Abstract: Models that capture the human circadian clock on a macroscopic level have been used effectively in predicting circadian phase, particularly for day worker and healthy populations. Adapting and applying these models to work with wearable data from populations with disrupted circadian phases (such as shift workers, cancer patients, and individuals with COVID-19) has been a recent key area of research. This talk will present limit cycle oscillator models for the circadian pacemaker, show their ability to predict human circadian phase based on real-world activity data from consumer-grade wearable devices, and consider the effects of lighting schedules and parameters on the model outputs. We will further discuss algorithms for the analysis of oscillatory wearable data such as heart rate and body temperature, and the application of these techniques to varying populations. Through this framework we see changes to physiologically-relevant features at different times around COVID-19 symptom onset, enhancing our understanding of disease progression and speaking to the early detection potential. These projects aim to utilize mathematical and computational tools to generate meaningful additions to our understanding of circadian rhythms, personal health, and disease in the real-world.

Contact: S. Alben