Understanding obesity treatment choices: a mixed-methods study of patient preferences and shared decision-making

Abstract

Background

Shared decision-making (SDM) is recommended in obesity treatment. Yet, little is known about how it occurs in real-world practice, particularly given recent advances in treatment options.

Purpose

This prospective mixed-methods study aimed to understand patient preferences and decision-making processes for obesity treatment.

Methods

Adults with obesity (N = 103) seeking specialty weight management treatment completed a survey, and a subset (n = 17) participated in qualitative interviews. Both assessed weight loss history, knowledge and expectations of obesity treatment options, decision-making factors, and perceptions of SDM.

Results

Participants reported an average of 10.1 (SD = 13.5) prior weight loss attempts and were primarily motivated by physical well-being (86.4%). Key factors influencing decision-making included desired weight loss (84.5%), provider recommendations (76.7%), and cost (62.1%). Anti-obesity medications were viewed most favorably, followed by behavioral and surgical approaches. Qualitative analysis generated four themes: (i) a chronic cycle of weight loss and regain; (ii) guided by stories, not science alone; (iii) navigating trade-offs in choosing weight-loss strategies; and (iv) feeling heard, but still seeking direction.

Conclusions

Patients demonstrate strong awareness of obesity treatments and value SDM and personalized recommendations when making decisions. Findings highlight the need for interventions that strengthen SDM and deliver tailored, high-quality health communication.

Graphical Abstract

Graphical Abstract.

Implications.

Practice: Effective obesity treatment should move beyond eliciting patient preferences to providing clear, personalized guidance that helps patients navigate treatment trade-offs and translate shared decision-making into actionable choices.

Policy: Policymakers should enact regulations that enable shared decision-making by supporting adequate visit structures, the routine use of evidence-based decision aids, and comprehensive coverage of obesity treatment options.

Research: Future research should develop and test interventions that enhance clinicians’ ability to deliver tailored, high-quality communication that balances patients’ values with evidence-based recommendations in obesity treatment.

Introduction

Obesity is a chronic, multifactorial disease that requires long-term individualized management strategies, often involving complex choices among multiple treatment options. Current clinical guidelines reflect this, recommending three core interventions: behavioral, pharmacological, and/or surgical. Foundational guidelines jointly published by the American College of Cardiology, the American Heart Association, and The Obesity Society in 2014 [1], followed by guidelines from the American Association of Clinical Endocrinologists and American College of Endocrinology shortly after in 2016 [2], established a framework for the long-term management of obesity among adults. Subsequent updates have been summarized in recent reviews [3, 4]. Since the Food and Drug Administration (FDA) approval of the glucagon-like peptide-1 receptor agonist (GLP-1) semaglutide as an anti-obesity medication (AOM) in 2021, the therapeutic landscape has rapidly evolved. In response, multiple international and national organizations have issued guidance addressing the use of GLP-1s as an AOM. For example, the World Health Organization issued its first global guideline on GLP-1 use for obesity in adults in 2025 [5].

As treatment options expand, shared decision-making (SDM) has become increasingly important for high-quality obesity care. Clinical guidelines explicitly recommend SDM [6, 7] given its benefits for improving health outcomes and quality of care [8]. Defined as a collaborative process in which patients and providers jointly determine the most appropriate treatment approach, SDM models vary but often involve components such as describing treatment options, tailoring information, reviewing patient preferences, and making a treatment decision [9]. Recent commentary highlights the heightened relevance of SDM in the modern treatment landscape [10, 11], which has been shaped by the rapid evolution of pharmacotherapies like GLP-1s. Although these medications produce significantly greater weight loss in comparison to older AOMs [12–15], their emergence has increased clinical complexity, further underscoring the need for SDM.

Despite growing emphasis on SDM, limited evidence describes how patients experience and navigate treatment decisions in real-world obesity care, particularly in the GLP-1 era. Much of the literature on patient preferences for obesity care predates FDA approval of semaglutide [16–18] and does not reflect recent therapeutic shifts. While newer studies examine perceptions of GLP-1s [15–18], few assess how patients weigh pharmacological options alongside behavioral or surgical approaches. Emerging evidence of variability in treatment uptake by sociodemographic factors, insurance type, and comorbidities further highlights the need to better understand patient-level decision drivers [19–21].

