Randomized, double-blind, placebo-controlled, crossover study of the effects of repeated-dose caffeine on neurobehavioral performance during 48 h of total sleep deprivation.

RATIONALE: Caffeine is widely used as a countermeasure against neurobehavioral impairment during sleep deprivation. However, little is known about the pharmacodynamic profile of caffeine administered repeatedly during total sleep deprivation. OBJECTIVES: To investigate the effects of repeated caffeine dosing on neurobehavioral performance during sleep deprivation, we conducted a laboratory-based, randomized, double-blind, placebo-controlled, crossover, multi-dose study of repeated caffeine administration during 48 h of sleep deprivation. Twelve healthy adults (mean age 27.4 years, six women) completed an 18-consecutive-day in-laboratory study consisting of three 48 h total sleep deprivation periods separated by 3-day recovery periods. During each sleep deprivation period, subjects were awakened at 07:00 and administered caffeine gum (0, 200, or 300 mg) at 6, 18, 30, and 42 h of wakefulness. The Psychomotor Vigilance Test and Karolinska Sleepiness Scale were administered every 2 h. RESULTS: The 200 and 300 mg doses of caffeine mitigated neurobehavioral impairment across the sleep deprivation period, approaching two-fold performance improvements relative to placebo immediately after the nighttime gum administrations. No substantive differences were noted between the 200 mg and 300 mg caffeine doses, and adverse effects were minimal. CONCLUSIONS: The neurobehavioral effects of repeated caffeine dosing during sleep deprivation were most evident during the circadian alertness trough (i.e., at night). The difference between the 200 mg and 300 mg doses, in terms of the mitigation of performance impairment, was small. Neither caffeine dose fully restored performance to well-rested levels. These findings inform the development of biomathematical models that more accurately account for the time of day and sleep pressure-dependent effects of caffeine on neurobehavioral performance during sleep loss.

2B-Alert Web: An Open-Access Tool for Predicting the Effects of Sleep/Wake Schedules and Caffeine Consumption on Neurobehavioral Performance.

STUDY OBJECTIVES: Computational tools that predict the effects of daily sleep/wake amounts on neurobehavioral performance are critical components of fatigue management systems, allowing for the identification of periods during which individuals are at increased risk for performance errors. However, none of the existing computational tools is publicly available, and the commercially available tools do not account for the beneficial effects of caffeine on performance, limiting their practical utility. Here, we introduce 2B-Alert Web, an open-access tool for predicting neurobehavioral performance, which accounts for the effects of sleep/wake schedules, time of day, and caffeine consumption, while incorporating the latest scientific findings in sleep restriction, sleep extension, and recovery sleep. METHODS: We combined our validated Unified Model of Performance and our validated caffeine model to form a single, integrated modeling framework instantiated as a Web-enabled tool. 2B-Alert Web allows users to input daily sleep/wake schedules and caffeine consumption (dosage and time) to obtain group-average predictions of neurobehavioral performance based on psychomotor vigilance tasks. 2B-Alert Web is accessible at: https://2b-alert-web.bhsai.org. RESULTS: The 2B-Alert Web tool allows users to obtain predictions for mean response time, mean reciprocal response time, and number of lapses. The graphing tool allows for simultaneous display of up to seven different sleep/wake and caffeine schedules. The schedules and corresponding predicted outputs can be saved as a Microsoft Excel file; the corresponding plots can be saved as an image file. The schedules and predictions are erased when the user logs off, thereby maintaining privacy and confidentiality. CONCLUSIONS: The publicly accessible 2B-Alert Web tool is available for operators, schedulers, and neurobehavioral scientists as well as the general public to determine the impact of any given sleep/wake schedule, caffeine consumption, and time of day on performance of a group of individuals. This evidence-based tool can be used as a decision aid to design effective work schedules, guide the design of future sleep restriction and caffeine studies, and increase public awareness of the effects of sleep amounts, time of day, and caffeine on alertness.

A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine.

