Amy L. Ahern

Background: Behavioural weight management programmes can prevent type 2 diabetes and reduce risk factors for complications and medication use in those with type 2 diabetes. However, few are sufficiently scalable, and weight is typically regained. We evaluated whether scalable behavioural weight management programmes can achieve and maintain weight loss and reduce type 2 diabetes incidence and complications in two populations: people at high risk, and those recently diagnosed. Objectives: To quantify the impact of scalable behavioural weight management programmes on type 2 diabetes incidence, related complications and health resource use (work package 1). To develop and test the feasibility and acceptability of a scalable weight loss maintenance programme (work package 2). Design and methods: Work package 1: Work package 1.1: to evaluate the effectiveness and cost-effectiveness of a scalable behavioural weight management programme for individuals at risk of type 2 diabetes, we conducted a 5-year follow-up of the Weight loss Referrals for Adults in Primary care randomised controlled trial ( N = 1267). Work package 1.2: to evaluate the effectiveness and cost-effectiveness of a scalable weight loss programme for individuals recently diagnosed with type 2 diabetes, we conducted a new randomised controlled trial ( N = 577). For both trials, we evaluated effectiveness (change in weight and glycated haemoglobin) and within-trial cost-effectiveness from a National Health Service perspective. Semistructured interviews explored the experiences of participants and practitioners. Work package 1.3: using the National Institute for Health and Care Research School for Public Health Research Diabetes Prevention microsimulation model, we evaluated the lifetime cost-effectiveness of the interventions evaluated in work package 1.1 and work package 1.2. To reduce uncertainty and increase confidence in our findings, we compared findings to a separate microsimulation model (National Health Service Health Checks model). Work package 2: Work package 2.1: we used an evidence-, theory- and person-based approach to develop a programme for weight loss maintenance (Supporting Weight Management). Work package 2.2: we evaluated the feasibility and acceptability of Supporting Weight Management in a 6-month randomised controlled trial ( N = 61) with embedded qualitative study of participants ( N = 28) and coaches ( N = 2). Work package 2.3: we used the School for Public Health Research Diabetes model and expected value of sample information methods to estimate the value of alternative trial designs in reducing uncertainty in the cost-effectiveness estimates. Setting and participants: Adults living with overweight and obesity who are at risk of type 2 diabetes or who have a recent diagnosis. Primary care and community settings in England. Interventions: Scalable behavioural interventions for weight management. Work package 1.1: brief advice, 12-week weight watchers or 52-week weight watchers. Work package 1.2: six-month weight watchers with tailored diabetes education and dietitian support via telephone [diabetes education and behavioural weight management programme (intervention group in the GLoW trial)] or diabetes education only. Work package 2: Supporting Weight Management (a digital, acceptance-based, guided self-help programme for weight loss maintenance) or standard advice (leaflet on weight loss maintenance). Main outcome measures: Primary outcomes were weight or glycated haemoglobin. Secondary outcomes included healthcare resource use, quality of life and psychosocial measures. Results: Work package 1.1: mean 5-year weight change was: −0.46 (standard deviation 8.31) kg in the brief intervention group, −1.95 (9.55) kg in the 12-week programme group and − 2.67 (9.81) kg in the 52-week programme. The adjusted difference in weight change between groups was not statistically significant. During the trial, the 12-week programme incurred the lowest cost and produced the highest quality-adjusted life-years. Qualitative interviews identified key differences in the cognitive and behavioural strategies used by ‘maintainers’ and ‘regainers’. Work package 1.2: we found no evidence for an intervention effect on change in glycated haemoglobin but diabetes education and behavioural weight management programme participants lost more weight than diabetes education at 6 [−1.77 (−2.86 to −0.67) kg] and 12 months [−1.38 (−2.56 to −0.19) kg]. During the trial, diabetes education and behavioural weight management was cost-effective compared with diabetes education. Qualitative interviews identified how the intervention was effective and key barriers to effectiveness. Work package 1.3: in the lifetime cost-effective analysis, 52-week weight watcher was cost-effective compared with brief intervention, 12-week weight watcher and a simulated do-nothing scenario, producing more health benefit and cost savings. A lifetime cost-effectiveness of diabetes education and behavioural weight management compared with diabetes education was cost-effective with an incremental cost-effectiveness ratio of £2290 per quality-adjusted life-year gained. Work package 2.1: we codeveloped the Supporting Weight Management intervention for weight loss maintenance. It involves 14 psychoeducational sessions delivered via a web-based platform over 18 weeks, and telephone support from trained, non-specialist coaches. Work package 2.2: pre-trial average weight loss was 8 kg; during the trial Supporting Weight Management participants lost a further 2.2 (± 6.4 standard deviation) kg and control participants gained 2.2 (± 6.6) kg. Supporting Weight Management participants described that Supporting Weight Management reinforced existing knowledge and supported skill development for weight loss maintenance. Work package 2.3: the simulated justifiable cost of a weight maintenance intervention was £104.64 (body mass index ≥ 28) and £88.14 (type 2 diabetes), based on a cost-effectiveness threshold of £20,000. A large prospective randomised controlled trial to evaluate Supporting Weight Management was found to be cost-effective in expected value of sample information analysis. Limitations: The coronavirus disease discovered in 2019 pandemic resulted in changes to study and intervention procedures, delayed recruitment, and higher-than-anticipated attrition. The ethnic profile of participants limits the generalisability of findings to minority ethnic groups. Conclusions: This research supports scalable behavioural weight management programmes as cost-effective for preventing and treating type 2 diabetes. Findings highlight the need for targeted support for maintaining weight loss. Acceptance-based guided self-help is an acceptable and feasible approach to support weight maintenance. Future work: We have identified a range of research needs going forward, including developing and testing interventions to prevent weight regain and improve long-term health economic outcomes, improving the reach into and engagement with under-represented groups, as well as enhancing and validating long-term health economic models using real-world weight trajectories to better predict lifetime cost-effectiveness and equity impacts. Study registration: The study WRAP is registered as ISRCTN64986150; GLoW is registered as ISRCTN18399564 and the SWiM feasibility study is registered as ISRCTN12685964. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Programme Grants for Applied Research Programme (NIHR award ref: RP-PG-0216-20010) and is published in full in Programme Grants for Applied Research ; Vol. 14, No. 22. See the NIHR Funding and Awards website for further award information.

