Effects of total sleep deprivation on components of top-down attentional control using a flexible attentional control task

Sleep deprivation consistently decreases vigilant attention, which can lead to difficulty in performing a variety of cognitive tasks. However, sleep-deprived individuals may be able to compensate for degraded vigilant attention by means of top-down attentional control. We employed a novel task to measure the degree to which individuals overcome impairments in vigilant attention by using top-down attentional control, the Flexible Attentional Control Task (FACT). The FACT is a two-choice task that has trials with valid, invalid, and neutral cues, along with an unexpected switch in the probability of cue validity about halfway in the task. The task provides indices that isolate performance components reflecting vigilant attention and top-down attentional control. Twelve healthy young adults completed an in-laboratory study. After a baseline day, the subjects underwent 39 hours of total sleep deprivation (TSD), followed by a recovery day. The FACT was administered at 03:00, 11:00, and 19:00 during sleep deprivation (TSD condition) and at 11:00 and 19:00 after baseline sleep and at 11:00 after recovery sleep (rested condition). When rested, the subjects demonstrated both facilitation and interference effects on cued trials. While sleep deprived, the subjects showed vigilant attention deficits on neutral cue trials, and an impaired ability to reduce these deficits by using predictive contextual cues. Our results indicate that the FACT can dissociate vigilant attention from top-down attentional control. Furthermore, they show that during sleep deprivation, contextual cues help individuals to compensate partially for impairments in vigilant attention, but the effectiveness of top-down attentional control is diminished.

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

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

Shift work and overtime across a career in law enforcement: a 15-year study

PURPOSE –

To characterize changes in work hours across a career in law enforcement.

DESIGN/METHODOLOGY/APPROACH –

N = 113 police officers enrolled in the BCOPS cohort were studied. The police officers started their careers in law enforcement between 1994 and 2001 at a mid-sized, unionized police department in northwestern New York and continued to work at this police department for at least 15 years. Day-by-day work history records were obtained from the payroll department. Work hours, leave hours and other pay types were summarized for each calendar year across their first 15 years of employment. Linear mixed-effects models with a random intercept over subject were used to determine if there were significant changes in pay types over time.

FINDINGS –

A total of 1,617 individual-years of data were analyzed. As the police officers gained seniority at the department, they worked fewer hours and fewer night shifts. Total paid hours did not significantly change due to seniority-based increases in vacation time. Night shift work was increasingly in the form of overtime as officers gained seniority. Overtime was more prevalent at the beginning of a career and after a promotion from police officer to detective.

ORIGINALITY/VALUE –

Shiftwork and long work hours have negative effects on sleep and increase the likelihood of on-duty fatigue and performance impairment. The results suggest that there are different points within a career in law enforcement where issues surrounding shiftwork and long work hours may be more prevalent. This has important implications for predicting fatigue, developing effective countermeasures and measuring fatigue-related costs.

0179 Poor Sleep Quality Increases Odds of On-Duty Injuries in Policing

Introduction

Shiftwork is inevitable in law enforcement. Officers are scheduled around-the-clock to protect and serve communities. Many police departments are also understaffed; consequentially, officers’ work schedules often include long work hours. Shift work and long work hours can result in sleep loss, poor sleep quality, and fatigue. In turn, these factors can impair police officers’ operational performance. We investigated whether sleep loss and poor sleep quality increase odds of on-duty injuries or disciplinary actions in policing.

Methods

Officers (n=113) that started their careers as police officers at the Buffalo Police Department between 1994–2001 were studied. Work and injury data were obtained for each officer starting with their hire date and continuing day-by-day for 15-years. Between 2004–2009, officers reported any disciplinary actions in the prior two years and their sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) and Survey Screen for Apnea. Data were analyzed using logistic regression with logit link functions (PROC GLIMMIX, SAS 9.4). Covariates included sex, ethnicity, experience, shift type, workload, and secondary employment.

