National Academies Press: OpenBook

Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance (2004)

Chapter: 3 Monitoring Overall Physical Status to Predict Performance

« Previous: 2 The Study of Individual Differences: Statistical Approaches to Inter- and Intraindividual Variability
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

3
Monitoring Overall Physical Status to Predict Performance

There is an extensive body of literature dealing with the prediction of maximal physical performance in a variety of settings, including both the prediction of optimal performance and the prediction of performance deterioration. Most of this literature is based on research carried out with healthy men and women performing various physical tasks, such as prolonged endurance efforts in exercise and sports settings, but some has involved combat service members performing military tasks under controlled laboratory and field conditions. There are two main types of measures used to predict physical performance: physiological measures and self-assessment measures.

The usual predictor variables employed in the research have consisted of physiological markers, such as heart rate; core temperature; blood and muscle lactate; plasma levels of epinephrine, norepinephrine, and beta-endorphin; plasma and salivary levels of cortisol; circulating glucose; ventilatory minute volume and related metabolic measures; plasma creatine phosphokinase; glycogen stores as determined by serial muscle biopsy; and regional cerebral blood flow. This chapter describes some of the physiological measures used to indicate overall physical status, such as vital signs and temperature, while more specific surrogate measures for muscle fatigue, bone health, and renal and immune function are described in Chapter 4.

Although measuring overall physical status in the field presents a challenge, the importance of measuring total daily energy expenditure as an indication of energy intake needs cannot be overemphasized. Limitations of the direct and indirect measurements of energy expenditure are described in this chapter, along with potential technological advances for the future.

There is evidence that self-assessment measures also possess efficacy in predicting both optimal physical performance and deterioration in performance. Self-assessment measures include perceived exertion, muscle soreness, muscle pain, ratings of sleep quality, and mood states. A number of investigations suggest that a single measure of effort sense or mood state may be superior to each of the above-mentioned physiological measures when used singly or in combi-

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

nation. This chapter describes some of the research, along with some of the advantages and limitations, of self-assessment.

GENERAL CONSIDERATIONS WHEN MONITORING PHYSICAL STATUS

The ultimate goal of collecting data on biomarkers that measure or predict the status of physiological and cognitive function of military personnel is to assess any change in these functions that could compromise an individual’s health and ability to perform mission tasks. The usual approach to the interpretation of these data is to compare them with the range of values determined to support normal physiological and cognitive function. If the data are outside this range, then there is a risk that the health of the individual and/or the mission success will be compromised. Corrective actions should be available to bring the physiological or cognitive function back to the normal range or to save the individual and accomplish the mission objective.

To implement such a system, several steps must be accomplished. First, there must be devices for continuously or intermittingly monitoring the biomarkers. Second, there must be some system for transmitting the data to a command and control unit or to the individual so that corrective action can be taken. Third, there must be baseline or reference data (normal range) that can be used to interpret the data. (The development of devices for measuring biomarkers and the system for transmission of data is beyond the scope of this report.)

For practical reasons it is likely that the data-monitoring system will be able to calculate and screen the incoming data so only those data that require action will be brought to the attention of the individual and/or the command and control unit. This means that the standard used in the analysis (the baseline data) becomes important. It is widely recognized that many individuals have biomarker values that may fall outside the normal range for some physiological or cognitive functions (Sargent and Weinman, 1966). Although the normal range is useful in the practice of clinical medicine because there are other opportunities to make judgments about a patient’s condition, a more rigorous approach may be needed for a system monitoring the vital functions of a combat service member.

A biomarker is a surrogate marker for an important outcome and therefore the choice of biomarkers will have a significant impact on the types and design of the devices and systems that will be needed. Major issues that must be considered are related to the validation of the biomarker, such as reliability and the potential for false positive or false negative results. Therefore, prior to implementing performance testing to assess “readiness to perform,” careful planning is necessary. Test development and validation can be a rather daunting and complex problem. For instance, even when a given measure has good reliability and validity under laboratory conditions, the efficacy of the procedure may not generalize to field settings. At the most basic level, it is first necessary to de-

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

velop an idea about the model that underlies the performance of interest and how specific tests relate to this model (paradigm development). Next, evidence must be gathered that proves the validity of specific individual tests for measuring what they purport to measure (e.g., does a psychomotor tracking task really predict flying ability?). Finally, if several cognitive tests are going to be combined into a generalized assessment battery, the entire battery (as opposed to the individual tests) should be validated. It should be noted that the validation results generally will apply only to a set of standardized testing conditions that must be maintained in the actual assessment context (this may be a significant obstacle to the implementation of test batteries for use in field environments).

Once an assessment battery is formulated and validated, personnel must be trained to the point at which no more learning effects would be expected to occur on the tests to be used. Then, during operational use of the battery, specific test outcome measures (e.g., reaction time, percent correct, accuracy) will need to be subjected to standard statistical treatments, and scores from individual test sessions will need to be compared with the individual’s baseline performance (defined as the average of his or her passing scores over numerous past sessions). Based on past results (or on unique validation studies, if desired) “cut” scores can be determined using traditional psychometric approaches. These cut scores can be used to determine whether or not the individual is within his or her normal performance envelope. Some test batteries use a cut score of 1.5 standard deviations from a person’s running average of numerous past sessions to indicate an alerting (nonsafety-critical) change, and a cut score of 2.0 standard deviations to indicate a safety-critical change. However, these values could be different for different criterion groups, which is another issue that must be addressed (Robert O’Donnell, NTI, Inc., Dayton, OH, personal communication, January 2004). In summary, the introduction of any type of fitness-for-duty test (whether for medical health, psychological well-being, or cognitive performance) will require a great deal of “up-front” work prior to implementation if valid and useful results are to be expected.

PHYSIOLOGICAL MEASUREMENTS

There are two general categories of physiological parameters that are used to monitor physical changes in humans: conventional and surrogate. These parameters have been used in a variety of settings (e.g., hospitals, military operations, clinical trials). When disease is present, measures of conventional physiological parameters, such as vital signs (e.g., pulse, temperature, blood pressure, and respiratory rate), are sensitive and specific for predicting the potential for adverse outcomes. More specifically, the level of blood glucose of diabetics or the level of blood urea nitrogen or creatine in those with chronic renal failure has considerable value for prognostic and treatment purposes. In addition to using vital signs and other conventional parameters to monitor physiological

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

status, surrogate measures (e.g., hormones, metabolic substrates) have also been used. The difficulty with using these conventional or surrogate physiological measures in normal humans under extreme conditions is their general lack of predictability for the individual, which is generally due to their poor sensitivity in asymptomatic healthy individuals.

In the critically ill, physiological measurements have been used in the Acute Physiologic and Chronic Health Evaluation Score (APACHE II and APACHE III). The APACHE II and III use conventional parameters to determine the risk of death of acutely ill patients (Knaus et al., 1985). These scores have been validated (Knaus et al., 1985; Rivera-Fernandez et al., 1998; Rosenberg, 2002) and have universal acceptance in defining the risk of mortality. As a result, APACHE has become the benchmark for comparing outcomes of care and for the evaluation of the efficacy of new therapies. There is also a simpler version of these scores, the Simplified Applied Physiology Score, which also primarily uses physiological variables (Le Gall et al., 1984).

Surrogate measures, on the other hand, use hormonal levels, such as cortisol, insulin-like growth factor-1, growth hormone, or metabolic substrates (e.g., glucose, lactate, ketone bodies, or amino acids). Unlike some conventional parameters, surrogate measures do not have validated scores or available algorithms to assess overall health status and predict the performance of individual combat service members in hostile situations.

Another significant limitation with many physiological measures is that they are based upon average group data, referred to as nomothetic data, which are frequently ineffective in predicting the performance of an individual. In circumstances where average group data may not appropriately correlate with the performance of an individual, prediction models will need to be based on the unique characteristics of the individual (see also Chapter 2). One approach is to monitor each combat service member during rigorous training to determine the values of the critical biomarkers that are “normal” for that individual under a variety of situations. For example, the concentration of electrolytes in sweat varies widely among individuals in very hot, humid conditions, and an estimate of normal electrolyte loss based on water loss may underestimate the actual loss by a large margin. If the individual has a major disconformity (in this example, either a very high or a very low electrolyte concentration in sweat), then that data can be inserted in the personal profile used to monitor his or her condition. Similarly, it may be found that some individuals are capable of optimum performance outside of the “normal range” for some biomarker of a physiological or cognitive function. If such observations are verifiable, then the profiles of those individuals could be modified to take advantage of those observations.

Despite the limitations mentioned above, some conventional measures are valuable for monitoring the physical status of combat service members in field operations; however, more research is needed to validate these measures. The following sections review current physiological monitoring methods and suggest potential uses of these conventional measures for monitoring in the field.

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

Energy Expenditure

Accurate measurement of total daily energy expenditure provides an estimate of total energy needs if weight is to be maintained. An individual is said to be in energy balance if energy input (calories consumed) matches energy expenditure. When energy expenditure is larger than energy intake, balance is not maintained, weight is lost and the energy available for physical activity is decreased. Severe weight loss can dramatically impair performance and cognitive ability in high physical and mental stress situations.

