Exergame (ExerG)-Based Physical-Cognitive Training for Rehabilitation in Adults With Motor and Balance Impairments: Usability Study
Notice bibliographique
Résumé
Background: Exergames are increasingly used in rehabilitation, yet their usability and user experience for patients and therapists, particularly for functional model systems, are underresearched. The diverse needs and preferences of users make conducting usability studies challenging, emphasizing the need for further investigation in real-world settings. Objective: This study aimed to evaluate the usability, safety, and user experience of a novel exergame functional model, the ExerG, from the perspectives of patients and therapists in a rehabilitation setting. Methods: In this mixed methods study, 15 patients undergoing rehabilitation (primary end users [PEUs]) and 20 therapists (secondary end users [SEUs]) from 2 rehabilitation centers in Switzerland and Austria participated in exercising and observation sessions with the ExerG. SEUs received training on system use and technical issue management, enabling them to fulfill their therapist roles while treating patients or mock patients. Rapid Iterative Testing and Evaluation was used and the training software adjusted based on participant feedback. Usability was assessed with questionnaires, semistructured interviews, and through observations during the ExerG testing. System acceptability was evaluated using specific quantitative thresholds based on PEU performance and feedback. An observation protocol tracked SEUs' correct use, errors, hesitations, task completion time, and needed assistance across scenarios. Results: Patients and therapists reported overall good usability and positive experiences with the exergame. PEUs rated 23/29 (79%) instructions as acceptable, showed good-to-very-good exercise performance in 19/29 (65%) tasks, and completed 28/29 (97%) tasks. Patients reported no adverse events, showing improved performance and enjoyment across ExerG exercising rounds, with 79/90 (88%) expressing positive emotions and reporting median scores of 9 (IQR 7.5-10) on a 1-10 user satisfaction scale. Patients were willing to continue using the device if the graphic design was improved (5/15), tracking systems and projector quality were enhanced (each 3/15), instructions clarified (12/15), and the game variety increased (2/15). PEUs felt secure in the safety harness (15/15) but recommended swivel arm movement enhancements (5/15). SEUs effectively executed scenarios, with hesitation and difficulties observed in only 14/41 tasks and 2/41 tasks, across all 20 therapists, accounting for 1.7% and 0.2% of the 820 total task cases, respectively. Therapists' quantitative usability ratings were high (median System Usability Scale score 82.5, IQR 65-95). All SEUs expressed their willingness to use the ExerG (20/20) and reported being able to operate the system using the user handbook (20/20). They emphasized the motivation-enhancing effect of video-game based training (12/20) and considered the activities supportive for physical and cognitive skills (20/20). They suggested incorporating daily living task simulations (13/20), more customizable options (6/20), more targeted motivational feedback (9/20), clearer performance ratings (9/20), and more concise activity instructions (6/20). Conclusions: The interdisciplinary, iterative ExerG development approach shows promise. The findings will inform future optimizations. Future work will assess long-term impact.
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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 | 0,001 |
| 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 | 0,000 | 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 ».