Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 159
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance 5 Strategies for Monitoring Cognitive Performance Up to this point the central focus of this report has been on the assessment or monitoring of the combat service member’s capacity to perform physical tasks. In this regard, the importance of factors that influence bone and muscle health, as well as other processes that underlie and optimize physical endurance and resistance to physical injury, have been highlighted, and for good reason. Clearly it is necessary to ensure that operational personnel are as physically fit as possible because success on the battlefield is to a great extent dependent on the ability of combat service members to carry and operate weapons, overcome physical obstacles, traverse distances in harsh environments, and endure a host of physical stresses and strains that could easily overwhelm unfit individuals. However, optimal performance in today’s military also is increasingly dependent on a high level of cognitive fitness. The widespread use of computerized weapon systems; complicated communications and targeting devices; high-performance aircraft, tanks, and maritime vessels; and the technologically advanced diagnostic systems used in the maintenance of military equipment demands the highest levels of cognitive readiness. In the following sections, operator cognitive fatigue, one of the principal threats to military readiness, is discussed. Also included is an overview of the primary operational causes of fatigue, followed by a brief synopsis of strategies that should be considered for monitoring the cognitive status of servicemembers. The fact that the focus here is on the fatigue that results from sleep deprivation should in no way imply that this is the only stressor of concern in the operational environment. As noted earlier in this report, combat service members are routinely exposed to a wide variety of physical and environmental stresses that, if ignored, will ultimately degrade operational performance. Heat stress and dehydration pose major threats to the cognitive readiness of ground combat service members, and these factors can be expected to exacerbate the fatigue from sleep loss and strenuous work. In the aviation arena, uncomfortable levels of noise, heat, vibration, and mental workload must be dealt with by pilots on a day-to-day basis, and these stresses likewise can be expected to compromise cognitive
OCR for page 160
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance capacity. However, since a detailed discussion of each of these areas is beyond the scope of this report, it is hoped that the reader can generalize many of the concepts from the forthcoming discussion of the most common cause of operator fatigue (sleep deprivation) to the fatigue stemming from other operational stressors. THE PROBLEM OF SLEEPINESS AND COGNITIVE DEGRADATION IN MILITARY SETTINGS Current military doctrine requires that units operate around the clock during times of conflict because the success of battlefield operations depends, at least in part, on maintaining the momentum of continuous day-night operations (U.S. Army, 1997). Technological advances, such as night vision devices, have enhanced the night-fighting capabilities of both ground and air combat military personnel, making around-the-clock missions a highly feasible component of the modern military strategy. Combining efficient day and night fighting capabilities across successive 24-hour periods places a significant strain on enemy resources and presents a clear tactical advantage for U.S. forces. In fact, the Air Force Chief of Staff recently noted that persistent and sustained operations “24 hours a day, seven days a week” are essential to attaining U.S. victory in today’s battle space (Elliott, 2001). However, there are difficulties inherent in maintaining effective around-the-clock operations. For example, aircraft can function for extended periods without adverse effects, but human operators need periodic sleep for the restoration of both body and cognitive function (Home, 1978). Depriving humans of proper restorative sleep produces attention lapses and slower reaction times, which are associated with poor performance (Krueger, 1991). It has been determined that sleep-deprived personnel lose approximately 25 percent of their ability to perform useful mental work with each 24-hour period of sleep loss (Belenky et al., 1994). Thus by the end of 3 days without sleep, combat service members may be considered totally ineffective in the operational setting, especially if they are performing complex tasks, such as operating computerized command-and-control centers or flying an aircraft. This is a significant problem given that an Army manual makes it clear that “Soldiers in continuous operations can expect to be deprived of extended regular sleep, possibly any sleep, for as long as three to five days” (U.S. Army, 1991, P. 3–10). Over the past several years the problem of sleep loss and fatigue has escalated because of increased requirements on military forces due to reductions in manpower and other resources. Over the past 10 to 15 years Army funding has been cut 38 percent and the number of personnel has been cut 35 percent, while missions have increased 300 percent (U.S. Army, 1996). A similar problem exists in the Air Force, where there has been a 37.7 percent reduction in military personnel and about a 50 percent reduction in the number of active Air Force tactical wings (Daggett and Belasco, 2002), while the operational tempo has
OCR for page 161
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance increased by as much as 400 percent (Correll, 1998). U.S. military capabilities are increasingly strained as understaffed units strive to accomplish more work with fewer resources. The ultimate result has been diminished military combat readiness (Spencer, 2000), in part because of increased levels of physical and cognitive fatigue. Although reductions in available resources do not guarantee that sleep deprivation will be a problem in the operational environment, they create a situation in which the available personnel are more likely to work prolonged shifts without the benefit of sufficient rest. Krueger (1991) reported that the efficiency of combatants in sustained operations can be significantly compromised by inadequate sleep. Vigilance and attention suffer, reaction time is impaired, mood declines, and some personnel begin to experience perceptual disturbances. Naitoh and Kelly (1992) warned that poor sleep management in extended operations quickly leads to motivational decrements, impaired attention, short-term memory loss, carelessness, reduced physical endurance, degraded verbal communication skills, and impaired judgment. Angus and Heslegrave (1985) noted that cognitive abilities suffer 30 percent reductions after only 1 night without sleep, and 60 percent reductions after a second night. Although all types of performance are not affected to the same degree by sleep loss, the fatigue from prolonged duty periods clearly is a threat to unit readiness in the operational context. This is especially the case for tasks that are lengthy, devoid of performance feedback, and boring. Caldwell and Ramspott (1998), Wilkinson (1969), and Wilkinson and colleagues (1966) found that when task durations extend beyond 15 to 20 minutes, performance deteriorations from fatigue become far more pronounced than when the task durations are shorter. Wilkinson (1961) found that knowledge of results alone can significantly attenuate the effects of sleep deprivation on some types of vigilance tasks. In addition, Wilkinson (1964) reported that while reaction-time tasks and vigilance tasks are most degraded by sleep loss, more interesting learning tasks and performance tasks often are less affected, presumably because the subject’s level of interest provides greater motivation and ability to resist attention lapses or outright sleep episodes (although, as warned by Dinges and Kribbs , this works only up to a point). In addition to the impact of task characteristics, it should be noted that there are individual differences in resiliency to sleep loss. Although this is an area that has not been sufficiently researched at this point, Van Dongen and colleagues (2003) found that there are basic interindividual differences in vulnerability to sleep debt that cannot be explained on the basis of differences in sleep need (i.e., short vs. long sleepers). This source of variability no doubt contributes to findings that there are wide differences in the accuracy with which several currently available methods can predict performance decrements. As the reader later reviews the strategies proposed to monitor alertness in the field, a recent U.S. Highway Traffic Safety report should be kept in mind (Dinges et al., 1998). In that report, even the eye-closure measure PERCLOS, which was found to be one
