73

Measuring and Predicting Sleep and Performance During Military Operations

Chapter 3

MEASURING AND PREDICTING SLEEP 

AND PERFORMANCE DURING 

MILITARY OPERATIONS

ADAM FLETCHER, PhD*; NANCY J. WESENSTEN, PhD†; KATIE KANDELAARS, PhD‡; and THOMAS J. BALKIN, PhD§

INTRODUCTION

Background and Definitions

Operational Context

MEASURING SLEEP AND PERFORMANCE

Laboratory Sleep Measures

Field Sleep Measures

Measuring Performance in the Field

PREDICTING SLEEP AND PERFORMANCE

Theoretical Aspects of Prediction

Practical Model Use

Limitations of Current Models

TACTICAL USE OF SLEEP

SUMMARY

*Executive Director, Integrated Safety Support, 1/192 Argyle Street, Fitzroy 3065, Australia; formerly, Research Psychologist, Department of Behavioral 

Biology, Walter Reed Army Institute of Research, 503 Robert Grant Avenue, Silver Spring, Maryland 20910-7500

†Supervisory Psychologist, Department of Behavioral Biology, Walter Reed Army Institute of Research, 503 Robert Grant Avenue, Silver Spring, Maryland 20910-7500

‡Consultant, Integrated Safety Support, 1/192 Argyle Street, Fitzroy 3065, Australia; formerly Researcher, The Centre for Sleep Research, University 

of South Australia, Level 7, Playford Building, City East Campus, Frome Road, Adelaide 5000, South Australia

§

Department Chief, Department of Behavioral Biology, Walter Reed Army Institute of Research, 503 Robert Grant Avenue, Silver Spring, Maryland 

20910-7500
74

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

INTRODUCTION

the degree of impairment depends substantially on the 

task, the individual’s environment, and the level of 

sleepiness. Operationally, the definition of impairment 

should encompass the necessary task(s) and the level 

of acceptable risk for the circumstances.

Operational Context

Success in military operations depends on effective 

performance at all levels of command and control. Effective performance at the individual level depends 

on the successful completion of complex cognitive 

tasks (eg, a commander must maintain near-constant 

concentration and an awareness of the overall mission 

goals and progress in the face of competing demands, 

including detection and management of threats and 

errors, while successfully firing and maneuvering). 

There are many factors that shape military effectiveness, including the following: 

	 •	 battle intensity,

	 •	 combat experience,

	 •	 training,

	 •	 morale,

	 •	 hydration,

	 •	 nutritional status, and

	 •	 sleep.9

Falling asleep while on duty in an operational 

setting can lead to catastrophic accidents. Less well 

appreciated is the fact that sleep deprivation systematically degrades performance long before people 

actually fall asleep. There is a cascade of degradation 

that predictably reflects a decline in mood, communication, reaction speed, reaction accuracy, and physical 

capacity. The natural endpoint of this spectrum is 

involuntary sleep. Figure 3-1 shows a representation 

of this cascade (see also Exhibit 3-1 and accompanying 

Exhibit Figure E1-1).

Background and Definitions

It is widely believed that a full night’s sleep is an 

unnecessary, inefficient luxury. However, this belief is 

even stronger in military and political settings in which 

a reduced need for sleep is seen often as a badge of 

honor. Throughout history, it has been reported that 

many noted individuals (eg, Alexander the Great, Napoleon Bonaparte, and Winston Churchill) only slept a 

maximum of 4 to 6 hours/day. Whether they slept for 

a short time every day—and did not get longer sleeps 

and/or naps regularly—is open for debate. More importantly, whether these individuals performed at an 

optimal level is another issue for discussion, which 

leads to the following questions: 

	 •	 How is sleep assessed quantitatively?

	 •	 What measurable performance effects exist?

By addressing these questions, a clearer picture of 

minimum and optimal sleep needs emerges.

Throughout this chapter, there are specific terms 

used to describe this process and the performance 

measures affiliated with it, including:

	 •	 sleep,

	 •	 sleepiness,

	 •	 circadian,

	 •	 fatigue, and

	 •	 impairment.

At the most basic level, sleep is a reversible behavioral state in which there is disengagement from the environment.1

 There are many proposed functions of sleep, 

including restoration2 and memory consolidation.3

Sleepiness is a state of wakefulness in which an individual has an increased inclination to fall asleep.4

 Both 

sleep and sleepiness can be measured objectively using 

standardized methods.5 Most people are awake during 

the day and asleep at night. People who do not work at 

night—especially between 0000 and 0600 hours—are 

most likely asleep. This is not surprising because the 

physical and psychological pressures to sleep at this 

time are often irresistible, even in people who are motivated to maintain wakefulness. The daily rhythm of 

wake and sleep states is a result of the circadian system, 

which is a persistent, internal, homeostatic mechanism 

controlled by the suprachiasmatic nucleus (a distinct 

group of cells located in the hypothalamus).6

Unlike sleepiness, it is not possible to measure 

fatigue quantitatively.7 Fatigue relates to both physiological performance decrements and psychological 

impairments, such as decreased morale, judgment, and 

mood.8 Impairment is equally difficult to define because 

Fig. 3-1. Schematic representation of (from left to right) the 

psychological and physical degradation associated with 

increasing sleepiness.

Mood

Fully Alert Asleep

Communication Reaction time Accuracy Physical skill
75

Measuring and Predicting Sleep and Performance During Military Operations

EXHIBIT 3-1

HISTORY OF INADEQUATE SLEEP IN GULF WAR OPERATIONS

On February 25, 1991, at 1800 hours, during the 100-hour ground war, a platoon of Bradley Fighting Vehicles from the 

Second Armored Cavalry Regiment (2ACR) was ordered to halt its advance and go into a screen line (Exhibit Figure 

E1-1a). They were to resume their advance the next morning. They remained awake and monitored their thermal sights 

for “hot spots,” which indicated a possible Iraqi approach to their position.

About 7 hours later—at approximately 0200 hours—the Bradley crews observed hot spots approaching (Exhibit Figure 

E1-1b). They were uncertain whether these were friend or foe; regardless, the crews continued to observe. The Iraqis had 

no thermal sights of their own; therefore, the Iraqis were unaware of the Bradley screen line and proceeded forward. 

Only when the lead Iraqi vehicles reached the screen line did the Bradley crews realize that these were the enemy. A 

brief firefight ensued, during which all the Iraqi vehicles were destroyed (Exhibit Figure E1-1c).

During the firefight, the two Bradley crews on the right flank of the screen line had turned to their left; they were no 

longer parallel to the other Bradley crews, but were instead facing into their own screen line (Exhibit Figure E1-1d). 