Results

Seventy-four percent of officers had poor sleep quality (PSQI global score ≥ 5). Officers with poorer sleep quality had greater odds of injury (OR=1.3 [95% CI: 1.0–1.5], p=0.03). Officers’ sleep duration was not a significant predictor of injuries (OR=1.0 [95% CI: 0.3–3.2], p=0.96). Officers with sleep disturbances (OR=3.5 [95% CI: 1.0–11.8], p<0.05) and/or using sleep medications (OR=15.7 [95% CI: 2.8–89.3], p<0.01) had higher odds of injury. None of the variables were significant predictors of disciplinary actions.

Conclusion

Poor sleep quality was prevalent among the officers. The natures of the injuries were likely multi-factorial and complex. Notwithstanding, poor sleep quality was associated with higher odds of on-duty injuries. The source of officers’ sleep disturbances (e.g. shift work, insomnia, and/or policing-related stresses) remains to be determined.

Support

CDC/NIOSH grant 1R01OH009640-01A1; NIJ grant 2005-FS-BX-0004

Generalizability of a biomathematical model of fatigue's sleep predictions

Introduction: Biomathematical models of fatigue (BMMF) predict fatigue during a work-rest schedule on the basis of sleep-wake histories. In the absence of actual sleep-wake histories, sleep-wake histories are predicted directly from work-rest schedules. The predicted sleep-wake histories are then used to predict fatigue. It remains to be determined whether workers organize their sleep similarly across operations and thus whether sleep predictions generalize.Methods: Officers (n = 173) enrolled in the Buffalo Cardio-Metabolic Occupational Police Stress study were studied. Officers' sleep-wake behaviors were measured using wrist-actigraphy and predicted using a BMMF (FAID Quantum) parameterized in aviation and rail. Sleepiness (i.e. Karolinska Sleepiness Scale (KSS) ratings) was predicted using actual and predicted sleep-wake data. Data were analyzed using sensitivity analyses.Results: During officers' 16.0 ± 1.9 days of study participation, they worked 8.6 ± 3.1 shifts and primarily worked day shifts and afternoon shifts. Across shifts, 7.0 h ± 1.9 h of actual sleep were obtained in the prior 24 h and associated peak KSS ratings were 5.7 ± 1.3. Across shifts, 7.2 h ± 1.1 h of sleep were predicted in the prior 24 h and associated peak KSS ratings were 5.5 ± 1.2. The minute-by-minute predicted and actual sleep-wake data demonstrated high sensitivity (80.4%). However, sleep was observed at all hours-of-the-day, but sleep was rarely predicted during the daytime hours.Discussion: The sleep-wake behaviors predicted by a BMMF parameterized in aviation and rail demonstrated high sensitivity with police officers' actual sleep-wake behaviors. Additional night shift data are needed to conclude whether BMMF sleep predictions generalize across operations.

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

Study Objectives

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

Methods

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

Results

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

Conclusions

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

Psychomotor Vigilance Impairment During Total Sleep Deprivation Is Exacerbated in Sleep-Onset Insomnia

PURPOSE

Individuals with primary insomnia frequently report cognitive impairment as a next-day consequence of disrupted sleep. Studies attempting to quantify daytime impairment objectively in individuals with insomnia have yielded mixed results, with evidence suggesting impairments in aspects of executive functioning but not psychomotor vigilance. It has been suggested that persons with insomnia may have latent performance deficits for which they would be able to compensate effectively under normal daytime circumstances - suggesting that any such deficits may be exposed through perturbation. In this context, we used a laboratory-based total sleep deprivation (TSD) paradigm to investigate psychomotor vigilance performance in individuals with chronic sleep-onset insomnia as compared to healthy normal controls.

PARTICIPANTS AND METHODS

Fourteen participants, seven individuals with chronic sleep-onset insomnia (ages 24-40y) and seven age-matched, healthy normal sleepers completed a highly controlled in-laboratory study involving 38 h of TSD. A 10 min and a 3 min version of the psychomotor vigilance test (PVT) were administered every 3 h during TSD.

