High risk of near-crash driving events following night-shift work

Eye blink parameters to indicate drowsiness during naturalistic driving in participants with obstructive sleep apnea: A pilot study

OBJECTIVES

To determine whether continuous eye blink measures could identify drowsiness in patients with obstructive sleep apnea (OSA) during a week of naturalistic driving.

DESIGN

Observational study comparing OSA patients and healthy controls.

SETTING

Regular naturalistic driving across one week.

PARTICIPANTS

Fifteen untreated moderate to severe OSA patients and 15 age (± 5 years) and sex (female = 6) matched healthy controls.

MEASUREMENTS

Participants wore an eye blink drowsiness recording device during their regular driving for one week.

RESULTS

During regular driving, the duration of time with no ocular movements (quiescence), was elevated in the OSA group by 43% relative to the control group (mean [95% CI] 0.20[0.17, 0.25] vs 0.14[0.12, 0.18] secs, P = .011). During long drives only, the Johns Drowsiness Scale was also elevated and increased by 62% in the OSA group relative to the control group (1.05 [0.76, 1.33] vs 0.65 [0.36, 0.93], P = .0495). Across all drives, critical drowsiness events (defined by a Johns Drowsiness Scale score ≥2.6) were twice as frequent in the OSA group than the control group (rate ratio [95% CI] =1.93 [1.65, 2.25], P ≤ .001).

CONCLUSIONS

OSA patients were drowsier than healthy controls according to some of the continuous real time eye blink drowsiness measures. The findings of this pilot study suggest that there is potential for eye blink measures to be utilized to assess fitness to drive in OSA patients. Future work should assess larger samples, as well as the relationship of eye blink measures to conventional fitness to drive assessments and crash risk.

The impact of sleep loss on performance monitoring and error-monitoring: A systematic review and meta-analysis

Awareness of performance deficits and errors during sleep loss could be protective against the consequences of sleep deprivation, however, it is unclear whether sleep deprived individuals have insight into their performance. We conducted a systematic review and meta-analysis of the impact of sleep loss (sleep duration <6 h) on monitoring of performance and errors using Embase, MEDLINE, PsycINFO & Cochrane Central. We identified 28 studies, 11 of which were appropriate for meta-analysis. The systematic review indicated limited consensus regarding sleep loss impacts on performance monitoring, due to substantial differences in study methodology. However, participants typically demonstrated more conservative estimates of performance during sleep loss. Error-monitoring literature was more consistent, indicating an impairment in error-monitoring following sleep loss. Meta-analyses supported the findings of the systematic review. In terms of methodology, we found the performance monitoring literature is limited by an overreliance on correlational designs, which are likely confounded by response bias. The error-monitoring literature is limited by very few studies utilising behavioural measures to directly measure error-awareness. Future performance monitoring studies must employ methods which control for confounds such as bias, and error-monitoring studies must incorporate combined behavioural and ERP measures to better understand the impact of sleep loss on error-monitoring.

The impact of the wake maintenance zone on attentional capacity, physiological drowsiness, and subjective task demands during sleep deprivation

We aimed to investigate the impact of the Wake Maintenance Zone (WMZ) on measures of drowsiness, attention, and subjective performance under rested and sleep deprived conditions. We studied 23 healthy young adults (18 males; mean age = 25.41 ± 5.73 years) during 40 hr of total sleep deprivation under constant routine conditions. Participants completed assessments of physiological drowsiness (EEG-scored slow eye movements and microsleeps), sustained attention (PVT), and subjective task demands every two hours, and four-hourly ocular motor assessment of inhibitory control (inhibition of reflexive saccades on an anti-saccade task). Tests were analyzed relative to dim light melatonin onset (DLMO); the WMZ was defined as the 3 hr prior to DLMO, and the preceding 3 hr window was deemed the pre-WMZ. The WMZ did not mitigate the adverse impact of ~37 hr sleep deprivation on drowsiness, sustained attention, response inhibition, and self-rated concentration and difficulty, relative to rested WMZ performance (~13 hr of wakefulness). Compared to the pre-WMZ, though, the WMZ improved measures of sustained attention, and subjective concentration and task difficulty, during sleep deprivation. Cumulatively, these results expand on previous work by characterizing the beneficial effects of the WMZ on operationally-relevant indices of drowsiness, inhibitory attention control, and self-rated concentration and task difficulty relative to the pre-WMZ during sleep deprivation. These results may inform scheduling safety-critical tasks at more optimal circadian times to improve workplace performance and safety.

Association between shift work and cognitive performance on the Trail Making Test in emergency department health officers

OBJECTIVE

Shift work has been proposed to disturb alertness and decrease cognitive efficiency. However, studies so far have had varied findings. The aim of the present study was to compare cognitive function following shifts at different times of the day in an Australian ED context.

METHODS

A prospective, self-controlled observational study was conducted on medical and nursing staff at a tertiary referral centre and regional hospital ED. Participants were required to complete the Trail Making Test (TMT), a neurocognitive test consisting of two parts (TMT-A and TMT-B), at baseline (at the start of the day) and at the end of their shift (day, evening or night). Related samples Wilcoxon signed-rank tests were used to compare post-shift TMT performance to baseline in medical and nursing staff.

RESULTS

Over a 5-month period, 140 ED staff were recruited including 109 doctors and 31 nurses. After a night shift, medical staff (n = 85) and nursing staff (n = 29) took longer to complete the TMT-B by 3.4 s (P < 0.001) and 7.1 s (P = 0.01), respectively, compared to baseline. Post-evening shift, medical staff (n = 59) took longer to complete the TMT-A by 0.3 s (P = 0.02).

CONCLUSIONS

Night shift work was associated with a longer TMT time. This may indicate a decrease in cognitive performance, in particular, visual attention, processing speed, task switching and executive function and may implicate the quality of care for patients and worker safety.

Acoustic enhancement of slow wave sleep on consecutive nights improves alertness and attention in chronically short sleepers

INTRODUCTION

Chronic sleep restriction has been linked to occupational errors and motor vehicle crashes. Enhancing slow wave sleep may alleviate some of the cognitive deficits associated with chronic sleep restriction. However, the extent to which acoustic stimulation of slow wave activity (SWA) may improve alertness and attention is not well established, particularly with respect to consecutive nights of exposure.

METHODS

Twenty-five healthy adults (32.9 ± 8.2 years; 16 female) who self-restricted their sleep during workdays participated in a randomized, double-blind, cross-over study. Participants wore an automated acoustic stimulation device for two consecutive nights. Acoustic tones (50 ms long) were delivered on the up-phase of the slow wave first and then at constant 1-s inter-tone-intervals once N3 was identified (STIM), until an arousal or shift to another sleep stage occurred, or at inaudible decibels during equivalent stimulation periods (SHAM). Subjective alertness/fatigue (KSS, Samn-Perelli) was assessed across both days, and objective measures of alertness (MSLT) and attention (PVT) were assessed after two nights of stimulation.

