1

Predicting Human Limits—The Special Relationship Between Physiology Research and the Army Mission

Chapter 1

PREDICTING HUMAN LIMITS—THE 

SPECIAL RELATIONSHIP BETWEEN 

PHYSIOLOGY RESEARCH AND 

THE ARMY MISSION 

KARL E. FRIEDL, PhD*

INTRODUCTION

HISTORY OF MILITARY PHYSIOLOGICAL MODELING

Civil War and Early Physiological Models

World War II and the Harvard Fatigue Laboratory

Ethical Considerations in Studies of Human Performance Limits

USES OF PHYSIOLOGICAL MODELS

Models Help Institutionalize Science-based Policy

Complexity Drives the Need for Models 

Models Provide Rapid Solutions to New Needs

Better Models Reduce Restrictions on Materiel Design Options

COMPUTATIONAL BIOLOGY

All Models Are Wrong, But Some Models Are Useful 

Criterion Measures and Militarily Relevant Endpoints

Generalizability and Applicability 

Maturity of a Model—Testing in Realistic Environments

DATA MINING

Sociological Barriers to Data Sharing

Preserving and Distributing Data Sets

Information Technology Solutions for Managing Massive Amounts of Data

TRANSITIONING PREDICTIVE MODELS TO USE

Spiral Development

Responsibilities to Maintain, Improve, and Supervise Use of a Model

Validation, Verification, and Accreditation

Army Computational Biology Applications

Virtual Prototyping and Virtual Training Environments and Simulations

SUMMARY

*Colonel, Medical Service Corps, US Army; Director, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel 

Command, Building 1054, Patchel Street, Fort Detrick, Maryland 21702-5012; formerly, Commander, US Army Research Institute of Environmental 

Medicine, Natick, Massachusetts 
2

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

INTRODUCTION

issues are constantly improving with new technological and scientific breakthroughs. This knowledge base 

can be passed on to future generations, with constant 

improvement and enlargement of the predictive models. Technological advances in the ability to acquire, 

store, and manage vast amounts of physiological data 

are helping to close some of these centuries-old gaps 

in retaining and integrating the “lessons observed” 

so that predictive models will truly represent “lessons learned.” These lessons can also be disseminated 

through decision support systems and through other 

new information technologies emerging in this new 

era of network-centric operations.3

Quantitation of biological relationships is at the 

heart of physiology. Classic examples of these defined 

relationships should be well known to students of 

physiology and are usually identified by the names 

of the creators (eg, the Siggaard-Andersen nomogram 

for acid-base balance, the Frank-Starling law of dynamic regulation of cardiac pumping, and the ÅstrandRyhming prediction of maximal oxygen uptake from 

submaximal heart rate).4 At the organismal level, these 

The US Army has entered a new era of low-level 

persistent conflict. This changing operational environment is shaped by new threats and challenges, such 

as globalization, disaffected populations vulnerable 

to radical ideologies, catastrophic terrorist attacks, 

failed or failing states, competition for energy, and 

climate change and natural disasters.1 The Army’s 

modernization strategy to meet these challenges includes developing more agile protection of health and 

performance of the individual soldier. The soldier is 

the Army’s most important military system (ie, “the 

soldier as a system”), as compared to other services 

with major platforms such as stealth fighters and destroyers. However, new technological complexity, the 

lethality of weapons systems, and rapid worldwide 

response capabilities make the performance of the 

individual soldier more critical to mission success than 

ever before. The near- and long-term health of individual soldiers is also potentially at risk from military 

technologies that can surpass operator capabilities and 

safety. Advances in warfighting technology and the 

changes in tactics and strategy that result from new 

capabilities make improved understanding of human 

limits critical to materiel and doctrine developers. The 

predictive models that provide developers and commanders access to critical physiological information are 

the subject of this book (Figure 1-1). No other agency 

or industry has a comparable need; thus, military 

needs have kept the Army at the forefront of human 

performance physiology. The continuously evolving 

threats drive more sophisticated predictive tools to 

improve selecting, training, monitoring, equipping, 

resting/recovering, and fueling the “soldier system” 

(Figure 1-2). 

A good first sergeant has special skills in recognizing 

human performance limits, but a good physiological 

model can greatly extend leader intuitive abilities to 

assess the status and capabilities of his troops. For 

example, soldiers are protected from heat injury by 

predictive work/rest and hydration tables. These limits are based on computational modeling of heat strain 

from an enormous amount of data and findings from 

more than 50 years of experimentation (summarized 

in TB MED 5072). These predictions provide guidance 

appropriate to any ambient conditions and mission 

posture to prevent environmental heat injuries, while 

also limiting overconsumption of water that might 

produce hyponatremia. Military leaders and medical providers need such well-founded physiological 

models that go beyond any one individual’s empirical 

experience. Even though every military leader has expertise acquired through careers in feeding, clothing, 

and training soldiers, the solution sets to these age-old 

Fig. 1-1. Physiological stressors encountered by soldiers 

and marines affect health and performance. Human performance optimization involves strategies to sustain health 

and performance in the face of these stressors. Physiological 

modeling defining human tolerance limits and the effect of 

moderating factors provides scientifically based strategies 

to interventions that ultimately involve how we feed, rest, 

train, and equip individuals and teams. It is important to 

consider models that combine multiple stressors because 

individuals are rarely subjected to only one stressor at a 

time. The highlighted individual stressors are described in 

various chapters throughout this book.

Immersion Cold

Freezing Cold

Hypoxia

Solar Radiation

Heat

Inadequate Energy Intake

Detraining

Overtraining

Dehydration

Hyponatremia

Sleep Deprivation

Traumatic Events

Isolation

New & Conflicting Roles

Family Separation

Information Overload

Spatial Disorientation

Soldier

Performance

Blast Overpressire

Non-Ionizing Radiation

Jolt/Impact

Head-Supported Mass

Load Carriage

Environmental Stressors

Materiel Hazards

Metabolic Stressors

Neuropsychiatric Stressors

EXTERNAL

STRESSORS

INTERNAL

STRESSORS

Energy Demands

Toxic Chemicals Fatigue
3

Predicting Human Limits—The Special Relationship Between Physiology Research and the Army Mission

models help to describe integrated biophysical and 

biochemical responses to stressors, such as exercise 

or hypoxia (Table 1-1).5–23 In this volume, readers will 

find Army models, such as the INJURY model of blast 

injury risk, the US Army Research Institute of Environmental Medicine (USARIEM) heat strain model, 

and successors of the Walter Reed Army Institute of 

Research (WRAIR) sleep and performance model. 

Military physiological models are the primary 

products of the Military Operational Medicine research 

program, wherein the findings of various laboratory 

and field studies are brought together in the form of 

useful algorithms, models, and simulations. Testing, 

refining, and validating these knowledge products 

comprise an additional step involving advanced development and engineering. This step has been typically 

neglected because there is no regulatory requirement 

for performance and preventive medicine predictions 

comparable to the US Food and Drug Administration’s 

approval of products that influence medical treatment. 

Computational biology is the modern science that 

encompasses this form of advanced development that 

makes the Military Operational Medicine program’s 

physiological and psychological research useful to 

Army customers. Computational biology is the interdisciplinary combination of mathematics, computer 

science, and life sciences. This chapter summarizes 

Army experiences and challenges in advancing physiological models developed through methods of computational biology. 

Fig. 1-2. Soldiers and marines today face many of the same 

physiological challenges presented to their counterparts 

in previous centuries. Today’s technologically enabled 

warfighters should be better prepared and better protected 

because of the knowledge accumulated from experience and 

research on how to feed, rest, train, and equip individuals 

and teams. This knowledge is especially useful when assembled into predictive physiological models used to optimize mission planning and rehearsal, for virtual prototyping 

of equipment, and for decision support tools. (a) Roger’s 

Rangers fought the Battle on Snowshoes in January 1757 

against French, Chippewa, and Ottawa warriors. In similar 

a b

environmental conditions, (b) US Marine Lance Corporal Harris C Bienn provides security for a simulated casualty evacuation during a 2009 winter training course at the Marine Corps Mountain Warfare Training Center in Bridgeport, California.

Painting: (a) Courtesy of Walking the Berkshires Web site, “Right Rangers, Wrong Fight,” October 1, 2007, http://greensleeves.

typepad.com/berkshires/2007/10/right-rangers-w.html.

Photograph: (b) Courtesy of the National Archives and Records Administration, Washington, DC. 

HISTORY OF MILITARY PHYSIOLOGICAL MODELING

Predicting soldier performance has always been 

of great interest to military commanders. In earlier 

times, recruit selection was based on anecdotal experience and may have involved some test of strength or 

endurance, or typically a simple visual assessment of 

physical robustness and behavior. By the mid-1800s, 

objective measures of fitness were considered. Height 

was a key selection criteria as a surrogate measure 

for health and fitness.24 During the Civil War, data 

were systematically collected by investigators who 

were interested in topics that are still relevant today, 

such as recruiting healthy and fit soldiers, sustaining 
4

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine TABLE 1-1 KEY PHYSIOLOGY MODELS IN THE ARMY’S MILITARY OPERATIONAL MEDICINE RESEARCH PROGRAM Prediction System Hazard TRL Inputs [Key References] Test System Applications/Users Blast overpressure TRL 8—model in use by Pressure tracing from Risk Assessment Code Blast Test Device CHPPM; Army Research (INJURY 8.0) the Army four sensors (based on lung injury) Laboratory/Survivability [Stuhmiller et al, 1996(1)] and Lethality Directorate Laser eye injury TRL 7—findings used in Frequency, wavelength, Retinal injury Evaluation cards CHPPM; ANSI standard ANSI standard and duration [Ness et al, 2000(2)] Thermal strain TRL 6—not yet generalized Temperature, heart rate, Water requirements; work/ Heat strain monitor HHA, conducted at (SCENARIO, for export fluid intake, activity, rest cycle; and core body (includes model) USARIEM HSDA) and clothing temperature [Kraning and Gonzalez, 1991(3); Moran et al, 1998(4)] Head-supported TRL 5—some key design Equipment mass and Neck fatigue and injury risk — HHA, conducted at USAARL equipment factors predicted center of mass [Ashrafiuon et al, 1997(5)] Jolt/vibration TRL 5—ANSI standard, Vibration signatures Fatigue [Cameron et al, — HHA, conducted at USAARL but limited data 1998(6); Smith, 1995(7)] Inhaled toxic gases TRL5—model predicts Metabolic deficits, Performance/escape Survivability assessments, (TGAS 2.0) serious risk primarily oxygen [Stuhmiller and Stuhmiller, — conducted at WRAIR delivery 2001,(8) 2005(9); Januszkiewicz et al, 1997(10)]

Radiofrequency TRL5—models can predict SAR Body heat deposition — HHA, conducted by CHPPM

radiation thermal risks [Adair and Berglund, 

1992(11)]

Impulse noise TRL5—models can predict Decibel exposure Risk of eardrum rupture and — HHA, conducted by CHPPM

eardrum rupture, but permanent threshold shiftArmy has inadequate in hearingpredictor of hearing [Chan et al, 2001(12);damage Stuhmiller, 1989(13)]

(Table 1-1 continues)
5

Predicting Human Limits—The Special Relationship Between Physiology Research and the Army Mission

Prediction

System Hazard TRL Inputs [Key References] Test System Applications/Users Behind body TRL5—new model that Lethality of torso impact Injury risk assessment — Materiel developer

armor effects may eventually replace [Stuhmiller, 2006(14)](BABTA) National Institute of

Justice standard

Head impact and TRL5—thresholds of injury Neuropsychological Neurocognitive impairments — Medical personnel assessing

concussion and reinjury risk have testing [Bleiberg et al, 2004(15); return to duty and(ANAM) not been well defined Warden et al, 2001(16)] rehabilitation

Physical training TRL5—first version model Gender; training intensity, Physical performance/ — Military trainers

enhancement/ in testing frequency, and duration; physical injuryinjury ground reaction forces [Stuhmiller et al, 2006(17)](TOP 1.0)

Sleep and TRL5—early version Sleep history; use of Cognitive performance — Military planners, Army

performance models in refinement stimulants; scheduling [Belenky et al, 2000(18); modeling, and simulation(SAFTE) of naps Hursh et al, 2004(19)]

Altitude ascent TRL5—first version model Acclimatization status; Probability of AMS symptoms — Military planners, Army

(ARMS) in development speed of ascent [Vann et al, 2005(20)] modeling, and simulation

