Evaluation of Mobile Nutrition Management Apps for Diabetes: Quality, Dietary Guideline Adherence, and Recommendations: A Mixed Method Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Diabetes mellitus (DM) is a chronic metabolic disorder marked by elevated blood glucose levels and has emerged as a global epidemic that needs management strategies for effective glycemic control through diet. In recent years, mobile applications have emerged as valuable tools for supporting self-management in chronic diseases, such as diabetes, particularly in the nutritional aspect of the disease. However, the quality, accuracy, and adherence of these applications to established dietary guidelines remain undiscovered and inconsistent. OBJECTIVE The study aims to evaluate the functionality and adherence to guidelines of digital nutrition management applications for diabetes, with a focus on dietary guidelines from the American Diabetes Association (ADA), World Health Organization (WHO), European Association for the Study of Diabetes (EASD), and Canadian Diabetes Association (CDA). METHODS A mixed-methods approach was used, involving a systematic review of mobile applications from the Google Play Store and Apple App Store. A total of 24 applications were selected based on predefined inclusion and exclusion criteria. Applications were analyzed for their content and features using a compliance checklist derived from official dietary guidelines for diabetes. Additionally, the Mobile App Rating Scale (MARS) was used to evaluate application quality on engagement, functionality, aesthetics, and information. RESULTS Only two applications showed full compliance with the dietary guidelines, while most applications showed partial adherence. The MARS evaluation revealed significant variability in application quality, where only two applications had a mean score above 4.0. This shows major gaps in user engagement, functionality, educational content, and personalization. CONCLUSIONS Even with the growing availability of nutrition management applications for diabetes, many lack full compliance with dietary guidelines and show room for improvement in their content quality. A collaboration between healthcare professionals, developers, and patients is essential for the future development of these tools to effectively support diabetes self-management.
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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 | 0.048 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".