Innovating Stroke Recovery: A Systematic Review of Virtual Reality in Cognitive Rehabilitation
Bibliographic record
Abstract
Virtual reality (VR), a surging therapeutic tool especially in stroke rehabilitation, has emerged as a powerhouse for VR-based rehabilitation in stroke patients, which is not only restricted to motor dysfunction but also encompasses cognitive impairment. Given the central role of cognition in activities of daily living, identifying effective interventions to address post-stroke cognitive impairment is critical. Hence, this systematic review aims to evaluate the effects of VR on cognitive function in stroke patients. An extensive search was conducted on PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), Turning Research into Practice (TRIP), Google Scholar, Science Direct, Web of Science, and Scopus databases, with Medical Subject Headings (MeSH) terms "virtual reality", "cognitive function", and "stroke" for relevant articles. Eligible randomized controlled trials (RCTs) published from January 2019 to August 2025 were reviewed. The Cochrane risk-of-bias tool (RoB 2) was used for methodological appraisal. The review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure transparency and compliance with standard guidelines. Out of 91,713 articles initially identified, 9 RCTs met the inclusion criteria and encompassed a total of 601 stroke patients, comprising 53% females. The primary outcome was global cognitive function assessed by the Montreal Cognitive Assessment, which was statistically significant in 78% of the articles following VR-based interventions compared to conventional therapy. The secondary outcomes measured included: mental health by Mini-Mental State Examination (n=2), Beck Depression Inventory-II (n=1), Hospital Anxiety and Depression Scale (n=1) or Hamilton Anxiety Rating Scale (n=1); disability and autonomy measured by Modified Barthel index (n=1) and Functional Independence Measure (n=2); and quality of life (QoL) assessed by Short form-12 Health Survey (n=1) and EuroQoL (n=1). Statistical improvement was observed in only two studies for mental health and in one for QoL. VR-based cognitive rehabilitation is associated with meaningful improvements in global cognitive function. The findings of this review support integration of VR as a complementary tool in the multidisciplinary rehabilitation of stroke survivors with cognitive impairments.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".