Patient Engagement and Empowerment Using a mHealth Application for Management of Inflammatory Arthritis
Notice bibliographique
Résumé
Objectives Despite the rise of mHealth applications for inflammatory arthritis, research on patient-reported outcomes related to usability and confidence in disease management is limited. While mHealth tools can improve self-management and patient-provider communication in chronic conditions, their impact on patient empowerment in arthritis remains underexplored. This study aims to address this gap by evaluating the “Arthritis+Patient” mHealth app, focusing on usability, self-management, and empowerment. Methods The “Arthritis+Patient” app, developed by Dr. Mulgund from Trillium Health Partners and available for free download, was evaluated through 2 cross-sectional surveys. The first survey, adapted from the Post-Study System Usability Questionnaire, evaluated user experience and was administered to 73 patients diagnosed with inflammatory arthritis by a rheumatologist between January 2019 and January 2024. A second survey on arthritis self-management was administered to 16 patients. We included patients from 3 community rheumatology clinics who consented to survey completion. Data was collected in office, either electronically or on paper, and descriptive statistics were used for analysis. There was some missing data, and denominators were provided accordingly. Results Among the 73 usability survey respondents, 81.9% (n = 59/72) agreed the app’s interface was user-friendly, and 83.5% (n = 61/73) found it easy to understand and navigate. Regarding health management, 69.8% (n = 51/73) found the app useful, and 62.5% (n = 45/72) reported increased confidence in managing their condition. Moreover, 70.8% (n = 51/72) intended to continue using the app, and 72.6% (n = 53/73) would recommend it. Of those who completed the self-management survey (n = 16), 86.7% (n = 13/15) found the educational material useful. App features for tracking medical history, and managing sleep and anxiety were valued by 92.3% (n = 12/13) and 69.2% (n = 9/13) of respondents, respectively. Additionally, 50% (n = 7/14) reported improved tracking of arthritis between appointments, and 28.6% (n = 4/14) felt more confident in decision-making. Most patients (75%, n = 9/12) noted feeling empowered through active symptom tracking and 50% (n = 7/14) used the app 1-2 times per week. Free-text feedback suggested adding trend tracking for symptoms. Conclusion The “Arthritis+Patient” app shows potential for enhancing patient engagement, empowerment, and self-management in inflammatory arthritis care. Usability and educational content were well-received, with users reporting increased confidence in disease management. Integrating mHealth apps into routine arthritis care could improve patient-centered outcomes and support quality improvement in health services.
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 | 0,006 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».