modeling in physiology

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1 modeling in physiology Heat strain models applicable for protective clothing systems: comparison of core temperature response R. R. GONZALEZ, 1 T. M. MCLELLAN, 2 W. R. WITHEY, 3 S. K. CHANG, 1 AND K. B. PANDOLF 1 1 US Army Research Institute of Environmental Medicine, Natick, Massachusetts ; 2 Defence and Civil Institute of Environmental Medicine, North York, Ontario, Canada M3M 3B9; and 3 Centre for Human Sciences, Defence Research Agency, Farnborough, Hampshire GU146TD, United Kingdom Gonzalez, R. R., T. M. McLellan, W. R. Withey, S. K. Chang, and K. B. Pandolf. Heat strain models applicable for protective clothing systems: comparison of core temperature response. J. Appl. Physiol. 83(3): , Core temperature (T c ) output comparisons were analyzed from thermal models applicable to persons wearing protective clothing. The two models evaluated were the United States (US) Army Research Institute of Environmental Medicine (USARIEM) heat strain experimental model and the United Kingdom (UK) Loughborough (LUT25) model. Data were derived from collaborative heat-acclimation studies conducted by three organizations and included an intermittentwork protocol (Canada) and a continuous-exercise/heat stress protocol (UK and US). Volunteers from the US and the UK were exposed to a standard exercise/heat stress protocol (ambient temperature 35 C/50% relative humidity, wind speed 1 m/s, level treadmill speed 1.34 m/s). Canadian Forces volunteers did an intermittent-work protocol (15 min moderate work/15 min rest at ambient temperature of 40 C/30% relative humidity, wind speed 0.4 m/s). Each model reliably predicted T c responses (within the margin of error determined by 1 root mean square deviation) during work in the heat with protective clothing. Models that are analytically similar to the classic Stolwijk-Hardy model serve as robust operational tools for prediction of physiological heat strain when modified to incorporate clothing heat-exchange factors. heat acclimation; exercise; clothing heat exchange; core temperature; thermal models MATHEMATICAL MODELING of thermal responses allows testing of wide performance limitations in individuals exposed to environmental extremes. Use of models is, therefore, especially important when experimental settings with human subjects are restricted to finite thermal limits necessary to protect the individual. In essence, the ideal mathematical model of heat strain should incorporate all essential variables active in thermoregulation. Although it seems to be an almost insolvable task, a great many worthwhile models do a reliable job describing the heat-balance equation. Models describing steady-state responses apply best when quasi-heat balance exists (2, 29, 30). They are quite useful in a first approach prediction of physiological effector response (e.g., sweating rate, skin blood flow), particularly when a given metabolic activity stays constant over the time of the given heat exposure. A regulating system is usually described in two distinct ways: in terms of a passive or controlled system and an informational or controlling system. In physiological terms, the controlled system in human thermoregulation is considered as the body with its inclusive anatomical features, heat capacities, and energy fluxes from various tissues: core, muscle, adipose, and skin sites. The controlling system includes the complete central nervous system transmitting information in a network manner (23, 30). Early forerunners of classic rational thermal models (which employ elements of heat exchange that predict physiological response) incorporated extensive descriptions of the passive system in terms of a steadystate bio-heat equation (18) or open-loop systems without a full description of control or regulatory action. Such models were established on a scanty experimental database (8, 29). As data became available, closed-loop characterizations of the thermoregulatory system appeared, which included a rudimentary feedback-control formulation of internal body temperature (8, 29). A significant database has been collected by using human experimental studies and wide clothing systems from which predictive modeling equations can be developed for individuals working in temperate and hot environments (1, 4, 5, 8, 11, 25). However, few comparisons of the results from various model outputs have ever been carried out. The present study describes approaches conventionally used in both rational and operational models and presents a comparison between measured and predicted core temperature (T c ) responses during exercise and heat exposure. Data from collaborative efforts conducted by three separate laboratories were evaluated against two models with separate model formats that employ distinct mathematical approaches but have analogous utility in prediction of heat strain, since they derive their form from fundamentals of heat exchange. This report covers one aspect of the collaboration. MODEL CHARACTERISTICS Operational Model In 1972, Givoni and Goldman (7) developed an empirical approach to prediction of T c response. They inferred 1017

2 1018 HEAT STRAIN MODEL COMPARISONS that for any given combinations of metabolic rate, environment, and clothing a theoretically determined equilibrium (final) T c and matching skin temperature would be generated, and unified biophysical formulas could be constructed to adequately predict that response. A series of predictive equations were subsequently developed that proved useful in describing human heat exchange while subjects wore a variety of clothing systems and that reliably fit patterns of rectal temperature (T re ) response observed during rest in the heat, rises during work, and decreases during recovery. Recent enhancements to the original equations (17) include three major components in the present United States (US) Army Research Institute of Environmental Medicine (USARIEM) operational model, incorporating thermal, sweating, and heart rate (HR) responses. Only the thermal and sweating aspects will be covered briefly here; an extensive documentation of the clothing coefficient analyses and equations used is found in the APPENDIX and other reviews (2, 17, 19). Predictive equations for implementing work/rest disciplines with various clothing systems, environments, or workload sequences in the original Givoni-Goldman model are based on a series of functions (2, 6, 7). The main function is the equation that establishes the difference in T c expected at equilibrium. The basic Givoni-Goldman (7) model has the mathematical form X(H) 0 1 H 1 2 H 2 3 exp ( 4 H 3 ) (1) where H 1 is the net metabolic heat load, H 2 is the environmental heat load, and H 3 is the difference between total heat load (H 1 H 2 ) and maximum evaporative cooling. The coefficients i, where i 0, 1, 2, 3, and 4, are determined empirically from actual experimentation or database values. Any one of the covariates is measurable during an experiment or precisely determined by theoretically applied heat-transfer equations (e.g., partitional calorimetry) or by the other covariates. H 1 is expressed as a function of body weight, weight of clothing and load, walking velocity, terrain factors, and grade of walking. H 2 is expressed as a function of body surface area (DuBois), dry bulb temperature, average skin temperature (T sk ), and total dry thermal insulation of clothing worn. H 3 is expressed as a function of H 1 and H 2 covariates and water vapor permeability index (Woodcock s i m factor; Ref. 27) of clothing worn, which, in turn, is a function of effective wind speed and relative humidity of the air and skin (based on saturated vapor pressure of the skin and ambient) (2). Days of heat acclimation, solar heat load, physical fitness [maximal oxygen uptake (V O2max )], gender, and state of hydration have been also incorporated as modifying factors in the present model (17, 21). Parameter adjustments that describe delay characteristics after a change in environment or clothing and that predict the rate of change of T c as a function of time during work have been substantially improved in the present USARIEM heat strain model described in the APPENDIX. Transient T re equations as a function of time of exposure to heat (t) have been developed for the resting state and exposure to heat stress by the following expression X(t) X 0 (X f X 0 ) exp [(log 5 ) 6 (t 7) ] t 7 (2) where X f and X 0 are the final and initial temperatures computed, respectively; t is duration of heat stress; and 0 i 1, where i 5, 6, and 7 are parameters determined empirically. The model is a modified form of the Gompertz curve forming a sigmoid shape (28). Duplications of the initial rise [inflection of T re f(t)] and family of curves compared with experimental data for various work rates, heat-stress exposures, and clothing systems are critical to the model s utility. For the case where increased heat stress includes the initiation of work and t 0 X(t) X 0 (X f X 0 ) (1 exp 5 8 (t 9 ) exp[ 10 (X f X 0 )]6) t 9 (3) A delay function in T c caused by exercise at a given metabolic work and the rate of change function is currently implemented by four subroutines. The delay time is a period where changes in T c are driven by the rate of change of T c at the start of the delay period (e.g., inflection at initiation of exercise) and the magnitude of the change in T c necessary to reach equilibrium that results from a change in metabolic rate described by an exponential rate coefficient (K work factor) (APPENDIX, Eqs. A17 A19). The T c is calculated every minute during the delay time. T c at every minute is dependent on the initial T c, the equilibrium T c, the time in the period from the end of a proceeding delay time during work or rest, and the exponential coefficient, K work. The original delay time in the Givoni-Goldman model (7) was based on a minimal delay time of 6 min observed when 580 W of work are initiated. The time lag (min) for work-induced T re is now based on a fit from multiple studies in our laboratory covering work rates [metabolic free energy (M)] of W and is estimated by t delay M M 2 (4) Thermal component evaluated from partitional calorimetry. The basic analytical form discussed may be easily calculated for coding in computers by analysis of heat-exchange properties (2, 3, 5) in which T re,f T re, H sk Dry Evap (5) where T re,f ( C) refers to the final steady-state level at a given time interval (from an initial T re,0 ), predicted as a function of the following variables: skin heat transfer (H sk ; in W) composed of (M W ex ) (in W), with M being a function of weight, walking velocity, grade, and terrain factors; W ex being rate of work done on an organism by a external system as a function of grade, terrain, body weight, and clothing plus equipment weight, the latter generally assessed at three wind speeds (2, 5, 6, 17). Dry (in W) incorporates the sensible environmental heat load (R C) on the person, where

