Using mHealth to Provide Mobile App Users With Visualization of Health Checkup Data and Educational Videos on Lifestyle-Related Diseases: Methodological Framework for Content Development
Bibliographic record
Abstract
BACKGROUND: The number of people with lifestyle-related diseases continues to increase worldwide. Improving lifestyle behavior with health literacy may be the key to address lifestyle-related diseases. The delivery of educational videos using mobile health (mHealth) services can replace the conventional way of educating individuals, and visualization can replace the provision of health checkup data. OBJECTIVE: This paper aimed to describe the development of educational content for MIRAMED, a mobile app aimed at improving users' lifestyle behaviors and health literacy for lifestyle-related diseases. METHODS: All videos were based on a single unified framework to provide users with a consistent flow of information. The framework was later turned into a storyboard. The final video contents were created based on this storyboard and further discussions with leading experts and specialist physicians on effective communication with app users about lifestyle-related diseases. RESULTS: The app uses visualization of personal health checkup data and educational videos on lifestyle-related diseases based on the current health guidelines, scientific evidence, and expert opinions of leading specialist physicians in the respective fields. A total of 8 videos were created for specific lifestyle-related diseases affecting 8 organs: (1) brain-cerebrovascular disorder, (2) eyes-diabetic retinopathy, (3) lungs-chronic obstructive pulmonary disease, (4) heart-ischemic heart disease, (5) liver-fatty liver, (6) kidneys-chronic kidney disease (diabetic kidney disease), (7) blood vessels-peripheral arterial disease, and (8) nerves-diabetic neuropathy. CONCLUSIONS: Providing enhanced mHealth education using novel digital technologies to visualize conventional health checkup data and lifestyle-related diseases is an innovative strategy. Future studies to evaluate the efficacy of the developed content are planned.
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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 | 0.051 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".