Dynamic circadian modulation in a biomathematical model for the effects of sleep and sleep loss on waking neurobehavioral performance

Biomathematical modeling of fatigue due to sleep inertia

Biomathematical models of fatigue capture the physiology of sleep/wake regulation and circadian rhythmicity to predict changes in neurobehavioral functioning over time. We used a biomathematical model of fatigue linked to the adenosinergic neuromodulator/receptor system in the brain as a framework to predict sleep inertia, that is, the transient neurobehavioral impairment experienced immediately after awakening. Based on evidence of an adenosinergic basis for sleep inertia, we expanded the biomathematical model with novel differential equations to predict the propensity for sleep inertia during sleep and its manifestation after awakening. Using datasets from large laboratory studies of sleep loss and circadian misalignment, we calibrated the model by fitting just two new parameters and then validated the model's predictions against independent data. The expanded model was found to predict the magnitude and time course of sleep inertia with generally high accuracy. Analysis of the model's dynamics revealed a bifurcation in the predicted manifestation of sleep inertia in sustained sleep restriction paradigms, which reflects the observed escalation of the magnitude of sleep inertia in scenarios with sleep restriction to less than ∼ 4 h per day. Another emergent property of the model involves a rapid increase in the predicted propensity for sleep inertia in the early part of sleep followed by a gradual decline in the later part of the sleep period, which matches what would be expected based on the adenosinergic regulation of non-rapid eye movement (NREM) sleep and its known influence on sleep inertia. These dynamic behaviors provide confidence in the validity of our approach and underscore the predictive potential of the model. The expanded model provides a useful tool for predicting sleep inertia and managing impairment in 24/7 settings where people may need to perform critical tasks immediately after awakening, such as on-demand operations in safety and security, emergency response, and health care.

Cortical waste clearance in normal and restricted sleep with potential runaway tau buildup in Alzheimer's disease

Accumulation of waste in cortical tissue and glymphatic waste clearance via extracellular voids partly drives the sleep-wake cycle and modeling has reproduced much of its dynamics. Here, new modeling incorporates higher void volume and clearance in sleep, multiple waste compounds, and clearance obstruction by waste. This model reproduces normal sleep-wake cycles, sleep deprivation effects, and performance decreases under chronic sleep restriction (CSR). Once fitted to calibration data, it successfully predicts dynamics in further experiments on sleep deprivation, intermittent CSR, and recovery after restricted sleep. The results imply a central role for waste products with lifetimes similar to tau protein. Strong tau buildup is predicted if pathologically enhanced production or impaired clearance occur, with runaway buildup above a critical threshold. Predicted tau accumulation has timescales consistent with the development of Alzheimer’s disease. The model unifies a wide sweep of phenomena, clarifying the role of glymphatic clearance and targets for interventions against waste buildup.

Dynamic ensemble prediction of cognitive performance in spaceflight

During spaceflight, astronauts face a unique set of stressors, including microgravity, isolation, and confinement, as well as environmental and operational hazards. These factors can negatively impact sleep, alertness, and neurobehavioral performance, all of which are critical to mission success. In this paper, we predict neurobehavioral performance over the course of a 6-month mission aboard the International Space Station (ISS), using ISS environmental data as well as self-reported and cognitive data collected longitudinally from 24 astronauts. Neurobehavioral performance was repeatedly assessed via a 3-min Psychomotor Vigilance Test (PVT-B) that is highly sensitive to the effects of sleep deprivation. To relate PVT-B performance to time-varying and discordantly-measured environmental, operational, and psychological covariates, we propose an ensemble prediction model comprising of linear mixed effects, random forest, and functional concurrent models. An extensive cross-validation procedure reveals that this ensemble outperforms any one of its components alone. We also identify the most important predictors of PVT-B performance, which include an individual's previous PVT-B performance, reported fatigue and stress, and temperature and radiation dose. This method is broadly applicable to settings where the main goal is accurate, individualized prediction of human behavior involving a mixture of person-level traits and irregularly measured time series.

Prediction of shiftworker alertness, sleep, and circadian phase using a model of arousal dynamics constrained by shift schedules and light exposure

STUDY OBJECTIVES

The study aimed to, for the first time, (1) compare sleep, circadian phase, and alertness of intensive care unit (ICU) nurses working rotating shifts with those predicted by a model of arousal dynamics; and (2) investigate how different environmental constraints affect predictions and agreement with data.

METHODS

The model was used to simulate individual sleep-wake cycles, urinary 6-sulphatoxymelatonin (aMT6s) profiles, subjective sleepiness on the Karolinska Sleepiness Scale (KSS), and performance on a Psychomotor Vigilance Task (PVT) of 21 ICU nurses working day, evening, and night shifts. Combinations of individual shift schedules, forced wake time before/after work and lighting, were used as inputs to the model. Predictions were compared to empirical data. Simulations with self-reported sleep as an input were performed for comparison.

RESULTS

All input constraints produced similar prediction for KSS, with 56%-60% of KSS scores predicted within ±1 on a day and 48%-52% on a night shift. Accurate prediction of an individual's circadian phase required individualized light input. Combinations including light information predicted aMT6s acrophase within ±1 h of the study data for 65% and 35%-47% of nurses on diurnal and nocturnal schedules. Minute-by-minute sleep-wake state overlap between the model and the data was between 81 ± 6% and 87 ± 5% depending on choice of input constraint.

CONCLUSIONS

The use of individualized environmental constraints in the model of arousal dynamics allowed for accurate prediction of alertness, circadian phase, and sleep for more than half of the nurses. Individual differences in physiological parameters will need to be accounted for in the future to further improve predictions.

Understanding fatigue in a naval submarine: Applying biomathematical models and workload measurement in an intensive longitudinal design

Fatigue is a critically important aspect of crew endurance in submarine operations, with continuously high fatigue being associated with increased risk of human error and long-term negative health ramifications. Submarines pose several unique challenges to fatigue mitigation, including requirements for continuous manning for long durations, a lack of access to critical environmental zeitgebers (stimuli pertinent to circadian physiology; e.g., natural sunlight), and work, rest and sleep occurring within an encapsulated environment. In this paper, we examine the factors that underlie fatigue in such a context with the aim of evaluating the predictive utility of a biomathematical model (BMM) of fatigue. Three experience sampling studies were conducted with submarine crews using a participant-led measurement protocol that included assessments of subjective sleepiness, workload (NASA-Task Load Index [TLX] and a bespoke underload-overload scale), and sleep. As expected, results indicated that predicting KSS with a BMM approach outperformed more conventional linear modelling approaches (e.g., time-of-day, sleep duration, time awake). Both the homeostatic and circadian components of the BMM were significantly associated with KSS and used as controls in the workload models. We found increased NASA-TLX workload was significantly associated with increased average KSS ratings at the between-person level. However, counter to expectations, the two workload measures were not found to have significant linear or quadratic relationship with fatigue at the within-person level. An important outcome of the research is that applied fatigue researchers should be extremely cautious applying conventional linear predictors when predicting fatigue. Practical implications for the submarine and related extreme work context are discussed. Important avenues for continued research are outlined, including directly estimating BMM parameters.

