Review and Analysis of German Mobile Apps for Inflammatory Bowel Disease Management Using the Mobile Application Rating Scale: Systematic Search in App Stores and Content Analysis
Bibliographic record
Abstract
BACKGROUND: Patients suffering from inflammatory bowel disease (IBD) frequently need long-term medical treatment. Mobile apps promise to complement and improve IBD management, but so far there has been no scientific analysis of their quality. OBJECTIVE: This study evaluated the quality of German mobile apps targeting IBD patients and physicians treating IBD patients using the Mobile Application Rating Scale (MARS). METHODS: The German Apple App Store and Google Play Store were systematically searched to identify German IBD mobile apps for patient and physician use. MARS was used by 6 physicians (3 using Android smartphones and 3 using iPhones) to independently assess app quality. Apps were randomly assigned so that the 4 apps with the most downloads were rated by all raters and the remaining apps were rated by 1 Android and 1 iOS user. RESULTS: In total, we identified 1764 apps in the Apple App Store and Google Play Store. After removing apps that were not related to IBD (n=1386) or not available in German (n=317), 61 apps remained. After removing duplicates (n=3) and apps for congresses (n=7), journals (n=4), and clinical studies (n=6), as well as excluding apps that were available in only 1 of the 2 app stores (n=20) and apps that could only be used with an additional device (n=7), we included a total of 14 apps. The app "CED Dokumentation und Tipps" had the highest overall median MARS score at 4.11/5. On the whole, the median MARS scores of the 14 apps ranged between 2.38/5 and 4.11/5. As there was no significant difference between iPhone and Android raters, we used the Wilcoxon comparison test to calculate P values. CONCLUSIONS: The MARS ratings showed that the quality of German IBD apps varied. We also discovered a discrepancy between app store ratings and MARS ratings, highlighting the difficulty of assessing perceived app quality. Despite promising results from international studies, there is little evidence for the clinical benefits of German IBD apps. Clinical studies and patient inclusion in the app development process are needed to effectively implement mobile apps in routine care.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".