Given these gaps and the rapidly evolving therapeutic landscape, updated empirical evidence is needed to clarify how patients form expectations, weigh risks and benefits, and engage in SDM regarding obesity treatment. Improved understanding of these processes may inform more effective SDM discussions, support equitable and person-centered care, and enhance patient satisfaction and clinical outcomes. Accordingly, this mixed-methods study examines patient knowledge and expectations of obesity treatments, identifies factors influencing these domains, and explores patient perceptions of SDM in contemporary obesity care.

Methods

Study overview

We conducted an explanatory sequential mixed-methods study to explain the patient decision-making process around obesity treatment. Patients seeking specialty care for obesity at a large integrated healthcare system provided survey data and a subset completed qualitative interviews. Both were designed to obtain insight into patients’ knowledge and expectations of obesity treatment options, factors influencing these domains, and perceptions of SDM. Informed consent was obtained from all participants, and the Geisinger Institutional Review Board approved the protocol. To ensure methodological rigor and transparency, we adhere to STROBE [22], and COREQ-32 [23], guidelines.

Reflexivity

The first three authors (K.M.H.N., L.W., and D.C.) were directly involved in data collection and/or analysis. All identify as female and are actively involved in health services research with a focus on nutrition. D.C. is a project manager whereas K.M.H.N. and L.W. hold dual roles as clinicians and researchers. K.M.H.N. is a psychologist specializing in eating and weight disorders and L.W. is a registered dietitian nutritionist. All researchers have experience with mixed-methods and qualitative research. Participants were informed of purpose of the study, and no prior relationships existed between researchers and participants. To enhance reflexivity, the team engaged in regular discussions to critically examine assumptions, interpretations, and potential influences of their backgrounds on the research process.

Participants and procedures

Potential participants were recruited from patients at a nutrition and weight management center at a large integrated healthcare system in central Pennsylvania. The health system serves both urban and rural populations, with 2022 internal data showing ∼32 of the 45 counties in the service area are designated as rural, which is higher than national averages [24]. A validated electronic health record (EHR) algorithm identified adults (>18 years) with a body mass index (BMI) >30 kg/m² scheduled for a new patient visit who spoke English; individuals who were pregnant were excluded. Recruitment occurred via email. Of the 701 patients contacted, 138 responded and were eligible, and 103 completed the REDCap survey. Purposive sampling was used to select 17 participants for semi-structured qualitative interviews to ensure diversity in weight management experiences. Data were collected from 1 September 2024 through 13 February 2025. Qualitative data collection concluded when data saturation was achieved. Participants were provided with renumeration for their time.

Measures

Data were obtained from the EHR, surveys, and qualitative interviews. Participants’ BMI was extracted from the EHR. The measurement closest to the date of survey completion was used. The survey and interview moderators guide can be found in the Supplementary Material; additional information about both is as follows.

Survey

The survey was investigator-created and included questions assessing sociodemographic factors, weight loss treatment history, knowledge and expectations of obesity treatment options, factors that influence decision-making, and perceptions of SDM. Survey items were selected from the National Awareness, Care, and Treatment in Obesity Management (ACTION) Study [13, 14, 18, 25].

Qualitative interview

An interview moderator’s guide was developed based on prior literature [13–15] and informed by input from a multidisciplinary team of professionals (including study authors), a patient advisory council comprised adults with a history of obesity, and survey results. Interviews were conducted via televideo or telephone using Microsoft Teams by trained study personnel (D.C.).

Analysis

Quantitative data

Descriptive statistics were used to summarize data for all participants. Continuous variables are reported using means and standard deviations. Categorical variables are described using frequencies and percentages. Statistical analysis was performed using R Studio.

Qualitative data

Interviews were digitally transcribed and deidentified prior to analysis. All transcripts were reviewed for accuracy by the first author through a process of listening to the audio recording and cross-checking the digital transcription. Data were coded inductively using Microsoft Excel and Word by the first author and another member of the research team (L.W.). Data analysis followed the tenets of reflexive thematic analysis [26, 27]. An open-coding approach was used to generate initial codes. Following independent coding, the coders met to discuss codes, compare interpretations, and reconcile discrepancies. Based on these discussions, the researchers refined coding approaches, and the first author applied them to all transcripts using NVivo. After initial coding was complete for all transcripts, an inductive, constant comparative strategy was used to derive themes and reach consensus. Final themes and subthemes were iteratively named, defined, and documented with exemplar quotes.