Optimal Sleep and Work Schedules to Maximize Alertness

STUDY OBJECTIVES

Working outside the conventional "9-to-5" shift may lead to reduced sleep and alertness impairment. Here, we developed an optimization algorithm to identify sleep and work schedules that minimize alertness impairment during work hours, while reducing impairment during non-work hours.

METHODS

The optimization algorithm searches among a large number of possible sleep and work schedules and estimates their effectiveness in mitigating alertness impairment using the Unified Model of Performance (UMP). To this end, the UMP, and its extensions to estimate sleep latency and sleep duration, predicts the time course of alertness of each potential schedule and their physiological feasibility. We assessed the algorithm by simulating four experimental studies, where we compared alertness levels during work periods for sleep schedules proposed by the algorithm against those used in the studies. In addition, in one of the studies we assessed the algorithm's ability to simultaneously optimize sleep and work schedules.

RESULTS

Using the same amount of sleep as in the studies but distributing it optimally, the sleep schedules proposed by the optimization algorithm reduced alertness impairment during work periods by an average of 29%. Similarly, simultaneously optimized sleep and work schedules, for a recovery period following a chronic sleep restriction challenge, accelerated the return to baseline levels by two days when compared to the conventional 9-to-5 work schedule.

CONCLUSIONS

Our work provides the first quantitative tool to optimize sleep and work schedules and extends the capabilities of existing fatigue-management tools.

Fatigue risk management based on self-reported fatigue: Expanding a biomathematical model of fatigue-related performance deficits to also predict subjective sleepiness

Biomathematical models of fatigue can be used to predict neurobehavioral deficits during sleep/wake or work/rest schedules. Current models make predictions for objective performance deficits and/or subjective sleepiness, but known differences in the temporal dynamics of objective versus subjective outcomes have not been addressed. We expanded a biomathematical model of fatigue previously developed to predict objective performance deficits as measured on the Psychomotor Vigilance Test (PVT) to also predict subjective sleepiness as self-reported on the Karolinska Sleepiness Scale (KSS). Four model parameters were re-estimated to capture the distinct dynamics of the KSS and account for the scale difference between KSS and PVT. Two separate ensembles of datasets - drawn from laboratory studies of sleep deprivation, sleep restriction, simulated night work, napping, and recovery sleep - were used for calibration and subsequent validation of the model for subjective sleepiness. The expanded model was found to exhibit high prediction accuracy for subjective sleepiness, while retaining high prediction accuracy for objective performance deficits. Application of the validated model to an example scenario based on cargo aviation operations revealed divergence between predictions for objective and subjective outcomes, with subjective sleepiness substantially underestimating accumulating objective impairment, which has important real-world implications. In safety-sensitive operations such as commercial aviation, where self-ratings of sleepiness are used as part of fatigue risk management, the systematic differences in the temporal dynamics of objective versus subjective measures of functional impairment point to a potentially significant risk evaluation sensitivity gap. The expanded biomathematical model of fatigue presented here provides a useful quantitative tool to bridge this previously unrecognized gap.

Robust stability of trait-like vulnerability or resilience to common types of sleep deprivation in a large sample of adults

STUDY OBJECTIVES

Sleep loss produces large individual differences in neurobehavioral responses, with marked vulnerability or resilience among individuals. Such differences are stable with repeated exposures to acute total sleep deprivation (TSD) or chronic sleep restriction (SR) within short (weeks) and long (years) intervals. Whether trait-like responses are observed to commonly experienced types of sleep loss and across various demographically defined groups remains unknown.

METHODS

Eighty-three adults completed two baseline nights (10 h-12 h time-in-bed, TIB) followed by five 4 h TIB SR nights or 36 h TSD. Participants then received four 12-h TIB recovery nights followed by five SR nights or 36 h TSD, in counterbalanced order to the first sleep loss sequence. Neurobehavioral tests were completed every 2 h during wakefulness.

RESULTS

Participants who displayed neurobehavioral vulnerability to TSD displayed vulnerability to SR, evidenced by substantial to near perfect intraclass correlation coefficients (ICCs; 78%-91% across measures). Sex, race, age, body mass index (BMI), season, and sleep loss order did not impact ICCs significantly. Individuals exhibited significant consistency of responses within, but not between, performance and self-reported domains.

CONCLUSIONS

Using the largest, most diverse sample to date, we demonstrate for the first time the remarkable stability of phenotypic neurobehavioral responses to commonly experienced sleep loss types, across demographic variables and different performance and self-reported measures. Since sex, race, age, BMI, and season did not affect ICCs, these variables are not useful for determining stability of responses to sleep loss, underscoring the criticality of biological predictors. Our findings inform mathematical models and are relevant for the general population and military and health professions.

Effects of six weeks of chronic sleep restriction with weekend recovery on cognitive performance and wellbeing in high-performing adults

Chronic sleep loss is associated with escalating declines in vigilant attention across days of sleep restriction. However, studies exceeding two weeks of chronic sleep loss are scarce, and the cognitive performance outcomes assessed are limited. We assessed the effects of six weeks of chronic sleep restriction on a range of cognitive domains in 15 high-performing individuals (38.5±8.2 years, 6 women) confined to small space in groups of four. Sleep opportunities were limited to 5h on weekdays and 8h on weekends. Individual sleep/wake patterns were recorded with actigraphy. Neurobehavioral performance was assessed in evenings with Cognition, a computerized battery of ten tests assessing a range of cognitive domains. There were some small to moderate effects of increasing sleep debt relative to pre-mission baseline, with decreases in accuracy across cognitive domains (standardized β=0.121, p=0.001), specifically on tests of spatial orientation (β=0.289, p=0.011) and vigilant attention (β=0.688, p<0.001), which were not restored by two nights of weekend recovery sleep. Cognitive and subjective decrements occurred despite occasional daytime napping in breach of study protocol, evening testing around the circadian peak, and access to caffeine before 14:00. Sensorimotor speed, spatial learning and memory, working memory, abstraction and mental flexibility, emotion identification, abstract reasoning, cognitive throughput and risk decision making were not significantly affected by sleep debt. Taken together with modest lower subjective ratings of happiness and healthiness, these findings underline the importance of sufficient sleep, on both an acute and chronic basis, for performance in selected cognitive domains and subjective wellbeing in operationally-relevant environments.

Efficient embedded sleep wake classification for open-source actigraphy

This study presents a thorough analysis of sleep/wake detection algorithms for efficient on-device sleep tracking using wearable accelerometric devices. It develops a novel end-to-end algorithm using convolutional neural network applied to raw accelerometric signals recorded by an open-source wrist-worn actigraph. The aim of the study is to develop an automatic classifier that: (1) is highly generalizable to heterogenous subjects, (2) would not require manual features' extraction, (3) is computationally lightweight, embeddable on a sleep tracking device, and (4) is suitable for a wide assortment of actigraphs. Hereby, authors analyze sleep parameters, such as total sleep time, waking after sleep onset and sleep efficiency, by comparing the outcomes of the proposed algorithm to the gold standard polysomnographic concurrent recordings. The relatively substantial agreement (Cohen's kappa coefficient, median, equal to 0.78 ± 0.07) and the low-computational cost (2727 floating-point operations) make this solution suitable for an on-board sleep-detection approach.