RESULTS

After one night of acoustic stimulation, increased slow wave energy was observed in 68% of participants, with an average significant increase of 17.7% (p = 0.01), while Night 2 was associated with a 22.2% increase in SWA (p = 0.08). SWE was highly stable across the two nights of STIM (ICC 0.93, p < 0.001), and around half (56%) of participants were consistently classified as responders (11/25) or non-responders (3/25). Daytime testing showed that participants felt more alert and awake following each night of acoustic stimulation (p < 0.05), with improved objective attention across the day following two nights of acoustic stimulation.

DISCUSSION

Consecutive nights of acoustic stimulation enhanced SWA on both nights, and improved next day alertness and attention. Given large individual differences, we highlight the need to examine both the long-term effects of stimulation, and to identify inter-individual differences in acoustic stimulation response. Our findings suggest that the use of an acoustic device to enhance slow wave sleep may alleviate some of the deficits in alertness and attention typically associated with sleep restriction.

The impact of a meal, snack, or not eating during the night shift on simulated driving performance post-shift

Objective The commute home following a night shift is associated with an increased risk for accidents. This study investigated the relationship between food intake during the night shift and simulated driving performance post-shift. Methods Healthy non-shift working males (N=23) and females (N=16), aged 18-39 years (mean 24.5, standard deviation 5.0, years) participated in a seven-day laboratory study and underwent four simulated night shifts. Participants were randomly allocated to one of three conditions: meal at night (N=12; 7 males), snack at night (N=13; 7 males) or no eating at night (N=14; 9 males). During the night shift at 00:30 hours, participants either ate a large meal (meal at night condition), a snack (snack at night condition), or did not eat during the night shift (no eating at night condition). During the second simulated night shift, participants performed a 40-minute York driving simulation at 20:00, 22:30, 01:30, 04:00, and 07:30 hours (similar time to a commute from work). Results The effects of eating condition, drive time, and time-on-task, on driving performance were examined using mixed model analyses. Significant condition×time interactions were found, where at 07:30 hours, those in the meal at night condition displayed significant increases in time spent outside of the safe zone (percentage of time spent outside 10 km/hour of the speed limit and 0.8 meters of the lane center; P<0.05), and greater lane and speed variability (both P<0.01) compared to the snack and no eating conditions. There were no differences between the snack and no eating conditions. Conclusion Driver safety during the simulated commute home is greater following the night shift if a snack, rather than a meal, is consumed during the shift.

Neurophysiological adaptations to spaceflight and simulated microgravity

Changes in physiological functions after spaceflight and simulated spaceflight involve several mechanisms. Microgravity is one of them and it can be partially reproduced with models, such as head down bed rest (HDBR). Yet, only a few studies have investigated in detail the complexity of neurophysiological systems and their integration to maintain homeostasis. Central nervous system changes have been studied both in their structural and functional component with advanced techniques, such as functional magnetic resonance (fMRI), showing the main involvement of the cerebellum, cortical sensorimotor, and somatosensory areas, as well as vestibular-related pathways. Analysis of electroencephalography (EEG) led to contrasting results, mainly due to the different factors affecting brain activity. The study of corticospinal excitability may enable a deeper understanding of countermeasures' effect, since greater excitability has been shown being correlated with better preservation of functions. Less is known about somatosensory evoked potentials and peripheral nerve function, yet they may be involved in a homeostatic mechanism fundamental to thermoregulation. Extending the knowledge of such alterations during simulated microgravity may be useful not only for space exploration, but for its application in clinical conditions and for life on Earth, as well.

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.

The Association of Sleep Hygiene and Drowsiness with Adverse Driving Events in Emergency Medicine Residents

Introduction

Prior research shows that physicians in training are at risk for drowsy driving following their clinical duties, which may put them in danger of experiencing adverse driving events. This study explores the relationship between sleepiness, overall sleep hygiene, level of training, and adverse driving events following an overnight shift in emergency medicine (EM) residents.

Methods

Throughout the 2018–2019 academic year, 50 EM residents from postgraduate years 1–4 completed self-administered surveys regarding their sleepiness before and after their drive home following an overnight shift, any adverse driving events that occurred during their drive home, and their overall sleep hygiene.

Results

Fifty out of a possible 57 residents completed the survey for a response rate of 87.7%. Sleepiness was significantly associated with adverse driving events (beta = 0.31; P < .001). Residents with high sleepiness levels reported significantly more adverse driving events. Residents reported significantly higher sleepiness levels after completing their drive home (mean = 7.04, standard deviation [SD] = 1.41) compared to sleepiness levels before driving home (mean = 5.58, SD = 1.81). Residency training level was significantly associated with adverse driving events (beta = −0.59, P < .01). Senior residents reported significantly fewer adverse driving events compared to junior residents.

Conclusion

Emergency physicians in training are at risk for drowsy driving-related motor vehicle crashes following overnight work shifts. Trainees of all levels underestimated their true degree of sleepiness prior to initiating their drive home, while junior residents were at higher risk for adverse driving events.

Driving impairments and duration of distractions: Assessing crash risk by harnessing microscopic naturalistic driving data

Distracted and impaired driving is a key contributing factor in crashes, leading to about 35% of all transportation-related deaths in recent years. Along these lines, cognitive issues like inattentiveness can further increase the chances of crash involvement. Despite its prevalence and importance, little is known about how the duration of these distractions is associated with critical events, such as crashes or near-crashes. With new sensors and increasing computational resources, it is possible to monitor drivers, vehicle performance, and roadway features to extract useful information, e.g., eyes off the road, indicating distraction and inattention. Using high-resolution microscopic SHRP2 naturalistic driving data, this study conducts in-depth analysis of both impairments and distractions. The data has more than 2 million seconds of observations in 7394 baselines (no event), 1228 near-crashes, and 617 crashes. The event data was processed and linked with driver behavior and roadway factors. The intervals of distracted driving during the period of observation (15 seconds) were extracted; next, rigorous fixed and random parameter logistic regression models of crash/near-crash risk were estimated. The results reveal that alcohol and drug impairment is associated with a substantial increase in crash/near-crash event involvement of 34%, and the highest correlations with crash risk include duration of distraction through dialing on a cellphone, texting while driving, and reaching for an object. Using detailed pre-crash data from instrumented vehicles, the study contributes by quantifying crash risk vis-à-vis detailed driving impairment and information on secondary task involvement, and discusses the implications of the results.

