Pupillary instability as an accurate, objective marker of alertness failure and performance impairment

Sleepiness and the transition from wakefulness to sleep

The transition from wakefulness to sleep is a progressive process that is reflected in the gradual loss of responsiveness, an alteration of cognitive functions, and a drastic shift in brain dynamics. These changes do not occur all at once. The sleep onset period (SOP) refers here to this period of transition between wakefulness and sleep. For example, although transitions of brain activity at sleep onset can occur within seconds in a given brain region, these changes occur at different time points across the brain, resulting in a SOP that can last several minutes. Likewise, the transition to sleep impacts cognitive and behavioral levels in a graded and staged fashion. It is often accompanied and preceded by a sensation of drowsiness and the subjective feeling of a need for sleep, also associated with specific physiological and behavioral signatures. To better characterize fluctuations in vigilance and the SOP, a multidimensional approach is thus warranted. Such a multidimensional approach could mitigate important limitations in the current classification of sleep, leading ultimately to better diagnoses and treatments of individuals with sleep and/or vigilance disorders. These insights could also be translated in real-life settings to either facilitate sleep onset in individuals with sleep difficulties or, on the contrary, prevent or control inappropriate sleep onsets.

Accurate detection of acute sleep deprivation using a metabolomic biomarker-A machine learning approach

Sleep deprivation enhances risk for serious injury and fatality on the roads and in workplaces. To facilitate future management of these risks through advanced detection, we developed and validated a metabolomic biomarker of sleep deprivation in healthy, young participants, across three experiments. Bi-hourly plasma samples from 2 × 40-hour extended wake protocols (for train/test models) and 1 × 40-hour protocol with an 8-hour overnight sleep interval were analyzed by untargeted liquid chromatography-mass spectrometry. Using a knowledge-based machine learning approach, five consistently important variables were used to build predictive models. Sleep deprivation (24 to 38 hours awake) was predicted accurately in classification models [versus well-rested (0 to 16 hours)] (accuracy = 94.7%/AUC 99.2%, 79.3%/AUC 89.1%) and to a lesser extent in regression (R2 = 86.1 and 47.8%) models for within- and between-participant models, respectively. Metabolites were identified for replicability/future deployment. This approach for detecting acute sleep deprivation offers potential to reduce accidents through "fitness for duty" or "post-accident analysis" assessments.

Psychophysiological models of hypovigilance detection: A scoping review

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

A hypoarousal model of neurological post-COVID syndrome: the relation between mental fatigue, the level of central nervous activation and cognitive processing speed

BACKGROUND

Knowledge on the nature of post-COVID neurological sequelae often manifesting as cognitive dysfunction and fatigue is still unsatisfactory.

OBJECTIVES

We assumed that cognitive dysfunction and fatigue in post-COVID syndrome are critically linked via hypoarousal of the brain. Thus, we assessed whether tonic alertness as a neurocognitive index of arousal is reduced in these patients and how this relates to the level of central nervous activation and subjective mental fatigue as further indices of arousal.

METHODS

40 post-COVID patients with subjective cognitive dysfunction and 40 matched healthy controls underwent a whole-report paradigm of briefly presented letter arrays. Based on report performance and computational modelling according to the theory of visual attention, the parameter visual processing speed (VPS) was quantified as a proxy of tonic alertness. Pupillary unrest was assessed as a measure of central nervous activation. The Fatigue Assessment Scale was applied to assess subjective mental fatigue using the corresponding subscale.

RESULTS

VPS was reduced in post-COVID patients compared to controls (p = 0.005). In these patients, pupillary unrest (p = 0.029) and mental fatigue (p = 0.001) predicted VPS, explaining 34% of the variance and yielding a large effect with f2 = 0.51.

CONCLUSION

In post-COVID patients with subjective cognitive dysfunction, hypoarousal of the brain is reflected in decreased processing speed which is explained by a reduced level of central nervous activation and a higher level of mental fatigue. In turn, reduced processing speed objectifies mental fatigue as a core subjective clinical complaint in post-COVID patients.

