0190 Propensity for Sleep Inertia during Sleep and Its Manifestation After Awakening in a Dynamic Biomathematical Fatigue Model

Abstract Introduction Sleep inertia, the transient period of cognitive impairment experienced immediately after awakening, is of concern in on-call and around-the-clock operational settings and merits development of a predictive tool to manage the associated fatigue risk. Recent work on our previously published biomathematical fatigue model revealed that predictive information about sleep inertia can be gained from tracking the propensity for sleep inertia across time spent asleep. In this approach, a neurobiological process with relatively fast dynamics (in the order of several minutes) interacts with the much slower dynamics of circadian, homeostatic, and allostatic sleep/wake regulation. Here we refine our modeling approach by mathematically dissociating the magnitude of sleep inertia from its dissipation rate. Methods N=280 healthy young adults (ages 21–49y) each participated in one of four laboratory studies of total sleep deprivation, sustained sleep restriction, and/or daytime napping. At 2–4 hour intervals while awake, participants performed a Psychomotor Vigilance Test (PVT), assessing number of lapses (RT>500ms), and rated their sleepiness on the Karolinska Sleepiness Scale (KSS). Sleep periods were recorded polysomnographically. Data were divided into a calibration set used to estimate two new model parameters capturing sleep inertia, and a validation set used to independently verify model goodness-of-fit. Sleep inertia was modeled with two ordinary differential equations – one during wakefulness to track manifest impairment, and one during sleep to track the propensity for sleep inertia upon awakening. Two parameters were fit, one modulating sleep inertia magnitude and one providing a dynamic time constant. Results Based on the calibration data set, the sleep inertia dissipation half-life was found to be 0.43h±0.07h. Based on the validation data set, the goodness-of-fit root-mean-square-error was 2.56 for PVT and 1.05 for KSS, indicating moderate-to-high predictive accuracy. As an emergent property of the model, the propensity of sleep inertia first increased and then decreased over sleep time, peaking 1–2h into the sleep period. Conclusion A two-parameter expansion of our biomathematical fatigue model captured the transient effect of sleep inertia accurately for sleep deprivation, sleep restriction, and daytime napping scenarios, and revealed a novel, emerging dynamic of sleep inertia propensity during sleep. Support (if any) Federal Express Corporation and WSU HPC