One Size Does Not Fit All: Financial Incentives Needed to Change Physical Exercise Levels for Different Groups
Bibliographic record
Abstract
This study estimated the distribution of willingness to accept (WTA) for a physical activity behavior change intervention entailing the completion of 10,000 steps/day to shed light on which levels of incentives trigger a change in behavior for different proportions of the population and for more at-risk subgroups. An online contingent valuation (CV) survey was administered to 1130 respondents in the Basque Autonomous Community, Spain. The survey queried respondents about their physical activity levels and intention to engage in physical activity before presenting the WTA questions. Nonparametric WTA values were estimated for the whole sample and for subsamples of active, inactive, and overweight and obese people. One-quarter of respondents would engage with the hypothetical program even without payment, but if a monetary incentive was offered them, they would take it. The median WTA for committing to complete 10,000 steps/day is €0.23 for the full sample, €0.21 for active, €0.25 for inactive, and €0.23 for overweight and obese people. The WTA at 75th percentile is €4 for the full sample, €1.70 for active, €10.80 for inactive, and €5 for overweight and obese respondents. WTA is positively affected by a person's lack of disposable time to increase their physical activity and, for inactive people, by their poor intention to become physically active.
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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 | 0.030 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".