OCR for page 162
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance of the most predictive indicators of fatigue-related performance lapses under laboratory conditions (r=0.7), sometimes correlated with performance only at about r=0.3 in some individuals. Two electroencephalographic (EEG) algorithms showed a median predictive capability of only 0.3 to 0.4, and one type of eye-blink monitoring device correlated with minute-to-minute lapse frequency at a median level of 0.17. Thus despite the known dangers of fatigue and the established need to accurately measure it in some contexts, it is clear that much work remains to be done on monitoring technologies that can accurately predict moment-to-moment performance fluctuations. Clearly sleep loss from prolonged duty periods is a major threat to unit readiness in the operational environment. In addition, factors related to the requirement for shift work or night operations also pose difficulties. During military operations a number of personnel are rotated from the day shift to the night shift so that operations can be continuous. Night-shift work in and of itself presents problems associated with insufficient sleep, increased fatigue, and sleepiness on the job because people are working at times when their bodies are programmed for sleep (Åkerstedt, 1988; Åkerstedt and Gillberg, 1982; Härmä, 1995; Penn and Bootzin, 1990). These same people are trying to sleep at times when their bodies are accustomed to being awake. Studies have shown that even small amounts of shift-work-related sleep disruption can decrease sleep length by 2 or more hours per night, and even this small amount of sleep loss can lead to significant performance and alertness decrements (Gillberg, 1995; Rosenthal et al., 1993; Taub and Berger, 1973). The initial period of adjustment from days to nights is particularly problematic since work must still be accomplished despite the fact that the human body is incapable of changing its internal sleep/wake rhythms quickly. Thus, personnel are faced with the problem of performing during their circadian low points until their internal rhythms adapt to the new schedule. In addition, impaired alertness and performance can result from the requirement for personnel to awaken at inopportune times. For example, early-morning report times require personnel to rise while their core body temperatures are still low, leading to difficulties in awakening and feelings of being inadequately rested (Åkerstedt et al., 1991). Clearly, one of the greatest threats to military readiness is the insufficient sleep that results from prolonged duty periods, shift work, and a related phenomenon, jet lag. Dinges (1995) summarized the impact of sleepiness/fatigue by pointing out that people who work when overly tired must expend increased energy simply to remain awake while suffering from poor, inefficient, and variable performance; impaired attention, information processing, and reaction time; reduced short-term memory capacity; and increased involuntary lapses into varying durations of actual sleep episodes. Momentary episodes of sleep and the periods of drowsiness preceding these “sleep attacks” are thought to underlie many serious accidents and incidents that are typically attributed to “insufficient operator attention.”
OCR for page 163
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance USEFUL APPROACHES FOR PREDICTING OPERATOR ALERTNESS Sleep As indicated previously, sleep quality and quantity are important determinants of operator cognitive status. Frequent sleep disturbances can adversely affect next-day mood and performance as much as severely truncated sleep periods can. Sleep Quality Examination of the structure and sequence of an individual’s sleep cycles offers crucial information about the restorative value of the sleep period. Although adequate sleep duration exerts a substantial impact on subsequent cognitive function, it is also important that the sleep be of high quality. The precise impact of changes in sleep content (i.e., distribution and amount of the sleep stages described below) remains a matter of some debate, since some investigators have shown that the loss of slow-wave sleep adversely impacts alertness (Walsh et al., 1994), whereas others have reported that neither slow-wave sleep restriction nor rapid-eye-movement (REM) sleep restriction lead to performance decrements (Agnew et al., 1967). Nonetheless, it is clear that sleep fragmentation (one aspect of sleep quality) exerts an important influence on next-day alertness (Roehrs et al., 2000). Many clinical sleep disorders are characterized by frequent sleep disruptions (Roehrs et al., 2000), and experimentally induced sleep fragmentation has been shown to degrade the recuperative value of sleep (Gillberg, 1995). The usual sleep cycle is characterized by a series of stages that can be distinguished using polygraphic techniques. Attenuation of alpha activity (8–12 Hz) is the first sign of a transition from wakefulness to sleep. This is followed by increased theta (3–7 Hz) and vertex sharp waves accompanied by slow eye movements and loss of facial muscle tone. Next, during stage 2 sleep, there are bursts of K-complexes (a special type of delta wave) and 12 to 14 Hz activity (sleep spindles) in the virtual absence of typical delta waves (0.5–2 Hz). After stage 2 sleep, there is a progression into slow-wave sleep (stages 3 and 4) that is characterized by increasing amounts of delta activity (0.5–2 Hz). Stages 1 through 4 sleep are all generally considered to be non-REM sleep. These stages are interspersed with REM periods, which consist of a desynchronized, low-amplitude EEG with no K-complexes or spindles, sporadic rapid eye movements, and the virtual absence of muscle activity. As the night progresses, the REM periods typically become more numerous, whereas the amount of very deep (slow-wave) sleep decreases. Adults typically cycle through non-REM and REM sleep approximately every 90 minutes during an 8-hour sleep period. Disruptions to normal sleep architecture have been correlated with daytime sleepiness. Frequent transitions into a very light stage of sleep during the night
OCR for page 164
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance clearly impact the restorative value of the sleep period. Several studies in which subjects have been aroused (but not necessarily awakened) by auditory stimuli have shown that next-day performance deteriorates and both subjective and objective measures of sleepiness increase (Roehrs et al., 1994; Thiessen, 1988). It is important to note that these are sleep disturbances that may not produce behavioral arousals, so the affected individuals are often unaware that their sleep is being disrupted. In a military field environment there are obviously many factors that can produce such disruptions. Although a discussion of each of these is beyond the scope of this report, the presence of high levels of ambient light, excessive environmental noise, temperature extremes, and uncomfortable sleep surfaces rank high on the list. Often these problems create outright sleep fragmentation (which produces shortened sleep periods) but, in many cases, they produce their deleterious effects by simply degrading sleep quality. Unfortunately it is unlikely that in the near future it will be possible to precisely monitor sleep-quality decrements in the field. Thus more attention has been aimed at monitoring sleep quantity, another major contributor to on-the-job alertness. Sleep Quantity As noted earlier, sleep restriction and sleep deprivation impair mood and performance. Balkin and colleagues (2000) found that chronic sleep reductions of even 2 hours per night result in performance decrements on vigilance tasks, and that even after 7 consecutive days of shortened sleep, there is no evidence of an adaptive response. Furthermore, these authors reported that severe sleep restriction not only hampered a wide variety of functions during the deprivation period itself (including the ability to accurately drive through a simulated course), but it continued to adversely affect performance capabilities for several days after full 8-hour sleep periods were once again permitted. Bonnet (1994) found that total sleep deprivation exerted especially noticeable effects on tasks that were lengthy, tasks that did not offer immediate performance feedback, and tasks that were externally paced. Sleep loss had a greater effect on newly learned skills as opposed to well-established skills, and it degraded complex tasks more than simple ones and those that had short-term memory requirements. Subjective feelings of sleepiness and fatigue often begin to appear before actual performance decrements, as do EEG indications of increased slow-wave activity, and thus may have value as predictors of performance decrements. Circadian Effects Regardless of the exact nature of the effects of insufficient sleep on different types of activities or physiological processes, it is clear that insufficient sleep quality or quantity degrades performance. In addition, working at times that are incompatible with circadian rhythms can produce problems that are separate from those associated with simply being awake or being on the job for a long
