William R. Santee

Thermoregulatory model to predict physiological status from ambient environment and heart rate

A real-time thermoregulatory model was developed for predicting real-time physiological responses of workers engaged in various tasks for prolonged time. The unique feature of the present model is primarily on metabolic activity inputs derived from minimum non-invasive measures (i.e., heart rate and ambient temperature). In addition, it utilizes individual anthropological characteristics (height, weight, and clothing) as an input to estimate core temperatures (T(c)). The model was validated using data from five laboratory studies (n=63) with varied environments, clothing, and heat acclimation status. Overall, T(c) predictions using this simplified model, corresponded well with measured values (root mean square deviation: 0.05-0.31 degrees C).

Comparison of methods for estimating Wet-Bulb Globe Temperature index from standard meteorological measurements.

Environmental heat illness and injuries are a serious concern for the Army and Marines. Currently, the Wet-Bulb Globe Temperature (WBGT) index is used to evaluate heat injury risk. The index is a weighted average of dry-bulb temperature (Tdb), black globe temperature (Tbg), and natural wet-bulb temperature (Tnwb). The WBGT index would be more widely used if it could be determined using standard weather instruments. This study compares models developed by Liljegren at Argonne National Laboratory and by Matthew at the U.S. Army Institute of Environmental Medicine that calculate WBGT using standard meteorological measurements. Both models use air temperature (Ta), relative humidity, wind speed, and global solar radiation (RG) to calculate Tnwb and Tbg. The WBGT and meteorological data used for model validation were collected at Griffin, Georgia and Yuma Proving Ground (YPG), Arizona. Liljegren (YPG: R(2) = 0.709, p < 0.01; Griffin: R(2) = 0.854, p < 0.01) showed closer agreement between calculated and actual WBGT than Matthew (YPG: R(2) = 0.630, p < 0.01; Griffin: R(2) = 0.677, p < 0.01). Compared to actual WBGT heat categorization, the Matthew model tended to underpredict compared to Liljegrens classification. Results indicate Liljegren is an acceptable alternative to direct WBGT measurement, but verification under other environmental conditions is needed.

Total energy expenditure estimated using foot-ground contact pedometry.

Routine walking and running, by increasing daily total energy expenditure (TEE), can play a significant role in reducing the likelihood of obesity. The objective of this field study was to compare TEE estimated using foot-ground contact time (Tc)-pedometry (TEE(PEDO)) with that measured by the criterion doubly labeled water (DLW) method. Eight male U.S. Marine test volunteers [27 +/- 4 years of age (mean +/- SD); weight = 83.2 +/- 10.7 kg; height = 182.2 +/- 4.5 cm; body fat = 17.0 +/- 2.9%] engaged in a field training exercise were studied over 2 days. TEE(PEDO) was defined as (calculated resting energy expenditure + estimated thermic effect of food + metabolic cost of physical activity), where physical activity was estimated by Tc-pedometry. Tc-pedometry was used to differentiate inactivity, activity other than exercise (i.e., non-exercise activity thermogenesis, or NEAT), and the metabolic cost of locomotion (M(LOCO)), where M(LOCO) was derived from total weight (body weight + load weight) and accelerometric measurements of Tc. TEE(PEDO) data were compared with TEEs measured by the DLW (2H2(18)O) method (TEE(DLW)): TEE(DLW) = 15.27 +/- 1.65 MJ/day and TEE(PEDO) = 15.29 +/- 0.83 MJ/day. Mean bias (i.e., TEE(PEDO) - TEE(DLW)) was 0.02 MJ, and mean error (SD of individual differences between TEE(PEDO) and TEE(DLW)) was 1.83 MJ. The Tc-pedometry method provided a valid estimate of the average TEE of a small group of physically active subjects where walking was the dominant activity.

