Compliance of Type 2 Diabetes Applications to International Guidelines: Protocol for a Quantitative App Assessment
Bibliographic record
Abstract
BACKGROUND: Diabetes is among the most common chronic conditions people live with across the world. While it can be managed to a substantial degree, it can result in significant complications. As such, easy access to accurate tools to aid diabetes management is useful in minimizing these complications. Mobile apps are highly accessible and widely used, but there is a gap in the literature examining their compliance with medical guidelines. OBJECTIVE: The aims of this study are to develop the Analysis of Diabetes Apps (ADA) checklist to evaluate apps' compliance to guidelines set by the International Diabetes Federation (IDF) on the treatment and management of type 2 diabetes; to assess type 2 diabetes apps in the Apple App Store and the Android Google Play Store, and their compliance with international guidelines using the ADA framework; and to compare the novel ADA checklist against both the Mobile App Rating Scale (MARS) tool kit and app ratings for each store. METHODS: We will develop a checklist based on the "IDF Clinical Practice Recommendations for Managing Type 2 Diabetes in Primary Care." Type 2 diabetes apps will be scraped from 6 countries' app stores using web scraping tools. These countries include Australia, Brazil, India, Nigeria, the United States, and the United Kingdom, which were selected based on the largest population of English-speaking people in each continent. The apps will be searched on the web-based scraper using the search terms "blood sugar," "diabetes," "glucose level," "insulin," "sugar level," and "type 2 diabetes." Apps will be excluded if they are paid or are not in English. The apps will be assessed using the ADA checklist to evaluate their compliance to the international diabetes guidelines. Once scored, the results will be analyzed with descriptive statistics. The most popular apps will be further analyzed using the MARS tool kit. The ADA checklist scores will then be compared to both the MARS tool kit score and app ratings for each store. RESULTS: The ADA checklist developed based on the IDF guidelines focuses on general information, risk factors, diagnosis, pharmacology, lifestyle modification, glycemic recommendations, and medications. The initial stress testing of the protocol resulted in 173 included apps. This will vary in the final search as the app stores are constantly changing. CONCLUSIONS: The protocol presents the development of a checklist to investigate the compliance of type 2 diabetes apps with international guidelines. The checklist will hopefully form the basis of a scoring system for future research on compliance of mobile apps with international guidelines. High standardization of the ADA checklist will make it a robust tool for people with diabetes and their health care providers alike in assessing type 2 diabetes apps in the future. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/48781.
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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 | 0.100 | 0.100 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.014 |
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".