The components of total daily energy expenditure are generally divided into three main categories: (1) basal metabolic rate, also known as resting metabolic rate (RMR), or resting energy expenditure; (2) the thermic effect of food (TEF); and (3) energy expended in physical activity or, as it is frequently called, the thermic effect of activity (TEA).

RMR is the energy required to maintain the systems of the body and to regulate body temperature at rest. It is measured by indirect calorimetry in the morning after an overnight fast (12 hours) and while the individual is resting in a bed. The individual must be comfortable and free from stress, medications, or any other stimulation that could increase metabolic activity (Manore and Thompson, 2000). In addition, the room where RMR is measured needs to be quiet, temperature controlled, and free of distractions. In most sedentary, healthy adults, RMR accounts for approximately 60 to 80 percent of total daily energy expenditure (Poehlman, 1989; Ravussin and Bogardus, 1989). However, this percentage varies greatly in active individuals. It is not unusual for some active individuals to expend 1,000 to 2,000 kcals/day in exercise activities. Thompson and colleagues (1993) determined energy balance in 24 elite, male endurance athletes over a 3- to 7-day period and found that their RMR represented only about 35 percent of total daily energy expenditure. Similar results have been reported in active females (Beidleman et al., 1995). During days of repetitive heavy competition, such as ultramarathons, RMR may represent less than 20 percent of total energy expenditure (Rontoyannis et al., 1989).

TEF is the increase in energy expenditure above RMR that results from the consumption of food throughout the day and includes the energy cost of food digestion, absorption, transport, metabolism, and storage. It usually accounts for approximately 7 to 10 percent of total daily energy expenditure, with women sometimes having a lower value (Poehlman, 1989; Ravussin et al., 1986). However, this value varies depending on the total number of kilocalories in the meal, the types of foods consumed, and the degree of obesity.

TEA is the most variable component of energy expenditure in humans. It includes the energy cost of daily activities above RMR and TEF, such as purposeful activities of daily living (e.g., making dinner, dressing, cleaning house) or planned exercise events (e.g., running, weight training, walking). It also includes the energy cost of involuntary muscular activity, such as shivering and fidgeting (also called spontaneous physical activity). TEA may account for only 15 percent of total daily energy expenditure in sedentary individuals, but it may

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

be as high as 30 percent in active individuals (Poehlman, 1989). The addition of RMR, TEF, and TEA should account for 100 percent of total energy expenditure. However, there are a variety of factors that may increase energy expenditure above normal, such as cold, fear, stress, and various medications or drugs. The thermic effect of these factors is frequently referred to as adaptive thermogenesis, which represents a temporary increase in thermogenesis that may last for hours or even days, depending on the duration and magnitude of the stimulus. For example, a serious physical injury, the stress associated with an upcoming event, or going to a higher altitude may all increase RMR above normal levels.

The measurement of total daily energy expenditure or its components can be conducted in the laboratory using direct measures, such as calorimetry, doubly labeled water (DLW), motion sensors, or observation. In general, field methods of measuring or predicting energy expenditure use indirect methods (e.g., self-report questionnaires, surveys, fitness measures) or devices (e.g., movement devices, heart-rate monitors) that have been validated against more precise laboratory methods. Energy expenditure prediction equations have also been developed and are typically based on age, gender, and body size.

Laboratory Methods

Calorimetry. Energy expenditure in humans can be assessed by either direct or indirect calorimetry. Direct calorimetry measures the amount of heat given off by the body through radiation, convection, and evaporation and must be conducted in an airtight calorimetric chamber in which the amount of heat produced by the body warms the water surrounding the chamber. The change in water temperature is recorded, and the amount of energy expended is calculated. This method is very expensive and is not currently used to any extent. However, some field devices are based on the direct calorimetry principle and use changes in body heat to predict total energy expenditure. Under basal conditions, both direct and indirect calorimetry give identical results, but due to the cyclical changes in body temperature throughout the day, direct calorimetry cannot be used to assess heat production for periods of less that 24 hours (Jequier and Schutz, 1983).

Indirect calorimetry uses a much less expensive method for assessing energy expenditure and is frequently the method of choice for many researchers. A metabolic chamber is used, and a mask, hood, or mouthpiece is used to collect gases. This method assumes that metabolic rate can be estimated by measuring the rate of transformation of chemical energy into heat. The amount of oxygen and carbon dioxide exchanged in the lungs closely represents the use and release of these substances by the body tissues, so the amount of oxygen consumed and the amount of carbon dioxide produced are measured during various activities to estimate the amount of energy being expended. The ratio between the volume of carbon dioxide produced (VCO2) and the volume of oxygen consumed (VO2)

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

can be calculated (VCO2/VO2). This ratio is termed the “nonprotein respiratory quotient” and represents the ratio between the oxidation of carbohydrate and lipid. By knowing the amount of each energy substrate oxidized, the amount of oxygen consumed, and the amount of carbon dioxide produced, total energy expenditure (in kilocalories) can be estimated using various published formulas (Manore and Thompson, 2000). In general, consuming 1 L of oxygen results in the expenditure of approximately 4.81 kcals if the fuels oxidized represent a mixture of protein, fat, and carbohydrate.

Doubly Labeled Water. Recently, the DLW technique has been validated and accepted as a gold standard method for determining free-living total daily energy expenditure. This method was first developed for use in animals and was eventually applied to humans (Schoeller et al., 1986). The DLW method is a form of indirect calorimetry based on the differential elimination of deuterium (2H2) and 18oxygen (18O) from body water following a load dose of water labeled with these two stable isotopes. The 2H2 is eliminated as water, while the 18O is eliminated as both water and carbon dioxide. The difference between the two elimination rates is a measure of carbon dioxide production (Coward and Cole, 1991; Prentice et al., 1991). This method differs from traditional indirect calorimetry in that it only measures carbon dioxide production, not oxygen consumption. The major disadvantages of this technique are that it requires frequent urine collection and it is very expensive, thus making it prohibitive for use in field situations. This method has become a valuable tool for the validation of other less-expensive field methods of measuring energy expenditure, such as the use of accelerometers (Schoeller and Racette, 1990).

Field Methods

Because measurements of calorimetry require that an individual be confined to a laboratory setting or a metabolic chamber, it is difficult to measure an individual’s free-living or habitual activity. However, there is a new hand-held indirect calorimetry instrument (BodyGem, HealtheTech, Inc., Golden, Colorado) available that could be used in the field. This instrument measures RMR in 12 minutes and has been validated against oxygen consumption measured with a metabolic cart or a Douglas bag. These studies (Melanson et al., 2003; Nieman et al., 2003) showed high correlations (r=0.81–0.97) between the BodyGem and the laboratory methods used for validation. Unfortunately, this instrument measures resting energy expenditure. Total daily energy expenditure still needs to be estimated from the methods outlined below or from prediction equations. Thus the usefulness of this instrument in the field is still limited.

Subjective Measures. These measures include the direct observation of physical activity by a trained observer or the recording of daily physical activity by the subject. Use of direct observation is limited because it requires a trained individual for each participant being measured. Recording daily physical activity

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

using activity logs is time consuming and requires training of the individual since each activity needs to be quantified as to time, intensity, and type. Both of these methods require that the input of the data and the calculation of total energy expenditure be conducted by trained researchers (Chen, 2003). These two methods are impractical for large military field operations.

Objective Measures. These measures use some type of mechanical or electronic device (e.g., pedometers, heel- or foot-strike monitors, accelerometers, heart-rate monitors, heat-flow sensors) that measures changes in body movement, heart rate, or body temperature. The data acquired from these devices are generally integrated with personal data (e.g., age, weight, stride length, gender) and then converted into a mathematical formula that predicts total physical activity or energy expenditure. The advantages of these devices are that they can generally be worn either on the wrist, waist, arm, or ankle; they require little manipulation once they are attached; and they can measure free-living movement over an extended period of time. Recorded data are either directly integrated into a formula that predicts energy expenditure or are downloaded to a computer for further analysis.

Validation of these devices is typically conducted by using whole-room indirect calorimetry or DLW (Chen, 2003) for total energy expenditure and by using treadmills or measured distances for physical activity. Many of these devices incorporate a number of methods for assessing body movement, motion, and heat production. Below are the methods used by three different sensors currently being sold:

  • The SenseWear Armband (BodyMedia, Inc., Pittsburgh, Pennsylvania) utilizes a two-axis accelerometer, a heat-flux sensor, a galvanic skin-response sensor, a skin-temperature sensor, and a near-body ambient temperature sensor to gather the data used to calculate energy expenditure from an algorithm (Liden et al., 2002).

  • The IDEEA (Intelligent Device for Energy Expenditure and Activity) (MiniSun, Fresno, California) measures body and limb motions constantly through five sensors attached to the chest, thighs, and feet, and can correctly provide identification of 98.9 percent of posture and limb movements and 98.5 percent of gait movements (Zhang et al., 2003).

  • The Actical Activity Monitor (Mini Mitter Co., Inc., Bend, Oregon) utilizes a motion sensor known as a piezoelectric accelerometer to monitor motion. This type of sensor integrates the degree and intensity of motion and produces a voltage output signal with varying magnitudes and durations that are dependent on the amount of motion. Based on recent validation studies using whole-room calorimetry, this monitor is a good predictor of total energy expenditure in children (Puyau et al., 2002). Validation studies using portable SensorMedics (Yorba Linda, California) systems performed with adolescents, teens, and adults to predict activity energy expenditure are still being conducted and algorithms are being refined (Heil and Klippel, 2003; Klippel and Heil, 2003).