Comparison of Parallel and Serial Methods for Determining Clothing Insulation

This paper examines the fundamental differences between the parallel and serial methods for the calculation of clothing insulation using a thermal manikin and demonstrates the differences in the insulation values calculated using these two methods. The parallel method is based on the condition that manikin surface temperatures remain uniform (UST), while the serial method is based on the condition that manikin heat fluxes remain uniform (UHF). Eleven clothing ensembles were evaluated on manikins in UST mode. Three of them were also evaluated on manikins in UHF mode. Insulation values were then calculated using both the serial and parallel methods. Results from UST mode showed that the parallel insulation values ranged from 1.24 to 5.79 clo, while the serial insulation values ranged from 1.43 to 7.98 clo. Differences in the parallel and serial insulations increased as the insulation increased, and the serial insulations were approximately 14–38 % higher than the parallel insulations. Results from UHF mode showed that the parallel insulations were 1.30 clo to 5.89 clo and close to the serial insulations of 1.34 clo to 5.99 clo. In conclusion, the methods of insulation calculation should be determined by the operating mode of the manikin. Only the parallel method should be used when manikins are operated in UST mode and only the serial method should be used when manikins are operated in UHF mode. Insulation values calculated using the incorrect method will be misleading.

Effect of WBGT Index Measurement Location on Heat Stress Category Classification.

UNLABELLED The location of the wet bulb globe temperature (WBGT) index measurement may affect heat stress flag category classification. PURPOSE This study aimed to compare WBGT measurements at three locations along the Boston Marathon race course and compare WBGT estimates for meteorological stations and 72-h advanced WBGT forecasts. METHODS WBGT was measured hourly from 1000 to 1400 h at approximately 7 km, approximately 18 km, and approximately 30 km on the Boston Marathon race course. Simultaneous WBGT estimates were made for two meteorological stations southeast of the course via a commercial online system, which also provided 72-h advanced forecasts. RESULTS The measurement difference (mean ± SD) among course locations was 0.2°C ± 1.8°C WBGT (ANOVA, P > 0.05). The difference between course and stations was 1.9°C ± 2.4°C WBGT (t-test, P < 0.05). Station values underestimated (n = 98) or overestimated (n = 13) course values by >3°C WBGT (>0.5 flag category) in 111 of 245 paired comparisons (45%). Higher black globe and lower wet bulb temperatures explained over- and underestimates, respectively. Significant underestimates of WBGT resulted in misclassification of green (labeled white) and black (labeled red) course flag categories (χ2, P < 0.05). Forecast data significantly underestimated red (labeled amber) and black (labeled red) course flag categories. CONCLUSIONS Differences in WBGT index along 23 km of the Boston Marathon race route can be small enough to warrant single measurements. However, significant misclassification of flag categories occurred using WBGT estimates for meteorological stations; thus, local measurements are preferred. If the relation between station WBGT forecasts and the race sites can be established, the forecast WBGT values could be corrected to give advanced warning of approximate flag conditions. Similar work is proposed for other venues to improve heat stress monitoring.

Prediction of hand manual performance during cold exposure

A model was developed to predict hand manual performance (HMP) impairment during cold exposure. HMP is defined as the performance (tasks completed per unit time) normalized relative to performance at a finger/hand skin temperature (FST) of approximately 33°C. An empirical algorithm describing the relationship between HMP and FST was developed from published data. This algorithm adequately predicts the critical FST thresholds for reduced HMP. The combination of this algorithm and FST predictive models is useful for risk assessment, evaluation of handwear and rescue equipment design when human testing is precluded for ethical or practical reasons.

