Optimal Sleep and Work Schedules to Maximize Alertness

Improving children's alertness and neuromuscular response by using a blue-enriched white light in the kindergarten playroom

Preschool children, who spend most of their time indoors, and the effects of artificial light on children's health and performance are important. Previous studies show that blue-enriched white light (BWL) has significant effects on human bodies, but only a few studies have specifically examined its effects in young children. Moreover, due to the significant physiological differences between children and adults, findings from BWL studies in adults cannot be directly applied to children. Therefore, investigating the effects of BWL on young children living in indoor environments is crucial. We recruited 24 preschool children (age: 5 ± 0.8 years; 12 girls and 12 boys) to participate in a within-subject, randomized crossover study involving common white light (CWL) (450 lx, Melanopic EDI: 354.04 lx) and BWL (450 lx, Melanopic EDI: 746.05 lx) in a kindergarten playroom. Under different light conditions, the children underwent tests for cardiac activity and critical flicker fusion frequency (CFF), as well as psychomotor vigilance task (PVT) and ruler drop test (RDT). The results indicated that BWL had significant effects on preschool children. Compared to CWL exposure, BWL exposure significantly improved cardiac activity, alertness, and neuromuscular response but slightly increased visual fatigue. Our study reveals that BWL has significant potential to improve children's physiological and cognitive functions, particularly to improve cardiac activity, alertness, and neuromuscular response. This study broadens the understanding of the effects of indoor lighting on children and provides a theoretical basis for designing a healthy indoor environment for children.

When to sleep and consume caffeine to boost alertness

STUDY OBJECTIVES

Sleep loss can cause cognitive impairments that increase the risk of mistakes and accidents. However, existing guidelines to counteract the effects of sleep loss are generic and are not designed to address individual-specific conditions, leading to suboptimal alertness levels. Here, we developed an optimization algorithm that automatically identifies sleep schedules and caffeine-dosing strategies to minimize alertness impairment due to sleep loss for desired times of the day.

METHODS

We combined our previous algorithms that separately optimize sleep or caffeine to simultaneously identify the best sleep schedules and caffeine doses that minimize alertness impairment at desired times. The optimization algorithm uses the predictions of the well-validated Unified Model of Performance to estimate the effectiveness and physiological feasibility of a large number of possible solutions and identify the best one. To assess the optimization algorithm, we used it to identify the best sleep schedules and caffeine-dosing strategies for four studies that exemplify common sleep-loss conditions and compared the predicted alertness-impairment reduction achieved by using the algorithm's recommendations against that achieved by following the U.S. Army caffeine guidelines.

RESULTS

Compared to the alertness-impairment levels in the original studies, the algorithm's recommendations reduced alertness impairment on average by 63%, an improvement of 24 percentage points over the U.S. Army caffeine guidelines.

CONCLUSIONS

We provide an optimization algorithm that simultaneously identifies effective and safe sleep schedules and caffeine-dosing strategies to minimize alertness impairment at user-specified times.

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.

Can we rely on wearable sleep-tracker devices for fatigue management?

STUDY OBJECTIVES

Wearable sleep-tracker devices are ubiquitously used to measure sleep; however, the estimated sleep parameters often differ from the gold-standard polysomnography (PSG). It is unclear to what extent we can tolerate these errors within the context of a particular clinical or operational application. Here, we sought to develop a method to quantitatively determine whether a sleep tracker yields acceptable sleep-parameter estimates for assessing alertness impairment.

METHODS

Using literature data, we characterized sleep-measurement errors of 18 unique sleep-tracker devices with respect to PSG. Then, using predictions based on the unified model of performance, we compared the temporal variation of alertness in terms of the psychomotor vigilance test mean response time for simulations with and without added PSG-device sleep-measurement errors, for nominal schedules of 5, 8, or 9 hours of sleep/night or an irregular sleep schedule each night for 30 consecutive days. Finally, we deemed a device error acceptable when the predicted differences were smaller than the within-subject variability of 30 milliseconds. We also established the capability to estimate the extent to which a specific sleep-tracker device meets this acceptance criterion.

RESULTS

On average, the 18 sleep-tracker devices overestimated sleep duration by 19 (standard deviation = 44) minutes. Using these errors for 30 consecutive days, we found that, regardless of sleep schedule, in nearly 80% of the time the resulting predicted alertness differences were smaller than 30 milliseconds.

CONCLUSIONS

We provide a method to quantitatively determine whether a sleep-tracker device produces sleep measurements that are operationally acceptable for fatigue management.