OCR for page 165
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance period of time. Performance on the night shift is often less optimal than performance on the day shift regardless of the nature of the work. The probability of accidents on the highways, in industry, and in aviation is higher at night in part because of increased sleepiness (Åkerstedt, 1995). Monk and Folkard (1985) have shown that nighttime work impairs even the simplest tasks. Night workers are slower to handle a telephone switchboard, more error prone when reading meters, more sluggish at the task of spinning thread, less able to remain alert while driving, and less vigilant at operating freight trains. Dinges (1995) has shown that nontraditional work hours, in combination with increased automation, have substantially increased the risk of fatigue-related problems throughout the industrialized world. Furthermore, there is evidence that a number of high-profile catastrophes (i.e., the grounding of the Exxon Valdez, the Space Shuttle Challenger accident, the crash of Korean Air flight 801, and the near meltdown at Three Mile Island) were at least partially attributable to the fatigue associated with night work (Mitler et al, 1988; NTSB, 1990, 2000). Of particular concern to the military aviation community is the considerable evidence that night flights are especially vulnerable to cognitive lapses, or “micro sleeps” (i.e., brief periods during which sleep uncontrollably intrudes into wakefulness). Moore-Ede (1993) found that while micro sleeps occurred in the cockpits of flight simulators regardless of the time of day, there was a tenfold increase between the hours of 0400 and 0600; pilots made the greatest number of errors during this time. Wright and McGown (2001) found that long-haul pilots were especially compromised by sleepiness on flights that departed late in the night compared with those that departed earlier. Furthermore, many of the micro sleeps experienced by these pilots were so short (less than 20 seconds) that the crewmembers may not have been aware that they had fallen asleep. Rosekind and colleagues (1994) also found a substantial increase in microevents (slow-wave EEG activity and slow eye movements) on long-haul flights, with night flights being particularly affected compared with day flights. Vigilance performance and subjective alertness ratings were degraded more at night as well. Caldwell and colleagues (2002) found that the combination of sleep loss and night flying significantly accentuated the type of slow-wave EEG activity that has been associated with insufficient alertness, while concurrently causing the types of mood and cognitive deteriorations that impair crew coordination and responses to system deviations or failures. Because of findings like these it has become clear that both sleep and circadian effects must be considered in any attempt to estimate the impact of work and sleep schedules on performance. Circadian cycles can be fairly well tracked by continuously measuring core body temperature, and sleep quantity and quality can be assessed by EEG techniques (see below). However, besides utilizing direct measures of physiological indices to help predict performance, predictive computerized models have been developed to estimate fatigue and cognitive performance capacity based on what is generally known about sleep and circadian influences.
OCR for page 166
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance Computerized Cognitive Performance Prediction Models Several organizations and individual scientists in the United States and abroad have developed computerized models (and scheduling tools based on these models) that predict cognitive performance decrements using known information about sleep and circadian rhythms. Such tools do not actually monitor any aspect of individual physiology, but they make predictions via keyboard or actigraphic inputs about work and sleep schedules. Two related prediction models are the Sleep Performance Model and the Sleep, Activity, Fatigue and Task Effectiveness (SAFTE) model, both of which were developed by Dr. Hursh of Science Applications International Corporation under Army and Air Force sponsorship (Eddy and Hursh, 2001). An additional model is the System for Aircrew Fatigue Evaluation (SAFE), which was developed at QinetiQ Centre for Human Sciences in the United Kingdom (Belyavin and Spencer, 2004). The Sleep Performance Model is an early version of the SAFTE model that was designed to be used in conjunction with wrist actigraphy. Both the SAFTE and SAFE versions are models that are applied to proposed work/sleep schedules (based on operator input provided via a computer keyboard) in order to identify the changes in cognitive readiness that would be expected to occur in personnel at various times during select work cycles. (SAFTE can also take “after-the-fact” scheduling input from actigraphic recordings.) Although other models and implementations are available, a complete review is beyond the scope of this report. However, this subject is treated in detail in a special edition of Aviation, Space, and Environmental Medicine (2004, Vol 75, Sup 3). The present state of the art permits only general predictions about the impact of specific work/rest schedules on the cognitive alertness of personnel, and additional work will be needed before such models can accurately predict the performance of any specific individual. This is because the models do not account for individual differences and because they do not monitor any physiological parameter to make their predictions. Since, for instance, the models do not actually monitor body temperature, they must rely on averaged data to predict circadian phase. In addition, since they do not examin physiological sleep quality, they can only make assumptions about the restorative value and amount of sleep that is being obtained. Thus, even if all of the prediction equations are perfect, guesswork remains due to the absence of direct physiological inputs, especially with regard to sleep quality and quantity. SAFTE A schematic of the SAFTE model appears in Figure 5–1. Note that SAFTE is based on the concept of a sleep reservoir that quantifies the impact of sleep-related processes on cognitive readiness, or “cognitive effectiveness.” Sufficient sleep time fills the sleep reservoir, and hours of wakefulness deplete the reser-
OCR for page 167
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance FIGURE 5–1 A schematic of the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model. SOURCE: Eddy and Hursh (2001). Figure reprinted with permission of Biodynamics and Protection Division, Human Effectiveness Directorate. voir. The sleep accumulation process is affected by sleep intensity (which is modulated by existing sleep debt and circadian factors) and quality of sleep (which is affected by sleep continuity). Cognitive readiness or effectiveness is predicted based on the level of the sleep reservoir and the time of day (circadian phase), as well as on the potential influence of short-term, postsleep grogginess (referred to as “sleep inertia”). This model has been implemented through the Fatigue Avoidance Scheduling Tool. This tool is useful for identifying times at which performance might be compromised within a given work/sleep schedule, and it is useful for optimizing schedule development because it allows an operator/planner to ask a series of “what if” questions. For example, as shown in Figure 5–2, a planner can view the predicted effects of 2 days without sleep, and then ask “What if we placed a 4-hour nap during this 40-hour period of otherwise continuous wakefulness?” As the figure indicates, such a napping strategy could offer a 20 percent
