Developing a Patient-Centered mHealth App: A Tool for Adolescents With Type 1 Diabetes and Their Parents
Bibliographic record
Abstract
BACKGROUND: Type 1 diabetes (T1D) afflicts approximately 154,000 people under 20 years of age. Three-quarters of adolescents are not achieving glycosylated hemoglobin (HbA1c) targets, which leads to negative health outcomes. Mobile health (mHealth), the use of technology in health, has been used successfully to improve health in many chronic conditions, including diabetes. OBJECTIVE: The purpose of this study was to use patient-centered research methods to inform and improve the design and functionality of our T1D app, MyT1DHero, and to provide insight for others who are designing a health app for adolescents and parents. METHODS: This study included data from focus groups with participants recruited from the Juvenile Diabetes Research Foundation (JDRF) southeast Michigan's family network. All data collected during the sessions were audio-recorded, transcribed, and coded. RESULTS: Four key themes were identified: (1) diabetes is unpredictable, (2) negative and frustrated communication, (3) motivations to use an app, and (4) feedback specific to our app. CONCLUSIONS: A patient-centered approach was used to assist in the development of an app for adolescents with T1D. Participants were satisfied with overall app design; customization, interactivity, and tangible rewards were identified as being necessary for continued use. Participants believed the app would help improve the communication between parents and adolescents. Many apps developed in the health context have not used a patient-centered design method or have seen vast improvements in health. This paper offers suggestions to others seeking to develop apps for adolescents and their parents.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".