Imputing missing sleep data from wearables with neural networks in real-world settings

Sleep is a critical component of health and well-being but collecting and analyzing accurate longitudinal sleep data can be challenging, especially outside of laboratory settings. We propose a simple neural network model titled SOMNI (Sleep data restOration using Machine learning and Non-negative matrix factorIzation [NMF]) for imputing missing rest-activity data from actigraphy, which can enable clinicians to better handle missing data and monitor sleep-wake cycles of individuals with highly irregular sleep-wake patterns. The model consists of two hidden layers and uses NMF to capture hidden longitudinal sleep-wake patterns of individuals with disturbed sleep-wake cycles. Based on this, we develop two approaches: the individual approach imputes missing data based on the data from only one participant, while the global approach imputes missing data based on the data across multiple participants. Our models are tested with shift and non-shift workers' data from three independent hospitals. Both approaches can accurately impute missing data up to 24 hours of long dataset (>50 days) even for shift workers with extremely irregular sleep-wake patterns (AUC > 0.86). On the other hand, for short dataset (~15 days), only the global model is accurate (AUC > 0.77). Our approach can be used to help clinicians monitor sleep-wake cycles of patients with sleep disorders outside of laboratory settings without relying on sleep diaries, ultimately improving sleep health outcomes.

A real-time, personalized sleep intervention using mathematical modeling and wearable devices

The prevalence of artificial light exposure has enabled us to be active any time of the day or night, leading to the need for high alertness outside of traditional daytime hours. To address this need, we developed a personalized sleep intervention framework that analyzes real-world sleep-wake patterns obtained from wearable devices to maximize alertness during specific target periods. Our framework utilizes a mathematical model that tracks the dynamic sleep pressure and circadian rhythm based on the user's sleep history. In this way, the model accurately predicts real-time alertness, even for shift workers with complex sleep and work schedules (N = 71, t = 13~21 days). This allowed us to discover a new sleep-wake pattern called the adaptive circadian split sleep, which incorporates a main sleep period and a late nap to enable high alertness during both work and non-work periods of shift workers. We further developed a mobile application that integrates this framework to recommend practical, personalized sleep schedules for individual users to maximize their alertness during a targeted activity time based on their desired sleep onset and available sleep duration. This can reduce the risk of errors for those who require high alertness during nontraditional activity times and improve the health and quality of life for those leading shift work-like lifestyles.

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.

Working around the Clock: Is a Person’s Endogenous Circadian Timing for Optimal Neurobehavioral Functioning Inherently Task-Dependent?

Neurobehavioral task performance is modulated by the circadian and homeostatic processes of sleep/wake regulation. Biomathematical modeling of the temporal dynamics of these processes and their interaction allows for prospective prediction of performance impairment in shift-workers and provides a basis for fatigue risk management in 24/7 operations. It has been reported, however, that the impact of the circadian rhythm—and in particular its timing—is inherently task-dependent, which would have profound implications for our understanding of the temporal dynamics of neurobehavioral functioning and the accuracy of biomathematical model predictions. We investigated this issue in a laboratory study designed to unambiguously dissociate the influences of the circadian and homeostatic processes on neurobehavioral performance, as measured during a constant routine protocol preceded by three days on either a simulated night shift or a simulated day shift schedule. Neurobehavioral functions were measured every 3 h using three functionally distinct assays: a digit symbol substitution test, a psychomotor vigilance test, and the Karolinska Sleepiness Scale. After dissociating the circadian and homeostatic influences and accounting for inter-individual variability, peak circadian performance occurred in the late biological afternoon (in the “wake maintenance zone”) for all three neurobehavioral assays. Our results are incongruent with the idea of inherent task-dependent differences in the endogenous circadian impact on performance. Rather, our results suggest that neurobehavioral functions are under top-down circadian control, consistent with the way they are accounted for in extant biomathematical models.

Alertness during working hours among eight-hour rotating-shift nurses: An observational study

PURPOSE

The aim of this study was to identify the patterns of the decline in the alertness of rotating-shift nurses during working hours across different shift types (day, evening, and night) using an objective measure.

DESIGN

An observational study using ReadiBand wrist actigraphs was conducted.

METHODS

Data were collected from June 2019 to February 2020. Participants were rotating-shift nurses (N = 82) who provided direct nursing care for patients in acute care hospitals in South Korea. Nurses wore actigraphs continuously for 14 days on their non-dominant hand to identify sleep-wake cycles and predict their alertness scores hourly. All participants completed a sleep diary.

FINDINGS

Nurses working during night shifts had lower average alertness scores (mean = 77.12) than nurses working during day (mean = 79.05) and evening (mean = 91.21). Overall, alertness showed a declining trend and the specific patterns of decline differed across shifts. Participants with alertness scores less than 70 or 80 demonstrated a significant decline in alertness across all shifts.

CONCLUSIONS

Distinct patterns of decline in alertness per nursing shift were revealed. Each shift feature should be considered when developing interventions to increase nurses' alertness, promote high-quality care provision, and ensure patient safety.

CLINICAL RELEVANCE

The implementation of interventions to increase alertness among shift nurses is needed at the organizational level, and the cooperation of nursing managers and administrators is required.