OCR for page 168
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance FIGURE 5–2 An example of output from a Fatigue Avoidance Scheduling Tool (FAST) to help predict cognitive effectiveness (%). Example: 2 days with no sleep compared with 2 days with 4 hours of sleep. improvement in cognitive effectiveness over what would be expected with no sleep at all. The predictive capability of the SAFTE model has been established by comparing the model’s output with laboratory data collected during various sleep-deprivation studies. For example, model predictions accounted for 89 percent of the variance (degradations) in throughput on serial addition-subtraction across 72 hours of sleep loss in one study, and 98 percent of the variance in throughput on a variety of cognitive tests across 54 hours of sleep deprivation in another (Hursh et al., 2004). Throughput is a combined speed/accuracy measure that on many basic cognitive tests is presumed to reflect the individual’s capacity to perform mental discriminations, react to incoming stimuli, think logically, process information, and comprehend language. Although the tasks on which SAFTE was validated are not typical military tasks, it is assumed that anything that degrades such basic mental facilities would also degrade operationally relevant performance. Future studies will validate the relationship between SAFTE model predictions and decrements in a variety of “real-world” performances. SAFE The SAFE model (Belyavin and Spencer, 2004) is similar to SAFTE in that it takes keyboard input about specific work/rest schedules and estimates per-
OCR for page 169
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance formance risk based on what is known about the impact of the body’s clock and time since sleep on alertness. In addition, the model accounts for the effects of sleep inertia. SAFE predictions were initially validated via comparisons with laboratory data collected during several studies of sleep deprivation and shift work. Select variables were used from tests of visual vigilance, continuous memory recall, psychomotor tracking, and the Multi-Attribute Task Battery. The results indicated that the basic model effectively predicted group performance on most aspects of these basic tasks. Later, SAFE predictions were compared with the subjective alertness ratings of a sample of commercial airline pilots across 72 flights (comprised of long-haul international trips). On some schedules, the model predicted mean alertness levels moderately well, with the exception of a sudden increased arousal that occurred at the end of some of the return flights. On other schedules, although the model tracked fatigue-related changes fairly well from the beginning to the end of each flight segment, the model underestimated alertness on outbound flights and overestimated alertness on return flights. Because of these discrepancies, the designers of SAFE performed additional studies and ultimately included prediction modifiers that considered not only the three basic alertness processes (time since sleep, circadian rhythms, and sleep inertia), but added the effects of: (a) multiple flight legs, (b) duration of time on duty, (c) the effects of consecutive and/or long tours of duty, (d) the impact of early report times, (e) the impact of daytime vs. nighttime sleep, and (f) the effects of sleep degradations during on-board rest periods. The addition of these factors substantially increased the predictive accuracy of the model. Sleep Monitoring There are two basic approaches for monitoring human sleep. The first, and most accurate, consists of electrophysiological recordings (polysomnograms). The second, and most practical for nonlaboratory settings, consists of activity-based recording (actigraphy). Polysomnography Polysomnographic recordings involving the collection of EEG, electromy-ographic (EMG), and electrooculographic (EOG) data from skin-mounted electrodes represent the most accurate way to monitor sleep parameters. For clinical and research purposes, sleep recordings are usually made in a sleep laboratory because of the available level of environmental control and the instrumentation required. EEG data are acquired with silver-silver chloride or gold electrodes attached to the scalp with collodion before the person retires for the night. A minimum of one or two EEG electrodes are attached, along with a mastoid (or ear-lobe) reference, two EOG electrodes, and two EMG electrodes placed underneath the subject’s chin. Amplification of the signals is accomplished via
OCR for page 184
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance els of optimal physical performance and the extent of performance deteriorations that would be of concern to combat service members engaged in demanding physical tasks. Thus self-ratings of operational status deserve serious consideration for their potential usefulness in status monitoring. SUMMARY No doubt there are strategies under development (and under refinement) that may contribute significantly to a further understanding of the basis of cognitive processing, as well as to the effects of fatigue, workload, and other factors that influence human performance. In all probability most will be useful only in laboratory environments or in fixed-based operational facilities (such as posts in which radar and sonar equipment are monitored or stations from which remote-controlled vehicles are piloted) where complex equipment can be housed, lengthy recording procedures can be conducted, and rigid controls can be maintained. Only a small subset of the strategies will likely be suitable for operational settings. Based on a general review of the literature, it appears that the most promising techniques for accomplishing real-time, continuous assessments of foot-soldier cognitive readiness in military field settings are: (1) actigraphy based, or (2) EEG based, although neither technique is currently ready for widespread application. As noted, the Walter Reed Army Institute of Research has made substantial progress in the development and validation of an actigraph-based, sleep/fatigue monitor that could be worn like a wristwatch in almost any environment. This device may be available by 2005. Concurrent work with high-impedance EEG and ECG electrodes will soon make it possible to continuously record brain activity, heart-rate data, and other electrophysiological parameters and, as noted above, both the EEG and ECG offer useful information about operator status. However, once these new sensors are sufficiently refined, work will remain in terms of mounting them in combat helmets or integrating them into combat clothing. Speech-pattern analysis at one time seemed to hold promise for the future since there is a fair amount of verbal radio communication in the modern operational environment, but the work on this particular measure has not been particularly encouraging. The most promising techniques for accomplishing real-time, continuous evaluations of the operators of military vehicles; the personnel responsible for manning radar, sonar, or other monitoring equipment; and those whose jobs consist of interfacing with computers and communications devices are: (1) EEG based, or (2) eye-movement based. The recording and evaluation of EEG activity becomes much more straightforward in settings in which operators are physically stationary and quiet because muscle and movement artifacts are attenuated. Furthermore, military aviators are required to wear flight helmets in which newly developed, high-impedance sensors could be mounted. Eye movement parameters (i.e., PERCLOS) have already proven feasible for the detection of
OCR for page 185
