Heart rate variability can be used to estimate sleepiness-related decrements in psychomotor vigilance during total sleep deprivation

Acute combined effects of concurrent physical activities on autonomic nervous activation during cognitive tasks

Backgrounds: The validity of heart rate variability (HRV) has been substantiated in mental workload assessments. However, cognitive tasks often coincide with physical exertion in practical mental work, but their synergic effects on HRV remains insufficiently established. The study aims were to investigate the combined effects of cognitive and physical load on autonomic nerve functions. Methods: Thirty-five healthy male subjects (aged 23.5 ± 3.3 years) were eligible and enrolled in the study. The subjects engaged in n-back cognitive tasks (1-back, 2-back, and 3-back) under three distinct physical conditions, involving isotonic contraction of the left upper limb with loads of 0 kg, 3 kg, and 5 kg. Electrocardiogram signals and cognitive task performance were recorded throughout the tasks, and post-task assessment of subjective experiences were conducted using the NASA-TLX scale. Results: The execution of n-back tasks resulted in enhanced perceptions of task-load feelings and increased reaction times among subjects, accompanied by a decline in the accuracy rate (p < 0.05). These effects were synchronously intensified by the imposition of physical load. Comparative analysis with a no-physical-load scenario revealed significant alterations in the HRV of the subjects during the cognitive task under moderate and high physical conditions. The main features were a decreased power of the high frequency component (p < 0.05) and an increased low frequency component (p < 0.05), signifying an elevation in sympathetic activity. This physiological response manifested similarly at both moderate and high physical levels. In addition, a discernible linear correlation was observed between HRV and task-load feelings, as well as task performance under the influence of physical load (p < 0.05). Conclusion: HRV can serve as a viable indicator for assessing mental workload in the context of physical activities, making it suitable for real-world mental work scenarios.

Psychophysiological models of hypovigilance detection: A scoping review

Hypovigilance represents a major contributor to accidents. In operational contexts, the burden of monitoring/managing vigilance often rests on operators. Recent advances in sensing technologies allow for the development of psychophysiology-based (hypo)vigilance prediction models. Still, these models remain scarcely applied to operational situations and need better understanding. The current scoping review provides a state of knowledge regarding psychophysiological models of hypovigilance detection. Records evaluating vigilance measuring tools with gold standard comparisons and hypovigilance prediction performances were extracted from MEDLINE, PsychInfo, and Inspec. Exclusion criteria comprised aspects related to language, non-empirical papers, and sleep studies. The Quality Assessment tool for Diagnostic Accuracy Studies (QUADAS) and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were used for bias evaluation. Twenty-one records were reviewed. They were mainly characterized by participant selection and analysis biases. Papers predominantly focused on driving and employed several common psychophysiological techniques. Yet, prediction methods and gold standards varied widely. Overall, we outline the main strategies used to assess hypovigilance, their principal limitations, and we discuss applications of these models.

Evaluation of Correlation between Psychomotor Vigilance Task Scores and Drowsiness Estimation Levels Obtained from Facial Videos

This paper reports the results of an experiment to evaluate the relationship between results obtained with a drowsiness estimation system we have developed using facial videos and those obtained with the Psychomotor Vigilance Task (PVT), which is a standard index of sleepiness used in sleep medicine. The correlation between PVT scores and the output of the drowsiness estimation system, which outputs drowsiness levels from assigned facial expressions, was calculated using data from 30 subjects. The Spearman's correlation coefficients between the drowsiness estimation results and the PVT mean response time, the slowest 10% response time, and the number of lapses were 0.36 (p <0.001), 0.43 (p <0.001), and 0.40 (p <0.001), respectively. Since this experiment showed a correlation between the drowsiness estimation results and those with PVT, it would seem possible to make specific interventions based on drowsiness estimation results learned from ground-truth drowsiness levels. Such estimation results could help prevent accidents resulting from drowsiness or insufficient vigilance while driving or working.

The Acute Readiness Monitoring Scale: Assessing Predictive and Concurrent Validation

To complement and enhance readiness-monitoring capability, the Acute Readiness Monitoring Scale (ARMS) was developed: a widely applicable, simple psychometric measure of perceived readiness. While this tool may have widespread utility in sport and military settings, it remains unknown if the ARMS demonstrates predictive and concurrent validity. Here, we investigated whether the ARMS is: (1) responsive to an acute manipulation of readiness using sleep deprivation, (2) relates to biological markers of readiness [cortisol/heart-rate variability (HRV)], and (3) predicts performance on a cognitive task. Thirty young adults (aged 23 ± 4 years; 18 females) participated. All participants engaged in a 24-h sleep deprivation protocol. Participants completed the ARMS, biological measures of readiness (salivary cortisol, HRV), and cognitive performance measures (psychomotor vigilance task) before, immediately after, 24-, and 48-h post-sleep deprivation. All six of the ARMS subscales changed in response to sleep deprivation: scores on each subscale worsened (indicating reductions in perceived readiness) immediately after sleep deprivation, returning to baseline 24/48 h post. Lower perceived readiness was associated with reduced awakening responses in cortisol and predicted worse cognitive performance (slower reaction time). No relationship was observed between the ARMS and HRV, nor between any biological markers of readiness (cortisol/HRV) and cognitive performance. These data suggest that the ARMS may hold practical utility in detecting, or screening for, the wide range of deleterious effects caused by sleep deprivation; may constitute a quick, cheap, and easily interpreted alternative to biological measures of readiness; and may be used to monitor or mitigate potential underperformance on tasks requiring attention and vigilance.

Response speed measurements on the psychomotor vigilance test: how precise is precise enough?

STUDY OBJECTIVES

The psychomotor vigilance test (PVT) is frequently used to measure behavioral alertness in sleep research on various software and hardware platforms. In contrast to many other cognitive tests, PVT response time (RT) shifts of a few milliseconds can be meaningful. It is, therefore, important to use calibrated systems, but calibration standards are currently missing. This study investigated the influence of system latency bias and its variability on two frequently used PVT performance metrics, attentional lapses (RTs ≥500 ms) and response speed, in sleep-deprived and alert participants.