Concussive head injury exacerbates neuropathology of sleep deprivation: Superior neuroprotection by co-administration of TiO2-nanowired cerebrolysin, alpha-melanocyte-stimulating hormone, and mesenchymal stem cells

Sleep deprivation (SD) is common in military personnel engaged in combat operations leading to brain dysfunction. Military personnel during acute or chronic SD often prone to traumatic brain injury (TBI) indicating the possibility of further exacerbating brain pathology. Several lines of evidence suggest that in both TBI and SD alpha-melanocyte-stimulating hormone (α-MSH) and brain-derived neurotrophic factor (BDNF) levels decreases in plasma and brain. Thus, a possibility exists that exogenous supplement of α-MSH and/or BDNF induces neuroprotection in SD compounded with TBI. In addition, mesenchymal stem cells (MSCs) are very portent in inducing neuroprotection in TBI. We examined the effects of concussive head injury (CHI) in SD on brain pathology. Furthermore, possible neuroprotective effects of α-MSH, MSCs and neurotrophic factors treatment were explored in a rat model of SD and CHI. Rats subjected to 48h SD with CHI exhibited higher leakage of BBB to Evans blue and radioiodine compared to identical SD or CHI alone. Brain pathology was also exacerbated in SD with CHI group as compared to SD or CHI alone together with a significant reduction in α-MSH and BDNF levels in plasma and brain and enhanced level of tumor necrosis factor-alpha (TNF-α). Exogenous administration of α-MSH (250μg/kg) together with MSCs (1×10⁶) and cerebrolysin (a balanced composition of several neurotrophic factors and active peptide fragments) (5mL/kg) significantly induced neuroprotection in SD with CHI. Interestingly, TiO₂ nanowired delivery of α-MSH (100μg), MSCs, and cerebrolysin (2.5mL/kg) induced enhanced neuroprotection with higher levels of α-MSH and BDNF and decreased the TNF-α in SD with CHI. These observations are the first to show that TiO₂ nanowired administration of α-MSH, MSCs and cerebrolysin induces superior neuroprotection following SD in CHI, not reported earlier. The clinical significance of our findings in light of the current literature is discussed.

Modeling melanopsin-mediated effects of light on circadian phase, melatonin suppression, and subjective sleepiness

A physiologically based model of arousal dynamics is improved to incorporate the effects of the light spectrum on circadian phase resetting, melatonin suppression, and subjective sleepiness. To account for these nonvisual effects of light, melanopic irradiance replaces photopic illuminance that was used previously in the model. The dynamic circadian oscillator is revised according to the melanopic irradiance definition and tested against experimental circadian phase resetting dose-response and phase response data. Melatonin suppression function is recalibrated against melatonin dose-response data for monochromatic and polychromatic light sources. A new light-dependent term is introduced into the homeostatic weight component of subjective sleepiness to represent the direct alerting effect of light; the new term responds to light change in a time-dependent manner and is calibrated against experimental data. The model predictions are compared to a total of 14 experimental studies containing 26 data sets for 14 different spectral light profiles. The revised melanopic model shows on average 1.4 times lower prediction error for circadian phase resetting compared to the photopic-based model, 3.2 times lower error for melatonin suppression, and 2.1 times lower error for subjective sleepiness. Overall, incorporating melanopic irradiance allowed simulation of wavelength-dependent responses to light and could explain the majority of the observations. Moving forward, models of circadian phase resetting and the direct effects of light on alertness and sleep need to use nonvisual photoreception-based measures of light, for example, melanopic irradiance, instead of the traditionally used illuminance based on the visual system.

Changes in performance and bio-mathematical model performance predictions during 45 days of sleep restriction in a simulated space mission

Lunar habitation and exploration of space beyond low-Earth orbit will require small crews to live in isolation and confinement while maintaining a high level of performance with limited support from mission control. Astronauts only achieve approximately 6 h of sleep per night, but few studies have linked sleep deficiency in space to performance impairment. We studied crewmembers over 45 days during a simulated space mission that included 5 h of sleep opportunity on weekdays and 8 h of sleep on weekends to characterize changes in performance on the psychomotor vigilance task (PVT) and subjective fatigue ratings. We further evaluated how well bio-mathematical models designed to predict performance changes due to sleep loss compared to objective performance. We studied 20 individuals during five missions and found that objective performance, but not subjective fatigue, declined from the beginning to the end of the mission. We found that bio-mathematical models were able to predict average changes across the mission but were less sensitive at predicting individual-level performance. Our findings suggest that sleep should be prioritized in lunar crews to minimize the potential for performance errors. Bio-mathematical models may be useful for aiding crews in schedule design but not for individual-level fitness-for-duty decisions.

An ensemble mixed effects model of sleep loss and performance

Sleep loss causes decrements in cognitive performance, which increases risks to those in safety-sensitive fields, including medicine and aviation. Mathematical models can be formulated to predict performance decrement in response to sleep loss, with the goal of identifying when an individual may be at highest risk for an accident. This work produces an Ensemble Mixed Effects Model that combines a traditional Linear Mixed Effects (LME) model with a semi-parametric, nonlinear model called Mixed Effects Random Forest (MERF). Using this model, we predict performance on the Psychomotor Vigilance Task (PVT), a test of sustained attention, using biologically motivated features extracted from a dataset containing demographic, sleep, and cognitive test data from 44 healthy participants studied during inpatient sleep loss laboratory experiments. Our Ensemble Mixed Effects Model accurately predicts an individual's trend in PVT performance, and fits the data better than prior published models. The ensemble successfully combines MERF's high rate of peak identification with LME's conservative predictions. We investigate two questions relevant to this model's potential use in operational settings: the tradeoff between additional model features versus ease of collecting these features in real-world settings, and how recent a cognitive task must have been administered to produce strong predictions. This work addresses limitations of previous approaches by developing a predictive model that accounts for interindividual differences and utilizes a nonlinear, semi-parametric method called MERF. We methodologically address the modeling decisions required for this prediction problem, including the choice of cross-validation method. This work is novel in its use of data from a highly-controlled inpatient study protocol that uncouples the influence of the sleep-wake cycle from the endogenous circadian rhythm on the cognitive task being modeled. This uncoupling provides a clearer picture of the model's real-world predictive ability for situations in which people work at different circadian times (e.g., night- or shift-work).

Earlier shift in race pacing can predict future performance during a single-effort ultramarathon under sleep deprivation

Objective

We constructed research camps at single-effort ultramarathons (50 and 100 miles) in order to study human endurance capabilities under extreme sleep loss and stress. It takes > 24h, on average, to run 100 miles on minimal sleep, allowing us to construct 24h human performance profiles (HPP).

Methods

We collected performance data plotted across time (race splits) and distance (dropout rates; n=257), self-reported sleep and training patterns (n=83), and endpoint data on cardiovascular fitness/adaptation to total sleep deprivation and extreme exercise/stress (n=127).

Results

In general, we found that self-reported napping was higher for 100-miler versus 50-miler runners and that ultra-endurance racing may possibly pre-select for early morning risers. We also compared HPPs between the first 50 miles completed by all runners in order to examine amplitude and acrophase differences in performance using a cosinor model. We showed that even though all runners slowed down over time, runners who completed a 100-miler ultramarathon had an earlier acrophase shift in race pace compared to non-finishers.