Background: Young adults increasingly rely on social media for nutrition information. However, little is known about (1) which types of eating-related content they actively engage with and why, and (2) how they interpret, evaluate, and incorporate this content into their everyday food choices and health behaviors. Objective: This qualitative study explored how UK young adults (aged 18-25 years) interact with food and nutrition content across social media platforms to inform the design of future social media interventions. Methods: Semistructured online interviews, guided by the Capability, Opportunity, Motivation–Behavior (COM-B) model, were conducted with 25 active social media users (18/25, 72% women, mean age 22.2, SD 1.9 years, ethnically diverse) in the United Kingdom between August and October 2024. The study design was informed by patient and public involvement to ensure relevance and acceptability. Data were analyzed using reflexive thematic analysis. To guide intervention development, key findings (coded as barriers and facilitators) were systematically mapped to the Theoretical Domains Framework, and the COM-B. Ethics approval was obtained from the University of Cambridge (24.368). Results: Five key themes were identified: (1) evolving engagement patterns (passive scrolling to active interaction and mixed feelings on algorithmic control), (2) conflicted information seeking (frustration with contradictory advice, varied strategies to assess credibility), (3) multifaceted behavioral impact (simultaneous positive impacts such as cooking inspiration and negative impacts such as restrictive eating triggers), (4) shifting goals (a movement from appearance-focused to health-centered goals; yet, vulnerability to body-image issues), and (5) intervention preferences (demand for credible professionals, customizable content, and privacy protection). Participants demonstrated a reactive learning process, developing “digital nutrition literacy” often after negative experiences. Social influences were identified as the most frequently cited domain (mapped to TDF [theoretical domains framework]/COM-B) shaping interactions with social media content. Conclusions: This study challenges assumptions of passive social media consumption, showing that young adults actively develop protective strategies yet remain vulnerable to misinformation. Digital interventions should leverage user agency and address diverse perceptions through customizable, credible content delivered with privacy and emotionally safe messaging. The COM-B and TDF mapping provide specific, evidence-based behavioral targets, particularly within the domain of Social Opportunity and Reflective Motivation, to guide the development of effective eHealth interventions. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2023-083465

