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

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.

Hilaire MA, Kirkley CL, Gregory KB, Clark T, Hanifin JP, Barger LK, Czeisler CA, Brainard GC, Lockley SW, Flynn-Evans EE. The effect of a dynamic lighting schedule on neurobehavioral performance during a 45-day simulated space mission

Study Objectives

We previously reported that during a 45-day simulated space mission, a dynamic lighting schedule (DLS) improved circadian phase alignment and performance assessed once on selected days. This study aimed to evaluate how DLS affected performance on a 5-minute psychomotor vigilance task (PVT) administered multiple times per day on selected days.

Methods

Sixteen crewmembers (37.4 ± 6.7 years; 5F) underwent six cycles of 2 × 8-hour/night followed by 5 × 5-hour/night sleep opportunities. During the DLS (n = 8), daytime white light exposure was blue-enriched (~6000 K; Level 1: 1079, Level 2: 76 melanopic equivalent daytime illuminance (melEDI) lux) and blue-depleted (~3000-4000 K; L1: 21, L2: 2 melEDI lux) 3 hours before bed. In the standard lighting schedule (SLS; n = 8), lighting remained constant (~4500K; L1: 284, L2 62 melEDI lux). Effects of lighting condition (DLS/SLS), sleep condition (5/8 hours), time into mission, and their interactions, and time awake on PVT performance were analyzed using generalized linear mixed models.

Results

The DLS was associated with fewer attentional lapses (reaction time [RT] > 500 milliseconds) compared to SLS. Lapses, mean RT, and 10% fastest/slowest RTs were worse following 5 compared to 8 hours of sleep but not between lighting conditions. There was an effect of time into mission on RTs, likely due to sleep loss. Overall performance differed by time of day, with longer RTs at the beginning and end of the day. There were more lapses and slower RTs in the afternoon in the SLS compared to the DLS condition.

Conclusions

Future missions should incorporate DLS to enhance circadian alignment and performance. This paper is part of the Sleep and Circadian Rhythms: Management of Fatigue in Occupational Settings Collection.

Night work, season and alertness as occupational safety hazards in the Arctic: protocol for the Noralert observational crossover study among Norwegian process operators

INTRODUCTION

The objective of this study is to determine the effects of night work, Arctic seasonal factors and cold working environments on human functions relevant to safety. The study aims to quantify the contribution of (1) several consecutive night shifts, (2) seasonal variation on sleepiness, alertness and circadian rhythm and (3) whether a computational model of sleep, circadian rhythms and cognitive performance can accurately predict the observed sleepiness and alertness.

METHODS AND ANALYSIS

In an observational crossover study of outdoor and indoor workers (n=120) on a three-shift schedule from an industrial plant in Norway (70 °N), measurements will be conducted during the summer and winter. Sleep duration and quality will be measured daily by smartphone questionnaire, aided by actigraphy and heart rate measurements. Sleepiness and alertness will be assessed at regular intervals by the Karolinska Sleepiness Scale and the psychomotor vigilance test, respectively. Saliva samples will assess melatonin levels, and a blood sample will measure circadian time. Thermal exposures and responses will be measured by sensors and by thermography.

ETHICS AND DISSEMINATION

All participants will give written informed consent to participate in the study, which will be conducted in accordance with the Declaration of Helsinki. The Norwegian Regional Committee for Medical Research Ethics South-East D waivered the need for ethics approval (reference 495816). Dissemination plans include academic and lay publications, and partnerships with national and regional policymakers.

Pilot feasibility testing of biomathematical model recommendations for personalising sleep timing in shift workers

Sleep disturbances and circadian disruption play a central role in adverse health, safety, and performance outcomes in shift workers. While biomathematical models of sleep and alertness can be used to personalise interventions for shift workers, their practical implementation is undertested. This study tested the feasibility of implementing two biomathematical models-the Phillips-Robinson Model and the Model for Arousal Dynamics-in 28 shift-working nurses, 14 in each group. The study examined the overlap and adherence between model recommendations and sleep behaviours, and changes in sleep following the implementation of recommendations. For both groups combined, the mean (SD) percentage overlap between when a model recommended an individual to sleep and when sleep was obtained was 73.62% (10.24%). Adherence between model recommendations and sleep onset and offset times was significantly higher with the Model of Arousal Dynamics compared to the Phillips-Robinson Model. For the Phillips-Robinson model, 27% of sleep onset and 35% of sleep offset times were within ± 30 min of model recommendations. For the Model of Arousal Dynamics, 49% of sleep onset, and 35% of sleep offset times were within ± 30 min of model recommendations. Compared to pre-study, significant improvements were observed post-study for sleep disturbance (Phillips-Robinson Model), and insomnia severity and sleep-related impairments (Model of Arousal Dynamics). Participants reported that using a digital, automated format for the delivery of sleep recommendations would enable greater uptake. These findings provide a positive proof-of-concept for using biomathematical models to recommend sleep in operational contexts.