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance changes in truck driver alertness, and efforts are underway to establish an automated PERCLOS that could be used in aviation settings. Since many eye monitoring systems require the use of cameras that are aimed at the faces of the operators, this is a technology that is clearly more applicable for stationary operators who are already staring straight ahead (at least most of the time) in order to complete some type of monitoring or computer-based task. Questions about where these new monitoring approaches will be implemented are best considered first by assessing the feasibility of using them in specific environments (as noted above), and second by performing an analysis of the cost of the technology versus the cost of the mishap that the technology would be expected to prevent. Obviously, it is likely to be quite expensive to put some of the newest and most complicated monitoring devices in the hands of every foot soldier or to mount them in every military vehicle, and this in and of itself will pose a substantial barrier to widespread implementation. Thus a jeep driver or a member of a rifle platoon probably will not see the common use of operational alertness monitors for several years after such monitors first become available because of the initial expenses. Furthermore, a performance failure on the part of such individuals is unlikely to be a multimillion dollar catastrophe, so it would ultimately take the military years to reap sufficient savings from the technology to justify implementation in these segments of the overall force structure. The pilot of a B-2 bomber, however, or those operating other highly complex modern aircraft may be among the first to benefit from newly developed status-monitoring approaches because there are relatively few of these aircraft, and the cost of losing even one would be significant by any standard. Each B-2 aircraft costs more than $1 billion, and the expenses likely to result from a single B-2 air mishap would no doubt be far greater depending on what type of munitions were on board and what the aircraft crashed into during the mishap. On top of these considerations is the fact that B-2s are long-range, two-crew bombers in which aircrew fatigue is known to be an operational hazard (some missions extend well beyond 33 hours of continuous flight time). In light of these facts, automated, onboard alertness monitors would be an obvious choice for fulfilling a much-needed fatigue countermeasure role. Therefore, the costs associated with instrumentation of such a platform are easily justifiable based on the aircraft’s mission and the savings that would result from the prevention of even a single mishap. Such considerations and calculations will no doubt be applied to every potential site for future monitoring applications, at least until a relatively inexpensive and easy solution to the general status monitoring problem is found. While the search is underway, individuals and their commanders will be forced to rely upon the same types of general alertness predictions (based on group data) and the same subjective impressions about “go” and “no-go” status that have been used for years. A great deal of progress has been made toward helping the armed forces address fatigue-related cognitive decrements once they have been identified. However, highly reliable, efficient, and cost-effective
OCR for page 186
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance technological means of initially detecting and predicting those decrements remain to be developed. REFERENCES Agnew HW Jr, Webb WB, Williams RL. 1967. Comparison of stage four and 1-REM sleep deprivation. Percept Mot Skills 24:851–858. Åkerstedt T. 1988. Sleepiness as a consequence of shift work. Sleep 11:17–34. Åkerstedt T. 1995. Work hours, sleepiness and the underlying mechanisms. J Sleep Res 4:15–22. Åkerstedt T, Gillberg M. 1982. Displacement of the sleep period and sleep deprivation. Hum Neurobiol 1:163–171. Åkerstedt T, Kecklund G, Knutsson A. 1991. Spectral analysis of sleep electroencephalography in rotating three-shift work. Scand J Work Environ Health 17:330–336. Andreassi JL. 1989. Psychophysiology: Human Behavior and Physiological Response. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates. Angus RG, Heslegrave RJ. 1985. Effects of sleep loss on sustained cognitive performance during a command and control simulation. Behav Res Methods Instrum Comput 17:55–67. Balkin J, Thorne D, Sing H, Thomas M, Redmond D, Wesensten N, Williams J, Hall S, Belenky G. 2000. Effects of Sleep Schedules on Commercial Motor Vehicle Driver Performance. Federal Motor Carrier Safety Administration. DOT-MC-00–133. Washington, DC: U.S. Department of Transportation. Belenky G, Penetar DM, Thorne D, Popp K, Leu J, Thomas M, Sing H, Balkin T, Wesensten N, Redmond D. 1994. The effects of sleep deprivation on performance during continuous combat operations. In: Marriott BM, ed. Food Components to Enhance Performance. Washington, DC: National Academy Press. Pp. 127–135. Belyavin AJ, Spencer MB. 2004. Modelling performance and alertness: The QinetiQ approach. Aviation Space Environ Med 75:A93-A103. Blanc C, LaFontaine E, Medvedeff M. 1966. Radiotelemetric recordings of the electroencephalograms of civil aviation pilots during flight. Aerospace Med 37:1060–1065. Bonnet MH. 1994. Sleep deprivation. In: Kryger MH, Roth T, Dement WC, eds. Principles and Practice of Sleep Medicine. 2nd ed. Philadelphia: W.B. Saunders. Pp. 50–67. Brenner M, Cash JR. 1991. Speech analysis as an index of alcohol intoxication—The Exxon Valdez accident. Aviat Space Environ Med 62:893–898. Brenner M, Doherty ET, Shipp T. 1994. Speech measures indicating workload demand. Aviat Space Environ Med 65:21–26. Brookhuis K. 1995. Driver impairment monitoring by physiological measures. In: Hartley L, ed. Fatigue and Driving: Driver Impairment, Driver Fatigue and Driver Simulation. Bristol, PA: Taylor & Francis. Pp. 181–188.
OCR for page 187
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance Brookhuis KA, de Waard D. 1993. The use of psychophysiology to assess driver status. Ergonomics 36:1099–1110. Cacioppo JT, Tassinary LG, eds. 1990. Principles of Psychophysiology: Physical, Social, and Inferential Elements. New York: Cambridge University Press. Caldwell JA, Ramspott S. 1998. Effects of task duration on sensitivity to sleep deprivation using the multi-attribute task battery. Behav Res Methods Instrum Comput 30:651–660. Caldwell JA, Roberts KA. 2000. Differential sensitivity of using simulators versus actual aircraft to evaluate the effects of a stimulant medication on aviator performance. Mil Psychol 12:277–291. Caldwell JA, Wilson GF, Cetingue M, Gaillard AWK, Gunder A, Lagarde D, Makeig S, Myhre G, Wright NA. 1994. Psychophysiological Assessment Methods. AGARD-AR-324. Neuilly-Sur-Seine, France: North Atlantic Treaty Organization. Caldwell JA, Caldwell JL, Crowley JS. 1996. Sustaining helicopter pilot alertness with Dexedrine during sustained operations. In: Advisory Group for Aerospace Research and Development Conference Proceedings CP-579, Aerospace Medical Symposium on Neurological Limitations of Aircraft Operations: Human Performance Implications. Neuilly-Sur-Seine, France: North Atlantic Treaty Organization. Pp. 38–1–38–11. Caldwell JA, Kelly CF, Roberts KA, Jones HD, Lewis JA, Woodrum L, Dillard RM, Johnson PP. 1997. A Comparison ofEEG and Evoked Response Data Collected in a UH-1 Helicopter to Data Collected in a Standard Laboratory Environment. USAARL 97–30. Fort Rucker, AL: U.S. Army Aeromedical Research Laboratory. Caldwell JA, Caldwell JL, Smythe NK, Hall KK. 2000a. A double-blind, placebo-controlled investigation of the efficacy of modafinil for sustaining the alertness and performance of aviators: A helicopter simulator study. Psychopharmacology 150:272–282. Caldwell JA, Smythe NK, LeDuc PA, Caldwell JL. 2000b. Efficacy of Dexedrine® for maintaining aviator performance during 64 hours of sustained wakefulness: A simulator study. Aviat Space Environ Med 71:7–18. Caldwell JA, Hall KK, Erickson BS. 2002. EEG data collected from helicopter pilots in flight are sufficiently sensitive to detect increased fatigue from sleep deprivation. Int J Aviat Psychol 12:19–32. Caldwell J, Caldwell JL, Brown D, Smythe N, Smith J, Mylar J, Mandichak M, Schroeder C. 2003. The Effects of 37 Hours of Continuous Wakefulness on the Physiological Arousal, Cognitive Performance, Self-Reported Mood, and Simulator Flight Performance of F-117A Pilots. AFRL-HE-BR-TR-2003–0086. Brooks City Base, TX: U.S. Air Force Research Laboratory. Carskadon MA, Dement WC. 1994. Normal human sleep: An overview. In: Kryger MH, Roth T, Dement WC, eds. Principles and Practice of Sleep Medicine. 2nd ed. Philadelphia: WB Saunders. Pp. 16–25.