Note: Except for the head impact/neuropsychological testing effort, all of these research model development programs have undergone scientific review at least once, and sometimes three or more times in external peer review site visits organized by the American Institute of Biological Sciences, in addition to review of the publications.AMS: Acute mountain sickness; ANAM: Automated Neuropsychological Assessment Metric; ANSI: American National Standards Institute; ARMS: Altitude Readiness Management System; BABTA: Body Armor Blunt Trauma Assessment; CHPPM: US Army Center for Health Promotion and Preventive Medicine (now undergoing a name change with integration into the US Army Public Health Command); HHA: health hazard assessment; SAFTE: Sleep, Activity, Fatigue, and Task Effectiveness; SAR: specific absorption rate; TGAS: toxic gas assessment; TOP: Training, Overuse Injury, and Performance; TRL: technology readiness level; USAARL: US Army Aeromedical Research Laboratory; USARIEM: US Army Research Institute of Environmental Medicine; WRAIR: Walter Reed Army Institute of Research. Data sources: (1) Stuhmiller JH, Ho KH, Vander Vorst MJ, Dodd KT, Fitzpatrick T, Mayorga M. A model of blast overpressure injury to the lung. J Biomech. 1996;29:227–234. (2) Ness JW, Zwick H, Stuck BE, et al. Retinal image motion during deliberate fixation: implications to laser safety for long duration viewing. Health Phys. 2000;78:131–142. (3) Kraning KK, Gonzalez RR. Physiological consequences of intermittent exercise during compensable and uncompensable heat stress. J Appl Physiol. 1991;71:2138–2145. (4) Moran DS, Shitzer A, Pandolf KB. A physiological strain index to evaluate heat stress. Am J 

Physiol. 1998;275:R129–R134. (5) Ashrafiuon H, Alem NM, McEntire BJ. Effects of weight and center of gravity location of head-supported devices on neck loading. Aviat Space Environ Med. 1997;68:915–922. (6) Cameron B, Morrison J, Robinson D, Roddan G, Springer M. Development of a Standard for the Health Hazard Assessment of Mechanical Shock and Repeated Impact in Army Vehicles. Fort Rucker, Ala: US Army Aeromedical Research Laboratory; 1998: 201 pp. Final Report. AD A339 243. (7) Smith SD. The effects of whole-body vibration on human biodynamic response. J Gravit Physiol. 1995;2:96–99. (8) Stuhmiller JH, Stuhmiller LM. An internal dose model for interspecies extrapolation of immediate incapacitation risk from inhalation of fire gases. Inhal Toxicol. 2001;14:929-957. (9) Stuhmiller JH, Stuhmiller LM. A 

mathematical model of ventilation response to inhaled carbon monoxide. J Appl Physiol. 2005;98:2033–2044. (10) Januszkiewicz AJ, Mundie TG, Dodd KT. Maximal exercise performance-impairing effects of simulated blast overpressure in sheep. Toxicology. 1997;121:51–63. (11) Adair ER, Berglund LG. Predicted thermophysiological responses of humans in MRI fields. Ann NY Acad Sci. 1992;649:188–200. (12) Chan PC, Ho KH, Kan KK, Stuhmiller JH, Mayorga MA. Evaluation of impulse noise criteria using human volunteer data. J Acoust Soc Am. 2001;110:1967–1975. (13) Stuhmiller JH. Use of modeling in predicting tympanic membrane rupture. Ann Otol Rhinol Laryngol. 1989;140(suppl):53–60. (14) Stuhmiller JH. Mathematical Modeling in Support of Military Operational Medicine. San Diego, Calif: Jaycor, Inc/L-3 Communications/Titan Corporation; 2006: 76 pp. Final Report DAMD17-00-C-0031. AD A458 419. (15) Bleiberg J, Cernich AN, Cameron K, et al. Duration of cognitive impairment after sports concussion. Neurosurgery. 2004;54:1073–1078. (16) Warden DL, Bleiberg J, Cameron KL, et al. Persistent prolongation of simple reaction time in sports concussion. Neurology. 2001;57:524–526. (17) Stuhmiller JH, Sih BL, Shen W, Amankwah K, Negus C. Overuse Injury Assessment Model. San Diego, Calif: Jaycor, Inc/Titan Corporation; 2006: 115 pp. Annual Report DAMD17-02-C-0073. AD A463 099. (18) Belenky G, Balkin TJ, Redmond DP, et al. Sustaining performance during continuous operations: the Army’s Sleep Management System. In: Friedl KE, Lieberman H, Ryan DH, Bray GA, eds. Countermeasures for Battlefield Stressors. Baton Rouge, La: Louisiana State University Press; 2000: 197–205. (19) Hursh SR, Redmond DP, Johnson ML, et al. Fatigue models for applied research in warfighting. Aviat Space Environ Med. 2004;75(suppl 3):A54–A60. (20) Vann RD, Pollock NW, Pieper CF, et al. Statistical models of acute mountain sickness. High Altitude Med Biol. 2005;6:32–42.Table 1-1 continued
6

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

endurance on road marches, preventing injuries, and 

reviewing recruitment and casualty statistics and associations.25 The same questions are still being asked 

more than a century later, but, clearly, technological 

advances provide new options for the enduring problem set of soldier health and performance issues (see 

Figure 1-1). Although some soldier problems are new, 

and generated by our own technology, most of these 

new issues still fall into the same broad categories of 

research that have been pursued for generations. For 

example, information overload is a relatively recent 

information-age issue, but cognitive performance in 

the face of other psychological stressors (eg, sleep deprivation) has been formally investigated at least since 

the World War II era, with findings that are relevant to 

this new problem. Similarly, radiofrequency radiation 

is a modern technology problem, but body heating 

has been a relevant Army focus for many years. This 

highlights the fact that there are enduring research 

requirements for the Army, with new understanding 

and technology always providing greater advantage 

to the warfighter (see Figure 1-2). 

Civil War and Early Physiological Models

During the Civil War, study teams were sent out 

to gather data at Union Army encampments that 

related body dimensions, nutrition, and nativity to 

physiological outcomes, such as spirometry measures, 

pulse, lifting strength, and general indicators of fitness 

for duty25 (Figure 1-3). Along with other statistics on 

recruitment demographics and casualty rates, various 

analyses were conducted to develop predictors of success for recruitment standards and battlefield planning 

Fig. 1-3. Research teams were sent to Union camps to collect physiological data from a large sample of men during the Civil 

War. Test devices were constructed for a variety of measurements, such as the calipers for body breadths, spirometers for 

lung volume, and an upright lift device to measure strength performance (top). Twelve anthropometry “machines” were 

constructed, based on a device developed by an Edinburgh tailor, to efficiently collect a large number of measurements on 

each individual (bottom right). Much of these data were summarized by age and ethnicity, such as the graphical display of 

mean statures plotted by age, demonstrating the physical maturation in men for stature after age 21 (bottom left). 

Illustrations: Adapted with permission from Gould BA. Investigations in the Military and Anthropological Statistics of American 

Soldiers. Cambridge, Mass: Riverside Press; 1869.
7

Predicting Human Limits—The Special Relationship Between Physiology Research and the Army Mission

factors. Researchers also gathered data on road march 

performance and its relationship to food provisions 

(including coffee rations), topics that are frequently 

revisited today in Army research.25–27 Unfortunately, 

the program had more ambitious objectives that were 

never realized because the secretary of war decided 

that research was not an important priority. 

Gould25 tested several new statistical approaches that 

describe normal height and weight relationships in humans based on this very large sample of healthy males, 

thus confirming Quetelet’s observations that, in metric 

units, weight increases normally against height squared. 

Gould found that this did not work for adolescent 

soldiers in a growth phase up to age 21, but then held 

constant across older age categories. This relationship 

between height and weight (now referred to as body 

mass index) is used to calculate the Army weight-forheight screening tables used today in Army Regulation 

600-9.27a Some of the Civil War data have been reused to 

assess body composition trends in young soldiers over 

time. This calculation of the average body fat of young 

soldiers in 1864 was not an application conceived of at 

the time, but was made possible because of the preservation of a well-defined data set (Figure 1-4).24,28

The struggle to preserve this unique data archive, 

as well as the challenges to productively analyze these 

measures from many thousands of Union soldiers, was 

articulated by Dr Benjamin Apthorp Gould (1869): 

These statistics greatly surpass in amount all that has 

been previously gathered on the same subjects, and 

it may be long before opportunity again offers for 

an equal collection of similar material. On the other 

hand, the proper reduction, elaboration, and discussion of this grand store of numerical data demands 

special training and peculiar gifts. No pains have 

been spared in their elaboration, and the enormous 

amount of work bestowed on the materials will be 

apparent only to those who are in some degree familiar with arithmetical computations. But the variety of 

topics is great; and medical and physiological knowledge of a high order is needed for eliciting such information as they may contain, as well as for deducing 

the best results.25(p vi)

Gould’s plea for the unique importance of his data 

set has been a familiar refrain of scientists with their 

military data sets from deployments, large training 

studies, and experimental studies ever since. 

In World War I, the seeds were planted for a new 

field of human nutrition science. A special commission 

of scientists was established to evaluate nutritional requirements for health and performance of soldiers.29,30

This commission led to the formation of the National 

Research Council and today’s Food and Nutrition 

Board in the Institute of Medicine. It was also the 

genesis of modern nutrition and metabolism research, 

which is now a credible scientific discipline. Detailed 

studies led by Major John R Murlin assessed 427 Army 

mess halls, representing 135,000 men, to determine 

that an average of 3,900 kcal were provided per man 

per day (including 131 g of protein and 134 g of fat), 

but only 3,600 kcal/day were consumed.30 Additional 

studies considered the effects of season, duration in 

the camps, and strength performance of the men in 

relationship to food consumption to predict requirements for various circumstances. The Army research 

focus was heavily influenced by concepts of nutrition, 

genetics, and health that were popular at the start of the 

20th century. One remarkable study examined health 

consequences of weight change using a longitudinal 

analysis of annual physical examination data. In this 

study, Reed and Love31 defined a “cardiac type,” reporting associations between weight gain and abdominal obesity with heart disease in Army officers. These 

important observations preceded the use of waist-hip 

ratio and abdominal fat as expressions for obesityrelated diseases that gained prominence in the 1950s. 

Modern tools of genomics and proteomics provide us 

with new data handling and analytical challenges, with 

opportunities to revisit these old issues and move from 

predictions based on group membership to individual 

predictions for personalized medicine and individualized training and protective interventions. 

Fig. 1-4. Body composition of US male soldiers in various 

eras. These estimates are derived from large anthropometric 

data sets and the DoD body-fat (BF) model developed by 

Dr James Hodgdon at the Naval Health Research Center. 

For more than a century, the typical young male soldier 

has benefited from improved health and nutrition with an 

increase in height (Ht) and body weight (BW), primarily 

reflected in an increase in the fat free mass (FFM) component 

comprising bone and muscle. Relative body fat of active 

young men (17%) is similar between Civil War soldiers and 

soldiers in 2000. 
8

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

Fig. 1-5. Nomograms and slide rules are graphical analog computational devices that were common tools before the advent of 

electronic computers. Slide rules are general purpose computers, whereas nomograms are usually intended to perform a specific 

set of calculations. This nomogram for human blood was developed to combine known biochemical relationships, such as the 

oxygen dissociation curve, into a more complete model to describe more complex relationships. This was developed to make 

predictions about new studies conducted with men at altitude in the famous US medical research expeditions into the Andes. 

Illustration: Reproduced with permission from Dill DB, Edwards HT, Consolazio WV. Blood as a physicochemical system. 

XI. Man at rest. J Biol Chem. 1937;118:635–648.

ARTERIAL

VENOUS

32

30

28

26

24

22

20

18

16

14

7.6

7.5

7.4

7.3

7.2

7.1

7.0

HbO2

pO2

pH2

pCO pH3 2

(TOTAL CO3

)b

(BHCO3

)3

(BHCO3

)2

(H2

O)3

9

8

7

6

5

4

100

80

60

50

40

30

25

20

15

10

5

5

6

7

8

9

10

12

14

16

20

25

30

40

35

45

50

60

70

80

90

100

3

2

0

1

44.0

45.6

45.4

45.2

45.0

44.8

44.6

44.4

44.2

7.1

7.2

7.3

7.4

7.5

7.6

7.7

7.8

7.9

8.0

8

0.74

0.72

0.70

0.68

0.66

0.64

0.62

22

20

18

16

14

718

717

716

715

714

723

722

724

721

720

719

12

10

11

18

17

16

15

14

13

12

19

26

25

24

23

22

31

30

29

28

27

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20

0.60
9

Predicting Human Limits—The Special Relationship Between Physiology Research and the Army Mission

In 1927, the Harvard Fatigue Laboratory was 

founded by Lawrence J Henderson and Elton Mayo 

as a research center for industrial physiology and 

psychology. Although the Army commitment to research on these problems rapidly waned after World 

War I, the work on occupational fitness standards; 

early occupational medicine standards, such as problems of inhaled gases in enclosed spaces (notably 

carbon monoxide); and responses to environmental 

stressors prepositioned the Harvard Fatigue Laboratory to deal with many important soldier problems 

in World War II.32,33 In this precomputer era, “computational biology” took the form of nomograms. 

