Optimizing physical fitness in chronic stroke patients: the impact of exercise training modality and dosage on maximal and sub-maximal fitness – a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effects of different exercise training modalities on maximal and sub-maximal physical fitness in chronic stroke patients and determine the optimal training dosage. DESIGN: Systematic review and meta-analysis of 38 randomized controlled trials. METHODS: A comprehensive search was conducted across seven databases (MedLine, Embase, ScienceDirect, Cochrane Library, CINAHL, and SPORTSDiscus) up to March 31, 2024. Maximal fitness was measured by VO2 max/peak, and sub-maximal fitness by the 6- or 12-minute walk test (6MWT) Results: Aerobic and mixed training significantly improved VO2 max/peak (MD = 3.16 [2.83, 3.49], p < 0.00001; I² = 22%). Only aerobic training significantly enhanced 6MWT performance (MD = 34.30 [25.08, 43.53], p < 0.00001; I² = 25%). Sensitivity analysis revealed that VO2 max/peak gains were greater with moderate-to-high intensity, while moderate intensity sufficed for 6MWT improvement. The optimal regimen was 45-minute sessions of moderate-to-high intensity aerobic training, at least three times weekly for a minimum of eight weeks. CONCLUSION: Moderate-to-vigorous aerobic training enhances physical fitness in chronic stroke. High-intensity and mixed training programs yield greater maximal fitness improvements, while moderate intensity benefits sub-maximal capacity. Targeted, intensity-monitored exercise programs of ≥45 minutes, three times weekly over ≥8 weeks, are recommended for significant fitness gains.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.026 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".