3 HEAT STRAIN MODEL COMPARISONS 1019 Dry 6.45/I T A D (T sk T a ), in which total clothing thermal insulation (I T ) (assessed over a minimum of three wind speeds), body surface (A D ), and average skin-to-ambient temperature (T sk T a ) are generally evaluated on a copper manikin in a specific garment (Table 1) (2, 3). Evap is accounted for by an exponential function 50.8 exp[ (E req E max )]6 of the difference in required evaporative cooling (E req ) and maximum evaporative heat exchange (E max ; in W), where E max 6.45 LR (i m /I T ) A eff (P s,sk P a ) (6) in which LR is the Lewis relation 2.2 C/Torr (16.5 K/kPa) (2, 5) that is used to evaluate, at sea level, evaporative heat transfer to radiative and convective heat transfer, respectively (LR h e /h c ); i m /I T describes the evaporative potential of a specific clothing system based on the Woodcock s factor i m to thermal insulation (I T ), and (P s,sk P a ) is the body skin saturation vapor pressure (P s,sk ) to ambient water vapor pressure (P a ) gradient depending on an effective body surface area (A eff ) (2, 3, 5, 6). Sweating rate and net water requirements. The change in T re from rest to a given time point during transients depending on metabolic activity can be determined by T re / t (S A D )/ ṁ b, where S (W m 2 ) is rate of heat storage, evaluated from partitional calorimetry and accounting for all energy exchanges in the heat balance equation, M is the latent heat constant (680 W h kg 1 ), and ṁ b is the nude body weight loss (kg) (3, 5, 7). The necessary water to supplement that lost during work and environmental heat is an added parameter adjustment to the original Givoni-Goldman operational model in the present USARIEM heat strain model. This equation is derived from sweating rate ṁ sw (g m 2 h 1 ), which is a function of the maximum evaporative power of the environment E max (3, 5) and required evaporative heat loss from the heat balance equation (21) Eqs. A15 A16 in APPENDIX. Water requirements (Wtr; ml/min) for a wide range of heat-stress conditions can be analytically determined by the following relationship (21) Wtr 2,000 ml/min if E max 0 Wtr f(e req, E max ) 27.9 A D E req E max if E max 0 Thermoregulatory Models The most complete description of the human thermal passive system resident in present thermal models to date derives from the work of Stolwijk and Hardy (23), which quantifies human body heat exchange from six segments, further subdivided into 25 compartments or nodes. During the last 30 years, attention to the mechanisms involved in operation of the controlling system has taken precedence over description of the passive heat flux between various segments of the body (3, 8, 29, 30). As a result, significant progress continues in the modeling of central and peripheral thermal controller activity and deriving parameter modifications to older controller equations for both cold (26) and (7) heat stress (12, 30). The classic approach used in building a thermal model is first to describe the passive state and develop algorithms to validate controller activity by rational analysis or actual experimental results. Both theoretical and experimental approaches must closely represent physiological responses ascribed to the controlling system. In general, in all rationally based thermoregulatory models, the controlling system active in body temperature regulation is divided into three components. The sensing elements consist of thermoreceptors, active in recognizing deviation in the thermal state of the controlled system. The integrating component receives thermal signals, integrates them, and relays appropriate effector commands. The final facilitator component adds or negates effector commands, modulates a command signal, depending on circumstances existing at particular loci in the brain stem, and elicits appropriate coding to cause effector action. The thermoregulatory controlling system has been considered as existing singularly or with both linear and nonlinear control operations, with and without a discrete temperature reference (set) point (12, 23, 29). The United Kingdom (UK) Loughborough (LUT25) model, as used here, was transcribed by Haslam and Parsons (9, 19) from the the Stolwijk-Hardy 25-node model of thermoregulation (23), which represents the human body as 25 compartments. In brief, the model depicts the head as a sphere and the trunk, arms, hands, and legs and feet as cylinders. A symmetrical construct of the body is assumed to limit the number of iterations required. Each segment is further divided into four layers: core, muscle, fat, and skin compartments to equal 24 compartments. The major arterial and venous vessels are represented as the 25th compartment. Each compartment is assigned a mass, volume, and specific heat. The values were obtained partly via direct experimentation and partly from the literature (23) and relate to an average-sized male with a body weight of 74.4 kg and 1.89 m 2 DuBois surface area A D (3). In the model s passive structure, heat flows radially by conduction from compartment to adjacent compartment. From segment to segment, heat flow is by convective transfer to and from the blood. Metabolic heat production is divided proportionately between the various segments and layers. External body compartments exchange heat with the environment by means of convection, radiation, and evaporation of thermoregulatory sweating. The controlling system is based on a set-point theory of human thermoregulatory control. Signals controling vasodilation, vasoconstriction, sweating, and shivering are calculated as a function of the difference of the actual temperatures of the compartments, from reference temperatures for each respective compartment. Local thermal signals are modified based on density of thermoreceptors present in each respective compartment. The thermal signals are integrated to produce core, core and skin, and skin signals. The effector regulator interprets the integrated signals and produces effector commands. Each effector command is implemented by an appropriate effector outcome: shiv-

