Physical activity and mental health: a systematic review and best-evidence synthesis of mediation and moderation studies
Bibliographic record
Abstract
BACKGROUND: While evidence consistently demonstrates that physical activity is beneficial to mental health, it remains relatively unknown how physical activity benefits mental health, and which factors influence the effect of physical activity on mental health. This understanding could vastly increase our capacity to design, recommend, and prescribe physical activity in more optimal ways. The purpose of this study was to systematically review and synthesise evidence of all mediators and moderators of the relationship between physical activity and mental health. METHODS: Systematic searches of four databases (i.e., Scopus, PsycINFO, PubMed, and SPORTDiscus) identified 11,633 initial studies. Empirical studies that quantitatively assessed physical activity, or conducted a physical activity intervention, measured a mental health outcome, and tested one or more mediator or moderator of the relationship between physical activity and mental health were included. A total of 247 met the inclusion criteria; 173 studies examined mediation and 82 examined moderation. RESULTS: Results of the best-evidence synthesis revealed strong evidence for 12 mediators including affect, mental health and wellbeing, self-esteem, self-efficacy, physical self-worth, body image satisfaction, resilience, social support, social connection, physical health, pain, and fatigue. Moderate evidence was identified for a further 15 mediators and eight moderators. CONCLUSIONS: Findings should inform the design of future physical activity interventions to ensure optimal effects on mental health related outcomes. Additionally, if health professionals were to take these mediators and moderators into consideration when prescribing or recommending physical activity, physical activity would likely have a greater impact on population mental health.
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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 | 0.048 | 0.176 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.019 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".