However, the crews of these two Bradleys were unaware that they had turned to face their own screen line. They still 

believed that they were facing in the correct orientation to the other Bradleys; therefore, they also believed that forward of them was the enemy. Because of this disorientation, the crews mistook the two Bradley crews on the left flank 

Fig. E1-1. Sequence of events from the 100-hour ground 

war—friendly fire incident (Gulf War, 1991). Black triangles represent US Bradley fighting vehicles. White 

triangles represent enemy vehicles.

d. February 26, approximately

0207 Hours 

a. February 25, 1800 Hours b. February 26, 0200 Hours 

c. February 26, 0206 Hours

(which were maneuvering around burning Iraqi vehicles) 

for enemy vehicles and proceeded to enfilade their own 

line with fire. They destroyed the two Bradley vehicles 

on the left flank (Exhibit Figure E1-1d).

Because the Bradley Fighting Vehicles were equipped 

with Kevlar (E. I. Du Pont de Nemours and Company, 

Wilmington, Delaware) spall curtains and Halon (H3R 

Performance, Larkspur, California), fire suppression, 

and the crew members wearing Nomex (E. I. Du Pont 

de Nemours and Company) fire-retardant suits, all of 

them escaped without injury. A medical officer with the 

parent unit of the Bradley platoon was able to assemble 

the platoon members a few days later and conduct a 

reconstructive debriefing of this incident.(1) By their own 

report, the Bradley crews had obtained only 3 to 4 hours of 

sleep per night over the previous 5 days and would have 

been performing at a level much lower than their optimal 

capacity. In addition, the firefight took place during the 

early morning hours, when the crew members’ complex 

mental operations were naturally waning because of their 

circadian rhythms.

MEASURING SLEEP AND PERFORMANCE

Laboratory Sleep Measures

Behaviorally, it is possible to characterize sleep as a 

broadly homogeneous state of quiescence and reduced 

responsivity to sensory stimuli. Physiologically, it is 

dynamic, with intermittent/phasic changes in brain 

activity, as well as in endocrine and peripheral nervous 

system activity. The gold standard for determining 

sleep/wake state is polysomnography. 

In the sleep scoring system currently accepted as 

the standard,10 sleep is divided into five stages—stages 

1 to 4 and rapid eye movement (REM)—which are 

determined visually based on the pattern of polysomnography activity. Stage 1 sleep is characterized by 

low-amplitude, mixed-frequency electroencephalographic (EEG) activity and is considered a transitional 

Data source: (1) Belenky G, Martin JA, Marcy SC. After-action critical incident stress debriefing and battle reconstruction following 

combat. In: Martin JA, Sparacino LR, Belenky G, eds. The Gulf War and Mental Health: A Comprehensive Guide. Westport, Conn: Praeger; 

1996: 105–113.
76

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

state between wakefulness and the deeper (and more 

recuperative) non-REM sleep stages 2 to 4.11 Characteristically, stage 2 sleep has the appearance, in the EEG, of 

sleep spindles (12- to 14-Hz activity occurring in 0.5- to 

2.0-second bursts) and K-complexes (a sharp negative 

excursion followed by a slower positive excursion and 

often quickly followed by a sleep spindle). 

High-amplitude delta or “slow” waves (slower than 

2 Hz, with peak-to-peak amplitude of at least 75 μV) 

can emerge during stage 2 sleep. When delta waves 

comprise 20% to 49% of a 30-second epoch, then that 

epoch is scored as stage 3 sleep. Epochs composed of 

50% or more delta wave activity are scored as stage 4 

sleep. Because of the waveforms that characterize sleep 

stages 3 and 4, these stages are collectively known as 

slow-wave sleep (SWS) stages.

REM sleep is characterized by a low-amplitude, 

mixed-frequency EEG (similar to that seen during 

stage 1); reduced muscle tonus (relative to the other 

sleep stages, as well as to wakefulness); and intermittent REMs. REM is the sleep stage during which most 

dreaming occurs.

Until recently, little was known about the neurophysiology underlying the various stages of sleep. The 

advent of imaging techniques (eg, positron emission 

tomography) has made it possible to create images of 

the living human brain during sleep. Recent results 

show that those brain regions mediating the ability to 

maintain alertness and vigilance, and so-called executive functions (eg, information assessment, problemsolving, and detection of conflicting information), are 

deactivated to a greater extent than other brain regions 

during sleep. This is true regardless of sleep stage; these 

brain regions deactivate during REM, SWS, and stage 

2 sleep, with stage 2 sleep and SWS differing mainly in 

degree of deactivation (greater deactivation during SWS 

than during stage 2 sleep).12 Also, these are the same 

brain regions most deactivated by sleep deprivation.13

On awakening, activity is restored to these regions.14

The various functions and mechanisms of sleep will 

continue to be studied and debated. But, in this chapter, we will focus on the apparent role of sleep in the 

restoration/sustenance of metabolic activity in brain 

regions that mediate (a) the ability to maintain wakefulness under nonstimulating conditions and (b) executive 

functions. Questions regarding the extent to which sleep 

stages are differentially recuperative for performance are 

obviously important when attempting to quantify the 

relationship between sleep and subsequent operational 

function. However, previous experiments have failed 

to discern stage-dependent differences in the rate that 

performance recuperation accrues during sleep. This 

does not mean that sleep-stage–related differences do 

not exist. Rather, the lack of experimental control over 

sleep stages has precluded definitive comparisons (ie, 

studies have failed generally to compare sleep periods 

that are equivalent in all potentially relevant respects, 

except for sleep stage of interest).15

There are good (albeit nonexperimental) reasons to 

hypothesize that SWS has greater recuperative value 

than the other sleep stages. First, stage 3 and stage 4 

(SWS) sleep tend to predominate during the first half 

of the night, whereas REM occupies more of the latter 

half of the night—an order suggesting that SWS might 

be the more important stage of sleep. Furthermore, the 

finding that even relatively brief sleep periods (eg, a 

4-hour daily nap following 90 hours of continuous 

wakefulness) can restore performance to near-normal 

(presleep deprivation) levels on some tasks16 suggests 

that the recuperative benefits of sleep are, to a significant extent, “front-loaded,” in much the same way that 

SWS is front-loaded within a typical sleep period. 

Finally, recovery sleep (ie, sleep following significant sleep loss) is typically characterized by increased 

(or “rebound”) SWS, both in terms of the percentage 

and the absolute amounts of SWS obtained. Because 

normal performance levels are restored recovery sleep 

periods that include much less sleep time than the 

amount actually “lost,” the implication is that SWS is 

likely to be the most “restoratively efficient” sleep.

Accuracy of mathematical models describing the 

relationship between sleep and subsequent performance—as detailed in the section on Predicting Sleep 

and Performance—can be enhanced if the relative 

recuperative powers of the various sleep stages are 

known and quantified. However, at this time, the importance of determining the relative recuperative value 

of the various sleep stages is not critical to modeling, 

because total sleep time is known to impact subsequent 

performance capacity. Obtaining polysomnography is 

impractical in most operational environments; and the 

extent to which sleep stages are differentially recuperative, if they are at all, is unknown. 

