Development and Testing of an Electronic Diabetes Diary Integrated With a Hospital Information System for Individuals With Type 2 Diabetes Mellitus: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes mellitus (T2DM) is one of the leading noncommunicable diseases that require diabetes self-management (DSM) practices. This study proposes to develop a customized mobile health (mHealth) app integrated with a hospital information system (HIS) to enable real-time, two-way transfer of information between the patient and physician. The captured information in the electronic health record will facilitate physicians to have a chronological account of the patient's diabetes history and enable tweaking of the treatment. OBJECTIVE: The objectives of the study are (1) to develop the HIS-integrated Electronic Diabetes Diary (EDDy) per the end-user expectations at a tertiary care hospital in a south Indian state with a high prevalence of T2DM and (2) to evaluate and test adherence to EDDy in the management of T2DM. METHODS: The study will be carried out in 3 phases. Phase 1 involved in-depth interviews with primary end users to gather information regarding their expectations from the hospital-based EDDy. Phase 2 will use this information to develop a customized mHealth app using an iterative model of software development. Phase 3 will involve a pre- and posttest design; the developed app will be tested among consenting patients, where physicians will receive the patients' data through the HIS-integrated mHealth app. The pre- and posttest values will be analyzed for adherence leading to improvement in patients' self-management of blood glucose, user experience, glycemic control, and clinical utility. RESULTS: Phase 1 was completed on November 28, 2023. Phase 2 commenced in December 2023 and will end in May 2025. Phase 3 will follow afterward. CONCLUSIONS: The proposed app will include a convenient and simple alert system that enables the patient to test glucose values at self-selected intervals, provide grading options to enter diabetic-related complications, enhance patients' knowledge of tracking and managing the complications of diabetes, and help in maintaining the visual representation of glucose values and complications. The simplicity and usability of the modules are its novelty, which may motivate the patients to keep track of their glucose values and help them attain better health outcomes. TRIAL REGISTRATION: Clinical Trial Registry India CTRI/2023/03/051077; http://tinyurl.com/4tau4ndb. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/50732.
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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 | 0.049 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".