2B-Alert Web 2.0, an Open-Access Tool for Predicting Alertness and Optimizing the Benefits of Caffeine: Utility Study.

BACKGROUND: One-third of the US population experiences sleep loss, with the potential to impair physical and cognitive performance, reduce productivity, and imperil safety during work and daily activities. Computer-based fatigue-management systems with the ability to predict the effects of sleep schedules on alertness and identify safe and effective caffeine interventions that maximize its stimulating benefits could help mitigate cognitive impairment due to limited sleep. To provide these capabilities to broad communities, we previously released 2B-Alert Web, a publicly available tool for predicting the average alertness level of a group of individuals as a function of time of day, sleep history, and caffeine consumption. OBJECTIVE: In this study, we aim to enhance the capability of the 2B-Alert Web tool by providing the means for it to automatically recommend safe and effective caffeine interventions (time and dose) that lead to optimal alertness levels at user-specified times under any sleep-loss condition. METHODS: We incorporated a recently developed caffeine-optimization algorithm into the predictive models of the original 2B-Alert Web tool, allowing the system to search for and identify viable caffeine interventions that result in user-specified alertness levels at desired times of the day. To assess the potential benefits of this new capability, we simulated four sleep-deprivation conditions (sustained operations, restricted sleep with morning or evening shift, and night shift with daytime sleep) and compared the alertness levels resulting from the algorithm's recommendations with those based on the US Army caffeine-countermeasure guidelines. In addition, we enhanced the usability of the tool by adopting a drag-and-drop graphical interface for the creation of sleep and caffeine schedules. RESULTS: For the 4 simulated conditions, the 2B-Alert Web-proposed interventions increased mean alertness by 36% to 94% and decreased peak alertness impairment by 31% to 71% while using equivalent or smaller doses of caffeine as the corresponding US Army guidelines. CONCLUSIONS: The enhanced capability of this evidence-based, publicly available tool increases the efficiency by which diverse communities of users can identify safe and effective caffeine interventions to mitigate the effects of sleep loss in the design of research studies and work and rest schedules.

2B-Alert App: A mobile application for real-time individualized prediction of alertness.

Knowing how an individual responds to sleep deprivation is a requirement for developing personalized fatigue management strategies. Here we describe and validate the 2B-Alert App, the first mobile application that progressively learns an individual's trait-like response to sleep deprivation in real time, to generate increasingly more accurate individualized predictions of alertness. We incorporated a Bayesian learning algorithm within the validated Unified Model of Performance to automatically and gradually adapt the model parameters to an individual after each psychomotor vigilance test. We implemented the resulting model and the psychomotor vigilance test as a smartphone application (2B-Alert App), and prospectively validated its performance in a 62-hr total sleep deprivation study in which 21 participants used the app to perform psychomotor vigilance tests every 3 hr and obtain real-time individualized predictions after each test. The temporal profiles of reaction times on the app-conducted psychomotor vigilance tests were well correlated with and as sensitive as those obtained with a previously characterized psychomotor vigilance test device. The app progressively learned each individual's trait-like response to sleep deprivation throughout the study, yielding increasingly more accurate predictions of alertness for the last 24 hr of total sleep deprivation as the number of psychomotor vigilance tests increased. After only 12 psychomotor vigilance tests, the accuracy of the model predictions was comparable to the peak accuracy obtained using all psychomotor vigilance tests. With the ability to make real-time individualized predictions of the effects of sleep deprivation on future alertness, the 2B-Alert App can be used to tailor personalized fatigue management strategies, facilitating self-management of alertness and safety in operational and non-operational settings.

Caffeine dosing strategies to optimize alertness during sleep loss.

