A73 CANADIAN INFLAMMATORY BOWEL DISEASE MOBILE APPS: CURRENT LANDSCAPE AND NEEDS
Bibliographic record
Abstract
Abstract Background Evidence-based digital health applications (apps) offering comprehensive lifestyle therapies for inflammatory bowel disease (IBD) patients are limited in Canada. Aims The aims of this study were to explore the Canadian IBD digital app landscape and review preliminary data from a recently launched digital app for IBD, LyfeMD. (www.lyfemd.ca). Methods “IBD”, “Inflammatory bowel disease”, “UC”, “Ulcerative colitis”, “Crohns” and “Crohn’s disease (CD)” were searched by one team member (BC) on the App Store. Apps were included if they offered any type of lifestyle therapy, including education. The mobile application rating system (MARS) was used to evaluate each app and is a validated tool used to assess the quality of mobile health apps. For the LyfeMD app, 35 IBD users completed a baseline assessment survey to identify: 1) physical activity, sitting, and screen time, and; 2) stress, sleep, depression and anxiety. Eleven participants completed in-depth user experience evaluations after 4 weeks. Survey scores were calculated using published scoring protocols and descriptive data were prepared. Results The LyfeMD and My IBD Care app scored highest on the MARS with a total score of 4.8/5. Of the other eight apps identified, scores ranged from 2.4 to 4.6 (overall mean=4.0). LyfeMD differentiated itself from other apps by providing lifestyle programs to improve nutrition, physical activity and mental health. Of the LyfeMD users, 74% had CD (median Harvey Bradshaw index=3.1, IQR=1.1–4.8) and 26% had ulcerative colitis (median partial mayo score=1.0, 0.5–6.0), 60% had a BMI ≥25 kg/m2, 57% were meeting 150 minute/week activity guidelines, 49% had high sitting time, 100% had high screen time, 69% had a moderate to high level of stress, 100% experienced sleep problems, 69% reported depression, and 49% reported anxiety. Eleven people completed the detailed user experience evaluations. They reported the app helped them identify behaviour changes to improve overall wellness; most often what they eat (64%), overall well-being (64%) and physical activity (46%). Conclusions Two IBD apps available in Canada had a high MARS rating, however only the LyfeMD app offered comprehensive lifestyle therapies. The growing literature supports benefit for lifestyle therapies in IBD, and the LyfeMD app may be effective to identify areas amenable to lifestyle modification. Funding Agencies Ascend, Alberta Innovates
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".