The evidence for physical activity in the management of major mental illnesses
Bibliographic record
Abstract
PURPOSE OF REVIEW: Physical activity has established the efficacy in improving physical health, mental health and cognition in the general population. Recent research has examined its potential as a preventive measure and/or adjunctive treatment for various mental health conditions. This review summarizes the recent evidence for physical activity in the management of major mental illnesses. RECENT FINDINGS: Emerging evidence suggests that physical activity may confer protection against depression and anxiety/stress disorders. There is robust evidence that structured and supervised physical activity, including aerobic and resistance training, can improve multiple outcomes in major depression, pre/postnatal depression, anxiety/stress disorders and schizophrenia. Emerging evidence suggests a potential role for physical activity in bipolar disorder and alcohol use disorders. SUMMARY: The quantity and quality of evidence regarding the efficacy of physical activity for mental illnesses is increasing. Given the established and further potential benefits and low adverse risk profile, physical activity should be offered as an adjunctive part of core mental health treatment. However, there is a need for high-quality multisite randomized controlled trials that can be replicated in routine care in mental health services. Future population-level trials are needed to examine the potential use of physical activity in those at risk of mental health conditions to see if physical activity can prevent the development of mental disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".