Background
Abdominal pain is a common symptom in patients with inflammatory bowel disease (IBD). Up to 70 % of IBD patients experience pain when the disease is active. Even when patients with IBD are in remission, 20-50 % of patients experience ongoing pain. The precise mechanism of developing chronic abdominal pain in patients with IBD in remission remains unknown.
The aim of this study is to identify psychophysiological and biological risk factors for the development of chronic abdominal pain in patients with newly diagnosed IBD. We will then use cutting edge techniques in machine learning to ascertain if artificial intelligence can predict the development of chronic abdominal pain.
Study design
This study consists of 4 sections (Study 1A, 1B, 2, and 3).
Study 1A: We will perform a longitudinal study in 150 patients with new-onset IBD (ulcerative colitis and Crohn's disease) over 18 months. We will ask the participants to answer online questionnaires and record heart rate using a mobile app every 6 months. We will also ask them to participate in the IBD BioResource 'Inception study' as well. Data collected by the IBD BioResource 'Inception study' will be used in the analysis.
Study 1B: 450 patients who are newly registered in the 'Inception study' will be recruited and followed for at least 12 months. We will ask them to answer online questionnaires every 6 months. Data collected by the IBD BioResource 'Inception study' will be used in the analysis.
Study 2 and 3: Study 2 and 3 are a questionnaire-based cross-sectional studies in patients with IBD (both Crohn's disease and ulcerative colitis patients). The participants for study 2 are patients who are registered in the 'IBD BOOST study', and those for study 3 are patients who are registered in IBD BioResource (but not in the 'IBD Boost study'). We will ask them to answer online questionnaires once.
