Perspectives and Needs of Malaysian Patients With Diabetes for a Mobile Health App Support on Self-Management of Diabetes: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Effective self-management of diabetes is crucial for improving clinical outcomes by maintaining glucose levels and preventing the exacerbation of the condition. Mobile health (mHealth) has demonstrated its significance in enhancing self-management practices. However, only 20% of Malaysians are familiar with mHealth technologies and use them for health management. OBJECTIVE: This study aims to explore the perceived benefits and challenges, needs and preferences, and willingness of patients with diabetes to use mHealth apps for self-management of diabetes. METHODS: The study involved one-on-one semistructured online interviews with a total of 15 participants, all of whom were aged 18 years or older and had been diagnosed with diabetes for more than 6 months. An interview guide was developed based on the constructs of the Technology Acceptance Model (TAM), the Health Information Technology Acceptance Model (HITAM), and the aesthetics factor derived from the Mobile Application Rating Scale. All interviews were recorded in audio format and transcribed verbatim. The interview content was then organized and coded using ATLAS.ti version 8. Thematic analysis was conducted in accordance with the recommended guidelines for analyzing the data. RESULTS: From the interviews with participants, 3 key themes emerged regarding the perceived benefits of using mHealth app support in diabetes self-management. These themes were the ability to track and monitor diabetes control, assistance in making lifestyle modifications, and the facilitation of more informed treatment decision-making for health care professionals. The interviews with participants revealed 4 prominent themes regarding the perceived barriers to using mHealth app support for diabetes self-management. These themes were a lack of awareness about the availability of mHealth support, insufficient support in using mHealth apps, the perception that current mHealth apps do not align with users' specific needs, and limited digital literacy among users. The interviews with participants unveiled 4 key themes related to their needs and preferences concerning mHealth app support for diabetes self-management. These themes were the desire for educational information, user-friendly design features, carbohydrate-counting functionality, and the ability to engage socially with both peers and health care professionals. The majority of participants expressed their willingness to use mHealth apps if they received recommendations and guidance from health care professionals. CONCLUSIONS: Patients generally perceive mHealth app support as beneficial for diabetes self-management and are willing to use these apps, particularly if recommended by health care professionals. However, several barriers may hinder the utilization of mHealth apps, including a lack of awareness and recommendations regarding these apps from health care professionals. To ensure the effective development of mHealth app support systems for diabetes self-management, it is crucial to implement user-centered design processes that consider the specific needs and preferences of patients. This approach will help create apps that are tailored to the requirements of individuals managing diabetes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".