Sleep loss, which affects about one-third of the US population, can severely impair physical and neurobehavioural performance. Although caffeine, the most widely used stimulant in the world, can mitigate these effects, currently there are no tools to guide the timing and amount of caffeine consumption to optimize its benefits. In this work, we provide an optimization algorithm, suited for mobile computing platforms, to determine when and how much caffeine to consume, so as to safely maximize neurobehavioural performance at the desired time of the day, under any sleep-loss condition. The algorithm is based on our previously validated Unified Model of Performance, which predicts the effect of caffeine consumption on a psychomotor vigilance task. We assessed the algorithm by comparing the caffeine-dosing strategies (timing and amount) it identified with the dosing strategies used in four experimental studies, involving total and partial sleep loss. Through computer simulations, we showed that the algorithm yielded caffeine-dosing strategies that enhanced performance of the predicted psychomotor vigilance task by up to 64% while using the same total amount of caffeine as in the original studies. In addition, the algorithm identified strategies that resulted in equivalent performance to that in the experimental studies while reducing caffeine consumption by up to 65%. Our work provides the first quantitative caffeine optimization tool for designing effective strategies to maximize neurobehavioural performance and to avoid excessive caffeine consumption during any arbitrary sleep-loss condition.

Real-time individualization of the unified model of performance.

Existing mathematical models for predicting neurobehavioural performance are not suited for mobile computing platforms because they cannot adapt model parameters automatically in real time to reflect individual differences in the effects of sleep loss. We used an extended Kalman filter to develop a computationally efficient algorithm that continually adapts the parameters of the recently developed Unified Model of Performance (UMP) to an individual. The algorithm accomplishes this in real time as new performance data for the individual become available. We assessed the algorithm's performance by simulating real-time model individualization for 18 subjects subjected to 64 h of total sleep deprivation (TSD) and 7 days of chronic sleep restriction (CSR) with 3 h of time in bed per night, using psychomotor vigilance task (PVT) data collected every 2 h during wakefulness. This UMP individualization process produced parameter estimates that progressively approached the solution produced by a post-hoc fitting of model parameters using all data. The minimum number of PVT measurements needed to individualize the model parameters depended upon the type of sleep-loss challenge, with ~30 required for TSD and ~70 for CSR. However, model individualization depended upon the overall duration of data collection, yielding increasingly accurate model parameters with greater number of days. Interestingly, reducing the PVT sampling frequency by a factor of two did not notably hamper model individualization. The proposed algorithm facilitates real-time learning of an individual's trait-like responses to sleep loss and enables the development of individualized performance prediction models for use in a mobile computing platform.

Rhythmic alternating patterns of brain activity distinguish rapid eye movement sleep from other states of consciousness.

Rapid eye movement (REM) sleep constitutes a distinct "third state" of consciousness, during which levels of brain activity are commensurate with wakefulness, but conscious awareness is radically transformed. To characterize the temporal and spatial features of this paradoxical state, we examined functional interactions between brain regions using fMRI resting-state connectivity methods. Supporting the view that the functional integrity of the default mode network (DMN) reflects "level of consciousness," we observed functional uncoupling of the DMN during deep sleep and recoupling during REM sleep (similar to wakefulness). However, unlike either deep sleep or wakefulness, REM was characterized by a more widespread, temporally dynamic interaction between two major brain systems: unimodal sensorimotor areas and the higher-order association cortices (including the DMN), which normally regulate their activity. During REM, these two systems become anticorrelated and fluctuate rhythmically, in reciprocally alternating multisecond epochs with a frequency ranging from 0.1 to 0.01 Hz. This unique spatiotemporal pattern suggests a model for REM sleep that may be consistent with its role in dream formation and memory consolidation.

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.

A PILOT STUDY ON THE ENCODING OF A PERCEPTUAL LEARNING TASK FOLLOWING SLEEP DEPRIVATION.

Memory encoding sometimes must occur during a period of sleep deprivation. The question was whether one night of sleep deprivation inhibits encoding on a perceptual learning task (the texture discrimination task). The sample was 18 human participants (M age=22.1 yr., SEM=0.5; 8 men). The participants were randomized to a sleep deprivation or sleep control condition and, after the manipulation, were given two administrations of the texture discrimination task. All participants were given an opportunity for a 90 min. nap between the two administrations. Performance was measured by the interpolated stimulus-to-mask-onset asynchrony (i.e., the inter-stimulus interval), at which the percentage of correct responses for the stimuli in the participant's peripheral vision fell below 80%. Offline consolidation was defined as a decrease in this index between the two administrations. Participants who were sleep deprived prior to encoding exhibited similar offline consolidation (M=-5.3 msec., SEM=2.3) compared to participants who were not sleep deprived prior to encoding (M=-6.2 msec., SEM=3.9); the two-way interaction between time and condition was not significant. In light of reports in the literature, these results indicate encoding following sleep deprivation may be influenced by both the type of task encoded and the brain regions involved in memory processing.

