A87 LEVERAGING DIGITAL HEALTH: SYMPTOM TRACKING IN PEDIATRIC IBD USING A SMARTPHONE APP FOR ENHANCED PATIENT-REPORTED OUTCOMES
Bibliographic record
Abstract
Abstract Background Patient-reported outcome measures (PROMs) are essential for managing chronic diseases, as they provide valuable insights into patients’ perspectives on their condition’s impact and help tailor treatments accordingly. However, collecting this information from pediatric patients with inflammatory bowel disease (IBD) presents several challenges, such as the reluctance of patients to discuss sensitive topics such as bowel movements, defecation, and rectal bleeding. Additionally, the subjective nature of physicians’ evaluations compared to the experiences of patients affect the symptom assessment. An IBD app could help teenagers to routinely assess the clinical symptoms. Aims The objectives of the study were twofold: first, to describe the trajectory of symptoms as self-reported by patients through a digital application, and second, to compare these symptoms with those evaluated by physicians during medical visits. Methods We conducted a prospective study involving teenagers with IBD aged 15-18 years old. All participants had a smartphone and an understanding of English. They received an app (Injoy Gut Health tracking by Phyla Technologies Inc.) that allowed for the collection of symptoms at home, which they were asked to use daily. At inclusion, participants completed the IBD Control questionnaire. Additionally, we collected information on disease status such as the physician global assessment (PGA). Results We included 25 children (60% female) with a median age at diagnosis of 14.5 years (interquartile range (IQR): 13.2-14.5) for IBD (64% Crohn’s disease) for a median of 3.6 years (IQR: 2.7-5.4). Most participants (84%) were in complete remission and 16% had mild active disease, according to the PGA. The teenagers completed the app survey for a median of 122 (IQR: 44-200) days. Participants reported gastrointestinal symptoms during daily data collection: 48% experienced rectal pain, 64% experienced nausea, 96% experienced gas, 76% experienced bloating and 80% experienced abdominal pain. Finally, 28% noted blood in their stools representing a median of 7.8% (IQR 8.88-11.9) of their stools. The PROMs filled at baseline depicted a spectrum of disease activity. The median IBD-control-8 subscore was 12 (IQR: 10-15). The PROMs and the data self-collected daily at home were not in agreement with the PGA. Indeed, symptom data showed that participants experienced a median of 7 (IQR: 4-8) symptoms while 92% (23) of patients reported more than two symptoms. Conclusions These results highlight the importance of incorporating PROMs for more precise and personalized management of IBD in children. The use of an app for at-home daily data collection proved to be a valuable tool in capturing real-time symptomatology. Funding Agencies:
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".