Discussion

We were able to identify time-dependent predictions on overall performance under minimal sleep, warranting the ultramarathon athlete as a unique demographic for future study of sleep and chronobiological relationships in the real world.

Recent advances in modeling sleep: from the clinic to society and disease

In the past few decades, advances in understanding sleep-wake neurophysiology have occurred hand-in-hand with advances in mathematical modeling of sleep and wake. In this review, we summarize recent updates in modeling the timing and durations of sleep and wake, the underlying neurophysiology of sleep and wake, and the application of these models in understanding cognition and disease. Throughout, we highlight the role modeling has played in developing our understanding of sleep and its underlying mechanisms. We present open questions and controversies in the field and propose the utility of individualized models of sleep for precision sleep medicine.

Sleep–wake behaviors exhibited by shift workers in normal operations and predicted by a biomathematical model of fatigue

Study Objectives

To compare rail workers’ actual sleep–wake behaviors in normal operations to those predicted by a biomathematical model of fatigue (BMMF). To determine whether there are group-level residual sources of error in sleep predictions that could be modeled to improve group-level sleep predictions.

Methods

The sleep–wake behaviors of 354 rail workers were examined during 1,722 breaks that were 8–24 h in duration. Sleep–wake patterns were continuously monitored using wrist-actigraphy and predicted from the work–rest schedule using a BMMF. Rail workers’ actual and predicted sleep–wake behaviors were defined as split-sleep (i.e. ≥2 sleep periods in a break) and consolidated-sleep (i.e. one sleep period in a break) behaviors. Sleepiness was predicted from the actual and predicted sleep–wake data.

Results

Consolidated-sleep behaviors were observed during 1,441 breaks and correctly predicted during 1,359 breaks. Split-sleep behaviors were observed during 280 breaks and correctly predicted during 182 breaks. Predicting the wrong type of sleep–wake behavior resulted in a misestimation of hours of sleep during a break. Relative to sleepiness predictions derived from actual sleep–wake data, predicting the wrong type of sleep–wake behavior resulted in a misestimation of sleepiness predictions during the subsequent shift.

Conclusions

All workers with the same work–rest schedule have the same predicted sleep–wake behaviors; however, these workers do not all exhibit the same sleep–wake behaviors in real-world operations. Future models could account for this group-level residual variance with a new approach to modeling sleep, whereby sub-group(s) may be predicted to exhibit one of a number of sleep–wake behaviors.

A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling

This part of the review aims to reduce the start-up burden of data collection and descriptive analytics for statistical modeling and route optimization of risk associated with motor vehicles. From a data-driven bibliometric analysis, we show that the literature is divided into two disparate research streams: (a) predictive or explanatory models that attempt to understand and quantify crash risk based on different driving conditions, and (b) optimization techniques that focus on minimizing crash risk through route/path-selection and rest-break scheduling. Translation of research outcomes between these two streams is limited. To overcome this issue, we present publicly available high-quality data sources (different study designs, outcome variables, and predictor variables) and descriptive analytic techniques (data summarization, visualization, and dimension reduction) that can be used to achieve safer-routing and provide code to facilitate data collection/exploration by practitioners/researchers. Then, we review the statistical and machine learning models used for crash risk modeling. We show that (near) real-time crash risk is rarely considered, which might explain why the optimization models (reviewed in Part 2) have not capitalized on the research outcomes from the first stream.

Sleep in the United States Military

The military lifestyle often includes continuous operations whether in training or deployed environments. These stressful environments present unique challenges for service members attempting to achieve consolidated, restorative sleep. The significant mental and physical derangements caused by degraded metabolic, cardiovascular, skeletomuscular, and cognitive health often result from insufficient sleep and/or circadian misalignment. Insufficient sleep and resulting fatigue compromises personal safety, mission success, and even national security. In the long-term, chronic insufficient sleep and circadian rhythm disorders have been associated with other sleep disorders (e.g., insomnia, obstructive sleep apnea, and parasomnias). Other physiologic and psychologic diagnoses such as post-traumatic stress disorder, cardiovascular disease, and dementia have also been associated with chronic, insufficient sleep. Increased co-morbidity and mortality are compounded by traumatic brain injury resulting from blunt trauma, blast exposure, and highly physically demanding tasks under load. We present the current state of science in human and animal models specific to service members during- and post-military career. We focus on mission requirements of night shift work, sustained operations, and rapid re-entrainment to time zones. We then propose targeted pharmacological and non-pharmacological countermeasures to optimize performance that are mission- and symptom-specific. We recognize a critical gap in research involving service members, but provide tailored interventions for military health care providers based on the large body of research in health care and public service workers.

Precision Medicine for Sleep Loss and Fatigue Management

Sleep loss is a widespread phenomenon and a public health threat. Sleep disorders, medical conditions, lifestyles, and occupational factors all contribute to insufficient sleep. Regardless of the underlying cause, insufficient sleep has well-defined consequences and the severity of said consequences partially influenced by individual characteristics. It is here where precision medicine needs to understand and define sleep insufficiency in hopes for personalizing medical approach to improve patient outcomes. Following a discussion on causes and consequences of sleep loss, this article discusses tools for assessing sleep sufficiency, mitigating strategies to sleep loss, and sleep loss in the context of fatigue management.

Computational Modeling of the Effects of Sleep Deprivation on the Vigilance Decrement

OBJECTIVE

We developed a computational model of the effects of sleep deprivation on the vigilance decrement by employing the methods of system dynamics modeling.

BACKGROUND

Situations that require sustained attention for a prolonged duration can cause a decline in cognitive performance, the so-called vigilance decrement. One factor that should influence the vigilance decrement is fatigue in the form of sleep deprivation.

METHOD

We employed the methods of system dynamics modeling (numerical-integration techniques for modeling complex feedback systems) to create a computational model of the vigilance decrement. We then simulated the computational effects of sleep deprivation on the behavior of that model, using empirical data obtained from the literature for calibrating such effects.

RESULTS

Sleep deprivation of 2 hr over a 14-day period should produce an additional decline of 9% in detection performance over that found with the typical vigilance decrement, whereas 4 hr of sleep deprivation over 14 days should produce an additional decline of 14% in detection performance.

CONCLUSION

With respect to dual-process theory, it is through its deleterious effects on analytical cognition that sleep deprivation should impact the vigilance decrement.

APPLICATION

Such computational modeling may be advantageous for human-machine teaming by theoretically allowing a future autonomous software agent to anticipate the decline of human performance and compensate accordingly.