Mixed methods

Quantitative and qualitative data were mixed using an explanatory sequential approach. The quantitative data were weighted more heavily and used to identify overall patterns. The qualitative data were then integrated to explain and contextualize those trends, offering nuanced insight into participants’ experiences.

Results

Participant characteristics are summarized in Table 1. Participants were primarily White (95.2%), female (71.8%), and middle-aged (M = 46.9, SD = 13.7). Most participants were married or partnered (55.3%), college educated (69.9%), employed (69.9%), and had a household income >$50 000 (54.5%). The average BMI was 41.6 (SD = 8.4). Sociodemographic characteristics are generally representative of the region, though there were a greater percentage of participants who identified as White when compared to county-level and US Census data [28, 29].

Table 1.

Participant characteristics of the full study sample.

Measure	Total sample (N = 103)
Continuous variables	M (SD)
Age	46.9 (13.7)
Body mass index (kg/m2)	41.6 (8.4)
Categorical variables	N (%)
Gendera
Female	74 (71.8)
Male	22 (21.4)
Non-binary/genderqueer	2 (1.9)
Race and ethnicity
American Indian or Alaskan Native	0 (0)
Black	2 (1.9)
Hispanic or Latino	2 (1.9)
Middle Eastern or North African	0 (0)
Native Hawaiian or Pacific Islander	0 (0)
White	98 (95.2)
Multiracial	1 (1.0)
Education
No high school diploma	2 (1.9)
High school graduate/GED	29 (28.2)
Some college	21 (20.3)
Associates degree	15 (14.6)
Bachelor’s degree	25 (24.3)
Professional or graduate degree	11 (10.7)
Marital statusb
Married/Living with partner	57 (55.3)
Divorced/Separated	17 (16.5)
Widowed	25 (24.3)
Never married	2 (1.9)
Employment status
Employed	72 (69.9)
Unemployed	13 (12.6)
Receive social security or  disability benefits	10 (9.7)
Retired	8 (7.8)
Household incomec
<$10 000	5 (4.9)
$10 000–$50 000	35 (34.0)
$50 000–$100 000	29 (28.2)
$100 000–$150 000	17 (16.5)
$150 000–$200 000	5 (4.9)
$200 000+	5 (4.9)

Full sample N = 103.

^a N = 5 prefer not to answer.

^b N = 2 prefer not to answer.

^c N = 7 prefer not to answer.

Quantitative

Participants’ experiences with attempting weight loss varied widely. On average, participants reported making 10.1 (SD = 13.5) serious attempts to lose weight throughout their adult life. Table 2 summarizes weight-loss strategies endorsed by participants. The most common strategy was making general improvements in eating habits (99%) whereas the least common was therapy/counseling/lifestyle modification (9%).

Table 2.

Reported lifetime history of use of various weight-loss strategies.

Weight-loss strategy	N (%)
General improvement in eating habits	99 (96.1)
Generally be more active	81 (77.9)
Specific diet program	69 (66.3)
Elimination diet	67 (64.4)
Meal or nutrient tracking	63 (60.6)
Skipping meals or fasting	63 (60.6)
Visiting a nutritionist/dietician	53 (51.0)
Formal exercise program/gym membership/personal trainer	52 (50.0)
Exercise tracking	50 (48.1)
Over-the-counter medication	44 (42.3)
Prescription medication	34 (32.7)
Visiting a weight loss specialist	31 (29.8)
Bariatric surgery	12 (11.5)
Taking laxatives, diuretics, or making yourself vomit	11 (10.6)
Therapy/counseling/lifestyle modification	9 (8.7)

Data are presented in descending order. Participants were asked to endorse all that apply.

Table 3 summarizes factors influencing participant’s desire to lose weight. Participants were also asked to rank the top three factors motivating them to lose weight. The most highly ranked items were wanting to feel better physically (86.4%), general health concerns (78.6%), and wanting to be more fit (78.6%).

