Assessment of the Efficacy of Virtual Reality Rehabilitation in Stroke Patients
Bibliographic record
Abstract
Background: Stroke is still one of the most prominent causes of adult disability globally and often results in both long-term physical and cognitive impairments. While traditional rehabilitation methods do have some effectiveness, they are sometimes hampered by low engagement and adherence levels from the patients. The use of virtual reality technology (VR) offers a new way to approach rehabilitation which could result in better outcomes because it creates an immersive experience that is inherently interactive. This study aimed to assess the effectiveness of VR-based rehabilitation in improving motor function, functional independence, and cognitive performance in stroke patients, compared with traditional therapy. Methods: Seventy-one stroke patients participated in the study and were randomized into two groups: one receiving virtual reality rehabilitation and the other receiving standard physiotherapy as a control arm to conventional treatment. The therapies were both provided over a six-week period, five days per week. The primary outcome measures were Fugl-Meyer Assessment and Barthel Index. Other outcome measures included cognitive assessment using Montreal Cognitive Assessment (MoCA), mobility measured by Timed Up and Go (TUG) test, and satisfaction level reported by the patients which were all considered as secondary outcomes. Results: Participants in the VR group demonstrated significantly greater improvements in motor function and independence in daily activities compared to the control group (p < 0.01). Cognitive gains were higher in the VR group, although this did not reach statistical significance (p = 0.058). Patient adherence and satisfaction were notably higher among VR participants. Conclusion: VR-based rehabilitation is a promising and effective approach to enhance post-stroke recovery, offering better patient outcomes and engagement than conventional methods. Further large-scale studies are recommended to confirm these findings and explore long-term effects. Keywords: Stroke rehabilitation, virtual reality therapy, motor recovery, cognitive function, patient engagement, Fugl-Meyer, Barthel Index.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".