Predicting performance and safety based on driver fatigue

Fatigue causes decrements in vigilant attention and reaction time and is a major safety hazard in the trucking industry. There is a need to quantify the relationship between driver fatigue and safety in terms of operationally relevant measures. Hard-braking events are a suitable measure for this purpose as they are relatively easily observed and are correlated with collisions and near-crashes. We developed an analytic approach that predicts driver fatigue based on a biomathematical model and then estimates hard-braking events as a function of predicted fatigue, controlling for time of day to account for systematic variations in exposure (traffic density). The analysis used de-identified data from a previously published, naturalistic field study of 106 U.S. commercial motor vehicle (CMV) drivers. Data analyzed included drivers' official duty logs, sleep patterns measured around the clock using wrist actigraphy, and continuous recording of vehicle data to capture hard-braking events. The curve relating predicted fatigue to hard-braking events showed that the frequency of hard-braking events increased as predicted fatigue levels worsened. For each increment on the fatigue scale, the frequency of hard-braking events increased by 7.8%. The results provide proof of concept for a novel approach that predicts fatigue based on drivers' sleep patterns and estimates driving performance in terms of an operational metric related to safety. The approach can be translated to practice by CMV operators to achieve a fatigue risk profile specific to their own settings, in order to support data-driven decisions about fatigue countermeasures that cost-effectively deliver quantifiable operational benefits.

Working Time Society consensus statements: Prescriptive rule sets and risk management-based approaches for the management of fatigue-related risk in working time arrangements

Traditionally, working time arrangements to limit fatigue-related risk have taken a prescriptive approach, which sets maximum shift durations in order to prevent excessive buildup of fatigue (and the associated increased risk) within shifts and sets minimum break durations to allow adequate time for rest and recovery within and/or between shifts. Prescriptive rule sets can be successful when, from a fatigue-related risk standpoint, they classify safe work hours as permitted and unsafe work hours as not permitted. However, prescriptive rule sets ignore important aspects of the biological factors (such as the interaction between circadian and homeostatic processes) that drive fatigue, which are critical modulators of the relationship between work hours and fatigue-related risk. As such, in around-the-clock operations when people must work outside of normal daytime hours, the relationship between regulatory compliance and safety tends to break down, and thus these rule sets become less effective. To address this issue, risk management-based approaches have been designed to regulate the procedures associated with managing fatigue-related risk. These risk management-based approaches are suitable for nighttime operations and a variety of other non-standard work schedules, and can be tailored to the particular job or industry. Although the purpose of these fatigue risk management approaches is to curb fatigue risk, fatigue risk cannot be measured directly. Thus, the goal is not on regulating fatigue risk per se, but rather to put in place procedures that serve to address fatigue before, during, and after potential fatigue-related incidents. Examples include predictive mathematical modeling of fatigue for work scheduling, proactive fatigue monitoring in the workplace, and reactive post-incident follow-up. With different risks and different needs across industries, there is no "one size fits all" approach to managing fatigue-related risk. However, hybrid strategies combining prescriptive rule sets and risk management-based approaches can create the flexibility necessary to reduce fatigue-related risk based on the specific needs of different work environments while maintaining appropriate regulatory oversight.

Effects of caffeine on sleep quality and daytime functioning

Caffeine (particularly in the form of coffee) is one of the most widely consumed stimulants in the world, with 90% of American adults consuming caffeine-infused beverages almost daily. While there is substantial evidence that caffeine enhances performance, caffeine withdrawal leads to deficits at both the individual (eg, cognitive, emotional, and behavioral processes) and societal (eg, increases in work accidents) level. Scholars for some time have considered that the supposed psychoactive benefits of caffeine may be the result of the mere reversal of deleterious effects of caffeine withdrawal, rather than a net benefit of caffeine ingestion. In this integrative review, we examine evidence illuminating the relationship between caffeine consumption and subsequent quality and quantity of nighttime rest. Secondly, we consider evidence as to whether performance deficits caused by sleep deprivation linked to caffeine can be reversed by caffeine consumption during the subsequent daytime period. Finally, we consider how these two stages can be reconciled in a single model that enables calculation of the net caffeine effect on daytime functioning. The literature highlights a range of positive impacts of caffeine consumption on both physical and cognitive functioning. There are also a number of factors that complicate any conclusions that can be drawn regarding the potential of caffeine to improve performance. Most critically, performance improvements the next day may simply be a result of the reversal of caffeine withdrawal. Animal studies and well-controlled human studies involving high habitual and low habitual users tend to confirm a "net benefit" for caffeine use. Further research, particularly with (necessarily rare) caffeine-naive populations, is required to elucidate the complexities of the relationship between caffeine, sleep, and daytime functioning. However, the convenience of accessing caffeine compared to ensuring adequate restorative sleep means that caffeine has applied advantages that are likely to see its use as a performance "enhancing" substance persist.

Spiking patterns and synchronization of thalamic neurons along the sleep-wake cycle

Spiking patterns and synchronization dynamics of thalamic neurons along the sleep-wake cycle are studied in a minimal model of four coupled conductance-based neurons. The model simulates two thalamic neurons coupled via a gap junction and driven by a synaptic input from a two-neuron model of sleep regulation by the hypothalamus. In accord with experimental data, the model shows that during sleep, when hypothalamic wake-active neurons are silent, the thalamic neurons discharge bursts of spikes. During wake, the excitatory synaptic input from the hypothalamus drives the coupled thalamic neurons to a state of tonic firing (single spikes). In the deterministic case, the thalamic neurons synchronize in-phase in the bursting regime but demonstrate multi-stability of out-of-phase, in-phase, and asynchronous states in the tonic firing. However, along the sleep-wake cycle, once the neurons synchronize in-phase during sleep (bursting), they stay synchronized in wake (tonic firing). It is thus found that noise is needed to reproduce the experimentally observed transitions between synchronized bursting during sleep and asynchronous tonic firing during wake. Overall, synchronization of bursting is found to be more robust to noise than synchronization of tonic firing, where a small disturbance is sufficient to desynchronize the thalamic neurons. The model predicts that the transitions between sleep and wake happen via chaos because a single thalamic neuron exhibits chaos between regular bursting and tonic activity. The results of this study suggest that the sleep- and wake-related dynamics in the thalamus may be generated at a level of gap junction-coupled clusters of thalamic neurons driven from the hypothalamus which would then propagate throughout the thalamus and cortex via axonal long-range connections.

Computational cognitive modeling of the temporal dynamics of fatigue from sleep loss

Computational models have become common tools in psychology. They provide quantitative instantiations of theories that seek to explain the functioning of the human mind. In this paper, we focus on identifying deep theoretical similarities between two very different models. Both models are concerned with how fatigue from sleep loss impacts cognitive processing. The first is based on the diffusion model and posits that fatigue decreases the drift rate of the diffusion process. The second is based on the Adaptive Control of Thought - Rational (ACT-R) cognitive architecture and posits that fatigue decreases the utility of candidate actions leading to microlapses in cognitive processing. A biomathematical model of fatigue is used to control drift rate in the first account and utility in the second. We investigated the predicted response time distributions of these two integrated computational cognitive models for performance on a psychomotor vigilance test under conditions of total sleep deprivation, simulated shift work, and sustained sleep restriction. The models generated equivalent predictions of response time distributions with excellent goodness-of-fit to the human data. More importantly, although the accounts involve different modeling approaches and levels of abstraction, they represent the effects of fatigue in a functionally equivalent way: in both, fatigue decreases the signal-to-noise ratio in decision processes and decreases response inhibition. This convergence suggests that sleep loss impairs psychomotor vigilance performance through degradation of the quality of cognitive processing, which provides a foundation for systematic investigation of the effects of sleep loss on other aspects of cognition. Our findings illustrate the value of treating different modeling formalisms as vehicles for discovery.