4 1020 HEAT STRAIN MODEL COMPARISONS ering, vasodilation, vasoconstriction, and sweating, after first being modified according to local compartmental thermal state. The LUT25 computer program used in this comparison is a recoded version of the 25-node model adapted from the original FORTRAN program listing given by Stolwijk and Hardy (S-H) (23). In the version provided by Centre for Human Sciences to USARIEM, there were modifications to the program listing and corrections to program logic to allow execution on a personal computer. The program code was changed to prevent the S-H model from shivering in the heat, which the original consistently demonstrates. The original S-H model predicts responses for the unclothed condition only. The published computer code of the model was adapted to account for clothing and thermal radiant loads by implementing the coefficients from the Gagge et al. (3) J. B. Pierce model, extended to include intrinsic clothing (i cl ) and water vapor permeability factors (Woodcock s i m ; Refs. 3, 6, 27). The LUT25 version used in this study implements clothing coefficients equally to each body compartment of the S-H model. Trunk temperature was used to predict the responses of T re from the experimental trials. All other parameters and control coefficients were exact facsimilies of the original code. METHODS The experimental study was conducted in three separate laboratory sites (US, UK, and Canada). Comparisons of thermoregulatory responses were done on volunteer subjects dressed in completely encapsulating chemical protective (CP) ensembles. The emphasis of the study was to compare T re responses vs. exercise time obtained from individuals exercising with the various protective ensembles. A standard level of environmental stress agreed on by country members was used: T a T g 35 C/50% relative humidity (RH), where T g is globe temperature [water vapor pressure (P w ) 2.81 kpa; 21 Torr], at constant wind speed of 1 m/s. Subjects attempted a 100-min walk on a level treadmill at a pace of 1.34 m/s (metabolic heat production W) until self withdrawal or until T re reached 39 C. UK and US experiments followed the above common protocol design. Data garnered from all laboratory experiments were then compared with model simulations obtained by using the USARIEM heat strain model (our present experimental version) and the LUT25 model (9). Model predictions were also conducted on experimental data obtained from a previous intermittentwork protocol (1) carried out by Canada, utilizing the Canadian CP ensemble. USARIEM Procedures Subjects. The subjects were 10 male military personnel volunteers, in accordance with US Army regulation AR 70 25, Use of Volunteers for Research. The volunteers received a verbal briefing on the purpose, procedures, and risks of the study, and each signed an informed consent agreement. Each volunteer received a medical clearance from a medical officer before participation. The physical characteristics of the subject pool were as follows (means SD): height m; weight kg; (Dubois) body surface area m 2, and %body fat (hydrostatic weighing method) of %. The age of the group was yr. Average ( SD) maximum aerobic power (V O2max ) of the subjects, determined by conventional incremented treadmill exercise procedures (5), was l O 2 /min ( ml kg 1 min 1 ). Experimental design. The study was conducted from February to early June in the US Army Doriot tropical environmental chamber during all phases of the study. Following a familiarization session to the test environment (35 C/50% RH, wind speed of 1 m/s), all 10 subjects underwent a 10-day heat-acclimation period. A 48-h period intervened between pre- and postacclimation experiments. The subjects were in good health and had not taken any prescribed or unprescribed medication or alcohol during the course of the experiments. A medical monitor was on site throughout the testing. Some exercise bouts were terminated before the 100-min time schedule when a subject voluntarily withdrew, when a subject s T re reached a terminal point of 39 C, or when HR exceeded 180 beats/min for 5 min. Termination was also allowed at any time based on the medical monitor s decision. The relative percent V O2max ranged from 27 to 30%. Heat acclimation. During heat acclimation, subjects wore only gym shorts and gym shoes and walked on a level treadmill at the same speed as in the preacclimation phase (1.34 m/s) at a constant environment of T a 49 C/20% RH and wind speed of 1 m/s. Heat acclimation was confirmed when T re and/or HR had leveled off by the 10th day of exposures. Water intake was allowed ad libitum. Exercise-heat tests. Before and after the 10-day heatacclimation program, subjects were exposed to the standard heat-stress test environment agreed on by the laboratories. Other than an initial hydration of 500 ml occurring 20 min before exercise, water was not given during pre- and postacclimation continuous work phases. In both the pre- and postacclimation experimental runs, subjects donned either the US Army battle dress overgarment (BDO), worn over the battle dress uniform (BDU), or the US Air Force CP ensemble worn over underclothing. In brief, the US Army temperate zone BDO consists of a two-layer, two-piece garment with coat and trousers. The outer garment shell is a 50:50 nylon-cotton twill, which is durable and water repellent against liquid agents. The outer shell is laminated to an inner layer of polyurethane foam liner impregnated with activated carbon. The outer-layer pattern is either olive green or four-color woodland camouflage. For maximum chemical protection, the BDO is worn over a regular issue BDU. The fully encapsulated configuration also entails donning a M17A1 CP mask, butyl rubber hood, butyl rubber gloves with cotton liners, and vinyl rubber overboots over the regular issue leather combat boots. The Air Force chemical defense flight suit (CWU/77P) consists of a one-piece coverall with similar two-layer construction as the US Army BDO. The exterior color is tan. The coverall is intended primarily for ground-crew operations. During testing, the coverall was worn over undershirt and underwear, as dictated by US Air Force requirements. The same US Army M17A1 gas masks, protective gloves, and rubber overboots were used for both the Army nuclear biological-chemical (NBC) and the Air Force flight suit experiments. Thermal and water vapor resistance values shown in Table 1 were evaluated at USARIEM by using a copper manikin (6). T re, T sk, and HR were continuously monitored throughout the exercise. T re was measured with a vinyl-covered calibrated thermistor probe (Yellow Springs Instruments 44033) inserted 10 cm past the anal sphincter. T sk was determined by using a calibrated six-point surface area weighting (16) (temperatures from the forehead, chest, back, upper arm, thigh, and calf) recorded from a skin temperature/skin heat flux harness (Concept Engineering, Old Saybrook, CT; FR-025-

