Real-Time Digitized Visual Feedback in Exercise Therapy for Lower Extremity Functional Deficits: Qualitative Study of Usability Factors During Prototype Testing
Notice bibliographique
Résumé
BACKGROUND: Osteoarthritis is one of the most common degenerative diseases of the musculoskeletal system and can ultimately lead to the need for surgery, such as total knee or hip arthroplasty. Functional movement deficits can be a prognostic factor for osteoarthritis in the lower extremities. Thus, training physiological movement patterns may help in the treatment of such functional deficits. Motivation to exercise frequently is of utmost importance and can be promoted by using digital real-time feedback. OBJECTIVE: This qualitative study aims to gather user recommendations for prototype feedback visualizations in a real-time exercise-feedback system called homeSETT for the treatment of functional deficits. The system provides real-time feedback to participants while performing exercises that focus on functional deficits, such as lateral trunk lean, pelvic drop, and valgus thrust. The findings of this study should help to optimize the prototype feedback visualizations. Thus, the main research questions were how patients, physiotherapists, and physicians evaluate the presented, current state of prototype feedback visualizations for selected functional exercises, and what improvements and variations would be recommended. METHODS: Testing of the prototype feedback visualizations took place at a movement laboratory using a 3D optoelectronic movement analysis system. Data on usability factors were acquired using the thinking aloud method during and semistructured interviews after prototype testing. Transcribed audio recordings of semistructured interviews as well as scribing logs of the thinking aloud method were examined using qualitative content analysis. RESULTS: Data were analyzed from 9 participants, comprising 2 (22%) patients, 2 (22%) physicians, and 5 (56%) physiotherapists. The mean age of the participants was 45 (SD 9) years and the mean work experience among the participating physiotherapists and physicians was 22 (SD 5) years. Each participant tested 11 different exercise-feedback combinations. Overall, results indicated that participants enjoyed the prototype feedback visualizations and believed that they could be used in therapeutic settings. Participants appreciated the simplicity, clarity, and self-explanatory nature of the feedback visualizations. While most participants quickly familiarized themselves, some struggled to recognize the feedback goals and connect the visualizations to their movements. Recommendations for improvement included optimizing color schemes, sensitivity, and difficulty adjustments. Adding instructional information and game design elements, such as repetition counting and reward systems, was deemed useful. The main study limitations were the small sample size and the use of feedback on performance as the sole feedback modality. CONCLUSIONS: The prototype feedback visualizations were positively perceived by the participants and were considered applicable in therapy settings. Insights were gathered on improving the color scheme, sensitivity, and recognizability of the feedback visualizations. The implementation of additional gamification and instructional elements was emphasized. Future work will optimize the prototype feedback visualizations based on study results and evaluate the homeSETT system's efficacy in eligible patient populations.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 ».