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24 4.4.4 Other Medical Conditions long-distance truck drivers reported falling asleep at the wheel. They found six underlying, independent factors, including Medical conditions having an impact on the crash severity (1) greater daytime sleepiness, (2) more arduous schedules, of commercial motor vehicle crashes were identified by with more hours of work and fewer hours off-duty, (3) older, Laberge-Nadeau et al. (1996). In their study, crash severity was more experienced drivers, (4) shorter, poorer sleep on the measured by the total number of injured victims. Their study road, (5) symptoms of sleep disorder, and (6) greater ten- indicated that truck drivers with binocular vision problems and dency to nighttime drowsy driving. The authors also suggest bus drivers with hypertension had more severe crashes than that if these six factors were to be ranked, a tendency toward healthy drivers. No other medical condition considered in daytime sleepiness was most highly predictive of falling this study--including diabetes, mellitus, and coronary heart asleep at the wheel, followed by an arduous work schedule disease--was significantly associated with crash severity. and older, long-time drivers. Hakkanen and Summala (2001) A study done with non-commercial drivers indicates that found similar findings in an analysis they conducted on 567 other health problems, including heart disease and stroke, were professional drivers that included five different commercial also associated with an increased likelihood of being involved driver types (long-haul drivers, short-haul drivers, bus drivers, in both at-fault and not-at-fault automobile crashes (McGwin drivers transporting wood, and drivers transporting dangerous et al. 2000). The study also found an increased risk of crash goods). They found that regardless of the commercial driver involvement for drivers with arthritis and diabetic neuropathy. type, sleepiness-related problems was strongly related to pro- A confounding factor in this study is that many people with longed driving, sleep deficit, and driver's health status. these conditions are taking prescription drugs that can impact Sagberg (1999) conducted a study of crashes caused by driv- their cognitive, perceptual, and psychomotor abilities. ers falling asleep. The study showed that fatigue was a strong contributing factor in nighttime accidents, run-off-road acci- dents, and accidents after driving more than 150 km on one 4.5 ALCOHOL AND DRUG ABUSE trip. Although his study was conducted on non-commercial drivers, many findings were consistent with McCartt et al.'s Commercial driver alcohol use while driving is infrequent, study. In addition, Sagberg found that more males than females especially in comparison with non-commercial drivers. In were involved in sleep-related accidents. Sagberg also suggests 2001, alcohol use on the part of large truck drivers was involved that drivers' lack of awareness of important precursors of falling in 2% of their fatal crashes, 1% of their injury crashes, and less asleep in addition to the reluctance to discontinue driving than 0.5% of their PDO crashes. Alcohol use on the part of despite feeling tired contributed to sleep-related accidents. bus drivers represented 3% of their fatal crashes and less than Pack et al. (1995) investigated the characteristics associ- 0.5% for both injury and PDO crashes. For passenger vehicles ated with sleep-related crashes among the general population (cars and light trucks), the comparable percentages were 27% of drivers. They found that the crashes were primarily drive- fatal, 5% injury, and 3% PDO (NHTSA 2002). Craft (2004) off-the road type and took place at higher speeds. The crashes reported preliminary findings on 210 light-truck vehicle crashes occurred primarily at two times of day: during the over- from the FMCSA/NHTSA Large Truck Crash Causation night hours (midnight to 7 a.m.) and during mid-afternoon Study. None of these 210 crashes involved alcohol or illegal (3:00 p.m.). Young drivers were overrepresented, especially drug use by truck drivers. Of the light vehicle drivers involved in overnight crashes. The times of occurrence of fatigue- in these crashes, 11% were under the influence of alcohol and related crashes corresponded to the known circadian variation 9% had used illegal drugs. in sleepiness. There is a major peak during the night with a Federal law requires all motor carriers employing commer- secondary peak during the mid-afternoon. When older drivers cial drivers to have drug and alcohol testing programs. The random testing rates are 10% for alcohol and 50% for con- were involved in these crashes, it tended to be in the after- trolled substances (illegal drugs). In 1999, 0.2% of CDL hold- noon rather than in the overnight hours. ers tested positive for alcohol use and 1.3% tested positive for Lyznicki et al. (1998) present a comprehensive review of controlled substances (FMCSA 2001). sleepiness, driving, and motor vehicle crashes in a report to These statistics indicate that commercial driver alcohol the Journal of the American Medical Association. Their report and illegal drug use are not major factors in the crashes. Nev- indicates that drivers at high risk for fatigue or sleep-related ertheless, any commercial driver identified as an alcohol or crashes include (1) younger drivers who lack sleep due to drug abuser should be considered a high-risk driver. demands from school and jobs, extracurricular activities, late-night socializing, and poor sleep habits, (2) shift work- ers, who may have reduced opportunities for sleep due to 4.6 DRIVER FATIGUE disruptions of the biological process that programs daytime wakefulness and nighttime sleepiness, (3) drivers who use Drivers who are sleep deprived have significant deficits alcohol and other drugs, and (4) drivers with sleep disorders. in vigilance and other cognitive abilities related to driving. In the landmark FHWA-sponsored Driver Fatigue and McCartt et al. (2000) identified factors associated with why Alertness Study (DFAS, Wylie et al. 1996), 80 long-haul com-

