Determining the Optimal Virtual Reality Exergame Approach for Balance Therapy in Persons With Neurological Disorders Using a Rasch Analysis: Longitudinal Observational Study
Notice bibliographique
Résumé
BACKGROUND: Virtual reality (VR) exergames have gained popularity in the rehabilitation of persons with neurological disorders as an add-on therapy to increase intensity of training. Intensity is strongly dependent on the motivation of the patient. Motivation can be increased by delivering variation within training and challenging exercises. However, patients are often underchallenged, as exergame difficulty often does not match the patient's ability. A Rasch analysis can establish hierarchy of exergame items in order to assist the delivery of patient-centered therapy. OBJECTIVE: The aim of this study was to apply the Rasch model to create a hierarchical order of existing VR balance exergames and to relate these exergames to the abilities of persons with neurological disorders, in order to deliver challenge and variation. METHODS: A total of 30 persons with stroke and 51 persons with multiple sclerosis (MS) were included in the study. All participants performed a training program, lasting 3 weeks for persons with MS and 4 weeks for persons with stroke, in which they performed VR balance exergames with a movement recognition-based system (MindMotion GO; MindMaze SA). VR exercise scores, Berg Balance Scale scores, and clinical descriptive data were collected. Berg Balance Scale and device scores were analyzed with the Rasch model using a repeated-measures approach to examine whether the distribution of exercise scores fitted the Rasch model. Secondly, a person-item map was created to show the hierarchy of exercise difficulty and person ability. RESULTS: Participants completed a selection of 56 balance exercises (ie, items), which consisted of a combination of various balance tasks and levels (ie, exercises). Using repeated measures, this resulted in a count of 785 observations. Analysis showed strong evidence for unidimensionality of the data. A total of 47 exercises (ie, items) had a sufficiently good fit to the Rasch model. Six items showed underfit, with outfit mean square values above 1.5. One item showed underfit but was kept in the analysis. Three items had negative point-biserial correlations. The final model consisted of 47 exercises, which were provided for persons with low to moderate balance ability. CONCLUSIONS: The VR exercises sufficiently fitted the Rasch model and resulted in a hierarchical order of VR balance exercises for persons with stroke and MS with low to moderate balance ability. In combination with the Berg Balance Scale, the results can guide clinical decision-making in the selection of patient-focused VR balance exercises. TRIAL REGISTRATION: ClinicalTrials.gov NCT03993275; https://clinicaltrials.gov/ct2/show/NCT03993275.
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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 | 0,000 | 0,000 |
| 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 | 0,000 |
| 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 ».