For example, blood-gas nomograms relating physicochemical characteristics, such as P50 and blood 

pH, were developed to provide useful mathematical 

predictions in respiratory metabolic assessments34

(Figure 1-5). This was a sophisticated predecessor 

of the current TGAS (toxic gas assessment) model 

that relies on modern computing power to link these 

well-developed respiratory metabolic factors with 

more recent physiologically based pharmacokinetic 

modeling to predict the effects of inhaled toxic gases, 

especially if combined with other variables (eg, 

ventilation).35

World War II and the Harvard Fatigue Laboratory

Military physiology research reached maturity in 

World War II, with the mobilization of members of 

the Harvard Fatigue Laboratory (Cambridge, Mass) 

(Figure 1-6) to military research activities at the Army 

Climatic Research Laboratory (Lawrence, Mass), 

Bethesda Naval Medical Research Institute (Bethesda, 

Md), Fort Knox Armored Medical Research Laboratory 

(Fort Knox, Ky), Pensacola Navy School and Aeromedical Laboratory (Pensacola, Fla), and Wright Field 

Aero Medical Laboratory (Wright Field, Ohio). Some 

of the individuals from these laboratories later helped 

form the Army Chemical Corps Medical Laboratories 

(Aberdeen Proving Ground, Md), US Army Medical 

Research and Nutrition Laboratory (Denver, Colo), 

Quartermaster Army Medical Nutrition Laboratory 

(Chicago, Ill), and the Quartermaster Climatic Research 

Laboratory (Natick, Mass), each of which has spawned 

more recent successor efforts.32 Today, the majority of 

integrated physiological research that relates to human 

performance limits are the primary mission of only 

one laboratory in the federal government: USARIEM 

(Natick, Mass).36 Other laboratories that include some 

missions in this area are the US Army Aeromedical 

Fig. 1-6. (a) D B Dill with his favorite experimental tools—the 

Van Slyke apparatus and the Haldane apparatus for blood 

gas analyses. Dill was the informal director of the Harvard 

Fatigue Laboratory, mentoring many of the next generation 

a b

of physiologists. One of these was his future son-in-law, Steve Horvath, founder of the Institute of Environmental Stress in 

Santa Barbara, the civilian parallel of the US Army Research Institute of Environmental Medicine. After World War II, Dill 

became the director of medical research for the US Army Chemical Research and Development Laboratory (1947–1961). 

Photograph: Horvath SM, Horvath EC. The Harvard Fatigue Laboratory. Upper Saddle River, NJ: Prentice-Hall; 1973. (b) 

Treadmill experiment at the Harvard Fatigue Laboratory in 1938. The subject is Sid Robinson, one of the two physiologists 

to receive their doctorate degree at the laboratory (the other was Steve Horvath). Robinson’s work on physical fitness and 

age is still frequently cited today, as is his pioneering body of work on thermoregulation in exercise. The observer is R E 

Johnson, famous for his studies on nutrition metabolism and work with C Frank Consolazio, a later investigator at the Military Nutrition Laboratory in Denver. Photograph: Reproduced with permission from Horvath SM, Horvath EC. The Harvard 

Fatigue Laboratory. Upper Saddle River, NJ: Prentice-Hall; 1973.
10

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

Research Laboratory in Fort Rucker, Alabama (biodynamics models) and WRAIR in Forest Glen, Maryland 

(fatigue and performance models).

Wartime efforts included quick turnaround studies in nutrition, fitness, clothing and protection in hot 

and cold environments, and respiratory physiology.32

Fatigue on long foot marches and during continuous 

operations was addressed, with countermeasures 

ranging from stimulants to nutritional supplements.27,37

Amphetamines were extensively tested for use in 

military operations,37 and these investigations of neurological factors associated with fatigue and fatigue 

interventions involving amphetamines provided 

insights that recent studies with more sophisticated 

methods confirm and extend. Fueling the soldier has 

always been an important consideration in sustaining performance. In World War II, the K ration was 

nutritionally complete, but was not eaten (even by 

hungry soldiers); thus, it was ineffective.38 Ration studies examined macronutrient composition for optimal 

performance, exploring the use of high-fat rations and 

considering the importance of energy balance.39,40 This 

led to modern studies by the Army to determine that 

carbohydrates are a preferred source of energy and still 

form the basis of current approaches to optimal composition of combat rations.41,42 Predictions of the amount 

of work that an individual could accomplish relative to 

the caloric intake were developed from underfeeding 

studies during this era. These studies provided clear 

evidence of a ceiling effect produced by limited energy 

intake.39 The Office of The Surgeon General contracted 

Ancel Keys and his group at the University of Minnesota to conduct the now famous Minnesota Starvation 

Study.43 Fit young male volunteers for this study lost 

24% of their body weight over 24 weeks and were 

then put on various refeeding regimens to determine 

the optimal recovery diet for returning prisoners of 

war and other starving populations. This study was 

published in two volumes, with the complete data set 

reutilized for metabolic modeling to this day.44,45

Environmental heat and cold exposures were significant factors in World War II, and extensive efforts 

to speed acclimation and improve soldier protection 

were investigated.32 More sophisticated analyses and 

predictions of those same challenges are being conducted today.46,47 Incorporation of accumulated data 

into thermal models provided early demonstrations of 

mathematical model approaches to predictive models 

for decision aids. This also preserved the key experimental results, even if data were not preserved. Physiological models that combined ambient conditions, 

clothing, etc, to predict heat strain were developed 

out of these data, with principles of the heat balance 

equation presented for the first time.48,49 This provided 

a basis for current methods that have since been extensively expanded and redefined for biophysical evaluation of clothing, evaluation, and heat strain modeling, 

as described in Chapter 8. The thermal modeling and 

biophysical methods developed for assessment of 

clothing and personal equipment were a World War II 

success story and represent one of the most advanced 

military physiological modeling efforts today (Figure 

1-7). This success is due, in part, to the close relationship between physiologists and materiel developers 

of personal protective equipment. 

Although there was extensive evidence of health 

hazards and impaired operator performance in tanks, 

even in World War I, military systems were traditionally engineered with the operator as an afterthought. 

The Fort Knox Armored Medical Research Laboratory 

conducted a large series of tests in tanks to evaluate 

the risks from heat, impulse noise, toxic gases, and 

other factors.50–53 These important findings were 

largely ignored. The problems were repeatedly demonstrated, including one event in which the senior 

officers attempted to fire 10 rounds from a Sherman 

tank with the hatches closed. However, they quit the 

test after firing four rounds because of the oppressive 

ammonia fumes.54,55 To protect against exposure to 

heat and engine combustion products, vehicles were 

generally operated with all hatches open. Years later, 

systems such as the Bradley Fighting Vehicle were 

still being developed with an incomplete appreciaFig. 1-7. Biophysical testing models, such as the copper foot, 

have been highly refined by the US Army for thermal insulating and vapor transmissibility properties of individual 

clothing. This copper foot at the USARIEM laboratory is 

being used to test a Norwegian boot.

Photograph: Courtesy of USARIEM, T Rice, Public Affairs 

Office.
11

Predicting Human Limits—The Special Relationship Between Physiology Research and the Army Mission

tion for the human occupant and operators. In 1983, 

the Army Health Hazard Assessment program was 

instituted to address issues of operator safety and 

crew survivability starting at the earliest stages of 

planning of Army vehicles and equipment. This Army 

regulation drives the requirement for medical input 

with validated predictive physiological models and 

damage risk criteria. 

Impulse noise was an issue from weapons and tunnel blasts in the Pacific Islands during World War II, 

addressed by goat studies in caves at Fort Knox (S M 

Horvath, personal communication, December 1982). 

This research was later refined with development of 

shock tubes for controlled blast exposures in a laboratory setting at Fort Knox.56 Research to improve auditory protection with various forms of earplugs was 

conducted at the Fort Knox Armored Medical Research 

Laboratory, and other aspects were also considered, 

including burn risks from firing large weapons.50–52 Today, impulse noise is an issue from new, high-powered 

weapons systems and has been addressed through an 

entire research program to protect against auditory and 

nonauditory effects of our own systems. 

As the United States entered World War II, clothing became appreciated for its significant roles in 

protecting soldier health and enhancing survival. 

This, and nutrition research efforts, provided the 

major impetus for the Army’s physiological research 

and modeling that continues today. In World War I, 

personal equipment was inadequate and too heavy. 

It was common practice for soldiers to discard clothing and equipment, because the standard individual 

load was about two thirds of the typical soldier’s 

body weight.57 Shoes were not waterproof, and 90% 

of the men suffered foot problems in the winter of 

1917–1918.57 These problems did not prompt much 

new development, and the US Army entered World 

War II still unprepared and with substantially the 

same inadequately designed cold weather uniforms 

and personal equipment, even for World War I conditions. A much wider range of conditions faced the 

American soldier in World War II, including, for 

example, dry heat in the north African desert, moist 

heat in the South Pacific, cold winters in Italy and 

western Europe, and damp cold in the Aleutians. In 

a remarkable move, borne out of wartime necessity, 

the Army established a multidisciplinary scientific 

team to deal with clothing issues. The group, modeled 

on the unique approach to practical problem-solving 

at the Harvard Fatigue Laboratory, included physicians, physiologists, textile experts, clothing design 

experts, and meteorologists. This was established 

as the Quartermaster Climatic Research Laboratory 

and later as USARIEM and Natick Labs. Much of the 

basic thermal modeling and studies in the protection 

against specific hazards were conducted at the Fort 

Knox Armored Medical Research Laboratory and 

later at the Pierce Laboratory (New Haven, Conn) at 

Yale University. This effort to study and predict the 

interaction of clothing, fatigue, and supplementary 

substances was guided by the National Research 

Council.57 Tropical environmental problems were 

investigated by a team of preventive medicine specialists in the Panama Canal Department. Specialized 

clothing protection was investigated across multiple 

laboratories and with new technologies to enhance 

fire proofing, protection against chemical warfare 

agents, and even fabric weaves and chemical impregnation to protect against disease-bearing arthropods. 

Skin diseases, especially in hot climates, were an 

important cause of disability and lost duty time.57

More than 90,000 cold injuries were reported in US 

troops during World War II, and much of this was attributable to inadequate supplies and training in their 

use.57 At the end of the war, detailed anthropometric 

measures and photographic studies of a statistically 

derived sample of more than 100,000 male and 8,000 

female soldiers were produced for future sizing and 

other studies.58

Ethical Considerations in Studies of Human 

Performance Limits

Ethical standards for the conductance of medical 

research have changed considerably in recent years. 

Greater awareness of what we do not understand 

and what constitutes misconduct in research has 

led to ever-improving human subject protections 

and regulatory safeguards. This obviously produces 

new challenges in conducting studies on the limits of 

healthy human tolerances, especially in the military 

environment where there is an even greater expectation of protection of healthy young men and women 

in an autocratic command structure (considered for 

research purposes as a special vulnerable population).59,60 In a previous era, physiologists often first 

exposed themselves to new stressors and risks to 

observe these limits. The famous 40-40-40 Club 

of researchers at the Harvard Fatigue Laboratory 

included nude exposure to –40°C, ascent to 40,000-

ft altitude, and 40-mile walks in 12 hours to study 

nutritional balance.32 Military psychologists were 

among the first researchers to point lasers into their 

own eyes to acquire data on laser dazzle and performance effects. In the present, more enlightened 

research environment, safety of human subjects is 

of paramount importance, and even physiologists 

are protected in “self-testing” experiments and pilot 
12

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

that impulse noise may have subtle effects on the 

brain, thus affecting mood and cognition. Similarly, 

studies of jolt and forces acting on the neck and laser 

eye exposures are carefully safeguarded, with exposures stopping within a conservative safety margin 

of the lowest known or suspected amount of harmful 

exposures. Therefore, the challenges of obtaining 

data for outcomes based on failure or approaching 

failure (eg, heat injury, hypothermia limits, muscle 

injury, etc) prevent direct laboratory-based validation of some models. 

