Abstract
We explored the predictive value of a neurobehavioral performance assessment under rested baseline conditions (evaluated at 8 hours awake following 8 hours of sleep) on neurobehavioral response to moderate sleep loss (evaluated at 20 hours awake two days later) in 151 healthy young participants (18–30 years). We defined each participant’s response-to-sleep-loss phenotype based on the number of attentional failures on a 10-min visual psychomotor vigilance task taken at 20 hours awake (resilient: less than 6 attentional failures, n = 26 participants; non-resilient: 6 or more attentional failures, n = 125 participants). We observed that 97% of rested participants with 2 or more attentional failures (n = 73 of 151) and 100% of rested participants with 3 or more attentional failures (n = 57 of 151) were non-resilient after moderate sleep loss. Our approach can accurately identify a significant proportion of individuals who are at high risk for neurobehavioral performance impairment from staying up late with a single neurobehavioral performance assessment conducted during rested conditions. Additional methods are needed to predict the future performance of individuals who are not identified as high risk during baseline.
Highlights
As resilient, intermediate, or vulnerable to sleep loss with an overall accuracy of 73–75%, but was less accurate (27–56%) in classifying the response to sleep loss in individuals under operational conditions
We have found that attentional failures on a single neurobehavioral performance assessment when rested are sufficient to identify a high-risk subset of healthy young individuals who are non-resilient after 20 hours awake in this sample of 151 young participants
We explored four metrics (Table S1) for defining the response to sleep loss phenotype: attentional failures on the visual PVT (vPVT) only (Metric 1) (Fig. 1B); a metric derived from principal component analysis of 74 variables from all 6 neurobehavioral tests, including vPVT (Metric 2); a metric derived from principal component analysis of 8 representative variables (Metric 3; mean RT on the vPVT, number of attentional failures on the vPVT, Karolinska Sleepiness Scale (KSS) score, Visual Analog Scales (VAS) Alert score, number attempted on the addition calculation test (ADD), number attempted on the digit symbol substitution test (DSST), mean RT on the auditory psychomotor vigilance task (aPVT), and number of lapses on the aPVT); and a metric derived from principal component analysis of 12 variables on all neurobehavioral tests excluding the vPVT and aPVT (Metric 4)
Summary
As resilient, intermediate, or vulnerable to sleep loss with an overall accuracy of 73–75%, but was less accurate (27–56%) in classifying the response to sleep loss in individuals under operational conditions. Other models that have been developed to predict individual response to sleep loss require real-time monitoring over several hours or days that include sleep deprivation[22,23,24], which is not practical as a screening tool for prospectively identifying individuals at high risk. The goal of the present analysis was to develop a metric that can predict an individual’s neurobehavioral response to moderate sleep loss (~20 hours awake) from neurobehavioral performance data collected two days earlier during a single rested baseline testing session (after ~8 hours awake following an 8-hour sleep opportunity). We have found that attentional failures on a single neurobehavioral performance assessment when rested are sufficient to identify a high-risk subset of healthy young individuals who are non-resilient after 20 hours awake in this sample of 151 young participants
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