5 HEAT STRAIN MODEL COMPARISONS 1021 Table 1. Clothing insulation, thermal resistance, water vapor permeability, and evaporative potential of NBC clothing systems Clothing System V, m/s I T, clo R T, m 2 K W i cl, 1 clo R cl, m2 K W 1 i m /I T UK Army NBC UK Royal Navy NBC US Army US Air Force Canadian NBC Intrinsic clothing value (i cl ) from direct manikin evaluation for each system [clothing surface area (A cl 2.23 m 2 )]. clo, Unit of thermal resistance based on heat transfer of m 2 K W 1 ; V, air speed; I T, total clothing thermal insulation; R T, thermal resistance; i m /I T, evaporative potential; NBC, nuclear biological-chemical protective suit. TH44018). All body temperatures were recorded every 10 s by using a personal computer data-acquisition system. Metabolic heat production was calculated by open-circuit spirometry (5). Total body sweating rates were determined from body weight changes before and after exercise utilizing a Sauter balance ( kg). Oxygen uptake (V O2 ) was measured by indirect calorimetry with the Douglas bag method (5) at the 10th minute and 30th minute time periods of the treadmill walk. The chemical mask was detached for a 2-min collection of expired air while the subjects continued to walk on the treadmill. After each collection period, subjects reconnected the chemical mask. These V O2 data were used to evaluate the transient and steady-state metabolic heat production. The electrocardiogram was monitored continuously with a dedicated telemetry system (Hewlett-Packard 78100A, 78101A). HR data were recorded every 10 min. UK Procedures A complete description of the methods and procedures can be found in Millard et al. (13). Subjects. Thermal responses were observed in 13 male subjects over the full 60 min, although subject attrition was noticeable before the 100-min milestone of the experiment. For this paper, data were truncated past the 65th minute for the comparison with model output. Subjects were dressed in either the UK Army NBC clothing or UK Royal Navy NBC clothing system. Average ( SD) physical characteristics were age ( yr), weight ( kg), height ( cm), and body fat (4 skin sites; %). Clothing and water vapor transfer characteristics were determined at USARIEM for all the UK ensembles (6), shown in Table 1. Experimental design. All experiments were conducted at the Centre for Human Sciences, Defence Research Agency, Farnborough, UK. The experimental protocol was approved by a local Human Ethics Committee. Heat acclimation. Experiments were conducted in the standard heat-stress environment before and after 10 successive days of heat acclimation. The heat-acclimation phases were conducted with subjects wearing light clothing (shorts, T-shirts, boots) on a level treadmill (1.33 m/s) in a hot environment (calculated WBGT of C) until T re reached 38.8 C, after which subjects rested in the chamber. T c was maintained at this level for an additional hour by further rest or intermittent exercise, as needed. T re, a weighted four-site skin temperature (20), HR, and total weight loss were recorded at 1-min intervals. Endurance times were determined by time to self-withdraw or, whenever withdrawal limits were reached, based on the Defence Research Agency UK Ethics Committee criteria. Canadian Procedures Subjects. Sixteen male military personnel and university students volunteered for the protocol after it was approved by the Human Ethics Committees at the Defence and Civil Institute of Environmental Medicine and University of Toronto. Before inclusion as a subject in the study, each person was medically screened. Subjects were informed of potential risks and discomforts, and they signed a volunteer affidavit of informed consent. Mean ( SD) physical characteristics for age, height, weight, Dubois surface area, and body fatness, determined from four skinfolds were yr, m, kg, m 2, and %, respectively. V O2max, determined by inclined treadmill running, was l/min or ml kg 1 min 1. Experimental design. All trials were conducted during the months of November through March when outside T a varied from 20 to 10 C. Following a 60-min familiarization session to the heat-stress test environment (T a 40 C/30% RH, air movement 0.4 m/s), subjects were assigned to one of two groups: 1) one group underwent heat acclimation for 6 days (n 8); and 2) the other group underwent a 12-day heatacclimation procedure (n 8). The two groups were matched as closely as possible on the basis of their initial physical characteristics and V O2max. Because there was no difference between groups in the reduction in heat strain while wearing the CP clothing after 6 or 12 days of heat acclimation (1), the data were analyzed as one group (n 16) for this report preand postacclimation. Physiological responses to a standard heat-exercise stress test were collected twice before and twice after no more than 4 days following the heat-acclimation period. A 48-h period intervened between these pre- and postacclimation trials. Before and after heat acclimation, subjects were evaluated while wearing lightweight combat clothing and the CP clothing ensemble used in the Canadian Forces. All experiments were performed at the same time of day between 0800 and Heat acclimation. Subjects wore jogging shorts and a T-shirt. Heat acclimation was carried out by a daily 1-h bout of treadmill exercise (1.34 m/s, 3 12% grade) in a hot environment (T a 40 C, 30% RH). Water was given ad libitum. The exposure was repeated over 6 consecutive or 12 days (two 6-day periods, separated by 1 day). Exercise-heat tests. Before and after the heat-acclimation program, subjects were exposed to the heat-stress environment while wearing the Canadian Forces NBC protective clothing ensemble. This ensemble consists of a one-piece protective overgarment with a similar two-layer construction as the US BDO system. The overgarment was worn over lightweight cotton combat clothing and underwear, together with impermeable rubber gloves and overboots (worn over jogging shoes or combat boots to the subject s preference) and a C4 respirator and cannister. Clothing and vapor characteristics of this clothing configuration were determined at USA- RIEM (Table 1). Subjects alternated 15 min of level treadmill walking at 1.34 m/s with 15 min of seated rest. These trials continued for a maximum of 150 min or until T re (measured 15 cm beyond the anal spinchter) reached 39.3 C, HR reached or exceeded 95% of the individual s maximum for 3 min, or when nausea or dizziness precluded further exercise. Dependent parameters besides T re included T sk (12-point area weighted average), HR (Sport tester), and V O2 (from opencircuit spirometry determined as a 2-min average every 15 min), and whole body sweating rates were determined from

6 1022 HEAT STRAIN MODEL COMPARISONS Table 2. Metabolic heat production before and after heat acclimation in subjects wearing various countries protective clothing ensembles Ensemble pre- and posttrial nude and clothed body weight measurements corrected for respiratory and metabolic weight losses. Statistical Evaluation Unacclimated 30th min Data are presented as means SD. For the US experimental data, all data were analyzed by analysis of variance (experimental variable by time) with repeated measures. Tukey s test of critical differences was performed as a post hoc analysis of a given parameter (P 0.05). Paired or nonpaired t-tests were used as appropriate to analyze and compare the differences in root mean square deviation (RMSD) and other physiological variables (9, 24). All statistical contrasts were accepted at the P 0.05 level of significance (24). RMSD. Comparison of each T c time series data from model predictions and experimental data was accomplished by using RMSD (9, 19). The statistic was used as formulated in Haslam and Parsons (9) for goodness of fit comparison of model output predictions. The RMSD ( C) of model prediction output to observed T c is defined as RMSD 1 n i 1 n 10-day Heat Acclimation 30th min d i 2 %Change UK Royal Navy NBC (n 6) (n 12) P 0.22, NS UK Army NBC (n 9) (n 10) P 0.13, NS US Army BDO BDU , NS (n 10) (n 10) US Air Force CWU/77P , NS (n 10) (n 10) Values are means SD in W; n no. of subjects. BDO, battle dress overgarment; BDU, battle dress uniform. where d i is the difference between observed (from experimental trials) and predicted T c response at each time point ( C) and n is the number of time points examined. Model input techniques. The USARIEM heat strain model has the option of querying the user for initial T c levels (T re,start ) to match observed levels. In using the model, we employed a recursive programing operation (24, 28) to establish K work (Eqs. A17a A19, APPENDIX), which matched the rise in T c based on the actual average metabolic heat production (Table 2) observed in experimental runs. In the LUT25 model, the trunk T c option was utilized to simulate observed T re response (9). Additionally, intrinsic thermal resistance values were determined by a copper manikin analysis over three wind speeds at USARIEM and utilized as input into the LUT25 model (Table 1). For each specific standard heat-stress experiment, a T re ceiling level of 39 C (102.2 F) was implemented as a heatcasualty limit, instead of the conventional 39.5 C (103.1 F) (USARIEM Human Use Review Committee limits). This T c limit helped curb excessive fatigue in the subjects, brought on by repeated daily exposures. Evaporative efficiency. Differences in nude and dressed weights before and after each experiment were corrected for urine, respiratory, and metabolic weight losses (1, 3, 5, 14). The amount of sweat secreted (Ṁ sw, kg/h) was calculated as follows: pretrial minus posttrial nude weight (corrected) plus the weight of water drunk during the trial. Evaporative sweat loss (Ev, kg/h) from the clothing was calculated as the ratio of the difference in corrected dressed weight to the amount of sweating (Ev/Ṁ sw ) and expressed as a percentage (Ev/Ṁ sw 100). This percentage determined the evaporative potential observed through a clothing system with which to compare the predicted evaporative potential from manikin values of i m /I T (Table 3). RESULTS Table 2 shows the steady-state mean SD metabolic heat production (30th minute) before and after heat acclimation from the UK and US experimental runs. Time Series US experiments. The individuals were dressed fully encapsulated in the two US CP garments and exposed Table 3. Comparison of final T sk, sweating rates, and evaporative potential observed and predicted for each clothing system T sk Final, C Ṁ sw, kg/h Ev, kg/h Ev/Ṁ sw,% Predicted Evaporative Potential From i m /clo, % UK Army NBC Preacclimation (n 13) Postacclimation (n 13) * UK Royal Navy NBC Preacclimation (n 13) Postacclimation (n 13) * US Army Preacclimation (n 10) Postacclimation (n 10) * US Air Force Preacclimation (n 10) Postacclimation (n 10) * * Canadian NBC Preacclimation (n 16) Postacclimation (n 16) * Values are means SD; n no. of subjects. Ṁ sw, amount of sweat secreted; Ev, sweat evaporation. *Significant decrease in final mean skin temperature (T sk ) and increases in sweating rate postacclimation (P 0.05). Predicted evaporative potential evaluated by i m /clo on completely wetted skin (parallels fully heat-acclimated person).

