Prospective Evaluation of Flare Detection in IBD With Digital Biomarkers: Bring Your Own Device Study
Trial Snapshot
- Phase
- Not Applicable
- Status
- Not yet recruiting
- Enrollment
- 600
- Locations
- 3
- Primary Endpoint
- Prediction of mucosal inflammation (calprotectin≥250 µg/g and/or CRP≥5mg/l with UCEIS≥1 or Mayo≥1 (UC) and SES CD≥3 (CD) on endoscopic/radiological evaluation) using HRV-changes relative to individual baseline.
Study Overview
Brief Summary
The aim of this study is to identify HRV changes predictive of IBD flares using patient-own wearable devices in a large cohort supplemented by additional data layers including sleep parameters, step count and clinical data extracted from electronic patient files.
Detailed Description
IBD, comprising Crohn's disease and ulcerative colitis, is a chronic inflammatory condition of the gastrointestinal tract, with a globally rising prevalence. Current medical care consists of outpatient visits combined with blood and stool tests, radiological examination and endoscopy. The disease is costly to manage, also leading to indirect costs such as reduced work productivity. Telemonitoring platforms, including MyIBDCoach, IBD@home(Luscii) and IBDream, can alleviate these factors by enabling remote cost-effective monitoring of patient's health. However, data collection largely relies on burdensome questionnaires which are susceptible to bias. Emerging technologies such as wearable devices and smartphones enable continuous remote monitoring through digital biomarkers. This Bring Your Own Device (BYOD) study is part of the IBDigital project, which aims to enrich existing monitoring platforms by integrating digital biomarkers.
In this prospective, observational, multicenter cohort study, patients with IBD will be recruited from the outpatient clinics of the participating centers. Data will be collected from participants' personal smartwatches (and smartphones). Following an inclusion period of approximately four months, participants will enter a one-year follow-up period. During follow-up, standard-of-care clinical data will be collected during outpatient visits. In addition, wearable-derived data, including heart rate metrics, sleep parameters, and physical activity measures, will be continuously collected.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •18 years or older
- •Crohn's Disease or ulcerative colitis
- •Having a smartwatch and/or a smartphone
- •Apple watch, Samsung Watch, Fitbit, Garmin, Polar (devices will not be made available)
Exclusion Criteria
- •No specific exclusion criteria will be used. Subgroup analysis will be performed for patients with e.g. heart problems or arrhythmia, medication impacting HR (such as beta blockers), pregnancy, thyroid problems, shift work...
Arms & Interventions
Patients with IBD
≥18 years or older with Crohn's disease or ulcerative colitis, and having a smartwatch (Apple watch, Samsung Watch, Fitbit, Garmin, Polar (devices will not be made available) ) and/or a smartphone.
Intervention: Wearing their own wearable device. (Device)
Outcomes
Primary Outcomes
Prediction of mucosal inflammation (calprotectin≥250 µg/g and/or CRP≥5mg/l with UCEIS≥1 or Mayo≥1 (UC) and SES CD≥3 (CD) on endoscopic/radiological evaluation) using HRV-changes relative to individual baseline.
Time Frame: 1 year
Secondary Outcomes
- To study the difference in step count measured by smartwatches compared with smartphones.(1 year)
- Prediction of mucosal inflammation (assessed by laboratory tests and/or imaging in IBD) using changes in physical activity and sleep metrics relative to individual baseline.(1 year)
- Identification of a discrepancy between inflammatory flares and symptomatic flares (MIAH-CD>3.6 and MIAH-UC>3.5) regarding HRV.(1 year)
- To investigate if changes in HRV, PA and sleep relative to baseline predict clinical/symptomatic remission in IBD.(1 year)
- To study the inter-device variability in mean HRV metrics (including MESOR, acrophase, amplitude, SDNN, RMSSD), PA and sleep parameters, compared to resting state.(1 year)
- Investigation of additional digital phenotypes that can be derived from the available (wearable/clinical) data and how these phenotypes differ across relevant subgroups within the study population.(1 year)