Dashcam video footage-based analysis of microsleep-related behaviors in truck collisions attributed to falling asleep at the wheel

OBJECTIVE

With the rapid spread of dashcams, many car accidents have been recorded; however, behavioral approaches using these dashcam video footage have not been sufficiently examined. We employed dashcam video footage to evaluate microsleep-related behaviors immediately prior to real-world truck collisions in professional drivers to explore a new solution to reduce collisions attributed to falling asleep at the wheel.

METHODS

In total, 3,120 s of video footage (60 s/case × 52 cases) from real-world truck collisions of 52 professional drivers obtained from interior and exterior dashcams were used and visually analyzed in a second-by-second manner to simultaneously evaluate any eye changes and microsleep-related behaviors (the driver's anti-sleepiness behavior, behavioral signs of microsleep, and abnormal vehicle behavior) during driving.

RESULTS

Assessment of the frequency of occurrence of each item of microsleep-related behavior in the 52 collisions revealed that the item "touching" in terms of anti-sleepiness behavior, "absence of body movement" in terms of behavioral signs of microsleep, and "inappropriate line crossing" in terms of abnormal vehicle behavior were observed at the highest rate in all drivers (46.2%, 75.0%, and 78.8%, respectively). Decreases in anti-sleepiness behavior coincided with increases in behavioral signs of microsleep and abnormal vehicle behavior, with collisions occurring within approximately 40 s of these changes. Collisions were more common among young people and in the early morning and evening.

CONCLUSION

Our dashcam video footage-based analysis in truck collisions attributed to falling asleep at the wheel revealed the process of changes in microsleep-related driver and vehicle behaviors, classified as anti-sleepiness behavior, behavioral signs of microsleep, and abnormal vehicle behavior. Based on these findings, to prevent collisions caused by falling asleep at the wheel, it is crucial to monitor not only the driver's eyes, but also the driver's whole body and vehicle behavior simultaneously to reliably detect microsleep-related behaviors.

A brief assessment of eye blink drowsiness immediately prior to or following driving detects drowsiness related driving impairment

Drowsy driving is a major cause of fatal and serious injury motor vehicle accidents. The inability objectively to assess drowsiness has hindered the assessment of fitness to drive and the development of drowsy driving regulations. This study evaluated whether spontaneous eye blink parameters measured briefly pre- and post-drive could be used to detect drowsy driving impairment. Twelve healthy participants (6 female) drove an instrumented vehicle for 2 h on a closed-loop track during a rested (8-10 h awake) and an extended wake condition (32-34 h awake). Pre- and post-drive, the participants completed a 5 min eye blink task, a psychomotor vigilance task (PVT), and the Karolinska sleepiness scale (KSS). Whole drive impairment was defined as >3.5 lane departures per hour. Severe end of drive impairment was defined as ≥2 lane departures in the last 15 min. The pre-drive % of time with eyes closed best predicted the whole drive impairment (area under the curve [AUC] 0.87). KSS had similar prediction ability (AUC 0.85), while PVT reaction time (AUC 0.72) was less accurate. The composite eye blink parameter, the Johns drowsiness scale was the best retrospective detector of severe end of drive impairment (AUC 0.99). The PVT reaction time (AUC 0.92) and the KSS (AUC 0.93) were less accurate. Eye blink parameters detected drowsy driving impairment with an accuracy that was similar to, or marginally better than, PVT and KSS. As eye blink measures are simple to measure, are objective and have high accuracy, they present an ideal option for the assessment of fitness for duty and roadside drowsiness.

The sleep, cancer and rest (SleepCaRe) trial: Rationale and design of a randomized, controlled trial of cognitive behavioral and bright light therapy for insomnia and fatigue in women with breast cancer receiving chemotherapy

BACKGROUND

Insomnia and fatigue symptoms are common in breast cancer. Active cancer treatment, such as chemotherapy, appears to be particularly disruptive to sleep. Yet, sleep complaints often go unrecognised and under treated within routine cancer care. The abbreviated delivery of cognitive behavioral therapy for Insomnia (CBTI) and bright light therapy (BLT) may offer accessible and cost-effective sleep treatments in women receiving chemotherapy for breast cancer.

