Aerobic exercise alleviates depressive symptoms in patients with a major non-communicable chronic disease: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To assess whether aerobic exercise was superior to usual care in alleviating depressive symptoms in patients living with a major non-communicable disease. DATA SOURCES: Data were obtained from online databases (PubMed, PsycINFO and SPORTDiscus) as well as from reference lists. The search and collection of eligible studies was conducted up to 18 October 2018 (PROSPERO registration number CRD42017069089). STUDY SELECTION: We included interventions that compared aerobic exercise with usual care in adults who reported depressive symptoms (ie, not necessarily the clinical diagnosis of depression) and were living with a major non-communicable disease. RESULTS: Twenty-four studies were included in the meta-analysis (4111 patients). Aerobic exercise alleviated depressive symptoms better than did usual care (standardised mean difference (SMD)=0.50; 95% CI 0.25 to 0.76; Grading of Recommendations Assessment, Development and Evaluation: low quality). Aerobic exercise was particularly effective in alleviating depressive symptoms in cardiac patients (SMD=0.67; 95% CI 0.35 to 0.99). CONCLUSION: Aerobic exercise alleviated depressive symptoms in patients living with a major non-communicable disease, particularly in cardiac populations. Whether aerobic exercise treats clinically diagnosed depression was outside the scope of this study.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.021 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".