BACKGROUND Young adults increasingly rely on social media for nutrition information. However, little is known about (1) which types of eating-related content they actively engage with and why, and (2) how they interpret, evaluate, and incorporate this content into their everyday food choices and health behaviors. OBJECTIVE This qualitative study explored how UK young adults (aged 18-25 years) interact with food and nutrition content across social media platforms to inform the design of future social media interventions. METHODS Semistructured online interviews, guided by the Capability, Opportunity, Motivation–Behavior (COM-B) model, were conducted with 25 active social media users (18/25, 72% women, mean age 22.2, SD 1.9 years, ethnically diverse) in the United Kingdom between August and October 2024. The study design was informed by patient and public involvement to ensure relevance and acceptability. Data were analyzed using reflexive thematic analysis. To guide intervention development, key findings (coded as barriers and facilitators) were systematically mapped to the Theoretical Domains Framework, and the COM-B. Ethics approval was obtained from the University of Cambridge (24.368). RESULTS Five key themes were identified: (1) evolving engagement patterns (passive scrolling to active interaction and mixed feelings on algorithmic control), (2) conflicted information seeking (frustration with contradictory advice, varied strategies to assess credibility), (3) multifaceted behavioral impact (simultaneous positive impacts such as cooking inspiration and negative impacts such as restrictive eating triggers), (4) shifting goals (a movement from appearance-focused to health-centered goals; yet, vulnerability to body-image issues), and (5) intervention preferences (demand for credible professionals, customizable content, and privacy protection). Participants demonstrated a reactive learning process, developing “digital nutrition literacy” often after negative experiences. Social influences were identified as the most frequently cited domain (mapped to TDF [theoretical domains framework]/COM-B) shaping interactions with social media content. CONCLUSIONS This study challenges assumptions of passive social media consumption, showing that young adults actively develop protective strategies yet remain vulnerable to misinformation. Digital interventions should leverage user agency and address diverse perceptions through customizable, credible content delivered with privacy and emotionally safe messaging. The COM-B and TDF mapping provide specific, evidence-based behavioral targets, particularly within the domain of Social Opportunity and Reflective Motivation, to guide the development of effective eHealth interventions. CLINICALTRIAL INTERNATIONAL REGISTERED REPORT RR2-10.1136/bmjopen-2023-083465

Objective: To understand delivery features and intervention strategies of adolescent weight management interventions which may influence eating disorder risk. Methods: Systematic searches in four databases and two trial registries to identify randomised controlled trials in adolescents with overweight/obesity measuring eating disorder risk pre‐ and post‐intervention. Delivery features and intervention strategies were coded from published descriptions using a project‐specific codebook, validated by trial investigators and narratively synthesised. Results: Of 11 860 records screened, 23 trials, with 54 intervention arms, were included in the analysis. Most interventions focused on weight loss and maintenance (54%) and were informed by a cognitive behavioural framework (43%). Interventions commonly targeted an individual with a support person (70%). Median intervention duration was 26 weeks, with weekly (35%) or staged (e.g., weekly, then monthly) visit (41%) frequency. Interventions had a mean (SD) of 30 (16.1) intervention strategies. Most included healthy eating education (89%), physical activity education (89%) and problem‐solving barriers to dietary change (80%). Few included mental health strategies (17%). Interventions included ‘dietary prescription’ (65%), and 78% promoted ‘healthful/helpful eating behaviours’. Conclusion: Weight management interventions are complex and vary in delivery approach and strategies used to change behaviors. Characterising interventions is a critical first step to understanding how weight management interventions' influence eating disorder risk.

