Exploring the Constituent Elements of a Successful Mobile Health Intervention for Prediabetic Patients in King Saud University Medical City Hospitals in Saudi Arabia: Cross-sectional Study
Notice bibliographique
Résumé
BACKGROUND: Self-management of prediabetic patients is crucial since they are at high risk of developing type 2 diabetes. Mobile health (mHealth) apps could contribute to potentially reducing the burden of diabetes by supporting the self-management of prediabetic patients. OBJECTIVE: This study aimed to explore the constituent elements of a successful mHealth intervention for prediabetic patients in King Saud University Medical City (KSUMC) hospitals in Saudi Arabia using the Centre for eHealth Research (CeHRes) roadmap. METHODS: This study used the CeHRes roadmap as a developmental guideline for proposing mHealth app features for self-management of prediabetic patients and was performed in 3 phases with one round in each phase. First, a contextual inquiry was conducted via an online self-administered questionnaire for both health care providers and patients. Second, the value specification phase elaborated on the outcomes from the contextual inquiry phase. Finally, prototype user design was performed in cocreation with end users. The design phase was also conducted via an online self-administered questionnaire to evaluate the proposed features of mHealth apps by prediabetic patients. RESULTS: A total of 20 health care providers participated in the study. The results revealed that the most powerful intervention for prediabetes was a combination of medication, physical activity, and healthy diet plans (12/20, 60%). Furthermore, the most common challenge faced by prediabetes patients was patient adherence to healthy diet and physical activity recommendations (10/20, 50%). Almost all patients believed that mHealth apps would be useful for prediabetic patients. A total of 48 prediabetic patients participated in the study. The results indicated that the most powerful intervention for prediabetic patients is a combination of healthy diet and physical activity plans (21/48, 44%), and the most frequent challenge that may lead the patients to discontinue the current intervention was the commitment to a physical activity plan (35/48, 75%). Furthermore, 15% (17/48) of patients use well-being and health apps to manage their current health status. The most common difficulties faced by the patients were navigating app features (mean 2.02 [SD 1.7]) followed by the app language (mean 1.88 [SD 2.0]); these difficulties occurred at a significantly higher rate among those with secondary or lower educational levels as compared to undergraduate and postgraduate levels (P<.05). Finally, the features proposed in the prototype design scored more than 2.5 points higher and indicate the need for these features to be included in the mHealth app. CONCLUSIONS: This study aimed to provide real-world insights into the development of an mHealth app for a diabetes prevention intervention by involving both health care providers and prediabetic patients in KSUMC hospitals. Therefore, the proposed app, which comprises all necessary features, may aid patients with prediabetes in self-management and making changes in their lifestyle.
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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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 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 ».