Daily circadian misalignment impairs human cognitive performance task-dependently

Shift work increases the risk for human errors, such that drowsiness due to shift work has contributed to major industrial disasters, including Space Shuttle Challenger, Chernobyl and Alaska Oil Spill disasters, with extraordinary socio-economical costs. Overnight operations pose a challenge because our circadian biology inhibits cognitive performance at night. Yet how the circadian system modulates cognition over multiple days under realistic shift work conditions remains to be established. Importantly, because task-specific cognitive brain regions show different 24-h circadian dynamics, we hypothesize that circadian misalignment impacts cognition task-dependently. Using a biologically-driven paradigm mimicking night shift work, with a randomized, cross-over design, we show that misalignment between the circadian pacemaker and behavioral/environmental cycles increases cognitive vulnerability on sustained attention, cognitive throughput, information processing and visual-motor performance over multiple days, compared to circadian alignment (day shifts). Circadian misalignment effects are task-dependent: while they acutely impair sustained attention with recovery after 3-days, they progressively hinder daily learning. Individuals felt sleepier during circadian misalignment, but they did not rate their performance as worse. Furthermore, circadian misalignment effects on sustained attention depended on prior sleep history. Collectively, daily circadian misalignment may provide an important biological framework for developing countermeasures against adverse cognitive effects in shift workers.

Further Evidence That Sleep Deprivation Effects and the Vigilance Decrement Are Functionally Equivalent: Comment on Altmann (2018)

Veksler and Gunzelmann (2018) argue that the vigilance decrement and the deleterious effects of sleep loss reflect functionally equivalent degradations in cognitive processing and performance. Our account is implemented in a cognitive architecture, where these factors produce breakdowns in goal-directed cognitive processing that we refer to as microlapses. Altmann (2018) raises a number of challenges to microlapses as a unified account of these deficits. Under scrutiny, however, the challenges do little to discredit the theory or conclusions in the original paper. In our response, we address the most serious challenges. In so doing, we provide additional support for the theory and mechanisms, and we highlight opportunities for extending their explanatory breadth.

Modeling the adenosine system as a modulator of cognitive performance and sleep patterns during sleep restriction and recovery

Sleep loss causes profound cognitive impairments and increases the concentrations of adenosine and adenosine A1 receptors in specific regions of the brain. Time courses for performance impairment and recovery differ between acute and chronic sleep loss, but the physiological basis for these time courses is unknown. Adenosine has been implicated in pathways that generate sleepiness and cognitive impairments, but existing mathematical models of sleep and cognitive performance do not explicitly include adenosine. Here, we developed a novel receptor-ligand model of the adenosine system to test the hypothesis that changes in both adenosine and A1 receptor concentrations can capture changes in cognitive performance during acute sleep deprivation (one prolonged wake episode), chronic sleep restriction (multiple nights with insufficient sleep), and subsequent recovery. Parameter values were estimated using biochemical data and reaction time performance on the psychomotor vigilance test (PVT). The model closely fit group-average PVT data during acute sleep deprivation, chronic sleep restriction, and recovery. We tested the model's ability to reproduce timing and duration of sleep in a separate experiment where individuals were permitted to sleep for up to 14 hours per day for 28 days. The model accurately reproduced these data, and also correctly predicted the possible emergence of a split sleep pattern (two distinct sleep episodes) under these experimental conditions. Our findings provide a physiologically plausible explanation for observed changes in cognitive performance and sleep during sleep loss and recovery, as well as a new approach for predicting sleep and cognitive performance under planned schedules.

Functional Equivalence of Sleep Loss and Time on Task Effects in Sustained Attention

Research on sleep loss and vigilance both focus on declines in cognitive performance, but theoretical accounts have developed largely in parallel in these two areas. In addition, computational instantiations of theoretical accounts are rare. The current work uses computational modeling to explore whether the same mechanisms can account for the effects of both sleep loss and time on task on performance. A classic task used in the sleep deprivation literature, the Psychomotor Vigilance Test (PVT), was extended from the typical 10-min duration to 35 min, to make the task similar in duration to traditional vigilance tasks. A computational cognitive model demonstrated that the effects of time on task in the PVT were equivalent to those observed with sleep loss. Subsequently, the same mechanisms were applied to a more traditional vigilance task-the Mackworth Clock Task-providing a good fit to existing data. This supports the hypothesis that these different types of fatigue may produce functionally equivalent declines in performance.

How should a bio-mathematical model be used within a fatigue risk management system to determine whether or not a working time arrangement is safe?

Bio-mathematical models that predict fatigue and/or sleepiness have proved a useful adjunct in the management of what has been typically referred to as fatigue-related risk. Codifying what constitutes appropriate use of these models will be increasingly important over the next decade. Current guidelines for determining a safe working time arrangement based on model outputs generally use a single upper threshold and are, arguably, over-simplistic. These guidelines fail to incorporate explicitly essential aspects of the risk assessment process - namely, the inherent uncertainty and variability in human sleep-wake behavior; the non-linear relationship between fatigue, task performance and safety outcomes; the consequence of a fatigue-related error and its influence on overall risk; and the impact of risk mitigation or controls in reducing the likelihood or consequence of a fatigue-related error. As industry and regulatory bodies increasingly move toward performance-based approaches to safety management, any fatigue risk management system that includes a bio-mathematical model should specify what exactly is measured by the model, and how the model can be used in the context of a safety management system approach. This will require significant dialog between the various parties with an interest in bio-mathematical models, i.e. developers, vendors, end-users, and regulators.

Modeling Neurocognitive Decline and Recovery During Repeated Cycles of Extended Sleep and Chronic Sleep Deficiency

Study Objectives

Intraindividual night-to-night sleep duration is often insufficient and variable. Here we report the effects of such chronic variable sleep deficiency on neurobehavioral performance and the ability of state-of-the-art models to predict these changes.

Methods

Eight healthy males (mean age ± SD: 23.9 ± 2.4 years) studied at our inpatient intensive physiologic monitoring unit completed an 11-day protocol with a baseline 10-hour sleep opportunity and three cycles of two 3-hour time-in-bed (TIB) and one 10-hour TIB sleep opportunities. Participants received one of three polychromatic white light interventions (200 lux 4100K, 200 or 400 lux 17000K) for 3.5 hours on the morning following the second 3-hour TIB opportunity each cycle. Neurocognitive performance was assessed using the psychomotor vigilance test (PVT) administered every 1-2 hours. PVT data were compared to predictions of five group-average mathematical models that incorporate chronic sleep loss functions.

Results

While PVT performance deteriorated cumulatively following each cycle of two 3-hour sleep opportunities, and improved following each 10-hour sleep opportunity, performance declined cumulatively throughout the protocol at a more accelerated rate than predicted by state-of-the-art group-average mathematical models. Subjective sleepiness did not reflect performance. The light interventions had minimal effect.

Conclusions

Despite apparent recovery following each extended sleep opportunity, residual performance impairment remained and deteriorated rapidly when rechallenged with subsequent sleep loss. None of the group-average models were capable of predicting both the build-up in impairment and recovery profile of performance observed at the group or individual level, raising concerns regarding their use in real-world settings to predict performance and improve safety.