An ensemble mixed effects model of sleep loss and performance

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

The relationship between alertness and spatial attention under simulated shiftwork

Higher and lower levels of alertness typically lead to a leftward and rightward bias in attention, respectively. This relationship between alertness and spatial attention potentially has major implications for health and safety. The current study examined alertness and spatial attention under simulated shiftworking conditions. Nineteen healthy right-handed participants (M = 24.6 ± 5.3 years, 11 males) completed a seven-day laboratory based simulated shiftwork study. Measures of alertness (Stanford Sleepiness Scale and Psychomotor Vigilance Task) and spatial attention (Landmark Task and Detection Task) were assessed across the protocol. Detection Task performance revealed slower reaction times and higher omissions of peripheral (compared to central) stimuli, with lowered alertness; suggesting narrowed visuospatial attention and a slight left-sided neglect. There were no associations between alertness and spatial bias on the Landmark Task. Our findings provide tentative evidence for a slight neglect of the left side and a narrowing of attention with lowered alertness. The possibility that one’s ability to sufficiently react to information in the periphery and the left-side may be compromised under conditions of lowered alertness highlights the need for future research to better understand the relationship between spatial attention and alertness under shiftworking conditions.

"It is really so exhausting": Exploring intensive care nurses' perceptions of 24-hour long shifts

AIM AND OBJECTIVES

To explore the perceptions and experiences of Cambodian ICU registered nurses regarding their working 24-hr shifts.

BACKGROUND

In Europe and the USA, nurses are moving to a 12-hr shift, and numerous studies have revealed the positive and negative effects of these. However, lesser known is the impact of 24-hr nursing shifts on care quality, and health and safety. In Cambodia, 100% of nurses are rostered for these in their shift patterns, but until this study no research had been conducted on such shifts.

DESIGN

A qualitative descriptive study.

METHOD

Three focus group discussions were conducted with 30 registered nurses in July 2019, ten in each group, from three intensive care units of three hospitals in Cambodia. Data saturation was obtained. Data were analysed using content analysis, and the COREQ was applied for reporting this study.

FINDINGS

The ICU nurses' perspectives revealed significant and unacceptable effects of working shifts of ~25-hr length, taking into account staff handover. Two major themes arose: It is so exhausting and Compromised hospital care. Alarmingly, participants worked on average 72 hr per week, were exhausted, and nursed between 6 and 10 critically ill patients per shift.

CONCLUSION

To our knowledge this is the first study on nurses working 24-hr shifts, revealing unacceptable, high risks for the health and safety of nurses and patients, with nursing activities left undone, and a lack of quality care.

RELEVANCE TO CLINICAL PRACTICE

Improving nurse and patient health and safety, and quality of care requires hospital leaders to work with government and nursing organisations to develop better shift strategies. Resources need to be provided so that: nurses can work a maximum 12-hr shifts; the ratio of nurses to patients is improved; and nurses can have decent break times. This has major implications, for not only practice, but also management, administration, budgets and education.

The experience of sleep deprivation for midwives practicing in the united states

INTRODUCTION

Midwives in the United States who work shifts longer than 12 h have higher rates of excessive daytime sleepiness than midwives who work shifts of 12 h or less. Increased levels of excessive daytime sleepiness can lead to negative life impacts and may increase the risk for accidents and professional burnout.

OBJECTIVE

To describe midwives' experiences related to sleep and sleep deprivation as a result of their work and call-shift schedules.

METHODS

A survey designed to explore the experience and impact of work on sleep and sleepiness among midwives in the United States was sent to members of the American College of Nurse-Midwives (N = 4358). The survey included an open-ended question about midwives' experiences related to sleep or sleep deprivation. This analysis of the qualitative data was conducted using qualitative description and qualitative content analysis by two of the authors.

RESULTS

There were a total of 753 midwife respondents (response rate = 17%); of those 268 responded to the qualitative question about sleep. Three main themes were identified: barriers and challenges contributing to sleep deprivation; negative consequences of sleep deprivation; and strategies that helped midwives cope with or reduce sleep deprivation.

DISCUSSION

Midwives reported suffering health and safety consequences as a result of insufficient sleep, including impacts to their personal health, clinical errors, and errors in driving after an extended period awake. Nurses, midwives, physicians, and administrators are encouraged to work together to develop strategies and policies to ameliorate the risks and impacts of sleep deprivation for all clinicians, including midwives.

An improved vehicle-pedestrian near-crash identification method with a roadside LiDAR sensor

PROBLEM

Potential conflicts between pedestrians and vehicles represent a challenge to pedestrian safety. Near-crash is used as a surrogate metric for pedestrian safety evaluations when historical vehicle-pedestrian crash data are not available. One challenge of using near-crash data for pedestrian safety evaluation is the identification of near-crash events.

METHOD

This paper introduces a novel method for pedestrian-vehicle near-crash identification that uses a roadside LiDAR sensor. The trajectory of each road user can be extracted from roadside LiDAR data via several data processing algorithms: background filtering, lane identification, object clustering, object classification, and object tracking. Three indicators, namely, the post encroachment time (PET), the proportion of the stopping distance (PSD), and the crash potential index (CPI) are applied for conflict risk classification.

RESULTS

The performance of the developed method was evaluated with field-collected data at four sites in Reno, Nevada, United States. The results of case studies demonstrate that pedestrian-vehicle near-crash events could be identified successfully via the proposed method. Practical applications: The proposed method is especially suitable for pedestrian-vehicle near-crash identification at individual sites. The extracted near-crash events can serve as supplementary material to naturalistic driving study (NDS) data for safety evaluation.

Objective Assessment of Fitness to Perform (FTOP) After Surgical Night Shifts in the Netherlands: An Observational Study Using the Validated FTOP Self-test in Daily Surgical Practice

BACKGROUND

Surgical skills and decision making are influenced by alertness, reaction time, eye-hand coordination, and concentration. Night shift might impair these functions but it is unclear to what extent. The aim of this study was to investigate whether a night shift routinely impairs the surgeon's fitness to perform and whether this reaches a critical limit as compared to relevant frames of reference.

METHODS

Consultants (n = 59) and residents (n = 103) conducted fitness to perform measurements at precall, postcall, and noncall moments. This validated self-test consists of an adaptive tracker that is able to objectively measure alertness, reaction time, concentration, and eye-hand coordination, and multiple visual analog scales to subjectively score alertness. Results are compared to sociolegal (ethanol) and professional (operative skills) frames of reference that refer to a decrease under the influence of 0.06% ethanol.

RESULTS

Residents spent 1.7 call hours asleep on average as compared to 5.4 for consultants. Subjective alertness decreased in residents after night shifts (-13, P < 0.001) but not in consultants (-1.2, P = NS). The overnight difference in tracker score was -1.17 (P < 0.001) for residents and 0.46 (P = NS) for surgeons. Postcall subjective alertness only correlated to objective alertness in consultants. For residents, hours slept on-call correlated to objective alertness. For consultants, subsequent night calls significantly correlated to objective alertness, with the third subsequent call related to performance below the reference.

