A Culturally Sensitive Mobile App (DiaFriend) to Improve Self-Care in Patients With Type 2 Diabetes: Development Study
Bibliographic record
Abstract
BACKGROUND: Mobile apps designed with cultural sensitivity have demonstrated higher user acceptability and greater effectiveness in enhancing self-care skills. However, a significant gap exists in developing such apps for specific populations, such as Portuguese Americans living in southern Massachusetts, home to the second-largest Portuguese community in the United States. This group possesses unique cultural traditions, particularly in dietary practices, including a tendency toward high carbohydrate intake. Tailoring diabetes self-care apps to address these specific cultural requirements could substantially improve diabetes management within this population. OBJECTIVE: The aim of this app development project was to develop a prototype diabetes management app for Portuguese Americans with type 2 diabetes mellitus using the design thinking methodology, incorporating user-centered design principles and cultural sensitivity. This paper describes the phase-2 results, focusing on app design and development. METHODS: Phase 2 of this app development project adhered to the design thinking methodology delineated by the Hasso Plattner Institute of Design at Stanford University, focusing on 2 critical steps: ideation and prototyping. This phase started in March 2022 and continued until April 2024. The project was driven by a multidisciplinary team consisting of 2 nurse educators; an app development specialist; and 2 graduate research assistants from the university's Computer and Information Sciences Department, both well-versed in mobile app development. Data collected during phase 1, which will be published separately, informed the app design and development process. RESULTS: The prototype of the DiaFriend app (version 1) was designed and developed. The app comprises five features: (1) blood glucose monitoring, (2) weight tracking, (3) carbohydrate tracking, (4) exercise log, and (5) medication reminder. The carbohydrate tracking feature was explicitly tailored to correspond to Portuguese food culture. This paper presents the front-end interface flowchart, demonstrating how the user navigates through each screen. It also discusses the challenges faced during the backend development, such as data not being able to be stored and retrieved. CONCLUSIONS: The DiaFriend app (version 1) distinguishes itself from conventional diabetes self-care apps through its emphasis on cultural sensitivity. The development of this app underscores the importance of cultural considerations in health informatics. It establishes a foundation for future research in developing and evaluating culturally sensitive mobile health apps. The adaptation of such technologies has the potential to enhance self-care practices among Portuguese Americans with type 2 diabetes mellitus, with improved glycated hemoglobin levels as a potential outcome. The last step of the design thinking methodology, testing the app, will be conducted in phase 3 and the results will be published elsewhere.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".