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

Preliminary data show reasonably accurate predictions using the Actical Activity Monitor in these populations, depending on the sites chosen for monitor placement.

These second-generation devices may be much better at predicting energy expenditure than devices that only use one method for determining motion (e.g., vertical acceleration). Extensive research has examined the validity and usefulness of pedometers and accelerometers for the measurement of physical activity or energy expenditure (Hendelman et al., 2000; Jakicic et al., 1999; Tudor-Locke et al., 2002). In general, accelerometer- and pedometer-based monitors provide valid indicators of overall physical activity, but they are less accurate at predicting energy expenditure (Bassett and Strath, 2002; Welk, 2002). If pedometers are used to predict levels of physical activity, then the correlation is stronger (average r=0.82) than if they are used to predict total daily energy expenditure (average r=0.68; range=0.46–0.88) (Tudor-Locke et al., 2002). The same appears to be true for accelerometers that use only one dimension to measure physical activity. Single-method motion detectors appear to underestimate energy expenditure by 42 to 67 percent in field conditions where a variety of exercises are used (Welk et al., 2000) and during cycling as work intensities are increased (Iltis and Givens, 2000). In addition, single-axis accelerometers or pedometers and most multidimensional accelerometers are not useful in detecting increased energy cost of high-intensity exercise, upper-body exercise, carrying a load, or changes in surface or terrain (Bassett et al., 2000; Hendelman et al., 2000; Iltis and Givens, 2000; Jakicic et al., 1999). However, single-axis accelerometers may work well when estimating energy expenditure during low-intensity single activities, such as walking, and they may be useful in assessing daily activity patterns of individuals (Schutz et al., 2002) unless the subjects are the frail elderly with very slow gaits (Le Masurier and Tudor-Locke, 2003).

If predicting total energy expenditure is the goal of monitoring the activity of the combat service members, then more sophisticated devices must be used (multidimensional devices that include multiple types of metabolic measurements) since they are better at predicting energy expenditure (Chen, 2003; Hoyt et al., 1994; Schutz et al., 2001). Based on a review by Schutz and colleagues (2001), measuring the total daily energy expenditure of combat service members at the accuracy required by the military will require the development of a motion sensor that is inexpensive and is more convenient and reliable than current pedometers or accelerometers. When this instrument becomes available, researchers, those responsible for monitoring the combat service members, or the combat service members themselves will be able to accurately monitor their daily free-living energy expenditure.

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Summary

There are a number of methods for assessing total daily energy expenditure in an individual in an objective manner. In a more subjective manner, however, combat service members (or their peers) can easily tell whether they are maintaining energy balance by changes in their total body weight. If their weight does not change, then their energy intake matches their energy expenditure.

Vital Signs

There are three conventional measurements discussed below that may have success in monitoring the physiological status of combat service members under field conditions. These measures are: pulse rate, a combination of respiratory rate and pulse rate, and a combination of all the major vital measures. This section also includes measurements of core temperature because it is the most common method for assessing the impact of environmental conditions and exercise on the body, in addition to being an indicator of physical status (e.g., inflammation).

Pulse Rate

Pulse rate, which is easy to measure noninvasively and is amenable to telemetry, can be used to estimate the degree and duration of aerobic workload. It might also be used to assess periods of rest and sleep.

Respiratory Rate and Pulse Rate

In the present environment of potential exposure to chemical and biological agents, other parameters, such as respiratory rate and pulse rate, might be patterned. For instance, wearing chemical biological weapon suits and breathing apparatus is likely to alter respiratory rate and pulse rate, both at rest and in response to activity. How these parameters are affected would be important to determine under experimental conditions, both in the laboratory setting and in the field, to gather baseline data for the individual combat service member.

Overall Vital Signs

With the measurement of pulse rate, respiratory rate, and core body temperature, one could potentially design algorithms to distinguish the following conditions: moderate activity, more intense activity, cold exposure affecting performance, sleep, and systemic inflammatory response (usually due to infection under battlefield conditions if the soldier is otherwise unaware of injury) (see Table 3–1).

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

TABLE 3–1 Summary of Vital Signs Under Specific Activity and Environmental Conditions

Condition

Pulse Rate

Respiratory Rate

Core Body Temperature

Moderate activity

Moderately high (>120)

Increased

Normal

Intense activity

High (>160)

Elevated for a prolonged period

Prolonged elevated respiratory rate may lead to increased core body temperature

Cold exposure Sleep

Normal to low

Lower than awake pulse rate

Normal to low

Lower than awake pulse rate

Low

Slightly lower to normal

Systemic inflammatory responses

>90, but usually <120

Elevated, but less than intense activity

Higher

Ambient Temperature

Although ambient temperature is not a vital sign, it is included here because environmental temperature can have a dramatic effect on the body’s ability to maintain physiological stability, especially during exercise (Cheung et al., 2000). If extreme environmental conditions are combined with fluid losses and the development of dehydration or the wearing of protective clothing (Kulka and Kenney, 2002), a significant decrease in mental function and exercise performance can occur. As temperature and humidity increase, exercising becomes harder and the risk of heat-related problems increases. Hydration can also be a problem for individuals who exercise in cold environments because fluid is being lost while the desire to drink may be reduced. As temperature decreases, the ability to maintain body heat and normal body temperature may decline depending on the severity of the cold stress (e.g., temperature, altitude, wind chill index, humidity), the intensity of the exercise being performed, the level of sleep deprivation, the negative energy balance, and the insulating effect of the clothing worn. In addition, the body tries to minimize heat loss through vasoconstriction and to increase heat production through shivering. Thus cold can dramatically increase metabolic demands on the body.

Table 3–2 outlines recommendations and precautions that should be taken by individuals who exercise under conditions of varying air temperature, relative humidity, and solar radiation. As shown in the table, when the wet bulb globe temperature (WBGT) rises, the health risks associated with exercising also rise. The WBGT is comprised of three measurements. First is the wet bulb, an index of relative humidity. Second is the black bulb, an index of radiation of heat from the environment (e.g., from the sun). The third measurement is the dry bulb, an index of ambient temperature (the actual air temperature measured on a thermometer). If the wet bulb and the dry bulb temperatures are the same, then the

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

TABLE 3–2 Various Wet Bulb Globe Temperatures (WBGT) and Exercise Recommendations and Precautions That Need to Be Taken

WBGT

Exercise Recommendations

Comments

Less than 80ºF (<~27ºC)

All can exercise

Most individuals without a risk of heat problems can perform activities

80º to 85ºF (~ 27º to 29ºC)

Exercise with caution

All individuals should drink frequently; look for signs of heat illness (dizziness, rapid heart rate, nausea, chilling, headache, and decreased coordination)

Distances greater than 10 km should not be done or conducted with caution when the WBGT is greater than 82ºF (28ºC)

85º to 88ºF (~29º to 31ºC)

Limited exercise

Physical activity for unconditioned or unacclimatized individuals should be suspended

Frequent water breaks should be taken by exercising individuals

Greater than 88ºF (>~31ºC)

Suspend exercise

All activities should be suspended or moved indoors to a cooler environment

NOTE: WBGT=0.7 (wet bulb temperature) +0.2 (black bulb temperature) +0.1 (dry bulb temperature). Wet bulb temperature measures the temperature when the bulb is moist (relative humidity); black bulb temperature measures radiated heat (this bulb absorbs the radiated heat); and dry bulb temperature measures the ambient room temperature.

SOURCE: Adapted from Pivarnik and Palmer (1994).

air has a humidity of 100 percent and evaporation is impossible. The following method is used to calculate the WBGT:

WBGT=0.7 (wet bulb temperature)+0.2 (black bulb temperature) +0.1 (dry bulb temperature)

The greatest contributor to WBGT is the humidity (wet bulb), while the ambient temperature (dry bulb) contributes the least. Thus it is easier and safer to exercise in a hot environment with a low humidity than in a hot environment with a high humidity. As the humidity rises, it is harder for the body to cool itself through evaporation of sweat from the skin. By measuring WGBT before exercising in hot environments, proper precautions can be taken to reduce the risk of heat exhaustion.

Environmental conditions that predispose an active individual to heat exhaustion or stroke are hot, humid, windless conditions or unseasonably hot conditions where an individual is not acclimatized to the environment. Sunstroke, heat cramps, or heat exhaustion are likely, and heatstroke is possible with pro-

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

longed exposure or physical activity in temperatures ranging from 105° to 130°F (41°–54°C). If the temperature rises to 130°F (54°C) or more, heat stroke and sunstroke are highly likely with continued exposure (NWS, 2003). Individuals who are unfit, overweight, dehydrated, unacclimatized to the heat, or ill are more susceptible to heat stroke. Finally, the young and the old are more susceptible to heat-related injury due to less sensitive homeostatic mechanisms for fluid balance (Sutton, 1990). Studies that examined exertional heat illness in military recruits showed that risk was greatest when temperatures rose above 65°F (18°C), when strenuous exercise was performed (e.g., running), or when recruits had heat-stress exposure on previous days (Kark et al., 1996). For new recruits, a body mass index (kg/m2) over 22 and a 1.5-mile run time over 12 minutes also increased the risk of heat illness (Gardner et al., 1996).

