Mathematical Modeling of Circadian Rhythms: Applications to Phase Prediction and Fatigue Reduction

CalebMayer

Stanford University

"Mathematical Modeling of Circadian Rhythms: Applications to Phase Prediction and Fatigue Reduction"

As consumer-grade wearable technology has become more prevalent in recent years, large-scale collections of data have been made available for researchers. We analyze significant amounts of wearable data to determine the circadian features that differ across groups and time frames. Using wearable activity, steps, and heart rate data, we adapt mathematical models to accurately estimate circadian phase across populations. This has a number of applications, including chronotherapeutic drug delivery, reducing fatigue, and shift work scheduling. We demonstrate applications to estimating circadian phase (dim light melatonin onset, or DLMO) in a home-based cohort of later-life adults, showing that activity-based models perform similarly or better than light-based models in DLMO estimation. We further use these models to provide wearable-based lighting interventions for reducing cancer-related fatigue. In particular, we test whether these lighting interventions, delivered via a mobile app, reduce cancer-related fatigue in a randomized controlled trial with 138 breast cancer, prostate cancer, and hematopoietic stem cell transplant patients. These interventions, based on real-time assessment of circadian rhythm through wearable devices, improve certain measures of fatigue (e.g., daily measurements of fatigue) in cancer patients. Further studies are needed to tune these models and assess the effect of lighting interventions in broader and more diverse cancer care settings.
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