Field Sleep Measures

Under operational conditions, obtaining adequate 

sleep can be viewed as a managerial problem similar 

to other items of logistic resupply (eg, water, food, 

fuel, and ammunition). For example, to manage fuel 

consumption effectively, a commander must know (a) 

how much fuel is on hand and (b) how far that fuel 

will take the unit, given the anticipated mission profile. 

This information is critical in planning for timely and 

adequate resupply. The same is true for sleep. 

Historically, commanders had to manage the sleep 

of their soldiers based primarily on their experience 

and assessment of the need for sleep relative to other 

mission requirements. This is at least partly because 

of the following reasons: 
77

Measuring and Predicting Sleep and Performance During Military Operations

	 •	 they did not know objectively how much sleep

their soldiers had obtained over the previous days or weeks (ie, they had no means of 

measuring the relative amounts of sleep state 

versus the wake state in the field), and 

	 •	 they were unable to predict how long any

sleep obtained would sustain readiness (ie, 

they had no miles-to-the-gallon equivalent 

for sleep). 

Basic sleep management awareness material has 

been provided to leaders and soldiers in a card format 

(Figure 3-2), but recent research will make more sophisticated assistance available to the commanders. 

For purposes of managing sleep to sustain performance before and during deployments, the Walter 

Reed Army Institute of Research (WRAIR) has developed a sleep management system (SMS).17,18 This 

system consists of six components:

1. a wrist-worn activity monitor to continuously 

record wrist movements,

2. an algorithm to score sleep time versus wake 

time from recorded activity,

3. a mathematical sleep/performance model 

to predict real-time performance based on 

scored sleep,

4. recommendations for stimulant usage to 

sustain cognitive performance when sleep is 

not possible,

5. recommendations for the use of sleep-inducing agents to induce recuperative sleep under 

certain circumstances, and

6. guidelines and doctrine for the sleep management systems to manage sleep, alertness, and 

performance.

A cornerstone of the SMS is the means for measuring 

the amount of sleep and wake times under operational 

conditions. Wrist-mounted actigraphy (developed in 

the 1970s and 1980s) is a portable, field-use method for 

estimating sleep and wake amounts based on movement data (Figure 3-3).

When compared with the laboratory-based gold 

standard for recording sleep/wake times (polysomnography), actigraphy is considered to be a less reliable 

and valid means of measuring sleep. However, given 

the substantially cheaper cost, higher portability, and 

overall level of agreement with polysomnography 

assessments of sleep, actigraphy is considered practical and useful in operational conditions.19 Wrist-worn 

actigraphy presents a practical method for measuring 

(in a field environment) daily sleep amounts in a large 

number of individuals for weeks at a time. 

The six levels of the Army SMS were designed to 

work together to ensure that operational safety and 

performance were maximized in the field through the 

management, monitoring, and prediction of a soldier’s 

sleep and alertness levels. The principal function of 

the SMS is to create the best opportunity for soldiers 

to sleep successfully, measure their actual sleep, and 

predict their subsequent performance. This can lead 

to an overall shift to ensure that more soldiers have 

capabilities within the optimal part of the performance 

distribution (Figure 3-4). 

Of the stressors affecting warfighter operational 

effectiveness, sleep loss is the most thoroughly studied in the laboratory. To date, however, no study has 

been published in which sleep-loss effects have been 

described under actual combat conditions. In several 

unpublished studies, sleep-loss effects during field 

training exercises have been evaluated and detailed. 

Exhibit 3-2 describes several of these studies.

This information leads to one practical question: 

Fig. 3-2. Example of sleep-related guidelines provided to 

commanders and soldiers. 

Photograph: Reproduced from the US Army Center for 

Health Promotion and Preventive Medicine.
78

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

Given the clear implications of sleep loss on performance, how much sleep is the soldier actually getting?

In a series of studies, Colonel Daniel P. Redmond (US 

Army, Retired) and colleagues at WRAIR used actigraphy to measure sleep/wake amounts during training 

exercises at the US Army’s Ranger School (Fort Benning, Ga) and at the US Army’s National Training Center (Fort Irwin, Calif). In the US Army Ranger School 

exercise, soldiers wore the actigraph continuously 

throughout the 58 days of training across all phases.

As shown in Figure 3-5, soldiers were significantly 

sleep-deprived across all phases of training. Inadequate 

sleep is a deliberate stressor built into the lesson plan 

in Ranger training. The average sleep amounts for each 

phase were less than 4 hours/day—an amount inadequate to sustain optimal performance. The WRAIR 

group also recorded sleep/wake amounts actigraphically in soldiers during another field exercise conducted 

at the US Army’s National Training Center. Figure 3-6 

shows average sleep per day across the three training 

phases, broken down by rank and type of activity. 

Soldiers of the lowest rank obtained, on average, 

more than 8 hours of sleep per night—adequate to 

sustain cognitive performance indefinitely. However, 

the highest ranking individuals, on average, obtained 

the least sleep: about 5 hours/night—an amount insufficient for sustaining cognitive performance at high 

levels for the majority of individuals. This discrepancy 

was particularly noteworthy during the force-on-force 

training phase. This phase most realistically simulates 

combat, in that people and machines fought against 

each other. During this phase, key leaders got inadequate sleep.

Measuring Performance in the Field

In the laboratory, it has been shown that sleep loss 

directly impairs many cognitive capacities. However, 

historical attempts to measure capabilities in the field 

have met with minimal success because of the difficulties in quantifying effective performance in the 

operational environment. One solution has been to use 

simple metrics/tests from which operational performance capabilities are inferred. Exhibit 3-3 describes 

one example of such an application. The Psychomotor 

Vigilance Task is a simple reaction time task, developed 

by Dinges and Powell,20 that is usually administered 

for either 5 or 10 minutes.

Fig. 3-3. One type of wrist-worn actigraph for the measurement of movement during field operations. From the 

measurement of activity, it is possible to make predictions 

of sleep obtained by soldiers using automated sleep-scoring 

algorithms.

Photograph: Courtesy of the Department of Behavioral Biology, Walter Reed Army Institute of Research.

Fig. 3-4. Schematic diagram of the general shift, due to active 

sleep management, from a less safe/effective distribution of 

soldier capacity to a more safe/effective one. Heavy vertical 

line distinguishes safe from unsafe effectiveness. Shaded 

area represents the part of the sleep-deprived distribution 

considered operationally unsafe. 

Sleep

Deprived

UNSAFE

Increasing Effectiveness

Adequate

Sleep

SAFE

PREDICTING SLEEP AND PERFORMANCE

Theoretical Aspects of Prediction

More than 20 years ago, the first theoretical attempts 

were made to quantify sleep through the construction of mathematical models. Such models typically 

conceptualize the timing and duration of sleep as the 

interaction between prior sleep and wake states. This 

is referred to as process S21 or process H,

22 and the 

circadian system (typically referred to as process C). 

