Prescribing strength training for stroke recovery: a systematic review and meta-analysis of randomised controlled trials
Bibliographic record
Abstract
OBJECTIVE: To examine the effects of strength training on patient-important outcomes of stroke recovery and to quantify the influence of the exercise prescription on treatment effects. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Eight electronic databases (MEDLINE, EMBASE, EMCARE, AMED, PsycINFO, CINAHL, SPORTDiscus, and Web of Science) and two clinical trial registries (ClinicalTrials.gov and WHO International Clinical Trials Registry Platform) were searched from inception to 19 June 2024. ELIGIBILITY CRITERIA: Randomised controlled trials were eligible if they examined the effects of strength training compared with no exercise or usual care and reported at least one exercise prescription parameter. An advisory group of community members with lived experience of stroke helped inform outcomes most relevant to stroke recovery. RESULTS: Forty-two randomised trials (N=2204) were included. Overall risk of bias was high across most outcomes. Strength training improved outcomes rated as 'critical for decision-making' by the advisory group, including walking capacity (standardised mean difference (SMD)=0.95 (95% CI 0.34-1.56)), balance (SMD=1.13 (0.51-1.75)), functional ability and mobility (SMD=0.61 (0.09-1.14)), and habitual (mean difference (MD)=0.05 m/s (0.02-0.09)) and fast-paced walking speed (MD=0.09 m/s (0.01-0.17)), with very low to moderate certainty of evidence, mainly due to risk of bias and inconsistency. More frequent strength training, traditional strength training programmes and power-focused intensities (ie, emphasis on movement velocity) were positively associated with walking capacity, health-related quality of life and fast-paced walking speed. CONCLUSION: Strength training alone or combined with usual care improves stroke recovery outcomes that are important for decision-making. More frequent strength training, power-focused intensities and traditional programme designs may best support stroke recovery. PROSPERO REGISTRATION NUMBER: CRD42023414077.
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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 | 0.036 | 0.099 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".