An AI-Based Telerehabilitation Solution to Improve Mobility in People With Multiple Sclerosis (the PLATINUMS Project): Protocol for an Implementation and Evaluation Study
Notice bibliographique
Résumé
BACKGROUND: Multiple sclerosis (MS) is a chronic, progressive, and neurodegenerative disease affecting more than 2.8 million people globally. Mobility impairments are among the most significant challenges faced by people with MS, leading to physical inactivity, deconditioning, and disability progression (for some, even irreversible disability). This negatively impacts mental health, social participation, and quality of life while placing a considerable economic burden on society. Exercise can improve mobility and mitigate disability progression, but facility-based options are often inaccessible, especially for those in remote areas. Telerehabilitation offers a promising alternative, but current systems are limited by complexity and hardware requirements. OBJECTIVE: The PLATINUMS (Implementation of an Advanced Telerehabilitation Solution for People With Multiple Sclerosis) project proposes an AI-driven telerehabilitation system to deliver accessible, cost-effective, and home-based exercise therapy for people with MS. METHODS: The PLATINUMS project begins with working package (WP) 1, focusing on obtaining ethical approval and recruiting staff. Following this, WP2 involves a 4-week system feasibility and usability study (n=40) to assess and refine the digital platform. WP3 comprises a validity study (n=60) to evaluate remote mobility tests via the system, such as the Short Physical Performance Battery (SPPB), functional reach, and sit-to-stand tests, to ensure their reliability for use in WP4, the feasibility multicenter randomized controlled trial (RCT). The 10-week multicenter feasibility RCT will be conducted in MS centers across Denmark, Ireland, Israel, and Italy, with 96 participants varying in disability levels. The primary objective is to evaluate the efficacy of the AI-powered telerehabilitation system on mobility outcomes compared to usual care. Finally, WP5 will assess the cost-effectiveness of the telerehabilitation system by analyzing implementation costs, adherence, and use of health care. The PLATINUMS project aims to revolutionize exercise therapy for people with MS by demonstrating the feasibility, validity, and preliminary efficacy of the AI-driven telerehabilitation system. This approach addresses barriers such as accessibility, privacy, and standardization while promoting patient and therapist acceptance. RESULTS: Funding for the PLATINUMS project was obtained in February 2024. WP2 data collection began in April 2025 across 4 European sites. WP3 is scheduled to launch in July 2025, with WP4 (the feasibility RCT) planned for January 2026. Initial WP2 results are expected by October 2025, with first publications anticipated in mid-2026. CONCLUSIONS: The PLATINUMS project is expected to generate critical insights into the feasibility, usability, and preliminary efficacy of an AI-based telerehabilitation system for people with MS. By leveraging widely available technology and real-time feedback, the system addresses key barriers to traditional rehabilitation. Findings from this protocol may inform future large-scale trials and support the broader implementation of digital health solutions in neurological rehabilitation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/75983.
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 | 0,059 | 0,047 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,005 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,072 | 0,015 |
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 ».