Tracked Physical Activity Levels Before and After a Change in Incentive Strategy Among UK Adults Using a Rewards App: Retrospective Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: Financial incentives delivered via apps appear to be effective in encouraging physical activity. However, the literature on different incentive strategies is limited, and the question remains whether financial incentives offer a cost-effective intervention that could be funded at the population level. OBJECTIVE: This study aimed to explore patterns of tracked physical activity by users of an incentive-based app before and after a change in incentive strategy. A business decision to alter the incentives in a commercially available app offered a natural experiment to explore GPS-tracked data in a retrospective, quasi-experimental study. The purpose of this exploratory analysis was to inform the design of future controlled trials of incentives delivered via an app to optimize their usability and cost-effectiveness. METHODS: Weekly minutes of tracked physical activity were explored among a sample of 1666 participants. A Friedman test was used to determine differences in physical activity before and after the change in incentive strategies. Post hoc Wilcoxon tests were used to assess minutes of physical activity in the 2 weeks before and after the change. A secondary analysis explored longitudinal patterns of physical activity by plotting the mean and median minutes of physical activity from 17 weeks before and 13 weeks after the change in incentive strategy. CIs were calculated using bias-corrected bootstraps. Demographics were also explored in this way. RESULTS: =42, P<.001). However, a longitudinal view of the data showed a more complex and marked variation in activity over time that undermined the conclusions of the before/after analysis. CONCLUSIONS: Short-term before-and-after observational studies of app-tracked physical activity may result in misleading conclusions about the effectiveness of incentive strategies. Longitudinal views of the data show that important fluctuations are occurring over time. Future studies of app-tracked physical activity should explore such variations by using longitudinal analyses and accounting for possible moderating variables to better understand what an effective incentive might be, for whom, and at what cost.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".