Effects of high-intensity exercise on rehabilitation of patients after stroke: a systematic review and meta-analysis of randomized controlled trials with high quality
Bibliographic record
Abstract
Objectives To present the latest systematic review and meta-analysis of high-quality randomized controlled trials (RCTs) comparing high-intensity exercise with routine rehabilitation in stroke patients. Methods PubMed, Web of Science, and Cochrane were used to searching literature up to October 2024. RCTs with sample size of ≥50 individuals were included. Primary outcomes assessed were the Six-Minute Walking Test (6MWT), Ten-Meter Walk Test (10MWT), VO2peak, Berg Balance Scale (BBS), Timed Up and Go test (TUG), and Montreal Cognitive Assessment (MoCA). Standardized mean differences (SMD) with 95% confidence intervals (CI) were used for pooling data. Stability was evaluated by sensitivity analysis. Results Seven RCTs with 724 participants were included. Meta-analysis revealed significant improvements in the 6MWT (SMD: 0.28; 95% CI: 0.11, 0.45) and BBS (SMD: 0.35; 95% CI: 0.03, 0.67) in the high-intensity exercise group. However, high-intensity exercise had no significant effect on VO2peak, TUG, or 10MWT. Sensitivity analysis showed that all outcomes were stable except for the 10MWT. No significant publication bias was detected for any indicator. Conclusion High-intensity exercise significantly improves 6MWT and BBS in stroke patients, but does not significantly affect TUG, VO2peak, 10MWT, or MoCA. Clinicians should encourage stroke patients with walking function to engage in structured high-intensity exercise to improve cardiopulmonary function. Systematic review registration CRD42024623036 Publicly accessible URL: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024623036 .
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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 | 0.029 | 0.063 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.029 | 0.050 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".