Financial incentives for physical activity in adults: Systematic review and meta-analysis update
Bibliographic record
Abstract
OBJECTIVE: To update the evidence on the effects of financial incentives (FI) on physical activity (PA) in adults. METHODS: A systematic search of nine databases (Medline, EMBASE, PsychINFO, Scopus, Web of Science, CINAHL, EconLit, SPORTDiscus, and Cochrane) was conducted to identify randomised controlled trials (RCTs) and pilot RCTs published between June 1, 2018 and March 31, 2024 examining FI-for-PA interventions. 'Vote counting' and random-effects meta-analyses assessed short- (<6 months) and long-term (≥6 months) FI effects, as well as impact during follow-up (incentive withdrawal). Meta-regressions examined moderator effects. RESULTS: Twenty-nine studies (n = 21 RCT, n = 8 pilot RCTs; median FI size = $1.19 USD/day) involving 9604 participants were included (60.8 % female, mean age = 42.7 years). 17 of 21 studies reported positive short-term effects. 5 of 5 and 3 of 8 studies, respectively, reported positive long-term and follow-up effects. Among the 15 studies included in daily step count meta-analyses (most commonly reported PA outcome), FI had a moderate effect during short-term interventions (standardized mean difference [SMD] [95 % CI] = 0.52 [0.25-0.78], p < 0.001) and a small effect in follow-up (SMD [95 % CI] = 0.20 [0.01-0.40], p = 0.04). Too few long-term studies reported daily step count to conduct pooled analyses (n = 1). Meta-regressions suggest study length, incentive size, wearable device-use, and goal setting moderate FI effects (p < 0.05). CONCLUSIONS: Twenty-nine studies were identified over a 6-year span. Short-term FI interventions increase PA. The impact on daily step count is clinically significant (≥1000 steps/day). Key contextual factors moderate effects. Evidence is limited regarding long-term and follow-up effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".