CONCLUSIONS

Consultants remain fit to perform after night call, but subsequent calls may compromise clinical activities. This study provides insight and awareness of individual performance with clear frames of reference.

Assessing the validity of eyelid parameters to detect impairment due to benzodiazepines

OBJECTIVE

Benzodiazepines impair driving ability and psychomotor function. Eyelid parameters accurately reflect drowsiness; however, the effects of benzodiazepines on these measures have not been extensively studied. The aim of this study was to investigate the effect of benzodiazepines on eyelid parameters and evaluate their accuracy for detecting psychomotor impairment.

METHODS

Eyelid parameters were recorded during a psychomotor vigilance task (PVT) and driving simulation over 2 days, baseline, and after 20-mg oral temazepam. The utility of eyelid parameters for detecting PVT lapses was evaluated using receiver operating characteristic curves, and cut-off levels indicating impairment (≥1 and ≥2 PVT lapses per min) were identified. The accuracy of these cut-off levels for detecting driving simulator crashes was then examined.

RESULTS

PVT and driving simulator performance was significantly impaired following benzodiazepine administration (p < .05). Average eyelid closure duration (inter-event duration) was a reliable indicator of PVT lapses (area under the curve [AUC] of 0.87-0.90). The cut-off value of eyelid closure duration derived from PVT AUC was able to predict driving simulator crashes with moderately high sensitivity and specificity (76.23% and 75.00%).

CONCLUSIONS

Eyelid parameters were affected by benzodiazepines and accurately detected the psychomotor impairment. In particular, eyelid closure duration is a promising real-time indicator of benzodiazepine impairment.

Risk Assessment of Commercial dangerous -goods truck drivers using geo-location data: A case study in China

The primary objective of this study is to understand the relationship between driving risk of commercial dangerous-goods truck (CDT) and exposure factors and find a way to evaluate the risk of specific transportation environment, such as specific transportation route. Due to increasing transportation demand and potential threat to public, commercial dangerous goods transportation (CDGT) has drawn attention from decision makers and researchers within governmental and non-governmental safety organization. However, there are few studies focusing on driving risk assessment of commercial dangerous-goods truck by environmental factors. In this paper we employ survival analysis methods to analyze the impact of risk exposure factors on non-accident mileage of commercial dangerous-good truck and assess risk level of specific driving environment. Using raw location data from six transportation companies in China, we derive a set of 17 risk exposure factors that we use for model parameters estimation. The survival model and hazard model were estimated using the Weibull distribution as the baseline distribution. The results show that four factors - weather, traffic flow, travel time and average velocity have a significant impact on the non-accident mileage of driver in this company, and the assessment results of survival function and hazard function are robust to the different levels of testing data. The employment time has some effect on the results but does not result in a significant difference in most cases, and the task stability has little impact on the results. The findings of this study should be useful for decision makers and transportation companies to better risk assessment of CDT.

A pre-drive ocular assessment predicts alertness and driving impairment: A naturalistic driving study in shift workers

Sleepiness is a major contributor to motor vehicle crashes and shift workers are particularly vulnerable. There is currently no validated objective field-based measure of sleep-related impairment prior to driving. Ocular parameters are promising markers of continuous driver alertness in laboratory and track studies, however their ability to determine fitness-to-drive in naturalistic driving is unknown. This study assessed the efficacy of a pre-drive ocular assessment for predicting sleep-related impairment in naturalistic driving, in rotating shift workers. Fifteen healthcare workers drove an instrumented vehicle for 2 weeks, while working a combination of day, evening and night shifts. The vehicle monitored lane departures and behavioural microsleeps (blinks >500 ms) during the drive. Immediately prior to driving, ocular parameters were assessed with a 4-min test. Lane departures and behavioural microsleeps occurred on 17.5 % and 10 % of drives that had pre-drive assessments, respectively. Pre-drive blink duration significantly predicted behavioural microsleeps and showed promise for predicting lane departures (AUC = 0.79 and 0.74). Pre-drive percentage of time with eyes closed had high accuracy for predicting lane departures and behavioural microsleeps (AUC = 0.73 and 0.96), although was not statistically significant. Pre-drive psychomotor vigilance task variables were not statistically significant predictors of lane departures. Self-reported sleep-related and hazardous driving events were significantly predicted by mean blink duration (AUC = 0.65 and 0.69). Measurement of ocular parameters pre-drive predict drowsy driving during naturalistic driving, demonstrating potential for fitness-to-drive assessment in operational environments.

Scheduled afternoon-evening sleep leads to better night shift performance in older adults

OBJECTIVES

This study investigated whether an intervention designed to reduce homeostatic sleep pressure would improve night shift performance and alertness in older adults.

METHODS

Non-shift workers aged 57.9±4.6 (mean±SD) worked four day (07:00-15:00) and four night shifts (23:00-07:00). Two intervention groups were instructed to remain awake until ~13:00 after each night shift: the sleep timing group (ST; n=9) was instructed to spend 8 hours in bed attempting sleep, and the sleep ad-lib group (n=9) was given no further sleep instructions. A control group (n=9) from our previous study was not given any sleep instructions. Hourly Karolinska Sleepiness Scales and Psychomotor Vigilance Tasks assessed subjective sleepiness and performance.

RESULTS

The ST group maintained their day shift sleep durations on night shifts, whereas the control group slept less. The ST group were able to maintain stable performance and alertness across the initial part of the night shift, while the control group's alertness and performance declined across the entire night. Wake duration before a night shift negatively impacted sustained attention and self-reported sleepiness but not reaction time, whereas sleep duration before a night shift affected reaction time and ability to sustain attention but not self-reported sleepiness.

CONCLUSIONS

A behavioural change under the control of the individual worker, spending 8 hours in bed and waking close to the start of the night shift, allowed participants to acquire more sleep and improved performance on the night shift in older adults. Both sleep duration and timing are important factors for night shift performance and self-reported sleepiness.

Insufficient sleep and fitness to drive in shift workers: A systematic literature review

BACKGROUND

Insufficient sleep, <6.5 h per night, majorly affects shift workers, placing them at higher risk for motor vehicle crash related injury or fatality. While systematic reviews (SLRs) examine the effects of insufficient sleep and driving, to date, no SLR focuses on driver fitness or performance in shift workers.

OBJECTIVES

Determine the class of evidence (Class I-highest to Class IV-lowest), and level of confidence (Level A-high, to Level U-insufficient) in the determinants of driver fitness and performance in shift workers. Next, consider evidence-based recommendations for clinical practice, research, and policy.

