Boosting Physical Activity Among Individuals With Low Engagement Through Double-Point Incentives in a Community-Based mHealth Intervention: Retrospective Observational Study
Bibliographic record
Abstract
Background: The administration of incentives to promote physical activity, such as the amount or timing, can vary depending on target health behaviors, research settings, intervention delivery channels, and participants' preferences. Interventions implemented at scale necessitate the consideration of potential fiscal constraints for public health promotion. Since limited funding is a barrier to implementing community-based interventions, examining both immediate and sustained effects of temporary incentive increases on physical activity is important. Objective: This study aimed to evaluate the effect of a 1-week double-point event on increasing physical activity among low-engaged individuals in the context of a community-based mobile intervention. Methods: Using retrospective data from a Seoul Metropolitan Government mobile health (mHealth) intervention, we evaluated the effects of a 1-week double-point incentive on participants' physical activity. During 3 registration phases from November to December 2021, a total of 50,145 individuals enrolled. Our analysis focused on the low-engaged group (n=27,833, 55.5%), who averaged fewer than 3 days per week of meeting the daily step challenge (at least 7000 steps) before the intervention. We performed a segmented regression analysis to assess changes in physical activity before and after the event. Multivariable logistic regression and Cox proportional hazards models were used to identify factors associated with improving and maintaining physical activity after starting the intervention. Results: Of 27,833 low-engaged participants, only 13.7% (n=3835) improved their physical activity. Daily challenge engagements per week increased by 2.53 times, and average daily steps increased by 1924.97 (standardized mean difference 0.55, 95% CI 0.51-0.58). In multivariable logistic regression, older age was significantly associated with improved physical activity immediately after starting the intervention. However, 50% (1918/3835) of the improved group was likely to return to low engagement 3 weeks after the intervention ended. Older age and use of certain wearable devices were associated with maintaining physical activity after the intervention. Conclusions: Double-point incentives in the short term may serve as a cue-to-action to motivate low-engagement targets; however, they do not seem to guarantee long-term maintenance in the context of community-based mHealth interventions. Further research is needed to identify additional strategies beyond monetary incentives to sustain long-term healthy behavior.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".