Development and Testing of a Mobile App for Management of Gestational Diabetes in Nepal: Protocol for a User-Centered Design Study and Exploratory Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: The prevalence of gestational diabetes mellitus (GDM) is increasing, particularly in low- and middle-income countries (LMICs) like Nepal. GDM self-management, including intensive dietary and lifestyle modifications and blood glucose monitoring, is critical to maintain glycemic control and prevent adverse outcomes. However, in resource-limited settings, several barriers hinder optimal self-management. Mobile health (mHealth) technology holds promise as a strategy to augment GDM treatment by promoting healthy behaviors and supporting self-management, but this approach has not yet been tested in any LMIC. OBJECTIVE: This report describes the protocol to develop a culturally tailored mHealth app that supports self-management and treatment of GDM (GDM-Dhulikhel Hospital [GDM-DH] app, phase 1) and test its usability and preliminary efficacy (phase 2) among patients with GDM in a periurban hospital setting in Nepal. METHODS: The study will be conducted at Dhulikhel Hospital in Dhulikhel, Nepal. In the development phase (phase 1), a prototype of the GDM-DH app will be developed based on expert reviews and a user-centered design approach. To understand facilitators and barriers to GDM self-management and to gather feedback on the prototype, focus groups and in-depth interviews will be conducted with patients with GDM (n=12), health care providers (n=5), and family members (n=3), with plans to recruit further if saturation is not achieved. Feedback will be used to build a minimum viable product, which will undergo user testing with 18 patients with GDM using a think-aloud protocol. The final GDM-DH app will be developed based on user feedback and following an iterative product design and user testing process. In the randomized controlled trial phase (phase 2), newly diagnosed patients with GDM (n=120) will be recruited and randomized to either standard care alone or standard care plus the GDM-DH app from 24-30 weeks gestation until delivery. In this proof-of-concept trial, feasibility outcomes will be app usage, self-monitoring adherence, and app usability and acceptability. Exploratory treatment outcomes will be maternal glycemic control at 6 weeks post partum, birth weight, and rates of labor induction and cesarean delivery. Qualitative data obtained from phase 1 will be analyzed using thematic analysis. In phase 2, independent 2-tailed t tests or chi-square analyses will examine differences in outcomes between the 2 treatment conditions. RESULTS: As of July 2024, we have completed phase 1. Phase 2 is underway. The first participant was enrolled in October 2021, with 99 participants enrolled as of July 2024. We anticipate completing recruitment by December 2024 and disseminating findings by December 2025. CONCLUSIONS: App-based lifestyle interventions for GDM management are not common in LMICs, where GDM prevalence is rapidly increasing. This proof-of-concept trial will provide valuable insights into the potential of leveraging mHealth app-based platforms for GDM self-management in LMICs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04198857; https://clinicaltrials.gov/study/NCT04198857. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59423.
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 | 0,057 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,008 | 0,005 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,053 | 0,008 |
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 ».