The use of aerobic exercise training in improving aerobic capacity in individuals with stroke: a meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To determine whether aerobic exercise improves aerobic capacity in individuals with stroke. DESIGN: A systematic review of randomized controlled trials. DATABASES SEARCHED: MEDLINE, CINAHL, EMBASE, Cochrane Database of Systematic Reviews and Physiotherapy Evidence Database were searched. INCLUSION CRITERIA: Design: randomized controlled trials (RCTs). PARTICIPANTS: individuals with stroke. INTERVENTIONS: aerobic exercise training aimed at improving aerobic capacity. PRIMARY OUTCOMES: aerobic capacity (peak oxygen consumption (VO2), peak workload). SECONDARY OUTCOMES: walking velocity, walking endurance. DATA ANALYSIS: The methodological quality was assessed by the PEDro scale. Meta-analyses were performed for all primary and secondary outcomes. RESULTS: Nine articles (seven RCTs) were identified. The exercise intensity ranged from 50% to 80% heart rate reserve. Exercise duration was 20-40 min for 3-5 days a week. The total number of subjects included in the studies was 480. All studies reported positive effects on aerobic capacity, regardless of the stage of stroke recovery. Meta-analysis revealed a significant homogeneous standardized effect size (SES) in favour of aerobic exercise to improve peak VO2 (SES 0.42; 95% confidence interval (CI) 0.15-0.69; P=0.001) and peak workload (SES 0.50; 95% CI 0.26-0.73; P<0.001). There was also a significant homogeneous SES in favour of aerobic training to improve walking velocity (SES 0.26; 95% CI 0.05-0.48; P= 0.008) and walking endurance (SES 0.30; 95% CI 0.06-0.55; P= 0.008). CONCLUSIONS: There is good evidence that aerobic exercise is beneficial for improving aerobic capacity in people with mild and moderate stroke. Aerobic exercise should be an important component of stroke rehabilitation.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.062 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".