A217 A REVIEW OF MOBILE HEALTH APPLICATIONS FOR INDIVIDUALS LIVING WITH INFLAMMATORY BOWEL DISEASE USING MOBILE APPLICATION RATING SCALE (MARS)
Bibliographic record
Abstract
Abstract Background Inflammatory bowel disease (IBD) is a chronic disorder that can be managed but not cured. The relapsing and remitting symptoms of IBD impact patients' quality of life and is associated with considerable healthcare costs. Different mobile health apps have been developed around the world for IBD and other chronic medical conditions. These apps can potentially decrease healthcare utilization and the burden of the disease. Purpose We aimed to review IBD mobile health apps available in English and evaluate their quality and content. Method We searched the Apple Store and Play Store in July 2022 using IBD-related keywords (e.g., “IBD,” “Crohn,” “Crohns,” “colitis,” “ulcerative colitis,” and “inflammatory bowel disease.”) to identify apps for iOS and Android devices, respectively. We included apps available in English, designed specifically for IBD, and free to download and use. Two researchers reviewed, rated, and evaluated the retrieved apps independently. The Mobile App Rating Scale (MARS) was used to rate the included apps. The MARS rates apps on a scale of 1 to 5 and includes an overall score as well as scores for engagement, functionality, information, aesthetics, and subject quality. A score ≥3 is deemed acceptable. Result(s) Search queries yielded 88 relevant apps, of which 38 met the inclusion criteria and were included in this review. The included apps for IBD were most tracking and monitoring symptoms of the disease (n=22). Two of the apps were affiliated with universities, and three of the apps were specifically developed for children. In addition, 11 apps were designed for IBD, regardless of disease subtype (Crohn’s disease [CD] or ulcerative colitis [UC]), and 27 apps were designed specifically for either UC or CD. The mean MARS score for IBD apps was 3.3 (SD=0.6), with more than half deemed acceptable. Apps scored highest on the functionality (mean=3.7, SD=0.6) and information (mean=3.6, SD=0.5) dimension of MARS and lowest on the engagement dimension (mean=3, SD=0.8). Image Conclusion(s) Most IBD-related apps provide acceptable information and functionality but should improve their engagement to be welcomed by users. This review provides a roadmap for improving available apps and future app development for individuals with IBD. App developers in the field of IBD must ensure they create high-quality, engaging, esthetic and evidence-based apps. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".