7 HEAT STRAIN MODEL COMPARISONS 1023 Fig. 1. Rectal temperature (T re ; mean 1 SD) plotted as a function of minutes of exercise in a heat-stress environment [ambient temperature (T a ) 35 C/50% relative humidity, wind speed 1.1 m/s] before and after heat acclimation for US Air Force (A) and US Army (B) chemical protective clothing; n 10 subjects. to the standard heat-stress environment (35 C/50% RH, air speed 1.06 m/s). Figure 1 shows T re plotted as a function of minutes of exercise before and after the heat-acclimation runs in each US group. In the experiments with US Air Force CP clothing, there was an initial rapid rise in T sk, followed by leveling off in the final values of T sk to C in the preacclimation runs, but it declined to C (P 0.05) after the heat acclimation (Table 3). Because sensible heat loss (R C) was reduced by having T sk T a 35 C, the only avenue of heat exchange was brought about by the enhanced sweating rate cooling the skin under the clothing (some 0.8 C lower). Skin cooling was facilitated adequately while wearing the US Air Force CP ensemble, as indicated by the high evaporative potential (i m /I T ) evident from this ensemble (Table 3). No apparent differences in the T c responses were evident before and after heat acclimation, nor was there any evidence of convergence of T sk with T c.t c rose steadily ( 1.2 C/h) and leveled off to 38.5 C. During exposure to the standard heat stress, in both the pre- and postacclimation experiments with the US BDO BDU, T sk values rose rapidly but exhibited a leveling off at C pre- and at C (not significant), indicating that both sensible heat exchange (by R C) and some evaporative cooling potential were possible. T c values rapidly rose ( 2.14 C/h) to the limiting threshold of 39 C in 70 min of exercise without any obvious steady state or leveling off. There was no apparent convergence of T sk with T re in any of the experiments at this metabolic heat production. There was a significant increase in sweating rate after heat acclimation (Table 3) in both the US Army and US Air Force experiments. UK experiments. Figure 2 presents results of the UK experiments to the heat-stress exposure before and after the 10-day heat acclimation, showing changes found in T re response. Fig. 2. T re ( 1 SD) plotted as a function of exercise time during standard heat-stress test before and after heat acclimation. UK Army nuclear biological-chemical (NBC) clothing system (A) and UK Royal Navy NBC protective clothing (B); n 13 subjects.

8 1024 HEAT STRAIN MODEL COMPARISONS The runs with the UK Army clothing system show three clear-cut observations: 1) after the heat acclimation there was a lower final T re (P 0.05); 2) there was an offset in the slope of the T re vs. time plot, suggesting that significant evaporative cooling (Table 3, P 0.05) by sweating aided in increasing the endurance times by delaying the rise in T c at this metabolic activity; and 3) the rate of rise of T c up to a 65-min period in all subjects averaged 1.63 C/h before acclimation and 1.57 C/h after the heat acclimation. Final skin temperatures pre- and postacclimation were not significantly different (Table 3). In the experiments in which subjects wore the Royal Navy clothing system, curves of T re plotted for the first 60 min of exercise displayed a similar lowered T c offset after heat acclimation as in the UK Army runs. Rate of rise of the T c vs. minutes of exercise averaged 1.6 C/h before acclimation and some 1.35 C/h after heat acclimation (P 0.05). Final skin temperatures were lower after heat acclimation (P 0.05) (Table 3). Canadian experiments with intermittent work. No differences were evident in heat production attributable to heat acclimation during intermittent work or rest phases, although a gradual elevation was apparent at each 15-min rest-cycle phase heat production over time (from 200 W to as much as 300 W). The gradual increase in resting M appeared affected by the antecedent elevated rate of heat storage ensuing during each exercise bout. Figure 3 shows the T re response to the heat-stress exposures (T a 40 C/30% RH, air speed 0.4 m/s) plotted as a function of time before and after the heatacclimation phases. There was an overall offset toward a lower T re after heat acclimation. A significant increase in sweating rate (Table 3) was observed postacclimation. Fig. 3. T re ( 1 SD) during 15 min work/15 min rest cycles and as a function of time during heat preacclimation and postacclimation runs in subjects wearing Canadian NBC protective clothing system; n 16 men. USARIEM Heat Strain Model vs. Experimental Data Comparisons US experiments. Figure 4 compares the T c output generated from the USARIEM heat strain model with the actual T c values obtained in the US experimental trials. Relative fit of the model output compared with average T re SD of the observed data was generally overpredictive for the first min of exercise with the US Army CP ensemble. The final predicted T c values were within 1 SD of the observed values during both the pre- and postacclimation runs. Relative fit of model output to mean data observed with the US Air Force clothing system was within 1 SD during the unacclimated T re time course and overpredictive up until the final time point in the postacclimation experiments. Sensitivity analysis of the US data prediction vs. observed T c response was carried out by calculation of RMSD over all 1-min time points of the exercise until a final exercise period (9, 31) (Table 4). For all US data shown in Table 4, performance of model predictions of T re over the whole time course of runs was in reasonable agreement to observed T re with values occurring within a RMSD of 0.4 C for both the unacclimated and acclimated state. UK experiments. Figure 5 compares the USARIEM model predictions of T c with the experimental observations obtained from the UK studies comprising a 65- min period achieved by all subjects. To fulfill the 100 min at the required average metabolic rate while wearing the UK Army NBC ensemble (Table 1 clothing insulation), the USARIEM model calculated that final T re values would reach 39.5 C when subjects are unacclimated to heat but 38.6 C after becoming heat acclimated. Figure 5 shows that the model results matched the observed T c time records accurately, except for a slight initial overrise shown in the first min in the UK Army experiments in the preacclimation state and a slight underprediction evident in the postacclimation phase around minutes The actual cessation of exercise in 13 individuals wearing the UK Army chemical clothing system occurred at around 62 min (based on a final T re of 39 C) and 82 min after the heat-acclimation phase (32% higher, P 0.05). Similar reliable predictions using the USARIEM model were obtained while evaluating the UK Royal Navy CP clothing suit data before and after heat acclimation. The RMSD analysis is shown in Table 4, encompassing both UK experimental data vs. the model output. Model predictions of T re over the whole time course were in reasonable agreement with actual T re values falling within a RMSD of 0.25 C for both the unacclimated and acclimated state. Canadian experiments. Figure 6 shows the USA- RIEM model simulations carried out on the Canadian heat-stress intermittent-exercise experiments before and after heat acclimation. The USARIEM model output reliably tracked the T re recorded during the work/rest cycles; both curves from