STUDY OBJECTIVES: Existing mathematical models of neurobehavioral performance cannot predict the beneficial effects of caffeine across the spectrum of sleep loss conditions, limiting their practical utility. Here, we closed this research gap by integrating a model of caffeine effects with the recently validated unified model of performance (UMP) into a single, unified modeling framework. We then assessed the accuracy of this new UMP in predicting performance across multiple studies. METHODS: We hypothesized that the pharmacodynamics of caffeine vary similarly during both wakefulness and sleep, and that caffeine has a multiplicative effect on performance. Accordingly, to represent the effects of caffeine in the UMP, we multiplied a dose-dependent caffeine factor (which accounts for the pharmacokinetics and pharmacodynamics of caffeine) to the performance estimated in the absence of caffeine. We assessed the UMP predictions in 14 distinct laboratory- and field-study conditions, including 7 different sleep-loss schedules (from 5 h of sleep per night to continuous sleep loss for 85 h) and 6 different caffeine doses (from placebo to repeated 200 mg doses to a single dose of 600 mg). RESULTS: The UMP accurately predicted group-average psychomotor vigilance task performance data across the different sleep loss and caffeine conditions (6% < error < 27%), yielding greater accuracy for mild and moderate sleep loss conditions than for more severe cases. Overall, accounting for the effects of caffeine resulted in improved predictions (after caffeine consumption) by up to 70%. CONCLUSIONS: The UMP provides the first comprehensive tool for accurate selection of combinations of sleep schedules and caffeine countermeasure strategies to optimize neurobehavioral performance.

A Unified Model of Performance: Validation of its Predictions across Different Sleep/Wake Schedules.

STUDY OBJECTIVES: Historically, mathematical models of human neurobehavioral performance developed on data from one sleep study were limited to predicting performance in similar studies, restricting their practical utility. We recently developed a unified model of performance (UMP) to predict the effects of the continuum of sleep loss-from chronic sleep restriction (CSR) to total sleep deprivation (TSD) challenges-and validated it using data from two studies of one laboratory. Here, we significantly extended this effort by validating the UMP predictions across a wide range of sleep/wake schedules from different studies and laboratories. METHODS: We developed the UMP on psychomotor vigilance task (PVT) lapse data from one study encompassing four different CSR conditions (7 d of 3, 5, 7, and 9 h of sleep/night), and predicted performance in five other studies (from four laboratories), including different combinations of TSD (40 to 88 h), CSR (2 to 6 h of sleep/night), control (8 to 10 h of sleep/night), and nap (nocturnal and diurnal) schedules. RESULTS: The UMP accurately predicted PVT performance trends across 14 different sleep/wake conditions, yielding average prediction errors between 7% and 36%, with the predictions lying within 2 standard errors of the measured data 87% of the time. In addition, the UMP accurately predicted performance impairment (average error of 15%) for schedules (TSD and naps) not used in model development. CONCLUSIONS: The unified model of performance can be used as a tool to help design sleep/wake schedules to optimize the extent and duration of neurobehavioral performance and to accelerate recovery after sleep loss.

Human Performance Optimization Metrics: Consensus Findings, Gaps, and Recommendations for Future Research.

Human performance optimization (HPO) is defined as "the process of applying knowledge, skills and emerging technologies to improve and preserve the capabilities of military members, and organizations to execute essential tasks." The lack of consensus for operationally relevant and standardized metrics that meet joint military requirements has been identified as the single most important gap for research and application of HPO. In 2013, the Consortium for Health and Military Performance hosted a meeting to develop a toolkit of standardized HPO metrics for use in military and civilian research, and potentially for field applications by commanders, units, and organizations. Performance was considered from a holistic perspective as being influenced by various behaviors and barriers. To accomplish the goal of developing a standardized toolkit, key metrics were identified and evaluated across a spectrum of domains that contribute to HPO: physical performance, nutritional status, psychological status, cognitive performance, environmental challenges, sleep, and pain. These domains were chosen based on relevant data with regard to performance enhancers and degraders. The specific objectives at this meeting were to (a) identify and evaluate current metrics for assessing human performance within selected domains; (b) prioritize metrics within each domain to establish a human performance assessment toolkit; and (c) identify scientific gaps and the needed research to more effectively assess human performance across domains. This article provides of a summary of 150 total HPO metrics across multiple domains that can be used as a starting point-the beginning of an HPO toolkit: physical fitness (29 metrics), nutrition (24 metrics), psychological status (36 metrics), cognitive performance (35 metrics), environment (12 metrics), sleep (9 metrics), and pain (5 metrics). These metrics can be particularly valuable as the military emphasizes a renewed interest in Human Dimension efforts, and leverages science, resources, programs, and policies to optimize the performance capacities of all Service members.