METHODS

The Sleep, Cancer and Rest (SleepCaRe) Trial is a 6-month multicentre, randomized, controlled, 2 × 2 factorial, superiority, parallel group trial. Women receiving cytotoxic chemotherapy for breast cancer at tertiary Australian hospitals will be randomly assigned 1:1:1:1 to one of four, non-pharmacological sleep interventions: (a) Sleep Hygiene and Education (SHE); (b) CBTI; (c) BLT; (d) CBT-I + BLT combined and simultaneously delivered. Each sleep intervention is delivered over 6 weeks, and will comprise an introductory session, a mid-point phone call, and regular emails. The primary (insomnia, fatigue) and secondary (health-related quality of life, rest activity rhythms, sleep-related impairment) outcomes will be assessed via online questionnaires at five time-points: baseline (t0, prior to intervention), mid-point intervention (t2, Week 4), post-intervention (t3, Week 7), 3-months (t4, Week 18), and 6-months follow-up (t5, Week 30).

CONCLUSIONS

This study will report novel data concerning the comparative and combined efficacy of CBT-I and BLT during chemotherapy. Findings will contribute to the development of evidence-based early sleep and fatigue intervention during chemotherapy for breast cancer. Clinical trial information Registered with the Australian New Zealand Clinical Trials Registry (http://anzctr.org.au/), Registration Number: ACTRN12620001133921.

Revisiting the Concept of Vigilance

Vigilance deficits can be observed after a period of prolonged, continuous wakefulness. In this context there has been extensive research targeting the impact of sleep deficits on different aspects of vigilance, but the underlying concept of vigilance was hardly ever addressed and discussed. One reason for this shortcoming is the unclear and ambiguous definition of the term vigilance, which is commonly used interchangeably with sustained attention and even wakefulness. This confusion is the result of a wide range of misleading definitions, starting in the 1940s, as psychologists redefined the concept of vigilance suggested by British Neurologist, Henry Head, in 1923. Nevertheless, the concept of vigilance is still useful and innovative, especially in treating sleep problems in children and young adults. This paper reviews the current usage of the term vigilance in sleep-wake-research and describes not only the benefits, but even more clearly, its limitations. By re-focusing on the definitions given by Henry Head, the concept of vigilance is an innovative way to gather new insights into the interplay between sleep- and daytime behaviors. In addition, future research on vigilance should consider three perspectives: 1st vigilance perceived as a process to allocate resources, 2nd vigilance associated with compensatory behaviors and 3rd the role of vigilance in human environmental interactions. This approach, understood as a conceptual framework, provides new perspectives by targeting sleep-wake behaviors as a 'real life' outcome measure, reflecting both physical and cognitive performance as well as sleep quality and quantity.

Pupillary fluctuation amplitude before target presentation reflects short-term vigilance level in Psychomotor Vigilance Tasks

Our daily activities require vigilance. Therefore, it is useful to externally monitor and predict our vigilance level using a straightforward method. It is known that the vigilance level is linked to pupillary fluctuations via Locus Coeruleus and Norepinephrine (LC-NE) system. However, previous methods of estimating long-term vigilance require monitoring pupillary fluctuations at rest over a long period. We developed a method of predicting the short-term vigilance level by monitoring pupillary fluctuation for a shorter period consisting of several seconds. The LC activity also fluctuates at a timescale of seconds. Therefore, we hypothesized that the short-term vigilance level could be estimated using pupillary fluctuations in a short period and quantified their amplitude as the Micro-Pupillary Unrest Index (M-PUI). We found an intra-individual trial-by-trial positive correlation between Reaction Time (RT) reflecting the short-term vigilance level and M-PUI in the period immediately before the target onset in a Psychomotor Vigilance Task (PVT). This relationship was most evident when the fluctuation was smoothed by a Hanning window of approximately 50 to 100 ms (including cases of down-sampled data at 100 and 50 Hz), and M-PUI was calculated in the period up to one or two seconds before the target onset. These results suggest that M-PUI can monitor and predict fluctuating levels of vigilance. M-PUI is also useful for examining pupillary fluctuations in a short period for elucidating the psychophysiological mechanisms of short-term vigilance.

