Effectiveness of different exercise interventions on balance and cognitive functions in stroke patients: A network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Exercise interventions are proven to improve functional outcomes in stroke patients, yet the optimal and safest exercise modalities remain uncertain. This network meta-analysis (NMA) aims to systematically compare the effects of various exercise interventions on balance and cognitive functions, providing robust evidence to guide clinical decision-making. METHODS: Web of Science, PubMed, Embase, and Cochrane Library were searched up to September 2024. Randomized controlled trials (RCTs) evaluating exercise interventions for balance and cognitive improvements in stroke patients were included. Quality assessment and data extraction were performed, followed by Bayesian NMA using Stata 15.0 and R 4.41. RESULTS: This study ultimately included 40 RCTs with 2,302 patients. Six commonly employed exercise interventions in clinical treatment were covered, including aerobic exercise (AE), core stability exercise (CSE), physical/mental exercise (PME), resistance training (RT), high-intensity interval training (HIIT), and mixed-component exercise (Mixed). According to the surface under the cumulative ranking curve (SUCRA), Mixed was the most effective intervention for improving Berg balance scale scores (SUCRA = 82.89%). AE was the most effective intervention for improving patients' performance on the timed up and go test (SUCRA = 88.46%). PME exhibited superior effectiveness in improving Montreal cognitive assessment scores (SUCRA = 86.43%). CONCLUSIONS: Mixed and AE noticeably improves balance function in stroke patients, while PME and AE notably enhance cognitive function. The efficacy of other forms of exercise requires further validation. For patients whose primary objective is to improve balance, we recommend prioritizing Mixed. In cases of markedly impaired physical function, a single type of exercise should be selected. For patients aiming to enhance cognitive function, we recommend the selection of PME as the preferred option. TRIAL REGISTRATION: Registration date: 23 September 2024. PROSPERO registration number: CRD42024593741.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.064 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".