METHODS

PVT data from one acute total (N = 31 participants) and one chronic partial (N = 43 participants) sleep deprivation protocol were the basis for simulations in which response bias (±15 ms) and its variability (0-50 ms) were systematically varied and transgressions of predefined thresholds (i.e. ±1 for lapses, ±0.1/s for response speed) recorded.

RESULTS

Both increasing bias and its variability caused deviations from true scores that were higher for the number of lapses in sleep-deprived participants and for response speed in alert participants. Threshold transgressions were typically rare (i.e. <5%) if system latency bias was less than ±5 ms and its standard deviation was ≤10 ms.

CONCLUSIONS

A bias of ±5 ms with a standard deviation of ≤10 ms could be considered maximally allowable margins for calibrating PVT systems for timing accuracy. Future studies should report the average system latency and its standard deviation in addition to adhering to published standards for administering and analyzing the PVT.

Leaf-inspired homeostatic cellulose biosensors

An incompatibility between skin homeostasis and existing biosensor interfaces inhibits long-term electrophysiological signal measurement. Inspired by the leaf homeostasis system, we developed the first homeostatic cellulose biosensor with functions of protection, sensation, self-regulation, and biosafety. Moreover, we find that a mesoporous cellulose membrane transforms into homeostatic material with properties that include high ion conductivity, excellent flexibility and stability, appropriate adhesion force, and self-healing effects when swollen in a saline solution. The proposed biosensor is found to maintain a stable skin-sensor interface through homeostasis even when challenged by various stresses, such as a dynamic environment, severe detachment, dense hair, sweat, and long-term measurement. Last, we demonstrate the high usability of our homeostatic biosensor for continuous and stable measurement of electrophysiological signals and give a showcase application in the field of brain-computer interfacing where the biosensors and machine learning together help to control real-time applications beyond the laboratory at unprecedented versatility.

Effect of Sleep and Biobehavioral Patterns on Multidimensional Cognitive Performance: Longitudinal, In-the-Wild Study

Background

With nearly 20% of the US adult population using fitness trackers, there is an increasing focus on how physiological data from these devices can provide actionable insights about workplace performance. However, in-the-wild studies that understand how these metrics correlate with cognitive performance measures across a diverse population are lacking, and claims made by device manufacturers are vague. While there has been extensive research leading to a variety of theories on how physiological measures affect cognitive performance, virtually all such studies have been conducted in highly controlled settings and their validity in the real world is poorly understood.

Objective

We seek to bridge this gap by evaluating prevailing theories on the effects of a variety of sleep, activity, and heart rate parameters on cognitive performance against data collected in real-world settings.

Methods

We used a Fitbit Charge 3 and a smartphone app to collect different physiological and neurobehavioral task data, respectively, as part of our 6-week-long in-the-wild study. We collected data from 24 participants across multiple population groups (shift workers, regular workers, and graduate students) on different performance measures (vigilant attention and cognitive throughput). Simultaneously, we used a fitness tracker to unobtrusively obtain physiological measures that could influence these performance measures, including over 900 nights of sleep and over 1 million minutes of heart rate and physical activity metrics. We performed a repeated measures correlation (r_rm) analysis to investigate which sleep and physiological markers show association with each performance measure. We also report how our findings relate to existing theories and previous observations from controlled studies.

Results

Daytime alertness was found to be significantly correlated with total sleep duration on the previous night (r_rm=0.17, P<.001) as well as the duration of rapid eye movement (r_rm=0.12, P<.001) and light sleep (r_rm=0.15, P<.001). Cognitive throughput, by contrast, was not found to be significantly correlated with sleep duration but with sleep timing—a circadian phase shift toward a later sleep time corresponded with lower cognitive throughput on the following day (r_rm=–0.13, P<.001). Both measures show circadian variations, but only alertness showed a decline (r_rm=–0.1, P<.001) as a result of homeostatic pressure. Both heart rate and physical activity correlate positively with alertness as well as cognitive throughput.

Conclusions

Our findings reveal that there are significant differences in terms of which sleep-related physiological metrics influence each of the 2 performance measures. This makes the case for more targeted in-the-wild studies investigating how physiological measures from self-tracking data influence, or can be used to predict, specific aspects of cognitive performance.

The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), a heterogeneous and multifactorial sleep related breathing disorder with high prevalence, is a recognized risk factor for cardiovascular morbidity and mortality. Autonomic dysfunction leads to adverse cardiovascular outcomes in diverse pathways. Heart rate is a complex physiological process involving neurovisceral networks and relative regulatory mechanisms such as thermoregulation, renin-angiotensin-aldosterone mechanisms, and metabolic mechanisms. Heart rate variability (HRV) is considered as a reliable and non-invasive measure of autonomic modulation response and adaptation to endogenous and exogenous stimuli. HRV measures may add a new dimension to help understand the interplay between cardiac and nervous system involvement in OSA. The aim of this review is to introduce the various applications of HRV in different aspects of OSA to examine the impaired neuro-cardiac modulation. More specifically, the topics covered include: HRV time windows, sleep staging, arousal, sleepiness, hypoxia, mental illness, and mortality and morbidity. All of these aspects show pathways in the clinical implementation of HRV to screen, diagnose, classify, and predict patients as a reasonable and more convenient alternative to current measures.

Dissociation of Subjective and Objective Alertness During Prolonged Wakefulness

BACKGROUND

Although the deterioration of subjective and objective alertness during prolonged wakefulness has been investigated rigorously, whether perceived sleepiness and fatigue are consistent with actual decrements in behavioral performance in the time course is still disputed. The present study examined the dissociation between decrements of subjective alertness and performance deficits during prolonged wakefulness of one night and explored the relationship between body temperature and the impairments of subjective and objective alertness.

PARTICIPANTS AND METHODS

Thirty-eight participants (27 females; age: 21.76 ± 2.37 years old) underwent prolonged wakefulness for one night at habitual bedtime (0:00-6:00 am). Participants completed a 10-min PVT to assess objective alertness, fatigue, and sleepiness ratings to assess subjective alertness every 2 hours, and body temperature was measured every hour during scheduled wakefulness.

