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-



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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance 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-

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance 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-

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance 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

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance 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.

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance 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

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

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance 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

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

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance 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.

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

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance 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

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance 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.

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance 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

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance 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

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance 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.

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