Exploring Desired Features of Mobile Health Apps for Patients With Diabetes to Enhance Engagement and Self-Management: Qualitative Study
Bibliographic record
Abstract
Background: Diabetes mellitus (DM) is a chronic condition requiring effective self-management to maintain glycemic control and prevent complications. Mobile health (mHealth) apps offer potential solutions by providing real-time monitoring, personalized feedback, and educational resources. However, their long-term adoption is hindered by a lack of user involvement in the development process and insufficient cultural adaptation. This study aims to explore the perspectives of patients with DM in Hong Kong on the functionalities and features of mHealth apps, highlighting the importance of tailoring these apps to meet local cultural needs. Objective: The objective of this study is to understand the views of patients with DM on the development of mHealth apps and the demand for app functions in order to provide a basis for the development of DM prevention apps. Methods: This descriptive qualitative study conducted semi-structured interviews with 10 patients with DM attending a District Health Centre in Hong Kong in May 2024, using a purposive sampling strategy. The transcribed data were analyzed by the inductive content analytical method, and themes were extracted with the aid of NVivo (version 15.0; QSR International) software. Results: In total, 7 key themes were identified: accurate information resources, automatic tracking and monitoring of health metrics, reminders, personalized customization options, intuitive usability, efficient data-sharing capabilities, and interactive design. Additionally, the study emphasizes the importance of cultural adaptation and the potential of artificial intelligence-enabled mHealth apps to enhance personalized information delivery. Ensuring the credibility and professionalism of information sources is also essential. Conclusions: The results provide valuable insights for enhancing the self-management capabilities of patients with DM and inform the future development of mHealth apps focused on DM prevention.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 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".