Game Times and Higher Winning Percentages of West Coast Teams of the National Football League Correspond With Reduced Prevalence of Regular Season Injury

Brager, AJ and Mistovich, RJ. Game times and higher winning percentages of west coast teams of the National Football League correspond with reduced prevalence of regular season injury. J Strength Cond Res 31(2): 462-467, 2017-West coast teams of the National Football League are more statistically likely to win home night games against east coast opponents. The alignment of game times with daily rhythms of alertness is thought to contribute to this advantage. This study aims to determine whether rates of turnovers and injuries during the regular season, putative measures of mental and physical fatigue, impact winning percentages. Regular season schedules and rates of turnovers for each of the 32 teams were obtained from Pro-Football-Reference. We developed our own metric of injury risk for each position obtained from depth charts and regular season schedules. This metric compared cumulative weeks on injury reserve with cumulative time zone travel. West coast teams traveled 4 times as often as east coast teams. However, teams traveling eastward won twice as many games. There was no relationship between the extent and direction of travel and number of turnovers. Losing teams had more turnovers. The offensive and defensive lines in Central Time (CT) were placed on injury reserve 4 times as often as offensive and defensive lines in Pacific Time (PT). Injury prevalence in CT vs. PT was most prominent midseason. Plotting midseason game time relative to biological time revealed that PT teams play games closer to endogenous peaks in alertness, whereas CT teams play games closer to endogenous troughs in alertness. Overall, closer alignment of game time with the endogenous "alerting" signal may protect west coast teams from fatigue-related injuries and suggests for modified strength and conditioning programs.

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.

The influence of sleep deprivation and oscillating motion on sleepiness, motion sickness, and cognitive and motor performance

Our goal was to determine how sleep deprivation, nauseogenic motion, and a combination of motion and sleep deprivation affect cognitive vigilance, visual-spatial perception, motor learning and retention, and balance. We exposed four groups of subjects to different combinations of normal 8h sleep or 4h sleep for two nights combined with testing under stationary conditions or during 0.28Hz horizontal linear oscillation. On the two days following controlled sleep, all subjects underwent four test sessions per day that included evaluations of fatigue, motion sickness, vigilance, perceptual discrimination, perceptual learning, motor performance and learning, and balance. Sleep loss and exposure to linear oscillation had additive or multiplicative relationships to sleepiness, motion sickness severity, decreases in vigilance and in perceptual discrimination and learning. Sleep loss also decelerated the rate of adaptation to motion sickness over repeated sessions. Sleep loss degraded the capacity to compensate for novel robotically induced perturbations of reaching movements but did not adversely affect adaptive recovery of accurate reaching. Overall, tasks requiring substantial attention to cognitive and motor demands were degraded more than tasks that were more automatic. Our findings indicate that predicting performance needs to take into account in addition to sleep loss, the attentional demands and novelty of tasks, the motion environment in which individuals will be performing and their prior susceptibility to motion sickness during exposure to provocative motion stimulation.

Prediction Accuracy in Multivariate Repeated-Measures Bayesian Forecasting Models with Examples Drawn from Research on Sleep and Circadian Rhythms

In study designs with repeated measures for multiple subjects, population models capturing within- and between-subjects variances enable efficient individualized prediction of outcome measures (response variables) by incorporating individuals response data through Bayesian forecasting. When measurement constraints preclude reasonable levels of prediction accuracy, additional (secondary) response variables measured alongside the primary response may help to increase prediction accuracy. We investigate this for the case of substantial between-subjects correlation between primary and secondary response variables, assuming negligible within-subjects correlation. We show how to determine the accuracy of primary response predictions as a function of secondary response observations. Given measurement costs for primary and secondary variables, we determine the number of observations that produces, with minimal cost, a fixed average prediction accuracy for a model of subject means. We illustrate this with estimation of subject-specific sleep parameters using polysomnography and wrist actigraphy. We also consider prediction accuracy in an example time-dependent, linear model and derive equations for the optimal timing of measurements to achieve, on average, the best prediction accuracy. Finally, we examine an example involving a circadian rhythm model and show numerically that secondary variables can improve individualized predictions in this time-dependent nonlinear model as well.

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.

Fatiguing effect of multiple take-offs and landings in regional airline operations

Fatigue is a risk factor for flight performance and safety in commercial aviation. In US commercial aviation, to help to curb fatigue, the maximum duration of flight duty periods is regulated based on the scheduled start time and the number of flight segments to be flown. There is scientific support for regulating maximum duty duration based on scheduled start time; fatigue is well established to be modulated by circadian rhythms. However, it has not been established scientifically whether the number of flight segments, per se, affects fatigue. To address this science gap, we conducted a randomized, counterbalanced, cross-over study with 24 active-duty regional airline pilots. Objective and subjective fatigue was compared between a 9-hour duty day with multiple take-offs and landings versus a duty day of equal duration with a single take-off and landing. To standardize experimental conditions and isolate the fatiguing effect of the number of segments flown, the entire duty schedules were carried out in a high-fidelity, moving-base, full-flight, regional jet flight simulator. Steps were taken to maintain operational realism, including simulated airplane inspections and acceptance checks, use of realistic dispatch releases and airport charts, real-world air traffic control interactions, etc. During each of the two duty days, 10 fatigue test bouts were administered, which included a 10-minute Psychomotor Vigilance Test (PVT) assessment of objective fatigue and Samn-Perelli (SP) and Karolinska Sleepiness Scale (KSS) assessments of subjective sleepiness/fatigue. Results showed a greater build-up of objective and subjective fatigue in the multi-segment duty day than in the single-segment duty day. With duty start time and duration and other variables that could impact fatigue levels held constant, the greater build-up of fatigue in the multi-segment duty day was attributable specifically to the difference in the number of flight segments flown. Compared to findings in previously published laboratory studies of simulated night shifts and nighttime sleep deprivation, the magnitude of the fatiguing effect of the multiple take-offs and landings was modest. Ratings of flight performance were not significantly reduced for the simulated multi-segment duty day. The US duty and flight time regulations for commercial aviation shorten the maximum duty duration in multi-segment operations by up to 25% depending on the duty start time. The present results represent an important first step in understanding fatigue in multi-segment operations, and provide support for the number of flight segments as a relevant factor in regulating maximum duty duration. Nonetheless, based on our fatigue results, a more moderate reduction in maximum duty duration as a function of the number of flight segments might be considered. However, further research is needed to include investigation of flight safety, and to extend our findings to nighttime operations.

Suicide and sleep: Is it a bad thing to be awake when reason sleeps?

Suicide is the second leading cause of death, worldwide, for those between the ages of 24 and 44 y old. In 2013, more than 41,000 suicides occurred in the United States. These statistics underscore the need to 1) understand why people die by suicide and 2) identify risk factors that are potentially modifiable. While it has been posited that sleep disturbance may represent one such factor, systematic research in this arena did not begin until the 2000s. Since that time, sleep disturbance has been reliably identified as a risk factor for suicidal ideation, suicide attempts, and suicide. While insomnia, nightmares, and other sleep disorders have each been found to contribute to the risk for suicidal ideation and behavior, it is also possible that these factors share some common variance. One possibility is that sleep disturbance results in being awake at night, and being awake at night also confers risk. The hypothesis proffered here is that being awake when one is not biologically prepared to be so results in "hypofrontality" and diminished executive function, and that this represents a common pathway to suicidal ideation and behavior. Such a proposition is highly testable under a variety of possible protocols. The current review summarizes the extant literature on suicide rates by time-of-day, and discusses circadian, psychosocial, and neurocognitive explanations of risk. Such a focus promises to enhance our understanding of how sleep disturbance may confer risk, allows for the identification of future lines of research, and further justifies the need for interventions that promote good sleep continuity among at-risk individuals.

