The Design, Development, and Testing of a Virtual Reality Device for Upper Limb Training in People With Multiple Sclerosis: Single-Center Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: Multiple sclerosis (MS) is a common nontraumatic, neurological, disabling disease that often presents with upper limb dysfunction. Exercise training has resulted in improvement for patients; however, there can be a lack of compliance due to access because of location and lack of MS experts. Virtual reality (VR) is a promising technology that can offer exercise therapy/rehabilitation at a distance. This type of remote training can be motivational and effective for patients with MS and can improve range of motion and muscle strength for those with upper limb dysfunction. OBJECTIVE: The aim of this study is to evaluate the safety and feasibility of the XRHealth software and the Oculus Rift Station for patients with MS with upper limb motor dysfunction. METHODS: A single-center, prospective, feasibility study was conducted with patients with MS who had upper limb motor dysfunction. Patients participated in a single 45-minute digital environment session with VR and completed a questionnaire about the quality of the training and fatigability. The clinician also completed a questionnaire to evaluate the suitability and safety of the training. RESULTS: Overall, 30 patients were enrolled between the ages of 20 and 81 years. Patients reported that the training sessions within the digital environment were helpful, challenging, fun, and simple to understand, and that they would be willing to repeat the sessions again. The physical therapist that oversaw the patients reported that the training was suitable for 87% (n=26) of the patients. Anticipated adverse events were fatigue, temporary dizziness, and temporary nausea. The operator complications included that the cable of the head-mounted display interrupted the training (n=2, 7%) and fatigue that caused cessation of the VR training session (n=2, 7%). No serious adverse events were reported. CONCLUSIONS: These preliminary results demonstrated that the use of the XRHealth software and Oculus Rift Station platform is feasible, safe, and engaging for patients, and has the potential to improve the functionality of the upper limbs in patients with MS. This study provides support for future studies of implementing a series of training sessions with virtual reality in a home-based environment.
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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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 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 ».