This means that more studies have to be designed 

around the discovery of basic principles that predict 

limits and models that interpolate and extrapolate 

biomedical data, supplemented by post-hoc observations of adverse outcomes from field data acquired in 

high-risk training and operations. Access to this kind 

of unique and interesting extreme of physiological 

performance in military training and operational missions is another reason that Army physiology has been 

in the forefront of performance modeling. Modeling 

existing data can improve efficiency and safety of new 

studies, thus helping to ensure the highest level of 

protection for human subjects with better predictions 

of limits and individual variability, as well as minimizing the suffering and use of laboratory animals. 

experiments. This means that laboratory studies on 

tolerable limits will be stopped within a conservative safety margin of the lowest known or suspected 

amount of harmful exposures. For example, laboratory heat exposure studies are typically terminated 

before an individual’s core temperature reaches 

40°C, even though 40°C may be maintained with 

peak marathon running performance for more than 

2 hours for some individuals. 

There is greater caution today to respect what 

we may not know about health risks. A study that 

passed through multiple levels of ethical review 

more than a decade ago exposed young soldiers to 

small explosive detonations to determine new auditory protection standards. This was based on graded 

exposures leading up to temporary threshold shifts 

in hearing of 25 dB, from which every subject had 

full and rapid auditory recovery.15 The most severe 

impairment noted in these subjects occurred in a 

single individual for whom baseline measures were 

obtained before he slipped out of his barracks to attend a rock concert; the impairment occurred without any experimental exposure. Nevertheless, it is 

doubtful that this study protocol would be approved 

today in light of new concerns (so far without supporting data, but also without assurance of safety) 

USES OF PHYSIOLOGICAL MODELS

Physiological models are important for the Army, 

providing ways to organize bits of knowledge dispersed 

in journals and among subject matter experts; and to 

generate testable, verifiable, and, ultimately, validated 

predictions. These predictions of actual outcomes 

reduce costs, improve experimental control of conditions, improve safety, and provide rapid and repeatable 

testing to accelerate research and development and 

improve military decision-making. Predictive models 

serve several broad purposes, including the following:

• capture and generalize lessons learned to 

make these physiological discoveries more 

useful; 

• produce predictions from complicated interactions that cannot be efficiently addressed by 

test matrices of all the relevant conditions; 

• rapidly address new problems from a sophisticated and generalizable model; 

• provide real-time sensor data fusion; 

• enable virtual prototyping of new equipment 

or tasks, thus improving efficiency in the exploration of optimized designs; and

• identify key questions for hypothesis-driven 

research. 

Models Help Institutionalize Science-based Policy

Lessons Learned to Prevent Physical Training Injuries

A family of thermal models has been used to ensure 

safe military training. These efforts originated from 

heat and cold physiology research in World War II. 

Computerized thermal strain models have been used 

by USARIEM to generate easily interpretable heat 

strain tables that are now widely used. These Army 

standards have been incorporated into national occupational standards.61 New cold water immersion 

tables were constructed following the 1995 deaths of 

Ranger students in moderately cold water conditions 

in Florida.62,63 These types of predictive models (ie, 

published table of training limits) are so important 

to the Army that they have become authoritative 

statements of policy that commanders are expected 

to follow. In this instance, physiological models have 

been used to make available the rules learned from 

many years of experiments and field data collection 

to improve safety Army-wide. 

It is far more difficult to manage risks and enforce 

safety policies in the absence of validated models. 

For example, the Army has always contended with 
13

Predicting Human Limits—The Special Relationship Between Physiology Research and the Army Mission

ish the need and enhance the efficiency of costly and 

time-consuming testing. The Army maintains research 

capabilities to develop risk assessment tools that can 

then be handed off to users to conduct their own risk 

assessments (Figure 1-8). These tools continuously 

improve the ability of materiel developers to conduct 

virtual prototyping; the US Army Public Health Command and others to conduct health hazards and survivability assessments; and commanders and planners to 

implement safe and effective training, doctrine, and 

soldier management policies to optimize performance 

and readiness. The same laboratories may also use their 

capabilities to conduct expedient tests for an urgently 

needed piece of equipment or strategy. 

For example, a new “bunker buster” armament 

for soldiers operating in urban terrain in Southwest 

Asia could not be fired by human testers because it 

exceeded an existing standard for permissible human 

exposure. The recoil force to the shoulder exceeded 

70 ft-lbs, an old testing standard of uncertain derivation. The system had to be redesigned to reduce the 

recoil, causing delays in system fielding. New data 

were needed to provide quick field guidance on how 

this system should be used, particularly given the 

limited knowledge of actual human tolerances to 

firing this system by individuals of various sizes and 

strength, singly and repeatedly, and in various positions (eg, prone or standing). However, rather than 

embark on a major new effort to study and model the 

complex articulation of the shoulder and understand 

how recoil forces act on it, a single empirical study 

by USARIEM measured indicators of performance 

degradation and injury with repeated firing by soldiers.66 The study included detailed assessment of 

injury, including magnetic resonance imaging of the 

shoulder, measures of edema, subjective assessments 

of soreness, biochemical markers of tissue damage, 

and markers of performance degradation (eg, changes 

in marksmanship accuracy). This test clearly demonstrated that the existing standard was conservative 

for these conditions; however, it could not address 

all of the important questions relevant to field use, 

such as health risks associated with firing position, 

repeated firings and fatigue, out-of-position firing, 

etc. This is an example of how core expertise in Army 

laboratories can be used to rapidly address a problem, but avoid the misstep of embarking on a major 

modeling effort for a relatively specialized problem 

in a resource-constrained environment. 

In contrast to this expedient evaluation of shoulder 

recoil standards, a major modeling effort focused 

on understanding the forces placed on the human 

neck has been under way for more than a decade. 

Neck-related issues have become more important to 

musculoskeletal injuries from excessive physical training and with fatigue-related mishaps resulting from 

inadequate restorative sleep. Even after decades of research to understand the predictive factors and appropriate interventions to reduce these, the rules learned 

about training injuries and fatigue21,64 are only poorly 

imparted to new leaders. The solution to making this 

accumulated knowledge useful beyond a small group 

of subject matter experts in the Department of Defense 

(DoD) is to produce validated predictive models that 

can be queried when needed. New models of fatigue 

and performance are now being developed,65 and the 

effort is reviewed in Chapter 3 of this volume. 

The Training, Overuse Injury, and Performance 

model will help to institutionalize knowledge about 

physical training injury prevention.20 Using extensive 

data sets on physical training injuries and expanding 

the qualitative rules summarized in TB MED 592 on 

prevention of physical training injuries, this model 

highlights the “cross-over point” wherein increased 

daily running mileage results in high injury rates and 

diminished training benefits.66 One use of this overuse 

injury model is to help instructors plan recruit training 

schedules. Another application is to guide scientists 

to areas wherein work is needed to close gaps in our 

predictive capabilities. Model inputs include training regimen, characteristics of the individual(s), and 

physical training “dose” (biomechanical and physiological loadings). Model outputs predict performance 

enhancement, fatigue, and injury. The first version of 

this model for physical training and injury (Training, 

Overuse Injury, and Performance 1.0), which undergoes revisions as new data come available, reuses data 

from several large Army and Navy data sets from 

studies of metabolic responses of muscle and bone to 

create predictions about group outcomes.20 A future 

iteration will attempt to refine the contributing factors 

to improve the accuracy of outcome predictions for 

individuals, not just groups.

Complexity Drives the Need for Models 

Biomechanics of the Shoulder and Neck

Test and evaluation of every new product or strategy, of every new dietary supplement or technology, or 

of every combination of stressors encountered during 

deployment are simply impossible. More importantly, 

simple tests are usually not adequate for answering 

truly complex questions, and conclusions are not 

readily transferable to the next variation of the problem. Predictive models that grow with each new set 

of conditions or understanding of the underpinning 

physiological relationships should increasingly dimin-
14

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

the Army because of the increasing participation of 

women (with thinner and weaker necks, compared 

with most men) and because of the trend to add 

more technologies to head-supported equipment (eg, 

heads-up helmet displays, night vision goggles). DoD 

and the National Highway Traffic Safety Administration have evolved predictive models of both mannequin and human neck responses to external forces 

acting on the head.9,67,68 Previous design criteria for 

head-worn devices have been estimated from what 

was tolerated by the typical young male, and not what 

was actually safe and tolerable for extended periods 

or what could be tolerated by the neck of an average 

female.69 Injury risks have been related to dimensions 

and strength of the neck (eg, “giraffe necks” vs “bull 

necks”), but little is known about actual neck and 

spine damage that may be caused by single or accumulated jolts in vehicles running over rough terrain 

or from the shock of a parachute opening. Few neckconditioning studies involving healthy individuals 

have been conducted,70 in part because of the concern 

for the potential neck injury risk associated with neck 

strength-conditioning studies. Until new validated 

neck models are available, the neck will continue to 

be treated very conservatively. For example, neck stabilization is part of the standard of care for casualties 

who have received a helmet strike or penetrating head 

wound. This is apparently driven by caution, rather 

than based on any data that neck injury can actually 

occur as a result of the forces imparted by a bullet to 

the head in a survivable injury. Ultimately, the neck 

model needed by the DoD will assess muscle fatigue, 

Fig. 1-8. Three examples of Army applications of medical research models that contribute to the safety of systems and better 

protection of teams and individuals. The blast injury model was developed for the Health Hazard Assessment process to 

determine safety of new high-powered weapons systems for the human operators. No such standards exist in the civilian 

community, but this model has found broad applications, including safety assessment of air bags in vehicles. The Toxic 

Gas Assessment Software (TGAS) model was developed for the Soldier Survivability Program to optimize military vehicle 

design and protection systems to protect crews against the effects of combinations of toxic fire gases. The Body Armor Blunt 

Trauma Assessment (BABTA) Injury Prediction model has been developed for the materiel developers of personal protective 

equipment (PPE) to improve the accuracy of the assessment of body armor effectiveness, permitting tradeoffs in flexibility 

and mass not previously possible. 

CFD: computational fluid dynamics; FEM: finite element modeling.

Photographs: Courtesy of Michael J Leggieri, Jr, DoD Blast Injury Research Program.
15

Predicting Human Limits—The Special Relationship Between Physiology Research and the Army Mission

degraded human performance (eg, head tracking), 

and risk of injury for various forces applied to the 

head with and without various masses. 

Models Provide Rapid Solutions to New Needs

Problem Solving for Issues of Thermal Strain

Validated research models can rapidly generate 

answers to questions from commanders, materiel developers, or preventive medicine specialists. Usually, 

problems identified from the field demand immediate 

answers. The research response to write a research 

protocol and conduct some studies is not a satisfactory answer. The ability to respond in a timely manner comes from existing Army research programs in 

areas corresponding to critical physiological threats. 

For example, many of the practical decision support 

tools needed in the field can be rapidly generated from 

a primary research model. Many different types of 

applications have been produced on short notice using the research models for thermal strain prediction 

at USARIEM. The Ranger Training Brigade needed 

guidance for safe heat exposures during their standard 

distance runs and road marches. A simple slide rule 

was promptly generated from the heat strain decision 

aid model to provide run time limits for various ambient conditions. This quick turnaround application 

benefited from years of thermal physiology studies and 

modeling. Another problem, hyponatremia, emerged 

in recent years, leading to an assessment of maximum 

limits of water that should be prescribed for various 

heat, clothing, and workloads to prevent overhydration without incurring an increase in heat injury in 

susceptible individuals. The adequacy of the new 

guidance predicted from USARIEM heat models was 

tested in field studies at Army training sites to confirm 

the validity of the model predictions.71

Rapid applications from Army thermal models 

included the development of a cooling strategy for 

soldiers operating in high-mobility multipurpose 

wheeled vehicles (or “Humvees”) in Iraq in the summertime. USARIEM models were used to predict 

thermal comfort and survivability in various cabin 

heat conditions, and estimates of the required cooling power to favorably shift these conditions within 

the constraints of the amount of cooling that could 

be practically achieved in these vehicles operating 

in high radiant heat conditions.72 This information 

was used by the equipment developers at Natick 

Soldier Center (Natick, Mass) to modify a lightweight, 

water-cooled vest (or the Air Warrior Microclimate 

Cooling Garment) with rapid release for individual 

occupants in a vehicle. The Tank and Automotive 

Command used this design to produce a simple refit 

air conditioning system that would provide cooling 

to four occupants per vehicle. In 2004, this system 

was deployed in 5,000 high-mobility multipurpose 

wheeled vehicles in Iraq and proved to be a useful 

enhancement to soldier effectiveness. This project 

earned a Research and Development Collaboration 

Award in 2005 as an example of seamless interaction 

between biomedical research and materiel developers, and the systems went into widespread use. Other 

examples of successful quick response model applications are provided in Chapter 2.

Better Models Reduce Restrictions on Materiel 

Design Options

Impulse Noise and Laser Eye Exposure Standards 

Development of new standards with a scientific basis almost always produces less conservative restrictions on materiel developers, because actual tolerance 

is refined from necessarily conservative initial limits. 