In the original models of sleep regulation, the 
79

Measuring and Predicting Sleep and Performance During Military Operations

not fixed points, but vary over time. These thresholds 

were determined from sleep/wake structures during 

internal desynchrony protocols.24

All of these processes (ie, S/H, C, h, and l) are based 

on physiological parameters that were measured (and 

propensity of sleep at any point in time is the sum 

of processes S and C.23 Sleep occurs when process S

extends past a high threshold h, and wake-up occurs 

when process S drops below a low threshold l. The 

high (h) and low (l) thresholds are “noisy”; they are 

EXHIBIT 3-2 

STUDIES TO INVESTIGATE THE IMPACT OF SLEEP LOSS ON OPERATIONAL EFFECTIVENESS

Artillery Fire Operations Simulation

Results from a detailed and realistic simulation of artillery fire operations exemplify the effects of sleep loss on operational 

effectiveness. In that study, five-person teams were evaluated for their ability to conduct simulated continuous combat 

operations lasting 36 hours. Each team’s task was to (a) plot target locations; (b) derive range, bearing, and angle of gun 

elevation; (c) charge immediately on receipt of the target; and (d) update situation maps. Across 36 hours of sleep deprivation, each team’s ability to derive range, bearing, and gun elevation was unimpaired, as was each team’s ability to charge. 

However, after approximately 24 hours without sleep, team members stopped updating their situation maps and 

stopped computing preplanned targets immediately on receipt of new information. As a consequence, the teams 

disrupted their smooth, accurate flow of work; fired on prohibited targets; and generally lost control of the operation.

Early Call Studies

In a series of studies, Haslam and Abraham(1) evaluated the effects of sleep loss (total or severely restricted) on military 

relevant aspects of performance. In the first exercise (Early Call I), platoons were assigned to 0, 1.5, or 3.0 hours of sleep 

per 24 hours (one platoon per sleep group) for 9 days. Shooting, weapon handling, digging, marching, patrolling, and 

cognitive performance (including map plotting, encoding/decoding, short-term memory, and logical reasoning) were 

evaluated periodically throughout the exercise. 

Haslam and Abraham reported that all soldiers in the 0-hour sleep platoon withdrew from the exercise after four nights 

without sleep (approximately 96 hours of total sleep deprivation); 39% of the 1.5-hours sleep platoon withdrew after 

five nights. Just more than half (52%) of the 1.5-hour and 91% of the 3-hour sleep platoons completed the entire 9-day 

exercise. Encoding performance (number correct) decreased across the exercise in all groups. 

The number of on-target deliveries during a 20-minute shooting task decreased across days in all three platoons. Across 

the last 5 days, performance seemed to drop more precipitously in the 1.5-hour group, compared with the 3-hour group. 

In contrast to on-target deliveries, “grouping capacity” (the ability to fire five rounds in as small an area as possible) 

did not vary across days or among sleep groups. 

Haslam and Abraham also observed that personal hygiene, self-care, and leadership deteriorated across the exercise. 

One aspect of leadership deterioration was defined as a change from direct order to exhortation (ie, urging, requesting, 

or advising), although it was not experimentally tested how lacking in effectiveness this change in communication style 

actually was. It is, however, possible that the different style was more effective than direct orders for sleep-deprived 

soldiers and, thus, not a leadership deterioration at all. This specific question would benefit from further assessment. 

In the second exercise (Early Call II), 10 experienced infantry soldiers were sleep deprived for 90 hours. This total sleepdeprivation period was then followed for 6 days by 4 hours sleep per 24 hours. Similar to Early Call I, performance 

on the 20-minute shooting task in this group degraded across the 90-hour sleep deprivation period and showed some 

rebound during the 4-hour sleep phase. This 10-soldier unit was marked by a high level of unit cohesion and morale. 

Nonetheless, as the sleep deprivation period continued, the section leader reported increased isolation from his soldiers (who became more docile and more united) and progressive difficulty keeping his soldiers motivated. Based on 

the results from Early Call I and Early Call II, Haslam and Abraham concluded that soldiers are likely to be militarily 

ineffective in a defensive role after 48 hours without sleep.

Implications of this case history and field studies are clear: even well-equipped, well-trained, highly motivated soldiers 

operating within cohesive units with good morale are not resistant to the effects of sleep loss. 

Data source: (1) Haslam DR, Abraham P. Sleep loss and military performance. In: Belenky G, ed. Contemporary Studies 

in Combat Psychiatry. Westport, Conn: Greenwood Press; 1987: 167–184. 
80

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

subsequently estimated) in a laboratory environment 

during extended sleep deprivation and internal desynchrony protocols.25–30 A schematic diagram of modeled 

sleep regulation is shown in Figure 3-7 (step 1).

During the late 1980s and early 1990s, the two- and 

three-process models of sleep regulation and alertness were created based on Borbély’s sleep regulation 

model.21,27,31 In the three-process model, a third process 

(W) was introduced to represent sleep inertia, which 

typically occurs following waking. Sleep inertia is the 

feeling of sleepiness or grogginess that occurs after 

arousal from sleep. According to the creators of the 

three-process model, modeled sleep inertia symptoms 

disappear completely after approximately 2 hours.21

Recent models have further broadened the scope of 

the initial sleep regulation models (Figure 3-7, step 2). 

In particular, such models have since been refined and 

developed to predict symptoms, for example, changes 

in subjective alertness21,32–34 and fatigue,7,35–38 changes 

in objective vigilance27,31 and performance,39,40 as well 

as variations in sleep latency and sleep length.39,41,42

These models have evolved from being used 

primarily in a laboratory setting where sleep/wake 

states and other behaviors are closely monitored and 

controlled, to extensive use in field environments 

where large variations in all facets of the sleep/wake 

schedule exist.43 Recent investigations have discovered 

that there are a number of variations in the specific 

formulation, input and output variables, and parameterization of each model that are directly related to 

their application areas.43

Practical Model Use

Models of sleepiness, fatigue, and/or performance 

are regularly applied in complex field environments. 

As referenced previously, there is a range of models in 

the literature that are available for use, with the major 

differences between these models being related to 

input requirements. The first group of models22,38 are 

one-step models that use the actual timing of sleep/

wake states to predict fatigue (Figure 3-8a). The timing 

of this sleep/wake state is obtained using polysomnography, actigraphy, or subjective sleep/wake diaries. 

One of the strengths of this modeling approach is that 

accurate predictions of fatigue, performance, or alertness can be made from observed sleep timing, which 

differs for each individual. However, this one-step 

technique can only be used retrospectively—post-hoc 

Training Phase

Average Daily Sleep (Hours)

Measured by Actigraphy

0 2 4 6

Benning

Desert

Mountain

Swamp

Fig. 3-5. Average daily sleep amounts obtained from soldiers 

during 58 days of US Army Ranger School (Fort Benning, Ga) 

broken down by training phase in studies conducted in 1992 

and 1993. The current Ranger school curriculum does not 

include a desert phase. Cognitive function was significantly 

impaired, but recovered with episodes of sleep.