Table 3.

Factors influencing desire to lose weight.

Measure	N (%)
Wanting to feel better physically/have more energy/be more active	89 (86.4)
Having general health concerns	81 (78.6)
Wanting to be more fit/in better shape	81 (78.6)
Wanting to be more confident/improve self-esteem	73 (70.9)
Wanting to fit into smaller clothes	60 (58.3)
Reaching the upper end of weight range comfortable with	46 (44.7)
Wanting to be a positive role model for family/children	43 (41.7)
Wanting to stop or not need to take medication	27 (26.2)
Encouragement from healthcare provider	23 (22.3)
Wanting to improve sex life	22 (21.4)
Encouragement from family or friend	20 (19.4)
Encouragement from others trying to lose weight (e.g. work health competition)	18 (17.5)
Wanting to improve job performance	14 (13.6)
Specific medical event (e.g. heart attack)	11 (10.7)
Major life change (e.g. divorce, retirement)	9 (8.7)
Financial incentive (e.g. lower co-pay)	6 (5.8)
Upcoming special event or occasion	6 (5.8)
Othera	2 (1.9)

Data are presented in descending order.

^aOther answers included to be eligible for other surgical procedure (e.g. total knee replacement).

Table 4 summarizes factors influencing participant’s choice of treatment. Participants were also asked to rank the top three factors. The most highly ranked items were how much weight I want to lose (84.5%), recommendations from healthcare providers (76.7%), and cost (62.1%).

Table 4.

Factors influencing decision-making when deciding which weight management strategy or treatment to use.

Measure	Yes
How much weight I want to lose	87 (84.5%)
Recommendations from healthcare providers	79 (76.7%)
Treatment cost	64 (62.1%)
Other health conditions which affect ability to diet or exercise	62 (60.2%)
Other health conditions which impact candidacy for specific treatment	54 (52.4%)
How quickly I want to lose weight	53 (51.5%)
Commitment/motivation	50 (48.5%)
Mental health	45 (43.7%)
Recommendations or experiences of family and friends	36 (35.0%)
How much time I can dedicate to weight management	36 (35.0%)
Medical guidelines	26 (25.2%)
Recommendations from a wellness coach	10 (9.7%)
Recommendations from books, magazines, or online sources	10 (9.7%)
Recommendations from social media	6 (5.8%)
Advertising/commercials	3 (2.9%)

Data are presented in descending order.

Figure 1 depicts participants’ perceptions of the three primary weight loss treatments [i.e. behavioral weight loss and lifestyle modification (BWL), AOMs, and metabolic and bariatric surgery (MBS)]. In general, AOMs were viewed the most favorably. Table 5 summarizes participants’ perceived effectiveness of various weight-loss strategies. Lifestyle changes (i.e. general changes in eating habits and being more active) and AOMs were perceived to be the most effective.

Figure 1.

Perceptions of obesity treatment options.

Note. Response options ranged from 1 = “Do not agree at all” to 5 = “Completely agree.” Error bars represent SD.

Table 5.

Perceived effectiveness of weight-loss strategies.

Measure	Mean (SD)
General improvement in eating habits	4.2 (1.0)
Prescription medication	4.0 (0.9)
Generally be more active	4.1 (1.0)
Visiting a weight loss specialist	3.9 (0.9)
Visiting a nutritionist/dietician	3.9 (1.0)
Formal exercise program/personal trainer	3.6 (1.0)
Meal or nutrient tracking	3.6 (1.1)
Exercise tracking	3.4 (1.1)
Bariatric surgery	3.4 (1.4)
Specific diet program	3.1 (1.1)
Therapy/counseling/lifestyle modification	3.1 (1.1)
Over-the-counter medication	2.7 (1.2)

Answers could range from 1 = “not at all effective” to 5 = “extremely effective.” Data are presented in descending order.

Qualitative

Characteristics of participants who completed qualitative interviews are summarized in Table 6. Participants were between the ages of 20 and 73 with 58.8% identifying as cis-gendered females. Although we successfully recruited participants with varying weight loss histories and across a range of socioeconomic stratum, we were less successful at recruiting racial and ethnic minorities.

