Mediators of physical activity behaviour change interventions among adults: a systematic review and meta-analysis
Bibliographic record
Abstract
An understanding of physical activity through mediators of behaviour change is important to evaluate the efficacy of interventions. The purpose of this review is to update prior reviews with meta-analysis to evaluate the state of physical activity interventions that include proposed mediators of behaviour change. Literature was identified through searching for five key databases. Studies were eligible if they described a published experimental or quasi-experimental trial in English examining the effect of an intervention on physical activity behaviour and mediators in non-clinical adult populations with the necessary statistical information to be included in the meta-analytic structural equation modelling analysis. Fifty-one articles (49 samples) met the eligibility criteria. Small overall effects were identified for mediation paths a (r = .16; 95% CI = .10 to .22), b (r = .21; 95% CI .16 to .27), and c (r = .24; 95% CI .12 to .35), c′ (r = .05 to .19) and ab (r = .02 to .07) that showed similar findings by theory and construct. The effect sizes seen in physical activity interventions are mediated by our current theories, but the effects are very small and no one construct/theory appears to be a critical driver of the mediated effect compared to any other. Innovation and increased fidelity of interventions is needed.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.022 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".