I think I'm sleepy, therefore I am - Awareness of sleepiness while driving: A systematic review

Driver drowsiness contributes to 10-20% of motor vehicle crashes. To reduce crash risk, ideally drivers would be aware of the drowsy state and cease driving. The extent to which drivers can accurately identify sleepiness remains under much debate. We systematically examined whether individuals are aware of sleepiness while driving, and whether this accurately reflects driving impairment, using meta-analyses and narrative review. Within this scope, there is high variability in measures of subjective sleepiness, driving performance and physiologically-derived drowsiness, and statistical analyses. Thirty-four simulated/naturalistic driving studies were reviewed. To summarise, drivers were aware of sleepiness, and this was associated to physiological drowsiness and driving impairment, such that high levels of sleepiness significantly predicted crash events and lane deviations. Subjective sleepiness was more strongly correlated (i) with physiological drowsiness compared to driving outcomes; (ii) under simulated driving conditions compared to naturalistic drives; and (iii) when examined using the Karolinska sleepiness scale (KSS) compared to other measures. Gaps remain in relation to how age, sex, and varying degrees of sleep loss may influence this association. This review provides evidence that drivers are aware of drowsiness while driving, and stopping driving when feeling 'sleepy' may significantly reduce crash risk.

An evaluation and comparison of commercial driver sleepiness detection technology: a rapid review

Objective. Sleepiness-related motor vehicle crashes, caused by lack of sleep or driving during night-time hours, often result in serious injury or fatality. Sleepiness detection technology is rapidly emerging as a sleepiness risk mitigation strategy for drivers. Continuous monitoring technologies assess and alert to driver sleepiness in real-time, while fit for duty technologies provide a single assessment of sleepiness state. The aim of this rapid review was to evaluate and compare sleepiness detection technologies in relation to specifications, cost, target consumer group and validity.Approach. We evaluated a range of sleepiness detection technologies suitable for consumer groups ranging from regular drivers in private vehicles through to work-related drivers within large businesses.Main results. Continuous monitoring technologies typically ranged between $100 and $3000 AUD and had ongoing monthly costs for telematics functionality and manager alerts. Fit for duty technologies had either a one-off purchase cost or a monthly subscription cost. Of concern, the majority of commercial continuous monitoring technologies lacked scientific validation. While some technologies had promising findings in terms of their ability to detect and reduce driver sleepiness, further validation work is required. Field studies that evaluate the sensitivity and specificity of technology alerts under conditions that are regularly experienced by drivers are necessary. Additionally, there is a need for longitudinal naturalistic driving studies to determine whether sleepiness detection technologies actually reduce sleepiness-related crashes or near-crashes.Significance. There is an abundance of sleepiness detection technologies on the market, but a majority lacked validation. There is a need for these technologies and their validation to be regulated by a driver safety body. Otherwise, consumers will base their technology choices on cost and features, rather than the ability to save lives.

Awareness of sleepiness: Temporal dynamics of subjective and objective sleepiness

We systematically examined the temporal relationships between subjective sleepiness and both physiological drowsiness and performance impairment in a controlled laboratory setting. Eighteen healthy young adults (8 women; M_AGE  = 21.44 ± 3.24 years) underwent 40 hr of extended wakefulness, completing a bihourly Karolinska Sleepiness Scale (KSS) and 10-min Psychomotor Vigilance Task (PVT). Microsleeps and slow eye movements (SEMs) were scored during the PVT. KSS scores increased 3 hr prior to performance impairment (p < .001) and 4-6 hr prior to physiological sleepiness (p < .001). There were strong within-subject correlations between KSS and PVT lapses (r = 0.75, p < .001) and physiological drowsiness (r > 0.60, p < .001). Between-subjects product-moment correlations were more modest but showed a significant positive increase across time awake, suggesting that subjective sleepiness and objective outcomes were more tightly correlated after sleep loss. Cross-correlations showed significant positive correlations at 0-lag (p < .034); however, a high proportion of participants showed maximal correlations at positive lags, suggesting KSS was associated with future objective impairment. Within individuals, subjective sleepiness was highly correlated with objective impairment, between-subject correlations were more modest, possibly due to interindividual vulnerability to sleep loss. These results suggest that subjective sleepiness represents an inbuilt early warning system for subsequent drowsiness and performance impairment.