9 HEAT STRAIN MODEL COMPARISONS 1025 the USARIEM model tracked the changes observed before and after heat acclimation. The superimposed curves from the model output on the actual mean T c data during both the preacclimation and heat-acclimation model curves overlap within 1 SD of the actual observed data. Table 4 shows the RMSD values obtained in the simulation vs. observed data comparison. Model performance of predictions of T re for the intermittent exercise over the whole time course were in reasonable agreement to actual T re falling within a RMSD of 0.24 C for both the unacclimated and acclimated state. Table 4. Sensitivity analysis of USARIEM model and LUT25 model predictions using RMSD on time series rectal temperature response data 1) USARIEM Model vs. Experimental Data (Unacclimated) 2) USARIEM Model vs. Experimental Data (Heat-Acclimated) 3) LUT25 Model vs. Experimental Data (Unacclimated) US BDO BDU US Air Force CP UK Army NBC UK Royal Navy NBC Canadian NBC USARIEM, US Army Research Institute of Environmental Medicine; LUT25, United Kingdom Loughborough model; RMSD, root mean square deviation ( C); CP, chemical protective garment. LUT25 Model Evaluations Fig. 4. Model simulations of T re (no error bars) vs. observed T re response ( 1 SD) to standard heat-stress exposures before and after heat acclimation in US chemical protective clothing systems. US Army Research Institute of Environmental Medicine (USARIEM) model (A) vs. US Army data (B); and USARIEM model (C) vs. US Air Force CWU/77P data (D). A and C are preacclimation and B and D are postacclimation results; n 10 subjects. US and UK experiments. As reported in METHODS, the LUT25 was modified to simulate trunk T c (the form this model uses to simulate T re ) but simulates unacclimated data only. No provisions in the LUT25 (or in the original S-H model control coefficient algorithms) are currently available for predicting the heat-acclimatized state. Also, since the original LUT25 only uses intrinsic clothing thermal resistance values as input (19), all clothing thermal resistances were reassessed in terms of intrinsic unit of thermal resistance based on a heat transfer of m 2 K W 1 (clo) (i cl ) by separate analyses in which a USARIEM copper manikin was used (6), as shown in Table 1. Figure 7 displays the results of the LUT25 trunk node temperature simulations in comparison with the mean 1 SDT re data observed from the US and UK experiments. The LUT25 model consistently showed an initial decrease in the T c response for the first 5 10 min of exercise observed in all UK experiments. The model output was overpredictive in the T c response when comparing both US data sets, with forecasting temperatures approaching a hyperthermic state within min. However, the LUT25 model matched closely (within 1 SD) the observed T c response obtained from both UK experimental runs. Canadian experiments. The clothing thermal and evaporative resistances were first assessed in terms of

10 1026 HEAT STRAIN MODEL COMPARISONS Fig. 5. Comparison of USARIEM heat strain model predictions with UK Army (A and B) and of USARIEM model and UK Royal Navy T re ( 1 SD) experimental data (C and D). A and C are preacclimation and B and D are postacclimation results; n 13 subjects. Thin line, model. intrinsic clothing resistance before input into the LUT25 model (Table 1). Figure 8 shows the T re results from the LUT25 model modified to simulate the Canadian 15 min work/15 min rest procedure for the unacclimated state. Fig. 6. Comparison of USARIEM model vs. Canadian intermittent-work experiments T re predictions (lined and dashed curves) vs. T re ( 1 SD) observed data before (r) and after (s) heat acclimation. n 16 Men. The LUT25 model suitably matched observed T c values up to the first 105 min of intermittent exercise/ rest experiments. The saw-tooth pattern shown by intermittent work cycles and rest periods (as well as the slightly elevated T c values occurring during the rest periods) were tracked within 1 SD. Model output was slightly overpredictive of T c response toward the end period ( min). Table 5 presents an analysis of the mean difference for RMSD values (from Table 4) covering pooled model output results vs. the T c responses. Characteristically, the lower the RMSD, the closer is the fit of a particular model simulation of this parameter to the experimental observations. There was a significant difference between output of the USARIEM and LUT25 models in the RMSD value for the unacclimated data (P 0.05). No statistical differences in the RMSD value were apparent in the pre- and postacclimation analyses when using the USARIEM heat strain model. DISCUSSION This study investigated whether experimental results can be suitably matched with physiological outputs determined from various model predictions in individuals dressed in protective clothing. Simulations from two disparate thermal model constructs were tested as predictors of T c. It is useful to discuss several positive features and limitations of each of the two models and point out the physiological implications that may explain some of the model results.

11 HEAT STRAIN MODEL COMPARISONS 1027 Fig. 7. Comparison of T re ( trunk core) output (preacclimation runs only) from UK Loughborough (LUT25) model with experimental data from US Army (A) and US Air Force (B) and from UK Army (C) and UK Navy (D) heat-stress exposures. n 13 Subjects. Thin line, model. USARIEM Heat Strain Model The present USARIEM model output function derives estimates of percent heat-stress casualty rates when plotted vs. T c from 700 studies collected at USARIEM (11). The 95% heat casualty rates occurred at an average T c of 39.5 C. Probability curves of T c response encompass a wide variety of metabolic activities and environmental temperatures for persons wearing protective clothing. The model estimates adequate work/rest cycles and maximum work times and determines water requirements over various heat-stress scenarios, terrains, and work activities (17). A limitation of the model is that the equations are based on empirical predictions tested only within a finite range of thermal environments (4, 7, 25) and that it has no validity at T a 20 C. Another limitation stems from the model s conservative nature in overpredicting heat casualties based on final estimated T c of an average population of individuals (11). For example, very fit, experienced persons often exceed tolerance time periods and reach higher levels of T c than predicted without marked heat strain problems. In the original model construct, abrupt rates of rise in T c values as a function of time are often observed when applying the Givoni-Goldman (7) T re equation Fig. 8. LUT25 model T re predictions for the Canadian 15 min work (at 225 W/m 2 )/15 min rest cycles in NBC protective clothing. Observed T re (r 1 SD) for unacclimated state; T a 40 C/30% relative humidity, wind speed 0.4 m/s. Table 5. Paired sample t-test comparison of RMSD output from different models Data/Model Compared With Table 4 Columns Mean Difference of RMSD Value ( SD) t-value (Two-Tail) All country s unacclimated data/usariem model vs. LUT25 model (1 vs. 3) * All country s acclimated data/usariem model (1 vs. 2) , NS * Data assuming equal variances. P

