Tracy L. Rupp

Trait-Like Vulnerability to Total and Partial Sleep Loss

OBJECTIVE To determine the extent to which individual differences in vulnerability to total sleep deprivation also reflect individual differences in vulnerability to multiple nights of sleep restriction. DESIGN Two sleep loss conditions (order counterbalanced) separated by 2 to 4 weeks: (a) total sleep deprivation (TSD) of 2 nights (63 h continuous wakefulness); (b) sleep restriction (SR) of 7 nights of 3 h nightly time in bed (TIB). Both conditions were preceded by 7 in-laboratory nights with 10 h nightly TIB; and followed by 3 recovery nights with 8 h nightly TIB. Measures of cognitive performance (psychomotor vigilance, working memory [1-Back], and mathematical processing), objective alertness, subjective sleepiness, and mood were obtained at regular intervals under both conditions. Intra-class correlation coefficients (ICC) were computed using outcome metrics averaged over the last day (08:00-20:00) of TSD and SR. SETTING Residential sleep/performance testing facility. PARTICIPANTS Nineteen healthy adults (ages 18-39; 11 males, 8 females). INTERVENTIONS 2 nights of TSD and 7 nights SR (3 h nightly TIB). RESULTS volunteers who displayed greater vulnerability to TSD displayed greater vulnerability to SR on cognitive performance tasks (ICC: PVT lapses = 0.89; PVT speed = 0.86; 1-Back = 0.88; mathematical processing = 0.68, Ps < 0.05). In addition, trait-like responsivity to TSD/SR was found for mood variables vigor (ICC = 0.91), fatigue (ICC = 0.73), and happiness (ICC = 0.85) (all Ps < 0.05). CONCLUSION Resilience to sleep loss is a trait-like characteristic that reflects an individuals ability to maintain performance during both types of sleep loss (SR and TSD). Whether the findings extend to sleep schedules other than those investigated here (63 h of TSD and 7 nights of 3 h nightly TIB) will be the focus of future studies.

Sleep loss and sleepiness: current issues.

Awareness of the consequences of sleep loss and its implications for public health and safety is increasing. Sleep loss has been shown to generally impair the entire spectrum of mental abilities, ranging from simple psychomotor performance to executive mental functions. Sleep loss may also impact metabolism in a manner that contributes to obesity and its attendant health consequences. Although objective measures of alertness and performance remain degraded, individuals subjectively habituate to chronic partial sleep loss (eg, sleep restriction), and recovery from this type of sleep loss is slow, factors that may help to explain the observation that many individuals in the general population are chronically sleep restricted. Individual differences in habitual sleep duration appear to be a trait-like characteristic that is determined by several factors, including genetic polymorphisms.

Sleep Loss and Sleepiness

Awareness of the consequences of sleep loss and its implications for public health and safety is increasing. Sleep loss has been shown to generally impair the entire spectrum of mental abilities, ranging from simple psychomotor performance to executive mental functions. Sleep loss may also impact metabolism in a manner that contributes to obesity and its attendant health consequences. Although objective measures of alertness and performance remain degraded, individuals subjectively habituate to chronic partial sleep loss (eg, sleep restriction), and recovery from this type of sleep loss is slow, factors that may help to explain the observation that many individuals in the general population are chronically sleep restricted. Individual differences in habitual sleep duration appear to be a trait-like characteristic that is determined by several factors, including genetic polymorphisms.

Effects of dextroamphetamine, caffeine and modafinil on psychomotor vigilance test performance after 44 h of continuous wakefulness.

Prolonged sleep loss impairs alertness, vigilance and some higher‐order cognitive and affective capacities. Some deficits can be temporarily reversed by stimulant medications including caffeine, dextroamphetamine, and modafinil. To date, only one study has directly compared the effectiveness of these three compounds and specified the doses at which all were equally effective in restoring alertness and vigilance following 64 h of wakefulness. The present study compared the effectiveness of these same three stimulants/doses following a less extreme period of sleep loss (i.e., 44 h). Fifty‐three healthy adults received a single dose of modafinil 400 mg (n = 11), dextroamphetamine 20 mg (n = 16), caffeine 600 mg (n = 12), or placebo (n = 14) after 44 h of continuous wakefulness. After 61 h of being awake, participants obtained 12 h of recovery sleep. Psychomotor vigilance was assessed bi‐hourly during waking and following recovery sleep. Relative to placebo, all three stimulants were equally effective in restoring psychomotor vigilance test speed and reducing lapses, although the duration of action was shortest for caffeine and longest for dextroamphetamine. At these doses, caffeine was associated with the highest percentage of subjectively reported side‐effects while modafinil did not differ significantly from placebo. Subsequent recovery sleep was adversely affected in the dextroamphetamine group, but none of the stimulants had deleterious effects on postrecovery performance. Decisions regarding stimulant selection should be made with consideration of how factors such as duration of action, potential side‐effects, and subsequent disruption of recovery sleep may interact with the demands of a particular operational environment.

PER3 and ADORA2A polymorphisms impact neurobehavioral performance during sleep restriction.