RESULTS

Subjective alertness reflected a linear decline with time, but the magnitudes of objective performance deterioration increased significantly between 4:00 and 6:00 am. The increasing magnitudes of performance deficits were associated with the change of body temperature between 4:00 and 6:00 am.

CONCLUSION

These results indicate that the perceived degree of decline in alertness is temporally dissociated with the actual decline in objective vigilance with increased duration of wakefulness. The dissociation of magnitudes of subjective and objective alertness decrements mainly occurs between 4:00 and 6:00 am, and the changes of performance deficits have a relationship with body temperature.

The validity of the pupillographic sleepiness test at shorter task durations

The pupillographic sleepiness test (PST) is an accurate predictor of alertness failure and performance impairment across sleep deprivation. At 11 min in duration, the task is considered too long to be used in occupational or roadside settings. We therefore investigated the predictive capacity of the PST at seven shortened test durations. Eighteen healthy young adults (aged 21.4 ± 3.2 years, 10 men) underwent 40 h of continuous wakefulness, completing an 11-min PST and a 10-min psychomotor vigilance task (PVT) every 2 h. Waking electroencephalography was recorded and scored for microsleeps during PVTs. The PST was divided into eight equal 82-s blocks and the predictive capacity of the pupillary unrest index (PUI) calculated at descending PST durations by systematically removing blocks. PUI increased significantly with time awake for all test durations (p < .0001), with a similar amplitude of PUI observed for test durations of 5.5 min and longer. While all test durations accurately predicted PVT impairment (AUC: 0.72-0.86, p < .001) and microsleep (AUC: 0.74-0.84, p < .0001), 5.5 min was the shortest duration where accuracy remained high across level and type of impairment (AUC: 0.79-0.86). For the 5.5-min duration, the positive predictive value (PPV) and negative predictive value (NPV) were on average 50.1% and 89.4%, respectively, and were comparable to the full 11-min task (PPV: 49.2%; NPV: 91%). The PST can be shortened to 5.5 min without compromising accuracy in detecting performance impairment or physiological drowsiness. The PST is an ideal candidate for fitness-for-duty or fitness-to-drive testing, and future studies should examine its predictive capacity, at shorter durations, against operationally relevant outcomes.

Frequency-Dependent Light Stimulation Effects on Performance During Vigilance Tasks on a Laptop

Students, office workers, or other computer and mobile device users can suffer from decrements in alertness or productivity, but many intervention methods on these can be too distracting or even affect daily routines. Using heart rate (HR) to determine a fast and slow target frequency at which to oscillate light brightness stimulation on a laptop, thirty-six participants joined a cognitive task where we hypothesized that fast frequency stimulation would increase alertness and decrease relaxation, while slow frequency stimulation would have the opposite effects. We found that slow frequency stimulation produces a statistically significant delay in response time, users react more slowly (3.8e2 ± 5.5e1 ms), when compared to the no stimulation (3.7e2 ± 4.1e1 ms) (p = 9.0e-3) conditions. The (Slow - No Stimulation) response time (1.7e1 ± 2.7e2 ms) produced a statistically significant delay in response time versus the (Fast - No Stimulation) response time (-0.74 ± 2.4e1 ms) (p = .016). These delays due to slow stimulation occurred without influencing accuracy or subjective sleepiness ratings. We observed that frequency-dependent light stimulation can potentially influence HRV metrics such as the mean normal-to-normal intervals and mean HR. Future work will target breathing rate to determine light stimulation oscillations as we further investigate the potential of using the slow-frequency domain to unobtrusively influence user performance and physiology.

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human-machine interaction in a car and especially for driver state monitoring in the field of automated driving.

Sleep in Pregnancy and Maternal Hyperglycemia: a Narrative Review

PURPOSE OF REVIEW

Prevalence of gestational diabetes is increasing globally and sleep may be a modifiable lifestyle factor associated with it. However, existing findings have been inconsistent.

RECENT FINDINGS

Majority of studies reviewed found a link between extreme sleep durations and elevated risk of maternal hyperglycemia. The findings with sleep-disordered breathing are less consistent. Methodological differences across studies, in terms of sleep assessment methods (subjective vs. objective), study population (low vs. high risk), classification of gestational diabetes and sleep problems, may have contributed to the inconsistent findings. Some studies also suggest the possibility of trimester-specific association between sleep and maternal hyperglycemia. Large-scale prospective studies comprising objective measurements of sleep, preferably over three trimesters and preconception, are needed to better evaluate the relationship between sleep and maternal hyperglycemia.

Microsleep episodes in the borderland between wakefulness and sleep

Study objectives

The wake-sleep transition zone represents a poorly defined borderland, containing, for example, microsleep episodes (MSEs), which are of potential relevance for diagnosis and may have consequences while driving. Yet, the scoring guidelines of the American Academy of Sleep Medicine (AASM) completely neglect it. We aimed to explore the borderland between wakefulness and sleep by developing the Bern continuous and high-resolution wake-sleep (BERN) criteria for visual scoring, focusing on MSEs visible in the electroencephalography (EEG), as opposed to purely behavior- or performance-defined MSEs.

Methods

Maintenance of Wakefulness Test (MWT) trials of 76 randomly selected patients were retrospectively scored according to both the AASM and the newly developed BERN scoring criteria. The visual scoring was compared with spectral analysis of the EEG. The quantitative EEG analysis enabled a reliable objectification of the visually scored MSEs. For less distinct episodes within the borderland, either ambiguous or no quantitative patterns were found.

Results

As expected, the latency to the first MSE was significantly shorter in comparison to the sleep latency, defined according to the AASM criteria. In certain cases, a large difference between the two latencies was observed and a substantial number of MSEs occurred between the first MSE and sleep. Series of MSEs were more frequent in patients with shorter sleep latencies, while isolated MSEs were more frequent in patients who did not reach sleep.

Conclusion

The BERN criteria extend the AASM criteria and represent a valuable tool for in-depth analysis of the wake-sleep transition zone, particularly important in the MWT.

