A79 EAT, SLEEP, WORK, REPEAT! WEARABLES AND APPS TO TRACK IBD PATIENTS’ SYMPTOMS, DIET, SLEEP, AND PHYSICAL ACTIVITY
Bibliographic record
Abstract
Abstract Background Lifestyle factors like diet, physical activity, and sleep patterns are known to influence symptoms and progression in Inflammatory Bowel Disease (IBD). However, accurately determining temporal relationships between these factors and symptoms has been hampered by a lack of reliable data collection methods. New technologies, such as wearables and smartphone apps, offer real-time, remote monitoring and allow for better understanding of their relationship with IBD symptoms. Aims To assess the feasibility of using remote monitoring tools (wearables and mobile apps) to explore the temporal relationships between symptoms, diet, and sleep in IBD patients. Methods Adult IBD patients participated in a 3-month pilot study (Track-IBD), during which they wore a fitness monitor (Oura Ring) and tracked their diet and symptoms in real-time using Keenoa (diet tracking) and Zamplo (symptom tracking) smartphone apps. Results Fifteen IBD patients (9 - Crohn’s disease, 6 - ulcerative colitis; 8 female; 12 Caucasian; mean age 41.2 years) were enrolled. The usage rates were 69.8% for Zamplo, 77.0% for Keenoa, and 77.0% for the Oura Ring. The most commonly reported symptoms were abdominal pain, diarrhea, and fatigue, which together accounted for 67% of all recorded symptoms. Symptoms were predominantly reported in the afternoon (30.1%) and late-night (49.2%), while morning and early-night periods saw fewer reports (20.7%). Most symptoms occurred within 5 hours of a meal, with peaks in meal-to-symptom latency (MTSL) at 0.5–1 hour and 4–5 hours post-meal, demonstrating an association between meal timing and symptoms. No correlation was found between MTSL and the nature of the symptoms (upper, lower, or extra-intestinal) or macronutrient intake. Wearable data analysis revealed decreased sleep quality and increased average heart rate on symptomatic days (p<0.04). Most participants found the apps easy to use (Oura: 100%, Zamplo: 93%, Keenoa: 73%) and helpful in managing their IBD (Oura: 100%, Zamplo: 87%, Keenoa: 60%). Conclusions The Track-IBD study demonstrates high adherence to monitoring diet, symptoms, and physiological measures by IBD patients over 3 months. It also shows the feasibility of combining multiple tracking technologies to assess lifestyle patterns, physiological parameters, and symptoms in real-time. Preliminary analyses identified relationships between symptoms, diet and sleep quality, highlighting the potential to identify lifestyle triggers of disease and symptom flares and provide personalized therapeutic advice to IBD patients. Funding Agencies Balsam Foundation
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.008 | 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".