RESULTS

In both the individuals with sleep-onset insomnia and the age-matched normal sleepers, lapses of attention and false starts on the PVT were relatively infrequent during the first 16 h of the TSD period, but increased significantly when wakefulness was extended beyond 16 h. However, the effects of TSD on PVT performance were considerably exacerbated in the sleep-onset insomnia group, which showed about twice as many lapses of attention, more than twice as many false starts, and approximately twice as big a time-on-task effect on the 10 min PVT as the age-matched normal sleepers group, with similar findings on the 3 min PVT.

CONCLUSION

These findings indicate that daytime impairment reported by individuals with sleep-onset insomnia has an objective performance component that is exposed during TSD. Thus, persons with sleep-onset insomnia could be at increased risk of performance impairment in settings that involve extended wakefulness. This underscores the importance of treating insomnia and suggests that laboratory sleep deprivation studies could serve to document the effectiveness of treatment approaches.

Cognitive performance and self-reported sleepiness are modulated by time-of-day during a mountain ultramarathon

Ninety-two runners completed the study during a 168 km mountain ultramarathon (MUM). Sleepiness, self-reported sleep duration, and cognitive performance were assessed the day before the race and up to eight checkpoints during the race. Sleepiness was assessed using the Karolinska Sleepiness Scale. Cognitive performance was also assessed using the Digital Symbol Substitution Task (DSST). Runner reported 23.40 ± 22.20 minutes of sleep (mean ± SD) during the race (race time: 29.38 to 46.20 hours). Sleepiness and cognitive performance decrements increased across this race, and this was modulated by time-of-day with higher sleepiness and greater performance decrements occurring during the early morning hours. Runners who slept on the course prior to testing had poorer cognitive performance, which may suggest that naps on the course were taken due to extreme exertion. This study provides evidence that cognitive performance deficits and sleepiness in MUM are sensitive to time into race and time-of-day.

Signal-to-Noise Ratio in PVT Performance as a Cognitive Measure of the Effect of Sleep Deprivation on the Fidelity of Information Processing

Study Objectives

There is a long-standing debate about the best way to characterize performance deficits on the psychomotor vigilance test (PVT), a widely used assay of cognitive impairment in human sleep deprivation studies. Here, we address this issue through the theoretical framework of the diffusion model and propose to express PVT performance in terms of signal-to-noise ratio (SNR).

Methods

From the equations of the diffusion model for one-choice, reaction-time tasks, we derived an expression for a novel SNR metric for PVT performance. We also showed that LSNR-a commonly used log-transformation of SNR-can be reasonably well approximated by a linear function of the mean response speed, LSNRapx. We computed SNR, LSNR, LSNRapx, and number of lapses for 1284 PVT sessions collected from 99 healthy young adults who participated in laboratory studies with 38 hr of total sleep deprivation.

Results

All four PVT metrics captured the effects of time awake and time of day on cognitive performance during sleep deprivation. The LSNR had the best psychometric properties, including high sensitivity, high stability, high degree of normality, absence of floor and ceiling effects, and no bias in the meaning of change scores related to absolute baseline performance.

Conclusions

The theoretical motivation of SNR and LSNR permits quantitative interpretation of PVT performance as an assay of the fidelity of information processing in cognition. Furthermore, with a conceptual and statistical meaning grounded in information theory and generalizable across scientific fields, LSNR in particular is a useful tool for systems-integrated fatigue risk management.

Normal sleep requires the astrocyte brain-type fatty acid binding protein FABP7

Sleep is found widely in the animal kingdom. Despite this, few conserved molecular pathways that govern sleep across phyla have been described. The mammalian brain-type fatty acid binding protein (Fabp7) is expressed in astrocytes, and its mRNA oscillates in tandem with the sleep-wake cycle. However, the role of FABP7 in regulating sleep remains poorly understood. We found that the missense mutation FABP7.T61M is associated with fragmented sleep in humans. This phenotype was recapitulated in mice and fruitflies bearing similar mutations: Fabp7-deficient mice and transgenic flies that express the FABP7.T61M missense mutation in astrocytes also show fragmented sleep. These results provide novel evidence for a distinct molecular pathway linking lipid-signaling cascades within astrocytes in sleep regulation among phylogenetically disparate species.