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 individual's trait-like response to different sleep-loss conditions.

Legitimacy of concerns about caffeine and energy drink consumption.

Whether caffeine and energy drink consumption presents a critical emerging health problem is not currently known. Available evidence suggests that energy drink consumption represents a change in the ways in which individuals in the United States consume caffeine but that the amount of caffeine consumed daily has not appreciably increased. In the present review, the question of whether Americans are sleep deprived (a potential reason for using caffeine) is briefly explored. Reported rates of daily caffeine consumption (based on beverage formulation) and data obtained from both civilian and military populations in the United States are examined, the efficacy of ingredients other than caffeine in energy drinks is discussed, and the safety and side effects of caffeine are addressed, including whether evidence supports the contention that excessive caffeine/energy drink consumption induces risky behavior. The available evidence suggests that the main legitimate concern regarding caffeine and energy drink use is the potential negative impact on sleep but that, otherwise, there is no cause for concern regarding caffeine use in the general population.

Dose-dependent model of caffeine effects on human vigilance during total sleep deprivation.

Caffeine is the most widely consumed stimulant to counter sleep-loss effects. While the pharmacokinetics of caffeine in the body is well-understood, its alertness-restoring effects are still not well characterized. In fact, mathematical models capable of predicting the effects of varying doses of caffeine on objective measures of vigilance are not available. In this paper, we describe a phenomenological model of the dose-dependent effects of caffeine on psychomotor vigilance task (PVT) performance of sleep-deprived subjects. We used the two-process model of sleep regulation to quantify performance during sleep loss in the absence of caffeine and a dose-dependent multiplier factor derived from the Hill equation to model the effects of single and repeated caffeine doses. We developed and validated the model fits and predictions on PVT lapse (number of reaction times exceeding 500 ms) data from two separate laboratory studies. At the population-average level, the model captured the effects of a range of caffeine doses (50-300 mg), yielding up to a 90% improvement over the two-process model. Individual-specific caffeine models, on average, predicted the effects up to 23% better than population-average caffeine models. The proposed model serves as a useful tool for predicting the dose-dependent effects of caffeine on the PVT performance of sleep-deprived subjects and, therefore, can be used for determining caffeine doses that optimize the timing and duration of peak performance.

PC-PVT: a platform for psychomotor vigilance task testing, analysis, and prediction.

Using a personal computer (PC) for simple visual reaction time testing is advantageous because of the relatively low hardware cost, user familiarity, and the relative ease of software development for specific neurobehavioral testing protocols. However, general-purpose computers are not designed with the millisecond-level accuracy of operation required for such applications. Software that does not control for the various sources of delay may return reaction time values that are substantially different from the true reaction times. We have developed and characterized a freely available system for PC-based simple visual reaction time testing that is analogous to the widely used psychomotor vigilance task (PVT). In addition, we have integrated individualized prediction algorithms for near-real-time neurobehavioral performance prediction. We characterized the precision and accuracy with which the system as a whole measures reaction times on a wide range of computer hardware configurations, comparing its performance with that of the "gold standard" PVT-192 device. We showed that the system is capable of measuring reaction times with an average delay of less than 10 ms, a margin of error that is comparable to that of the gold standard. The most critical aspect of hardware selection is the type of mouse used for response detection, with gaming mice showing a significant advantage over standard ones. The software is free to download from http://bhsai.org/downloads/pc-pvt/ .

The challenge of sleep management in military operations.

