CROCO - Using Commercial Wearables for Circadian Rhythm Home Monitoring

CROCO study


  • Fan Wu, Patrick Langer, Jinjoo Shim, Elgar Fleisch, Filipe Barata (2024). Comparative Efficacy of Commercial Wearables for Circadian Rhythm Home Monitoring from Activity, Heart Rate, and Core Body Temperature. (under review)


We conducted a study with 36 participants during which we collected circadian rhythm related data (physical activity and core body temperature) over 14 days. We used commercially available devices like Samsung Smartwatches and CORE body temperature sensors and compared collected data to the clinical grade activity trackers (actigraph). Data collection was done using our CLAID framework.


Circadian rhythms govern biological patterns that follow a 24-hour cycle. Dysfunctions in circadian rhythms can contribute to various health problems, such as sleep disorders. Current circadian rhythm assessment methods, often invasive or subjective, limit circadian rhythm monitoring to laboratories. Hence, this study aims to investigate scalable consumer-centric wearables for circadian rhythm monitoring outside traditional laboratories. In a two-week longitudinal study conducted in real-world settings, 36 participants wore an Actigraph, a smartwatch, and a core body temperature sensor to collect activity, temperature, and heart rate data. We evaluated circadian rhythms calculated from commercial wearables by comparing them with circadian rhythm reference measures, i.e., Actigraph activities and chronotype questionnaire scores. The circadian rhythm metric acrophases, determined from commercial wearables using activity, heart rate, and temperature data, significantly correlated with the acrophase derived from Actigraph activities (r=0.96, r=0.87, r=0.79; all p<0.001) and chronotype questionnaire (r=-0.66, r=-0.73, r=-0.61; all p<0.001). The acrophases obtained concurrently from consumer sensors significantly predicted the chronotype (R2=0.64; p<0.001). Our study validates commercial sensors for circadian rhythm assessment, highlighting their potential to support maintaining healthy rhythms and provide scalable and timely health monitoring in real-life scenarios.
Patrick Langer
Patrick Langer
Doctoral Researcher

My research interests include machine learning systems, mobile- and wearableAI, realtime AI and digital health.