A Video Self-Modeling Intervention Using Virtual Reality Plus Physical Practice for Freezing of Gait in Parkinson Disease: Feasibility and Acceptability Study
Notice bibliographique
Résumé
BACKGROUND: Despite optimal medical and surgical intervention, freezing of gait commonly occurs in people with Parkinson disease. Action observation via video self-modeling, combined with physical practice, has potential as a noninvasive intervention to reduce freezing of gait. OBJECTIVE: The aim of this study is to determine the feasibility and acceptability of a home-based, personalized video self-modeling intervention delivered via a virtual reality head-mounted display (HMD) to reduce freezing of gait in people with Parkinson disease. The secondary aim is to investigate the potential effect of this intervention on freezing of gait, mobility, and anxiety. METHODS: The study was a single-group pre-post mixed methods pilot trial for which 10 participants with Parkinson disease and freezing of gait were recruited. A physiotherapist assessed the participants in their homes to identify person-specific triggers of freezing and developed individualized movement strategies to overcome freezing of gait. 180° videos of the participants successfully performing their movement strategies were created. Participants watched their videos using a virtual reality HMD, followed by physical practice of their strategies in their own homes over a 6-week intervention period. The primary outcome measures included the feasibility and acceptability of the intervention. Secondary outcome measures included freezing of gait physical tests and questionnaires, including the Timed Up and Go Test, 10-meter walk test, Goal Attainment Scale, and Parkinson Anxiety Scale. RESULTS: The recruitment rate was 24% (10/42), and the retention rate was 90% (9/10). Adherence to the intervention was high, with participants completing a mean of 84% (SD 49%) for the prescribed video viewing and a mean of 100% (SD 56%) for the prescribed physical practice. One participant used the virtual reality HMD for 1 week and completed the rest of the intervention using a flat-screen device because of a gradual worsening of his motion sickness. No other adverse events occurred during the intervention or assessment. Most of the participants found using the HMD to view their videos interesting and enjoyable and would choose to use this intervention to manage their freezing of gait in the future. Five themes were constructed from the interview data: reflections when seeing myself, my experience of using the virtual reality system, the role of the virtual reality system in supporting my learning, developing a deeper understanding of how to manage my freezing of gait, and the impact of the intervention on my daily activities. Overall, there were minimal changes to the freezing of gait, mobility, or anxiety measures from baseline to postintervention, although there was substantial variability between participants. The intervention showed potential in reducing anxiety in participants with high levels of anxiety. CONCLUSIONS: Video self-modeling using an immersive virtual reality HMD plus physical practice of personalized movement strategies is a feasible and acceptable method of addressing freezing of gait in people with Parkinson disease.
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 | 0,004 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 ».