Action Observation Combined With Motor Imagery Training to Improve Motor Function in People With Stroke: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Action observation combined with motor imagery (AO+MI) training is considered a potentially effective approach for improving motor function in patients after stroke. Therefore, it is important to review and analyze the existing research evidence of its effectiveness. Objective: This study aims to evaluate the effectiveness of AO+MI training on the limb motor function of patients with stroke. Methods: A systematic search was conducted in PubMed, Cochrane Library, Web of Science, Embase, Proquest, Physiotherapy Evidence Database, ClinicalTrials.gov, and ChiCTR. The last search was performed in June 2025. Three reviewers independently screened the articles, and 2 reviewers extracted data. Quality assessments of randomized controlled trials were done using the Cochrane Risk-of-Bias Tool. The certainty of evidence was evaluated with GRADEpro GDT (Evidence Prime, Inc). A meta-analysis was performed using RevMan 5.3 (The Cochrane Collaboration) software and Stata software. Results: A total of 13 articles were included with 399 patients with stroke. The results of the meta-analysis showed that compared with routine rehabilitation, AO+MI could improve the upper extremity function (standard mean difference [SMD]=1.02, 95% CI 0.28-1.75; P=.007) and the lower extremity function (SMD=6.31, 95% CI 4.75-7.87; P<.001) of patients with stroke. There was no significant difference between AO+MI and routine rehabilitation for improving activities of daily living (SMD=0.06, 95% Cl -0.35 to 0.47; P=.06). AO+MI could promote the recovery of upper extremity function in patients compared with MI independently (SMD=0.97, 95% Cl 0.13-1.80; P=.02). There was no significant difference between synchronous combination and asynchronous combination in upper extremity function rehabilitation of patients after stroke (SMD=-1.04, 95% Cl -2.56 to 0.48). Conclusions: AO+MI can improve the motor function of limbs and can be considered an effective limb rehabilitation therapy for patients after a 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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".