User Experience of a Web-Based Mobile Health Application Supporting Low-Income Pregnant Individuals with Diabetes: Mixed Methods Study (Preprint)
Bibliographic record
Abstract
Background: Diabetes mellitus management requires considerable patient self-efficacy, knowledge, and support for social determinants of health. These needs become particularly acute during pregnancy. Mobile health (mHealth) tools are a promising approach to enhance patient engagement with the health care system, education, and health promotion and may be particularly helpful during the period of rapid skills acquisition, which is a hallmark of experiencing diabetes during pregnancy. Therefore, we developed SweetMama, a web-based mHealth app designed to support and provide information to low-income pregnant individuals with gestational diabetes mellitus (GDM) or type 2 diabetes mellitus (T2DM). Objective: This study aimed to understand the user experiences of low-income pregnant people who were randomized to use SweetMama during a feasibility trial. Methods: This mixed methods secondary analysis of data from a feasibility randomized controlled trial (RCT) included participants randomized to SweetMama, an interactive, web-based mHealth app with multiple motivational and educational features that help reduce barriers to care, offer health education, and aim to improve diabetes self-care for low-income pregnant people. In the parent trial, English-speaking pregnant individuals with GDM or T2DM were randomized to use SweetMama during pregnancy or usual care. SweetMama users experienced an individualized curriculum from enrollment through 6 weeks postpartum. Upon exit, users completed 2 qualitative interviews (during the delivery hospitalization and at the postpartum visit) and surveys assessing standardized usability metrics. The surveys included the System Usability Scale (SUS), the Usefulness, Satisfaction, Ease of Use (USE) scale, and the mHealth App Usability Questionnaire (MAUQ) to assess usability. Qualitative data were analyzed using constant comparative techniques. Results: Of 30 SweetMama users, 60% (n=18) had GDM, 83.3% (n=25) had publicly funded prenatal care, and the majority identified as non-Hispanic Black (n=17, 56.7%) or Hispanic (n=11, 36.7%). Scores on the SUS (median 85.0/100, IQR 70.0-88.8; ≥71% indicates acceptable or higher usability), USE (overall median 84.5/100, IQR 81.0-91.4), and MAUQ (median 84.1/100, IQR 79.0-91.3) indicated favorable usability assessments, particularly for the "ease of learning" domain. Qualitative interviews supported these findings: participants described the app as easy to navigate, well organized, and helpful for staying on track, citing features such as clear visual design, timely text reminders, and actionable tips. Users valued motivational elements and content specificity, while recommending increased customization and enhanced esthetics. Conclusions: In this user experience evaluation of a web-based mHealth app for low-income pregnant individuals with diabetes, participants found the tool to be user-friendly, visually appealing, informative, and motivating. Constructive feedback for application improvement for use in a future larger trial of clinical effectiveness was collected.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".