Less has been written on the body’s response to cold weather exercise; however, when exercise is performed in cold environments, the body’s thermoregulation mechanisms are stressed (O’Brien et al., 1998b; Young et al., 1998). If exercise in the cold is combined with high altitude, the metabolic stresses on the body are extremely high, which increases the demand for adequate energy and fluid intake. Like in hot environments, fluid balance can be compromised while exercising in cold environments (Murray, 1995). First, cold can increase urinary fluid losses, while fluid intake is reduced. Individuals generally have less desire to drink in the cold, the need to drink is less obvious, fluids may be less available, and fluid intake may be reduced to avoid having to urinate. Active women may be more likely to restrict fluid intake in cold environments to avoid removing layers of clothing in order to urinate or to avoid traveling some distance to a restroom. Fluid losses are increased through respiration and sweat losses, especially if heavily insulated clothing is worn.

Body Temperature

A number of factors can influence body temperature. Therefore, a number of physiological parameters may need to be measured to assess thermal strain on the combat service member.

Core Body Temperature. Measuring core body temperature is the most commonly used method for assessing the impact of environmental conditions (either hot or cold) and exercise on the body. The most common places for measuring core body temperature are the esophagus, rectum, mouth, tympanum, and auditory meatus (Young et al., 2003), but most thermal physiologists consider the esophagus to be the best site (Moran and Mendal, 2002; Young et al., 2003). Measurement of esophageal temperature is best for research settings, but it is problematic in clinical or field assessments because the sensor causes irritation to the nasal passages and general subject discomfort (Moran and Mendal, 2002), and it is difficult to insert. Overall, these probes are impractical to use

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

TABLE 3–3 Definitions of Various Temperature Measurements Used in Wearable Body Activity Monitors

Measurement

Definition

Heat flux

Measurement of the heat being dissipated by the body; sensors in the wearable devices use very low thermally resistant materials and sensitive thermocouple arrays to determine this measurement

Skin temperature

Sensors placed in the device are in contact with the skin and measure changes in skin temperature

Near-body ambient temperature

Sensors measure air temperature immediately around the wearable device and are designed to directly reflect the change in environmental conditions; an example is walking into an air-conditioned building from outside on a hot day

Galvanic skin response

This measurement represents the electrical conductivity between two points on the wearer’s arm or leg, depending on where the device is worn; skin conductivity is affected by the sweat from physical activity and by emotional stimuli; it can also be used as an indicator of evaporative heat loss by identifying the onset, peak, and recovery of maximal sweat rates

 

SOURCE: Liden et al. (2002).

in a field setting where individuals are participating in strenuous physical activity.

Skin Temperature Sensors. These sensors measure skin temperature at a particular body site and may not correlate well with core body temperature. In order to use skin sensors, multiple sites may need to be measured, and the information gained from the sensors may need to be integrated with other temperature and thermal stress-related data (e.g., heart rate, ambient temperature, exercise intensity, level of hydration, wind speed, and perceived effort or exertion). As discussed previously, new, wearable body-activity monitors (typically worn on the arm, wrist, or ankle) are being used to assess total energy expenditure or physical activity. They also measure a variety of temperature-related variables, such as heat flux, skin temperature, near-body ambient temperature, and galvanic skin response (see Table 3–3). One device, the Mini-Logger (Mini-Metter Co., Inc., Bend, Oregon), has four temperature channels that can measure skin, rectal, and ear canal temperature. Only the assessment of skin temperature would be practical for military personnel in the field. Many of these devices can be worn continuously for 4 to 5 days without recharging their batteries, and they may have the capability for remote transmission of data.

Oral Temperature Thermometer. The measurement of oral temperature can readily track changes in core body temperature. Unfortunately, oral temperature

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

measurements are not always possible, because of equipment being worn over the face, or accurate, because the head and face can be easily influenced by the environment. Hot and cold drinks, smoking, or irregular breathing patterns can also alter oral temperature measures (Moran and Mendal, 2002).

Temperature Pill Telemetry System. The ingestible temperature pill provides a valid measure of core temperature during rest, exercise, and changes in environmental conditions (e.g., hot, cold) (Kolka et al., 1993; O’Brien et al., 1998a). The temperature pill has been validated by comparison with rectal and esophageal temperature. The pill contains a sensor that transmits a continuous, low-frequency radio wave that varies with temperature. This signal can be received and stored by a data logger and later downloaded to a computer (Castellani et al., 2002; O’Brien et al., 1998a). The pill moves through the gastrointestinal tract and most accurately measures core temperature when it reaches the small intestine. Because it is eventually eliminated, a new pill needs to be consumed if temperature monitoring is to continue over long periods of time. The use of this technology in the field is possible; however, a mechanism for data transmission over long distances to a collection site is required, as is a way of displaying the data so it can be easily observed by the soldier.

Summary. Currently there is no accurate and easy method to measure core body temperature in a field setting. The development of a simple, noninvasive, universally used device that can measure core body temperature in individuals exercising or working in extreme environments would be quite useful (Moran and Mendal, 2002). Such an instrument would help prevent many of the heat-related illnesses that occur in field settings.

Physiological Strain Index

The U.S. Army and the Israeli military have been working together to develop and test a physiological strain index (PSI) based on rectal temperature and heart rate—two physiological parameters that adequately depict the combined strain reflected by the cardiovascular and thermoregulatory systems (Moran et al., 1998b). The PSI is based on a scale of 1 to 10, with a high value indicating a high risk of heat stress. It was developed using individuals performing exercise in the heat under a variety of conditions (e.g., in different heat-related environments, with protective clothing, and with varying hydration levels) (Moran et al., 1998a, 1998b). Comparisons of the PSI based on gender, age, and level of exercise training and intensity have also been conducted (Moran et al., 1999b, 2002). Overall, the PSI may be a simple method for examining the impact of environmental temperatures and exercise stress on individuals in order to predict who might be at risk for heat stress (Moran, 2000). Once a field method for assessing core body temperature is developed, the PSI could be a useful tool for the prevention of heat illnesses.

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Cold Strain Index

Similar to the PSI, the U.S. Army and the Israeli military have developed a cold strain index (CSI) based on rectal temperature and mean skin temperature measured at multiple sites (Moran et al., 1999a). The CSI is also based on a rating scale of 1 to 10, where high numbers indicate risk of hypothermia. As with the PSI, the CSI’s usefulness in the field will depend on the development of a reliable method for measuring core body temperature.

SELF-ASSESSMENT MEASUREMENTS

As discussed in the previous section, it might be possible to employ some physiological markers in field settings, although some still need validation in the field and others encounter practical limitations. In addition to conventional measures, self-assessments have the potential for use as indicators of physical status in the field. Self-assessment measures include: perceived exertion, muscle soreness, muscle pain, ratings of sleep quality, and mood states. There is evidence that self-assessment measures possess efficacy in predicting both optimal physical performance and deterioration in performance.

In a number of investigations a single measure of effort sense or mood state has been found to be superior to specific physiological measures used singly or in combination. Relying on self-assessments to evaluate physical status, however, is not without limitations. For example, it is an established fact that sleep-deprived individuals lose their ability to accurately assess their own levels of sleepiness and impairment after the first day or two of sleep reduction. Also, it is well known that inexperienced individuals are often unable to pace themselves as well as people who have been frequently exposed to a given situation. In addition, peer and supervisory pressures continue to present major confounds to the validity of self-assessments in circumstances involving team relationships. For these reasons, more objective assessments that are immune to such judgment and social confounds, such as direct physiological measurements, are generally more desirable.

If these limitations to self-assessment can be overcome, then the advantages to this measurement method can be realized. First, the continued use of this approach does not require the development and application of sophisticated instrumentation; that is, “perceptual” models can be taught and used now. Second, the data based on the unique characteristics of the individual are not confounded by other individuals’ responses within a group. As discussed in Chapter 2, research on combat service members, with its focus on person-environment interactions, has a pressing need to elucidate those factors that contribute to interindividual differences and to distinguish them from sources of intraindividual variability. The educated athlete (combat service member) can learn how to monitor sensations provided by the working muscle, as well as other physiological systems, and he or she can titrate the pace (e.g., increase, decrease, maintain) without experiencing performance decay or the morbidity and mortality often

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

associated with performance of prolonged efforts under extreme conditions (Morgan, 1981, 2000; Morgan and Pollock, 1977). If educated athletes (combat service members) and coaches (commanders) learn how to train hard without overtraining, when they are placed in a situation (competition or combat) where maximal or supramaximal efforts are required their output can be titrated and optimized (Morgan et al., 1988; Verde et al., 1992). The body of scientific literature relevant to this hypothesis emphasizes the importance of considering the individual in efforts designed to prevent morbidity, as well as to predict maximal physical performance.

Perceived Exertion

Perceived exertion involves the individual’s sensation of effort, and the rating of perceived exertion (RPE) obtained during prolonged physical efforts can reliably predict performance. Physiological variables (e.g., heart rate, blood pressure, and cortisol, epinephrine, norepinephrine, muscle and blood lactate, glycogen, oxygen uptake, and ventilatory minute volume) and individual variables (e.g., gender, training state, personality structure, and mood state) contribute to RPE. According to Borg (1973, 1998), RPE can be viewed as the gestalt or whole (i.e., configuration of all sensory inputs responsible for the formation of the percept), while variables such as heart rate or lactate should be regarded as parts of the whole. Hence, perceptual ratings such as RPE could be more accurate in predicting or monitoring exertion than any part of the whole.

