Master's Thesis - Abstract

Inflammatory bowel diseases (IBD), including Crohn’s disease and ulcerative colitis, are chronic conditions marked by alternating periods of intestinal inflammation and remission. Current monitoring strategies rely largely on intermittent, often invasive assessments and patient-reported symptoms, limiting the timely detection of disease activity and contributing to predominantly reactive models of care. This thesis investigates the potential of multimodal data, combining wearable-derived physiological and sleep metrics with patient-reported outcomes, to enable continuous and personalized monitoring of disease activity and outcomes in IBD. Using a real-world dataset provided by Coreway UG, a digital-health company developing a patient-companion platform that integrates wearable sensor data and daily symptom tracking to support proactive IBD management, this work systematically evaluates the robustness and reproducibility of two landmark studies by \cite{hirten2025, hirten2025sleep}. Linear mixed-effects modeling is employed to examine associations between wearable-derived features and flare periods derived from patient-reported outcomes. In addition to replication analyses, supplementary investigations explore the relationship between wearable-derived metrics and subjective sleep quality, assess anomaly-detection approaches for longitudinal time-series data, and introduce an interactive visualization tool to support future exploratory analyses. The findings largely corroborate prior evidence that wearable-derived physiological and sleep metrics capture meaningful changes surrounding inflammatory flares, while also highlighting challenges related to data heterogeneity and real-world data quality. Overall, this thesis contributes to the growing evidence base supporting wearable technologies as a complementing component of proactive, predictive, and patient-centered digital health solutions in IBD care.