Illustration: Redrawn with permission from Shippee RL, 

Friedl KE, Kramer T, et al. Nutritional and Immunological 

Assessment of Ranger Students with Increased Caloric Intake. 

Natick, Mass: US Army Research Institute of Environmental 

Medicine; December 1994: p. 6-6. Technical Report T95-5. 

AD-A290252.

Fig. 3-6. Average daily sleep amounts of military personnel 

during military exercises conducted at the National Training 

Center (Fort Irwin, Calif). Data are presented by rank. COL: 

Colonel; LTC: Lieutenant Colonel; MAJ: Major; CPT: Captain; 

1LT: First Lieutenant; 2LT: Second Lieutenant; SFC: Sergeant 

First Class; MSG: Master Sergeant; SGT: Sergeant; SSG: Staff 

Sergeant; PVT/PV2: Private; Assembly Area: drawing, repairing, and organizing equipment; Live Fire: firing actual 

tank and artillery shells while advancing through terrain 

dotted with simulated enemy targets; Force-on-Force: battle 

using laser systems to simulate killing and being killed by a 

live enemy. The discrepancy in sleep obtained between the 

ranks is most clear in the most operationally realistic phase 

(Force-on-Force).

Illustration: Courtesy of Colonel (Retired) Greg Belenky, 

unpublished data. Rank Officer Enlisted

COL LTC

MAJ CPT

1LT 2LT

SFC/MSG

SGT/SSG

PVT/PV2

Assembly Area

Live Fire

Force-on-Force

Average Daily Sleep (Hours)

Measured by Actigraphy

0 2 4 6 8 10
81

Measuring and Predicting Sleep and Performance During Military Operations

rather than as a predictive tool to determine fatigue, 

performance, and/or alertness—unless there is a reasonable basis for first predicting what the likely sleep 

pattern will be. Thus, in their most frequently used 

form, one-step models are unable to determine whether 

a work schedule is likely to produce excessive levels of 

sleepiness/fatigue until after the fact. Such models are 

useful for analysis of incident/accident investigations 

EXHIBIT 3-3

THE PSYCHOMOTOR VIGILANCE TASK 

The Psychomotor Vigilance Task (PVT) is a simple reaction time task, usually administered for either 5 or 10 minutes, 

developed by Dinges and Powell.(1) A reaction time test, in its most general form, measures the time it takes for a 

person to respond to the presentation of new information (usually a visual or auditory stimulus). Because the PVT 

has demonstrated sensitivity to even slight restrictions in daily sleep amounts well in advance of frank errors and 

accidents,(2,3) it has been adapted for field use and has been implemented using a personal digital assistant (PDA).(4)

The handheld PDA version of the PVT is a field-usable test that is sensitive to sleep loss and has a high reliability, due 

partly to the steep learning curve associated with the reaction time test. Questions regarding the PVT’s specificity and 

validity for operational tasks are not yet fully answered, but initial studies show promise. The PVT involves holding 

a PDA with a right or left thumb poised over the right or left button (as determined by right- or left-handedness) and 

when a bull’s eye target appears, pressing the button. The score is the time it takes (latency) to press the button after 

the target appears. Using the PDA-based PVT, McLellan and colleagues(5) at the Walter Reed Army Institute of Research 

have shown that speed of responding on the PVT correlates with vigilance shooting on a firing range and vigilance in 

detecting enemy movement in a Military Operations on Urbanized Terrain (MOUT) field training exercise. 

However, can one generalize from performance on the PVT to performance on the battlefield? Although this is an

empirical question, strategic and tactical analyses of combat operations—put forward by Colonel John Boyd(6)—suggest 

that the answer is yes. According to Colonel Boyd, operational success depends on being inside the opponent’s decision cycle. In this context, PVT (a measure of reaction speed) seems an appropriate match to the elegant simplicity of 

Boyd’s conception of the basis for operational success at all levels of command and control. 

Future work will link PVT metrics to cognitive capabilities underlying the warfighter’s ability to rapidly recognize and 

capitalize on emergent battlefield opportunities in the network-centric environment.(7) Consider the following scenario: 

An M1 tank is engaged in a battle. The commander is scanning for possible targets; finding one, he confirms identity as friend

or foe. Once he identifies the target as foe, he passes the target to his gunner. This process involves a positive hand-off in

which both commander and gunner confirm to their mutual satisfaction that they are looking at the same thing. The gunner

then ranges the target, decides the round, and communicates this to the loader. The loader loads the round. The gunner fires

the gun. This entire process takes time, less time when an otherwise well-trained and experienced crew is rested and more 

time when the crew is sleep deprived. 

This process can be broken down into a series of temporal latencies (eg, latency of the commander to acquire a target, 

latency to pass to the gunner). The sum of these latencies increases the total time taken to execute the task. If for any 

given target the crew of the M1 is able to complete its series of tasks in less time than it takes for the enemy to do 

the same, then the outcome will be favorable; thus, the tank crew will operate inside the opponent’s decision loop. It 

should be possible in the simulation environment to correlate PVT performance with the latencies to accomplish these 

real-world tasks. By predicting and summing these latencies, actual operational performance from the PVT can be 

predicted. Furthermore, it might be possible to measure these latencies directly in operations and use them as input 

to a performance prediction model.

Data sources: (1) Dinges DF, Powell JW. Microcomputer analyses of performance on a portable, simple visual RT task during sustained operations. Behav Res Methods Instrum Comput. 1985;17:652–655. (2) Belenky G, Wesensten NJ, Thorne DR, et al. Patterns of 

performance degradation and restoration during sleep restriction and subsequent recovery: a sleep dose-response study. J Sleep Res.

2003;12:1–12. (3) Balkin TJ, Bliese PD, Belenky G, et al. Comparative utility of instruments for monitoring sleepiness-related performance decrements in the operational environment. J Sleep Res. 2004;13:219–227. (4) Thorne DR, Johnson DE, Redmond DP, Sing HC, 

Belenky G, Shapiro JM. The Walter Reed palm-held psychomotor vigilance task. Behav Res Methods. 2005;37:111–118. (5) McLellan 

TM, Kamimori GH, Bell DG, Smith IF, Johnson D, Belenky G. Caffeine maintains vigilance and marksmanship in simulated urban 

operations with sleep deprivation. Aviat Space Environ Med. 2005;76:39–45. (6) Hammond GT. The Mind of War: John Boyd and American Security. Washington, DC: Smithsonian Institution Press; 2001. (7) Wesensten NJ, Belenky G, Balkin TJ. Cognitive readiness in 

network-centric operations. Parameters. 2005;Spring:94–105. 
82

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

Fig. 3-7. Schematic diagram of the two-process model of sleep regulation and the subsequent fatigue/alertness addition. 

