Evan M. Forman

Standard behavioral weight loss (SBT) is a first-line intervention for obesity, but its efficacy with Black adults is limited. Some preliminary work suggests that acceptance-based behavioral therapy (ABT), which integrates mindful decision making, experiential acceptance, and reflection on values, may be a better fit for the needs of Black adults. In this fully powered secondary data analysis, we compared the efficacy of SBT and ABT for Black adults. Black and non-Hispanic White adults (N = 737) with overweight or obesity (body mass index = 27-50 kg/m²) were enrolled in one of the five randomized controlled trials investigating the effectiveness of ABT versus SBT. Analyses assessed the impact of race, treatment condition, and their interaction on percent weight loss at posttreatment and first follow-up assessment while controlling for clinical trial. The interaction effects between race and treatment condition on weight loss were trending toward significance (p < .05) at posttreatment, F(1, 729) = 2.74, p = .089, but not at follow-up, F(1, 729) = .003, p = .957. Pairwise comparisons revealed that the disparity in posttreatment weight loss between Black and White participants in the SBT condition (M = 3.5%, p = .001) was attenuated in the ABT condition (M = 1.5%, p = .05). ABT has promise for supporting Black adults in weight loss attempts, although weight regain after treatment remains persistent for Black and White adults alike. Future research is warranted to continue developing this treatment approach as a tool to reduce health disparities. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Background: Approximately 2 billion adults seek lifestyle modification to improve their health and well-being. However, because of cost and access barriers, only a tiny fraction receive gold-standard lifestyle modification programs led by trained coaches. Digital programs without human coaches are more disseminable but have limited efficacy. Recent advances in large language model (LLM) AI chatbots have the potential to revolutionize digital programs by emulating human coaches, offering a safe, low-cost, and scalable lifestyle modification intervention. Yet, no studies have developed and tested such a program. Purpose: This study aims to evaluate the feasibility, safety, and user engagement of a new AI chatbot-powered lifestyle modification program named Lyra, which utilizes recent advances in LLM technology to potentially emulate the benefits of human coaching. Methods: We developed Lyra, which integrates structured psychoeducational programming, digital tracking of health behaviors, and text messaging, all powered by an LLM engine. We tested the 8-week program, which included interactive training in behavioral strategies, customized meal plans, tailored feedback on goal progress, and on-demand coaching, with 17 participants in a sample of participants with overweight/obesity seeking lifestyle modification. Results: The program was successfully implemented at a cost of $1.78/participant/week. Coders rated 2288 messages from and 475 conversations with Lyra. All were deemed safe, and the mean expertise was 8.5/10. Participants (mean BMI = 31.5) rated Lyra as helpful (69%), empathic (88%), and easy to use (94%); 88% valued having 24-h availability, and 75% developed a connection to Lyra. Participants initiated an average of 6.2 conversations, ranging from coping with cravings to unprompted “confessions” of having consumed unhealthy food. Conclusion: This new form of intervention proved feasible, acceptable, safe, clinically adept, and capable of fostering a connection. However, rigorous testing of Lyra’s effectiveness is necessary.

Objective: From October 18 to 20, 2022, the National Institutes of Health held a workshop to examine the state of the science concerning obesity interventions in adults to promote health equity. The workshop had three objectives: (1) convene experts from key institutions and the community to identify gaps in knowledge and opportunities to address obesity, (2) generate recommendations for obesity prevention and treatment to achieve health equity, and (3) identify challenges and needs to address obesity prevalence and disparities and develop a diverse workforce. Methods: A three‐day virtual convening. Results: Several key themes emerged from the workshop discussions that describe directions to build on the currently limited amount of research on obesity, disparities, and equity. Key themes centered on the determinants of health, leveraging technology, clinical, community, commercial, and policy approaches. Community‐engaged work, particularly in populations that have received little focus (e.g., sexual gender minorities, Asian communities), was also discussed. Conclusions: Future research may be impactful when multilevel approaches are undertaken that leverage equity‐minded tools and can be scaled up to meet community‐informed population needs in a variety of settings. Funding priorities and workforce development will be critical to realizing health equity.

Automated coaching messages for weight control can save time and costs, but their repetitive, generic nature may limit their effectiveness compared to human coaching. Large language model (LLM) based artificial intelligence (AI) chatbots, like ChatGPT, could offer more personalized and novel messages to address repetition with their data-processing abilities. While LLM AI demonstrates promise to encourage healthier lifestyles, studies have yet to examine the feasibility and acceptability of LLM-based BWL coaching. Eighty-seven adults in a weight-loss trial (BMI ≥ 27 kg/m ² ) rated ten coaching messages’ helpfulness (five human-written, five ChatGPT-generated) using a 5-point Likert scale, providing additional open-ended feedback to justify their ratings. Participants also identified which messages they believed were AI-generated. The evaluation occurred in two phases: messages in Phase 1 were perceived as impersonal and negative, prompting revisions for messages in Phase 2. In Phase 1, AI-generated messages were rated less helpful than human-written ones, with 66% receiving a helpfulness rating of 3 or higher. However, in Phase 2, the AI messages matched the human-written ones regarding helpfulness, with 82% scoring three or above. Additionally, 50% were misidentified as human-written, suggesting AI’s sophistication in mimicking human-generated content. A thematic analysis of open-ended feedback revealed that participants appreciated AI’s empathy and personalized suggestions but found them more formulaic, less authentic, and too data-focused. This study reveals the preliminary feasibility and perceived helpfulness of LLM AIs, like ChatGPT, in crafting potentially effective weight control coaching messages. Our findings also underscore areas for future enhancement.