Objective: To analyse whether conducting a randomised controlled trial (RCT) to evaluate an online weight maintenance guided self-help intervention (Supporting Weight Management (SWiM)) would offer good value for money in the United Kingdom. Method: We examined 24 RCT designs by varying inclusion criteria (participants completing behavioural weight management, specialist-led weight management, diabetes prevention programme, type 2 diabetes remission, digital weight management, all weight management services), trial duration (1–2 years), and sample size (n = 500 or 2000). Trial benefits were estimated by the method of expected value of sample information analysis using a health economic model. The model examines how the proposed intervention affects weight maintenance over time (with uncertainty), and generates estimated lifetime Quality Adjusted Life Years (QALYs) and National Health Service (NHS) costs. Structured expert elicitation with 4 experts was undertaken to quantify pre-trial uncertainty in the effectiveness of SWiM compared with usual care. All trial designs were simulated to estimate trial benefits: the reduction in the costs of an inefficient decision for future populations over 10 years. Trial designs offer value for money if trial benefits exceed trial costs. Results: For three inclusion criteria options (groups recently completing ‘diabetes remission’, ‘digital weight management’ or ‘specialist weight management’), the cost of the proposed trials was estimated to exceed the estimated trial benefit (value of the reduction in decision uncertainty) over 10 years. For the other three inclusion criteria options (groups recently completed ‘behavioural weight management’, ‘diabetes prevention programme’, or ‘all weight loss programmes’), 12 trial designs produced greater benefits than costs. The optimal trial design option would include ‘all weight loss programmes’, with 2 years follow-up and sample size n = 2000. Conclusion: Investment in a large RCT to evaluate the SWiM intervention for patients completing a range of weight loss interventions offers the greatest value to the NHS.

Background: Obesity care may benefit from precision approaches, matching patients to treatment types based on their individual characteristics, including eating behaviour traits (EBTs) like emotional eating, uncontrolled eating, external eating, internal disinhibition and restraint. Initial evidence suggests that Acceptance and Commitment Therapy (ACT)-based interventions might address dysregulated EBTs more effectively than standard behavioural treatments. However, it is unclear if ACT is more effective for certain EBT levels. Methods and analysis: This pre-registered (CRD42022359691) one-stage Individual Participant Data (IPD) meta-analysis explored the moderating effects of baseline EBTs on weight outcomes in trials of ACT-based interventions for adults with a BMI ≥ 25 kg/m². Unlike traditional meta-analyses, IPD meta-analyses re-analyse existing data to answer novel research questions. We identified 16 eligible trials through a systematic search of eight databases until June 20, 2022. We obtained, checked, and harmonised data from 15 trials (N = 2535). We used mixed regression models to investigate both continuous and categorical interaction effects. Results: We found no evidence of interactions between ACT vs. control and baseline EBTs as continuous variables on percentage weight change. However, we found evidence to suggest an added difference in weight change of −4.47% (95%CI −1.15, −7.73) from baseline to 12-months after intervention end in participants with medium levels of internal disinhibition compared to those with high levels. Sensitivity analyses similarly indicated a greater intervention benefit for participants with medium, rather than high, emotional eating levels (in trials that reduced experiential avoidance and in trials using the three-factor eating questionnaire) and internal disinhibition (in analyses of participants with at least 60% attendance). Given the exploratory nature of analyses, results should be interpreted with caution. Conclusion: Findings suggest potential non-linear interaction effects of ACT with internal disinhibition but require replication in confirmatory trials. These results may help guide further research on precision approaches based on EBTs.

Background: Most weight lost during weight‐loss programmes is eventually regained. Interventions based on Acceptance and Commitment Therapy (ACT) demonstrate good evidence for long‐term weight loss, but are often costly and difficult to scale up. Guided self‐help programmes delivered using technology and non‐specialist coaches could increase scalability, but it is unclear whether delivering ACT‐based interventions in this way is feasible and acceptable. Methods: In this feasibility study, 61 people who recently completed a behavioral weight management intervention (BWMI) for weight management were randomly allocated to SWiM (“Supporting Weight Management”: 4‐month digital guided self‐help ACT‐based intervention for weight loss maintenance) or a standard care group (leaflet about maintaining weight loss) using a 2:1 allocation ratio. At baseline and 6 months, participants completed measures of weight, mental health, eating behavior, and other psychosocial variables. Participants completed an intervention evaluation questionnaire. At 3 and 6 months, qualitative interviews were conducted with participants from both trial arms and SWiM coaches. The analysis integrated statistics and thematic analysis, informed by the Medical Research Council (MRC) framework for process evaluations. Since this was a feasibility study, analyses focused on process outcomes instead of interpreting statistical significance. Results: Eighty‐eight percent (36/41) of participants allocated to SWiM completed at least the first session and 22 (54%) completed all sessions. At 6 months, mean weight change was −2.2 (+/−6.4 SD) kg in SWiM participants and +2.2 (+/−6.6) kg in standard care participants. Descriptively, eating behavior and mental health scores improved in SWiM participants but not in standard care participants. In interviews, SWiM participants noted that they reinforced their existing knowledge while acquiring new skills and strategies, which were felt to contribute to positive behavioral changes. Conclusion: The SWiM intervention is practical and well‐received, and shows promise in supporting weight loss maintenance, though evaluation in a larger trial is needed to assess effectiveness. Trial Registration: ISRCTN12685964

