Platform Technology for Extended Reality Biofeedback Training Under Operant Conditioning for Functional Limb Weakness: Protocol for the Coproduction of an at-Home Solution (React2Home)
Notice bibliographique
Résumé
BACKGROUND: Functional neurological disorder (FND), including functional movement disorders (FMDs), arises from disruptions in the perception-action cycle, where maladaptive cognitive learning processes reduce the sense of agency and motor control. FND significantly impacts quality of life, with patients often experiencing physical disability and psychological distress. Extended reality (XR) technologies present a novel therapeutic opportunity by leveraging biofeedback training to target sensory attenuation and amplification mechanisms, aiming to restore motor function and the sense of agency. OBJECTIVE: This study aims to coproduce and evaluate the usability of an XR technology platform for FND rehabilitation, focusing on functional limb weakness. The platform integrates biofeedback training with haptic and visual feedback to support motor relearning and control. METHODS: We propose to use an experience-based co-design framework to engage patients with FND, caregivers, and health care professionals in collaboratively designing the XR platform. Stakeholders can share their experiences through narrative interviews and co-design workshops, which can identify emotional touchpoints and prioritized patient-centered needs. Insights will be synthesized through qualitative analysis and used to guide the development of system requirements via quality function deployment, ensuring that the platform aligns with user needs. XR training tasks-virtual reality relaxation, XR position feedback, and XR force feedback-will be integrated as needed into a unified therapeutic game experience through 4-week Agile sprints. Usability will be assessed using the System Usability Scale and qualitative feedback, with themes analyzed in NVivo to identify key areas for subsequent improvement. RESULTS: High usability scores (>85) were recorded for the XR position feedback tasks in the predesign study, reflecting excellent usability and participant satisfaction. However, the virtual reality relaxation and XR force feedback tasks exhibited interindividual variability, underscoring the need for personalization. Key themes included customization, comfort, accessibility, and XR technological quality, ensuring that the XR platform effectively addressed diverse patient needs. The predesign study highlighted the potential of XR technology for FMD rehabilitation by integrating biofeedback training into a patient-centered game design framework. Approaches such as experience-based co-design and quality function deployment can support coproduction by systematically addressing usability and accessibility challenges. Brain-based metrics may further strengthen this evaluation. Accordingly, this study will use portable brain imaging to capture dynamic functional connectivity in key brain regions, enabling personalized interventions. CONCLUSIONS: Through coproduction and iterative refinement, this study aims to demonstrate the promise of personalized XR gaming technology as a scalable, at-home solution for FMD rehabilitation. In this context, personalization and accessibility are critical for optimizing usability and long-term clinical outcomes, paving the way for at-home implementation within the FND stepped care model. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/70620.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 0,008 |
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 source (Gemma direct ou Codex distillé), 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 ».