This should make easy friends between physiology 

modelers and materiel developers.73 For example, 

new, high-powered weapons systems could not be 

fielded by the Army without impractical limitations 

to their use (eg, howitzers that must be fired with a 

1,000-ft lanyard) until new studies produced data, 

criteria, and a test methodology for the evaluation 

of operator risks to nonauditory impulse noise (blast 

overpressure) injury (see Chapter 10 in this volume). 

In the absence of criteria for blast overpressure injury, 

an existing standard (MIL-STD 1474D) designed for 

acoustic hazards had been adopted. Through data 

collection and modeling effort, this acoustic standard 

was shown to be orders of magnitude more restrictive than a scientifically based model for nonauditory 

blast effects. Risks to hearing were also found to be 

overly conservative by at least an order of magnitude, based on controlled human blast exposures.15

A similar phenomenon occurred with laser eye exposure thresholds when new research by the Army 

demonstrated constant motion of a nonanesthetized 

eye, even with a concerted effort to focus on a point.6

This led to an order of magnitude increase in safe 

permissible exposure limits (ANSI Z136.1-2000) in 

the design and use of field range-finding devices 

and other Army laser applications.6 Obviously, the 

reverse situation, with discovery of an unexpected 

hazard that produces new restrictions on materiel 

options, would be best made through a scientifically valid and relevant modeling effort and as early 

as possible in the development of a new system. 

Some modern technologies, such as directed energy 
16

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

(electromagnetic radiation and laser), pose new and 

very specific threats. Nevertheless, a basic scientific 

understanding of these technologies exists because of 

prior research, and the Army is positioned to avoid 

technological surprise. As an example, the effects of 

a new radiofrequency radiation weapon can be generally predicted from estimating energy deposition 

and heating in various tissues in the body.14 Relatively 

few nonthermal bioeffects have been discovered over 

several decades of Army research in this area.74 Thus, 

technical issues and potential threats are typically not 

completely new or surprising, and additional research 

can be quickly focused on any gaps in the existing 

knowledge base and models. 

COMPUTATIONAL BIOLOGY

All Models Are Wrong, But Some Models Are Useful

Models are only approximations of the behavior of 

complex natural systems; but, despite the simplifying assumptions, they can be useful instruments if 

they provide a clear predictive advantage. A model 

will be more useful if it includes some estimate of 

the accuracy of the predictions. For example, a sleep 

and performance model is not particularly useful to a 

commander if it only highlights well-known associations between inadequate sleep and decreasing mental 

performance. Modeling must go far beyond statistical 

associations and use some of the modern techniques 

available, such as support vector machines.75 Stochastic 

modeling and related approaches help to provide error 

estimates or probabilities associated with a prediction.76,77 Both inter- and intraindividual variabilities 

need to be determined to provide estimates of the 

repeatability of the prediction and variation between 

individuals.78 It will be even better if it has “learned 

its soldier” in a cybernetic type of individual process. 

Without baseline testing and individual response patterns, some predictions are of little value. Essentially 

all of the Army’s current physiological models are 

based on group means; individual predictions are a 

future objective. For most outcomes, rate of change 

within individuals will be more predictive than group 

means and thresholds of change; this is a fundamental 

concept in systems biology and in new approaches to 

personalized medicine. Neuropsychological testing to 

predict mental readiness status is an example in which 

individual values provide information about change 

from an individual’s usual performance in a much 

more helpful assessment than the information gained 

from comparison of a single assessment to group 

means.79 Like physical fitness testing, the normal range 

is very wide, and thresholds of success are somewhat 

arbitrary. However, a dramatic change in individual 

performance on either physical or mental tests may 

indicate important changes in the individual’s readiness status.

Other important examples of criterion selection 

include physical task performance. Selection for military occupations using a model that predicts strength 

capacity is grossly inadequate if strength is only one 

aspect of job performance, or if it is a unique requirement for task performance that can be accomplished 

through several strategies. Use of strength testing as 

the key determinant of suitability for certain Army 

jobs would predictably exclude most female soldiers 

from the job and would not identify individuals particularly suited to the tasks. Two decades of research 

on military occupational specialty job classifications 

by physical capabilities have concluded that the predictive criteria were wrong, or are certainly not valid 

today with improvements in task and equipment 

design, and strength is only one of many factors that 

play into job performance and safety.80,81 In this book, 

the chapter on load carriage highlights the correlation between certain types of strength and successful 

load carriage performance; successful performance 

also includes not being injured during performance 

of the task. 

Criterion Measures and Militarily Relevant 

Endpoints

Fatigue and Performance Models

A critical element of a model is the criterion measure 

or outcome against which it is developed. This determines a great deal about its validity and relevance in 

a particular application. Desired predictive endpoints 

of militarily relevant performance may not map easily to laboratory measures or even the outcomes that 

can be easily measured in the field. This is a special 

problem today for military performance research 

focused on domains of physical and mental capabilities. One difficulty in performance research is that the 

determinants of success on the battlefield also include 

unpredictable or intangible factors, such as coincidence 

and bravery. 

Although psychomotor vigilance testing is the most 

reliable single laboratory measure that is exquisitely 

associated with sleep deprivation and sleep latency,82

what this means is the prediction of mission success in 

a military operation is uncertain. This test can be cautiously generalized to military performance involving 

sustained concentration and appears to be a reasonable 

surrogate laboratory measure for high-order function-
17

Predicting Human Limits—The Special Relationship Between Physiology Research and the Army Mission

ing. However, it would not be appropriate for the prediction of other domains, such as mood and motivation 

or marksmanship performance. Sleepy soldiers can 

continue to march effectively even if they score poorly 

on the Psychomotor Vigilance Test.83 A research volunteer can rally long enough to do well on a test even 

when significantly fatigued, just as a soldier hearing a 

bullet pass his ear has a burst of adrenaline-stimulated 

alertness that does not reflect the probability of lapses 

in attention that might actually be better represented 

by the Psychomotor Vigilance Test or tests of sleep latency. In evaluation of the countermeasures to fatigue, 

delaying onset of sleep, high-dose caffeine, amphetamine, and modafinil all effectively sustain Psychomotor Vigilance Test performance while delaying sleep.84

However, with increasing sleep restriction, there is also 

increasing impairment of some potentially important 

capabilities that are not reversed by stimulants, such 

as interpretation of nonverbal communication and 

complex emotions.85 The countermeasures themselves 

can have effects on other aspects of higher cognitive 

function, including moral reasoning and humor appreciation and sleep recovery,86 demonstrating the 

limitations of a performance model based on one key 

test outcome. 

Mission failure outcomes may be a very different 

model and potentially less useful and generalizable. 

In sleep and performance modeling, occupational 

injuries have been modeled with respect to shifting 

work cycles. The likely outcome is a complex mix of 

time-of-day issues involving more than the human 

operator’s state, but also changes in staffing, changed 

room lighting, and other human factors that also 

contribute to injuries. The result is a model that is 

not generalizable to anything except a prediction of 

injuries by time of day for the circumstances in which 

it was developed. Thus, it might be appealing to use 

fatigue models to predict accident rates instead of the 

intended performance status of the individual. However, this is not a logical extension of the model that 

has been developed, and a model based on accident 

rate outcomes has human performance as only one 

component of the prediction.65

Generalizability and Applicability 

Body-Fat Standards/Personal Readiness Standards

The right endpoint measurement is not necessarily 

going to be the most sophisticated and accurate measurement obtainable. In an example using body-fat 

standards, techniques to predict a gold standard chemical model of the body do not provide the predictive 

value of an abdominal girth measurement technique 

obtained with a 50-cent tape measure. This is because it 

is specifically abdominal fat, not total body-fat assessment that best serves the intent of the Army’s Weight 

Control Program. When the DoD was directed in 1980 

to establish new standards to enhance fitness and 

prevent obesity, a new, practical prediction of body fat 

needed to be established.87 The interim method using 

four skinfold thicknesses was difficult to implement 

because of observer training demands.88,89 The previous standard based on weight-for-height standards 

described body size, but could not distinguish highly 

desirable big and lean (strong) recruits from undesirable big and fat recruits.90 New guidelines called for an 

objective assessment of body fat to prevent obesity and 

improve military readiness, including improved physical fitness, military appearance, and health.28 The 1980 

expert panel recognized that abdominal girth was not 

only the most important predictor of excess fat, but also 

a highly suitable metric for the intended application of 

driving fitness habits (marker of overnutrition and underexercise), ensuring military appearance (potbellies 

are a main affront to military bearing), and reducing 

health risks (abdominal fat was already recognized as 

being key to obesity-related health consequences). The 

circumference equations developed by the DoD have 

been repeatedly validated across different populations 

using sophisticated three- and four-compartment 

models (Figure 1-9). The standard error of the estimate 

against these criterion measures is consistently around 

Fig. 1-9. Body fat prediction algorithms in the 1990s for 

women in the services ranged from too simplistic (Air Force) 

to overly fitted (Army) models before the DoD adopted 

a single most appropriate model developed by Dr James 

Hodgdon at the Naval Health Research Center. Each of the 

four military services had developed circumference-based 

body-fat predictive models after a 1980 report on fitness in 

the military services concluded that the post-Vietnam military needed new focus on overnutrition and underexercise, 

and lauded the US Marine Corps’ first use of circumferencebased body-fat estimates to enforce body-fat limits. 
18

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

3% body fat; the reproducibility of the method is 1%; 

and the biological variability with normal ranges of 

hydrational changes, genetically determined fat deposition, etc, is at least several percentages of body fat. 

Thus, the criterion measure “calibrates” an abdominal 

measurement to body fat. Sophisticated methods of 

total body-fat estimation are less useful, accurately including fat located in genetically determined sites (eg, 

subcutaneous distribution to arms, back, and thighs) 

and locations that are less susceptible to environmental 

control (ie, exercise and nutrition). This was illustrated 

by a body composition study involving career male 

soldiers at the US Army Sergeants Major Academy 

(Fort Bliss, Tex). Comparison of body fat estimated by 

the abdominal circumference-based equation to dualenergy X-ray absorptiometry (a three-compartment 

model method) demonstrated a good correspondence 

up to the upper limit of acceptable male body fat and 

then plateaued to higher levels of dual-energy X-ray 

absorptiometry body fat, with nobody exceeding the 

Army standard by the circumference method.28 This 

demonstrated the effect of environmental factors 

(eg, exercise and nutrition habits) on maintaining an 

abdominal fat “standard,” even as age, genetics, and 

other less-controllable factors may produce changes 

in total body fat more accurately detected by sophisticated methods. This is an example in which relevance

of the endpoint measurement (abdominal girth) is 

more important than accuracy of the originally targeted 

endpoint (precise total body fat). 

Maturity of a Model—Testing in Realistic 

Environments

Technology Readiness Levels

The National Aeronautics and Space Administration (NASA) developed a rating scheme to categorize 

the level of research maturity known as technology 

readiness levels (TRLs). This came about because too 

many incompletely formed ideas were leaving the 

bench for actual use or advanced testing, with consequent problems and cost overruns when they did not 

meet expectations. TRLs have been adopted in Army 

research management. The modeling equivalents in 

the Army acquisition system are shown in Table 1-2. 

This is an important metric to apply to physiological 

models, because there are many commercial products 

dependent on models that have not been validated, 

wherein valid models have been misapplied and 

the models do not provide meaningful advantages. 

There is no trusted authority with a US Food and 

TABLE 1-2

TECHNOLOGY READINESS LEVELS AND PHYSIOLOGICAL MODELING ANALOGS 

Level NASA Definition—Status of Research Physiological Modeling Equivalent

3 Analytical and laboratory studies to determine and Qualitative relationships—conceptual exploration of 

test separate elements of a technology qualitative factors important to a predictive model 

4 “Low-fidelity” integration of components in the Semiquantitative relationships—laboratory identification 

laboratory of rough quantitative relationships contributing to a 

 physiological outcome

5 “High-fidelity” breadboard laboratory integration and Quantitative relationships—mathematical and 

testing in simulated environments computer-working predictive model, version 1.0

6 “High-fidelity” testing in a relevant laboratory or Expansion and generalized testing model predictions 

simulated operational environment against relevant human or surrogate data; peer review 

 validation 

7 Demonstration of an actual prototype in an operational Testing model predictions in operational environment 

environment (actual use by intended operators); special panel review, 

 ANSI, etc, field testing/experiment

8 Technology has been proven to work in final form and Customer is using the model predictions with clear 

under expected conditions advantages to use 

9 Product in use under mission conditions Valid model has been institutionalized as the approved 

 prediction model

ANSI: American National Standards Institute

NASA: National Aeronautics and Space Administration
19

Predicting Human Limits—The Special Relationship Between Physiology Research and the Army Mission

Drug Administration–like function to regulate the 

commercialization of a physiological model that is 

not used for a medical decision (eg, sports watches 

that provide training heart rates, calorimeter/pedometers, body-fat measurement devices, fatigue and 

performance actigraphs and planning tools). Even for 

the Army, physiological models can easily find their 

way into major Army models and systems without 

any rigorous authentication. 

