Computational approaches for individual circadian phase prediction in field settings. Issue 22 (August 2020)

• Record Type: Journal Article
• Authors: Stone, Julia E.; Postnova, Svetlana; Sletten, Tracey L.; Rajaratnam, Shantha M.W.; Phillips, Andrew J.K.
• Is Part Of: Current opinion in systems biology. Issue 22(2020)
• Page Start: 39
• Page End: 51
• Publication Date: 2020-08
• Subjects: Circadian rhythms -- Melatonin -- Computational models -- Light -- Actigraphy -- Ambulatory monitoring; Systems biology -- Periodicals; 570
• DOI: 10.1016/j.coisb.2020.07.011
• Languages: English
• ISSNs: 2452-3100

Abstract

Abstract: Knowledge of circadian phase is critical for timing interventions for circadian rhythm disorders, medications, or predicting alertness. Current gold-standard measures of circadian phase are impractical for continuous or real-time tracking. Mathematical modeling offers an alternative, whereby ambulatory monitoring of environmental, behavioral, and/or physiological variables can be used to predict circadian phase. This review examines available approaches for predicting circadian phase, ranging from statistical models to machine learning and dynamical systems models, and evaluates their readiness for individual phase predictions. Multiple models predicted circadian phase with similar accuracy when individuals were stably entrained. However, most models did not generalize, or were not tested, under more challenging conditions (e.g., circadian misalignment). One model performed similarly under a range of conditions: a limit-cycle oscillator model. Most models had been designed to predict circadian phase using group-level assumptions. Future work should focus on model individualization and improved wearables to capture more accurate ambulatory signals. Highlights: Real-time tracking of circadian phase in the field is currently not available. Statistical, machine learning, and dynamical systems models have been developed. Multiple models predict circadian phase in individuals who are stably entrained. Only one model has been successfully validated under more challenging conditions. Next steps include wider testing, individualization, and improved light sensing.