Prediction of Cognitive Performance and Subjective Sleepiness Using a Model of Arousal Dynamics. Issue 2 (April 2018)

• Record Type: Journal Article
• Authors: Postnova, Svetlana; Lockley, Steven W.; Robinson, Peter A.
• Is Part Of: Journal of biological rhythms. Volume 33:Issue 2(2018)
• Page Start: 203
• Page End: 218
• Publication Date: 2018-04
• Subjects: performance -- sleepiness -- alertness -- circadian -- homeostatic -- model -- prediction; Biological rhythms -- Periodicals; Circadian rhythms -- Periodicals; 571.77
• DOI: 10.1177/0748730418758454
• Languages: English
• ISSNs: 0748-7304

Abstract

A model of arousal dynamics is applied to predict objective performance and subjective sleepiness measures, including lapses and reaction time on a visual Performance Vigilance Test (vPVT), performance on a mathematical addition task (ADD), and the Karolinska Sleepiness Scale (KSS). The arousal dynamics model is comprised of a physiologically based flip-flop switch between the wake- and sleep-active neuronal populations and a dynamic circadian oscillator, thus allowing prediction of sleep propensity. Published group-level experimental constant routine (CR) and forced desynchrony (FD) data are used to calibrate the model to predict performance and sleepiness. Only the studies using dim light (<15 lux) during alertness measurements and controlling for sleep and entrainment before the start of the protocol are selected for modeling. This is done to avoid the direct alerting effects of light and effects of prior sleep debt and circadian misalignment on the data. The results show that linear combination of circadian and homeostatic drives is sufficient to predict dynamics of a variety of sleepiness and performance measures during CR and FD protocols, with sleep-wake cycles ranging from 20 to 42.85 h and a 2:1 wake-to-sleep ratio. New metrics relating model outputs to performance and sleepiness data are developed and tested against group average outcomes from 7 (vPVT lapses), 5 (ADD), and 8 (KSS) experimental protocols, showing good quantitative and qualitative agreement with the data (root mean squared error of 0.38, 0.19, and 0.35, respectively). The weights of the homeostatic and circadian effects are found to be different between the measures, with KSS having stronger homeostatic influence compared with the objective measures of performance. Using FD data in addition to CR data allows us to challenge the model in conditions of both acute sleep deprivation and structured circadian misalignment, ensuring that the role of the circadian and homeostatic drives in performance is properly captured.