The usefulness of TRLs can be illustrated in the development of soldier physiological status monitoring 

(PSM) systems. The original PSM concept developed 

in 1995 by Fred Hegge was intended to take information from noninvasive wearable sensors, conduct local sensor signal processing, connect to an executive 

processing unit on the soldier, and be transmitted to 

commanders or medics. This diagrammed concept 

is referred to as a TRL3—“still in the concept stage, 

without actually testing and prototypes.” Studies 

with actigraphs to describe recent sleep history and 

models to associate this with laboratory performance 

predictions might score as a TRL4—“prototype testing.” A TRL5 consisted of combining sensors (eg, actigraphy, core body temperature pills, and in-the-boot 

foot strike prototype monitors) to provide analyses 

of various fatigue and performance, thermal load 

and heat strain, and locomotory energy costs from 

a Norwegian Ranger training run that took place in 

1997. The hardware system itself—that proved technically challenging in these field conditions because 

of broken wires, improper synchronization of data, 

and computer download failures—was a premature 

test of a system in a realistic field environment. A 

subsequent test in a concept exploration program 

used more refined systems with a squad of infantry 

soldiers operating in hot conditions at Fort Benning.91

This test demonstrated other issues, such as sensor 

cross-talk between individuals. Another study with 

data collection from multiple sensors over 10 days 

on a squad of Marine officers in training highlighted 

other developmental gaps with data streaming and 

data storage protocols.92 Individual physiological 

models continued to be developed in the laboratory (TRL6), and a new Army Technology Objective 

pursued an integrated and field-hardened hardware 

solution with soldier wearability testing (TRL6). At 

the end of the 30-month Army Technology Objective, 

a hardware system had been tested in limited field 

conditions, resolving frequency interference and 

other issues. Heat models using simplified and better 

predictive algorithms had been tested for retroactive 

identification of heat strain occurrences in actual 

soldier field data.93,94 TRL7 has not been achieved for 

physiological monitoring systems and the predictive 

algorithms that make them useful. TRL7 requires a 

considerable investment and renewed focus on the 

prediction of specific outcomes. This current rating 

should clearly indicate that physiological monitoring 

is not ready for use on soldiers in an operational environment.79,95 TRLs can be applied to the individual 

components, including hardware and models, as well 

as to the maturity of an overall system.

DATA MINING 

Sociological Barriers to Data Sharing

It is a significant tragedy of the modern era that, 

within the relatively small circle of physiologists, data 

have not been routinely saved and distributed in useful 

forms that could be reutilized. This is especially important for data from studies that cannot be repeated 

because of the scope, cost, or unique opportunity. At 

last, both the sociological and technological barriers to 

data sharing are crumbling. Technological advances in 

information management are also making it easier to 

store and share data sets. Some journals, funding organizations, and regulatory agencies are beginning to 

require archiving of original data with access to others 

for examination and further analysis to protect against 

misinterpretation and fraud. Recognizing the importance of data sharing, the National Institutes of Health 

(NIH) have mandated the development of data-sharing 

plans in every major grant proposal. Recent policies on 

data sharing and guidelines for their implementation 

have been issued for all grant recipients: 

NIH reaffirms its support for the concept of data 

sharing. We believe that data sharing is essential for 

expedited translation of research results into knowledge, products, and procedures designed to improve 

human health. The NIH endorses the sharing of final research data to serve these and other important 

scientific goals. The NIH expects and supports the 

timely release and sharing of final research data from 

NIH-supported studies for use by other researchers.96

NIH has further defined the “timely release and 

sharing” of data to be no later than the acceptance for 

publication of the main findings from the final data set. 

Whereas initial investigators are expected to reap the 

“first and continuing” benefits of their data, there is 

no provision for “prolonged and exclusive use.” There 

are many reasons given for not sharing data, but the 

culture has changed, and these reasons are no longer 

acceptable (see Table 1-3).97
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Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

Preserving and Distributing Data Sets

One classic experiment demonstrates how DoDsponsored data have been effectively shared in the past. 

The Minnesota Starvation Study, highlighted earlier in 

this chapter, is famous not only because of the public 

attention elicited at the time the study occurred in 1945, 

but also because data were made available and have 

been repeatedly reused in metabolic studies (Figure 

1-10).43 Recently, these old data sets helped to (a) design 

and interpret studies of the health and performance 

effects of undernutrition in Ranger students,39,98 (b) test 

coefficients in a metabolic model,39,99 and (c) establish 

a new model of body composition balance.45 These 

data sets were successfully shared in a precomputer 

era through a simple but elegant two-volume book set 

that clearly described the experiments and presented 

individual qualified data in a series of tables.43 The 

report also interpreted the data, along with a comprehensive review of the rare literature, up to the time of 

the study. This form of data sharing (ie, publication of 

tables) worked well for a relatively small data set. The 

difference between science now and then is the amount 

of data that can be collected that may all be relevant to 

the production of a high-quality predictive model. It has 

been pointed out that the Human Genome Project could 

not have been undertaken if that massive amount of 

data on base pairs was to be published as a textbook.100

TABLE 1-3

SOCIOLOGICAL BARRIERS TO DATA SHARING 

Common Reasons for Not Sharing Data The New Reality

It’s my data! I conceived the study and conducted the Data reutilization is important to experimental efficiency, 

experiments. preventing unnecessary experiments and reducing costs 

and risks. It is government data if it was collected by an 

Army employee as part of his/her official duties. 

I have not finished analyzing the data, and I still have more Once the main papers are published, promptly sharing the 

papers to publish. data facilitates new research and should still bring credit 

to the original investigators and their subsequent papers. 

Nobody else knows how to interpret these data properly. An important challenge to making data available and useful is the adequate description of the relevant experimental conditions and variables. Journals and funding 

agencies are increasingly requesting fully documented 

data sets. 

It is unethical to share the information for an analysis that Data sets can be properly de-identified to protect the 

was not described in the protocol approved by the Human privacy of research volunteers and may have great value 

Use Committee. in analysis beyond the original hypothesis. This has 

been pioneered by federal agencies, such as the National 

Center for Health Statistics.

I’m afraid someone reanalyzing my data will come up with a Testing new and alternative hypotheses is a cornerstone of 

different interpretation. science. It is important to encourage alternate interpretations and opinions.

Data yielded negative results and are of no use to anyone. Well-designed, but negative, results are important to share, 

because the tendency to bury this data biases science. 

Making data available for an alternate use, including 

meta-analysis, may help salvage value from the study.

I cannot trust or understand data produced in another Sharing data sets is a good way to discover and resolve 

laboratory. methodological differences.

It is a lot of work to assemble the data and information on the The trend of using electronic laboratory notebooks, 

relevant conditions, protocol, etc, to help someone else. archiving protocols and data sets, and utilizing distributed computing technologies to enhance data sharing is 

making this easier.

Data source: Koslow SH. Should the neuroscience community make a paradigm shift to sharing primary data? Nature Neurosci. 2000;3:863–865.
21

Predicting Human Limits—The Special Relationship Between Physiology Research and the Army Mission

a

c

b

d

Fig. 1-10. (a) Ancel Keys in his laboratory at the University of 

Minnesota, where he and colleagues conducted the famous 

Minnesota Starvation Study for the Office of The Surgeon 

General in 1945. (b) Fit young men participating in this study 

lost 24% of their body weight over 24 weeks through energy 

restriction and were randomized to controlled refeeding regimens to determine how to best refeed returning prisoners of 

war and starving civilian populations. (c, d) Extensive physiological testing and psychological testing were conducted 

that produced a published data set that is still used to test 

new hypotheses and models today. 

Photographs: Reproduced with permission from Keys A, 

Brozek J, Henschel A, Mickelsen O, Taylor HL. The Biology 

of Human Starvation. Minneapolis, Minn: University of Minnesota Press; 1950.
22

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

Publication of raw data is not well tolerated by 

modern journals. Consequently, collateral archives 

may not suffice for the complicated forms of data 

produced in modern studies. Data must be properly 

qualified and stored in a form that is accessible and 

useful to all. The Army has explored various computer routines to automatically process and store 

data in useful and retrievable forms.17,94,101 Jaycor, 

Inc (San Diego, Calif) has experimented with data 

preservation that includes experimental context so 

that future investigators can access a data set with 

complete experimental context, methodology, and 

individual or summary data without having to go 

back to the original investigators. In one such important preservation effort, all of the important Armyfunded blast data sets from more than a decade of 

animal and human experiments have been stored in 

a relational database that handles multiple formats.17

This physiological data preservation effort has been 

extended to some other large Army data sets, such as 

a USARIEM boot biomechanics study. A recent effort 

retrieved and reutilized large epidemiological injury 

data sets from Army and Navy studies to develop a 

first prototype injury and fitness prediction model 

(Training, Overuse Injury, and Performance 1.0).20

Data sets have been developed for general purpose 

use and posted on the Internet. The National Center for 

Health Statistics conducts broad data collection efforts 

in national studies, such as the National Health and 

Nutrition Examination Survey, even though there is 

careful consideration regarding the types of hypotheses that will be addressed by data collection. These 

data are specifically intended to be shared with other 

researchers, and the de-identified data are publicly 

released as soon as they have been used to address 

key hypotheses advanced by various sponsoring 

agencies. The de-identification process has been carefully designed to ensure protection of volunteers. For 

example, unique characteristics, such as advanced age 

that might inadvertently single out an individual, are 

collapsed into larger categories. Similar protections 

against deductive disclosure of participant data in active duty military databases might include collapsing 

high ranks, such as general officers, years of service, 

older age, and other lower frequency characteristics.

Data enclaves are restricted data sets not openly 

shared beyond a group of investigators that agree to 

certain terms of data use. Recent NIH guidelines for 

implementation of data-sharing policies specify that 

data “should be made as widely and freely available 

as possible while safeguarding the privacy of participants, and protecting confidential and proprietary 

data.” Data enclaves protect sharing of sensitive or 

proprietary information.102 Military epidemiological 

data sets—such as the longitudinal Millenium Cohort 

Study (the military “Framingham” study),103 the biennial survey of health behaviors in the DoD,104 the Total 

Army Injury and Health Outcomes Database,105 and the 

registry of Gulf War veterans106,107—are examples of data 

enclaves established to protect sensitive patient information protected by Health Insurance Portability and 

Accountability Act (HIPAA) regulations. A repository 

of DNA samples from research volunteers participating 

in various physical and environmental experiments, 

along with their phenotypic data, was established to 

study gene polymorphisms of interest.108 This Environmental Medicine Genome Bank is an example of a 

potent capability for health modeling.109,110 The Wound 

Data and Munitions Effectiveness Team database, representing comprehensive data on US military casualties 

in Vietnam between 1965 and 1967, has been coded and 

placed in an electronic format that can be queried.111

Casualties from Iraq and Afghanistan are being summarized in the Joint Theater Trauma Registry database 

to provide important information on current practices 

and problems.112,113 Like the Wound Data and Munitions 

Effectiveness Team, these data are also being combined 

with incident data for future analyses and modeling 

of protective equipment failures and monitoring of 

the effectiveness of improved safety equipment in the 

new Joint Trauma Analysis and Prevention of Injury in 

Combat program. Access to these data may require a 

simple data use agreement or involve extensive protocol development and approval, as well as appropriate 

security clearances. 

The concept of a system of information analysis 

centers (IACs) has been explored by the DoD, recognizing the need for a “smart archive” and analytical cell 

within specialty areas.114 The execution of this concept 

has had mixed success, with only a few centers serving 

their science communities well. The Defense Technical 

Information Center (DTIC) is the recognized archive 

for reports of all kinds from data collected under the 

auspices of the DoD, as well as related information, 

especially in technical reports from other countries. 

Many of the technical reports in DTIC are also made 

available outside of the DoD through the Commerce 

Department systems, such as the National Technical 

Information System. The IAC concept was intended 

to fall under the DTIC in various relevant configurations related to specialty areas. For example, IACs with 

relevance to physiological problem-solving included 

the Human Systems IAC, Chemical and Biological 

IAC, Modeling and Simulation IAC, and Survivability 

IAC. From the standpoint of physiological modeling, 

only the Survivability IAC (SURVIAC) has become 

specialized in data archiving, modeling support, or 

new syntheses of information. 
23

Predicting Human Limits—The Special Relationship Between Physiology Research and the Army Mission

Information Technology Solutions for Managing 

Massive Amounts of Data

Although physiological modeling still suffers from 

the relative paucity of data, remote physiological data 

monitoring and other new data collection systems will 

soon change this into a glut of raw data. It is likely that, 

in future deployments beyond the Iraq War, soldiers 

will be intensively monitored, and a vast amount of 

physiological data—along with contextual information 

regarding ambient conditions, equipment, and outcomes—will be harvested and available for modeling. 

