Aerobic exercise interventions for promoting cardiovascular health and mobility after stroke: a systematic review with Bayesian network meta-analysis
Bibliographic record
Abstract
Objective To determine the superiority of aerobic exercise (AE) interventions on key outcomes of stroke recovery, including cardiorespiratory fitness (V̇O2peak, primary outcome), systolic blood pressure (SBP) and mobility (6 min Walk Test (6MWT) distance and 10 m Usual Gait Speed) after stroke. Data sources MEDLINE, EMBASE, Web of Science, CINAHL, CENTRAL, SPORTDiscus, PsycINFO and AMED Allied and Complementary Medicine were searched from inception to February 2023. Eligibility criteria Randomised controlled trials were included that compared the effects of any AE interventions (low-intensity, moderate-intensity, high-intensity continuous training (HICT), high-intensity interval training (HIIT)) to no exercise, usual care or other AE interventions in individuals poststroke. Analyses Systematic review with Bayesian network meta-analysis (NMA) methodology was employed. Surface under the cumulative ranking curve (SUCRA) values were used to rank interventions. The Grading of Recommendations, Assessment, Development and Evaluation minimally contextualised framework for NMA was followed. Results There were 28 studies (n=1298) included in the NMA for V̇O2peak, 11 (n=648) for SBP, 28 (n=1494) for 6MWT and 18 (n=775) for the 10 m Usual Gait Speed. The greatest effect on V̇O2peak, 6MWT and 10 m Usual Gait Speed was observed after HIIT and HICT. No differences between interventions were found for SBP. SUCRA values identified HIIT as the superior AE intervention for all outcomes of interest. HIIT was the most effective intervention for improving V̇O2peak (2.9 mL/kg/min (95% credible interval 0.8 to 5.0) moderate certainty) compared with usual care. Conclusion This NMA suggests that higher-intensity AE is superior to traditional low-intensity to moderate-intensity AE for improving outcomes after 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 | 0.033 | 0.067 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.038 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".