The literature dealing with self-assessment has been summarized in volumes by Borg (1998) and Noble and Robertson (1996). These comprehensive reviews of self-assessment demonstrate the efficacy of perceptual models in quantifying stress responses associated with exercise and, in some circumstances, their superiority to selected physiological models.

Borg (1998) developed a number of rating scales for use in quantifying the distress or strain associated with exercise, but the one most frequently employed has been his 6–20 category scale. This scale has verbal anchors associated with the odd-numbered ratings (7=very, very light, 9=very light, 11=fairly light, 13=somewhat hard, 15=hard, 17=very hard, 19=very, very hard) (Borg, 1973). The terms “easy,” “heavy,” and “moderate” are sometimes used in place of light, hard, and somewhat hard, respectively. The 6–20 category scale is employed in most exercise laboratories around the world and has been shown to possess good construct validity when employed with English-speaking individuals and across cultures.

In his early formulations, Borg (1973) asserted that RPE correlated very well with physiological measures (e.g., heart rate), and it was proposed that heart rate was equal to RPE×10. While this proposal may have been overly simplistic, it actually worked reasonably well in the case of healthy young men and women evaluated on maximal bicycle ergometer or treadmill tests. However, as the scale became more widely applied with younger and older age

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

groups, elite distance runners, and selected patient groups, it became apparent that RPE and heart rate were often uncoupled. In the case of healthy men and women performing selected types of exercise (e.g., isometric, concentric), as well as exercise in extreme environments (e.g., hot, cold, hyperbaric, hypobaric), the RPE-heart rate relationship is not strong. However, RPE works effectively in such settings because it includes other inputs that possess greater primacy.

In summary, perceptual ratings of effort, sense, fatigue, pain, and mood are often regarded as subjective, whereas physiological measures are regarded as objective. However, since perceptual ratings have some advantages and some studies have showed that they may be more accurate than some physiological measures in predicting performance, a case can be made for developing and employing perceptual models in efforts to monitor distress during training and special operations.

Perceived Exertion as a Predictor of Physical Strain and Physical Endurance

Although heart rate may be easy to measure, the case has been made that ratings of perceived exertion may be a better measure of the whole physiological situation of an individual. As a result, there are studies that compare the physiological measure of heart rate with RPE. These studies evaluate any possible correlations of these two measures, specifically through physical strain and physical endurance studies.

Patton and colleagues (1977) evaluated ratings of perceived exertion and heart rate in two groups of 60 male military personnel who differed in level of fitness (Group I untrained, Group II trained), measured by maximal oxygen uptake (VO2max). Group II scored significantly higher than Group I on VO2max at the outset of the study, as anticipated. When the two groups performed submaximal runs on the treadmill, Group I (untrained) had a significantly higher heart rate than Group II (trained) at each minute of exercise, as expected; however, the RPE for the two groups did not differ. While this finding is surprising, a similar observation was later reported by Dishman and colleagues (1994). These results represent examples of the uncoupling of heart rate and perceived exertion, and they suggest that heart rate may not be an adequate measure of strain during physical exertion. When both groups were retested following 6 months of training, they experienced a significant decrease in perceived exertion and heart rate during submaximal exercise (Patton et al., 1977). Furthermore, the RPE and heart-rate values were identical for the two groups following the training. This finding indicates that although a valuable measure of strain, effectiveness of the RPE model is dependent in part on habituation or familiarization; this phenomenon needs to be addressed during educational and training programs. In addition to these issues, it is important to support the honest reporting of RPE despite encouragement to report overly positive results. Individuals may

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

feel that it runs counter to military expectations of stoicism and toughness to admit to high levels of perceived exertion. Both habituation and overly positive reporting have to be monitored during actual field situations.

The efficacy of perceptual versus heart-rate monitoring in the development of physical endurance was evaluated by Koltyn and Morgan (1992) in a study involving two groups of college women engaged in aerobic dance classes. The two groups used either heart rate or perceived exertion to regulate exercise intensity. The outcome measure used in this study was endurance performance as measured by the amount of distance that could be covered during a 15-minute run. This test was administered at the beginning and the conclusion of the 14-week course. Both groups experienced an increase in endurance, but the gain for the perceived exertion group was 11 percent compared with 6 percent for the heart-rate group. This led to the conclusion that regulation of exercise intensity with the use of perceived-exertion monitoring is superior to heart-rate monitoring for improvement in endurance performance.

In a case study conducted with one of the participants in the above study (Morgan, 1981), a volunteer attempted to complete a simulated marathon (26.2 miles, 385 yards) on the treadmill at a pace of 7.5 mph and 0 percent grade. Heart rate, rectal temperature, state anxiety, and RPE were obtained throughout the simulation. There was a gradual increase in heart rate and rectal temperature during this simulation, but extrapolation from values obtained at 5, 10, and 15 miles into the run suggested that the individual would complete the planned run without difficulty. Ratings of state anxiety and RPE obtained at the same points in time predicted otherwise. The individual was unable to continue beyond the 23-mile point—the precise point predicted by the RPE data. This case study supports the theoretical views advanced by Borg (1973, 1998) that ratings of perceived exertion are more accurate in predicting endurance performance than measures of heart rate and rectal temperature.

In conclusion, it has been shown that heart rate is not an adequate measure of physical strain during exertion or during the development of physical endurance.

RPE as a Predictor of Maximal Physical Performance

A test of maximal physical performance involving progressive increments in workload on a bicycle ergometer was performed by Morgan and Borg (1976) using 30 trained male cyclists with a mean age of 23 years. Heart rates measured at submaximal levels of work (50, 100, and 150 W) were employed to predict the actual maximum and compared with a prediction based upon ratings of RPE. The actual maximal performance capacity was 14,316 kpm. The predicted maximal performance capacity using heart-rate values was 16,500 kpm, whereas the prediction using RPE was 14,250 kpm. This observation demonstrates that RPE values obtained at submaximal exercise intensities are superior to heart-rate values in predicting maximal performance capacity. This is important since the

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

most frequent measure employed to predict maximum capacity is submaximal heart rate (Åstrand and Rodahl, 1986).

RPE as a Predictor of Total Exhaustion Time

In research carried out at the U.S. Army Research Institute of Environmental Medicine by Horstman and colleagues (1979), the perception of effort was studied in healthy combat service members during constant work to self-imposed exhaustion. In the first experiment, 26 healthy male volunteers completed a test of VO2max on one day, and were retested at 80 percent of VO2max on two subsequent days in the walking and running modes. Heart rate, VO2, VCO2, minute ventilation, end tidal CO2, and RPE were obtained throughout the exercise. Plasma lactate, epinephrine, and norepinephrine concentrations were obtained following exercise. These values did not differ between the walking and running conditions. At the time of exhaustion, the test subjects reported less respiratory distress for the walk compared with the run, but perception of effort for the legs did not differ in the two conditions. Values of RPE were identical for the walking and running conditions, and these ratings increased in a linear fashion from a value of 12.9 at 25 percent of total exhaustion time to 18.9 at exhaustion. The results from this experiment were replicated in the walking mode with an independent sample involving another 28 combat service members. It was found that changes in perception of effort occurring early during work were sensitive predictors of exhaustion time in this study.

RPE as a Predictor of Coronary Heart Disease

There is recent evidence that RPE obtained from individuals for customary or usual exercise is predictive of coronary heart disease (CHD). Lee and colleagues (2003) reported that an inverse relationship exists between an individual’s RPE and the risk of CHD. These investigators studied 7,337 men who were free of CHD at the outset of the study; 551 of these men developed CHD at follow-up. The men who reported RPE as “moderate” to “strong” had a lower risk of CHD compared with those who reported RPE in the “weak” or “less intense” range. This study suggests that the efficacy of RPE could be extended from the performance domain to include morbidity and mortality due to CHD.

Other Potential Uses of Self-Assessment Measurements

Optimal Pace

Prolonged endurance efforts lasting several hours, as well as repeated efforts of shorter duration, are more likely to be optimal if a steady-state pace is employed (Wilmore and Costill, 1994). Use of a steady-state pace results in the more economical use of energy with conservation of energy stores, whereas accelerating and decelerating during a given endurance effort results in uneven

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

energy expenditure with a more rapid depletion of energy stores. In fact, it has been recognized for many years that athletes, industrial workers, military personnel, and individuals who engage in various forms of exercise for health, fitness, and recreational purposes (e.g., cycling, jogging, swimming, gardening, dancing, walking) do so at a self-regulated pace. In the case of the athlete performing vigorous exercise at a high intensity for prolonged periods, the concept of pace represents a very important principle in terms of both optimal performance and prevention of injury (Morgan, 2000; Morgan and Pollock, 1977). Indeed, the ability to maintain optimal pace in endurance events is not only a principal focus in the training programs of many athletes, but steady state-expenditure of energy is often developed in an exquisite manner. Runners and swimmers, for example, often repeat segments of a given distance within a second or fraction of a second throughout an event.