This is actually a congressional mandate, following on 

the experience of the first Gulf War and the challenges 

associated with diagnosing illnesses from subjective 

medical symptoms and a dearth of location, treatment, 

and environmental exposure data for individuals. Gulf 

War illness issues have propelled the development of 

an effective and seamless electronic health record system between the Department of Veterans Affairs (VA) 

and DoD, as well as significant efforts to acquire and 

record individual deployment exposure data.

The need to save physiological data was clearly noted 

by Frederick Hegge, an Army psychologist concerned 

with developing metrics of cognitive function to assess 

performance status of soldiers in elaborate models and 

simulations. As the Director of the Military Operational 

Medicine research program, Hegge set out to recruit 

retiring senior scientists to gather and summarize existing data in their own areas of specialization so that 

they could be modeled by future researchers. Hegge 

was particularly frustrated by the lack of reutilization 

of important data sets, including the loss of virtually 

all information gathered in the P2NBC2 (Physiological 

and Psychological Effects of the Nuclear/Biological 

and Chemical Environment on Systems and Sustained 

Operations) studies, over which he had presided during 

the 1970–1980 time frame. This was an important human 

performance database developed in response to concerns about performance degradation with use of nerve 

agent protective equipment and prophylactic drugs. 

To demonstrate his concept, Hegge helped to develop an approach using fuzzy logic and a breast cancer research data repository in the Defense Women’s 

Health Research Program. This breast cancer decision 

assist system was designed to provide answers to users who typed queries into a terminal.115 It provided 

the most up-to-date answer about the disease. The 

Army Medical Knowledge Engineering System was 

developed as a Java-based system with a back-end 

database, the software that accesses the database, and 

the connecting network server. The database was the 

heart of the system, requiring a one-time-only entry 

of new peer-reviewed study information that formed 

qualified “knowledge nuggets” or generic encapsulated knowledge objects.115 As the database grew, the 

information became increasingly accurate and more 

precise. Because of its broad applications, this project 

was recognized with a Smithsonian-Computerworld 

Innovation Award and accepted into the Smithsonian 

Institution’s permanent research collection of information technology. 

Meta-analytical techniques provide a useful approach to deriving new knowledge from the summary 

results of separate studies, but there is greater power 

in analyzing all of the available data, including the 

individual data points from each study. The challenge 

is to access the complete data. This was illustrated 

in a project conducted for the Army’s stress fracture 

research program. A vigorous attempt to obtain the 

original data from a large number of qualified data 

sets yielded only 25% of the data.116,117 Even prospective 

attempts to preserve important data sets by collecting data from multiple experiments into single data 

sets have often proven to be monumental wastes of 

resources, with unmanageable data sets that could 

be analyzed by nobody. Oracle-based databases at 

the Institute of Environmental Stress (Santa Barbara, 

Calif) and at USARIEM were established to “blend” 

key data fields that were common in physiological 

studies at each of these organizations; both of these 

efforts failed to produce more than some large sample 

descriptive data.118 Typically, the formal harvesting of 

data variables from very different studies has been 

a huge labor cost with little return. These data sets 

were not collected with a single hypothesis or goal 

in mind, and there is difficulty in matching even the 

same measurements collected in very different circumstances and for different purposes. New technologies 

for semantic interpretation using fuzzy logic and other 

artificial intelligence tools will eventually resolve these 

problems and make data reutilization an efficient and 

simple process.

New information management technologies will 

now make it possible to access the data from multiple 

studies without imposing additional demands on 

the investigator (eg, to reformat data, and to explain 

analytical methods and conditions of the studies).119,120

This global grid approach to combining data into 

more powerful data sets will provide unprecedented 

efficiency and greatly enhance biological modeling. 

This has already been demonstrated in the neurosciences through the Biomedical Informatics Research 

Network, which uses the tools of grid networking, 

distributed computing, and “database federation.”100

The software that will make this a useful tool to 

physiological modelers is rapidly developing for the 

discovery and pooling of computational resources 
24

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

(eg, Globus, Condor-G, MPI, and Pegasus), as well 

as for the “middleware” that will integrate resources, 

applications, and security to simplify access for the 

user.121 The future of military medicine depends on 

an efficient electronic medical record. Aggregation 

of electronic medical record data for hundreds and 

thousands of records in a “research data cube” will 

permit new healthcare modeling and outcomes research that should improve the quality of care and 

attenuate soaring medical costs. Creation of the data 

cube and future applications through grid technologies is a formidable challenge, considering the current 

problems in interoperability between DoD and the 

VA electronic databases for even seemingly simple 

issues. A major project following the first Gulf War 

failed in the attempt to efficiently combine the VA 

and DoD Gulf War illnesses data registries because 

of insurmountable differences in disease coding and 

other issues of comparability, including the way in 

which data were originally recorded.122 Major technological advances in the past decade now put a new 

era of healthcare modeling within reach. 

TRANSITIONING PREDICTIVE MODELS TO USE

Spiral Development

Data Fusion to Provide Real-Time Decision Support 

Models developed for soldier performance limits 

have to be developed in an iterative process, wherein 

a first “best-available” version is constructed and utilized on a limited basis. This may be used in parallel 

with an existing standard for health hazards assessment to gain confidence in the validity of the model 

or to further refine the model. This is how the INJURY 

(blast overpressure) model was developed, with bioengineering modelers at Jaycor, Inc, using a developing 

model to make predictions that could be evaluated in 

subsequent animal experiments at the Army’s blast 

test site in Albuquerque, New Mexico (now closed). 

Evolving models were similarly used to construct 

new experiments to examine the role of ventilation 

on exposure rates for various inhalation threats from 

“fire gases.”35 The iterative process may also be used 

to collect data (not available in real time) in actual 

field conditions to enhance the range of predictions 

to include actual limits of soldier performance that 

are not ethically or practically achievable in laboratory experiments. Physiological status monitoring is 

an example of this iterative process of collecting real 

field data with prototype sensor sets. 

The current version of a prototype soldier-wearable 

physiological monitoring ensemble was developed 

in a 3-year effort at USARIEM (the PSM concept 

was retitled Warfighter Physiological Status Monitor 

[WPSM], to distinguish the DoD effort from a plethora 

of emerging commercial efforts), and perfectly reflects 

the process of spiral development of models and the 

systems on which the models operate. Originally 

just science fiction as a concept in Heinlein’s Starship 

Troopers, this took hold as a feasible concept after 

Hegge brought together the key ingredients of computational biology, sensor technology, and the Infantry 

School’s Dismounted Battlelabs. Simple physiological 

telemetry had been contemplated as a live-dead detector,123,124 but Hegge introduced the concept of sensor 

data fusion (Hegge FW, collected briefings and summaries, unpublished data, 1997). The performance of 

a medic (or a leader) is not likely to be assisted, but 

more likely seriously degraded by the flow of raw 

heart rate data on a unit of instrumented soldiers. On 

the other hand, a medic’s performance in detecting 

and aiding casualties, or even preventing impending 

environmental injuries, could be greatly enhanced by 

decision-assisted tools that use physiological sensor 

data to provide amber alert for predictions of impending injury, or red alert for a casualty-producing 

event (followed by early triage to assist the medic in 

prioritizing patients). Models and algorithms for sensor data fusion are critically important in the analysis 

of the massive amount of modern data streams that 

can be elaborated from physiological sensors. Hegge’s 

expansive vision included wear-and-forget systems 

operating on extremely low (“flea”) power, with local 

sensor signal processing, an executive hub with the 

physiology algorithms and databases, and some kind 

of communications link to other squad members, leaders, and medics. He further envisioned analyses that 

would include sensor data and contextual information 

(eg, ambient conditions and activities, recent signal 

trends, and individual soldier patterns and baseline 

information) (Figure 1-11). 

Reed Hoyt, at USARIEM, pioneered many of the 

strategies to acquiring and managing data from wearable physiological sensors on soldiers.94 A long series 

of experiments collecting data with experimental 

sensor systems included laboratory treadmill studies 

on energy expenditure predictions125,126; thermoregulatory monitoring in Ranger students95,127; and massive 

data acquisition efforts with Norwegian cadets in a 

week-long course with no food and sleep, a squad of 

soldiers at Fort Benning in a hot weather patrolling 

scenario,91 a technology demonstration in the Infantry 

School’s Dismounted Battlelab urban terrain test,93
25

Predicting Human Limits—The Special Relationship Between Physiology Research and the Army Mission

Fig. 1-11. The concept for Warfighter Physiological Status Monitoring. A suite of wearable sensors collect physiological data 

that are translated into actionable intelligence about the status of an individual (a). These data are processed through physiological models and interpreted in the context of the environment, mission, and individual (concept; b). In this simplified 

hypothetical example, intense shivering measured by a wrist-worn actigraph would be interpreted by other sensor data to 

distinguish between impending hypothermia, intense psychological response, or a neurotoxic chemical exposure. 

EEG: electroencephalogram; EKG: electrocardiogram; EMG: electromyelogram; GPS: Global Positioning System.

a

b
26

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

and a group of Marine Corps officers in a field training exercise with unexpected snow conditions.92 Each 

field study provided iterative advances to the management of data and the ruggedization of equipment. 

Colonel Beau Freund transformed the concept from a 

collection of physiological modeling efforts and commercial telemetry systems into a first comprehensive 

field-ruggedized prototype developed to interface 

properly to Army doctrine and equipment.79 As the 

applications evolved during the development of this 

system, the inputs and outputs changed the modeling 

requirements. For example, heat strain predictions 

started with heart rate and impractical temperature 

pill data using a heat strain index analysis.128 By the 

end of the development, through several iterations of 

models, heat strain predictions had moved completely 

away from reliance on temperature estimates.129,130 This 

first version of WPSM has established specifications for 

soldier field monitoring and provided a system that 

can be used to collect new data on soldiers operating 

at physiological limits that are not obtainable in a 

laboratory or controlled field experiment. 

Another example of this iterative field process is 

the development of Army-wide neuropsychological 

assessment (Figure 1-12). The goal, originally articulated by Hegge, is to develop task-embedded neuropsychological assessment tools and physiological 

markers to predict changes in mental status (eg, using 

measurements such as voice stress analysis, oculomotor patterns, pupillometry, slow eyelid closure, blood–

brain flow changes, etc).131 Current efforts are using a 

20-minute version of the Automated Neuropsychological Assessment Metric (ANAM) to establish predeployment values for soldiers before deployment to Iraq 

and then to determine how these baselines should be 

used to assess soldiers following a concussive event. 

The reliability of the test system and the stability of the 

measurements and testing methods are known from a 

large series of studies, but a new iteration is required 

to determine thresholds for action, initially based on 

clinical experience and individual change. Eventually, 

these data will be modeled, perhaps in conjunction 

with other physiological measurements, to provide 

more robust and automatic interpretations.

Responsibilities to Maintain, Improve, and 

Supervise Use of a Model

To be useful, a model should be relevant, accurate, 

as simple as possible, understandable, predictive, testable, flexible to continuous updating/revision, and 

coherent with other models. These considerations are 

not necessarily foremost in the mind of a physiologist 

attempting to describe the important relationships in 

his/her data, but these are essential considerations in 

the elaboration of a model that will be useful to the 

Army. Along with these features, there must be consideration to continued technical support beyond the 

availability of the original producer. Open literature 

publication of the basis for models, as well as complete 

Fig. 1-12. (a) Psychological testing in World War I at Camp Lee, Virginia, during an era when Major Yerkes and others pioneered testing of mental function and return-to-duty standards. Photograph: Courtesy of Robert M Yerkes. The New World of 

Science—Its Development During the War. New York, NY: The Century Co.; 1920: 358. (b) Contemporary neuropsychological 

testing with a computerized version of established paper and pencil tests in soldiers at Fort Campbell, Kentucky, before 

deployment into a war zone. In the past century, testing the brain, including cognitive functioning, has been slow to develop 

beyond laboratory and clinical settings. Modern technologies vastly expand the range of opportunities for brain function 

testing beyond computerized versions of ancient tests that were limited by paper and pencil and need to be developed for 

practical use in realistic mass testing.

a b
27

Predicting Human Limits—The Special Relationship Between Physiology Research and the Army Mission

reports on the lessons learned from their applications 

to Army problems, is an essential first step. Shoulder 

recoil standards are an example of legacy models that 

have had to be reinvestigated because of inadequate 

documentation of their origin. Complex research 

models, from which specific applications are derived 

(eg, the SCENARIO thermoregulatory model), are 

perpetuated through open literature publication, sharing with other researchers, and continued support to 

core capabilities for which the DoD may always have 

to maintain leadership. There is no Army agency responsible for biomedical model sustainment, although 

this could become a function of an IAC-like concept 

for human performance. 

