Development of a Smartphone App for Women Living With Gestational Diabetes Mellitus: Qualitative Study
Bibliographic record
Abstract
Background: Gestational diabetes mellitus (GDM), a type of blood glucose intolerance or hyperglycemia that occurs during pregnancy, is a common condition increasing in prevalence both globally and in Australia. Mobile health apps have been shown to be a useful resource for women with type 1 diabetes and could successfully contribute to GDM management by facilitating healthy behaviors. Objective: This study aimed to seek the perspectives of health care consumers (HCCs) and health professionals (HPs) regarding the development of a smartphone app for women living with GDM. Methods: A co-design process with 4 distinct phases underpinned the development of SugarMumma. Phase 1 involved a nonsystematic literature search followed by the creation of an app functions wish list. In phase 2, semistructured interviews with HCCs and HPs were undertaken and then thematically analyzed. In phase 3, a prototype was designed based on social cognitive theory and stakeholder recommendations. Agile project management methodology was used, followed by "user acceptance testing." During phase 4, a second round of individual interviews was undertaken with HCCs and HPs. The same qualitative methods outlined in phase 2 were used. Results: In phase 2, individual and didactic interviews were undertaken with HCCs (n=2) and HPs (n=6). Two overarching themes encompassing recommendations for app development emerged: (1) functionality and (2) individualized care. SugarMumma was created in phase 3. Phase 4 involved a second round of individual interviews with HCCs (n=1) and HPs (n=5), resulting in the final theme (3) future directions. Conclusions: With increasing numbers of people using smartphones, mobile health apps can help manage chronic conditions such as GDM. SugarMumma was designed following extensive stakeholder input. Good functionality, regular notifications, appealing visual aids, positive feedback, relevant dietary advice, and exporting information to HPs are important features to include.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".