Background: Identifying mechanisms of action can aid the refinement of weight management interventions. Acceptance and Commitment Therapy (ACT)-based interventions may support long-term weight management by improving self-regulation of eating behavior traits (EBTs). However, it remains unclear if changing EBTs like emotional eating, external eating, internal disinhibition, and restraint during ACT causes improved weight management. Methods: For this 1-stage Individual Participant Data (IPD) meta-analysis, we requested IPD from 9 trials identified through a systematic search of ACT-based interventions for adults with a body mass index >25 kg/m2 across 8 databases until June 20, 2022. We obtained, checked, and harmonized data from 8 of those trials (N = 1391) and conducted separate structural equation models with complex survey analysis to estimate short- and long-term mediating effects of changes in each EBT on percent weight change. Results: In the short-term (ie, follow-up closest to intervention end), we found indirect effects of the intervention on percent weight change through changes in emotional eating, external eating, internal disinhibition, and restraint. Each 1-unit change in these EBTs led to a 0.02% (95% CI, 0.05–0.001), 0.03% (95% CI, 0.06–0.001), 0.05% (95% CI, 0.11–0.02), and 0.09% (95% CI, 0.14–0.04) decrease in weight, respectively. In the long term (ie, 12 months after intervention end), we found both indirect and total effects for emotional eating, internal disinhibition, and restraint, with EBT changes explaining 23.78%, 23.12%, and 25.64% of total effects. Conclusion: Findings suggest small partial mediating effects of ACT on weight through EBTs. Targeting EBTs may support improved weight management outcomes, particularly in the long term.

Obsessive-compulsive disorder (OCD) is a common, debilitating, and costly anxiety disorder. The gold standard treatment for OCD is exposure and ritual prevention (ERP) and despite its well-established efficacy, only a small percentage of OCD patients have access to this treatment. Remote treatments (e.g., videoconferencing) are becoming more popular avenues for delivering evidence-based treatments and show promise in increasing patient access to evidence-based mental health care. However, various barriers to successfully delivering ERP for OCD remain. High rates of treatment refusal and attrition may be even higher when utilizing remote treatment because the therapeutic presence is less potent than in face-to-face treatment settings. Thus it may be beneficial to supplement ERP with other interventions (e.g., acceptance and commitment therapy, ACT) aimed at facilitating greater engagement in ERP. The current study sought to address two primary questions. First, is it possible to sufficiently engage OCD patients via remote, Internet-based, state-of-the-art ERP therapy? Second, is it helpful to supplement standard ERP with interventions derived from ACT to facilitate exposures and promote treatment compliance? A randomized control led trial was designed to compare standard ERP to ERP+ACT delivered through Skype for adults with OCD. Fifteen adults with OCD were recruited from across the United States to participate in the study. Recruitment difficulties ultimately precluded between group analyses, but analyses across all participants revealed that videoconference-based delivery of ERP is feasible and acceptable to patients and therapists. Importantly, the intervention was effective in improving OCD symptoms and quality of life, and the effect sizes were comparable to other remote and in-person studies examining ERP for OCD. Additionally, ACT processes, i.e., increases in defusion and psychological flexibility, were associated with treatment outcome. This study adds to a growing body of literature suggesting that videoconference-based interventions are viable alternatives to face-to-face treatment methods. These technologies will likely play a crucial role in increasing the availability of empirically supported treatments to individuals who might not otherwise be able to access specialized mental health care.

Objective: This study experimentally tested whether coach access to participants' digital self‐monitoring data improved behavioral weight‐loss outcomes.Methods: Participants (N = 322) received 12 weeks of group‐based behavioral weight‐loss sessions via videoconference and were instructed to engage in daily self‐monitoring of weight, physical activity (PA), and dietary intake. For participants who were randomly assigned to Coach Share ON (n = 161), coaches regularly accessed a web‐based portal that displayed data from the participants' scale, PA sensor, and food record.Results: Weight loss at 12 weeks was significantly greater in Coach Share ON versus OFF (6.2% vs. 5.3%; p = 0.04). Self‐monitoring of PA (98.70% vs. 97.40% of days; p = 0.006) and eating (98.05% vs. 93.51% of days; p = 0.007) was more frequent in Coach Share ON versus OFF. There were no significant differences by condition in PA (p = 0.57), attendance (p = 0.42), working alliance (p = 0.62), or self‐monitoring of weight (p = 0.12). Perceived supportive accountability was significantly greater in Coach Share ON versus OFF (p < 0.001).Conclusions: The short‐term efficacy of behavioral weight loss was greater when coaches had direct access to self‐monitoring device data. Notably, there also was no evidence of iatrogenic effects of data sharing.