Mathematical Analysis of Extremity Immersion Cooling for Brain Temperature Management

Due to the low heat conductivity of body tissue, head surface cooling methods for management of the brain temperature during medical treatments often have limited utility. As blood flow rates and surface-to-volume ratios are generally high in the extremities, heat exchange between the body and the environment through the extremities is an important path for heat exchange. This study examines the effects of cold-water extremity immersion on brain temperature by simulation modeling. The work is based on a six-cylinder thermoregulatory model that predicts human thermoregulatory responses to heat, cold, and water immersion. An arteriovenous anastomosis (AVA) response algorithm was added to the base model. Arteriovenous anastomoses are assumed to be controlled by a combination of core and skin temperatures. Our series of simulation scenarios consists of resting in a hot environment (40°C, 75% relative humidity) until the brain temperature rises to 39°C, then continuing to rest for 1 h under one of the following treatments: (A) no cooling; (B) hands immersed in 10°C water; (C) feet immersed in 10°C water; (D) hands/feet immersed in 10°C water. The simulation results indicate that within the first 30 min, the hands, feet, or hands/feet immersion cooling resulted in brain temperature drops of 1.7°C, 2.4°C, and 3.3°C, respectively, which correspond to cooling rates of 0.03°C/min, 0.04°C/min, and 0.05°C/min. The predicted values show that extremity immersion cooling is a viable mechanism for simple and effective control of brain temperature.

Estimation of core body temperature from skin temperature, heat flux, and heart rate using a Kalman filter

Core body temperature (TC) is a key physiological metric of thermal heat-strain yet it remains difficult to measure non-invasively in the field. This work used combinations of observations of skin temperature (TS), heat flux (HF), and heart rate (HR) to accurately estimate TC using a Kalman Filter (KF). Data were collected from eight volunteers (age 22 ± 4 yr, height 1.75 ± 0.10 m, body mass 76.4 ± 10.7 kg, and body fat 23.4 ± 5.8%, mean ± standard deviation) while walking at two different metabolic rates (∼350 and ∼550 W) under three conditions (warm: 25 °C, 50% relative humidity (RH); hot-humid: 35 °C, 70% RH; and hot-dry: 40 °C, 20% RH). Skin temperature and HF data were collected from six locations: pectoralis, inner thigh, scapula, sternum, rib cage, and forehead. Kalman filter variables were learned via linear regression and covariance calculations between TC and TS, HF, and HR. Root mean square error (RMSE) and bias were calculated to identify the best performing models. The pectoralis (RMSE 0.18 ± 0.04 °C; bias -0.01 ± 0.09 °C), rib (RMSE 0.18 ± 0.09 °C; bias -0.03 ± 0.09 °C), and sternum (RMSE 0.20 ± 0.10 °C; bias -0.04 ± 0.13 °C) were found to have the lowest error values when using TS, HF, and HR but, using only two of these measures provided similar accuracy.

Cardiorespiratory responses to heavy military load carriage over complex terrain

This study examined complex terrain march performance and cardiorespiratory responses when carrying different Soldier loads. Nine active duty military personnel (age, 21 ± 3 yr; height, 1.72 ± 0.07 m; body mass (BM), 83.4 ± 12.9 kg) attended two test visits during which they completed consecutive laps around a 2.5-km mixed terrain course with either a fighting load (30% BM) or an approach load (45% BM). Respiratory rate and heart rate data were collected using physiological status monitors. Training impulse (TRIMP) scores were calculated using Banisters formula to provide an integrated measure of both time and cardiorespiratory demands. Completion times were not significantly different between the fighting and approach loads for either Lap 1 (p = 0.38) or Lap 2 (p = 0.09). Respiration rate was not significantly higher with the approach load than the fighting load during Lap 1 (p = 0.17) but was significantly higher for Lap 2 (p = 0.04). However, heart rate was significantly higher with the approach load versus the fighting load during both Lap 1 (p = 0.03) and Lap 2 (p = 0.04). Furthermore, TRIMP was significantly greater with the approach load versus the fighting load during both Lap 1 (p = 0.02) and Lap 2 (p = 0.02). Trained military personnel can maintain similar pacing while carrying either fighting or approach loads during short mixed terrain marches. However, cardiorespiratory demands are greatly elevated with the approach load and will likely continue to rise during longer distance marches.