Aims/hypothesis: UK standard care for type 2 diabetes is structured diabetes education, with no effects on HbA_1c, small, short-term effects on weight and low uptake. We evaluated whether remotely delivered tailored diabetes education combined with commercial behavioural weight management is cost-effective compared with current standard care in helping people with type 2 diabetes to lower their blood glucose, lose weight, achieve remission and improve cardiovascular risk factors. Methods: We conducted a pragmatic, randomised, parallel two-group trial. Participants were adults (≥18 years) with overweight or obesity (BMI≥25 kg/m²) and recently diagnosed with type 2 diabetes (≤3 years), recruited from 159 primary care practices in England. We randomised participants to a tailored diabetes education and behavioural weight management programme (DEW; delivered by Weight Watchers) or to current standard care diabetes education (DE; Diabetes Education and Self Management for Ongoing and Newly Diagnosed [DESMOND] programme), using a computer-generated randomisation sequence in a 1:1 allocation stratified by gender and diabetes duration, unknown to those collecting and analysing the data. Participants could not be blinded due to the nature of the interventions. Participants completed assessments at 0, 6 and 12 months. The primary outcome was 12 month change from baseline in HbA_1c. We also assessed bodyweight, blood pressure, cholesterol (total, HDL, LDL), glucose-lowering medication, behavioural measures (physical activity, food intake), psychosocial measures (eating behaviour, diabetes-related quality of life, wellbeing) and within-trial and modelled lifetime cost effectiveness. Results: We randomised 577 participants (DEW: 289, DE: 288); 398 (69%) completed 12 month follow-up. We found no evidence for an intervention effect on change in HbA_1c from baseline to 12 months (difference: −0.84 [95% CI −2.99, 1.31] mmol/mol, p=0.44) or 6 months (−1.83 [−4.05, 0.40] mmol/mol). We found an intervention effect on weight at 6 (−1.77 [−2.86, −0.67] kg) and 12 months (−1.38 [−2.56, −0.19] kg). Participants in DEW had a higher likelihood of achieving diabetes remission than participants in DE (6 months: RR 2.10 [95% CI 1.03, 4.47]; 12 months: RR 2.53 [1.30, 5.16]). DEW was cost-effective compared with DE in within-trial and lifetime analyses, in the latter generating an incremental cost effectiveness ratio of £2290 per quality-adjusted life year gained. Conclusions/interpretation: A commercial behavioural weight management programme combined with remote dietary counselling after diagnosis of type 2 diabetes did not improve HbA_1c up to 12 months post intervention in this trial. The intervention could help people achieve weight loss and be cost-effective compared with current standard National Health Service care. Trial registration: ISRCTN 18399564 Funding: National Institute for Health and Care Research (NIHR; RP-PG-0216-20010), Medical Research Council (MC_UU_00006/6), NIHR Cambridge Biomedical Research Centre (NIHR203312). Graphical

Obesity is the accumulation of excess adipose tissue (fat) and presents risks to physical and mental health. Health-related behaviours contribute to the occurrence of obesity, and both these behaviours and obesity itself may be responsible for some of the health risks. Additionally, societal stigmatisation of obesity may also account for these negative physical and psychological outcomes. Individual behaviour is embedded in biogenetic, environmental, socio-economic, and structural drivers. Approaches to reducing population obesity involve a synergy of interventions on multiple levels, the majority of which directly or indirectly influence health behaviours. Interventions at any level benefit from addressing the deleterious effects of weight stigma. This chapter introduces the condition of obesity and examines the role that health behaviours play in causing and treating excess adipose tissue. This chapter focuses on outlining the complexity of behaviour as embedded with a system of factors relevant for explaining obesity.