A Diabetes Education App for People Living With Type 2 Diabetes: Co-Design Study
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes (T2D) is a growing global health concern, including in Singapore. Diabetes education programs have been shown to be effective in improving health outcomes and diabetes self-management skills. Mobile health apps have emerged as useful tools for diabetes education; however, their use and acceptance by the target population remain inconsistent. Therefore, end-user participation in the design and development of a mobile health app is crucial for designing an acceptable app that can improve outcomes for populations with a chronic disease. OBJECTIVE: The objective of this study was to apply an end-user participatory approach to co-design a diabetes education app prototype for people living with T2D by exploring their perceptions, acceptance, and usability of an app prototype, as well as their diabetes experience and perspectives on digital diabetes education. METHODS: A total of 8 people with T2D, who were recruited from diabetes management Facebook groups, participated in 4 web-based surveys via Qualtrics and 2 structured interviews via Zoom (Zoom Video Communications, Inc) between August 20, 2021, and January 28, 2022. Descriptive statistics and thematic analyses of the discussion and iterative feedback on the app prototype were used to assess the participants' perceptions of living with T2D, attitudes toward digital diabetes education, and acceptance of the prototype. RESULTS: Analyses of the surveys and interview data revealed 3 themes: challenges of living with T2D; validation, acceptability, and usability of the diabetes education app prototype; and perspectives on digital diabetes education. In the first theme, participants highlighted the importance of solitary accountability, translating knowledge into practice, and developing pragmatic self-consciousness. The second theme indicated that the diabetes education app prototype was acceptable, with information and appearance being key; revealed ambivalent and polarized opinions toward the chatbot; and confirmed potential impact of the app on diabetes self-management skills and practice. The third theme comprised the necessity of using a variety of information-seeking strategies and recommendations for desired content and app qualities, including accessibility, adaptability, autonomy, evidence-based design and content, gamification, guidance, integration, personalization, and up-to-date content. The findings were used to reiterate the app design. CONCLUSIONS: Despite a small sample size, the study demonstrated the feasibility of engaging and empowering people living with T2D to consider digital therapeutics for diabetes self-management skills and practice. Participants gave rather positive feedback on the design and content of the app prototype, with some recommendations for improvements. The findings suggest that incorporating end-user feedback into app design can lead to the creation of feasible and acceptable tools for diabetes education, potentially improving outcomes for populations with a chronic disease. Further research is needed to test the impact of the refined diabetes education app prototype on diabetes self-management skills and practice and quality of life.
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".