Assessment of the unified model of performance: accuracy of group-average and individualised alertness predictions

To be effective as a key component of fatigue-management systems, biomathematical models that predict alertness impairment as a function of time of day, sleep history, and caffeine consumption must demonstrate the ability to make accurate predictions across a range of sleep-loss and caffeine schedules. Here, we assessed the ability of the previously reported unified model of performance (UMP) to predict alertness impairment at the group-average and individualised levels in a comprehensive set of 12 studies, including 22 sleep and caffeine conditions, for a total of 301 unique subjects. Given sleep and caffeine schedules, the UMP predicted alertness impairment based on the psychomotor vigilance test (PVT) for the duration of the schedule. To quantify prediction performance, we computed the root mean square error (RMSE) between model predictions and PVT data, and the fraction of measured PVTs that fell within the models' prediction intervals (PIs). For the group-average model predictions, the overall RMSE was 43 ms (range 15-74 ms) and the fraction of PVTs within the PIs was 80% (range 41%-100%). At the individualised level, the UMP could predict alertness for 81% of the subjects, with an overall average RMSE of 64 ms (range 32-147 ms) and fraction of PVTs within the PIs conservatively estimated as 71% (range 41%-100%). Altogether, these results suggest that, for the group-average model and 81% of the individualised models, in three out of four PVT measurements we cannot distinguish between study data and model predictions.

Human challenges to adaptation to extreme professional environments: A systematic review

NASA is planning human exploration of the Moon, while preparations are underway for human missions to Mars, and deeper into the solar system. These missions will expose space travelers to unusual conditions, which they will have to adapt to. Similar conditions are found in several analogous environments on Earth, and studies can provide an initial understanding of the challenges for human adaptation. Such environments can be marked by an extreme climate, danger, limited facilities and supplies, isolation from loved ones, or mandatory interaction with others. They are rarely encountered by most human beings, and mainly concern certain professions in limited missions. This systematic review focuses on professional extreme environments and captures data from papers published since 2005. Our findings provide an insight into their physiological, biological, cognitive, and behavioral impacts for better understand how humans adapt or not to them. This study provides a framework for studying adaptation, which is particularly important in light of upcoming longer space expeditions to more distant destinations.

Understanding the Need for Sleep to Improve Cognition

The restorative function of sleep is shaped by its duration, timing, continuity, subjective quality, and efficiency. Current sleep recommendations specify only nocturnal duration and have been largely derived from sleep self-reports that can be imprecise and miss relevant details. Sleep duration, preferred timing, and ability to withstand sleep deprivation are heritable traits whose expression may change with age and affect the optimal sleep prescription for an individual. Prevailing societal norms and circumstances related to work and relationships interact to influence sleep opportunity and quality. The value of allocating time for sleep is revealed by the impact of its restriction on behavior, functional brain imaging, sleep macrostructure, and late-life cognition. Augmentation of sleep slow oscillations and spindles have been proposed for enhancing sleep quality, but they inconsistently achieve their goal. Crafting bespoke sleep recommendations could benefit from large-scale, longitudinal collection of objective sleep data integrated with behavioral and self-reported data.

Effects of dynamic lighting on circadian phase, self-reported sleep and performance during a 45-day space analog mission with chronic variable sleep deficiency

Spaceflight exposes crewmembers to circadian misalignment and sleep loss, which impair cognition and increase the risk of errors and accidents. We compared the effects of an experimental dynamic lighting schedule (DLS) with a standard static lighting schedule (SLS) on circadian phase, self-reported sleep and cognition during a 45-day simulated space mission. Sixteen participants (mean age [±SD] 37.4 ± 6.7 years; 5 F; n = 8/lighting condition) were studied in four-person teams at the NASA Human Exploration Research Analog. Participants were scheduled to sleep 8 h/night on two weekend nights, 5 h/night on five weekday nights, repeated for six 7-day cycles, with scheduled waketime fixed at 7:00 a.m. Compared to the SLS where illuminance and spectrum remained constant during wake (~4000K), DLS increased the illuminance and short-wavelength (blue) content of white light (~6000K) approximately threefold in the main workspace (Level 1), until 3 h before bedtime when illuminance was reduced by ~96% and the blue content also reduced throughout (~4000K × 2 h, ~3000K × 1 h) until bedtime. The average (±SE) urinary 6-sulphatoxymelatonin (aMT6s) acrophase time was significantly later in the SLS (6.22 ± 0.34 h) compared to the DLS (4.76 ± 0.53 h) and more variable in SLS compared to DLS (37.2 ± 3.6 min vs. 28.2 ± 2.4 min, respectively, p = .04). Compared to DLS, self-reported sleep was more frequently misaligned relative to circadian phase in SLS RR: 6.75, 95% CI 1.55-29.36, p = .01), but neither self-reported sleep duration nor latency to sleep was different between lighting conditions. Accuracy in the abstract matching and matrix reasoning tests were significantly better in DLS compared to SLS (false discovery rate-adjusted p ≤ .04). Overall, DLS alleviated the drift in circadian phase typically observed in space analog studies and reduced the prevalence of self-reported sleep episodes occurring at an adverse circadian phase. Our results support incorporating DLS in future missions, which may facilitate appropriate circadian alignment and reduce the risk of sleep disruption.

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.

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.

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.