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.

Negative health outcomes in long sleepers: The societal sleep restriction hypothesis.

Society imposes work and school schedules, as well as social expectations, that militate against consistently obtaining more than 7-9 h of sleep every 24 h. For most but not all adults this sleep duration is adequate. But among those who consistently obtain more than 9 h of sleep per day ("long sleepers"), there likely exists a subpopulation of individuals who are nevertheless failing to obtain enough sleep to satisfy their physiological sleep needs - a consequence of "restricting" their daily sleep durations to whatever extent they can tolerate so as to conform as closely as possible to society's norms and expectations. It is hypothesized that the 'long sleep arm' of the seemingly paradoxical U-shaped relationship between sleep duration and negative health outcomes can be explained, at least in part, by the existence of a subpopulation of such 'sleep-restricted long sleepers.'

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.

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.

Increased rates of brain protein synthesis during [N1,N2] sleep: L-[1-11C]leucine PET studies in human subjects.

During sleep, reduced brain energy demands provide an opportunity for biosynthetic processes like protein synthesis. Sleep is required for some forms of memory consolidation which requires de novo protein synthesis. We measured regional cerebral protein synthesis rates (rCPS) in human subjects to ascertain how rCPS is affected during sleep. Subjects underwent three consecutive L-[1-11C]leucine PET scans with simultaneous polysomnography: 1. rested awake, 2. sleep-deprived awake, 3. sleep. Measured rCPS were similar across the three conditions. Variations in sleep stage times during sleep scans were used to estimate rCPS in sleep stages under the assumption that measured rCPS is the weighted sum of rCPS in each stage, with weights reflecting time and availability of [11C]leucine in that stage. During sleep scans, subjects spent most of the time in N2, N3, and awake and very little time in N1 and REM; rCPS in N1 and REM could not be reliably estimated. When stages N1 and N2 were combined [N1,N2], estimates of rCPS were more robust. In selective regions, estimated rCPS were statistically significantly higher (30-39%) in [N1,N2] compared with N3; estimated rCPS in N3 were similar to values measured in sleep-deprived awake scans. Results indicate increased rates of protein synthesis linked to [N1,N2] sleep.

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.

Decoupling of the brain's default mode network during deep sleep.

The recent discovery of a circuit of brain regions that is highly active in the absence of overt behavior has led to a quest for revealing the possible function of this so-called default-mode network (DMN). A very recent study, finding similarities in awake humans and anesthetized primates, has suggested that DMN activity might not simply reflect ongoing conscious mentation but rather a more general form of network dynamics typical of complex systems. Here, by performing functional MRI in humans, it is shown that a natural, sleep-induced reduction of consciousness is reflected in altered correlation between DMN network components, most notably a reduced involvement of frontal cortex. This suggests that DMN may play an important role in the sustenance of conscious awareness.

Acute sleep interventions as an avenue for treatment of trauma-associated disorders.

Scientific evidence that acute, posttrauma sleep disturbances (eg, nightmares and insomnia) can contribute significantly to the pathogenesis of trauma-induced disorders is compelling. Sleep disturbances precipitating from trauma are uniquely predictive of daytime posttrauma symptom occurrence and severity, as well as subsequent onset of mental health disorders, including post-traumatic stress disorder. Conversely, adequate sleep during the acute posttrauma period is associated with reduced likelihood of adverse mental health outcomes. These findings, which are broadly consistent with what is known about the role of sleep in the regulation of emotion, suggest that the acute posttrauma period constitutes a "window of opportunity" during which treatment of sleep disturbances may be especially effective for preventing or mitigating progression of aberrant psychophysiological processes. At this point, the weight of the scientific evidence supporting this possibility warrants initiation of clinical trials to confirm the benefits of targeted prophylactic sleep enhancement, and to establish treatment guidelines as appropriate. CITATION: Swift KM, Thomas CL, Balkin TJ, Lowery-Gionta EG, Matson LM. Acute sleep interventions as an avenue for treatment of trauma-associated disorders. J Clin Sleep Med. 2022;18(9):2291-2312.