The objective of the study was to determine whether ADORA2A or PER3 polymorphisms contribute to individual responsivity to sleep restriction. Nineteen healthy adults (ages 18–39, 11 males, 8 females) underwent sleep restriction (SR) which consisted of seven nights of 3 h time in bed (TIB) (04:00–07:00). SR was preceded by seven in‐laboratory nights of 10 h TIB (21:00–07:00) and followed by three nights of 8 h TIB (23:00–07:00). Volunteers underwent psychomotor vigilance, objective alertness, and subjective sleepiness assessments throughout. Volunteers were genotyped for the PER3 VNTR polymorphism (PER34/4 n = 7; PER34/5 n = 10; PER35/5 n = 2) and the ADORA2A c.1083T>C polymorphism, (ADORA2AC/T n = 9; ADORA2AT/T n = 9; ADORA2AC/C n = 1) using polymerase chain reaction (PCR). Separate mixed‐model anovas were used to assess contributions of ADORA2A and PER3 polymorphisms. Results showed that PER34/4 and ADORA2AC/T individuals expressed greater behavioral resiliency to SR compared to PER4/5and ADORA2AT/T individuals. Our findings contrast with previously reported non‐significant effects for the PER3 polymorphism under a less challenging sleep restriction regimen (4 h TIB per night for five nights). We conclude that PER3 and ADORA2A polymorphisms become more behaviorally salient with increasing severity and/or duration of sleep restriction (based on psychomotor vigilance). Given the small sample size these results are preliminary and require replication.

A unified mathematical model to quantify performance impairment for both chronic sleep restriction and total sleep deprivation.

Performance prediction models based on the classical two-process model of sleep regulation are reasonably effective at predicting alertness and neurocognitive performance during total sleep deprivation (TSD). However, during sleep restriction (partial sleep loss) performance predictions based on such models have been found to be less accurate. Because most modern operational environments are predominantly characterized by chronic sleep restriction (CSR) rather than by episodic TSD, the practical utility of this class of models has been limited. To better quantify performance during both CSR and TSD, we developed a unified mathematical model that incorporates extant sleep debt as a function of a known sleep/wake history, with recent history exerting greater influence. This incorporation of sleep/wake history into the classical two-process model captures an individuals capacity to recover during sleep as a function of sleep debt and naturally bridges the continuum from CSR to TSD by reducing to the classical two-process model in the case of TSD. We validated the proposed unified model using psychomotor vigilance task data from three prior studies involving TSD, CSR, and sleep extension. We compared and contrasted the fits, within-study predictions, and across-study predictions from the unified model against predictions generated by two previously published models, and found that the unified model more accurately represented multiple experimental studies and consistently predicted sleep restriction scenarios better than the existing models. In addition, we found that the model parameters obtained by fitting TSD data could be used to predict performance in other sleep restriction scenarios for the same study populations, and vice versa. Furthermore, this model better accounted for the relatively slow recovery process that is known to characterize CSR, as well as the enhanced performance that has been shown to result from sleep banking.

Sleep history affects task acquisition during subsequent sleep restriction and recovery

The aim of the present study was to examine if sleep amount prior to sleep restriction mediated subsequent task acquisition on serial addition/subtraction and reaction time (RT) sub‐tasks of the Automated Neuropsychological Assessment Metric. Eleven males and 13 females [mean (SD) age = 25 (6.5) years] were assigned to either an Extended [10 h time in bed (TIB)] (n = 12) or Habitual [Mean (SD) = 7.09 (0.7)] (n = 12) sleep group for 1 week followed by one baseline night, seven sleep restriction nights (3 h TIB) and five recovery nights (8 h TIB). Throughout baseline, restriction and recovery, mathematical and serial RT tasks were administered hourly each day (08:00–18:00 h). Math and serial RT throughput for each task (speed × accuracy product) was analysed using a mixed‐model anova with fixed effects for sleep group, day and time‐of‐day followed by post hoc t‐tests (Bonferroni correction). Math throughput improved for both groups during sleep restriction, but more so compared with baseline for the prior sleep Extended group versus the Habitual group during recovery. In sum, 1 week of sleep extension improved resilience during subsequent sleep restriction and facilitated task acquisition during recovery, demonstrating that nightly sleep duration exerts long‐term (days, weeks) effects.

Can a mathematical model predict an individual's trait-like response to both total and partial sleep loss?

Humans display a trait‐like response to sleep loss. However, it is not known whether this trait‐like response can be captured by a mathematical model from only one sleep‐loss condition to facilitate neurobehavioural performance prediction of the same individual during a different sleep‐loss condition. In this paper, we investigated the extent to which the recently developed unified mathematical model of performance (UMP) captured such trait‐like features for different sleep‐loss conditions. We used the UMP to develop two sets of individual‐specific models for 15 healthy adults who underwent two different sleep‐loss challenges (order counterbalanced; separated by 2–4 weeks): (i) 64 h of total sleep deprivation (TSD) and (ii) chronic sleep restriction (CSR) of 7 days of 3 h nightly time in bed. We then quantified the extent to which models developed using psychomotor vigilance task data under TSD predicted performance data under CSR, and vice versa. The results showed that the models customized to an individual under one sleep‐loss condition accurately predicted performance of the same individual under the other condition, yielding, on average, up to 50% improvement over non‐individualized, group‐average model predictions. This finding supports the notion that the UMP captures an individuals trait‐like response to different sleep‐loss conditions.

An Unfounded Conclusion from a Confounded Study

To the Editor: In the recent article by Edmonds and Vinson, “Three Measures of Sleep, Sleepiness, and Sleep Deprivation and the Risk of Injury: A Case-Control and Case-Crossover Study,”[1][1] the correlative relationships between self-reported sleepiness, sleep quality, and sleep amount with