OCR for page 188
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance Coles MGH, Donchin E, Porges SW, eds. 1986. Psychophysiology: Systems, Processes, and Applications. New York: Guilford Press. Comens P, Reed D, Mette M. 1987. Physiologic responses of pilots flying high-performance aircraft. Aviat Space Environ Med 58:205–210. Comperatore CA, Caldwell JA, Stephans RL, Chiaramonte JA, Pearson JY, Trast ST, Mattingly AD. 1992. The Use of Electrophysiological and Cognitive Variables in the Assessment of Degradation During Periods of Sustained Wakefulness. USAARL 93–5. Fort Rucker, AL: U.S. Army Aeromedical Research Laboratory. Cooper R, Osselton JW, Shaw JC. 1980. EEG Technology. 3rd ed. London: Butterworths. Correll JT. 1998. Strung out. We have too few forces and too little money chasing too many open-ended deployments. Online. Air Force Magazine. Available at http://www.afa.org/magazine/sept1998/09edit98_print.html. Accessed November 3, 2003. Daggett S, Belasco A. 2002. Defense Budget for FY2003: Data Summary. CRS Report for Congress, RL31349. Washington, DC: Congressional Research Service. de Waard D, Brookhuis KA. 1991. Assessing driver status: A demonstration experiment on the road. Accid Anal Prev 23:297–307. Dement WC. 1976. Some Must Watch While Some Must Sleep. New York: WW Norton & Co. Dinges DF. 1995. An overview of sleepiness and accidents. J Sleep Res 4:4–14. Dinges DF, Kribbs NB. 1991. Performing while sleepy: Effects of experimentally-induced sleepiness. In: Monk TH, ed. Sleep, Sleepiness, and Performance. New York: John Wiley & Sons. Pp. 97–128. Dinges DF, Mallis MM, Maislin G, Powell JW. 1998. Evaluation of Techniques for Ocular Measurement as an Index of Fatigue and as the Basis for Alertness Management. DOT-HS-808–762. Washington, DC: National Highway Traffic Safety Administration. Pp. 42–47, 79–80. Dorrian J, Lamond N, Dawson D. 2000. The ability to self-monitor performance when fatigued. J Sleep Res 9:137–144. Downing P, Liu J, Kanwisher N. 2001. Testing cognitive models of visual attention with fMRI and MEG. Neuropsychologia 39:1329–1342. Drummond SPA, Brown GG. 2001. The effects of total sleep deprivation on cerebral responses to cognitive performance. Neuropsychopharmacology 25:S68-S73. Drummond SPA, Brown GG, Gillin JC, Stricker JL, Wong EC, Buxton RB. 2000. Altered brain response to veral learning following sleep deprivation. Nature 403:655–657. Eddy DR, Hursh SR. 2001. Fatigue Avoidance Scheduling Tool (FAST). AFRL-HE-BR-TR-2001–0140. Brooks City Base, TX: U.S. Air Force Research Laboratory.
OCR for page 189
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance Elliott S. 2001. Chief of Staff Shares Views on Global Strike Task Force. Online. U.S. Air Force. Available at http://www.af.mil/news/Oct200l/n20011029_1543.shtml. Accessed November 4, 2003. Evinger C, Manning KA, Sibony PA. 1991. Eyelid movements. Invest Ophthalmo Vis Sci 32:387–400. Gevins A, Leong H, Du R, Smith ME, Le J, Du Rousseau D, Zhang J, Libove J. 1995. Towards measurement of brain function in operational environments. Biol Psychol 40:169–186. Gillberg M. 1995. Sleepiness and its relation to the length, content, and continuity of sleep. J Sleep Res 2:37–40. Gillberg M, Kecklund G, Åkerstedt T. 1994. Relations between performance and subjective ratings of sleepiness during a night awake. Sleep 17:236–241. Grace R, Steward S. 2001. Drowsy Driver Monitor and Warning System. Online. Public Policy Center, University of Iowa. Available at http://ppc.uiowa.edu/driving-assessment/2001/Summaries/Driving%20Assessment%20Papers/11_Grace_Richard.htm.Accessed October 10, 2003. Griffin GR, Williams CE. 1987. The effects of different levels of task complexity on three vocal measures. Aviat Space Environ Med 58:1165–1170. Hansen AL, Johnsen BH, Thayer JF. 2003. Vagal influence on working memory and attention. Int J Psychophysiol 48:263–274. Härmä M. 1995. Sleepiness and shiftwork: Individual differences. J Sleep Res 4:57–61. Hart SG, Hauser JR. 1987. Inflight application of three pilot workload measurement techniques. Aviat Space Environ Med 58:402–410. Hauri P, Orr WC. 1982. The Sleep Disorders. 2nd ed. Kalamazoo, MI: Upjohn. Hitchcock EM, Warm JS, Matthews G, Dember WN, Shear PK, Tripp LD, Mayleben DW, Parasuraman R. 2003. Automation cueing modulates cerebral blood flow and vigilance in a simulated air traffic control task. Theor Issues Ergon Sci 4:89–112. Hollander TD, Warm JS, Matthews GR, Dember WN, Parasuraman R, Hitchcock EM, Beam CA, Tripp LD. 2002. Effects of signal regularity and salience on vigilance performance and cerebral hemovelocity. In: Proceedings of the Human Factors and Ergonomics Society 46th Annual Meeting. Santa Monica: Human Factors and Ergonomics Society. Pp. 1654–1658. Horne JA. 1978. A review of the biological effects of total sleep deprivation in man. Biol Psychol 7:55–102. Hossain JL, Reinish LW, Kayumov L, Bhuiya P, Shapiro CM. 2003. Underlying sleep pathology may cause chronic high fatigue in shift-workers. J Sleep Res 12:223–230. Howitt JS, Hay AE, Shergold GR, Ferres HM. 1978. Workload and fatigue—Inflight EEG changes. Aviat Space Environ Med 49:1197–1202.
