Abstract
Sleep timing varies between individuals and can be altered in mental and physical health conditions. Sleep and circadian sleep phenotypes, including circadian rhythm sleep-wake disorders, may be driven by endogenous physiological processes, exogeneous environmental light exposure, social constraints or behavioural factors. Identifying the relative contributions of these driving factors to different phenotypes is essential for the design of personalised sleep interventions. The timing of the human sleep-wake cycle has been modelled as an interaction between a relaxation oscillator (the sleep homeostat), a stable limit cycle oscillator with a near 24-hour period (the circadian process), man-made light exposure, and the natural light-dark cycle generated by the Earth's rotation. However, these models have rarely been used to quantitatively describe sleep at the individual level. Here, we present a new Homeostatic-Circadian-Light model (HCL) which is simpler, more transparent, and more computationally efficient than other available models and is designed to run using longitudinal sleep and light exposure data from wearable sensors. We carry out a systematic sensitivity analysis for all model parameters and discuss parameter identifiability. We demonstrate we can describe individual sleep phenotypes in each of 34 older participants (65-83y) by feeding individual participant light exposure patterns into the model and fitting two parameters that capture individual average sleep duration and timing. The fitted parameters describe endogenous drivers of sleep phenotypes. We then quantify exogenous drivers using a novel metric which encodes the circadian phase dependence of the response to light. Combining endogenous and exogeneous drivers better explains individual mean mid-sleep (adjusted R-squared 0.64) than either driver on its own (adjusted R-squared 0.08 and 0.17 respectively). Critically, our model and analysis highlights different people exhibiting the same sleep phenotype may have different driving factors and opens the door to personalised interventions to regularize sleep-wake timing that are readily implementable with current digital health technology.Competing Interest StatementDJD and ACS are consultants to F. Hoffmann-La Roche, Ltd.Footnotes* This version of the manuscript has been revised to: - take account of referees comments - updated to correct minor errors, including typographical errors. The central messages and mathematical modelling remain unchanged.* https://github.com/anneskeldon/Homeostatic_circadian_light_model-factors_driving_sleep_phenotypes