Prediction of individual differences in circadian adaptation to night work among older adults: application of a mathematical model using individual sleep-wake and light exposure data

ABSTRACT

Circadian misalignment remains a distinct challenge for night shift workers. Variability in individual sleep-wake/light-dark patterns might contribute to individual differences in circadian alignment in night shift workers. In this simulation study, we compared the predicted phase shift from a mathematical model of the effect of light on the human circadian pacemaker to the observed melatonin phase shift among individuals who completed one of four interventions during simulated night shift work. Two inputs to the model were used to simulate circadian phase: sleep-wake/light-dark patterns measured from a wrist monitor (Simulation 1) and sleep-wake/light-dark patterns measured from a wrist monitor enhanced by known light levels measured at the level of the eye during simulated night shifts (Simulation 2). The estimated phase shift from the model was within 2 hours of the observed phase shift in ~80% of night shift workers for both simulations; none of the model-predicted phase shifts was more than ~3 hours from the observed phase shift. Overall, the root-mean-square error between observed and predicted phase shifts was better for Simulation 1. The light input from the wrist monitor informed by actual light level measured at the eye performed better in the sub-group exposed to bright light during their night shifts. The findings from this simulation study suggest that using a mathematical model combined with sleep-wake and light exposure data from a wrist monitor can facilitate the design of shift work schedules to enhance circadian alignment, which is expected to improve sleep, alertness, and performance.

KEYWORDS:

• Shiftwork
• mathematical model
• circadian phase
• phase shift
• light exposure
• DLMO
• melatonin phase
• actigraphy

Acknowledgements

The authors wish to thank Drs. Min Ju Kim and Jee Hyun Kim for serving as project leaders for some of the studies; Joyce Hong, Michael P. Harris, Audra S. Murphy, and John C. Wise for assistance with participant recruitment and data processing; and Joseph M. Ronda, M.S. for technical assistance.

Declaration of interest

The authors report no conflicts of interest. The studies were supported by NIH grant R01 AG044416 and the laboratory segments were carried out in the Brigham and Women’s Hospital Center for Clinical Investigation, part of Harvard Catalyst (Harvard Clinical and Translational Science Center) and supported by NIH Award UL1 TR001102 and financial contributions from Brigham and Women’s Hospital and from Harvard University and its affiliated academic health-care centers. EDC was supported by NIH fellowship T32 HL007901 during the time of data collection. MSH is supported by NIH grant R21 NR018974 and NASA grant 80NSSC20K0576.

Additional information

Funding

This work was supported by the National Center for Advancing Translational Sciences [TR001102]; National Heart, Lung, and Blood Institute [T32 HL007901]; National Institute on Aging [R01 AG044416]; National Institutes of Health [R21 NR018974]; NASA [80NSSC20K0576].