METHODS

A protocol was registered on PROSPERO (#CRD42018052905) using an established SLR methodology: a comprehensive electronic database search, study selection, data extraction, critical appraisal, analysis, and interpretation using published guidelines.

RESULTS

Searches identified 1226 unique records with 11(2 on-road, 9 simulator) meeting final inclusion criteria. Class III to IV evidence identified that exposure to overnight shift work possibly predicts (Level C confidence) drivers at risk for adverse on-road outcomes and likely predicts (Level B) drivers at risk for adverse driving simulator outcomes. Higher ratings of subjective sleepiness and extended time driving possibly predict (Level C) drivers at risk for adverse driving simulator outcomes.

CONCLUSIONS

This study demonstrates a low to moderate level of confidence in the determinants of driving in shift workers. A critical need exists for gold-standard on-road assessments integrating complex driving environments representative of real-world demands, targeting tactical and strategic outcomes in a broad spectrum of shift workers.

Acute moderate-intensity exercise improves 24-h sleep deprivation-induced cognitive decline and cerebral oxygenation: A near-infrared spectroscopy study

We evaluated the effects of moderate-intensity exercise in improving the decline in cognitive performance induced by a 24-h period of acute sleep deprivation (SD). We hypothesized that the positive effect of exercise is mediated by increased oxygenation (measured using near-infrared spectroscopy) of the dorsolateral prefrontal cortex (DLPFC). Cognitive performance was measured using the reaction time and interference scores of the Stroop colour and word test, in 12 healthy adults (eight males, 21.1 ± 0.3 years-old), at pre- and post-exercise. Cognitive scores were compared under two conditions: rested wakefulness (RW) and 24-h SD. The exercise consisted of 20-min of ergometer cycling at an intensity of 60 % VO_2peak. Oxygenation to the DLPFC increased, at 12 min after exercise onset, compared to the baseline and was maintained until the end of the exercise in both RW and SD conditions (P < 0.01). The change in RT correlated with sleepiness (P < 0.05), with no correlation for the interference score and oxygenation. Taken together, moderate-intensity exercise reverses SD-induced cognitive decline.

Microsleep is associated with brain activity patterns unperturbed by auditory inputs

Microsleeps are brief episodes of arousal level decrease manifested through behavioral signs. Brain activity during microsleep in the presence of external stimulus remains poorly understood. In this study, we sought to understand neural responses to auditory stimulation during microsleep. We gave participants the simple task of listening to audios of different pitches and amplitude modulation frequencies during early afternoon functional MRI scans. We found the following: 1) microsleep was associated with cortical activations in broad motor and sensory regions and deactivations in thalamus, irrespective of auditory stimulation; 2) high and low pitch audios elicited different activity patterns in the auditory cortex during awake but not microsleep state; and 3) during microsleep, spatial activity patterns in broad brain regions were similar regardless of the presence or types of auditory stimulus (i.e., stimulus invariant). These findings show that the brain is highly active during microsleep but the activity patterns across broad regions are unperturbed by auditory inputs.NEW & NOTEWORTHY During deep drowsy states, auditory inputs could induce activations in the auditory cortex, but the activation patterns lose differentiation to high/low pitch stimuli. Instead of random activations, activity patterns across the brain during microsleep appear to be structured and may reflect underlying neurophysiological processes that remain unclear.

A Review on Measuring Affect with Practical Sensors to Monitor Driver Behavior

Using sensors to monitor signals produced by drivers is a way to help better understand how emotions contribute to unsafe driving habits. The need for intuitive machines that can interpret intentional and unintentional signals is imperative for our modern world. However, in complex human–machine work environments, many sensors will not work due to compatibility issues, noise, or practical constraints. This review focuses on practical sensors that have the potential to provide reliable monitoring and meaningful feedback to vehicle operators—such as drivers, train operators, pilots, astronauts—as well as being feasible for implementation and integration with existing work infrastructure. Such an affect-sensitive intelligent vehicle might sound an alarm if signals indicate the driver has become angry or stressed, take control of the vehicle if needed, and collaborate with other vehicles to build a stress map that improves roadway safety. Toward such vehicles, this paper provides a review of emerging sensor technologies for driver monitoring. In our research, we look at sensors used in affect detection. This insight is especially helpful for anyone challenged with accurately understanding affective information, like the autistic population. This paper also includes material on sensors and feedback for drivers from populations that may have special needs.

Vehicle and Highway Adaptations to Compensate for Sleepy Drivers

Sleepiness remains a major contributor to road crashes. Driver monitoring systems identify early signs of sleepiness and alert drivers, using real-time analysis of eyelid movements, EEG activity, and steering control. Other vehicle adaptations warn drivers of lane departures or collision hazards, with higher vehicle automation actively taking over vehicle control to prevent run off the road incidents and institute emergency braking. Similarly, road adaptations warn drivers (rumble strips) or mitigate crash severity (barriers). Infrastructure to encourage drivers to use countermeasures, such as rest stops for napping, is also important. The effectiveness of adaptations varies for different road users.

Sleep Restriction, Sleep Hygiene, and Driving Safety: The Importance of Situational Sleepiness

Sleep-related accidents are a frequent cause of death and injury in the world. Poor sleep hygiene is responsible for sleep deprivation, which is clearly associated with an increased risk of accidents. Evidence shows that self-reported sleepiness at the wheel and reporting of inappropriate line-crossings are strong predictors of accident risk. Although the Epworth sleepiness scale is widely used in clinical practice, it is not the best to evaluate driving risks. Simple questions on the occurrence of near misses and sleepiness at the wheel should be asked systematically to address the issue of fitness to drive.

Eye-Blink Parameters Detect On-Road Track-Driving Impairment Following Severe Sleep Deprivation

STUDY OBJECTIVES

Drowsiness leads to 20% of fatal road crashes, while inability to assess drowsiness has hampered drowsiness interventions. This study examined the accuracy of eye-blink parameters for detecting drowsiness related driving impairment in real time.

METHODS

Twelve participants undertook two sessions of 2-hour track-driving in an instrumented vehicle following a normal night's sleep or 32 to 34 hours of extended wake in a randomized crossover design. Eye-blink parameters and lane excursion events were monitored continuously.

RESULTS

Sleep deprivation increased the rates of out-of-lane driving events and early drive terminations. Episodes of prolonged eyelid closures, blink duration, the ratio of amplitude to velocity of eyelid closure, and John's Drowsiness Score (JDS, a composite score) were also increased following sleep deprivation. A time-on-task (drive duration) effect was evident for out-of-lane events rate and most eye-blink parameters after sleep deprivation. The JDS demonstrated the strongest association with the odds of out-of-lane events in the same minute, whereas measures of blink duration and prolonged eye closure were stronger indicators of risk for out-of-lane events over longer periods of 5 minutes and 15 minutes, respectively. Eye-blink parameters also achieved moderate accuracies (specificities from 70.12% to 84.15% at a sensitivity of 50%) for detecting out-of-lane events in the same minute, with stronger associations over longer timeframes of 5 minutes to 15 minutes.