While most of this research has been carried out with trained athletes, there is no reason to believe that combat service members cannot be trained to perform prolonged efforts in a steady state. There is research evidence that perception of effort, the key component of steady-state energy expenditure, can be an effective tool in applications with military personnel engaged in physical efforts (Horstman et al., 1979; Morgan, 1977, 1981; Morgan et al., 1983; Patton et al., 1977; Soule and Goldman, 1973).

Some of the earliest research dealing with the subject of pace was performed by Ralph Goldman and his colleagues at the U.S. Army Research Institute of Environmental Medicine. It was reported by Hughes and Goldman (1970), for example, that an energy expenditure of 425 kcal/hr (±10 percent) is voluntarily adopted by healthy, physically fit young men engaged in hard physical work. As a matter of fact, this research group demonstrated that self-regulated pace is not only very consistent, but inclusion of terrain and load coefficients in mathematical models enables one to accurately predict the time requirement to traverse a given distance (Givoni and Goldman, 1971; Goldman and Iampietro, 1962; Hughes and Goldman, 1970; Soule and Goldman, 1969, 1972). This work has potential military applications as it has involved multiple terrains, and the resulting prediction models included load coefficients based on energy expenditure associated with loads positioned on the head, back, and legs.

Preferred Exertion

One of the most innovative lines of research carried out by Goldman’s group involved a study dealing with the pacing of intermittent work during a 31-hour period without sleep (Soule and Goldman, 1973), designed to examine what has come to be known as “preferred exertion.” This concept represents a distinct construct from RPE, but it is related since it represents the exertional level the individual chooses to adopt (Morgan, 2001).

In the study described by Soule and Goldman (1973), 10 men with a mean age of 21 years walked on a motor-driven treadmill at a self-selected pace. The

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

individuals in this study walked for 1 hour or until completion of 4.8 km, whichever occurred first, on six occasions at 5-hour intervals during a 31-hour period without sleep. This task was completed on one day while wearing a 15-kg pack, and on a second day while wearing a 30-kg pack. The speed of walking was self-regulated by means of a servo-controlled treadmill. The time required to walk each 400 m was recorded, as well as the distance covered during each 5-minute epoch. An RPE was obtained near the end of each 1-hour exercise bout, and heart rate was measured at the conclusion of each walk. Times for the 15-kg condition were faster than those for the 30-kg condition as expected, but walk times were not impaired significantly by sleep deprivation. The investigators suggested that the improved performance at 31 hours of sleep deprivation was due to what has traditionally been termed “end spurt” in human performance research, that is, the participants walked faster since they knew this was the last work bout.

Additional research dealing with the concept of preferred exertion has been conducted with civilian samples, and the findings have generally supported the earlier work by Goldman and his associates involving young combat service members. Furthermore, preferred exertion has been found to be consistent when exercise is performed in the early morning, at noon, and in the late afternoon, and the stability of this phenomenon has been shown to hold for both men and women (Trine and Morgan, 1997). A summary of additional research involving preferred exertion has been described by Morgan (2001).

Preferred Intensity

Indirect support for the use of perceptual monitoring is offered by Pollock and colleagues (1972). These investigators evaluated the influence of aerobic training in 22 men ranging in age from 30 to 45 years who were randomly assigned to one group that trained at 90 percent of maximum heart rate or a second group that trained at 80 percent of maximum heart rate. Both groups experienced significant increases in aerobic power, but they did not differ in the amount of improvement. The investigators had hypothesized that the group training at 90 percent of maximum heart rate would have the greatest gain in aerobic power, and the unexpected finding was explained on the basis of preferred exertion. The investigators reported that it was necessary throughout the study to encourage the 90-percent group to go faster and maintain the desired intensity, while at the same time it was necessary to urge the 80-percent group to slow their pace. It was found that both groups “preferred” an intensity of ~85 percent of maximum heart rate, and there was a regression toward this intensity. Hence the finding that training at 90 percent of maximum heart rate was no more effective than training at 80 percent was due to the fact that both groups were actually training at 85 percent of maximum heart rate most of the time.

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

There is additional evidence that healthy men and women tend to have a comfort zone based on preferred intensity. It has been reported by Morgan (1973) that male college students tested at five work loads (i.e., 50, 100, 150, 200, and 250 W) on a bicycle ergometer had a preferred intensity of 120 W for a 30-minute exercise bout. However, there was considerable individual variability in preferred intensity. The preferred exercise intensity in any given setting is undoubtedly due to many factors. In this particular study it was found that values of RPE were correlated with extroversion. This finding supports Borg’s view that perception of effort is a configuration of numerous physiological, psychological, and demographic factors (Borg, 1973, 1998).

Perception of effort was assessed in six well-trained endurance athletes by Farrell and colleagues (1982) on a treadmill test involving 30-minute runs at 60 percent and 80 percent of VO2max performed on separate days. These runners were assessed on a third occasion with instructions to select the pace they would prefer to use for a 30-minute run. The preferred intensity was found to be 75 percent of VO2max, and the mean ratings of perceived exertion for this intensity were below those observed at 80 percent and above those observed at 60 percent of VO2max. In this case there was considerable individual variability that further demonstrates the limitation of nomothetic guidelines.

Overtraining

There is also a great deal of research demonstrating that physical training, when carried to excess, usually results in performance decrements (see also Chapter 4). The reduced performance in such cases has been associated with a number of undesirable physiological and psychological changes. Examples of the physiological changes associated with overtraining include:

  • elevated heart rate and blood pressure;

  • elevated cortisol, creatine kinase, epinephrine, and norepinephrine at rest, along with greater increases in these values following a standard exercise stimulus; and

  • decreased glycogen stores (Costill et al., 1988; Kirwan et al., 1988; Morgan et al., 1987, 1988; O’Connor et al., 1989, 1991; Wilmore and Costill, 1994).

There is also evidence that mood disturbance occurs with overtraining, and common changes include increases in:

  • tension and state anxiety,

  • depression,

  • anger,

  • fatigue,

  • confusion, and

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
  • decreased vigor as measured by the Profile of Mood States (POMS) (Morgan et al., 1987, 1988; O’Connor et al., 1989, 1991; Verde et al., 1992).

In the early phases of overtraining, performance is not affected, but this is followed by a decrease in performance as the overtraining continues (Costill et al., 1988; Morgan et al., 1987, 1988). The decreased performance resulting from excessive physical training has consistently been shown to be associated with mood disturbance, and this relationship can be viewed as causal for the following reasons: (a) the strength of association is strong, (b) there is a temporal sequence, (c) there is consistency for the observed relationship, (d) there is an association independent of other factors, (e) there is a dose-response gradient between increased training volume and mood disturbance, (f) there is biological plausibility for the association (e.g., hypercortisolism), and (g) there is experimental confirmation showing a causal link. There is also evidence that titration of training volume in a systematic manner is effective in preventing the onset of mood disturbance and performance decline. The resulting syndrome is sometimes termed “staleness” in the sports medicine literature, and this breakdown is also associated with reports of muscle soreness, decrements in physical performance, loss of appetite, sleep disturbance, and reduced libido (Costill et al., 1988; Morgan et al., 1987, 1988; O’Connor et al., 1989, 1991).

One of the overtraining studies cited above included a sudden increase in training volume from 4,000 to 9,000 m/day at 94 percent of VO2max in 12 male competitive swimmers. Measures of perceived intensity of exercise, muscle soreness, and mood disturbance (total POMS score) progressively increased through training until midway, at which point self-reports of perceived exertion reached a plateau. Four of the twelve individuals were unable to adapt, and these individuals experienced performance decrements. The self-report (total POMS score [Morgan et al., 1988]) and physiological data (Costill et al., 1988; Kirwan et al., 1988) were in agreement on predicting the negative impact of the sudden increase in training volume for three of the four swimmers, but the psychological data identified all of the impaired swimmers. In a subsequent study by Verde and colleagues (1992) involving heavy training in highly trained distance runners, it was reported that the self-report measure of mood state as measured by POMS was superior to a battery of physiological variables in the prediction and identification of disturbed function.

SUMMARY

The overall physical status of service members in the field can be evaluated by analyzing either objective physiological measurements or subjective measurements of self-assessments (or assessments by peers). For many of the physiological measurements (e.g., energy expenditure), technological advances need to be achieved before the parameters are practical for field situations. Even with

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

this limitation, objective measurements of physiological factors, such as heart rate or temperature, are generally preferable to more subjective methods of measurement for a variety of reasons. For example, individuals that self-report their status tend to overestimate endurance and performance due to peer and commander pressure. The validity of self-assessments is also compromised when, as is often the case, the service members are tired or sleep deprived. Last, unfamiliarity with a given situation may alter pace and therefore also may confound the results of a test. However, when measuring overall physical status, the subjectivity of self-assessments may offer an advantage over other more objective measurements. In fact, self-assessments often include the influence of psychological factors, which are not accounted for when physiological measurements are used. This may be one of the reasons why studies have shown that self-assessment measurements, such as rating of perceived exertion, are better indicators of physical performance than a single physiological measurement.

Whether physiological measurements or self-assessments are used to measure performance, it is critical that before implementation in the field, the biomarker is validated not only in the laboratory, but also in the field. This validation is a complex problem that requires a great deal of planning and research.

REFERENCES

Åstrand PO, Rodahl K. 1986. Textbook of Work Physiology. Physiological Bases of Exercise. 3rd ed. New York: McGraw-Hill.