Validity of the Pupillographic Sleepiness Test for the diagnosis of daytime sleepiness in children and adolescents and its relationship to sleepiness-associated outcomes

OBJECTIVES

To report validation data for the Pupillographic Sleepiness Test (PST) in children and adolescents, evaluate its applicability for diagnosing excessive daytime sleepiness and its relationship to sleepiness-associated outcomes.

METHODS

A cross-sectional diagnostic test accuracy study was performed. Patients underwent three PST at 9 a.m. (T1), 11 a.m. (T2) and 1 p.m. (T3) plus the Multiple Sleep Latency Test (MSLT) on a single day. Additionally, two neurocognitive tests were performed and three questionnaires about quality of life, sleep-related self-efficacy and behavioural aspects completed. Gender-stratified z-values of the natural logarithm of the Pupillary Unrest Index (z-lnPUI) were correlated to Sleep Latency (SL) and Mean Sleep Latency (MSL) and to variables of neurocognitive tests and questionnaires using Spearman's rank correlation. Cut-off values were determined by receiver operating characteristic (ROC) analysis.

RESULTS

47 patients were recruited (median 10.6 years, range 6-18). Correlation between z-lnPUI and SL was r_T1 = -0.373 (p = 0.011); r_T2 = -0.320 (p = 0.028) and r_T3 = -0.336 (p = 0.022). Correlation between z-lnPUI and MSL was r_T1 = -0.338 (p = 0.020); r_T2 = -0.202 (p = 0.173); r_T3 = -0.117 (p = 0.433). ROC analysis showed an area under the curve of 90.7% and PUI cut-off values of 12.6 mm/min (boys) and 11.6 mm/min (girls). There were moderate correlations between z-lnPUI_T1 and reaction time and omission errors in neurocognitive tests (r = 0.394, p = 0.007 and 0.391, p = 0.008).

CONCLUSIONS

We found satisfactory correlations between PST and MSLT results. The z-lnPUI_T1 was related to MSL and objective measures of attention ability. Given this accuracy, the PST may be used as a screening tool for evaluating daytime sleepiness in children and adolescents. Corresponding gender-related reference values are now available.

Sleep deprivation and compensatory cognitive effort on a visual information processing task

STUDY OBJECTIVES

Total sleep deprivation (TSD) is often associated with worse performance on tasks of attention and working memory, but some studies show no performance changes. One possibility is that greater compensatory cognitive effort is put forth to achieve similar results after TSD. We aimed to better understand the relationship between TSD, cognitive engagement, and performance outcomes following TSD.

METHODS

Twenty healthy adults completed cognitive testing following a night of normal sleep and again after ~55 hours of TSD. Participants detected target letters in low (3-item) and high (10-item) load visual letter displays on the span of apprehension task with concurrent pupillometry, a measure of cognitive effort.

RESULTS

We found significantly poorer detection accuracy and marginally longer response times following TSD across both arrays. In both arrays, significantly greater preparatory pupillary responses were found just prior to array onset. There was also a significant session by array interaction for pupillary responses, such that significantly greater dilation was found for the 3-letter array after TSD, while a nonsignificant decline in dilation was found following the 10-letter array after TSD.

CONCLUSIONS

These results suggest a complex relationship between attentional control and cognitive resource allocation following TSD. Sleep-deprived individuals may allocate more compensatory cognitive effort to easier tasks but choose to disengage from more challenging cognitive tasks that have little perceived reward or probability of success to preserve diminishing cognitive resources. More work is needed to better delineate the underlying neurological systems involved in these processing load-dependent attentional control mechanisms after TSD.

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.