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25 mercial drivers in the United States and Canada were moni- total driving hours of the study but were responsible for 226 tored over a 4- to 5-day work week. In the study, there was (70%) of the 323 high-drowsiness episodes. In contrast, the continuous video monitoring of drivers' faces, which enabled 9 most alert drivers (the first bar in Figure 11) drove 24% of judgments of alertness based on eyelid droop, facial expres- the driving hours but had no high-drowsiness episodes. Among sion, and muscle tone. Approximately 4.9% of the sampled the 27 drivers, there was a moderate correlation between video segments during the 4,000 hours of subject driving driver high-drowsiness rates and other CI involvement rates. were scored as drowsy based on reviewers' assessments. One In the study, a situational factor contributing to the dispersion of the major observations of the study was the pronounced of driver drowsiness incidence was team versus solo driving. individual differences in the incidence of drowsiness among The study included both types, and the solo drivers exhibited the 80 drivers. Twenty-nine of the drivers (36%) were never significantly more drowsiness than the team drivers. judged drowsy whereas, at the other extreme, 11 of the drivers Indeed, there are numerous situational factors that increase (14%) were responsible for 54% of all the drowsiness episodes the probability of drowsy driving, such as night driving, irreg- observed in the study. Figure 10 shows the frequency distri- ular schedules, sleeper berth use (versus sleep in a bed), length bution of drowsiness episodes among the 80 drivers, plotted of working shift, delivery schedule pressure, and amount of with five frequency ranges. The two drivers in the far right sleep. "Sleep hygiene" refers to sleep and alertness-related bin had 38 and 40 drowsiness episodes, respectively. Their personal habits and schedules. In a survey of 511 commercial total of 78 was greater than the total drowsiness episodes drivers, Abrams, Schultz, and Wylie (1997) identified many exhibited by the best 51 of the DFAS drivers. This distribution sleep hygiene-related variables, including work shift length, differs very significantly from the distribution expected from sleeper berth use, split sleeper berth sleep, hours resting, chance variation alone. frequency and duration of napping, drowsiness episodes in the Personal factors possibly related to the high-drowsiness past month, willingness to forgo sleep when behind schedule, incidence for the two driver subjects were not identified in the and other health-related behaviors (i.e., diet and exercise). DFAS report. Interestingly, two of the 80 drivers were diag- A wide range of responses were given on most of these topics, nosed as having sleep apnea, but they were not the two highest- indicating the sleep hygiene practices of drivers vary widely. drowsiness subjects. One question relating directly to fatigue risk asked how often As noted, each DFAS driver drove for only 1 week, so the drivers had dozed or fallen asleep at the wheel in the past study did not address the question of whether individual dif- month. The distribution of responses was as follows: 0 inci- ferences in drowsiness incidence were enduring. Enduring dents, 72.0%; 1 to 5 incidents, 22.8%; 6 to 15 incidents, 4.0%, individual differences in fatigue susceptibility would imply and >15 incidents, 1.4%. Of the seven drivers constituting the the existence of a fatigue susceptibility trait (i.e., a long-term highest category, four reported 30 episodes in the past month characteristic), whereas the lack of such enduring differences and one reported 60. would imply that situational or other factors lead to short-term Of course, the above survey data are subject to a number of differences in driver states. vagaries, including variations in driver memory, candidness, An instrumented vehicle study of long-haul drivers using criteria for "dozed or fallen asleep," and self-assessment of sleeper berths yielded a similar positively skewed distribution drowsiness level. On the latter point, the DFAS and other stud- of high-drowsiness episodes. Figure 11 shows the distribution ies (e.g., Itoi et al. 1993) have found that drivers are not very of high-drowsiness episodes per hour for 27 drivers. Of the good judges of their own levels of drowsiness, in particular the 27 truck drivers, 7 drivers had high-drowsiness episode rates probability of imminent sleep episodes. The Itoi et al. study of greater than 0.30/hour. These 7 drivers drove 25% of the found variations in sleepiness across subjects for the same level 35 Number of Drivers 30 25 (N=80) 20 15 10 5 0 0 1-10 11-20 21-30 31+ High-Drowsiness Episodes Figure 10. Frequency distribution of long-haul truck driver high-drowsiness episodes among 80 drivers of the DFAS.

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26 20 Number of Drivers 15 (N=27) 10 5 0 0 .01-.15 .16-.30 .31-.45 >.45 High-Drowsiness Episodes/Hr Figure 11. Frequency distribution of long-haul truck driver high-drowsiness episode rates among 27 drivers of the sleeper berth study (Dingus et al. 2001). of sleep deprivation and also variations in the ability of sleep- The Balkin study also included a field study where 25 long- deprived subjects to accurately predict the imminent occur- haul and 25 short-haul commercial drivers wore wrist acti- rence of involuntary sleep. Of 31 subjects, accurate predictions graphs for 20 days to assess their amount of sleep and factors of imminent involuntary sleep (i.e., "I will fall asleep in the influencing it. Not surprisingly, they found that total off-duty next two minutes.") ranged from 25% to 97%. Accurate pre- period had a strong effect on principal sleep duration. Some dictions of non-sleep (i.e., "I will not fall asleep in the next two drivers had highly variable sleep durations from night to night, minutes.") ranged from 9% to 84%. Several of the 31 subjects whereas others were very consistent in their sleep routines. performed at or near chance levels of accuracy in predicting the Overall sleep hygiene habits may be long term and thus a imminent occurrence of involuntary sleep. source of enduring individual differences among drivers in In a large FMCSA-sponsored study of sleep apnea, Pack their levels of alertness. And the Balkin et al. sleep depriva- et al. (2002) recorded amounts of nightly sleep for 340 com- tion study showed that the effects of sleep deprivation may mercial drivers, including drivers at high risk and low risk for vary widely among drivers during a week of partial sleep sleep apnea. For both groups, they found wide ranges in aver- deprivation. But, over a longer period of time, would differ- age hours of nightly sleep, from less than 6 hours for about ent drivers respond characteristically differently to lack of 10% (both subsamples combined) to more than 8 hours for sleep? Dinges et al. (1998) deprived 14 subjects of sleep about 24%. The study employed four subjective measures of over 40 hours in a test of different physiological measures sleepiness (e.g., Stanford Sleepiness Scale) and four objective of alertness. A principal, and previously validated, perfor- tests (e.g., PVT) and found that average sleep duration signif- mance measure of alertness in the study was the frequency icantly affected measures on all scales. Clearly, variations in of subject lapses (non-responses) on the PVT. The PVT was amount of nightly sleep are a major source of variations in administered during 20 "bouts" in the 40 hours. To test the commercial driver alertness and performance. physiological measures of alertness more rigorously, the Are there large individual differences in alertness for indi- researchers created two subject subgroups post hoc: six viduals with controlled amounts of sleep? In a major FMCSA- "high lapsers" and eight "low lapsers." The high lapsers sponsored controlled experiment on the effects of different were 42% of the subjects but accounted for 69% of the lapses amounts of sleep, Balkin et al. (2000) permitted driver subjects observed in the study. The researchers split the 40 hours in 3, 5, 7, or 9 hours in bed nightly for 1 week. As expected, it was half--2 to 22 hours and 22 to 42 hours--and observed the found that, between groups, alertness and performance var- marked lapse-frequency differences between the subject ied directly with amount of sleep and that these differences groups during both halves of the sleep deprivation. Indeed, increased over successive days. Another finding, however, the high-lapser lapse incidence during the first 20 hours of was that sleepiness and alertness performance varied signif- sleep deprivation was almost as high as the low-lapser inci- icantly between subjects within the same rest duration group. dence during the second 20 hours. The best physiological For example, mean sleep latency, a standard measure of measure of alertness in the study was found to be Percent drowsiness, varied widely among subjects, from about 1 to Eyelid Closure (PERCLOS), a measure of eyelid droop 20 minutes. At the extremes, some 3- and 5-hour subjects associated with drowsiness. PERCLOS was almost equally had sleep latencies of more than 10 minutes, whereas some accurate across both the high and low lapser groups and 7- and 9-hour subjects had sleep latencies of 1 minute. Indi- across both halves of the deprivation period. This implies vidual sleepiness was not a direct function of the amount of that the same or similar physiological processes are occur- sleep; marked individual differences and distribution overlaps ring among all the subjects, but that the rate of alertness dete- among groups were observed. rioration is different for different subjects.

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27 A separate follow-up experiment in the study brought back inter-individual differences in the response to sleep depriva- four of the subjects (4 to 7 months later) for the same 40 hours tion for all the tests employed. That is, individual subjects of sleep deprivation, but this time with occasional auditory and tended to respond similarly on all tests during their three vibrotactile alerting stimuli. These stimuli were found to have sleep deprivation periods, but differed considerably among no overall effect on the time course of alertness deterioration each other. Intraclass correlation coefficients (used to quan- for any of the subjects. Remarkably, each subject nearly dupli- tify trait-like inter-individual variance in of each of the tests) cated their original time course of alertness deterioration as showed that, across the 13 tests, 68% to 92% of the variance measured by PVT lapses. "There is an apparently reproducible in the neurobehavioral data were explained by stable individ- `fingerprint' quality to the overall bout-to-bout profile of PVT ual differences. The effect of the amount of sleep obtained in lapses for each of the subjects between experiments I and II" days before (i.e., prior sleep history) on performance during (Dinges et al. 1998, Page 91). Figure 12 shows bout-to-bout sleep deprivation was statistically significant, but modest in PVT lapses for a single typical subject during the first experi- comparison with the observed inter-individual differences. ment without alerting stimuli and the second experiment with Although each subject tended to show a stable response on them. Although this part of the study involved only four sub- specific tests, those showing the greatest deficits on one test did jects, the individual differences in fatigue susceptibility were not necessarily show the same level of impairment on other significant and remarkably reliable. tests. In particular, inter-individual differences in subjec- Important new research findings (Van Dongen et al. 2004) tive measures of alertness did not correspond well with inter- strongly corroborate the view that there are significant "trait- individual differences in objective measures of alertness. like" individual differences in susceptibility to alertness loss Figure 13 provides a sample of these results for one measure as a result of sleep deprivation. In the study, 21 healthy (PVT) and two of the sleep deprivation periods. In the figure, adults were sleep-deprived in a laboratory for 36 hours three data for 18 subjects are plotted. The horizontal axis is the aver- different times, separated by intervals of at least 2 weeks. age PVT lapses in the last 24 hours of the first sleep depriva- Every 2 hours they underwent "neurobehavioral" testing con- tion session, and the vertical axis is the corresponding measure sisting of 13 objective and subjective measures of alertness. for the second sleep deprivation session. The scatter plot shows There were two main factors under examination in the study: huge differences (about a sixfold difference) between the best inter-individual variation and variation due to prior sleep his- and worst performances. The scatter plot also illustrates a high tory. A striking finding of the study was that there were stable correlation between the first and second sleep deprivation PVT PVT Lapses - no alerting PVT Lapses - with alerting Figure 12. Time course of vigilance deterioration for a single subject sleep deprived twice several months apart, once without alerting stimuli and once with alerting stimuli. (SOURCE: Dinges et al. 1998.)