OCR for page 190
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance Hursh SR, Redmond DP, Johnson ML, Thorne DR, Belenky G, Balkin TJ, Storm WF, Miller JC, Eddy DR. 2004. Fatigue models for applied research in warfighting. Aviat Space Environ Meet 75:A44-A53. Itoh Y, Hayashi Y, Tsukui I, Saito S. 1989. Heart rate variability subjective mental workload in flight task validity of mental workload measurement using the H.R.V. method. In: Smith MJ, Salvendy G, eds. Work With Computers: Organizational, Management, Stress and Health Aspects. Amsterdam: Elsevier. Pp. 209–216. Johannes B, Salnitski VP, Gunga HC, Kirsch K. 2000. Voice stress monitoring in space—Possibilities and limits. Aviat Space Environ Med 71 :A58-A65. Johnsen BH, Thayer JF, Laberg JC, Wormnes B, Raadal M, Skaret E, Kvale G, Berg E. 2003. Attentional and physiological characteristics of patients with dental anxiety. J Anxiety Disord 17:75–87. Kakimoto Y, Nakamura A, Tarui H, Nagasawa Y, Yagura S. 1988. Crew workload in JASDF C-1 transport flights: I. Change in heart rate and salivary cortisol. Aviat Space Environ Med 59:511–516. Kingsley SA, Sriram S, Pollick A, Marsh J. 2003. Photrode™ optical sensor for electrophysiological monitoring. Aviat Space Environ Med 74:1215–1216. Kithil PW, Jones RD, MacCuish J. 2001. Development of Driver Alertness Detection System Using Overhead Capacity Sensor Array. Online. Available at http://www.lascruces.com/~rfrye/complexica/d/ASCI%20drowsy%20driver%20paper%2011-20-01.doc. Accessed November 4, 2003. Kramer AF. 1991. Physiological metrics of mental workload: A review of recent progress. In: Damos DL, ed. Multiple-Task Performance. Washington, DC: Taylor & Francis. Pp. 279–328. Krueger GP. 1991. Sustained military performance in continuous operations: Combatant fatigue, rest and sleep needs. In: Gal R, Mangelsdorff AD, eds. Handbook of Military Psychology. New York: John Wiley & Sons. Pp. 255–277. Leder RS, Gale H, Stamp C, Yamasaki DS, Webster JG. 1996. Eyelid activity measurement: A new retroreflective sensor. Sleep Res 25:509. LeDuc PA, Caldwell JA, Ruyak PS. 2000. The effects of exercise as a countermeasure for fatigue in sleep-deprived aviators. Mil Psychol 12:249–266. Lewis CE, Jones WL, Austin F, Roman J. 1967. Flight research programs: IX. Medical monitoring of carrier pilots in combat—II. Aerosp Med 38:581–592. Lindholm E, Cheatham C, Koriath J, Longridge TM. 1984. Physiological Assessment of Aircraft Pilot Workload in Simulated Landing and Simulated Hostile Threat Environments. AFHRL-TR-83–49. Williams Air Force Base, AZ: U.S. Air Force Human Resources Laboratory. Lindqvist A, Keskinen E, Antila K, Halkola L, Peltonen T, Valimaki I. 1983. Heart rate variability, cardiac mechanics, and subjectively evaluated stress during simulator flight. Aviat Space Environ Med 54:685–690.
OCR for page 191
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance Lorenzo I, Ramos J, Arce C, Guevara MA, Corsi-Cabrera M. 1995. Effect of total sleep deprivation on reaction time and waking EEG activity in man. Sleep 18:346–354. Maulsby RL. 1966. Electroencephalogram during orbital flight. Aerosp Med 37:1022–1026. Miller JC. 1997. Quantitative analysis of truck driver EEG during highway operations. Biomed Sci Instrum 34:93–98. Mitler MM, Carskadon MA, Czeisler CA, Dement WC, Dinges DF, Graeber RC. 1988. Catastrophes, sleep, and public policy: Consensus report. Sleep 11:100–109. Monk TH, Folkard S. 1985. Shiftwork and performance. In: Folkard S, Monk TH, eds. Hours of Work. Temporal Factors in Work-Scheduling. New York: John Wiley & Sons. Pp. 239–252. Monk TH, Buysse DJ, Rose LR. 1999. Wrist actigraphic measures of sleep in space. Sleep 22:948–954. Moore-Ede M. 1993. Aviation safety and pilot error. In: Twenty-Four Hour Society. Reading, MA: Addison-Wesley Publishing. Pp. 81–95. Mulder G, Mulder LJM. 1980. Coping with mental work load. In: Levine S, Ursin H, eds. Coping and Health. New York: Plenum Press. Pp. 233–258. Mulder LJM. 1992. Measurement and analysis methods of heart rate and respiration for use in applied environments. Biol Psychol 34:205–236. Naitoh P, Kelly TL. 1992. Sleep Management User’s Guide for Special Operations Personnel. Naval Health Research Center Report No. 92–28. Bethesda, MD: Naval Medical Research and Development Command. Nicholson AN, Hill LE, Borland RG, Ferres HM. 1970. Activity of the nervous system during the let-down, approach and landing: A study of short duration high workload. Aerosp Med 41:436–446. NTSB (National Transportation Safety Board). 1990. Marine Accident Report-Grounding of the U.S. Tankship Exxon Valdez on Bligh Reef, Prince William Sound, Near Valdez, Alaska, 24 Mar 1989. Report No. NTSB/Mar-90/04. Washington DC: NTSB. NTSB. 2000. Aircraft Accident Report: Controlled Flight into Terrain, Korean Air Flight 801, Boeing 747–300, HL7468, Nimitz Hill, Guam, August 6, 1997. Report No. NTSB/AAR-00–01. Washington, DC: NTSB. Opmeer CHJM, Krol JP. 1973. Towards an objective assessment of cockpit workload: I. Physiological variables during different flight phases. Aerosp Med 44:527–532. Penn PE, Bootzin RR. 1990. Behavioural techniques for enhancing alertness and performance in shift work. Work Stress 4:213–226. Petit C, Chaput D, Tarriere C, LeCoz JY, Planque S. 1990. Research to prevent the driver from falling asleep behind the wheel. In: 34th Annual Proceedings, Association of the Advancement of Automotive Medicine Conference, October 1–3, 1990. Barrington, IL: Association of the Advancement of Automotive Medicine. Pp. 505–522.
OCR for page 192
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance Pietrini P, Alexander GE, Furey ML, Hampel H, Guazzelli M. 2000. The neurometabolic landscape of cognitive decline: In vivo studies with positron emission tomography in Alzheimer’s disease. Int J Psychophysiol 37:87–98. Pigeau RA, Heslegrave RJ, Angus RG. 1987. Psychophysiological measures of drowsiness as estimators of mental fatigue and performance degradation during sleep deprivation. In: Electrical and Magnetic Activity of the Central Nervous System: Research and Clinical Applications in Aerospace Medicine. Paris, France: NATO Advisory Group for Aerospace Research and Development. Pp. 21–1–21–16. Pollak CP, Tryon WW, Nagaraja H, Dzwonczyk R. 2001. How accurately does wrist actigraphy identify the states of sleep and wakefulness? Sleep 24:957–965. Portas CM, Rees G, Howseman AM, Josephs O, Turner R, Frith CD. 1998. A specific role for the thalamus in mediating the interaction of attention and arousal in humans. J Neurosci 18:8979–8989. Rechtschaffen A, Kales A, eds. 1968. A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. Washington, DC: U.S. Government Printing Office. Rees G. 2001. Neuroimaging of visual awareness in patients and normal subjects. Curr Opin Neurobiol 11:150–156. Roehrs T, Merlotti L, Petrucelli N, Stepanski E, Roth T. 1994. Experimental sleep fragmentation. Sleep 17:438–443. Roehrs T, Carskadon MA, Dement WC, Roth T. 2000. Daytime sleepiness and alertness. In: Kryger MH, Roth T, Dement WC, eds. Principles and Practice of Sleep Medicine. Philadelphia: WB Saunders. Pp. 43–52. Roffwarg HP. 1979. Diagnostic classification of sleep and arousal disorders. Sleep 2:5–15. Roscoe AH. 1978. Stress and workload in pilots. Aviat Space Environ Med 49:630–636. Roscoe AH. 1980. Heart-rate changes in test pilots. In: Kitney RI, Rompelman O, eds. The Study of Heart-Rate Variability. Oxford, England: Clarendon Press. Pp. 178–190. Roscoe AH. 1992. Assessing pilot workload. Why measure heart rate, HRV and respiration? Biol Psychol 34:259–287. Rosekind MR, Gander PH, Miller DL, Gregory KB, Smith RM, Weldon KJ, Co EL, McNally KL, Lebacqz JV. 1994. Fatigue in operational settings: Examples from the aviation environment. Hum Factors 36:327–338. Rosenthal L, Roehrs TA, Rosen A, Roth T. 1993. Level of sleepiness and total sleep time following various time in bed conditions. Sleep 16:226–232. Ruffell-Smith HP. 1967. Heart rate of pilots plying aircraft on scheduled airline routes. Aerosp Med 38:1117–1119. Ruiz R, Legros C, Guell A. 1990. Voice analysis to predict the psychological or physical state of a speaker. Aviat Space Environ Med 61:266–271.