Table 6.

Participant characteristics of qualitative sample.

Measure	N = 17
Continuous variables	M (SD)
Age (years)	47.0 (13.4)
Body mass index (kg/m2)	42.5 (7.6)
Categorical variables	N (%)
Weight loss history
Mean number of weight loss attempts	14.6 (24.1)
History of AOM use	8 (47.1)
History of MBS	2 (11.8)
Gender
Female	10 (58.8)
Male	7 (41.2)
Race and ethnicity
Hispanic or Latino	1 (5.9)
White	15 (88.2)
Multiracial	1 (5.9)
Educationa
No high school diploma	1 (5.9)
High school graduate/GED	3 (17.6)
Some college	1 (5.9)
Associates degree	2 (11.8)
Bachelor’s degree	8 (47.1)
Professional or graduate degree	1 (5.9)
Marital statusb
Married/Living with partner	10 (58.8)
Widowed	1 (5.9)
Never married	5 (29.4)
Employment statusc
Employed	13 76.5)
Receive social security or disability benefits	3 (17.6)
Retired	1 (5.9)
Household incomed
$10 000–$50 000	5 (26.4)
$50 000–$100 000	3 (17.6)
$100 000–$150 000	6 (35.3)
$200 000+	2 (11.8)

^a N = 1 prefer not to answer.

^b N = 1 prefer not to answer.

^c N = 1 prefer not to answer.

^d N = 1 prefer not to answer.

Four primary themes and several subthemes emerged from the inductive analysis. They are summarized in Table 7 and described in more detail below.

Table 7.

Themes and subthemes from qualitative interviews.

Themes and subthemes
A chronic cycle of weight loss and regain
The physical and emotional toll of repeated weight loss attempts
Resilience despite longstanding struggle
Guided by stories, not science alone
Learning through lived and observed experience
Media as a catalyst for awareness and understanding of obesity treatment options
Navigating trade-offs in choosing weight loss strategies
Coverage and cost as gatekeepers to care
Seeking effective and sustainable solutions
Viewing treatment as a stepped care model
Feeling heard, but still seeking direction
Shared decision-making as a foundation for trust and confidence
Wanting more than empathy and the need for specific direction
Searching for the why: unanswered questions about weight struggles

A chronic cycle of weight loss and regain

All but one participant described a lifetime of dieting attempts, marked by patterns of weight loss followed by regain. Many characterized their history as a cycle of intense but unsustainable efforts. Two subthemes emerged: (i) the physical and emotional toll of repeated weight loss attempts; and (ii) resilience despite longstanding struggle. Together, these themes situate decision-making for obesity within a broader narrative of longstanding struggle and resilience.

Participants described the impact of repeated weight loss attempts on their physical, emotional, and mental health. These ranged from mild effects to more serious results such as one participant who reported losing their gallbladder after use of over-the-counter weight loss pills. Several described resorting to unhealthy weight loss strategies consistent with disordered eating. Many described the emotional toll reporting their lifetime of dieting attempts contributed to feelings of frustration and discouragement.

“It was just a roller coaster of, lose, gain, lose, gain, lose, gain. And it was, it felt like it was just impossible to do it.”—48-year-old White female

Participants described a pattern of setbacks and prolonged challenges. Yet rather than interpreting these as signs of failure, most expressed a belief that improvement was still possible. Many framed their continued engagement in the context of personal determination and moral commitment. Across participant narratives, ongoing weight loss efforts were portrayed not simply as endurance, but as an active commitment to keep striving even when progress felt slow or uncertain.

“I just I’m missing a component to my weight loss. And I just, I cannot pinpoint it. You know, like I’m doing the best I know to do for my health and for my weight loss journey, but it’s something missing.” P350—61-year-old Hispanic female

Guided by stories, not science alone

Participants were asked about their knowledge of different obesity treatments including how they’ve learned about various treatments. This theme captures the multiple, intersecting ways patients learn about, and make sense of, obesity treatment options. Two subthemes emerged within the primary theme: (i) learning through lived and observed experience, and (ii) media as a catalyst for awareness and understanding of obesity treatment options. Though a few participants explicitly referenced scientific research and empirical evidence as below, many participants emphasized the former sources, suggesting they are used to construct and interpret their understanding of available treatments.