CONCLUSIONS

Eyelid closure parameters are useful tools for monitoring and predicting drowsiness-related driving impairment (out-of-lane events) that could be utilized for monitoring drowsiness and assessing the efficacy of drowsiness interventions.

CLINICAL TRIAL REGISTRATION

This study is registered with the Australian New Zealand Clinical Trial Registry (ANCTR), http://www.anzctr.org.au/TrialSearch.aspx ACTRN12612000102875.

CITATION

Shekari Soleimanloo S, Wilkinson VE, Cori JM,Westlake J, Stevens B, Downey LA, Shiferaw BA, Rajaratnam SMW, Howard ME. Eye-blink parameters detect on-road track-driving impairment following severe sleep deprivation. J Clin Sleep Med. 2019;15(9):1271-1284.

Sleep, Sleep Disorders, and Sexual Dysfunction

Good sleep is necessary for good health. Sleep health is increasingly recognized as important for physical and mental health by both the medical profession and the general public, and there is great interest in how to avoid and treat sleep disorders and problems. Recent research indicates that insufficient sleep, disrupted sleep, and sleep disorders affect many aspects of human health including sexual function. In fact, patients with urological disorders or erectile dysfunction (ED) may have a sleep disorder that contributes to their urological or sexual dysfunction. Obstructive sleep apnea, insomnia, shift work disorder, and restless legs syndrome are all common sleep disorders and are associated with ED and/or other urological disorders. Therefore, careful attention should be paid to the diagnosis and treatment of concomitant sleep disorders in patients with sexual dysfunction. In this review, we provide an overview of what sleep is and how it is assessed in the clinic or laboratory; our current understanding of the functions of sleep and sleep health; a description of common sleep disorders, as well as how they are diagnosed and treated; and how sleep and its disorders are associated with male sexual dysfunction. Sleep is considered to be a 'third pillar of health', along with diet and exercise. With an understanding of common sleep disorders and how they can impact male sexual function, the urologist can ensure that sleep disorders are considered as a contributor to sexual dysfunction in their patients in order to provide them with the optimal treatment for overall health.

Distinct pattern of oculomotor impairment associated with acute sleep loss and circadian misalignment

Key points

Inadequate sleep and irregular work schedules have not only adverse consequences for individual health and well‐being, but also enormous economic and safety implications for society as a whole.

This study demonstrates that visual motion processing and coordinated eye movements are significantly impaired when performed after sleep loss and during the biological night, and thus may be contributing to human error and accidents.

Because affected individuals are often unaware of their sensorimotor and cognitive deficits, there is a critical need for non‐invasive, objective indicators of mild, yet potentially unsafe, impairment due to disrupted sleep or biological rhythms.

Our findings show that a set of eye‐movement measures can be used to provide sensitive and reliable indicators of such mild neural impairments.

Abstract

Sleep loss and circadian misalignment have long been known to impair human cognitive and motor performance with significant societal and health consequences. It is well known that human reaction time to a visual cue is impaired following sleep loss and circadian misalignment, but it has remained unclear how more complex visuomotor control behaviour is altered under these conditions. In this study, we measured 14 parameters of the voluntary ocular tracking response of 12 human participants (six females) to systematically examine the effects of sleep loss and circadian misalignment using a constant routine 24‐h acute sleep‐deprivation paradigm. The combination of state‐of‐the‐art oculometric and sleep‐research methodologies allowed us to document, for the first time, large changes in many components of pursuit, saccades and visual motion processing as a function of time awake and circadian phase. Further, we observed a pattern of impairment across our set of oculometric measures that is qualitatively different from that observed previously with other mild neural impairments. We conclude that dynamic vision and visuomotor control exhibit a distinct pattern of impairment linked with time awake and circadian phase. Therefore, a sufficiently broad set of oculometric measures could provide a sensitive and specific behavioural biomarker of acute sleep loss and circadian misalignment. We foresee potential applications of such oculometric biomarkers assisting in the assessment of readiness‐to‐perform higher risk tasks and in the characterization of sub‐clinical neural impairment in the face of a multiplicity of potential risk factors, including disrupted sleep and circadian rhythms.

Inadequate sleep and irregular work schedules have not only adverse consequences for individual health and well‐being, but also enormous economic and safety implications for society as a whole.

This study demonstrates that visual motion processing and coordinated eye movements are significantly impaired when performed after sleep loss and during the biological night, and thus may be contributing to human error and accidents.

Because affected individuals are often unaware of their sensorimotor and cognitive deficits, there is a critical need for non‐invasive, objective indicators of mild, yet potentially unsafe, impairment due to disrupted sleep or biological rhythms.

Our findings show that a set of eye‐movement measures can be used to provide sensitive and reliable indicators of such mild neural impairments.

Driving Risk Classification Methodology for Intelligent Drive in Real Traffic Event

To solve the problem that existing driving data cannot correlate to the large number of vehicles in terms of driving risks, is the functionality of intelligent driving algorithm should be improved. This paper deeply explores driving data to build a link between massive driving data and a large number of sample vehicles for driving risk analysis. It sorted out certain driving behavior parameters in the driving data, and extracted some parameters closely related to the driving risk; it further utilized the principal component analysis and factor analysis in spatio-temporal data to integrate certain extracted parameters into factors that are clearly related to the specific driving risks; then, it selected factor scores of driving behaviors as indexes for hierarchical clustering, and obtained multi-level clustering results of the driving risks of corresponding vehicles; in the end, it interpreted the clustering results of the vehicle driving risks. According to the results, it is found that cluster for different risks proposed in this paper for driving behaviors is effective in the hierarchical cluster for typical driving behaviors and it also offers a solution for risk analyses between driving data and large sample vehicles. The results provide the basis for training on safe driving for the key vehicles, and the improvement of advanced driver assistance system, which shows a wide application prospect in the field of intelligent drive.

Commuting accidents of Spanish professional drivers: when occupational risk exceeds the workplace

Background. Work traffic accidents are an issue both in Spain and all over the world, and specific evidence on commuting accidents is scarce. Even though both industrial safety and welfare have been improved during the last decades, the commuting accidents rate is growing worldwide. Purpose. The aim of this study was to examine and describe the characteristics of commuting traffic crashes of Spanish professional drivers. Materials and methods. For this cross-sectional study, commuting accidents suffered by drivers during the last 12 years were analyzed. Crossed and heatmap-based analyses were performed in order to establish patterns and driver-based differences among commuting crashes. Results. Commuting crashes' features were found to be associated with demographic and job-related variables of professional drivers. Drivers' gender, time slots (peak/off-peak hours) and the specific hour of the event explained different trends in accident severity and characteristics. Conclusions. The results of this study suggest that commuting accidents involving professional drivers differ in demographic and situational issues from general and on-duty professional drivers' traffic crashes. Also, since in Spain commuting crashes are occupational accidents, more numerous and better actions should be taken in this regard, especially considering the association of professional drivers' accidents with fatigue and shift-working.

