Examining the impact of financial incentive removal on physical activity: A quasi-experimental study of 584,760 mobile health application users
Bibliographic record
Abstract
BACKGROUND: Government interest in using financial incentives (FIs) to stimulate physical activity (PA) is increasing. The cost of longer-term incentive interventions may be prohibitive, however. PURPOSE: To examine the impact of FI withdrawal on PA. METHODS: A 25-week retrospective pre-post quasi-experimental study was conducted with users of a FI-based mHealth app. Users from three Canadian provinces were included. Daily FI were removed in Ontario (ON; intervention) but not British Columbia (BC) and Newfoundland and Labrador (NL; control). Simple linear regression models were used to examine weekly mean daily step count after FI withdrawal. RESULTS: The total sample included 584,760 users (Female: 63.5%; Age: 34.3 years). Following FI withdrawal, weekly mean daily step count decreased in all provinces with the largest decrease observed in ON (i.e., 198 and 274 fewer steps/day vs. BC and NL, respectively). CONCLUSION: These findings may be relevant for governments looking to deploy time-limited FI-based PA programs.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".