Circadian Phenotype Composition is a Major Predictor of Diurnal Physical Performance in Teams

Team performance is a complex phenomenon involving numerous influencing factors including physiology, psychology, and management. Biological rhythms and the impact of circadian phenotype have not been studied for their contribution to this array of factors so far despite our knowledge of the circadian regulation of key physiological processes involved in physical and mental performance. This study involved 216 individuals from 12 different teams who were categorized into circadian phenotypes using the novel RBUB chronometric test. The composition of circadian phenotypes within each team was used to model predicted daily team performance profiles based on physical performance tests. Our results show that the composition of circadian phenotypes within teams is variable and unpredictable. Predicted physical peak performance ranged from 1:52 to 8:59 p.m. with performance levels fluctuating by up to 14.88% over the course of the day. The major predictor for peak performance time in the course of a day in a team is the occurrence of late circadian phenotypes. We conclude that circadian phenotype is a performance indicator in teams that allows new insight and a better understanding of team performance variation in the course of a day as often observed in different groupings of individuals.

Sleep Deprivation-Induced Blood-Brain Barrier Breakdown and Brain Dysfunction are Exacerbated by Size-Related Exposure to Ag and Cu Nanoparticles. Neuroprotective Effects of a 5-HT3 Receptor Antagonist Ondansetron

Military personnel are often subjected to sleep deprivation (SD) during combat operations. Since SD is a severe stress and alters neurochemical metabolism in the brain, a possibility exists that acute or long-term SD will influence blood-brain barrier (BBB) function and brain pathology. This hypothesis was examined in young adult rats (age 12 to 14 weeks) using an inverted flowerpot model. Rats were placed over an inverted flowerpot platform (6.5 cm diameter) in a water pool where the water levels are just 3 cm below the surface. In this model, animals can go to sleep for brief periods but cannot achieve deep sleep as they would fall into water and thus experience sleep interruption. These animals showed leakage of Evans blue in the cerebellum, hippocampus, caudate nucleus, parietal, temporal, occipital, cingulate cerebral cortices, and brain stem. The ventricular walls of the lateral and fourth ventricles were also stained blue, indicating disruption of the BBB and the blood-cerebrospinal fluid barrier (BCSFB). Breakdown of the BBB or the BCSFB fluid barrier was progressive in nature from 12 to 48 h but no apparent differences in BBB leakage were seen between 48 and 72 h of SD. Interestingly, rats treated with metal nanoparticles, e.g., Cu or Ag, showed profound exacerbation of BBB disruption by 1.5- to 4-fold, depending on the duration of SD. Measurement of plasma and brain serotonin showed a close correlation between BBB disruption and the amine level. Repeated treatment with the serotonin 5-HT3 receptor antagonist ondansetron (1 mg/kg, s.c.) 4 and 8 h after SD markedly reduced BBB disruption and brain pathology after 12 to 24 h SD but not following 48 or 72 h after SD. However, TiO2-nanowired ondansetron (1 mg/kg, s.c) in an identical manner induced neuroprotection in rats following 48 or 72 h SD. However, plasma and serotonin levels were not affected by ondansetron treatment. Taken together, our observations are the first to show that (i) SD could induce BBB disruption and brain pathology, (ii) nanoparticles exacerbate SD-induced brain damage, and (iii) serotonin 5-HT3 receptor antagonist ondansetron is neuroprotective in SD that is further potentiated byTiO2-nanowired delivery, not reported earlier.

Behavioral sleep-wake homeostasis and EEG delta power are decoupled by chronic sleep restriction in the rat

STUDY OBJECTIVES

Chronic sleep restriction (CSR) is prevalent in society and is linked to adverse consequences that might be ameliorated by acclimation of homeostatic drive. This study was designed to test the hypothesis that the sleep-wake homeostat will acclimatize to CSR.

DESIGN

A four-parameter model of proportional control was used to quantify sleep homeostasis with and without recourse to a sleep intensity function.

SETTING

Animal laboratory, rodent walking-wheel apparatus.

SUBJECTS

Male Sprague-Dawley rats.

INTERVENTIONS

Acute total sleep deprivation (TSD, 1 day × 18 or 24 h, N = 12), CSR (10 days × 18 h TSD, N = 5, or 5 days × 20 h TSD, N = 6).

MEASUREMENTS AND RESULTS

Behavioral rebounds were consistent with model predictions for proportional control of cumulative times in wake, nonrapid eye movement (NREM) and rapid eye movement (REM). Delta (D) energy homeostasis was secondary to behavioral homeostasis; a biphasic NREM D power rebound contributed to the dynamics (rapid response) but not to the magnitude of the rebound in D energy. REM behavioral homeostasis was little affected by CSR. NREM behavioral homeostasis was attenuated in proportion to cumulative NREM deficit, whereas the biphasic NREM D power rebound was only slightly suppressed, indicating decoupled regulatory mechanisms following CSR.

CONCLUSIONS

We conclude that sleep homeostasis is achieved through behavioral regulation, that the NREM behavioral homeostat is susceptible to attenuation during CSR and that the concept of sleep intensity is not essential in a model of sleep-wake regulation.

STUDY OBJECTIVES

Chronic sleep restriction (CSR) is prevalent in society and is linked to adverse consequences that might be ameliorated by acclimation of homeostatic drive. This study was designed to test the hypothesis that the sleep-wake homeostat will acclimatize to CSR.

DESIGN

A four-parameter model of proportional control was used to quantify sleep homeostasis with and without recourse to a sleep intensity function.

SETTING

Animal laboratory, rodent walking-wheel apparatus.

SUBJECTS

Male Sprague-Dawley rats.

INTERVENTIONS

Acute total sleep deprivation (TSD, 1 day × 18 or 24 h, N = 12), CSR (10 days × 18 h TSD, N = 5, or 5 days × 20 h TSD, N = 6).

MEASUREMENTS AND RESULTS

Behavioral rebounds were consistent with model predictions for proportional control of cumulative times in wake, nonrapid eye movement (NREM) and rapid eye movement (REM). Delta (D) energy homeostasis was secondary to behavioral homeostasis; a biphasic NREM D power rebound contributed to the dynamics (rapid response) but not to the magnitude of the rebound in D energy. REM behavioral homeostasis was little affected by CSR. NREM behavioral homeostasis was attenuated in proportion to cumulative NREM deficit, whereas the biphasic NREM D power rebound was only slightly suppressed, indicating decoupled regulatory mechanisms following CSR.

CONCLUSIONS

We conclude that sleep homeostasis is achieved through behavioral regulation, that the NREM behavioral homeostat is susceptible to attenuation during CSR and that the concept of sleep intensity is not essential in a model of sleep-wake regulation.