Designing financial incentives for health behaviour change: a mixed-methods case study of weight loss in men with obesity
Bibliographic record
Abstract
Abstract Aim Designing financial incentives for health behaviour change requires choices across several domains, including value (the size of the incentive), frequency of incentives, and direction (gain or loss). However, the rationale underlying complex incentive design is infrequently reported. Transparent reporting is important if we want to understand and improve the incentive development process. This paper describes a mixed methods approach for designing financial incentives for health behaviour change which involves stakeholders throughout the design process. Subject and methods The mixed methods approach focuses on incentives for weight loss for men with obesity living in areas with high levels of disadvantage. The approach involves: (a) using an existing framework to identify all domains of a financial incentive scheme for which choices need to be made, deciding what criteria are relevant (such as effectiveness, acceptability and uptake) and making choices on each domain on the basis of the criteria; (b) conducting a survey of target population preferences to inform choices for domains and to design the incentive scheme; and (c) making final decisions at a stakeholder consensus workshop. Results The approach was implemented and an incentive scheme for weight loss for men living with obesity was developed. Qualitative interview data from men receiving the incentives in a feasibility trial endorses our approach. Conclusion This paper demonstrates that a mixed methods approach with stakeholder involvement can be used to design financial incentives for health behaviour change such as weight loss. Trial registration number NCT03040518. Date: 2 February 2017.
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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 | 0.087 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".