Impact of Virtual Reality-Based Therapies on Cognition and Depression of Parkinson's Disease Patients: Systematic Review and Meta-analysis of Randomized Controlled Trials (Preprint)
Notice bibliographique
Résumé
Background: As a neurodegenerative disorder, Parkinson disease (PD) demonstrates significant prevalence worldwide. As the population ages, the number of patients with PD increases. Individuals with PD are susceptible to varying degrees of cognitive and psychological impairments. Virtual reality (VR)-based therapy is an emerging technology used for cognitive recovery and mental health treatment, yet controversy remains. Objective: This study aimed to assess the impact of VR-based therapies on cognitive function and depression in patients with PD. Methods: An extensive database search was conducted through PubMed, Web of Science, Embase, and the Cochrane Library to identify randomized controlled trials (RCTs) that investigated the impact of VR on patients with PD. Studies published before March 31, 2026, which met our inclusion and exclusion criteria, were included. A total of 13 RCTs involving 430 patients with PD were included. The Cochrane risk-of-bias tool was used to assess the risk of bias, indicating the included studies generally had a low risk of bias in randomization but a high or unclear risk concerning allocation concealment and blinding. Random-effects meta-analyses were performed using standardized mean differences (SMDs) with 95% CIs. Hartung-Knapp adjustments were applied, and prediction intervals (PIs) were calculated to assess the expected distribution of effects across future settings. The certainty of evidence was assessed using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). Results: In the meta-analysis, VR-based therapies were associated with statistically significant average improvements in global cognitive function (SMD=0.40, 95% CI 0.11-0.70; 95% PI 0.10-0.70; P=.01; I2=0%) and depressive symptoms (SMD=-0.77, 95% CI -1.42 to -0.12; 95% PI -1.82 to 0.27; P=.03; I2=31%). However, the PI for depression crossed the line of no effect, suggesting that this effect may vary across future settings. No significant average effects were observed for executive function (SMD=0.06, 95% CI -0.31 to 0.44; P=.66), memory (SMD=0.48, 95% CI -0.30 to 1.25; P=.15), attention (SMD=0.01, 95% CI -0.28 to 0.31; P=.94), or quality of life (QoL) outcomes (SMD=0.01, 95% CI -0.46 to 0.47; P=.97). Conclusions: The results suggest that VR-based therapies may be associated with improvements in global cognitive function and depressive symptoms in patients with PD, although evidence for executive function, attention, memory, and QoL remains inconclusive. This review provides an updated synthesis that differs from previous reviews by focusing on both global and domain-specific cognitive outcomes, as well as depressive symptoms and QoL, rather than mainly on motor outcomes. By incorporating recent RCTs and considering PIs, risk of bias, and GRADE certainty, this review offers a more cautious interpretation of the evidence. In practice, VR-based therapies may serve as an engaging adjunct to conventional rehabilitation, but larger and methodologically rigorous trials are needed before clinical recommendations can be made.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,015 | 0,029 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».