Physical Activity for Cognitive and Mental Health in Youth: A Systematic Review of Mechanisms
Bibliographic record
Abstract
CONTEXT: Physical activity can improve cognitive and mental health, but the underlying mechanisms have not been established. OBJECTIVE: To present a conceptual model explaining the mechanisms for the effect of physical activity on cognitive and mental health in young people and to conduct a systematic review of the evidence. DATA SOURCES: Six electronic databases (PubMed, PsycINFO, SCOPUS, Ovid Medline, SportDiscus, and Embase) were used. STUDY SELECTION: School-, home-, or community-based physical activity intervention or laboratory-based exercise interventions were assessed. Studies were eligible if they reported statistical analyses of changes in the following: (1) cognition or mental health; and (2) neurobiological, psychosocial, and behavioral mechanisms. DATA EXTRACTION: Data relating to methods, assessment period, participant characteristics, intervention type, setting, and facilitator/delivery were extracted. RESULTS: Twenty-five articles reporting results from 22 studies were included. Mechanisms studied were neurobiological (6 studies), psychosocial (18 studies), and behavioral (2 studies). Significant changes in at least 1 potential neurobiological mechanism were reported in 5 studies, and significant effects for at least 1 cognitive outcome were also found in 5 studies. One of 2 studies reported a significant effect for self-regulation, but neither study reported a significant impact on mental health. LIMITATIONS: Small number of studies and high levels of study heterogeneity. CONCLUSIONS: The strongest evidence was found for improvements in physical self-perceptions, which accompanied enhanced self-esteem in the majority of studies measuring these outcomes. Few studies examined neurobiological and behavioral mechanisms, and we were unable to draw conclusions regarding their role in enhancing cognitive and 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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| 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".