Models can be misused and inappropriately modified. There is usually no patent associated with the 

model that prevents legal remedies to misuse, even 

though failures associated with the misapplication 

may very well be ascribed to that “poor” Army 

model. The best way to avoid inadvertent problems 

with misuse is to ensure that subject matter experts, 

preferably the original developers, are involved in 

any modifications. 

Some models need to be more fully developed and 

managed for broader use. The ANAM is an example 

in which there is general need for neuropsychological 

tools, including, for example, sports concussion batteries to determine return-to-play evaluative criteria, 

just as the Army has a need for return-to-duty criteria. 

The ANAM has been licensed by the Army to the University of Oklahoma, wherein further development, 

maintenance, and general commercial distribution 

can be effectively accomplished.132 Commercial entities help to maintain and update products, including 

decision support tools, developed with government 

funding. This is an important product sustainment 

function typically specified in the terms of the grant 

agreement or through later licensing agreements.

The INJURY blast model has been maintained by 

Jaycor, Inc, the organization that developed the model 

through research contracts with the Army. The Army 

has no intrinsic capability to follow and maintain 

a model such as this for more than two decades of 

evolving applications. New requirements predictably 

emerge, and it is important to have a competent performer responsive to needs. As examples, the INJURY 

model was formally adopted by the USACHPPM (US 

Army Center for Health Promotion and Preventive) for 

health hazards assessment of blast overpressure, but 

further refinements to the automation of the process 

were required in the transition from experimental 

laboratory assessments at WRAIR to occupational 

evaluations at USACHPPM. When the INJURY model 

was applied to a novel type of explosive, thermobaric 

weapon, it was important to have experts who could 

evaluate new data against the data for which the model 

was originally developed.133 When the Army needed a 

better science-based approach to evaluation of modern 

body armor concepts, the blast modeling experience 

to predict injury to lungs was a key factor in rapid 

adaptation of a torso finite elements model to address 

questions about the new behind-body armor. New 

interest in traumatic brain injury from blast exposures 

from improvised explosive devices has led to a call for 

a blast dosimeter, and the efficient development of a 

candidate system will again draw heavily on the institutional memory of available blast data, experiments, 

and modeling to produce a new predictive model of 

human blast exposure. 

Validation, Verification, and Accreditation

The Army has a well-defined process of determining 

that an estimate produced by modeling and simulation meets acceptable thresholds of usefulness. This is 

referred to as validation, verification, and accreditation 

(VV&A). The intent of VV&A is to determine:

• if the model works as intended (Did I build the 

thing right?), 

• if the model is realistic (Did I build the right 

thing?), and

• if the users’ requirements have been met 

(Should it be used?), thus ensuring credible 

Army modeling and simulation (Should it be 

trusted?).

See the VV&A Recommended Practices Guide, available at www.amso.army.mil. The formal DoD definitions for the three processes (DoDI 5000.61)134 include :

1. Verification: the process of determining that 

a model implementation and its associated 

data accurately represent the developer’s 

description and specifications.

2. Validation: the process of determining the 

degree to which a model and its associated 

data provide an accurate representation of 

the real world from the perspective of the 

intended uses of the model.

3. Accreditation: official certification that a 

model, simulation, or federation of models 

and simulations and its associated data are 

acceptable for use for a specific purpose.

The Army recognizes that modeling and simulation is one approach to obtaining information for 

problem-solving or decision support—which saves 
28

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

development costs and provides many other advantages—if sufficient VV&A ensures correct program 

decisions from the modeling and simulation. 

Physiological models have not been taken, traditionally, through the formal VV&A process used for 

other Army modeling and simulation. With increasing 

interest in the integration of physiological models as 

components of Army mission planning tools, with 

virtual prototyping models for military acquisition 

programs, there is increased pressure for scientific and 

military certification of the models. 

Scientific validation of a biomedical model is an essential step to its approval for use. This includes peerreviewed publication of the complete model, including 

its basis and assumptions. Peer review is a cornerstone 

of modern science; commercial proprietary models and 

related efforts that limit full disclosure are generally 

unacceptable because they cannot be fully evaluated. 

Peer-reviewed publication has been traditionally accepted as “validation” of physiological models. Even 

so, it does not always occur, and it is not required by 

any regulatory body. If it does occur, it still does not 

provide any confidence that the model has been run 

through its paces by anyone except the original developer. The new standard in physiological modeling 

journals is becoming one of full disclosure, with the 

capability for anyone to test the model—that means 

providing the data and the source code. Previously, 

models were published with statistical parameters 

defining their performance and with descriptions of 

their assumptions and development, but could only be 

challenged with new data and without full knowledge 

of the assumptions in the mathematical model. 

Today, significant problems arise from commercially 

developed models with proprietary bases that are not 

disclosed and that rely on empirical testing to calibrate 

predictions to an outcome. Some of these have been 

aggressively marketed to the DoD. Because there is 

no regulation of predictive models unless they are 

specifically advertised for medical decision making 

or health claims, no testing is actually required. Thus, 

most commercial products available today for physiological monitoring of health and performance status 

(eg, sports watch programs or “wellness” monitors) are 

not transparent in their computational methods and 

need not have been tested or validated in any way. 

In some cases, in recent years, the Army models 

have been subjected to panel peer review scrutiny. For 

example, the heat modeling was reviewed, but no final 

agreement on validity of this long-invested and commonly used method occurred. The blast injury model 

was reviewed by an external panel of experts before 

adoption by the Health Hazard Assessment Office as 

the Army blast injury assessment standard. Auditory 

models were similarly reviewed, and it was agreed 

that these needed further documentation, transparency 

to the rest of the critical research community, and 

validation. Numerous other reviews of Army models 

have been conducted in the past decade. Some models 

have been critically analyzed through expert consensus 

meetings. In 1959, a landmark review of body composition predictive models was held in conjunction with 

the National Academy of Sciences in Natick.135 A more 

recent example is the intensive review and performance 

evaluation of the world’s leading sleep and performance 

models, including the Army/DoD sleep model.65 This 

consisted of a comparison of model performance using inputs from a data set that none of the researchers 

had previously seen. This workshop stimulated a new 

generation of more sophisticated sleep and performance 

modeling efforts.136,137 Another form of expert consensus 

meeting is illustrated by the Pensacola 2001 Summit 

Meeting to review software development, applications, 

and predictive thresholds of a DoD neuropsychological 

assessment tool (ANAM).138 This meeting put in motion 

the additional research and development of the tool that 

will now allow its application as a standard assessment 

tool of soldiers before deployment. For the most part, 

physiological models are a work in progress, and it is 

common to find models with no associated probabilities 

or validation statistics. Thus, all models must be viewed 

skeptically, and the starting assumption is that they have 

unknown reliability, reproducibility, and questionable 

thresholds for action.

Army Computational Biology Applications 

The American public is generally unaware of the 

wide positive impact on daily life that is accrued from 

military medical research discoveries. Thus, hot weather guidance that is used worldwide to limit physical 

activities at temperature extremes and ensure adequate 

hydration to protect against heat injury was developed 

by the Army. This guidance was derived from studies by World War II–era physiologists of endurance 

limits in desert environments in Army tankers. More 

recent studies address hyponatremia and the need to 

set upper limits to fluid intake and the need to avoid 

overexuberant hydration in military settings. Navy research and modeling have led to dive tables that today 

protect divers worldwide. Using a similar concept of 

staging, Army models for ascent to altitude currently 

in development may eventually become the standard 

for mountaineers worldwide.23 Laser pointer safety 

and vehicle jolt standards are derived from models 

of safe exposure limits that have been produced with 

significant contributions from Army researchers. The 

Department of Transportation has improved the safety 
29

Predicting Human Limits—The Special Relationship Between Physiology Research and the Army Mission

of airbags in automobiles by modifying a blast injury 

model developed by the Army. Even the National 

School Lunch Program has links to Army research. 

Other specific military uses of physiological models 

include the following: 

• health hazards assessment of new equipment 

based on predicted human tolerances (eg, 

thermal strain, TB MED 507, blast overpressure injury risk model, INJURY 8.0),

• survivability assessment for mission and 

equipment based on human performance limits (eg, performance predictions for defeated 

armor),

• training safety limits for environmental exposures (eg, safe limits for cold water immersion 

in high-intensity training, TB MED 508),

• medical standards for recruitment and retention (eg, body-fat prediction using the Hodgdon equations, DoD Instruction 1308.3, Army 

Regulation 600-9),

• readiness training optimization tools (eg, 

physical training planner that balances injury 

risk against training benefits, Transaction 

Online Processing System 1.0),

• mission planning tools (eg, performance 

predictions based on sleep and circadian 

rhythms, SAFTE [Sleep, Activity, Fatigue, and 

Task Effectiveness] model), 

• real-time decision support tools (eg, readiness 

status and health risk warning system, Physiological Status Monitor),

• virtual prototyping of equipment and doctrine 

optimized to human physiology (eg, biomechanical optimization of load carriage), and 

• hypothesis generator to pinpoint key research 

gaps (eg, ventilatory control and combined 

toxic gas modeling for enclosed spaces and 

fire gases).

Virtual Prototyping and Virtual Training 

Environments and Simulations 

Virtual prototyping is a major component of the 

MANPRINT (Manpower and Personnel Integration) 

program. Not only will new equipment concepts be 

testable at the earliest stages for human factors and 

medical safety with effectiveness considerations, 

but also equipment can be designed based on models of human tolerances so that its design meets 

optimal specifications of human capabilities and 

needs. This is a very significant advance over earlier trial-and-error designs, which were sometimes 

brilliant engineering solutions, but so disconnected 

from human capabilities that special selection and 

training of the users were required; in some cases, 

significant injuries and performance inefficiencies 

were created by the equipment. Load carriage equipment is a simple example of this, with rucksacks that 

impeded performance of the average female, and 

anthropometric considerations—including optimal 

center of mass—made huge differences in performance capabilities. Today, load carriage models are 

used in combination with prototype equipment tests 

in biomechanical laboratory studies to produce the 

best possible load carriage equipment for soldiers 

(see Chapter 11). 

An overarching goal of physiological modeling is to 

develop a complex family of research models, evolving from interlinked and poorly meshed “stovepipes” 

into the ultimate “total physiological human,” wherein 

models are syncytial and synergistic and include, 

for example, the following: (a) biodynamic models 

connecting brain, neck, thorax, spine, and weightbearing lower extremities; (b) models integrated with 

predicting cognitive function based on neurobiology; 

and (c) models merged with brain, muscle, and bone 

metabolic models. These predictive models should 

make major contributions to larger Army simulations, 

including training programs with decision aids and 

teaching, virtual reality tools, and war games.136 Dr 

James Stuhmiller, a longtime biomedical modeler, has 

articulated the current need to meet General Casey’s 

mission forecast1:

Threats to the soldier are growing in nontraditional 

areas, such as acoustic, electromagnetic, ionizing 

radiation, chemical, and biological weapons. These 

threats have both a physical component (coupling of 

the external threat to the organs of the body) and a 

systemic effect (disruption of the normal protection 

and response functions). Although many of the physical interactions are known, they have never been 

combined with physiological response to produce a 

quantitative and predictive methodology.136

SUMMARY

The US Army has been a significant force in the advancement of the physiology of human performance, 

with specific and urgent needs that have called for 

practical problem-solving, real-life military training, and operational paradigms that test the limits 

of human tolerance and performance, and support 

for research and development in the interests of national security. In this century, the confluence of new 
30

Military Quantitative Physiology: Problems and Concepts in Military Operational Medicine

computing technologies, the “convergence revolution” (mathematics and physical sciences coming to 

the assistance of the life sciences), and a new appreciation for the many military uses of physiological 

models and predictive decision support tools have the 

Army poised for even greater contributions. Current 

models have been largely data-driven; but with new 

computing resources and mathematical approaches, 

models can better incorporate and test physiological 

findings and move rapidly to a first principles quantitative physiology basis. This becomes especially 

important as human genome data are interpreted 

and used in human phenomics. The military gains 

advantage from this information through the ability to 

develop better human-centered materiel and systems, 

operational planning that more accurately predicts 

the human element in various courses of action, and 

decision support tools that make accurate group and 

individual predictions useful to commanders and 

clinicians. The Army is likely to continue to be the 

largest contributor to the knowledge of individual 

human performance limits. 

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