Prediction of drowsiness events in night shift workers during morning driving

The morning commute home is an especially vulnerable time for workers engaged in night shift work due to the heightened risk of experiencing drowsy driving. One strategy to manage this risk is to monitor the driver's state in real time using an in vehicle monitoring system and to alert drivers when they are becoming sleepy. The primary objective of this study is to build and evaluate predictive models for drowsiness events occurring in morning drives using a variety of physiological and performance data gathered under a real driving scenario. We used data collected from 16 night shift workers who drove an instrumented vehicle for approximately two hours on a test track on two occasions: after a night shift and after a night of rest. Drowsiness was defined by two outcome events: performance degradation (Lane-Crossing models) and electroencephalogram (EEG) characterized sleep episodes (Microsleep Models). For each outcome, we assessed the accuracy of sets of predictors, including or not including a driver factor, eyelid measures, and driving performance measures. We also compared the predictions using different time intervals relative to the events (e.g., 1-min prior to the event through 10-min prior). By examining the Area Under the receiver operating characteristic Curve (AUC), accuracy, sensitivity, and specificity of the predictive models, the results showed that the inclusion of an individual driver factor improved AUC and prediction accuracy for both outcomes. Eyelid measures improved the prediction for the Lane-Crossing models, but not for Microsleep models. Prediction performance was not changed by adding driving performance predictors or by increasing the time to the event for either outcome. The best models for both measures of drowsiness were those considering driver individual differences and eyelid measures, suggesting that these indicators should be strongly considered when predicting drowsiness events. The results of this paper can benefit the development of real-time drowsiness detection and help to manage drowsiness to avoid related motor-vehicle crashes and loss.

Performance and sleepiness in nurses working 12-h day shifts or night shifts in a community hospital

Hospitals are around-the-clock operations and nurses are required to care for patients night and day. The nursing shortage and desire for a more balanced work-to-home life has popularized 12-h shifts for nurses. The present study investigated sleep/wake cycles and fatigue levels in 22 nurses working 12-h shifts, comparing day versus night shifts. Nurses (11day shift and 11 night shift) were recruited from a suburban acute-care medical center. Participants wore a wrist activity monitor and kept a diary to track their sleep/wake cycles for 2 weeks. They also completed a fatigue test battery, which included the Psychomotor Vigilance Test (PVT) and the Karolinska Sleepiness Scale (KSS), at the beginning, middle and end of 4 duty shifts. Daily sleep duration was 7.1h on average. No overall difference in mean daily sleep duration was found between nurses working day shifts versus night shifts. Objective performance on the PVT remained relatively good and stable at the start, middle, and end of duty shifts in day shift workers, but gradually degraded across duty time in night shift workers. Compared to day shift workers, night shift workers also exhibited more performance variability among measurement days and between participants at each testing time point. The same pattern was observed for subjective sleepiness on the KSS. However, congruence between objective and subjective measures of fatigue was poor. Our findings suggest a need for organizations to evaluate practices and policies to mitigate the inevitable fatigue that occurs during long night shifts, in order to improve patient and healthcare worker safety. Examination of alternative shift lengths or sanctioned workplace napping may be strategies to consider.

A review of current approaches for evaluating impaired performance in around-the-clock medical professionals

The need for data to study the relationship between fatigued healthcare professionals and performance outcomes is evident, however, it is unclear which methodology is most appropriate to provide these insights. To address this issue, we performed a systematic review of relevant articles by searching the MEDLINE, EMBASE, Cochrane, Web of Science, and CINAHL databases. The literature search identified 2960 unique references, of which 82 were identified eligible. The impact on performance was studied on clinical outcomes, medical simulation, neurocognitive performance, sleep quantification and subjective assessment. In general results on performance are conflicting; impairment, no effect, and improvement were found. This review outlines the various methods currently available for assessing fatigue-impaired performance. The contrasting outcomes can be attributed to three main factors: differences in the operationalisation of fatigue, incomplete control data, and the wide variety in the methods used. We recommend the implementation of a clinically applicable tool that can provide uniform data. Until these data become available, caution should be used when developing regulations that can have implications for physicians, education, manpower planning, and ‒ ultimately ‒ patient care.

The psychomotor vigilance task: Role in the diagnosis of hepatic encephalopathy and relationship with driving ability

BACKGROUND & AIMS

Hepatic encephalopathy (HE) is a syndrome of decreased vigilance and has been associated with impaired driving ability. The aim of this study was to evaluate the psychomotor vigilance task (PVT), which is used to assess both vigilance and driving ability, in a group of patients with cirrhosis and varying degrees of HE.

METHODS

A total of 145 patients (120 males, 59 ± 10 years, model for end-stage liver disease [MELD] score 13 ± 5) underwent the PVT; a subgroup of 117 completed a driving questionnaire and a subgroup of 106 underwent the psychometric hepatic encephalopathy score (PHES) and an electroencephalogram (EEG), based on which, plus a clinical evaluation, they were classed as being unimpaired (n = 51), or as having minimal (n = 35), or mild overt HE (n = 20). All patients were followed up for an average of 13 ± 5 months in relation to the occurrence of accidents and/or traffic offences, HE-related hospitalisations and death. Sixty-six healthy volunteers evenly distributed by sex, age and education served as a reference cohort for the PVT.

RESULTS

Patients showed worse PVT performance compared with healthy volunteers, and PVT indices significantly correlated with MELD, ammonia levels, PHES and the EEG results. Significant associations were observed between neuropsychiatric performance/PVT indices and licence/driving status. PVT, PHES and EEG results all predicted HE-related hospitalisations and/or death over the follow-up period; none predicted accidents or traffic offences. However, individuals with the slowest reaction times and most lapses on the PVT were often not driving despite having a licence. When patients who had stopped driving for HE-related reasons (n = 6) were modelled as having an accident or fine over the subsequent 6 and 12 months, PVT was a predictor of accidents and traffic offences, even after correction for MELD and age.

CONCLUSIONS

The PVT is worthy of further study for the purposes of both HE and driving ability assessment.