12 1028 HEAT STRAIN MODEL COMPARISONS during exercise with different protective clothing systems. In the Givoni-Goldman equations, a time lag equation for rise in T c is based on a best estimate of 6 min for 580 W of work. The curve prediction of T re,f is an exponential rise to maximum. Consequently, the rate constant becomes too elevated because of the assumed high-intensity exercise. This rate constant overexaggerates T re,f at each time point, resulting in overprediction of model output vs. experimental values. The original Givoni-Goldman T c equation, used during uncompensable heat stress, frequently generates an RMSD 1.0 in the respective time-series comparisons between model and observed T c and skin temperatures. RMSDs 1.0 observed from model comparisons of a given physiological variable typically indicate that a model does not reasonably predict observed values. When this occurs, corrections should be made to the computer code or algorithm control coefficient structure before use of the model. We implemented a time-delay feature in the model that matches each average metabolic heat production (described by Eq. A19 in APPENDIX) (Figs. 4 6). This function accurately buffered the rise of T re paralleling observed mean T c values, as observed in the experimental trials. LUT25 Model In the LUT25 model, convective heat-transfer coefficients are adjusted according to activity mode. These are corrected in the code by a theoretically developed, intrinsic clothing resistance value (e.g., without air boundary layer resistance) evaluated for still-air wind speeds only. Sensible and insensible heat-transfer properties adapted for various clothing systems are based on steady-state lumped-parameter estimations from the Burton (for sensible heat loss) and Nishi and Gagge (vapor permeation flux) equations (3, 16). The difficulty with such estimations is that the various control algorithms (which form the basis of many of the model outputs) predict that evaporation and dry heat flux occur solely on the body skin surface. If heat loss is only accounted from the skin surface, model outputs calculate incorrect Burton and Nishi and Gagge clothing factors (3, 16), not wholly applicable for layers within protective clothing systems. In the LUT25 model, moisture vapor heat-transfer properties have been enhanced based on Woodcock s i m concept (2, 27), but they are only valid for the still-air condition. Because the original S-H model was based on unclothed simulations of thermoregulatory response, model clones such as the LUT25 can underestimate evaporative heat transfer coefficients. Only rough theoretical estimates of thermal and vapor resistance values can be applied when using the S-H model with protective clothing, which requires alterations to the algorithms in the original code. This may be one reason the LUT25 model used in this study calculated T c increases that became overpredictive ( 1 RMSD) in some circumstances, compared with actual observed data. Conversely, heat trapped in the clothing is accounted for in the USARIEM model (APPENDIX, Eqs. A9 and A11). One could assume that the values of evaporative potential were too small when determined from measurement of evaporative and thermal resistance by using a copper manikin. The consequences would be that latent heat loss simulated by the LUT25 model through the semipermeable membrane laminates of a given protective clothing system would be underpredictive and T c would be estimated too high. This hypothesis is not likely, since Table 3 shows that at 1 m/s wind speed, quite similar values of i m /I T of 0.26 and 0.28 were obtained from the UK Army NBC and the US Air Force suit, respectively. Yet, T c prediction was greater than observed with the US Air Force garment when using this model. Evaporative heat loss predictions through these clothing systems were also comparable at 88 and 95 W m 2. Overprediction of T c by use of the trunk node in the LUT25 model is possibly affected by the estimations of distribution of blood flow to the core (trunk node) and shell. In the unclothed state, effective shell thickness is a variable that increases with vasoconstriction but narrows with vasodilation. The fraction of body mass in the shell at any time (t) during exercise may be described by (3, 12) in which t SkBf(t)/A D (9) where the proportion of body mass in the core is given by (1 t ) and SkBf is skin blood flow. In the S-H clones, the algorithm for controlling sweating rate (Ṁ sw ) weights these proportions according to the relative influence of core and skin contribution. When subjects are unclothed or when wearing thin porous clothing, prediction of various responses is quite accurate from thermoregulatory models employing the above algorithm. However, a more complex model algorithm incorporating axial and radial heat conductance that deals with latent heat release or storage in multilayered clothing may be a future goal necessary to predict finite T c changes with protective clothing (2, 3, 30, 33). We noted a curious dip in T c ( trunk node temperature) occurring from the LUT25 output simulations at the start of exercise when this variable was plotted as a function of time (Fig. 7). One reason for the unique dip observed when applying the LUT25 simulation may be because the model follows passive state predictions at each node held over from the original S-H model (8, 23). Both models reflect axial heat conductance through each layer established for a horizontal cylinder. Additionally, in the original passive equations of the S-H model s algorithms, the redistribution of cooler skin blood flow occurs from the extremities toward the trunk core on the initiation of exercise. This often happens at the initiation of treadmill or cycle ergometer exercise. A recent report (15) suggests that the latter transient sequestration of cool blood from the lower body extremities toward the pelvic area is a reasonable interpretation for the initial drop in T c evident in the LUT25-node model.

13 HEAT STRAIN MODEL COMPARISONS 1029 One feature simulated equally well by both models was the tracking of T c response during intermittent exercise (Canadian experiments). The model prediction confirms the results of other studies (1, 2, 4, 17). These studies show that employment of work/rest cycles in which the work cycle is 1 l/min (or, roughly, 25 27% V O2max ) reduces final T c, improves tolerance to an environmental challenge, diminishes rate of heat storage, and maximizes evaporative potential through CP garments. Along with the LUT25 model used in this evaluation, several other physiological models have appeared that have embraced the original thermoregulatory control algorithms formulated in the S-H 25-node prototype (12, 26). One model developed by Kraning (12) is worth mentioning. This model has merged the clothing heat and mass transfer characteristics derived by copper manikin evaluations with the heat and mass transfer equations appearing in the S-H model. Kraning s model also integrates many positive features of both the S-H model and the prediction capabilities found in the USARIEM heat strain model. Kraning s SCENARIO simulation routines have coupled 1) the combined effects of posture, metabolic activity level, clothing coefficients, T c, and skin temperature influences on cardiac stroke volume; 2) the effect of cardiovascular overload during work-in-the-heat routines on increasing muscle oxygen extraction, thereby relieving the overload; 3) effects of both T c and skin temperature as modulators of the central temperature set-point for controlling skin blood flow; and 4) effects of age as a factor in decreasing maximal HR on thermoregulatory responses. Modeling of Heat-Acclimation Response Heat-acclimation modifications were accurately simulated by the USARIEM model as shown by the T re vs. time response. The UK and Canadian experimental data show responses pointing to a definite offset toward a lower internal core reference temperature (mirroring a central nervous system thermal reference point alteration). In other studies, this response has been observed during heat acclimation in unclothed exercising individuals (14) and during passive heat exposures (10). It is not apparent why the offset in T c was observed after heat acclimation with protective clothing in the UK and the Canadian Forces trials but not in the US experiments. The level of metabolic intensity associated with required evaporative potential through a specific clothing system may have been one critical factor. If metabolic heat production is too high and evaporative potential is not possible through protective clothing having a critical thermal resistance, too great a rate of heat storage is incurred. At this point, any physiological mechanism (e.g., increased sweating rate and/or skin blood flow) improving heat exchange during the heat acclimation becomes overwhelmed (1, 14). Heat acclimation during sustained operations with light work clothing or combat fatigues is certainly obvious, since evaporative potential and performance are maximized. However, its operational importance while subjects are wearing protective clothing is not certain and possibly becomes a limiting variable because of excessive sweating, which is dependent on work intensity and magnitude of evaporative potential through a clothing system. The enhanced sweating at a lower T c produces excessive skin wettedness (E sk /E max ) with limited cooling benefits (3, 25) when exercise continues for an extended period of time underneath protective clothing. If the water lost is not replaced adequately, individuals can become hypohydrated (22). Further investigations of sweating and skin blood flow responses might reveal additional interesting observations useful to modeling of the UK and Canadian heat acclimation phases not within the scope of this report. Simulations using either the USARIEM heat strain or UK LUT25 models offer reliable predictions of T c responses (within 1 RMSD) during work in the heat while subjects are wearing protective clothing. However, only the USARIEM heat strain model simulated responses adequately for both the unacclimated and heat-acclimated phases during continuous work (UK and US trials) and intermittent work (Canadian trial). From the data vs. model comparisons, it is clear that direct thermal and vapor resistance evaluations are especially important when protective clothing is used in which new semipermeable laminates are structured into the garment (Table 1). If manikin data are not available, a method to transform clothing and vapor transfer resistance values for civilian clothing may be applicable for protective clothing as well (19). In summary, our study demonstrated that analytically similar thermoregulatory models modified to predict clothing heat transfer can easily serve as discerning tools suitable for various occupational heat-stress scenarios. These models are appropriate tools for use as guidelines in guarding against overheating, ascertaining work/rest cycles, predicting appropriate stay times, and cooling power requirements based on radiative and evaporative heat loss in civilian workers wearing protective clothing. Prediction analysis by use of such models is a practical way to obviate direct T c monitoring in hazardousmaterials workers wearing CP clothing systems. APPENDIX An iterative software model employing the Givoni-Goldman equations was developed for implementation on a personal computer. A full explanation of the modifications to the original Goldman-Givoni (7) program with current algorithms may be found in Pandolf et al. (17). The modifications present in the version used in this study are given in the following sections. Improvements in parameter equations and the model s implementation on PC-DOS and Macintosh platforms include the ability to use multiple data sets for individual input variables; a user-friendly, pull-down menu user interface; the ability to use a variety of units when entering data; graphic analysis of data; and automatic file saving for future use and/or retrieval. These improvements greatly enhance the program s ability not only to generate final results for work and recovery categories but also to allow the user to graphically review the generated results.