“Typically, I’ll look at some of the, at least in my mind, are typically a little more credible, you know, like the WebMD, Mayo Clinic. Public published articles and professional medical journals that I’ll read…”—52-year-old White male

For many, firsthand or vicarious accounts carried substantial weight in shaping beliefs about what “works,” what feels risky, and what seems attainable. These lived and observed experiences felt more influential than clinical information alone, functioning as tangible evidence that shaped expectations and perceptions.

“My coworker, she was prediabetic, just bordering on becoming diabetic, and they gave her that shot. And it’s helping her. It curbs her appetite. She’s losing weight… That’s why I was looking to the shot. This is working for her… And I need to see that kind of loss too.”—73-year-old White female

Participants also frequently highlighted the powerful role of media in increasing awareness of treatment options. Sources ranged from commercials to social media to reality television. GLP-1 commercials were frequently mentioned, and the reality television show My 600 lb Life was cited several times regarding sources of knowledge about MBS.

“I think its Ozempic or whatever. And, obviously, I mean you see commercials every day. You can’t miss ‘em. I almost know the songs unfortunately.”—51-year-old White male

Navigating trade-offs in choosing weight loss strategies

Participants were asked about the factors they prioritize when selecting a treatment approach. This theme reflects the deliberative process of evaluating different treatment options, weighing the risks, benefits, and feasibility. Participants described considering factors such as convenience, provider recommendations, empirical evidence, cost, and insurance coverage when deciding which approach felt realistic.

Insurance and affordability emerged as dominant factors, often constraining choices and causing frustration when preferred treatments were financially inaccessible.

“I couldn’t afford, you know, $800 a month for you know, any of the other things because my insurance didn’t cover it. So, I opted to try the other medication, simply because it was more affordable.”—58-year-old White female

Participants emphasized the desire for the most effective treatment option. However, participants’ definitions of “effective” included not just substantial weight loss, but also sustainable weight loss. Participant narratives spoke to wanting to break the chronic cycle of weight loss and regain and select a treatment approach that will lead to durable weight loss.

“I don’t want to just lose the weight, but I want to keep it off.”—35-year-old White female

Many participants conceptualized treatment like a stepped care model. BWL was often viewed as the starting point, with AOMs and MBS considered progressively more intensive interventions. Many participants described wanting to attempt “natural” lifestyle changes before considering medical or surgical approaches. Although several saw MBS as effective, it carried stigma or was viewed as “extreme.”

“Everything else that I had tried historically, I’m in my 40s at this point, didn’t really work. I was nervous and not really excited to try a medication, but I felt kind of like it was, other than surgery, my last resort… So a weight loss medication seemed like if I really wanted to, to take a hold of my health, that that was going to be the best option to try. It’s also reversible so…”—46-year-old White female

Feeling heard, but still seeking direction

This theme reflects participant’s perceptions of SDM and the relational aspects of obesity care. Overall, this theme and subthemes underscore that while supportive, collaborative relationships are central to navigating complex treatment decisions, patients also need concrete, individualized recommendations to feel confident in their treatment path.

Participants consistently emphasized the importance of being included in care decisions, describing SDM as a central to feeling respected and understood. Conservations where clinicians asked for their input, acknowledged their preferences, and invited questions reinforced a sense of partnership in their relationship.

“I think listening to what my story was and then it felt like her answers on what I should do were motivated by that. Like, it definitely felt like she got what I was coming from and where I had been before and wasn’t like, alright, but have you considered carrot sticks?”—42-year-old White male

Many participants described feeling supported by their providers. These participants valued providers who listened to their concerns, acknowledged their experiences, and offered guidance without judgement. However, participants also expressed a desire for concrete, actionable recommendations to guide their next steps. They often wanted clear direction about which treatment options would be the most effective for them personally, rather than solely supportive dialogue.

“Some doctors I’ve seen in the past have always just been like. Oh yeah, you just need to diet and exercise, and I’m like, well, yeah, but I’ve been trying.”—20-year-old multiracial male

A recurring frustration emerged around not receiving meaningful explanations for why they had been unable to lose weight despite years of effort. Several participants described seeking clarity about the underlying causes of their struggles (biological, behavioral, or otherwise) and felt discouraged when providers could not offer satisfying answers. This gap between emotional support and practical guidance sometimes left patients feeling validated but not fully equipped to move forward in their care.

