EFFICACY OF VIRTUAL REALITY-BASED PHYSIOTHERAPY ON POST-STROKE MOTOR RECOVERY: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Stroke is a leading cause of adult disability worldwide, often resulting in long-term motor impairments that compromise independence and quality of life. While conventional physiotherapy remains the cornerstone of rehabilitation, limitations in patient engagement and treatment outcomes have led to the exploration of innovative approaches. Virtual reality (VR)-based physiotherapy has emerged as a promising modality that offers immersive, task-specific training. Despite growing interest, there remains a lack of consensus on its efficacy across diverse post-stroke populations, warranting a systematic synthesis of current evidence. Objective: This systematic review aims to evaluate the effectiveness of virtual reality-based physiotherapy interventions in improving motor function among post-stroke patients compared to conventional physiotherapy or no intervention. Methods: A systematic review was conducted following PRISMA guidelines. Electronic databases including PubMed, Scopus, Web of Science, and Cochrane Library were searched for studies published between 2019 and 2025. Inclusion criteria comprised randomized controlled trials, controlled clinical trials, and observational studies involving adult stroke patients receiving VR-based physiotherapy targeting motor recovery. Data extraction and risk of bias assessment were independently performed by two reviewers using standardized tools (Cochrane Risk of Bias 2.0, Newcastle-Ottawa Scale). Due to heterogeneity, a narrative synthesis was employed. Results: Eight studies met the inclusion criteria, encompassing a total of 439 participants. Interventions included immersive VR, exoskeleton-assisted systems, and haptic-enhanced platforms. Across studies, VR-based interventions significantly improved upper limb motor function, balance, trunk control, and functional independence, as measured by outcomes such as FMA-UE, BBS, TIS, and FIM (p < 0.05). Risk of bias was generally low to moderate. Conclusion: VR-based physiotherapy demonstrates significant benefits in post-stroke motor recovery and may serve as a valuable adjunct to traditional rehabilitation. However, variability in study design and small sample sizes limit the generalizability of findings. Further large-scale, standardized trials are needed to confirm long-term efficacy and optimize implementation strategies.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".