14 1030 HEAT STRAIN MODEL COMPARISONS USARIEM Experimental Heat Strain Model The following summarized equations and coefficients form the main system incorporated for use on a PC in the model employed in the present experimental comparisons T re,f ( C) T re, (M W ex ) Dry 0.8 exp[0.0047(e req E max )] (A1) where T re,0 is initial value for T re based on an experiment, defaults to 36.8 C; T re,f is equilibrium T c ( C); M is energy expenditure or metabolic rate (W); W ex is external work (W); Dry is radiative and convective heat exchange (W); E req is evaporative heat exchange required (W); and E max is maximum possible evaporative heat exchange (W) W ex (Wt L) V w (A2) where is incline of slope (%grade); Wt is weight of person (kg); L is weight of clothing, equipment, and load (kg); and V w is velocity of walking (m/s). Heat Exchange Routines Dry 6.45 I T A D (T db T sk ) (A3) where A D is Dubois surface area of person (m 2 ); T db is dry bulb temperature ( C); T sk is average skin temperature ( C); I T is total clothing insulation (I a I cl,i ) coefficient (clo) (where 1 clo m 2 K W 1 ), from copper manikin database values at a given wind speed. E req (M W ex ) Dry (A4) E max LR 6.45 i m A I D (P s,sk a P a ) (A5) T where i m is vapor permeability coefficient (dimensionless); P s,sk is saturation vapor pressure at skin temperature (Torr); a is relative humidity (%); P a is ambient water vapor pressure (Torr); and LR is Lewis relation (2.2 C/Torr), at sea level. Clothing-Related Routines V eff (M) W sp (M 105) (A6) where V eff is effective wind velocity (m/s); and W sp is wind speed (m/s). where U is heat trapped by the body; I Tc is static clothing insulation at V eff (clo) (database for wide clothing systems); and I Tvc is exponent of the clothing insulation vs. USARIEM windspeed curve (dimensionless). DuBois Body Surface Area A D Ht0.725 Wt (A10) where Ht is height (cm). E req f(dry, M,U) Dry M W ex U SlrF CldF (A11) where W ex is external energy (W); SlrF is solar factor; and CldF is cloud factor. E max (C evap ) C evap A D (P s,sk P a ) (A12) where E max is maximum evaporation heat transfer; P a is saturation vapor pressure at T a ; and is a constant depending on conductance of clothing (product of 6.46 W m 2 K 1 and the LR at sea level 2.2 C Torr 1 ). T re and Change Calculations T re,f (M, U, Dry, E req, E max ) T re, M U CldF SlrF Dry 0.8 exp[ (E req E max )] (A13) where T re,0 is the initial value for T re ( C), default is 36.8 C; T re,f is final T re ( C); E max is maximum evaporation heat transfer of the environment (W); and E req is required evaporative heat transfer (W) to achieve thermal balance Tre,f (T re,f, E max ) ( exp [0.5 (T re,0 T re,f )]6 (A14) [1 exp ( E max )]) [exp ( 0.3 DIH)] where Tre,f is change in final T re for heat acclimation and dehydration ( C); and DIH is days in heat (heat acclimation level base). Water Requirements Wtr(E req, E max ) 2,000 if E max Wtr(E req, E max ) 27.9 A D E req E max if E max 0 (A15) where Wtr is water required (canteens/h), based on original data (Refs. 17, 21). I I T (V eff ) I Tc V Tvc eff (A7) Swt(E req, E max ) 2,000, if Wtr(E req, E max ) 2,000 (A15a) where I Tc is still wind (at W sp 0.41 m/s) clothing insulation at V eff (clo); and I Tvc is exponent of thermal insulation vs. windspeed with USARIEM copper manikin (dimensionless). I C evap (V eff ) I mc V mvc eff (A8) where C evap is water vapor permeability of clothing (i m )asa function of V eff ; I mc is static moisture vapor permeability at V eff 1 m/s; and I mvc is exponent of the rate of change of I m vs. wind speed from USARIEM copper manikin 100% wetted skin layer. U(V eff ) 0.41 (0.43 I V Tvc) I eff Tc (A9) Swt(E req, E max ) 150, if Wtr f (E req, E max ) 150 (A15b) Swt(E req, E max ) Wtr(E req, E max ), where Swt is sweating rate. if 2,000 Wtr(E req, E max ) 150 (A15c) (A16) Maximum Work Time Calculation If T re,fwork T re,fwork maximum work temperature limit Max work time (min) (A17) where maximum work time (min) is maximum singleexposure work time; T re,fwork ( C) is final T re during work;

15 HEAT STRAIN MODEL COMPARISONS 1031 and T re,fwork ( C) is change in final T re for acclimation and dehydration during work. Else, if T re,fwork T re,fwork T re,fstart and if maximum work time 0.0, then maximum work time 0.0. Else Maximum work time t delay e 99.0 K work (A17a) where t delay is time lag (min) for work to induce T c changes at given M. Comparison of T re f(t) for various M (W) indicates that this value is shorter as M is higher (evaluated from a database of multiple studies by the equation t delay M M2 ). K work is exponential rate coefficient for work output ( C/min). The value of the change of T c, T re,fwork ( C) is used to calculate K work ( C/min) as well as the T c predicted at time t (T re,t ) is calculated from T re,t T re,0 T re,fwork 51 exp[ K work (time t delay )]6 where T re,0 is the T c at the end of the delay time period. [1 3 exp( 0.3 T re,fwork )] K work 225 (A18) (A19) We thank the soldiers who volunteered to participate as subjects in this investigation. We acknowledge many contributors who assisted in the experimental study, including Dr. Stefan Constable, Armstrong Laboratory, Brooks Air Force Base, TX, for providing the US Air Force CP ensemble; Dr. Claire E. Millard and Mike Neale, Research Assistant, Centre for Human Sciences, Defence Research Agency, Farnborough, UK; and Robert Wallace, Statistician, USARIEM, Natick, MA. This work was part of an international colloborative effort under The Technical Cooperation Program between the United States, United Kingdom, and Canada. The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official position, policy, or decision from the Department of the Army or any of the collaborative country laboratories. Address for reprint requests: R. R. 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