Efficacy of Virtual Reality–Based Interventions on Cognitive Function in Patients With Neuropsychiatric Disorders: Systematic Review and Meta-Analysis of Randomized Controlled Trials
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,029 | 0,008 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».