Bassett DR, Strath SJ. 2002. Use of pedometers to assess physical activity. In: Welk GJ, ed. Physical Activity Assessments for Health-Related Research. Champaign, IL: Human Kinetics. Pp. 163–177.

Bassett DR, Ainsworth BE, Swartz AM, Strath SJ, O’Brien WL, King GA. 2000. Validity of four motion sensors in measuring moderate intensity physical activity. Med Sci Sports Exerc 32:S471-S480.

Beidleman BA, Puhl JL, De Souza MJ. 1995. Energy balance in female distance runners. Am J Clin Nutr 61:303–311.

Borg GAV. 1973. Perceived exertion: A note on ‘history’ and methods. Med Sci Sports 5:90–93.

Borg G. 1998. Borg’s Perceived Exertion and Pain Scales. Champaign, IL: Human Kinetics.


Castellani JW, O’Brien C, Stulz DA, Blanchard LA, DeGroot DW, Bovill ME, Francis TJ, Young AJ. 2002. Physiological responses to cold exposure in men: A disabled submarine study. Undersea Hyperb Med 29:189–203.

Chen KY. 2003. The Use of Portable Accelerometers in Predicting Activity Energy Expenditure. Presented at the Institute of Medicine, Committee on Metabolic Monitoring Technologies for Military Field Applications Workshop on Metabolic Monitoring Technologies for Military Field Applications, San Antonio, Texas, January 8–9.

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

Cheung SS, McLellan TM, Tenaglia S. 2000. The thermophysiology of uncompensable heat stress. Physiological manipulations and individual characteristics. Sports Med 29:329–359.

Costill DL, Flynn MG, Kirwan JP, Houmard JA, Mitchell JB, Thomas R, Park SH. 1988. Effects of repeated days of intensified training on muscle glycogen and swimming performance. Med Sci Sports Exerc 20:249–254.

Coward WA, Cole TJ. 1991. The doubly labeled water method for the measurement of energy expenditure in humans: Risks and benefits. In: Whitehead RG, Prentice A, eds. New Techniques in Nutrition Research. San Diego : Academic Press. Pp. 139–176.


Dishman RK, Farquhar RP, Cureton KJ. 1994. Responses to preferred intensities of exertion in men differing in activity levels. Med Sci Sports Exerc 26:783–790.


Farrell PA, Gates WK, Maksud MG, Morgan WP. 1982. Increases in plasma beta-endorphin/beta-lipotropin immunoreactivity after treadmill running in humans. J Appl Physiol 52:1245–1249.


Gardner JW, Kark JA, Karnei K, Sanborn JS, Gastaldo E, Burr P, Wenger CB. 1996. Risk factors predicting exertional heat illness in male Marine Corps recruits. Med Sci Sports Exerc 28:939–944.

Givoni B, Goldman RF. 1971. Predicting metabolic energy cost. J Appl Physiol 30:429–433.

Goldman RF, Iampietro PF. 1962. Energy cost of load carriage. J Appl Physiol 17:675–676.


Heil DP, Klippel NJ. 2003. Validation of energy expenditure prediction algorithms in adolescents and teens using the actical activity monitor. Med Sci Sports Exerc 35:S285.

Hendelman D, Miller K, Baggett C, Debold E, Freedson P. 2000. Validity of accelerometry for the assessment of moderate intensity physical activity in the field. Med Sci Sports Exerc 32:S442-S449.

Horstman DH, Morgan WP, Cymerman A, Stokes J. 1979. Perception of effort during constant work to self-imposed exhaustion. Percept Motor Skill 48:1111–1126.

Hoyt RW, Knapik JJ, Lanza JF, Jones BH, Staab JS. 1994. Ambulatory foot contact monitor to estimate metabolic cost of human locomotion. J Appl

Hughes AL, Goldman RF. 1970. Energy cost of “hard work.” J Appl Physiol 29:570–572.


Iltis PW, Givens MW. 2000. Validation of the CALTRAC accelerometer during simulated multi-geared cycling at different work rates. J Exerc Physiol 3:21–27.


Jakicic JM, Winters C, Lagally K, Ho J, Robertson RJ, Wing RR. 1999. The accuracy of the tritrac-R3d accelerometer to estimate energy expenditure. Med Sci Sports Exerc 31:747–754.

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

Jequier E, Schutz Y. 1983. Long-term measurements of energy expenditure in humans using a respiration chamber. Am J Clin Nutr 38:989–998.


Kark JA, Burr PQ, Wenger CB, Gastaldo E, Gardner JW. 1996. Exertional heat illness in Marine Corps recruit training. Aviat Space Environ Med 67:354–360.

Kirwan JP, Costill DL, Flynn MG, Mitchell JB, Fink WJ, Neufer PD, Houmard JA. 1988. Physiological responses to successive days of intense training in competitive swimmers. Med Sci Sports Exerc 20:255–259.

Klippel JN, Heil DP. 2003. Validation of energy expenditure prediction algorithms in adults using the Actical Electronic Activity Monitor. Med Sci Sports Exerc 35:S284.

Knaus WA, Draper EA, Wagner DP, Zimmerman JE. 1985. APACHE II: A severity of disease classification system. Crit Care Med 13:818–829.

Kolka MA, Quigley MD, Blanchard LA, Toyota DA, Stephenson LA. 1993. Validation of a temperature telemetry system during moderate and strenuous exercise. J Therm Biol 18:203–210.

Koltyn KF, Morgan WP. 1992. Efficacy of perceptual versus heart rate monitoring in the development of endurance. Br J Sports Med 26:132–134.

Kulka TJ, Kenney WL. 2002. Heat balance limits in football uniforms. Physician Sportsmed 30:29–35.


Le Gall JR, Loirat P, Alperovitch A, Glaser P, Granthil C, Mathieu D, Mercier P, Thomas R, Villers D. 1984. A simplified acute physiology score for ICU patients. Crit Care Med 12:975–977.

Le Masurier GC, Tudor-Locke C. 2003. Comparison of pedometer and accelerometer accuracy under controlled conditions. Med Sci Sports Exerc 35:867–871.

Lee IM, Sesso HD, Oguma Y, Paffenbarger PF Jr. 2003. Relative intensity of physical activity and risk of coronary heart disease. Circulation 107:1110–1116.

Liden CB, Wolowicz M, Stivoric J, Teller A, Vishunubhatla S, Pelletier R, Farringdon J. 2002. Accuracy and Reliability of the SenseWear™ Armband as an Energy Expenditure Assessment Device. Online. BodyMedia. Available at http://www.bodymedia.com/pdf/Accuracy.pdf. Accessed September 23, 2003.


Manore M, Thompson J. 2000. Sport Nutrition for Health and Performance. Champaign, IL: Human Kinetics. Pp. 136–137, 148–149, 219–220, 224, 225, 228.

Melanson EL, Coelho LB, Tran ZV, Haugen HA, Kearney JT, Hill JO. 2003. Validation of the BodyGem™ hand-held indirect calorimeter. Abstract presented at the Nutrition Week Conference, San Antonio, Texas, January 18–22.

Moran DS. 2000. Stress evaluation by the physiological strain index (PSI). J Basic Clin Physiol Pharmacol 11:403–423.

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

Moran DS, Mendal L. 2002. Core temperature measurement: Methods and current insights. Sports Med 32:879–885.

Moran DS, Montain SJ, Pandolf KB. 1998a. Evaluation of different levels of hydration using a new physiological strain index. Am J Physiol 275:R854-R860.

Moran DS, Shitzer A, Pandolf KB. 1998b. A physiological strain index to evaluate heat stress. Am J Physiol 275:R129-R134.

Moran DS, Castellani JW, O’Brien C, Young AJ, Pandolf KB. 1999a. Evaluating physiological strain during cold exposure using a new cold strain index. Am J Physiol 277:R556-R564.

Moran DS, Shapiro Y, Laor A, Izraeli S, Pandolf KB. 1999b. Can gender differences during exercise-heat stress be assessed by the physiological strain index? Am J Physiol 276:R1798-R1804.

Moran DS, Kenney WL, Pierzga JM, Pandolf KB. 2002. Aging and assessment of physiological strain during exercise-heat stress. Am J Physiol Regul Integr Comp Physiol 282:R1063-R1069.

Morgan WP. 1973. Psychological factors influencing perceived exertion. Med Sci Sport 5:97–103.

Morgan WP. 1977. Perception of effort in selected samples of Olympic athletes and soldiers. In: Borg G, ed. Physical Work and Effort. Oxford: Pergamon Press. Pp. 267–277.

Morgan WP. 1981. The 1980 C.H.McCloy Research Lecture. Psychophysiology of self-awareness during vigorous physical activity. Res Q Exerc Sport 52:385–427.

Morgan W. 2000. Psychological factors associated with distance running and the marathon. In: Pedoe DT, ed. Marathon Medicine. London, UK: Royal Society of Medicine Press Limited. Pp. 293–310.

Morgan WP. 2001. Prescription of physical activity: A paradigm shift. Quest: The Academy Papers 53:137–161.

Morgan WP, Borg GAV. 1976. Perception of effort in the prescription of physical activity. In: Craig TT, ed. The Humanistic and Mental Health Aspects of Sports, Exercise and Recreation. Chicago, IL: American Medical Association. Pp. 126–129.