“So, you know, I, I’m wondering at this point is there something wrong with my, you know, my body… Why is this stuff not working?—58-year-old White female

Discussion

This mixed-methods study explored patient knowledge and perceptions of obesity treatment options, including preferences and factors impacting decision-making processes. Using a sequential explanatory design, quantitative findings showed that patients weigh several factors when selecting a treatment, with perceived effectiveness, recommendations from providers, and cost being dominant factors. Qualitative findings provided deeper insight into how decisions are formed, underscoring that patient knowledge and expectations are shaped not only by scientific evidence but also by personal testimonies and media exposure. Qualitative findings also highlighted the importance of SDM and personalized action plans in patient-centered obesity care.

History of weight loss attempts

Participants in the current study reported an extensive history of weight loss attempts using a range of strategies. The average number of weight loss attempts exceeded those reported in prior research [17, 30], though this may reflect sampling differences. Consistent with earlier findings [31–33], most participants described modifying eating habits and increasing physical activity. Of concern, 10.6% of participants reported a history of engaging in inappropriate compensatory behaviors, a prevalence much higher than rates in nationally representative community samples (0.37%–4.74%) [31–33]. These data highlight the necessity of routine screening and monitoring of eating disorders in weight management settings. Interestingly, therapy, counseling, or lifestyle modification were the least utilized approaches, despite lifestyle change being perceived as an essential component for weight loss. This discrepancy may reflect limited awareness about professional services, stigma surrounding behavioral health care, and/or access barriers. Future research should explore these factors as it may inform efforts to increase engagement with evidence-based behavioral interventions.

Knowledge of weight loss treatments and expectations

Although participants generally demonstrated a strong understanding and awareness of available treatment options, misconceptions regarding their relative effectiveness were common. For example, participants rated AOMs as more effective than MBS, contradicting evidence that MBS produces greater, more sustainable weight loss outcomes [34]. Additionally, a prevailing perception was that lifestyle changes are a highly effective intervention for weight reduction. Participants rated general improvements in eating habits as the most effective weight loss strategy in one survey item, despite evidence that lifestyle changes alone typically yield substantially less weight loss than AOMs or MBS [34, 35].

Qualitative interviews helped contextualize this discrepancy, revealing that participants rarely conceptualize weight-loss strategies in isolation. Instead, they described lifestyle changes as necessary components of long-term weight management, noting they should be used in conjunction with AOMs and MBS. Of note, participant recollections related to MBS often centered on surgical complications and weight regain. In contrast, GLP-1s were described as “popular” or a “miracle drug,” and participants noted repeated exposure to direct-to-consumer advertisements and other media, a theme noted in other studies [36, 37]. This is notable given a recent analysis of social media advertisements for GLP-1s which found advertisements commonly amplify benefits while downplaying risks [38]. Conversely, no participants mentioned encountering media content that promoted lifestyle change, and the only media reference to MBS was the reality television show My 600 lb Life, which typically portrays severe cases of obesity and may reinforce stigmatizing perceptions of surgical treatment. Taken together, these patterns suggest that widespread marketing and positive personal testimonials amplify perceptions of GLP‑1 effectiveness, whereas limited and often unfavorable depictions of MBS contribute to misconceptions about its safety and effectiveness. These dynamics appear to shape patient perceptions more strongly than scientific evidence alone.

Decision-making factors

Quantitative findings revealed patients consider multiple factors when determining a treatment approach and qualitative findings further highlighted the deliberative nature of these decisions. Like previous quantitative studies [16, 39], expected weight loss was the most influential factor. However, insurance coverage and cost emerged as major barriers. During interviews, patients often expressed frustration with restrictive insurance criteria for GLP-1s and prohibitive out-of-pocket costs, echoing findings from previous work [40]. As a result, patients cannot fully consider all evidence-based treatment options when many are effectively inaccessible.