Effects of Sleep Deprivation on Surgeons Dexterity

Sleep deprivation is an ordinary aspect in the global society and its prevalence is increasing. Chronic and acute sleep deprivation have been linked to diabetes and heart diseases as well as depression and enhanced impulsive behaviors. Surgeons are often exposed to long hour on call and few hours of sleep in the previous days. Nevertheless, few studies have focused their attention on the effects of sleep deprivation on surgeons and more specifically on the effects of sleep deprivation on surgical dexterity, often relying on virtual surgical simulators. A better understanding of the consequences of sleep loss on the key surgical skill of dexterity can shed light on the possible risks associated to a sleepy surgeon. In this paper, the authors aim to provide a comprehensive review of the relationship between sleep deprivation and surgical dexterity.

Noncontact RF Vital Sign Sensor for Continuous Monitoring of Driver Status

In this paper, a radio frequency vital sign sensor based on double voltage-controlled oscillators (VCOs) combined with a switchable phase-locked loop (PLL) is proposed for a noncontact remote vital sign sensing system. Our sensing system primarily detects the periodic movements of the human lungs and the hearts via the impedance variation of the resonator. With a change in impedance, both the VCO oscillation frequency and the PLL feedback voltage also change. Thus, by tracking the feedback voltage of the PLL, breath and heart rate signals can be acquired simultaneously. However, as the distance between the body and the sensor varies, there are certain points with minimal sensitivity, making it is quite difficult to detect vital signs. These points, called impedance null points, periodically occur at distances proportional to the wavelength. To overcome the impedance null point problem, two resonators operating at different frequencies, 2.40 and 2.76 GHz, are employed as receiving components. In an experiment to investigate the sensing performance as a function of distance, the measurement distance was accurately controlled by a linear actuator. Furthermore, to evaluate the sensing performance in a real environment, experiments were carried out with a male and a female subject in a static vehicle. To demonstrate the real-time vital sign monitoring capability, spectrograms were utilized, and the accuracy was assessed relative to reference sensors. Based on the results, it is demonstrated that the proposed remote sensor can reliably detect vital signs in a real vehicle environment.

Increased glymphatic influx is correlated with high EEG delta power and low heart rate in mice under anesthesia

The glymphatic system is responsible for brain-wide delivery of nutrients and clearance of waste via influx of cerebrospinal fluid (CSF) alongside perivascular spaces and through the brain. Glymphatic system activity increases during sleep or ketamine/xylazine (K/X) anesthesia, yet the mechanism(s) facilitating CSF influx are poorly understood. Here, we correlated influx of a CSF tracer into the brain with electroencephalogram (EEG) power, heart rate, blood pressure, and respiratory rate in wild-type mice under six different anesthesia regimens. We found that glymphatic CSF tracer influx was highest under K/X followed by isoflurane (ISO) supplemented with dexmedetomidine and pentobarbital. Mice anesthetized with α-chloralose, Avertin, or ISO exhibited low CSF tracer influx. This is the first study to show that glymphatic influx correlates positively with cortical delta power in EEG recordings and negatively with beta power and heart rate.

Heart Rate Variability-Based Driver Drowsiness Detection and Its Validation With EEG

OBJECTIVE

Driver drowsiness detection is a key technology that can prevent fatal car accidents caused by drowsy driving. The present work proposes a driver drowsiness detection algorithm based on heart rate variability (HRV) analysis and validates the proposed method by comparing with electroencephalography (EEG)-based sleep scoring.

METHODS

Changes in sleep condition affect the autonomic nervous system and then HRV, which is defined as an RR interval (RRI) fluctuation on an electrocardiogram trace. Eight HRV features are monitored for detecting changes in HRV by using multivariate statistical process control, which is a well known anomaly detection method.

RESULT

The performance of the proposed algorithm was evaluated through an experiment using a driving simulator. In this experiment, RRI data were measured from 34 participants during driving, and their sleep onsets were determined based on the EEG data by a sleep specialist. The validation result of the experimental data with the EEG data showed that drowsiness was detected in 12 out of 13 pre-N1 episodes prior to the sleep onsets, and the false positive rate was 1.7 times per hour.

CONCLUSION

The present work also demonstrates the usefulness of the framework of HRV-based anomaly detection that was originally proposed for epileptic seizure prediction.

SIGNIFICANCE

The proposed method can contribute to preventing accidents caused by drowsy driving.

Light, Alertness, and Alerting Effects of White Light: A Literature Overview

Light is known to elicit non-image-forming responses, such as effects on alertness. This has been reported especially during light exposure at night. Nighttime results might not be translatable to the day. This article aims to provide an overview of (1) neural mechanisms regulating alertness, (2) ways of measuring and quantifying alertness, and (3) the current literature specifically regarding effects of different intensities of white light on various measures and correlates of alertness during the daytime. In general, the present literature provides inconclusive results on alerting effects of the intensity of white light during daytime, particularly for objective measures and correlates of alertness. However, the various research paradigms employed in earlier studies differed substantially, and most studies tested only a limited set of lighting conditions. Therefore, the alerting potential of exposure to more intense white light should be investigated in a systematic, dose-dependent manner with multiple correlates of alertness and within one experimental paradigm over the course of day.

The role of sleep duration and sleep disordered breathing in gestational diabetes mellitus

Many women experience sleep problems during pregnancy. This includes difficulty initiating and maintaining sleep due to physiologic changes that occur as pregnancy progresses, as well as increased symptoms of sleep-disordered breathing (SDB). Growing evidence indicates that sleep deficiency alters glucose metabolism and increases risk of diabetes. Poor sleep may exacerbate the progressive increase in insulin resistance that normally occurs during pregnancy, thus contributing to the development of maternal hyperglycemia. Here, we critically review evidence that exposure to short sleep duration or SDB during pregnancy is associated with gestational diabetes mellitus (GDM). Several studies have found that the frequency of GDM is higher in women exposed to short sleep compared with longer sleep durations. Despite mixed evidence regarding whether symptoms of SDB (e.g., frequent snoring) are associated with GDM after adjusting for BMI or obesity, it has been shown that clinically-diagnosed SDB is prospectively associated with GDM. There are multiple mechanisms that may link sleep deprivation and SDB with insulin resistance, including increased levels of oxidative stress, inflammation, sympathetic activity, and cortisol. Despite emerging evidence that sleep deficiency and SDB are associated with increased risk of GDM, it has yet to be demonstrated that improving sleep in pregnant women (e.g., by extending sleep duration or treating SDB) protects against the development of hyperglycemia. If a causal relationship can be established, behavioral therapies for improving sleep can potentially be used to reduce the risk and burden of GDM.