Morgan WP, Pollock ML. 1977. Psychologic characterization of the elite distance runner. Ann NY Acad Sci 301:382–403.

Morgan WP, Horstman DH, Cymerman A, Stokes J. 1983. Facilitation of physical performance by means of a cognitive strategy. Cogn Ther Res 7:251–264.

Morgan WP, Brown DR, Raglin JS, O’Connor PJ, Ellickson KA. 1987. Psychological monitoring of overtraining and staleness. Br J Sports Med 21:107–114.

Morgan WP, Costill DL, Flynn MG, Raglin JS, O’Connor PJ. 1988. Mood disturbance following increased training in swimmers. Med Sci Sports Exerc 20:408–414.

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

Murray R. 1995. Fluid needs in hot and cold environments. Int J Sport Nutr 5:S62-S73.


Nieman DC, Trone GA, Austin MD. 2003. A new handheld device for measuring resting metabolic rate and oxygen consumption. J Am Diet Assoc 103:588–592.

Noble BJ, Robertson RJ. 1996. Perceived Exertion. Champaign, IL: Human Kinetics.

NWS (National Weather Service). 2003. Heat Index. Online. National Oceanic and Atmospheric Administration. Available at http://www.crh.noaa.gov/pub/heat.htm. Accessed September 24, 2003.


O’Brien C, Hoyt RW, Buller MJ, Castellani JW, Young AJ. 1998a. Telemetry pill measurement of core temperature in humans during active heating and cooling. Med Sci Sports Exerc 30:468–472.

O’Brien C, Young AJ, Sawka MN. 1998b. Hypohydration and thermoregulation in cold air. J Appl Physiol 84:185–189.

O’Connor PJ, Morgan WP, Raglin JS, Barksdale CM, Kalin NH. 1989. Mood state and salivary cortisol levels following overtraining in female swimmers. Psychoneuroendocrinol 14:303–310.

O’Connor PJ, Morgan WP, Raglin JS. 1991. Psychobiologic effects of 3 d of increased training in female and male swimmers. Med Sci Sports Exerc 23:1055–1061.


Patton JF, Morgan WP, Vogel JA. 1977. Perceived exertion of absolute work during a military physical training program. Eur J Appl Physiol 36:107–114.

Pivarnik JM, Palmer RA. 1994. Water and electrolyte balance during rest and exercise. In: Wolinsky I, Hickson JF, eds. Nutrition in Exercise and Sport. 2nd ed. Boca Raton, FL: CRC Press. Pp. 245–262.

Poehlman ET. 1989. A review: Exercise and its influence on resting energy metabolism in man. Med Sci Sports Exerc 21:515–525.

Pollock ML, Broida J, Kendrick Z, Miller HS, Janeway R, Linnerud AC. 1972. Effects of training two days per week at different intensities on middle-aged men. Med Sci Sports 4:192–197.

Prentice AM, Diaz EO, Murgatroyd PR, Goldberg GR, Sonko BJ, Black AE, Coward WA. 1991. Doubly labeled water measurements and calorimetry in practice. In: Whitehead RG, Prentice A, eds. New Techniques in Nutrition Research. San Diego: Academic Press. Pp. 177–206.

Puyau MR, Adolph AL, Vohra FA, Butte NF. 2002. Validation and calibration of physical activity monitors in children. Obes Res 10:150–157.


Ravussin E, Bogardus C. 1989. Relationship of genetics, age, and physical fitness to daily energy expenditure and fuel utilization. Am J Clin Nutr 49:968–975.

Ravussin E, Lillioja S, Anderson TE, Christin L, Bogardus C. 1986. Determinants of 24-hour energy expenditure in man. Methods and results using a respiratory chamber. J Clin Invest 78:1568–1578.

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

Rivera-Fernandez R, Vazquez-Mata G, Bravo M, Aguayo-Hoyos E, Zimmerman J, Wagner D, Knaus W. 1998. The Apache III prognostic system: Customized mortality predictions for Spanish ICU patients. Intensive Care Med 24:574–581.

Rontoyannis GP, Skoulis T, Pavlou KN. 1989. Energy balance in ultramarathon running. Am J Clin Nutr 49:976–979.

Rosenberg AL. 2002. Recent innovations in intensive care unit risk-prediction models. Curr Opin Crit Care 8:321–330.


Sargent F, Weinman KP. 1966. Physiological individuality. Ann NY Acad Sci 134:696–719.

Schoeller DA, Racette SB. 1990. A review of field techniques for the assessment of energy expenditure. J Nutr 120:1492–1495.

Schoeller DA, Ravussin E, Schutz Y, Acheson KJ, Baertschi P, Jequier E. 1986. Energy expenditure by doubly labeled water: Validation in humans and proposed calculation. Am J Physiol 250:R823-R830.

Schutz Y, Weinsier RL, Hunter GR. 2001. Assessment of free-living physical activity in humans: An overview of currently available and proposed new measures. Obes Res 9:368–379.

Schutz Y, Weinsier S, Terrier P, Durrer D. 2002. A new accelerometric method to assess the daily walking practice. Int J Obes Relat Metab Disord 26:111–118.

Soule RG, Goldman RF. 1969. Energy cost of loads carried on the head, hands, or feet. J Appl Physiol 27:687–690.

Soule RG, Goldman RF. 1972. Terrain coefficients for energy cost prediction. J Appl Physiol 32:706–708.

Soule RG, Goldman RF. 1973. Pacing of intermittent work during 31 hours. Med Sci Sports 5:128–131.

Sutton JR. 1990. Clinical implications of fluid imbalance. In: Lamb DR, Gisolfi CV, eds. Perspectives in Exercise Science and Sports Medicine. Fluid Homeostasis During Exercise. Vol 3. Carmel, IN: Benchmark Press. Pp. 425–455.


Thompson J, Manore MM, Skinner JS. 1993. Resting metabolic rate and thermic effect of a meal in low- and adequate-energy intake male endurance athletes. Int J Sport Nutr 3:194–206.

Trine MR, Morgan WP. 1997. Influence of time of day on the anxiolytic effects of exercise. Int J Sports Med 18:161–168.

Tudor-Locke C, Williams JE, Reis JP, Pluto D. 2002. Utility of pedometers for assessing physical activity: Convergent validity. Sports Med 32:795–808.


Verde T, Thomas S, Shephard RJ. 1992. Potential markers of heavy training in highly trained distance runners. Br J Sports Med 26:161–175.


Welk GJ. 2002. Use of accelerometry-based activity monitors to assess physical activity. In: Welk GJ, ed. Physical Activity Assessments for Health-Related Research. Champaign, IL: Human Kinetics. Pp. 125–141.

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

Welk GJ, Blair SN, Wood K, Jones S, Thompson RW. 2000. A comparative evaluation of three accelerometry-based physical activity monitors. Med Sci Sports Exerc 32:S489-S497.

Wilmore JH, Costill DL. 1994. Physiology of Sport and Exercise. Champaign, IL: Human Kinetics Publishers.


Young AJ, Castellani JW, O’Brien C, Shippee RL, Tikuisis P, Meyer LG, Blanchard LA, Kain JE, Cadarette BS, Sawka MN. 1998. Exertional fatigue, sleep loss, and negative energy balance increase susceptibility to hypothermia. J Appl Physiol 85:1210–1217.

Young AJ, Sawka MN, Pandolf KB. 2003. Biomarkers of Physiological Strain during Exposure to Hot and Cold Environments. Presented at the Institute of Medicine, Committee on Metabolic Monitoring Technologies for Military Field Applications Workshop on Metabolic Monitoring Technologies for Military Field Applications, San Antonio, Texas, January 8–9.


Zhang K, Werner P, Sun M, Pi-Sunyer FX, Boozer CN. 2003. Measurement of human daily physical activity. Obes Res 11:33–40.

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×

This page intentionally left blank.

Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 53
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 54
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 55
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 56
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 57
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 58
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 59
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 60
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 61
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 62
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 63
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 64
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 65
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 66
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 67
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 68
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 69
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 70
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 71
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 72
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 73
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 74
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 75
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 76
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 77
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 78
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 79
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 80
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 81
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 82
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 83
Suggested Citation:"3 Monitoring Overall Physical Status to Predict Performance." Institute of Medicine. 2004. Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance. Washington, DC: The National Academies Press. doi: 10.17226/10981.
×
Page 84
Next: 4 Physiological Biomarkers for Predicting Performance »
Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance Get This Book
×
Buy Paperback | $64.00 Buy Ebook | $49.99
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

The U.S. military’s concerns about the individual combat service member’s ability to avoid performance degradation, in conjunction with the need to maintain both mental and physical capabilities in highly stressful situations, have led to and interest in developing methods by which commanders can monitor the status of the combat service members in the field. This report examines appropriate biological markers, monitoring technologies currently available and in need of development, and appropriate algorithms to interpret the data obtained in order to provide information for command decisions relative to the physiological “readiness” of each combat service member. More specifically, this report also provides responses to questions posed by the military relative to monitoring the metabolic regulation during prolonged, exhaustive efforts, where nutrition/hydration and repair mechanisms may be mismatched to intakes and rest, or where specific metabolic derangements are present.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    Switch between the Original Pages, where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

    « Back Next »
  6. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  7. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  8. ×

    View our suggested citation for this chapter.

    « Back Next »
  9. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!