Effectiveness and cost-effectiveness of text messages with or without endowment incentives for weight management in men with obesity (Game of Stones): study protocol for a randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: Obesity increases the risk of type 2 diabetes, heart disease, stroke, mobility problems and some cancers, and its prevalence is rising. Men engage less than women in existing weight loss interventions. Game of Stones builds on a successful feasibility study and aims to find out if automated text messages with or without endowment incentives are effective and cost-effective for weight loss at 12 months compared to a waiting list comparator arm in men with obesity. METHODS: or more living in and around three UK centres (Belfast, Bristol, Glasgow), purposively targeting disadvantaged areas. Intervention groups: (i) automated, theory-informed text messages daily for 12 months plus endowment incentives linked to verified weight loss targets at 3, 6 and 12 months; (ii) the same text messages and weight loss assessment protocol; (iii) comparator group: 12 month waiting list, then text messages for 3 months. The primary outcome is percentage weight change at 12 months from baseline. Secondary outcomes at 12 months are as follows: quality of life, wellbeing, mental health, weight stigma, behaviours, satisfaction and confidence. Follow-up includes weight at 24 months. A health economic evaluation will measure cost-effectiveness over the trial and over modelled lifetime: including health service resource-use and quality-adjusted life years. The cost-utility analysis will report incremental cost per quality-adjusted life years gained. Participant and service provider perspectives will be explored via telephone interviews, and exploratory mixed methods process evaluation analyses will focus on mental health, multiple long-term conditions, health inequalities and implementation strategies. DISCUSSION: The trial will report whether text messages (with and without cash incentives) can help men to lose weight over 1 year and maintain this for another year compared to a comparator group; the costs and benefits to the health service; and men's experiences of the interventions. Process analyses with public involvement and service commissioner input will ensure that this open-source digital self-care intervention could be sustainable and scalable by a range of NHS or public services. TRIAL REGISTRATION: ISRCTN 91974895 . Registered on 14/04/2021.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".