Effects of total sleep deprivation on divided attention performance

Dividing attention across two tasks performed simultaneously usually results in impaired performance on one or both tasks. Most studies have found no difference in the dual-task cost of dividing attention in rested and sleep-deprived states. We hypothesized that, for a divided attention task that is highly cognitively-demanding, performance would show greater impairment during exposure to sleep deprivation. A group of 30 healthy males aged 21-30 years was exposed to 40 h of continuous wakefulness in a laboratory setting. Every 2 h, subjects completed a divided attention task comprising 3 blocks in which an auditory Go/No-Go task was 1) performed alone (single task); 2) performed simultaneously with a visual Go/No-Go task (dual task); and 3) performed simultaneously with both a visual Go/No-Go task and a visually-guided motor tracking task (triple task). Performance on all tasks showed substantial deterioration during exposure to sleep deprivation. A significant interaction was observed between task load and time since wake on auditory Go/No-Go task performance, with greater impairment in response times and accuracy during extended wakefulness. Our results suggest that the ability to divide attention between multiple tasks is impaired during exposure to sleep deprivation. These findings have potential implications for occupations that require multi-tasking combined with long work hours and exposure to sleep loss.

Sleep Deprivation in Young and Healthy Subjects Is More Sensitively Identified by Higher Frequencies of Electrodermal Activity than by Skin Conductance Level Evaluated in the Time Domain

We analyzed multiple measures of the autonomic nervous system (ANS) based on electrodermal activity (EDA) and heart rate variability (HRV) for young healthy subjects undergoing 24-h sleep deprivation. In this study, we have utilized the error awareness test (EAT) every 2 h (13 runs total), to evaluate the deterioration of performance. EAT consists of trials where the subject is presented words representing colors. Subjects are instructed to press a button (“Go” trials) or withhold the response if the word presented and the color of the word mismatch (“Stroop No-Go” trial), or the screen is repeated (“Repeat No-Go” trials). We measured subjects' (N = 10) reaction time to the “Go” trials, and accuracy to the “Stroop No-Go” and “Repeat No-Go” trials. Simultaneously, changes in EDA and HRV indices were evaluated. Furthermore, the relationship between reactiveness and vigilance measures and indices of sympathetic control based on HRV were analyzed. We found the performance improved to a stable level from 6 through 16 h of deprivation, with a subsequently sustained impairment after 18 h. Indices of higher frequencies of EDA related more to vigilance measures, whereas lower frequencies index (skin conductance leve, SCL) measured the reactiveness of the subject. We conclude that indices of EDA, including those of the higher frequencies, termed TVSymp, EDASymp, and NSSCRs, provide information to better understand the effect of sleep deprivation on subjects' autonomic response and performance.

Changes in Plasma Lipids during Exposure to Total Sleep Deprivation

STUDY OBJECTIVES

The effects of sleep loss on plasma lipids, which play an important role in energy homeostasis and signaling, have not been systematically examined. Our aim was to identify lipid species in plasma that increase or decrease reliably during exposure to total sleep deprivation.

DESIGN

Twenty individuals underwent sleep deprivation in a laboratory setting. Blood was drawn every 4 h and mass spectrometry techniques were used to analyze concentrations of 263 lipid species in plasma, including glycerolipids, glycerophospholipids, sphingolipids, and sterols.

SETTING

Chronobiology and Sleep Laboratory, Duke-NUS Graduate Medical School.

PARTICIPANTS

Healthy ethnic-Chinese males aged 21-28 y (n = 20).

INTERVENTIONS

Subjects were kept awake for 40 consecutive hours.

MEASUREMENTS AND RESULTS

Each metabolite time series was modeled as a sum of sinusoidal (circadian) and linear components, and we assessed whether the slope of the linear component differed from zero. More than a third of all individually analyzed lipid profiles exhibited a circadian rhythm and/or a linear change in concentration during sleep deprivation. Twenty-five lipid species showed a linear and predominantly unidirectional trend in concentration levels that was consistent across participants. Choline plasmalogen levels decreased, whereas several phosphatidylcholine (PC) species and triacylglycerides (TAG) carrying polyunsaturated fatty acids increased.

CONCLUSIONS

The decrease in choline plasmalogen levels during sleep deprivation is consistent with prior work demonstrating that these lipids are susceptible to degradation by oxidative stress. The increase in phosphatidylcholines and triacylglycerides suggests that sleep loss might modulate lipid metabolism, which has potential implications for metabolic health in individuals who do not achieve adequate sleep.

Classifying vulnerability to sleep deprivation using baseline measures of psychomotor vigilance

OBJECTIVES

To identify measures derived from baseline psychomotor vigilance task (PVT) performance that can reliably predict vulnerability to sleep deprivation.

DESIGN

Subjects underwent total sleep deprivation and completed a 10-min PVT every 1-2 h in a controlled laboratory setting. Participants were categorized as vulnerable or resistant to sleep deprivation, based on a median split of lapses that occurred following sleep deprivation. Standard reaction time, drift diffusion model (DDM), and wavelet metrics were derived from PVT response times collected at baseline. A support vector machine model that incorporated maximum relevance and minimum redundancy feature selection and wrapper-based heuristics was used to classify subjects as vulnerable or resistant using rested data.

SETTING

Two academic sleep laboratories.

PARTICIPANTS

Independent samples of 135 (69 women, age 18 to 25 y), and 45 (3 women, age 22 to 32 y) healthy adults.

INTERVENTIONS

In both datasets, DDM measures, number of consecutive reaction times that differ by more than 250 ms, and two wavelet features were selected by the model as features predictive of vulnerability to sleep deprivation. Using the best set of features selected in each dataset, classification accuracy was 77% and 82% using fivefold stratified cross-validation, respectively.

