Physiological Data Collected from Wearable Devices Identify and Predict Inflammatory Bowel Disease Flares

Background and aims: Wearable devices capture physiological signals non-invasively and passively. Many of these parameters have been linked to inflammatory bowel disease (IBD) activity. We evaluated the associative ability of several physiological metrics with IBD flares and how they change before the development of flare.

Methods: Participants throughout the United States answered daily disease activity surveys and wore an Apple Watch, Fitbit or Oura Ring. These devices collected longitudinal heart rate (HR), resting heart rate (RHR), heart rate variability (HRV), steps, and oxygenation (SpO2). C-reactive protein, erythrocyte sedimentation rate and fecal calprotectin were collected as standard of care. Linear mixed-effect models were implemented to analyze HR, RHR, steps, and SpO2, while cosinor mixed-effect models were applied to HRV circadian features. Mixed effect logistic regression was used to determine the predictive ability of physiological metrics.

Results: 309 participants were enrolled across 36 states. Circadian patterns of HRV significantly differed between periods of inflammatory flare and remission, and symptomatic flare and remission. Marginal means for HR and RHR were higher during periods of inflammatory flare and symptomatic flare. There was lower daily steps during inflammatory flares. HRV, HR, and RHR differentiated whether participants with symptoms had inflammation. HRV, HR, RHR, steps, and SpO2 were significantly altered up to 7 weeks prior to inflammatory and symptomatic flares.

Conclusions: Longitudinally collected physiological metrics from wearable devices can identify and change prior to IBD flares, suggesting their feasibility to monitor and predict IBD activity.