Step 1: Process C modulates the h and l thresholds. Process S/H rises during the wake state and declines during the sleep 

state. Interaction between these three functions determines the onset and duration of sleep episodes. Step 2: Latent alertness 

is then calculated using these estimated sleep/wake periods and sleep inertia information. 

Process C Process S or H

Process W

Fatigue/Alertness

Step 1:

Sleep Regulation Output

Step 2:

Fatigue/Alertness Output

Wake Sleep

h

l

Wake Sleep

Fig. 3-8. Comparison of the one-step and two-step approaches. (a) One-step models require both work/rest and sleep/wake 

information as input. (b) Two-step models infer sleep/wake timing either directly or indirectly from the timing of the work/

rest state and make subsequent predictions of fatigue, alertness, and/or performance.

One-Step Models

Input Output Input Sleep Estimates

Step 1 Step 1 Step 2

work periods sleep periods fatigue level

Two-Step Models

a b
83

Measuring and Predicting Sleep and Performance During Military Operations

in which sleepiness/fatigue was a potential factor 

and can also be used for testing hypotheses about 

how the model predictions relate to performance or 

other meaningful measures. In the future, if real-time 

activity/sleep data were available, then the utility of 

one-step models in certain contexts would become 

significantly more valuable. For example, if soldiers 

wore wrist actigraphs that allowed assessment of current and near-future status, then the one-step model 

predictions could be of considerable strategic value to 

a commander. In environments where collecting data 

from actigraphy or another objective measure was not 

possible, such as in civilian shiftwork operations, the 

potential utility of one-step approaches is therefore low. 

The second group of fatigue, performance, and alertness models7,21,32,34–37 are two-step models in which the 

timing of sleep/wake states is inferred either directly 

or indirectly from the pattern of work (Figure 3-8b). 

In the first step, the work pattern is used to estimate 

probabilistic distributions for the timing of sleep/

wake cycles and the circadian phase. In the second 

step, the estimated sleep/wake states are used in a 

similar manner to the actual sleep/wake states within 

one-step models. 

Unlike their one-step counterparts, the two-step 

models can prospectively estimate the timing of sleep/

wake states and subsequently make predictions of fatigue, alertness, and/or performance. Therefore, these 

models can be more useful in complex field environments in which the recording of sleep information 

can be impractical, expensive, and unethical, and, in 

which fatigue, performance, or alertness predictions 

are required prior to the task.

Limitations of Current Models

The strengths and limitations of different modeling 

approaches must consider the context of their development and intended use. For example, some models 

were developed and validated in the laboratory,22 and 

other models were developed in the laboratory but 

later refined to suit organizational settings.32,38 As stated 

previously, most of the current models in the literature 

predict fatigue, performance, and/or alertness levels 

in the body based on a combination of circadian and 

homeostatic components.21,22,28,32,38 The circadian component is sinusoidal, and the homeostatic component 

has been found to be either exponential,21 Gaussian 

(ie, the normal distribution curve),22 or broadly linear 

(with circadian variation).38 From a physiological perspective, these models contain elements representing 

many of the processes that control sleep regulation and 

subsequent levels of fatigue, performance, or alertness. 

Unfortunately, none of the models have been exhaustively validated, and published validation studies 

have not been typically independent of data used for 

model development.44 Therefore, further validation of 

models with previously unused datasets, as well as 

cross-validation between models, is highly desirable. 

Thus, a 2002 workshop conducted in Seattle, Washington, compared and examined each of the fatigue, 

alertness, and performance models currently based in 

the literature using five previously unseen scenarios.45

These scenarios ranged from laboratory-based total 

or partial sleep restriction protocols, to field data 

collected from locomotive engineers, to a theoretical 

flight schedule for an ultralong-range flight operation 

between New York and Hong Kong. Van Dongen’s 

article45 gives specific information on these scenarios.

Each of the models used various inputs to estimate 

fatigue, alertness, and/or performance for the given 

scenarios. Estimated and actual measures (including 

various neurobehavioral performance outputs and subjective measurements) were then compared with each 

model’s output generating a “goodness of fit” for each.45

Overall, the models produced reasonable goodness-offit measures. In general, however, there were relatively 

small differences between the performances of each 

model, and there was no model that was consistently 

better or worse using any of the comparison criteria.45

Models developed using laboratory-based data 

were able to closely fit scenarios based on other laboratory data, but were less reliable when used in the field. 

Similarly, models derived from field data generally did 

not perform as well for the laboratory protocols. This 

highlights the differences between data collected in a 

laboratory environment (in which factors such as social 

interaction, sleep strategies, ambient temperature, caffeine consumption, light levels, and tasks undertaken 

are controlled), compared with data collected in a 

field environment (in which those same factors are 

not controlled). These uncontrolled field situations 

can significantly reduce the amount of sleep obtained 

during a given break,46 compared with laboratory protocols in which the influence of these factors is tightly 

controlled. Currently, most models used to predict 

fatigue, alertness, or performance are unable to account 

for these factors, because they were developed and/

or validated in a controlled laboratory environment. 

Comparison of current fatigue, alertness, or performance models indicates that there was a tendency to 

underestimate fatigue in chronic, partial sleep-deprivation protocols.45,47 It has been reported that chronic 

sleep restriction is a major problem encountered by 

employees in various fields in which shiftwork is prevalent, including individuals in aviation and military 

settings.48,49 Therefore, the inability of current models 

to accurately predict fatigue and alertness levels during these conditions greatly limits their immediate 

applicability in a field environment. 
84

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

EXHIBIT 3-4

OPERATIONAL EXAMPLE OF INTERINDIVIDUAL DIFFERENCES AND MODELS

Current models used to predict fatigue, sleepiness/alertness, and/or performance do so based on data derived from 

healthy, young subjects. Recent scientific literature shows that interindividual differences in the neurobehavioral deficits 

exist and are significant in magnitude.(1) Unfortunately, the present cohort of mathematical fatigue, alertness, and/or 

performance models lag behind the current literature because they are unable either to account for these large individual 

differences in deficits or show this variation graphically in their output (eg, through the use of 95% confidence intervals). 

This problem is illustrated in the following example taken from a complex aviation setting. Actigraphically derived 

sleep/wake records of 13 pilots were recorded for a minimum of 15 days, during which time participants were instructed to perform their usual work and social activities. During the recording period, participants were scheduled 

to complete a routine flight pattern from Sydney, Australia, to Los Angeles, California. After a brief layover in Los 

Angeles (mean ± SD, 35.9 ± 1.2 hours), crews flew to Auckland, New Zealand, where another short layover was undertaken (23.6 ± 0.95 hours). 