Psychophysical stress and strain of maritime pilots in Germany. A cross-sectional study

Introduction

Maritime pilots work in an irregular deployment system (rotation system) with unpredictable work assignments under high levels of physical and mental stress. Fatigue or chronic diseases, e.g. coronary heart disease, peptic ulcers or gastritis can occur as a consequence. This can lead to long-term limitations of pilots’ work ability. The aim of this study is to analyse current stress and strain in maritime pilots.

Methods

Initially, all German pilots were interviewed with an online questionnaire about their living and working situation (response rate 43%). Subsequently, a medical and psychological examination of a random sample was carried out with pilots working in a 4-month rotation system compared with those working in a 1-week system. Most of the measurements took place at the beginning and the end of continuous work assignments each lasting several weeks (pre vs post-rotation). The questionnaires RESTQ-work 27, Resilience Scale RS-13 and Berlin Questionnaire were used as well as a sleeping diary. Furthermore, cardiovascular parameters (during rest and under ergometric stress), activity and blood parameters, urine stress hormones, and the pupillary unrest index were surveyed.

Results

60 pilots were recorded with an average age of 48.7 years (SD 8.3 years). Among the parameters collected, there were no significant differences between pre and post-rotation examinations. Pilots with a 4-month rotation system experienced a much higher subjective strain level in RESTQ work-27 (OR 10.12 (95% CI 1.21–84.59)). According to the sleep diaries of the pilots working in a 4-month rotation system, reduced levels were found concerning the pre and post-rotation subjective performance level (p = 0.042 and 0.029), subjective sleep duration (p = 0.032) and current subjective feeling post-rotation (p = 0.036). Objectively measured arterial hypertension was significantly more frequent among pilots working 4 months at a time (OR 21.41 (95% CI 1.26–364.05)). In addition, elevated levels of total cholesterol, triglycerides and uric acid were more common among this group of pilots (p = 0.038, p = 0.033 and p = 0.038). In particular, the risk of hypertriglyceridemia was increased (OR 4.41 (95% CI 1.15–16.91)).

Discussion

Maritime pilotage represents a very straining profession that has been studied very little up to this point. The present results indicate that 4-month rotation systems lead to higher levels of subjective and objective strain than 1-week rotation systems. Interventions are therefore recommended; especially a change in the rotation system should be considered.

Sleepiness of day workers and watchkeepers on board at high seas: a cross-sectional study

OBJECTIVES

To estimate the prevalence of sleepiness on duty among day workers and watchkeepers on board.

DESIGN

Cross-sectional survey in a maritime field study.

SETTING

10 shipping companies with container vessels under German management.

PARTICIPANTS

The whole crew (75 day workers and 123 watchkeepers) during 18 voyages on 18 different container ships.

OUTCOME MEASURES

Sleepiness on duty and efficiency of sleep using pupillometry (in a cross-shift design) and the SenseWear armband activity monitor.

RESULTS

The watchkeepers showed significantly shorter sleep periods than day workers (5.5 hours vs 5.8 hours). The average efficiency of sleep was 69.6% and significantly lower among watchkeepers (OR 0.48; 95% CI 0.26 to 0.88). 396 pupillometric examinations were carried out and revealed 88 study members (22.2%) with a pupillary unrest index (rPUI) in a range characterised as 'unfit for duty' and 110 seafarers (27.8%) categorised as 'particular attention required'. The average rPUI was similar between day workers and watchkeepers. The Epworth Sleepiness Scale revealed recent daytime sleepiness in 70 seafarers, which was similarly often stated by day workers and watchkeepers. Based on the Stanford Sleepiness Scale (SSS), a measurable cross-shift increase in the SSS value during the examined shift was observed, especially among watchkeepers. The amount of time already spent on the vessel at the time of the present examination was significantly associated with the rPUI (p=0.009).

CONCLUSION

Sleep periods of both the day workers and the watchkeepers aboard vessels were alarmingly short and sleep efficiency was low. Sleepiness on duty is similarly prevalent among day workers and watchkeepers and seems to depend partly on the cumulative working period on the vessels. Preventive measures need to be taken by the shipping industry to counteract fatigue (eg, by enabling sufficient rest and sleep times).

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems.