Individual differences in cognitive vulnerability to fatigue in the laboratory and in the workplace

H. P. A. Van Dongen and G. A. Kerkhof (Eds.) Progress in Brain Research, Vol. 190 ISSN: 0079-6123 Copyright Ó 2011 Elsevier B.V. All rights reserved.

CHAPTER 9

Individual differences in cognitive vulnerability to fatigue in the laboratory and in the workplace Hans P. A. Van Dongen{,*, John A. Caldwell, Jr.{ and J. Lynn Caldwell} {

Sleep and Performance Research Center, Washington State University, Spokane, WA, USA { Fatigue Science, Honolulu, HI, USA } Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH, USA

Abstract: Individual differences in cognitive functioning during extended work hours and shift work are of considerable magnitude, and observed both in the laboratory and in the workplace. These individual differences have a biological basis in trait-like, differential vulnerability to fatigue from sleep loss and circadian misalignment. Trait-like vulnerability is predicted in part by gene polymorphisms and other biological or psychological characteristics, but for the larger part it remains unexplained. A complicating factor is that whether individuals are vulnerable or resilient to sleep deprivation depends on the fatigue measure considered—subjective versus objective assessment, or one cognitive task versus another. Such dissociation has been observed in laboratory data published previously, and in data from a simulated operational setting first presented here. Discordance between subjective and objective measures of fatigue has been documented in various contexts, and may be one of the reasons why vulnerable individuals do not systematically opt out of professions involving high cognitive demands and exposure to fatigue. Discordance in vulnerability to fatigue among different measures of cognitive performance may be related to the “task impurity problem,” which implies that interrelated cognitive processes involved in task performance must be distinguished before overall performance outcomes can be fully understood. Experimental studies and cognitive and computational modeling approaches are currently being employed to address the task impurity problem and gain new insights into individual vulnerability to fatigue across a wide range of cognitive tasks. This ongoing research is driving progress in the management of risks to safety and productivity associated with vulnerability to cognitive impairment from fatigue in the workplace. Keywords: resilience to fatigue; interindividual differences; performance impairment; air force pilots; sleep deprivation; shift work.

*Corresponding author. Tel.: þ1-509-358-7755; Fax: þ1-509-358-7810 E-mail: [email protected] DOI: 10.1016/B978-0-444-53817-8.00009-8

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Trait individual differences in vulnerability to fatigue Individual differences in tolerance for, adaptation to, and impairment from extended work hours and shift work have been documented across a range of operational settings (Gillberg and Åkerstedt, 1985; Härmä, 1995; Monk and Folkard, 1985). Evidence is accumulating that these individual differences may have a biological basis (Van Dongen, 2006), involving differences in vulnerability to fatigue (sleepiness, loss of alertness) due to sleep deprivation and circadian misalignment. Fatigue is biologically regulated by a sleep/wake homeostatic process, which builds up pressure for sleep as a function of time awake and reduces this pressure as a function of time asleep, in interaction with a circadian process, which causes a waxing and waning of pressure for wakefulness as a function of time of day (Daan et al., 1984; Dijk and Czeisler, 1994; Mollicone et al., 2010; Van Dongen and Belenky, 2009). When these two processes are temporally misaligned due to sleep deprivation, night work, or transmeridian travel (jet lag), a state of fatigue ensues. Circadian adjustment (i.e., adaptation of the biological clock) and extended-duration recovery sleep reduce fatigue, but it can take several days to weeks before fatigue is dissipated (Axelsson et al., 2008; Banks et al., 2010; Belenky et al., 2003; Bjorvatn et al., 2006; McCauley et al., 2009). The sleep/wake and circadian regulation of fatigue is described in greater detail elsewhere (Dijk and Lockley, 2002; Van Dongen and Dinges, 2005; Van Dongen et al., 2010) . There are trait-like individual differences in the biological processes regulating fatigue, as was first demonstrated in a study of repeated exposure to sleep deprivation (Van Dongen et al., 2004a). A sample of 21 healthy young adults underwent three 36-h sleep-deprivation sessions under strictly controlled laboratory conditions. In the week preceding two of the three sleep-deprivation sessions, subjects were required to satiate their sleep need by extending time in bed to

12 h per day. In the week preceding the other sleep-deprivation session (randomly selected), they were required to restrict their sleep to no more than 6 h time in bed per day. Performance in a variety of cognitive tests and subjective measures of fatigue was measured every 2 h during each sleep-deprivation session. There were substantial individual differences in the magnitude of performance impairment and subjective fatigue, compared to which the effect of sleep history (sleep satiation versus sleep restriction in the week before) was negligible. The individual differences were stable within subjects across the two sleep-deprivation sessions with prior sleep satiation, with values for the intraclass correlation coefficient (ICC; a measure of within-subject replicability) ranging from 67.5% to more than 90% depending on the outcome measure. The individual differences also remained stable for the sleepdeprivation session with prior sleep restriction. The magnitude, replicability, and robustness of the individual differences in this study indicated that vulnerability to sleep deprivation is a trait (Van Dongen et al., 2004a), which has been referred to as “trototype” (Van Dongen et al., 2005) and is most likely biological in nature. Other aspects of fatigue and its biological regulation exhibit substantial individual differences as well (Van Dongen et al., 2005), although the experimental evidence is strongest for vulnerability to sleep deprivation. A wide search for predictors of the individual differences has been started (King et al., 2009). For vulnerability to sleep deprivation, baseline performance capability and a number of other candidate predictors have been ruled out (Van Dongen et al., 2004a). However, polymorphisms in genes associated with the homeostatic process (Rétey et al., 2006) and the circadian process (Viola et al., 2007) appear to predict individual differences in fatigue and its regulation to some extent (Landolt, 2008). In addition, neuroimaging studies have revealed brain structure and activation patterns that may be predictive of individual responses to sleep deprivation (Chee and Chuah, 2008; Drummond

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et al., 2005; Mu et al., 2005; Rocklage et al., 2009), albeit that the interpretation of such findings is not unambiguous (Van Dongen, 2005). A recent study suggested that an interaction between the personality trait of extraversion and engagement in social interaction predicts differences in vulnerability to psychomotor vigilance impairment during sleep deprivation (Rupp et al., 2010). Yet, whether these various predictors explain a substantive portion of the individual differences in vulnerability to sleep deprivation is not at all clear (King et al., 2009). A curious finding in the original study demonstrating trait-like individual differences in vulnerability to sleep deprivation (Van Dongen et al., 2004a) was that whether subjects were vulnerable or resilient depended on the outcome measure considered. In other words, responses to sleep deprivation were stable within subjects for any given measure of performance or fatigue, but were discordant across different measures. The fatigue variables observed during the study clustered in three distinct (orthogonal) dimensions: (1) eight different subjective measures of fatigue and mood; (2) four different objective performance tasks; and (3) the psychomotor vigilance test (PVT; Dinges and Powell, 1985). The dissociation between subjective experiences and objective performance outcomes is not surprising, as it has been reported in other studies and other contexts as well (e.g., Leproult et al., 2003; Van Dongen et al., 2003, 2004b). The four objective performance tasks in the second dimension were all relatively brief (6.5–8 min), exhibited baseline differences in aptitude, displayed learning curves, and relied on working memory performance. In contrast, the PVT version employed in the study was relatively long (20 min), showed little effect of aptitude and had no appreciable learning curve (Van Dongen et al., 2003), and relied primarily on sustained attention (Doran et al., 2001). Which of these factors, if any, are responsible for the dissociation in individual differences between the PVT and the other objective performance measures of the study has not yet been elucidated.

Individual differences in vulnerability to fatigue in operational settings The existence of trait-like individual differences in vulnerability to fatigue may be crucially important for workers in 24/7 operational settings, such as medical personnel (Czeisler, 2009), first responders (Lammers-van der Holst et al., 2006), and aviators (Caldwell et al., 2008). However, it is not a priori evident that laboratory-based assessments of individual variability translate reliably to the workplace. In populations that are highly trained and also frequently exposed to extended work hours and shift work schedules, practice effects and selection or self-selection effects could result in a strong bias for retaining only the most fatigue-resistant individuals. This issue was considered in a study of extended wakefulness in U.S. Air Force fighter pilots (Van Dongen et al., 2006), which are a highly trained and highly selected population. Ten active-duty F-117 “Nighthawk” stealth fighter pilots were deprived of sleep for 38 h, and during the last 24 h they were studied five times, at 5-h intervals, in a high-fidelity flight simulator. Systematic individual differences in the effects of sleep deprivation on the pilots’ performance were observed for a variety of flight maneuvers. This is illustrated in Fig. 1 for a 720 left turn, where systematic individual differences accounted for 57.8% of the variance after correcting for baseline differences (Van Dongen et al., 2006). Neuroimaging research has suggested that the individual differences in vulnerability to fatigue among pilots may be predictable by baseline differences in cortical activation (Caldwell et al., 2005). The findings of this line of research suggested that selection and self-selection mechanisms cannot be counted upon to eliminate individual differences in vulnerability to fatigue from the work force, even for highly demanding professions in which extended work hours and night shifts are commonplace and selection pressures are high. Among a variety of simulated flight performance measures and some subjective scales administered

148 0.8 Relative performance

0.6 0.4 0.2 0.0 –0.2 –0.4 –0.6 –0.8 Subjects Fig. 1. Individual fighter pilots’ performance for roll angle accuracy on a 720 left turn in a high-fidelity F-117 flight simulator, as measured every 5 h during the night and subsequent day of a period of 38 h of total sleep deprivation. Tick marks on the abscissa represent the 10 individual pilots in the study, ordered by the magnitude of their performance impairment (the pilots most resilient to sleep deprivation are on the left). The ordinate represents the systematic individual differences over time in the pilots’ performance, expressed relative to each other after correcting for baseline differences. Figure based on data described in Van Dongen et al. (2006), and adapted from Van Dongen and Belenky (2009) with permission.

in conjunction with the flight simulator sessions, nine variables as listed in Table 1 were significantly impacted by fatigue during the extended wakefulness and displayed at least moderately stable individual variability (defined here as an ICC value greater than 40% after correcting for baseline). For these nine outcome measures, which were not reliably predicted by age, experience, or prior night's sleep, it was again observed that whether subjects were vulnerable or resilient to impairment depended on the measure considered. The variables were subjected to principal component analysis (PCA)—see Table 1—which revealed that they clustered in three distinct (orthogonal) dimensions: (1) five different measures of simulated flight performance, (2) three different subjective measures of fatigue and mood, and (3) roll angle accuracy on the left 720 turn. The dissociation between subjective experiences and objective performance fits with the results discussed earlier. The finding may

provide a clue as to why people do not appear to self-select out of operational settings that put them at excessive risk due to fatigue: they may not be subjectively aware of their vulnerability. The distinction between roll angle accuracy on the left 720 turn and other performance measures of basic piloting skills was unexpected. It is possible (and has been anecdotally reported) that the left 720 turn is disorienting, or has the greatest sustained attention demand. The latter explanation would place it in the same category as the PVT, and set it apart for that reason. At present, this is speculation, but new lines of research, as discussed in the next section, have been initiated to shed further light on the issue. Continuing the analysis of the data, subject-specific time series representing each of the three orthogonal dimensions in Table 1 were formed using the standardized factor scores derived from the PCA. These time series were subjected to mixed-effects analysis of variance (ANOVA; see Van Dongen et al., 2004c) to assess the temporal profiles of change in each dimension across the five measurement times, as well as the individual pilots’ overall standing relative to each other. The left-hand panels in Fig. 2 show the group-average (baseline-corrected) responses to extended wakefulness for each of the dimensions of individual variability. The right-hand panels in Fig. 2 illustrate the systematic individual differences among the 10 pilots, which varied across the three dimensions as expected. The stability of the individual differences was high: ICC ¼ 63.7% for factor 1, ICC ¼ 57.8% for factor 2, and ICC ¼ 70.5% for factor 3. This suggests that all three dimensions of fatigue observed in this study may, at least in part, be trait-like.

New research into distinct cognitive dimensions of vulnerability to fatigue The curious finding that systematic individual differences in vulnerability to sleep loss depend on the outcome measure at hand, both in a highly

149 Table 1. Factor loadings on orthogonal dimensions of individual variability Variable

ICC (%)a

Factor 1b

Factor 2b

Factor 3b

Left 720 turn, altitude accuracy Climbing left 540 turn, airspeed accuracy Left 360 turn, altitude accuracy Descending right 360 turn, airspeed accuracy Straight and level flying, heading accuracy POMS fatigue-inertiac POMS vigor-activityc Visual analog scale of sleepiness Left 720 turn, roll angle accuracy

41.4 58.2 59.6 63.9 46.8 49.4 64.3 61.4 57.8

0.83 0.82 0.80 0.78 0.55 0.11 0.11 0.42 0.07

0.16 0.09 0.02 0.03 0.09 0.95 0.92 0.73 0.13

0.09 0.05 0.24 0.32 0.44 0.03 0.10 0.31 0.89

a

Intraclass correlation coefficient (corrected for baseline differences) measuring replicability of individual differences over time. The scree plot of eigenvalues indicated that three factors should be retained, which together explained 74.9% of the variance. Factor loadings (after varimax rotation) greater than 0.5 or less than  0.5 are underlined to help with interpretation of orthogonal dimensions. c Profile of Mood States (POMS) subjective scales (McNair et al., 1971), where the vigor-activity scale was inverted so that greater values corresponded to greater impairment for all variables. b

controlled study of healthy young adults from the general population using laboratory measures of performance and fatigue (Van Dongen et al., 2004a) and in a simulator study of highly selected, active-duty jet fighter pilots using high-fidelity simulated flight performance measures (Van Dongen et al., 2006), suggests that there is much to learn yet about vulnerability to fatigue and individual differences therein. Several lines of research are being pursued to address this issue. A common thread is the “task impurity problem,” which entails that performance tasks involve a number of interrelated cognitive processes that must be distinguished to understand the causal factors determining overall performance outcomes (Whitney and Hinson, 2010). The criticality of the task impurity problem was recently underscored by a laboratory study of the effects of sleep deprivation on executive functions (Tucker et al., 2010). In this study, performance tasks designed to dissociate components of cognition showed that sleep deprivation affects distinct cognitive processes differentially. This suggests that multiple, distinct cognitive pathways must be accounted for when considering how individual differences in vulnerability to fatigue may be expressed in performance on

cognitive tasks and clusters of tasks. New laboratory studies have been designed to systematically unravel this, guided in part by new neuroimaging studies resolving different cognitive pathways involved in sleep-deprivation responses to specific performance tasks (Chee and Tan, 2010; Lim et al., 2007; Stricker et al., 2006). It will likely take a number of years before a reasonably comprehensive understanding of task-specific vulnerability to fatigue emerges from laboratory data being collected. However, existing data sets can also be used to help address the issue, in at least two complementary ways. The first approach makes use of cognitive models previously developed to explain task performance in contexts other than fatigue. A good example is the diffusion model (Ratcliff and McKoon, 2008), which was developed to describe cognitive performance on two-choice decision tasks in considerable detail (e.g., accuracy rates and response time distributions as a function of task difficulty). Applying the diffusion model to data from a two-choice numerosity discrimination task performed after 57 h of total sleep deprivation, it was found that sleep deprivation adversely affects multiple components of cognitive processing including the decision process, but not

150 Factor 1

1.8

0.8

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EBLUPs

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time awake (h) Factor 2

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time awake (h) Factor 3

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⫺1.8

time awake (h) Fig. 2. Pilots’ responses to sleep deprivation in three orthogonal dimensions of individual variability. The left-hand panels show group-average (baseline-corrected) responses for the five measurement times plotted across time awake (in hours), based on the standardized factor scores for each of the three factors derived from PCA (see Table 1). The right-hand panels show the overall responses to extended wakefulness across the five measurement times for each individual (different symbols), expressed relative to the group average and offset horizontally for clarity. These are the subjects’ estimated best linear unbiased predictors (EBLUPs) for each of the three factors. Upward corresponds to greater impairment in every panel. Factor 1: basic piloting skills; factor 2: subjective fatigue; factor 3: roll angle accuracy on the left 720 turn.

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the duration of the stimulus-encoding and response-output processes (Ratcliff and Van Dongen, 2009). Further work will apply the diffusion model to data from other performance tasks, and examine communalities in the changes in model parameters due to sleep deprivation so as to build a generic account of cognitive impairment resulting from fatigue. The second approach utilizes cognitive architectures, which are computational models of brain function representing a general theory of human cognition. An example is the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture (Anderson et al., 2004), which incorporates computational modules for mechanisms of perception, cognition, and action, with a number of parameters determining the speed and effectiveness of these processes. Ongoing research focuses on how fatigue may influence those parameters, such that the cognitive architecture produces the correct moment-to-moment performance on simulated tasks (Gunzelmann et al., 2007). Individual differences in simulated task performance can be produced by manipulating certain parameters differentially, which provides insight into which distinct cognitive processes may be responsible for task-specific individual differences in vulnerability to fatigue (Gunzelmann et al., 2008, 2009). Over time, these lines of research will yield fundamental insights into individual vulnerability to fatigue across a wide range of cognitive tasks and operational environments. This is critical for managing the risks to safety and productivity associated with cognitive vulnerability to fatigue in operational settings. Cutting-edge approaches to fatigue risk management make use of mathematical models of fatigue to predict and mitigate cognitive impairment (Hursh and Van Dongen, 2010). Bayesian statistical techniques have even made it possible to account for individual differences in mathematical model predictions of fatigue (Olofsen et al., 2004; Smith et al., 2009; Van Dongen et al., 2007). However, these techniques do not yet capture individual vulnerability to fatigue at the level of specific tasks or

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