Motor imagery as an intervention to improve activities of daily living post-stroke: A systematic review of randomized controlled trials
Bibliographic record
Abstract
Introduction: Motor imagery (MI) may be an effective tool for improving activities of daily living (ADL) post-stroke. However, no review to date has examined ADL independence when investigating training effectiveness. This review aimed to evaluate the quality of evidence and the effectiveness of MI training for improving ADL independence post-stroke. Method: Randomized controlled trial (RCT) studies comparing MI to conventional therapies were reviewed. Methodological quality was assessed using the Physiotherapy Evidence Database (PEDro) scale. Results: Thirteen articles met inclusion criteria. The overall quality was considered moderate to good, with a PEDro score ranging from 3 to 8. Most studies (9 out of 13) were considered good quality, with one rating of poor quality and three of fair quality. The primary findings suggest that MI training is a low-risk tool that may facilitate ADL independence. Audio-based MI training seems to improve ADL independence when paired with other rehabilitation methods, but the results should be interpreted with caution. Conclusion: To our knowledge, this is the first systematic review to examine RCTs investigating MI effectiveness in improving ADL post-stroke. Results support the use of MI to facilitate ADL independence. However, more research is needed to establish practice guidelines for implementing MI training post-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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".