Naturalistic field study of the restart break in US commercial motor vehicle drivers: Truck driving, sleep, and fatigue

Commercial motor vehicle (CMV) drivers in the US may start a new duty cycle after taking a 34-h restart break. A restart break provides an opportunity for sleep recuperation to help prevent the build-up of fatigue across duty cycles. However, the effectiveness of a restart break may depend on its timing, and on how many nighttime opportunities for sleep it contains. For daytime drivers, a 34-h restart break automatically includes two nighttime periods. For nighttime drivers, who are arguably at increased risk of fatigue, a 34-h restart break contains only one nighttime period. To what extent this is relevant for fatigue depends in part on whether nighttime drivers revert back to a nighttime-oriented sleep schedule during the restart break. We conducted a naturalistic field study with 106 CMV drivers working their normal schedules and performing their normal duties. These drivers were studied during two duty cycles and during the intervening restart break. They provided a total of 1260days of data and drove a total of 414,937 miles during the study. Their duty logs were used to identify the periods when they were on duty and when they were driving and to determine their duty cycles and restart breaks. Sleep/wake patterns were measured continuously by means of wrist actigraphy. Fatigue was assessed three times per day by means of a brief psychomotor vigilance test (PVT-B) and a subjective sleepiness scale. Data from a truck-based lane tracking and data acquisition system were used to compute lane deviation (variability in lateral lane position). Statistical analyses focused on 24-h patterns of duty, driving, sleep, PVT-B performance, subjective sleepiness, and lane deviation. Duty cycles preceded by a restart break containing only one nighttime period (defined as 01:00-05:00) were compared with duty cycles preceded by a restart break containing more than one nighttime period. During duty cycles preceded by a restart break with only one nighttime period, drivers showed more nighttime-oriented duty and driving patterns and more daytime-oriented sleep patterns than during duty cycles preceded by a restart break with more than one nighttime period. During duty cycles preceded by a restart break with only one nighttime period, drivers also experienced more lapses of attention on the PVT-B and increased lane deviation at night, and they reported greater subjective sleepiness. Importantly, drivers exhibited a predominantly nighttime-oriented sleep schedule during the restart break, regardless of whether the restart break contained only one or more than one nighttime period. Consistent with findings in laboratory-based studies of the restart break, the results of this naturalistic field study indicate that having at least two nighttime periods in the restart break provides greater opportunity for sleep recuperation and helps to mitigate fatigue.

Validation of a portable, touch-screen psychomotor vigilance test

INTRODUCTION

The Psychomotor Vigilance Test (PVT) measures effects of fatigue from sleep loss and circadian misalignment on sustained vigilance performance. To promote PVT use in field environments, a 5-min PVT version has been implemented on a personal digital assistant (PDA) with a touch screen. The present laboratory study was conducted to validate this PVT against a standard 10-min laptop PVT across 38 h of total sleep deprivation (TSD).

METHODS

Following a baseline sleep night, subjects underwent 38 h of TSD, during which they performed the PVT every hour, alternating between the two test platforms. The study concluded with a night of recovery sleep.

RESULTS

The primary outcome was the number of PVT lapses (reaction times > 500 ms). Both PVT platforms showed significant effects for the number of lapses across TSD test times involving an increase with time awake modulated by circadian rhythm. Laptop PVT lapses across test times exhibited a large effect size (f2 = 0.36), whereas PDA PVT lapses exhibited a medium effect size (f2 = 0.17). The laptop PVT showed a significant effect for the number of false starts during TSD similar to the temporal profile of lapses, while the PDA PVT had false starts throughout the TSD period.

DISCUSSION

The 5-min PDA PVT provided performance testing functionality and results comparable to the 10-min laptop PVT. The number of PDA PVT lapses tracked fatigue similarly to the laptop PVT lapses, albeit with smaller average ranges and effect sizes.