LAY SUMMARY

Hepatic encephalopathy (HE) is a complication of advanced liver disease that can manifest as excessive sleepiness. Some patients with HE have been shown to have difficulty driving. Herein, we used a test called the Psychomotor Vigilance Task (PVT), which measures sleepiness and can also be used to assess driving competence. We showed that PVT performance is fairly stable in healthy individuals. We also showed that PVT performance parallels performance in tests which are commonly used in cirrhotic patients to measure HE. We suggest that this test is helpful in quantifying HE and identifying dangerous drivers among patients with cirrhosis.

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

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

Client-Centered Mobile Health Care Applications: Using the Mobile Application Rating Scale Instrument for Evidence-Based Evaluation

The use of mobile devices and applications (apps) to monitor or assist in health behaviors is rapidly expanding in many areas of society. Clinicians desire evidence-based app recommendations for their clients to increase self-care and wellness management in such areas as mindfulness, weight loss and activity tracking, glycemic control, and consumer medication information. Given the constant influx of new apps into the major app repositories, clinicians need to be able to ensure the quality of information and interaction that occurs within the mobile health (mHealth) marketplace. The Mobile Application Rating Scale (MARS) and the user version of the scale are valid and reliable instruments used to examine the engagement, functionality, aesthetics, and quality of information in mHealth apps. MARS-rated apps can be readily available resources for busy clinicians to make app suggestions to assist clients on a variety of topics that promote improved outcomes. This article reviews the MARS instrument and utilization of the instrument by clinicians and summarizes several primary care and wellness apps that have been evaluated using this tool.

The Impact of Shift Work on Sleep, Alertness and Performance in Healthcare Workers

Shift work is associated with impaired alertness and performance due to sleep loss and circadian misalignment. This study examined sleep between shift types (day, evening, night), and alertness and performance during day and night shifts in 52 intensive care workers. Sleep and wake duration between shifts were evaluated using wrist actigraphs and diaries. Subjective sleepiness (Karolinska Sleepiness Scale, KSS) and Psychomotor Vigilance Test (PVT) performance were examined during day shift, and on the first and subsequent night shifts (3^rd, 4^th or 5^th). Circadian phase was assessed using urinary 6-sulphatoxymelatonin rhythms. Sleep was most restricted between consecutive night shifts (5.74 ± 1.30 h), consecutive day shifts (5.83 ± 0.92 h) and between evening and day shifts (5.20 ± 0.90 h). KSS and PVT mean reaction times were higher at the end of the first and subsequent night shift compared to day shift, with KSS highest at the end of the first night. On nights, working during the circadian acrophase of the urinary melatonin rhythm led to poorer outcomes on the KSS and PVT. In rotating shift workers, early day shifts can be associated with similar sleep restriction to night shifts, particularly when scheduled immediately following an evening shift. Alertness and performance remain most impaired during night shifts given the lack of circadian adaptation to night work. Although healthcare workers perceive themselves to be less alert on the first night shift compared to subsequent night shifts, objective performance is equally impaired on subsequent nights.

Driver drowsiness detection based on classification of surface electromyography features in a driving simulator

Driver drowsiness is a significant cause of fatal crashes every year in the world. In this research, driver's drowsiness is detected by classifying surface electromyography signal features. The tests are conducted on 13 healthy subjects in a driving simulator with a monotonous route. The surface electromyography signal from the upper arm and shoulder muscles are measured including mid deltoid, clavicular portion of the pectoralis major, and triceps and biceps long heads. Signals are separated into 30-s epochs. Five features including range, variance, relative spectral power, kurtosis, and shape factor are extracted. The Observer Rating of Drowsiness evaluates the level of drowsiness. A binormal function is fitted for each feature. For classification, six classifiers are applied. The results show that the k-nearest neighbor classifier predicts drowsiness by 90% accuracy, 82% precision, 77% sensitivity, and 92% specificity.

Screening for Obstructive Sleep Apnea in Commercial Drivers Using EKG-Derived Respiratory Power Index

STUDY OBJECTIVES

Obstructive sleep apnea (OSA) is common in commercial motor vehicle operators (CMVOs); however, polysomnography (PSG), the gold-standard diagnostic test, is expensive and inconvenient for screening. OSA is associated with changes in heart rate and voltage on electrocardiography (EKG). We evaluated the utility of EKG parameters in identifying CMVOs at greater risk for sleepiness-related crashes (apnea-hypopnea index [AHI] ≥ 30 events/h).

METHODS

In this prospective study of CMVOs, we performed EKGs with concurrent PSG, and calculated the respiratory power index (RPI) on EKG, a surrogate for AHI calculated from PSG. We evaluated the utility of two-stage predictive models using simple clinical measures (age, body mass index [BMI], neck circumference, Epworth Sleepiness Scale score, and the Multi-Variable Apnea Prediction [MVAP] score) in the first stage, followed by RPI in a subset as the second-stage. We assessed area under the receiver operating characteristic curve (AUC), sensitivity, and negative posttest probability (NPTP) for this two-stage approach and for RPI alone.

RESULTS

The best-performing model used the MVAP, which combines BMI, age, and sex with three OSA symptoms, in the first stage, followed by RPI in the second. The model yielded an estimated (95% confidence interval) AUC of 0.883 (0.767-0.924), sensitivity of 0.917 (0.706-0.962), and NPTP of 0.034 (0.015-0.133). Predictive characteristics were similar using a model with only BMI as the first-stage screen.

CONCLUSIONS

A two-stage model that combines BMI or the MVAP score in the first stage, with EKG in the second, had robust discriminatory power to identify severe OSA in CMVOs.

Association between sleep and fatigue in nurses who are engaged in 16 h night shifts in Japan: Assessment using actigraphy

AIM

To determine the association between sleep and fatigue in nurses who are working in a two-shift system, including 16 h night shifts.

METHODS

Sixty-one nurses were assessed on their sleeping and napping over 9 days, using actigraphy and a sleep diary. Work-related feelings of fatigue were measured by using the "Jikaku-sho shirabe" questionnaire and the Cumulative Fatigue Symptoms Index.

RESULTS

The main night-time sleep started after 00:00 hours in half of the participants and the average start and end times were significantly delayed among the participants in their 20s, compared to those in their 40s . Although ~90% of the participants napped during and/or after a night shift, only 50.8% napped for >2 h during their shift and 32.8% napped in the morning after a night shift. In the high-fatigue group, significantly more nurses went to sleep after 00:25 hours than before 00:26 hours the night after a night shift. Furthermore, those nurses who napped for >2 h during their night shift exhibited a significantly lower rate of some cumulative fatigue symptoms, compared to those who did not. In addition, a combination of napping in the morning after a night shift and beginning the following night-time sleep before 00:26 hours were associated with a significant decrease in fatigue symptoms.

CONCLUSIONS

Naps at an appropriate time and of an appropriate duration, along with the practice of beginning the night-time sleep early after a night shift, might relieve cumulative mental fatigue in nurses who are working 16 h night shifts.