Identification and Evaluation of Mobile Applications for Self-Management of Diet and Lifestyle for Patients with Inflammatory Bowel Disease
Bibliographic record
Abstract
Abstract Background Mobile health applications (apps) providing diet and lifestyle self-management programs to patients with inflammatory bowel disease (IBD) are emerging. The objective of this study was to evaluate current apps available in the US and Canada based on app quality, perceived impact on diet and mental health and comprehensiveness to support self-management. Methods The Apple iOS and Google Play app stores were searched for terms related to IBD. Apps were included if they targeted diet and lifestyle behaviours for patients living with IBD and were available to the general public. Apps were excluded if they were not specific to IBD, not available in English, did not target diet or lifestyle therapy, were not available in the US and Canada, or did not offer stand-alone self-management programs. The Mobile App Rating Scale was used to assess mobile app quality. Results A total of 1,512 apps were identified through the app stores. Six apps met inclusion criteria. My IBD Care: Crohn’s and Colitis received the highest quality rating and LyfeMD received the highest overall app rating. Only these two apps provided behaviour tracking over time, and three (50 percent) apps provided good-quality information. Conclusions While many IBD-related apps exist, few support self-management of diet and lifestyle behaviours. The My IBD Care and LyfeMD apps had the highest ratings and can be used to track lifestyle behaviours. The effectiveness of these apps to improve behaviours, and subsequently impact the disease course and quality of life, should be explored in future studies.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".