A final flight returning to Sydney was then completed. All flight times were similar (SD < 1 hour), as were layover 

lengths and layover conditions (eg, hotels and lodging). The sleep/wake records were entered into a published onestep model to estimate the alertness of each individual. These individual alertness scores were then compared with 

the alertness profile obtained when only duty times were entered into the model and the two-step approach was used 

(Exhibit Figure E4-1). Using the scale of Samn and Perelli,(2) self-rated alertness levels recorded preduty and postduty 

are also included for comparison.

Analysis reveals that a strong relationship exists between the alertness estimated using the one-step and two-step 

methods (r = 0.912, P ≤ 0.0001; Exhibit Figure E4-1A). Correlations between the one-step and two-step methods and

16

14

12

10

8

6

4

2

21:00 21:00 21:00 21:00 21:00 21:00 21:00 21:00

21:00

9:00 9:00 9:00 9:00 9:00 9:00 9:00 9:00

9:00 21:00 9:00 21:00 9:00 9:00 21:00 9:00 21:00 9:00 21:00 9:00 21:00 9:00 9:00

7

6

5

4

3

2

1

0

One-Step (+ 95% Cl)

Two-Step

Samn-Perelli Scores (+ 95% Cl)

–

–

One-Step

Two-Step

a

(Exhibit 3-4 continues)
85

Measuring and Predicting Sleep and Performance During Military Operations

the self-rated scores indicate a strong negative relationship between the fatigue prediction methods (one-step method 

and Samn-Perelli scale, r = –0.90, P ≤ 0.0001; two-step method and Samn-Perelli scale, r = –0.85, P ≤ 0.0001). The actual sleep/wake data shown in the raster plot of Exhibit Figure E4-1B indicate that each participant’s sleep period is 

disrupted, especially on shorter layovers, thus supporting previously published data on transmeridian travel, which 

stated that sleep is usually disrupted during the time period when circadian rhythms are resynchronizing.

(3–6)

Data sources: (1) Van Dongen HP, Baynard MD, Maislin G, Dinges DF. Systematic interindividual differences in neurobehavioral 

impairment from sleep loss: evidence of trait-like differential vulnerability. Sleep. 2004;27:423–433. (2) Samn SW, Perelli LP. Estimating 

Aircrew Fatigue: A Technique with Application to Airlift Operations. Brooks Air Force Base, Tex: US Air Force School of Aerospace Medicine; 1982. No. SAM-TR-82-21. (3) Gander PH, Myhre G, Graeber RC, Andersen HT, Lauber JK. Adjustment of sleep and the circadian 

temperature rhythm after flights across nine time zones. Aviat Space Environ Med. 1989;60:733–743. (4) Samel A, Wegmann HM. Sleep 

and circadian rhythms of an airline pilot operating on the polar route: a case study. Aviat Space Environ Med. 1988;59:443–447. (5) 

Samel A, Wegmann HM, Summa W, Naumann M. Sleep patterns in aircrew operating on the polar route between Germany and east 

Asia. Aviat Space Environ Med. 1991;62:661–669. (6) Kandelaars KJ, Fletcher A, Eitzen G, Roach GD, Dawson D. Layover prediction 

for cockpit crews during transmeridian flight patterns. Aviat Space Environ Med. 2006;77:145–150.

Fig. E4-1. (a) Comparison of one-step and two-step model approaches using data obtained from airline pilots (n = 13) 

flying from Australia to Los Angeles and returning via Auckland, Australia. Included is a comparison of the alertness 

levels obtained using the one-step approach (with 95% confidence intervals [CIs]), with the two-step approach output 

and self-rated Samn-Perelli fatigue ratings completed preduty and postduty. The lower schematic diagram compares 

the periods during each layover where sleep propensity was greater than 33% and sleep estimations were obtained 

from the two-step approach. (b) Raster plot showing the timing of both sleep (black), duty periods (dark gray), and local 

night (red) for the 13 flight patterns included in the current analysis in both Australian Eastern Standard Time (AEST) 

and Universal Time Code (UTC). This plot highlights the differences in sleep strategies among pilots, and the disruption 

in sleep timing and duration caused by transmeridian travel. 

Exhibit 3-4 continued

00 08 16 00 08 16 00 08 16 00 08 16 00 08 16 00 08 16 00 08 16 00 08 16 00

10 18 02 10 18 02 10 18 02 10 18 02 10 18 02 10 18 02 10 18 02 10 18 02 10

Time of Day (AEST)

Australian Eastern Standard Time (UTC +10:00)

Universal Time (UTC +00:00)

b

Recent studies indicate that recovery from chronic 

sleep restriction can be considerably slower than 

initially believed.48,50 This implies that chronic sleep 

restriction can cause relatively longer term changes 

in brain physiology that are slower to recover. One 

recent finding suggests that the cumulative effect of 
86

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

excessive wakefulness should be included in future 

fatigue and alertness models to account for these effects.47–49 Other findings suggest that the current sleep 

homeostasis function should be modulated to alter the 

level of total recovery available during sleep.51,52 The 

findings of both groups are still in their infancy, and 

further definitive research is required. It is possible 

that the structure of the existing fatigue, performance, 

and/or alertness models needs to be altered to fit an 

updated recovery profile. 

Suitability of model use in the field setting versus 

laboratory setting is not the only factor that can influence model performance. When determining current 

and future values of a model’s proposed application, 

it is important to distinguish between its performances 

when the one-step and two-step techniques are used 

(ie, when the actual sleep/wake state is used as an input versus using estimated sleep/wake times inferred 

from hours of work). Goodness-of-fit levels are higher 

when actual sleep/wake data (one-step approach) are 

input directly.45 Furthermore, the output derived from 

different one-step models is significantly correlated, 

with little systematic difference observed. Good correlation between models, at the very least, represents 

convergence of the underlying scientific approaches.

However, marked differences in model outputs are 

seen when using a two-step approach to estimate the 

probabilistic distribution of prior sleep/wake states from 

a work pattern and fatigue.45 This discrepancy is caused 

by the inability of current models to account for key differences in sleep quality, as well as differences in sleep 

strategy caused by social interaction during break periods, commuting distances, and family responsibilities. 

All current models have been evaluated in terms of 

their ability to predict average rather than individual 

levels of fatigue.53 Thus, the general goodness-of-fit for 

group data does not apply when the model is used to 

predict individual levels of fatigue, or the likelihood 

that a single event can be associated with work-related 

fatigue. This is a significant word of caution for those 

considering the use of models for individual-level 

assessment without the perspective of group-level 

interpretation. This observation points to a further step 

of development for promising current models, which 

is to extend the predictions to the individual level. 

Another approach that could be applied to field-use 

models would be to start with a model based on data 

collected under controlled laboratory conditions, then 

validate the model, and explore generalizability and 

limitations using data collected under field conditions. 

