Acceptability, feasibility, and impact of the MyGut digital health platform in the monitoring and management of inflammatory bowel disease
Bibliographic record
Abstract
Abstract Background Digital health monitoring may help facilitate self-management strategies when caring for patients with inflammatory bowel disease (IBD). Aims This study investigated the feasibility of implementing the MyGut health application when caring for patients with IBD and evaluated whether its use improved health outcomes. Methods We conducted a prospective trial in 2 Canadian hospitals from 2020 to 2023. Patients with IBD were recruited from gastroenterology clinics, and the MyGut application was installed onto their mobile devices. Metrics such as acceptability, satisfaction, feasibility, quality-of-life scores (measured through the short IBD questionnaire [SIBDQ]), and resource utilization were collected throughout the 1-year follow-up period. Results Of the 84 patients enrolled, 58 patients (69%) continued to use the app until the study completion. At recruitment, all 84 patients (100%) were willing to use the MyGut application after a brief tutorial. There was a significant improvement in the SIBDQ scores after 1 year of MyGut use (mean = 56.0, SD 8.85 vs 52.0, SD 9.84) (P = .012). However, only 42.9% (21/49) of the patients were willing to continue using the application after 1 year, a significant decrease compared with the 71.4% (35/49) who were willing to continue after 2 months (P = .001). No differences were observed in the number of emergency room visits/hospitalizations (P = .78) before and after 1 year of MyGut use. Conclusions This study demonstrates that patients are willing to use digital health monitoring platforms and this may lead to improved quality of life. However, sustained efforts must be made to optimize its long-term feasibility.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".