It has long been known that short-term (days) insufficient sleep causes decrements in mental effectiveness that put individuals at increased risk of committing errors and causing accidents. More recently, it has been discovered that chronic poor sleep (over years) is associated with a variety of negative health outcomes (metabolic syndrome, obesity, degraded behavioral health). Implementing an effective sleep health program is, therefore, in the best interests of active duty personnel and their families both in the short- and long-term. Like managing physical activity or nutrition, effectively managing sleep health comes with its unique set of challenges arising from the fact that individuals who routinely do not obtain sufficient sleep are generally desensitized to feeling sleepy and are poor at judging their own performance capabilities--and individuals cannot be compelled to sleep. For these reasons, an optimally effective sleep health program requires 3 components: (1) a rigorous, evidence-based sleep education component to impart actionable knowledge about optimal sleep amounts, healthy sleep behaviors, the known benefits of sleep, the short- and long-term consequences of insufficient sleep, and to dispel myths about sleep; (2) a nonintrusive device that objectively and accurately measures sleep to empower the individual to track his/her own sleep/wake habits; and (3) a meaningful, actionable metric reflecting sleep/wake impact on daily effectiveness so that the individual sees the consequences of his/her sleep behavior and, therefore, can make informed sleep health choices.

Caffeine gum minimizes sleep inertia.

Naps are an effective strategy for maintaining alertness and cognitive performance; however, upon abrupt wakening from naps, sleep inertia (temporary performance degradation) may ensue. In the present study, attenuation of post-nap sleep inertia was attempted by administration of caffeine gum. Using a double-blind, placebo-controlled crossover design, 15 healthy, non-smoking adults were awakened at 1 hr. and again at 6 hr. after lights out (0100 and 0600, respectively) and were immediately administered a gum pellet containing 100 mg of caffeine or placebo. A 5-min. psychomotor vigilance task was administered at 0 min., 6 min., 12 min., and 18 min. post-awakening. At 0100, response speed with caffeine was significantly better at 12 min. and 18 min. post-awakening compared to placebo; at 0600, caffeine's effects were evident at 18 min. post-awakening. Caffeinated gum is a viable means of rapidly attenuating sleep inertia, suggesting that the adenosine receptor system is involved in sleep maintenance.

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 individual's 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.

A biomathematical model of the restoring effects of caffeine on cognitive performance during sleep deprivation.

RATIONALE: While caffeine is widely used as a countermeasure to sleep loss, mathematical models are lacking. OBJECTIVE: Develop a biomathematical model for the performance-restoring effects of caffeine in sleep-deprived subjects. METHODS: We hypothesized that caffeine has a multiplicative effect on performance during sleep loss. Accordingly, we first used a phenomenological two-process model of sleep regulation to estimate performance in the absence of caffeine, and then multiplied a caffeine-effect factor, which relates the pharmacokinetic-pharmacodynamic effects through the Hill equation, to estimate the performance-restoring effects of caffeine. RESULTS: We validated the model on psychomotor vigilance test data from two studies involving 12 subjects each: (1) single caffeine dose of 600mg after 64.5h of wakefulness and (2) repeated doses of 200mg after 20, 22, and 24h of wakefulness. Individualized caffeine models produced overall errors that were 19% and 42% lower than their population-average counterparts for the two studies. Had we not accounted for the effects of caffeine, the individualized model errors would have been 117% and 201% larger, respectively. CONCLUSIONS: The presented model captured the performance-enhancing effects of caffeine for most subjects in the single- and repeated-dose studies, suggesting that the proposed multiplicative factor is a feasible solution.

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 (PER3(4/4) n = 7; PER3(4/5) n = 10; PER3(5/5) n = 2) and the ADORA2A c.1083T>C polymorphism, (ADORA2A(C) (/T) n = 9; ADORA2A(T) (/T) n = 9; ADORA2A(C) (/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 PER3(4/4) and ADORA2A(C/T) individuals expressed greater behavioral resiliency to SR compared to PER(4/5) and ADORA2A(T/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.

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 individual's 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.

A new metric for quantifying performance impairment on the psychomotor vigilance test.

We have developed a new psychomotor vigilance test (PVT) metric for quantifying the effects of sleep loss on performance impairment. The new metric quantifies performance impairment by estimating the probability density of response times (RTs) in a PVT session, and then considering deviations of the density relative to that of a baseline-session density. Results from a controlled laboratory study involving 12 healthy adults subjected to 85 h of extended wakefulness, followed by 12 h of recovery sleep, revealed that the group performance variability based on the new metric remained relatively uniform throughout wakefulness. In contrast, the variability of PVT lapses, mean RT, median RT and (to a lesser extent) mean speed showed strong time-of-day effects, with the PVT lapse variability changing with time of day depending on the selected threshold. Our analysis suggests that the new metric captures more effectively the homeostatic and circadian process underlying sleep regulation than the other metrics, both directly in terms of larger effect sizes (4-61% larger) and indirectly through improved fits to the two-process model (9-67% larger coefficient of determination). Although the trend of the mean speed results followed those of the new metric, we found that mean speed yields significantly smaller (∼50%) intersubject performance variance than the other metrics. Based on these findings, and that the new metric considers performance changes based on the entire set of responses relative to a baseline, we conclude that it provides a number of potential advantages over the traditional PVT metrics.

Individualized performance prediction during total sleep deprivation: accounting for trait vulnerability to sleep loss.

Individual differences in vulnerability to sleep loss can be considerable, and thus, recent efforts have focused on developing individualized models for predicting the effects of sleep loss on performance. Individualized models constructed using a Bayesian formulation, which combines an individual's available performance data with a priori performance predictions from a group-average model, typically need at least 40 h of individual data before showing significant improvement over the group-average model predictions. Here, we improve upon the basic Bayesian formulation for developing individualized models by observing that individuals may be classified into three sleep-loss phenotypes: resilient, average, and vulnerable. For each phenotype, we developed a phenotype-specific group-average model and used these models to identify each individual's phenotype. We then used the phenotype-specific models within the Bayesian formulation to make individualized predictions. Results on psychomotor vigilance test data from 48 individuals indicated that, on average, ∼85% of individual phenotypes were accurately identified within 30 h of wakefulness. The percentage improvement of the proposed approach in 10-h-ahead predictions was 16% for resilient subjects and 6% for vulnerable subjects. The trade-off for these improvements was a slight decrease in prediction accuracy for average subjects.

Automated Neuropsychological Assessment Metrics: repeated assessment with two military samples.

INTRODUCTION: U.S. military troops deploying to war zones are currently administered the Automated Neuropsychological Assessment Metrics (ANAM4) Traumatic Brain Injury (TBI) Battery to establish individual neurocognitive performance baselines. In part, the utility of the ANAM4 TBI Battery baseline measurement depends on test-retest reliability of this instrument. The purpose of this report was to evaluate performance following multiple administrations of the ANAM4 TBI Battery: does performance in a repeated measures paradigm constitute a stable, interpretable indication of baseline neurocognitive ability? METHODS: The data presented here are from the ANAM4 TBI Battery administered four times to a group of U.S. Marines in Study 1 and eight times to a group of New Zealand Defence Force personnel in Study 2. RESULTS: The results show practice effect in five of six performance subtests in both Study 1 and Study 2. DISCUSSION: Results are consistent with expectations that multiple test sessions are required to reach stable performance on some computerized tasks. These results have implications for taking ANAM4 TBI Battery practice effects into account in test administration and in data interpretation.

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 x 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.

An improved methodology for individualized performance prediction of sleep-deprived individuals with the two-process model.

We present a method based on the two-process model of sleep regulation for developing individualized biomathematical models that predict performance impairment for individuals subjected to total sleep loss. This new method advances our previous work in two important ways. First, it enables model customization to start as soon as the first performance measurement from an individual becomes available. This was achieved by optimally combining the performance information obtained from the individual's performance measurements with a priori performance information using a Bayesian framework, while retaining the strategy of transforming the nonlinear optimization problem of finding the optimal estimates of the two-process model parameters into a series of linear optimization problems. Second, by taking advantage of the linear representation of the two-process model, this new method enables the analytical computation of statistically based measures of reliability for the model predictions in the form of prediction intervals. Two distinct data sets were used to evaluate the proposed method. Results using simulated data with superimposed white Gaussian noise showed that the new method yielded 50% to 90% improvement in parameter-estimate accuracy over the previous method. Moreover, the accuracy of the analytically computed prediction intervals was validated through Monte Carlo simulations. Results for subjects representing three sleep-loss phenotypes who participated in a laboratory study (82 h of total sleep loss) indicated that the proposed method yielded individualized predictions that were up to 43% more accurate than group-average prediction models and, on average, 10% more accurate than individualized predictions based on our previous method.