Such an approach would not only limit the amount of 

inherent noise from field data being incorporated into 

the model, but also provide guidance for commanders 

on conditions of use. A model built in this way could 

also be developed further to account for individual 

differences or, at the very least, provide an indication 

of variance in addition to the overall predictions (see 

Exhibit 3-4 and accompanying Exhibit Figure E4-1).

TACTICAL USE OF SLEEP

In operational settings, where adequate nightly 

sleep may not be possible, “tactical napping” can be 

of significant benefit. This confirms the practice of 

soldiers sleeping whenever they can and as often as 

possible during extended training or operations. A major reason why naps are a powerful countermeasure is 

that the improvements that can be gained from a short 

nap are disproportionately large, compared with a long 

sleep.54 Research studies have illustrated that napping 

leads to significant benefits of alertness, performance, 

and communication.55–58

As valuable and efficient as they can be, however, 

there are specific aspects of napping that need to be 

addressed in operational environments:

	 •	 Scientific studieshave shownthatnaps thatlast

longer than 10 minutes have many benefits.59,60

	 •	 The value of a nap is generally dependent on

its duration—the longer the nap, usually the 

more beneficial it is.56,61

	 •	 It is equivocal whether the recovery value of

a nap depends on the time of day that it is 

obtained. Thus, functionally, napping is more 

important than the time it is taken.55

	 •	 Some research has found that the time of day

does have an effect on the recovery value 

of a nap (eg, a nap at 0430 hours has higher 

recovery value than a nap at 2100 hours).62

During the waking process, performance can be 

impaired.63–71 When awaken from a nap, most individuals experience a 1- to 30-minute period of confusion.58,60,72–74 This confusion is sometimes defined as 

sleep inertia or sleep drunkenness.

75–77 The impact of sleep 

inertia on performance is similar to reactions observed 

during sleep deprivation.68

In military operational settings, when naps are used 

tactically, positive benefits are available without sleep 

inertia risks degrading the nap value. The duration of 

sleep inertia is typically longer when individuals are: 

	 •	 awakened from sleep, compared with naturally awaking from sleep,78

	 •	 awakened from a deep sleep, compared with
87

Measuring and Predicting Sleep and Performance During Military Operations

light sleep,66,79,80 and

	 •	 awakened at a low point of alertness in their

biological rhythm. 73

Exactly how long sleep inertia lasts remains a contentious issue. The only consensus is that waking up 

and eliminating sleep inertia are not physiologically 

or psychologically instantaneous. Military operations 

that utilize naps should know that napping can also 

have effects on the quality and quantity of subsequent 

sleep.81–83 Because of these additional factors, managing fatigue in operational settings is a major challenge, 

even despite the potential benefits of countermeasures 

(eg, napping).84–87

The capacity of warfighters to obtain sleep/naps 

in theater also relates to other significant factors not 

discussed in this chapter. For example, pharmacological substances taken to sustain alertness during 

demanding operations are known to influence falling 

asleep, remaining asleep, remaining awake, and 

maintaining certain aspects of performance. Each 

substance has its own pharmacological properties 

that need to be considered both in terms of their 

immediate potential benefit, as well as in terms of 

their subsequent hangover effects. In this context, 

a hangover can result from a stimulant’s impact on 

an individual’s ability to get subsequent sleep or a 

sleep-inducing agent’s impact on the performance 

capability. In addition, the environment that people 

have to sleep in (from both the physical and psychological perspectives) plays a role in their capacity to 

use any recovery opportunities. 

SUMMARY

Sleep is a biological need critical to successful operational outcomes. This is true in both military and 

nonmilitary settings. Under operational conditions, 

the need to obtain adequate sleep should be viewed 

as comparable with other requirements of logistic 

resupply, such as water, food, fuel, and ammunition. 

From a practical perspective, adequate sleep sustains 

mental performance; however, less than adequate sleep 

degrades performance over time. 

Commanders are generally aware of the importance 

of adequate sleep. However, other than their own experience, and limited information/tools, the proactive 

management of sleep has been a difficult undertaking.

There have been technological advances that help to 

answer key operational questions. The most practical 

tool available to measure sleep in the field is the wristworn actigraph, which measures movement as an 

index of sleep and wake states. Actigraphy outputs correlate strongly to much more expensive, less practical 

sleep measures that are usually used in the laboratory, 

such as PSG. Any data collected using actigraphy in 

the field are not only useful for mapping the amounts 

of sleep that all individuals have obtained, but also for 

estimating the future performance of each individual, 

groups of individuals, or when data are aggregated.

Quantitatively measuring performance in the field 

is a more challenging issue than measuring sleep. 

Principally, this is because the operational demands 

of engagement must take the highest priority, and, 

generally, performance is considered acceptable unless 

an obvious error is made. This retrospective view is 

not a particularly sensitive process to assess degrading 

performance under operational conditions; it is only 

sensitive to failure, which occurs near the impaired end 

of the performance spectrum. Currently, more simple 

tests like the handheld, field-use PVT have been used 

as surrogate assessments of performance change. Further studies are needed to assess the validity of using 

surrogate tests to infer performance on operational 

tasks. Ideally, separate tasks—such as the PVT—would 

not be necessary because they represent a distraction 

from a soldier’s primary operational task. Thus, automated measures of efficiency and performance using 

online assessment of primary task performance is an 

area of significant interest for future studies.

In addition to the currently available tools to measure sleep and performance, there are also mathematical models that predict sleepiness/alertness, fatigue, 

and performance. Like most tools, these models have 

strengths and limitations. Therefore, the predictions of 

such tools are one meaningful input considered in conjunction with other available sources of information. 

Relevant information includes criticality of planned 

missions, boundaries of risk tolerance in the operation, 

self-reports by soldiers, and expected field conditions.

Current evidence suggests that there are potential 

benefits for efficiency and safety if the sleep of soldiers 

can be managed in a more proactive way than it has 

been managed previously, although there is not yet a 

single, practical strategy to be applied to all circumstances. There is sufficient evidence to support the 

prediction of sleep timing and amounts, as well as 

the tracking of actual amounts obtained for missions, 

gaming, or simulations. Furthermore, the inclusion 

of sleep deprivation and associated performance loss 

needs to be considered in risk assessments and in 

the development of appropriate countermeasures to 

specific military operations. This occurs because even 

adequately equipped, well-trained, highly motivated 

soldiers operating within cohesive units with good 
88

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

morale are not resistant to sleep loss. Future studies 

should focus on improving knowledge about the sleep 

of soldiers and performance behaviors in battle zones. 

There is a need to investigate the range of differences 

between individual-level sleep and performance in 

such conditions. 

Acknowledgments

We thank Colonel Greg Belenky, MD (US Army, Retired) for his detailed and insightful explanations of 

sleep-loss–related impairment in military operational environments. We also thank Erica Lipizzi for her 

editing expertise. 

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