Designing and Developing a Mobile App for Management and Treatment of Gestational Diabetes in Nepal: User-Centered Design Study
Bibliographic record
Abstract
BACKGROUND: Mobile apps can aid with the management of gestational diabetes mellitus (GDM) by providing patient education, reinforcing regular blood glucose monitoring and diet/lifestyle modification, and facilitating clinical and social support. OBJECTIVE: This study aimed to describe our process of designing and developing a culturally tailored app, Garbhakalin Diabetes athawa Madhumeha-Dhulikhel Hospital (GDM-DH), to support GDM management among Nepalese patients by applying a user-centered design approach. METHODS: A multidisciplinary team of experts, as well as health care providers and patients in Dhulikhel Hospital (Dhulikhel, Nepal), contributed to the development of the GDM-DH app. After finalizing the app's content and features, we created the app's wireframe, which illustrated the app's proposed interface, navigation sequences, and features and function. Feedback was solicited on the wireframe via key informant interviews with health care providers (n=5) and a focus group and in-depth interviews with patients with GDM (n=12). Incorporating their input, we built a minimum viable product, which was then user-tested with 18 patients with GDM and further refined to obtain the final version of the GDM-DH app. RESULTS: Participants in the focus group and interviews unanimously concurred on the utility and relevance of the proposed mobile app for patients with GDM, offering additional insight into essential modifications and additions to the app's features and content (eg, inclusion of example meal plans and exercise videos).The mean age of patients in the usability testing (n=18) was 28.8 (SD 3.3) years, with a mean gestational age of 27.2 (SD 3.0) weeks. The mean usability score across the 10 tasks was 3.50 (SD 0.55; maximum score=5 for "very easy"); task completion rates ranged from 55.6% (n=10) to 94.4% (n=17). Findings from the usability testing were reviewed to further optimize the GDM-DH app (eg, improving data visualization). Consistent with social cognitive theory, the final version of the GDM-DH app supports GDM self-management by providing health education and allowing patients to record and self-monitor blood glucose, blood pressure, carbohydrate intake, physical activity, and gestational weight gain. The app uses innovative features to minimize the self-monitoring burden, as well as automatic feedback and data visualization. The app also includes a social network "follow" feature to add friends and family and give them permission to view logged data and a progress summary. Health care providers can use the web-based admin portal of the GDM-DH app to enter/review glucose levels and other clinical measures, track patient progress, and guide treatment and counseling accordingly. CONCLUSIONS: To the best of our knowledge, this is the first mobile health platform for GDM developed for a low-income country and the first one containing a social support feature. A pilot clinical trial is currently underway to explore the clinical utility of the GDM-DH app.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".