Men with long-term conditions in the Game of Stones text messaging and financial incentives trial: an exploratory mixed methods study
Bibliographic record
Abstract
Abstract Men living with multiple long-term conditions and obesity are under-represented in behavioural weight management trials. Within an effective text messaging and financial incentives trial, our aim was to explore retention, secondary mental health and wellbeing outcomes, and experiences of men with multiple long-term conditions. Methods Mixed methods process evaluation within a 3-group randomised controlled trial: behavioural text messages with financial incentives, texts alone and waiting-list control, for 583 of 585 men with obesity. Trial retention, mental health and wellbeing outcomes, and experiences were compared for 235 (40%) participants with multiple long-term conditions, 181 (31%) with single conditions, 167 (29%) with no conditions, and for 165 (29%) with disability. Semi-structured interviews, analysed using the Framework method, explored experiences with weight trajectories. Concurrent descriptive and qualitative analyses were undertaken. Results Of the 235 (40%) trial participants with multiple long-term conditions, 99 were disabled and 93 were living in deprived areas. Participants with multiple long-term conditions and/or disability were older, fewer had a degree level qualification, and fewer were in full time work. Retention at 12 months was higher for men with disability (76%) or no long-term conditions (75%), and lower for men with diabetes (65%). Self-reported weight stigma, wellbeing and quality of life scores improved or stayed the same for men living with multiple long-term conditions in the intervention groups, however, results for anxiety and depression screening scores were inconsistent. Participant experiences indicated complex dynamic health, social and life situations which could provide motivation to lose weight for some but not others. Hospitalisation and poor mobility, with inability to exercise, was de-motivating for making changes to reach weight loss targets. Conclusion Men with multiple long-term conditions varied from very successful weight loss and improved health, to not prioritising or feeling helped by the programme or disengagement due to immobility or diabetes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".