Exercise for depressive symptoms in stroke patients: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective: The objective was to conduct a systematic review and meta-analysis of studies that examined the effects of structured exercise on depressive symptoms in stroke patients. Methods: We searched for published randomized controlled trials that evaluated the effect of structured exercise programs (e.g. functional, resistance, or aerobic training) on depressive symptoms. The mean effect size, a 95% confidence interval (CI) and I-squared (I 2 ) for heterogeneity were estimated. Sensitivity analyses were conducted. Results: Thirteen studies ( n = 1022) were included in the meta-analysis. Exercise resulted in less depressive symptoms immediately after the exercise program ended, standardized mean difference = −0.13 [95% CI = −0.26, −0.01], I 2 = 6%, p = 0.03, but these effects were not retained with longer term follow-up. Exercise appeared to have a positive effect on depressive symptoms across both the subacute (≤6 months post stroke) and chronic stage of recovery (>6 months). There was a significant effect of exercise on depressive symptoms when higher intensity studies were pooled, but not for lower intensity exercise protocols. Antidepressant medication use was not documented in the majority of studies and thus, its potential confounding interaction with exercise could not be assessed. Conclusions: Exercise may be a potential treatment to prevent or reduce depressive symptoms in individuals with subacute and chronic stroke.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.006 | 0.006 |
| 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".