Exploring Opportunities and Challenges for the Spread, Scale-Up, and Sustainability of mHealth Apps for Self-Management of Patients With Type 2 Diabetes Mellitus in the Netherlands: Citizen Science Approach
Notice bibliographique
Résumé
BACKGROUND: Technologies evolve at a breakneck pace, and the success of mobile health (mHealth) for people with type 2 diabetes mellitus (T2DM) depends on whether health care professionals, care management, government regulators, and consumers will adopt the technology as a viable solution to enhance patient self-management. OBJECTIVE: In this study, we explored the challenges of the implementation of mHealth apps in care for patients with T2DM and determined to what extent these challenges complicate the dissemination, limit scale-up, and influence the sustainability of technological interventions for patients with T2DM. METHODS: The nonadoption, abandonment, and challenges to scale-up, spread, and sustainability (NASSS) framework served as the basis for our study. The 7 domains of the NASSS framework were explored with a citizen science approach using questionnaires, semistructured in-depth interviews, and focus groups together with patients with T2DM, care professionals, technology developers, policy officers, and a patient organization. RESULTS: Regarding the domain "condition," being aware of their condition and changing lifestyle were crucial for patients with T2DM to get to grips with their life. The rapid development of health apps for T2DM was highlighted in the domain "technology." Users should be aware of these apps and know how to use them. The domain "value proposition" included the patient perspective and elaborated on personal values, as well as care professionals who focus on personalized care and pressure on health care. Regarding the "adopters," it is crucial to know who needs to use and introduce the apps. Responsibility, a shared vision, and resistance among care professionals were mentioned as important determinants for "organization." Finally, the domain "wider system" showed the importance of involving multiple institutes, care guidelines, and reimbursements. CONCLUSIONS: This study investigated the implementation of mHealth apps in an early stage of the implementation process. Key stakeholders were involved, who attributed to the possibilities and limitations of the implementation. It is crucial to have a clear vision from an organizational perspective and specific prerequisites for implementation strategies at micro, meso, and macro levels. Essential strategies at the national level include guidelines for regulations, privacy, and security; the integration of mHealth into T2DM care guidelines; and sufficient reimbursement by health insurers.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».