Although prior research suggests a disconnect between patient’s weight loss goals and their chosen treatments [16], the current study indicates that such decisions reflect more complex trade-offs. Participants balanced weight loss goals against concerns about side effects, convenience, financial burden, etc. While recognized as effective, MBS was often viewed as “extreme,” illustrating that treatment selection reflects multifaceted values rather than a singular focus on efficacy [41]. Together, results indicate the perceived mismatch between weight loss goals and treatment selection is more complex than previously assumed.

SDM and obesity treatment

Participants reflected that SDM is necessary, but not sufficient, for effective obesity treatment. SDM fostered feelings of respect, trust, and partnership, consistent with prior conceptualizations of SDM [10, 11], even amid broader declines in public trust in science and medicine [42]. However, many participants desired more personalized and concrete guidance than they received. General recommendations without actionable detail limited patients’ confidence in implementing treatment plans, consistent with prior research [15].

This tension highlights an important nuance: while SDM enhances relational aspects of care (e.g. trust, communication) it may not, on its own, provide the level of structured support required for patients navigating the complexities of obesity treatment. Given the multifactorial nature of obesity, patients often require specific behavioral strategies or stepwise plans. Without sufficient specificity, patients may feel heard but not adequately helped.

Research and practice implications

Findings point to several research and practice implications. Given the influence of personal narratives and media messaging, clinicians must be prepared to address misconceptions that could stem from these sources. Narrative-based educational tools may help communicate risks and benefits in patient-centered ways [43]. Additionally, more balanced, accurate health communication that clearly conveys both risks and benefits of available treatments is needed. Calls for such efforts already exist [38, 44, 45], and there may be opportunities to learn from recent public health campaigns. For example, the collaboration between social media influencers and the Harvard T.H. Chan School of Public Health, aimed at reducing online mental health misinformation [46], may serve as a model for parallel initiatives focused on obesity.

In addition, findings suggest that SDM is most effective when paired with concrete behavioral guidance and individualized action planning [47, 48]. Provider training that incorporates behavioral counseling strategies, such as SMART (Specific, Measurable, Achievable, Relevant, Time-bound) goal setting, may enhance implementation. Participants endorsement of staged treatment approaches suggest value in explicitly incorporating stepwise planning into SDM discussions. At a research level, adaptive designs such as Sequential Multiple Assignment Randomized Trials could be used to evaluate acceptability and optimize individualized care pathways [49].

Strengths and limitations

The current study must be interpreted in the context of several strengths and limitations. Strengths include the mixed-methods design, patient advisory council engagement, and recruitment of a sociodemographically diverse US sample. Limitations include limited generalizability to the broader US population or to international contexts and use of a non-validated survey instrument. Future research using discrete choice experiments across multiple institutions/sites could address this. Additionally, qualitative approaches are subject to bias, though the use of reflexivity and triangulation methods helped enhance analytic rigor [26, 27].

Conclusion

Shared decision-making is becoming increasingly complex as obesity treatments continue to expand. Patients in this study not only demonstrated strong awareness of available treatments but also held misconceptions likely shaped by personal narratives and media. While patients valued SDM, they expressed a clear preference for personalized, actionable guidance. These findings highlight the need to strengthen SDM with tailored recommendations and high-quality health communication across clinical and public domains.

Funding sources

This project was funded by the Geisinger Henry Hood Clinical and Research Excellence Award, awarded to Dr Christopher Still.

Role of the funder

The funders had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Supplementary material

Supplementary material is available at Translational Behavioral Medicine online.

Human rights

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Welfare of animals

This article does not contain any studies with animals performed by any of the authors.

Transparency statement

Study registration: This study was not formally registered.

Analytic plan pre-registration: The analysis plan was not formally pre-registered.

Analytic code availability: Analytic code used to conduct the analyses presented in this study are not available in a public archive. They may be available by emailing the corresponding author.

Materials availability: The study survey and interview moderators guide are available in the Supplementary Material.

Data availability

De-identified data from this study are not available in a public archive. De-identified data from this study will be made available (as allowable according to institutional IRB standards) by emailing the corresponding author.

Artificial intelligence (AI)

Artificial intelligence was not used in the development of this manuscript. Generative AI tool (Google NotebookLM) was used to assist in the creation of the graphical abstract. The authors have verified the accuracy of these images.