A Remote Patient Monitoring System With Feedback Mechanisms Using a Smartwatch: Concept, Implementation, and Evaluation Based on the activeDCM Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Technological advances allow for recording and sharing health-related data in a patient-centric way using smartphones and wearables. Secure sharing of such patient-generated data with physicians would enable close management of individual health trajectories, monitoring of risk factors, and asynchronous feedback. However, most remote patient monitoring (RPM) systems currently available are not fully integrated into hospital IT systems or lack a patient-centric design. OBJECTIVE: The objective of this study was to conceptualize and implement a user-friendly, reusable, interoperable, and secure RPM system incorporating asynchronous feedback mechanisms using a broadly available consumer wearable (Apple Watch). In addition, this study sought to evaluate factors influencing patient acceptance of such systems. METHODS: The RPM system requirements were established through focus group sessions. Subsequently, a system concept was designed and implemented using an iterative approach ensuring technical feasibility from the beginning. To assess clinical feasibility, the system was used as part of the activeDCM prospective randomized interventional study focusing on dilated cardiomyopathy. Each patient used the system for at least 12 months. The System Usability Scale was used to measure usability from a subjective patient perspective. In addition, an evaluation was conducted on the objective wearable interaction frequency as well as the completeness of transmitted data classified into sensor-based health data (SHD) and patient-reported outcome measures (PROMs). Descriptive statistics using box plots and bootstrapped multiple linear regression with 95% CIs were used for evaluation analyzing the influence of age, sex, device experience, and intervention group membership. RESULTS: The RPM system comprised 4 interoperable components: patient devices, a data server, a data viewer, and a notification service. The system was evaluated with 95 consecutive patients with dilated cardiomyopathy (28/95, 29% female; mean age 50, SD 12 y) who completed the activeDCM study protocol. The system's app achieved a mean System Usability Scale score of 78 (SD 17), which was most influenced by device experience. In total, 87% (83/95) of the patients could integrate the use of the app well or very well into their daily routine, and 71% (67/95) saw a benefit of the RPM system for management of their health condition. On average, patients interacted with the wearable on 61% (SD 26%) of days enrolled in the study. SHD were available on average for 78% (SD 23%) of days, and PROM data were available on 64% (SD 27%) of weeks enrolled in the study. Wearable interaction frequency, SHD, and PROM completeness were most influenced by intervention group membership. CONCLUSIONS: Our results mark a first step toward integrating RPM systems based on a consumer wearable device for primary patient input into standardized clinical workflows. They can serve as a blueprint for creating a user-friendly, reusable, interoperable, and secure RPM system that can be integrated into patients' daily routines.
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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 | 0,043 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».