone who interacts with patients is aware of medical practices that can be stigmatizing and educated on weight-inclusive practices. Fourth, multiple providers shared clinical settings should modify their physical spaces and equipment to accommodate patients in larger bodies, including appropriately sized chairs for the waiting room, blood pressure cuffs, scales, and table height.

4. Discussion

The quantitative component of this study aimed to understand the relationship between experiences of weight bias and eating behaviors among patients enrolled in a lifestyle medicine program. Consistent with past research, more than half the sample reported experiencing weight bias [23]. Further, we found that patients who reported weight bias entered the program consuming fewer healthful plant-based foods and were more likely to engage in frequent emotional eating than their counterparts who did not report experiencing weight bias. These findings were congruent with extant literature [24,25].

Because experiences of weight bias were widespread among program patients, the study team sought to explore provider perceptions about weight bias and how it may influence program success. The team also aimed to understand how providers created a weight-inclusive program and what recommendations could be offered to other providers. Providers observed their patients had had these pernicious experiences throughout life. Accordingly, providers aimed to address the perceived consequences throughout the program and in proposed modifications (e.g., adding a psychologist). Four practice approaches emerged as being key to weight-inclusive care: acknowledging and listening to patient concerns, deemphasizing weight while emphasizing positive lifestyle behaviors, building confidence through small steps, and encouraging self-compassion. Four practice recommendations were made for addressing weight bias in healthcare: formalize assessment of weight bias, integrate mental health specialists, train care team members on weight inclusivity, and create size-inclusive spaces. We recommend that adoption of these recommendations be explored by providers as they represent high value opportunities for practice change.

Future research should explore which clinical practice settings are best suited for integrating a standardized weight bias screening process. Given the overwhelming evidence that experiencing weight bias is negatively associated with healthy lifestyle behaviors and health outcomes [24,25], identifying experiences of weight bias through formal assessment is critical for providers using lifestyle as a first line method of treatment. Our study provides evidence that practice settings supporting patient lifestyle change, including lifestyle medicine, but potentially extending to weight management and obesity medicine, may be particularly suited to implement screening. As screening for depression [26] and health-related social needs [27] is implemented in primary care, other research may explore what can be learned from these processes to inform practice and protocols for weight bias screening.

Additionally, the results of our study identified strategies to implement inclusive practice. Clinical practice aimed at health behavior change and lifestyle medicine must emphasize body diversity and ways to optimize health at every weight. However, this approach is not standard in medical training historically [28]. Scant studies have attempted to create curricula to address weight bias, with the first MedEdPORTAL publication in 2023 [28]. These curricula aim to shift the paradigm of medical education by acknowledging that medical education is currently weight-centered, while teaching with a body diversity framework [28]. While immersive early medical education is ideal, to truly shift medical culture, training is needed at all levels of medical education and practice [29]. One recent study demonstrated the promise of professional development with a 4-h Continuing Medical Education (CME) symposium (plus a one-time follow-up email) resulting in sustained changes in “attributions of responsibility of obesity, increasing empathy, creating self-awareness of weight bias” [30]. As calls to action for consistent weight bias training at all professional levels have been made [29], our study demonstrates how such intervention may impact patient experience and practice of lifestyle medicine.

The qualitative component of this study focused solely on provider perspectives. However, understanding patient perspectives is critical to the ultimate implementation of patient-centered clinical practice [31,32]. PBLM providers felt that building trust and rapport with their patients by actively listening to their concerns was an essential part of the weight-inclusive approach, and recommended modifications to clinical settings to accommodate patients in larger bodies. These suggestions are consistent with patient recommendations to address weight bias in healthcare settings, including having providers demonstrate empathy as well as build rapport with patients by communicating effectively and listening to patients' health concerns and experiences [33,34], and adapting clinical equipment to be more weight-inclusive [34]. Patients have also suggested that providers be sensitive to discomfort they may experience when being weighed in public [35], including honoring patients’ request to not be weighed, as seen in our results. Alternatively, one study found that some patients expressed they would like more support from and open discussions with their provider about their weight and possible weight management tools/treatment options [35] and for treatments to be tailored to their needs and lifestyle [33]. This highlights the importance of having a patient-centered approach – some patients may not want to focus on weight while others may want compassionate, individualized care and support from their provider in addressing their weight concerns.

4.1. Limitations

This study has a few important limitations. First, the quantitative portion of this study relies on cross-sectional data and it is not possible to establish causality. Second, experiences of weight bias were assessed using a single-item measure rather than a validated multi-item scale, which may have led to underestimation or misclassification of patients’ experiences of weight bias. This approach was chosen to minimize participant burden, but future research should incorporate multi-item, validated instruments to better measure weight bias. Relatedly, people may not able to accurately determine the reason they have been judged or treated differently/unfairly. This is particularly relevant for historically minoritized groups who experience multiple forms of oppression. However, our findings are consistent with literature on the negative association between weight bias and health behaviors, somewhat assuaging this concern. Third, the original evaluation was not designed or powered to answer questions about weight bias. Thus, these findings should be viewed as exploratory and additional research should be done. Additionally, the qualitative component exclusively explored provider perspectives from one lifestyle medicine program, so generalizability may be limited.

4.2. Conclusion

Addressing weight bias in healthcare is a public health priority as it is associated with the quality of care and outcomes for individuals living in larger bodies. Interventions to address weight bias may encourage the adoption of health promoting behaviors including more optimal use of healthcare, without fear of bias and judgement, ultimately contributing to improving population health and reducing health inequalities.

• •Our study revealed that weight bias is commonly experienced by patients and is negatively associated with their eating behaviors and mental health.
• •Lifestyle medicine providers identified strategies for programs and providers to implement weight-inclusive practice in healthcare settings, including addressing patient concerns, deemphasizing weight loss in favor of healthful behaviors, focusing on achievable steps/goals, and encouraging self-compassion with patients.
• •Recommendations for reducing weight bias in healthcare settings, including enhanced training for providers on non-biased, patient-centered care; inclusion of mental health professionals in the care team; formal assessment of patients for experiences of weight bias; and creating size-inclusive environments may encourage the adoption of health promoting behaviors among patients.

Author contribution

SLA jointly conceived of the study, designed data collection procedures, was responsible for the implementation of the study, led data analysis and interpretation, and drafted the manuscript. LK managed data, performed quantitative data analysis, and contributed to drafting the manuscript. RM contributed to study design, oversaw all data collection, led coding and analysis of qualitative data, and contributed to drafting the manuscript. MM led the program, contributed to the overall study design, and contributed to the manuscript. HEA advised on methods and contributed to the manuscript. RO jointly conceived of the study, helped with interpretation, and contributed to the manuscript.

Ethical adherence and ethical review

NYU Grossman School of Medicine IRB and Office of Research and Administration for Implementation at NYC Health + Hospitals/Bellevue approved study procedures and participants provided informed consent (i18-01319).

Declaration of artificial intelligence (AI) and AI-assisted technologies

Artificial intelligence was used to suggest potential word deletions to meet word count limits with explicit instructions not to change any wording, content, or structure, and it was not involved in the development, analysis, or writing of the manuscript. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

Funding

This work was supported by NYC Health + Hospitals.

Conflicts of interest

The Authors declare that there are no conflicts of interest.