Quantifying the effects of sleep loss: relative effect sizes of the psychomotor vigilance test, multiple sleep latency test, and maintenance of wakefulness test.

The psychomotor vigilance test (PVT) is a widely-used, minimally invasive, inexpensive, portable, and easy to administer behavioral measure of vigilance that is sensitive to sleep loss. We conducted analyses to determine the relative sensitivity of the PVT vs. the multiple sleep latency test (MSLT) and the maintenance of wakefulness test (MWT) during acute total sleep deprivation (TSD) and multiple days of sleep restriction (SR) in studies of healthy adults. Twenty-four studies met the criteria for inclusion. Since sleepiness countermeasures were administered in some of these studies, the relative sensitivity of the three measures to these interventions was also assessed. The difference in weighted effect size (eta-squared) was computed for each pair of sleepiness measures based on available raw test data (such as average PVT reaction time). Analyses revealed that the sleep measures were differentially sensitive to various types of sleep loss over time, with MSLT and MWT more sensitive to TSD than the PVT. However, sensitivity to SR was comparable for all three measures. The PVT and MSLT were found to be differentially sensitive to the administration of sleepiness countermeasures (drugs, sleep loss, etc.), but PVT and MWT were found to be comparably sensitive to these interventions. These findings suggest the potential utility of the PVT as a component of next-generation fatigue risk management systems.

Caffeine protects against increased risk-taking propensity during severe sleep deprivation.

Previous research suggests that sleep deprivation is associated with declines in metabolic activity within brain regions important for judgement and impulse control, yet previous studies have reported inconsistent findings regarding the effects of sleep loss and caffeine on risk-taking. In this study, 25 healthy adults (21 men, four women) completed the Balloon Analog Risk Task (BART) and Evaluation of Risks (EVAR) scale at regular intervals to examine behavioral and self-reported risk-taking propensity during 75 h of continuous sleep deprivation. Participants received either four double-blind administrations of 200 mg caffeine (n=12) or indistinguishable placebo (n=13) gum bi-hourly during each of the 3 nights of sleep deprivation. No significant effects of drug group or sleep deprivation were evident on the BART or EVAR when measured at 51 h of wakefulness. However, by 75 h, the placebo group showed a significant increase in risk-taking behavior on the cost-benefit ratio and total number of exploded balloons on the BART, whereas the caffeine group remained at baseline levels. On the EVAR, several factors of self-reported risk-taking propensity, including total risk, impulsivity and risk/thrill seeking, were reduced among subjects receiving caffeine across the 3 days of sleep deprivation, but remained at baseline levels for the placebo group. These results suggest that 3 nights of total sleep deprivation led to a significant increase in behavioral risk-taking but not self-reported perception of risk-propensity. Overnight caffeine prevented this increase in risky behavior.

Sleep disorders and work performance: findings from the 2008 National Sleep Foundation Sleep in America poll.

Chronic sleep deprivation is common among workers, and has been associated with negative work outcomes, including absenteeism and occupational accidents. The objective of the present study is to characterize reciprocal relationships between sleep and work. Specifically, we examined how sleep impacts work performance and how work affects sleep in individuals not at-risk for a sleep disorder; assessed work performance outcomes for individuals at-risk for sleep disorders, including insomnia, obstructive sleep apnea (OSA) and restless legs syndrome (RLS); and characterized work performance impairments in shift workers (SW) at-risk for shift work sleep disorders relative to SW and day workers. One-thousand Americans who work 30 h per week or more were asked questions about employment, work performance and sleep in the National Sleep Foundation's 2008 Sleep in America telephone poll. Long work hours were associated with shorter sleep times, and shorter sleep times were associated with more work impairments. Thirty-seven percent of respondents were classified as at-risk for any sleep disorder. These individuals had more negative work outcomes as compared with those not at-risk for a sleep disorder. Presenteeism was a significant problem for individuals with insomnia symptoms, OSA and RLS as compared with respondents not at-risk. These results suggest that long work hours may contribute to chronic sleep loss, which may in turn result in work impairment. Risk for sleep disorders substantially increases the likelihood of negative work outcomes, including occupational accidents, absenteeism and presenteeism.