Efficacy of Virtual Reality–Based Interventions on Cognitive Function in Patients With Neuropsychiatric Disorders: Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: Virtual reality (VR) technology has emerged as a promising tool for cognitive rehabilitation in patients with neuropsychiatric disorders. These patients often endure significant cognitive impairments, which are associated with decreased quality of life and increased disease burden. Traditional treatments are limited in improving cognitive functions, making VR-based interventions an area of growing interest. Objective: This meta-analysis aims to evaluate the efficacy of VR-based interventions on cognitive function in patients with neuropsychiatric disorders by synthesizing data from randomized controlled trials (RCTs). Methods: Following PRISMA guidelines, we conducted a comprehensive search across PubMed, Web of Science, MEDLINE, EMBASE, and Cochrane Library for RCTs from January 2010 to December 2024. Studies were included if they evaluated the impact of VR-based interventions on cognitive outcomes in patients with neuropsychiatric disorders. Data extraction and risk of bias assessment were performed independently by 2 researchers. Meta-analyses were conducted using random-effects models, and standardized mean differences (SMDs) as effect size. Results: A total of 21 RCTs involving 1051 participants were included. Overall, VR-based interventions significantly improved cognitive functions of patients with neuropsychiatric disorders (SMD 0.67, 95% CI 0.33-1.01, z=3.85; P<.001). Subgroup analyses revealed significant benefits for cognitive rehabilitation training (SMD 0.75, 95% CI 0.33-1.17, z=3.53; P<.001), exergame-based training (SMD 1.09, 95% CI 0.26-1.91, z=2.57; P=.01), and telerehabilitation and social functioning training (SMD 2.21, 95% CI 1.11-3.32, z=3.92; P<.001). Conversely, immersive cognitive training, music attention training, and vocational and problem-solving skills training did not yield significant improvements (z=1.86, P=.06; z=0.35, P=.72; z=0.88, P=.38; respectively). Disease-type subgroup analyses indicated significant improvements in schizophrenia (SMD 0.92, 95% CI 0.22-1.62, z=2.58; P=.01), and mild cognitive impairment (SMD 0.75, 95% CI 0.16-1.35, z=2.47; P=.01), but not in brain injuries, Parkinson disease, or stroke (z=0.34, P=.73; z=1.26, P=.21; z=1.16, P=.24; respectively). Conclusions: This meta-analysis revealed that VR-based interventions can improve cognitive functions among individuals with neuropsychiatric disorders, with notable improvements observed in cognitive rehabilitation training, exergame-based training, and tele-rehabilitation and social functioning training. These results offer valuable evidence supporting the use of VR technology in rehabilitation for neuropsychiatric conditions and inform the optimization of future intervention approaches.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.029 | 0.008 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".