App- and Wearable-Based Remote Monitoring for Patients With Myasthenia Gravis and Its Specialists: Feasibility and Usability Study
Notice bibliographique
Résumé
Background: Myasthenia gravis (MG) is rare, chronic autoimmune disorder of the neuromuscular junction that requires specialized care and often lifelong treatment, facing challenges due to its rarity and the limited availability of specialists. Telemedical solutions in specialized centers hold considerable promise in bridging this gap by increasing access to this care to a broader patient population in a timely manner. However, there is no research regarding interventional remote care solutions in the field of MG to date. Objective: This study aimed to assess the feasibility and usability among patients with MG and specialists of a telemedicine platform, tailored to patients with MG and designed to facilitate remote monitoring, treated in a specialized center. Methods: The telemedicine platform consisted of an app for patients and a web-based portal for physicians. Over a period of 3 months, 30 patients continuously monitored their vital parameters through external devices, including a digital spirometer and a wearable (activity tracker). Furthermore, patients completed 7 different patient-reported outcome measures (PROMs) through the app at predefined intervals. Specialists could review this monitoring data and adjust therapy as necessary. In addition, communication between patients and physicians was facilitated through a chat module. Feasibility was evaluated by total adherence rates for completing PROMs within the app, alongside the collection of spirometry and wearable data. Furthermore, user satisfaction was assessed among both patients with MG and physicians at the end of study. Results: Total adherence rates ranged from 74.3% (1830/2464) to 97.9% (327/334) across all data types, with the highest adherence observed for PROMs (1139/1179, 96.6%), followed by spirometry (293/334, 87.7%) and wearables (1830/2261, 80.9%). Notably, patients wore the wearable longer than required by protocol and conducted a higher number of spirometry measurements during the study than required per protocol (median 20 h/d [IQR 15-24] vs 14 h/d and median 49 [IQR 15-59] measurements vs 11 measurements, respectively). Technical issues and discomfort with wearables were factors affecting lower adherence in some patients. The System Usability Scale yielded a median score of 85 indicating "excellent usability." In addition, results from a more detailed user evaluation questionnaire showed high levels of user satisfaction among both patients and health care professionals across diverse categories, including their experience of the care program, communication, and evaluation of the program. Conclusions: Remote monitoring of patients with MG through the telemedical platform demonstrated good feasibility and acceptability, as evidenced by above-average adherence rates and user satisfaction for both patients and physicians. The majority of patients wanted to continue using the app. These findings highlight the potential for user-friendly digital tools to enhance care for patients with MG, although addressing technical challenges and ensuring patient comfort with wearables are essential for optimal implementation. Further research involving larger cohorts and longer study duration is warranted to validate these findings.
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 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».