OCR for page 193
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance Russo M, Thomas M, Sing H, Thorne D, Balkin T, Wesensten N, Redmond D, Welsh A, Rowland L, Johnson D, Cephus R, Hall S, Belenky G, Krichmar J. 1999. Sleep deprivation related changes correlate with simulated motor vehicle crashes. In: Carroll RJ, ed. Ocular Measures of Driver Alertness: Technical Conference Proceedings. FHWA-MC-99–136. Washington, DC: Office of Motor Carrier and Highway Safety/Federal Highway Administration. Pp. 119–124. Russo M, Thomas M, Thorne D, Sing H, Redmond D, Rowland L, Johnson D, Hall S, Krichmar J, Balkin T. 2003. Oculomotor impairment during chronic partial sleep deprivation. Clin Neurophysiol 114:723–736. Santucci G, Boer L, Farmer E, Goeters KM, Grissett JD, Schwartz E, Wetherall A, Wilson G. 1989. Human Performance Assessment Methods. AGARDograph No. 308. Neuilly-Sur-Seine, France: NATO Advisory Group for Aerospace Research and Development. Sekiguchi C, Handa Y, Gotoh M, Kurihara Y, Nagasawa Y, Kuroda I. 1979. Frequency analysis of heart rate variability under flight conditions. Aviat Space Environ Med 50:625–634. Spencer J. 2000. The Facts about Military Readiness. Online. The Heritage Foundation Backgrounder: No. 1394. Available at http://www.heritage.org/Research/MissileDefense/loader.cfm?url=/commonspot/security/getfile.cfm&PageID=10734. Accessed November 7, 2003. Stern A, Ranney T. 1999. Ocular based measures of driver alertness. In: Carroll RJ, ed. Ocular Measures of Driver Alertness: Technical Conference Proceedings. FHWA-MC-99–136. Washington, DC: Office of Motor Carrier and Highway Safety/Federal Highway Administration. Pp. 4–23. Stern JA. 1980. Aspects of Visual Search Activity Related to Attentional Processes and Skill Development. AFOSR-TR-81–0119. Washington, DC: U.S. Air Force Office of Scientific Research. Stickgold R, Neri DF, Pace-Schott E, Juguilon A, Czeisler CA, Hobson JA. 1995. Nightcap detection of decreased vigilance. Sleep Res 24:500. Stroobant N, Vingerhoets G. 2000. Transcranial Doppler ultrasonography monitoring of cerebral hemodynamics during performance of cognitive tasks: A review. Neuropsychol Rev 10:213–231. Taub JM, Berger RJ. 1973. Performance and mood following variations in the length and timing of sleep. Psychophysiology 10:559–570. Thiessen GJ. 1988. Effect of traffic noise on the cyclical nature of sleep. J Acoust Soc Am 84:1741–1743. Thomas M, Sing H, Belenky G, Holcomb H, Mayberg H, Dannals R, Wagner H, Thorne D, Popp K, Rowland L, Welsh A, Balwinski S, Redmond D. 2000. Neural basis of alertness and cognitive performance impairments during sleepiness. I. Effects of 24 h of sleep deprivation on waking human regional brain activity. J Sleep Res 9:335–352. U.S. Army. 1991. Soldier Performance in Continuous Operations. FM 22–9. Washington, DC: U.S. Department of the Army.
OCR for page 194
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance U.S. Army. 1996. Force of Decision: Capabilities for the 21st Century. White paper. Washington, DC: U.S. Department of the Army. U.S. Army. 1997. Army Aviation Operations. FM 1–100. Washington, DC: U.S. Department of the Army. Van Dongen HPA, Rogers NL, Dinges DF. 2003. Sleep debt: Theoretical and empirical issues. Sleep Biol Rhythms 1:5–13. Walsh JK, Hartman PG, Schweitzer PK. 1994. Slow-wave sleep deprivation and waking function. J Sleep Res 3:16–25. Whitmore J, Fisher S. 1996. Speech during sustained operations. Speech Commun 20:55–70. Wierwille WW. 1999. Historical perspective on slow eyelid closure: Whence PERCLOS? In: Carroll RJ, ed. Ocular Measures of Driver Alertness: Technical Conference Proceedings. FHWA-MC-99–136. Washington, DC: Office of Motor Carrier and Highway Safety/Federal Highway Administration. Pp. 31–53. Wierwille WW, Connor SA. 1983. Evaluation of 20 workload measures using a psychomotor task in a moving-base aircraft simulator. Hum Factors 25:1–16. Wilkinson RT. 1961. Interaction of lack of sleep with knowledge of results, repeated testing, and individual differences. J Exp Psychol 62:263–271. Wilkinson RT. 1964. Effects of up to 60 hours’ sleep deprivation on different types of work. Ergonomics 17:175–186. Wilkinson R. 1969. Some factors influencing the effect of environmental stressors upon performance. Psychol Bull 72:260–272. Wilkinson RT, Edwards RS, Haines E. 1966. Performance following a night of reduced sleep. Psychonomic Sci 5:471–472. Wilson GF. 1993. Air-to-ground training missions: A psychophysiological workload analysis. Ergonomics 36:1071–1087. Wilson GF, Eggemeier FT. 1991. Psychophysiological assessment of workload in multi-task environments. In: Damos DL, ed. Multiple-Task Performance. Washington, DC: Taylor & Francis. Pp. 329–360. Wilson GF, Purvis B, Skelly J, Fullenkamp P, Davis I. 1987. Physiological data used to measure pilot workload in actual flight and simulator conditions. Proceedings of the Human Factors Society, 31st Annual Meeting. Pp. 779–783. Wright N, McGown A. 2001. Vigilance on the civil flight deck: incidence of sleepiness and sleep during long-haul flights and associated changes in physiological parameters. Ergonomics 44:82–106. Wu JC, Gillin JC, Buchsbaum MS, Hershey T, Hazlett E, Sicotte N, Bunney WE. 1991. The effect of sleep deprivation on cerebral glucose metabolic rate in normal humans assessed with positron emission tomography. Sleep 14:155–162.
Representative terms from entire chapter: