Personalized and Culturally Tailored Features of Mobile Apps for Gestational Diabetes Mellitus and Their Impact on Patient Self-Management: Scoping Review
Notice bibliographique
Résumé
Background: Gestational diabetes mellitus (GDM) is an increasingly common high-risk pregnancy condition requiring intensive daily self-management, placing the burden of care directly on the patient. Understanding personal and cultural differences among patients is critical for delivering optimal support for GDM self-management, particularly in high-risk populations. Although mobile apps for GDM self-management are being used, limited research has been done on the personalized and culturally tailored features of these apps and their impact on patient self-management. Objective: This scoping review aims to explore the extent to which published studies report the integration and effectiveness of personalized and culturally tailored features in GDM mobile apps for patient self-management support. Methods: We examined English-language peer-reviewed articles published between October 2016 and May 2023 from PubMed, CINAHL, PsycINFO, ClinicalTrials.gov, Proquest Research Library, and Google Scholar using search terms related to digital tools, diabetes, pregnancy, and cultural tailoring. We reviewed eligible articles and extracted data using the Arskey and O'Malley methodological framework. Results: Our search yielded a total of 1772 articles after the removal of duplicates and 158 articles for full-text review. A total of 21 articles that researched 15 GDM mobile apps were selected for data extraction. Our results demonstrated the stark contrast between the number of GDM mobile apps with personalized features for the individual user (all 15 mobile apps) and those culturally tailored for a specific population (only 3 of the 15 mobile apps). Our findings showed that GDM mobile apps with personalized and culturally tailored features were perceived to be useful to patients and had the potential to improve patients' adherence to glycemic control and nutrition plans. Conclusions: There is a strong need for increased research and development to foster the implementation of personalized and culturally tailored features in GDM mobile apps for self-management that cater to patients from diverse backgrounds and ethnicities. Personalized and culturally tailored features have the potential to serve the unique needs of patients more efficiently and effectively than generic features alone; however, the impacts of such features still need to be adequately studied. Recommendations for future research include examining the cultural needs of different ethnicities within the increasingly diverse US population in the context of GDM self-management, conducting participatory-based research with these groups, and designing human-centered mobile health solutions for both patients and providers.
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 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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 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 ».