Predicting performance and safety based on driver fatigue.

Fatigue causes decrements in vigilant attention and reaction time and is a major safety hazard in the trucking industry. There is a need to quantify the relationship between driver fatigue and safety in terms of operationally relevant measures. Hard-braking events are a suitable measure for this purpose as they are relatively easily observed and are correlated with collisions and near-crashes. We developed an analytic approach that predicts driver fatigue based on a biomathematical model and then estimates hard-braking events as a function of predicted fatigue, controlling for time of day to account for systematic variations in exposure (traffic density). The analysis used de-identified data from a previously published, naturalistic field study of 106 U.S. commercial motor vehicle (CMV) drivers. Data analyzed included drivers' official duty logs, sleep patterns measured around the clock using wrist actigraphy, and continuous recording of vehicle data to capture hard-braking events. The curve relating predicted fatigue to hard-braking events showed that the frequency of hard-braking events increased as predicted fatigue levels worsened. For each increment on the fatigue scale, the frequency of hard-braking events increased by 7.8%. The results provide proof of concept for a novel approach that predicts fatigue based on drivers' sleep patterns and estimates driving performance in terms of an operational metric related to safety. The approach can be translated to practice by CMV operators to achieve a fatigue risk profile specific to their own settings, in order to support data-driven decisions about fatigue countermeasures that cost-effectively deliver quantifiable operational benefits.

Drowsiness measures for commercial motor vehicle operations.

Timely detection of drowsiness in Commercial Motor Vehicle (C MV) operations is necessary to reduce drowsiness-related CMV crashes. This is relevant for manual driving and, paradoxically, even more so with increasing levels of driving automation. Measures available for drowsiness detection vary in reliability, validity, usability, and effectiveness. Passively recorded physiologic measures such as electroencephalography (EEG) and a variety of ocular parameters tend to accurately identify states of considerable drowsiness, but are limited in their potential to detect lower levels of drowsiness. They also do not correlate well with measures of driver performance. Objective measures of vigilant attention performance capture drowsiness reliably, but they require active driver involvement in a performance task and are prone to confounds from distraction and (lack of) motivation. Embedded performance measures of actual driving, such as lane deviation, have been found to correlate with physiologic and vigilance performance measures, yet to what extent drowsiness levels can be derived from them reliably remains a topic of investigation. Transient effects from external circumstances and behaviors - such as task load, light exposure, physical activity, and caffeine intake - may mask a driver's underlying state of drowsiness. Also, drivers differ in the degree to which drowsiness affects their driving performance, based on trait vulnerability as well as age. This paper provides a broad overview of the current science pertinent to a range of drowsiness measures, with an emphasis on those that may be most relevant for CMV operations. There is a need for smart technologies that in a transparent manner combine different measurement modalities with mathematical representations of the neurobiological processes driving drowsiness, that account for various mediators and confounds, and that are appropriately adapted to the individual driver. The research for and development of such technologies requires a multi-disciplinary approach and significant resources, but is technically within reach.

Cardiac autonomic activity during simulated shift work.

Shift work leads to adverse health outcomes including increased risk of cardiovascular disease. Heart rate (HR) and heart rate variability (HRV) are measures of cardiac autonomic activity and markers of cardiovascular disease and mortality. To investigate the effects of shift work on cardiac autonomic activity, we assessed the influence of simulated night work on HR and HRV, and dissociated the direct effects of circadian misalignment from those of sleep displacement and altered physical activity patterns. A total of 29 subjects each participated in one of two in-laboratory, simulated shift work studies. In both studies, EKG was continuously monitored via Holter monitors to measure HR and the high frequency (HF) component of HRV (HF-HRV). We found endogenous circadian rhythmicity in HR and HF-HRV. Sleep and waking physical activity, both displaced during simulated night work, had more substantial, and opposite, effects on HR and HF-HRV. Our findings show systematic but complex, interacting effects of time of day, sleep/wake state, and physical activity on cardiac autonomic activity. These effects need to be taken into account when evaluating HR and HRV in shift work settings and when interpreting these measures of cardiac autonomic activity as markers of cardiovascular disease.

Sleep and performance in simulated Navy watch schedules.

To operate Navy ships 24h per day, watchstanding is needed around the clock, with watch periods reflecting a variety of rotating or fixed shift schedules. The 5/15 watch schedule cycles through watch periods with 5h on, 15h off watch, such that watches occur 4h earlier on the clock each day - that is, the watches rotate backward. The timing of sleep varies over 4-day cycles, and sleep is split on some days to accommodate nighttime watchstanding. The 3/9 watch schedule cycles through watch periods with 3h on, 9h off watch, allowing for consistent sleep timing over days. In some sections of the 3/9 watch schedule, sleep may need to be split to accommodate nighttime watchstanding. In both the 5/15 and 3/9 watch schedules, four watch sections alternate to cover the 24h of the day. Here we compared sleep duration, psychomotor vigilance and subjective sleepiness in simulated sections of the 5/15 and 3/9 watch schedules. Fifteen healthy male subjects spent 6 consecutive days (5 nights) in the laboratory. Sleep opportunities were restricted to an average of 6.5h daily. Actigraphically estimated sleep duration was 5.6h per watch day on average, with no significant difference between watch sections. Sleep duration was not reduced when sleep opportunities were split. Psychomotor vigilance degraded over watch days, and tended to be more variable in the 5/15 than in the 3/9 watch sections. These laboratory-based findings suggest that Navy watch schedules are associated with cumulative sleep loss and a build-up of fatigue across days. The fixed watch periods of the 3/9 watch schedule appear to yield more stable performance than the backward rotating watch periods of the 5/15 watch schedule. Optimal performance may require longer and more consistent daily opportunities for sleep than are typically obtained in Navy operations.

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

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