MEASUREMENTS AND RESULTS

In both datasets, DDM measures, number of consecutive reaction times that differ by more than 250 ms, and two wavelet features were selected by the model as features predictive of vulnerability to sleep deprivation. Using the best set of features selected in each dataset, classification accuracy was 77% and 82% using fivefold stratified cross-validation, respectively.

CONCLUSIONS

Despite differences in experimental conditions across studies, drift diffusion model parameters associated reliably with individual differences in performance during total sleep deprivation. These results demonstrate the utility of drift diffusion modeling of baseline performance in estimating vulnerability to psychomotor vigilance decline following sleep deprivation.

Regularity of cardiac rhythm as a marker of sleepiness in sleep disordered breathing

Aim

The present study aimed to analyse the autonomic nervous system activity using heart rate variability (HRV) to detect sleep disordered breathing (SDB) patients with and without excessive daytime sleepiness (EDS) before sleep onset.

Methods

Two groups of 20 patients with different levels of daytime sleepiness -sleepy group, SG; alert group, AG- were selected consecutively from a Maintenance of Wakefulness Test (MWT) and Multiple Sleep Latency Test (MSLT) research protocol. The first waking 3-min window of RR signal at the beginning of each nap test was considered for the analysis. HRV was measured with traditional linear measures and with time-frequency representations. Non-linear measures -correntropy, CORR; auto-mutual-information function, AMIF- were used to describe the regularity of the RR rhythm. Statistical analysis was performed with non-parametric tests.

Results

Non-linear dynamic of the RR rhythm was more regular in the SG than in the AG during the first wakefulness period of MSLT, but not during MWT. AMIF (in high-frequency and in Total band) and CORR (in Total band) yielded sensitivity > 70%, specificity >75% and an area under ROC curve > 0.80 in classifying SG and AG patients.

Conclusion

The regularity of the RR rhythm measured at the beginning of the MSLT could be used to detect SDB patients with and without EDS before the appearance of sleep onset.

Individual differences in physiologic measures are stable across repeated exposures to total sleep deprivation

Some individuals show severe cognitive impairment when sleep deprived, whereas others are able to maintain a high level of performance. Such differences are stable and trait‐like, but it is not clear whether these findings generalize to physiologic responses to sleep loss. Here, we analyzed individual differences in behavioral and physiologic measures in healthy ethnic‐Chinese male volunteers (n = 12; aged 22–30 years) who were kept awake for at least 26 h in a controlled laboratory environment on two separate occasions. Every 2 h, sustained attention performance was assessed using a 10‐min psychomotor vigilance task (PVT), and sleepiness was estimated objectively by determining percentage eyelid closure over the pupil over time (PERCLOS) and blink rate. Between‐subject differences in heart rate and its variability, and electroencephalogram (EEG) spectral power were also analyzed during each PVT. To assess stability of individual differences, intraclass correlation coefficients (ICC) were determined using variance components analysis. Consistent with previous work, individual differences in PVT performance were reproducible across study visits, as were baseline sleep measures prior to sleep deprivation. In addition, stable individual differences were observed during sleep deprivation for PERCLOS, blink rate, heart rate and its variability, and EEG spectral power in the alpha frequency band, even after adjusting for baseline differences in these measures (range, ICC = 0.67–0.91). These findings establish that changes in ocular, ECG, and EEG signals are highly reproducible across a night of sleep deprivation, hence raising the possibility that, similar to behavioral measures, physiologic responses to sleep loss are trait‐like.

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Individual differences in physiologic measures were examined in healthy ethnic‐Chinese males who underwent sleep deprivation in the laboratory on two different occasions. We found that between‐subject differences in ocular, electrocardiogram, and electroencephalogram measures were highly stable, even after adjusting for baseline individual differences in these measures. These results suggest that the brain responds predictably to the challenge of sleep deprivation and raise the possibility that physiologic responses to sleep loss are trait‐like.

Temporal dynamics of ocular indicators of sleepiness across sleep restriction

The current study characterized the temporal dynamics of ocular indicators of sleepiness during extended sleep restriction. Ten male participants (mean age ± SD = 23.3 ± 1.6 years) underwent 40 h of continuous wakefulness under constant routine (CR) conditions; they completed the Karolinska Sleepiness Scale (KSS) and a 10-min auditory psychomotor vigilance task (aPVT) hourly. Waking electroencephalography (EEG) and ocular measures were recorded continuously throughout the CR. Infrared-reflectance oculography was used to collect the ocular measures positive and negative amplitude-velocity ratio, mean blink duration, the percentage of eye closure, and a composite score of sleepiness levels (Johns Drowsiness Scale). All ocular measures, except blink duration, displayed homeostatic and circadian properties. Only circadian effects were detected in blink duration. Significant, phase-locked cross-correlations (p < 0.05) were detected between ocular measures and aPVT reaction time (RT), aPVT lapses, KSS, and EEG delta-theta (0.5-5.5 Hz), theta-alpha (5.0-9.0 Hz), and beta (13.0-20.0 Hz) activity. Receiver operating characteristic curve analysis demonstrated reasonable sensitivity and specificity of ocular measures in correctly classifying aPVT lapses above individual baseline thresholds (initial 16 h of wakefulness). Under conditions of sleep restriction, ocular indicators of sleepiness paralleled performance impairment and self-rated sleepiness levels, and demonstrated their potential to detect sleepiness-related attentional lapses. These findings, if reproduced in a larger sample, will have implications for the use of ocular-based sleepiness-warning systems in operational settings.

Heart rate variability for evaluating vigilant attention in partial chronic sleep restriction

STUDY OBJECTIVES

Examine the use of spectral heart rate variability (HRV) metrics in measuring sleepiness under chronic partial sleep restriction, and identify underlying relationships between HRV, Karolinska Sleepiness Scale ratings (KSS), and performance on the Psychomotor Vigilance Task (PVT).

DESIGN

Controlled laboratory study.

SETTING

Experimental laboratory of the Brain Work Research Centre of the Finnish Institute of Occupational Health, Helsinki, Finland.

PARTICIPANTS

Twenty-three healthy young males (mean age ± SD = 23.77 ± 2.29).

INTERVENTIONS

A sleep restriction group (N = 15) was subjected to chronic partial sleep restriction with 4 h sleep for 5 nights. A control group (N = 8) had 8 h sleep on all nights.

MEASUREMENTS AND RESULTS

Based on a search over all HRV frequency bands in the range [0.00, 0.40] Hz, the band [0.01, 0.08] Hz showed the highest correlation for HRV-PVT (0.60, 95% confidence interval [0.49, 0.69]) and HRV-KSS (0.33, 95% confidence interval [0.16, 0.46]) for the sleep restriction group; no correlation was found for the control group. We studied the fraction of variance in PVT explained by HRV and a 3-component alertness model, containing circadian and homeostatic processes coupled with sleep inertia, respectively. HRV alone explained 33% of PVT variance.

CONCLUSIONS

The findings suggest that HRV spectral power reflects vigilant attention in subjects exposed to partial chronic sleep restriction.

CITATION

Henelius A, Sallinen M, Huotilainen M, Müller K, Virkkala J, Puolamäki K. Heart rate variability for evaluating vigilant attention in partial chronic sleep restriction.

Sleepiness and Safety: Where Biology Needs Technology

Maintaining human alertness and behavioral capability under conditions of sleep loss and circadian misalignment requires fatigue management technologies due to: (1) dynamic nonlinear modulation of performance capability by the interaction of sleep homeostatic drive and circadian regulation; (2) large differences among people in neurobehavioral vulnerability to sleep loss; (3) error in subjective estimates of fatigue on performance; and (4) to inform people of the need for recovery sleep. Two promising areas of technology have emerged for managing fatigue risk in safety-sensitive occupations. The first involves preventing fatigue by optimizing work schedules using biomathematical models of performance changes associated with sleep homeostatic and circadian dynamics. Increasingly these mathematical models account for individual differences to achieve a more accurate estimate of the timing and magnitude of fatigue effects on individuals. The second area involves technologies for detecting transient fatigue from drowsiness. The Psychomotor Vigilance Test (PVT), which has been extensively validated to be sensitive to deficits in attention from sleep loss and circadian misalignment, is an example in this category. Two shorter-duration versions of the PVT recently have been developed for evaluating whether operators have sufficient behavioral alertness prior to or during work. Another example is online tracking the percent of slow eyelid closures (PERCLOS), which has been shown to reflect momentary fluctuations of vigilance. Technologies for predicting and detecting sleepiness/fatigue have the potential to predict and prevent operator errors and accidents in safety-sensitive occupations, as well as physiological and mental diseases due to inadequate sleep and circadian misalignment.

Effects of exposure to intermittent versus continuous red light on human circadian rhythms, melatonin suppression, and pupillary constriction

Exposure to light is a major determinant of sleep timing and hormonal rhythms. The role of retinal cones in regulating circadian physiology remains unclear, however, as most studies have used light exposures that also activate the photopigment melanopsin. Here, we tested the hypothesis that exposure to alternating red light and darkness can enhance circadian resetting responses in humans by repeatedly activating cone photoreceptors. In a between-subjects study, healthy volunteers (n = 24, 21–28 yr) lived individually in a laboratory for 6 consecutive days. Circadian rhythms of melatonin, cortisol, body temperature, and heart rate were assessed before and after exposure to 6 h of continuous red light (631 nm, 13 log photons cm⁻² s⁻¹), intermittent red light (1 min on/off), or bright white light (2,500 lux) near the onset of nocturnal melatonin secretion (n = 8 in each group). Melatonin suppression and pupillary constriction were also assessed during light exposure. We found that circadian resetting responses were similar for exposure to continuous versus intermittent red light (P = 0.69), with an average phase delay shift of almost an hour. Surprisingly, 2 subjects who were exposed to red light exhibited circadian responses similar in magnitude to those who were exposed to bright white light. Red light also elicited prolonged pupillary constriction, but did not suppress melatonin levels. These findings suggest that, for red light stimuli outside the range of sensitivity for melanopsin, cone photoreceptors can mediate circadian phase resetting of physiologic rhythms in some individuals. Our results also show that sensitivity thresholds differ across non-visual light responses, suggesting that cones may contribute differentially to circadian resetting, melatonin suppression, and the pupillary light reflex during exposure to continuous light.

Assessment of drowsiness based on ocular parameters detected by infrared reflectance oculography

STUDY OBJECTIVES

Numerous ocular parameters have been proposed as reliable physiological markers of drowsiness. A device that measures many of these parameters and then combines them into a single metric (the Johns Drowsiness Scale [JDS]) is being used commercially to assess drowsiness in professional drivers. Here, we examine how these parameters reflect changes in drowsiness, and how they relate to objective and subjective indices of the drowsy state in a controlled laboratory setting.

DESIGN

A within subject prospective study.

PARTICIPANTS

29 healthy adults (18 males; mean age 23.3 ± 4.6 years; range 18-34 years).

INTERVENTIONS

N/A.

MEASUREMENTS AND RESULTS

Over the course of a 30-h extended wake vigil under constant routine (CR) conditions, participants were monitored using infrared reflectance oculography (Optalert) and completed bi-hourly neurobehavioral tests, including the Karolinska Sleepiness Scale (KSS) and Psychomotor Vigilance Task (PVT). Ocular-defined increases in drowsiness were evident with extended time awake and during the biological night for all ocular parameters; JDS being the most sensitive marker of drowsiness induced by sleep regulatory processes (p < 0.0001). In addition, the associations between JDS in the preceding 10-min period and subsequent PVT lapses and KSS were stronger (AUC 0.74/0.80, respectively) than any other ocular metric, such that PVT lapses, mean response time (RT), and KSS increased in a dose-response manner as a function of prior JDS score (p < 0.0001).

CONCLUSIONS

Ocular parameters captured by infrared reflectance oculography detected fluctuations in drowsiness due to time awake and during the biological night. The JDS outcome was the strongest predictor of drowsiness among those tested, and showed a clear association to objective and subjective measures of drowsiness. Our findings indicate this real-time objective drowsiness monitoring system is an effective tool for monitoring changes in alertness and performance along the alert-drowsy continuum in a controlled laboratory setting.

Sleepiness and Safety: Where Biology Needs Technology

Maintaining human alertness and behavioral capability under conditions of sleep loss and circadian misalignment requires fatigue management technologies due to: (1) dynamic nonlinear modulation of performance capability by the interaction of sleep homeostatic drive and circadian regulation; (2) large differences among people in neurobehavioral vulnerability to sleep loss; (3) error in subjective estimates of fatigue on performance; and (4) to inform people of the need for recovery sleep. Two promising areas of technology have emerged for managing fatigue risk in safety-sensitive occupations. The first involves preventing fatigue by optimizing work schedules using biomathematical models of performance changes associated with sleep homeostatic and circadian dynamics. Increasingly these mathematical models account for individual differences to achieve a more accurate estimate of the timing and magnitude of fatigue effects on individuals. The second area involves technologies for detecting transient fatigue from drowsiness. The Psychomotor Vigilance Test (PVT), which has been extensively validated to be sensitive to deficits in attention from sleep loss and circadian misalignment, is an example in this category. Two shorter-duration versions of the PVT recently have been developed for evaluating whether operators have sufficient behavioral alertness prior to or during work. Another example is online tracking the percent of slow eyelid closures (PERCLOS), which has been shown to reflect momentary fluctuations of vigilance. Technologies for predicting and detecting sleepiness/fatigue have the potential to predict and prevent operator errors and accidents in safety-sensitive occupations, as well as physiological and mental diseases due to inadequate sleep and circadian misalignment.

Sustained attention performance during sleep deprivation associates with instability in behavior and physiologic measures at baseline

STUDY OBJECTIVES

To identify baseline behavioral and physiologic markers that associate with individual differences in sustained attention during sleep deprivation.

DESIGN

In a retrospective study, ocular, electrocardiogram, and electroencephalogram (EEG) measures were compared in subjects who were characterized as resilient (n = 15) or vulnerable (n = 15) to the effects of total sleep deprivation on sustained attention.

SETTING

Chronobiology and Sleep Laboratory, Duke-NUS Graduate Medical School Singapore.

PARTICIPANTS

Healthy volunteers aged 22-32 years from the general population.

INTERVENTIONS

Subjects were kept awake for at least 26 hours under constant environmental conditions. Every 2 hours, sustained attention was assessed using a 10-minute psychomotor vigilance task (PVT).

MEASUREMENTS AND RESULTS

During baseline sleep and recovery sleep, EEG slow wave activity was similar in resilient versus vulnerable subjects, suggesting that individual differences in vulnerability to sleep loss were not related to differences in homeostatic sleep regulation. Rather, irrespective of time elapsed since wake, subjects who were vulnerable to sleep deprivation exhibited slower and more variable PVT response times, lower and more variable heart rate, and higher and more variable EEG spectral power in the theta frequency band (6.0-7.5 Hz).

CONCLUSIONS

Performance decrements in sustained attention during sleep deprivation associate with instability in behavioral and physiologic measures at baseline. Small individual differences in sustained attention that are present at baseline are amplified during prolonged wakefulness, thus contributing to large between-subjects differences in performance and sleepiness.

Circadian adaptation to night shift work influences sleep, performance, mood and the autonomic modulation of the heart

Our aim was to investigate how circadian adaptation to night shift work affects psychomotor performance, sleep, subjective alertness and mood, melatonin levels, and heart rate variability (HRV). Fifteen healthy police officers on patrol working rotating shifts participated to a bright light intervention study with 2 participants studied under two conditions. The participants entered the laboratory for 48 h before and after a series of 7 consecutive night shifts in the field. The nighttime and daytime sleep periods were scheduled during the first and second laboratory visit, respectively. The subjects were considered "adapted" to night shifts if their peak salivary melatonin occurred during their daytime sleep period during the second visit. The sleep duration and quality were comparable between laboratory visits in the adapted group, whereas they were reduced during visit 2 in the non-adapted group. Reaction speed was higher at the end of the waking period during the second laboratory visit in the adapted compared to the non-adapted group. Sleep onset latency (SOL) and subjective mood levels were significantly reduced and the LF∶HF ratio during daytime sleep was significantly increased in the non-adapted group compared to the adapted group. Circadian adaptation to night shift work led to better performance, alertness and mood levels, longer daytime sleep, and lower sympathetic dominance during daytime sleep. These results suggest that the degree of circadian adaptation to night shift work is associated to different health indices. Longitudinal studies are required to investigate long-term clinical implications of circadian misalignment to atypical work schedules.

Parasympathetic nervous system activity and children's sleep

We examined indices of children's parasympathetic nervous system activity (PNS), including respiratory sinus arrhythmia during baseline (RSAB) and RSA reactivity (RSAR), to a laboratory challenge, and importantly the interaction between RSAB and RSAR as predictors of multiple parameters of children's sleep. Lower RSAR denotes increased vagal withdrawal (reductions in RSA between baseline and task) and higher RSAR represents decreased vagal withdrawal or augmentation (increases in RSA between baseline and task). A community sample of school-attending children (121 boys and 103 girls) participated [mean age = 10.41 years; standard deviation (SD) = 0.67]. Children's sleep parameters were examined through actigraphy for 7 consecutive nights. Findings demonstrate that RSAB and RSAR interact to predict multiple sleep quality parameters (activity, minutes awake after sleep onset and long wake episodes). The overall pattern of effects illustrates that children who exhibit more disrupted sleep (increased activity, more minutes awake after sleep onset and more frequent long wake episodes) are those with lower RSAB in conjunction with lower RSAR. This combination of low RSAB and low RSAR probably reflects increased autonomic nervous system arousal, which interferes with sleep. Results illustrate the importance of individual differences in physiological regulation indexed by interactions between PNS baseline activity and PNS reactivity for a better understanding of children's sleep quality.