Publications by authors named "William R Santee"

24 Publications

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A digital tool for prevention and management of cold weather injuries-Cold Weather Ensemble Decision Aid (CoWEDA).

Int J Biometeorol 2021 Apr 4. Epub 2021 Apr 4.

Biophysics and Biomedical Modeling Division, U.S. Army Research Institute of Environmental Medicine, 10 General Greene Avenue, Natick, MA, 01760-5007, USA.

This paper describes a Cold Weather Ensemble Decision Aid (CoWEDA) that provides guidance for cold weather injury prevention, mission planning, and clothing selection. CoWEDA incorporates current science from the disciplines of physiology, meteorology, clothing, and computer modeling. The thermal performance of a cold weather ensemble is defined by endurance times, which are the time intervals from initial exposure until the safety limits are reached. These safety limits correspond to conservative temperature thresholds that provide a warning of the approaching onset of frostbite and/or hypothermia. A validated six-cylinder thermoregulatory model is used to predict human thermal responses to cold while wearing different ensembles. The performance metrics, model, and a database of clothing properties were integrated into a user-friendly software application. CoWEDA is the first tool that allows users to build their own ensembles from the clothing menu (i.e., jackets, footwear, and accessories) for each body region (i.e., head, torso, lower body, hands, feet) and view their selections in the context of physiological strain and the operational consequences. Comparison of predicted values to skin and core temperatures, measured during 17 cold exposures ranging from 0 to -40°C, indicated that the accuracy of CoWEDA prediction is acceptable, and most predictions are within measured mean ± SD. CoWEDA predicts the risk of frostbite and hypothermia and ensures that a selected clothing ensemble is appropriate for expected weather conditions and activities. CoWEDA represents a significant enhancement of required clothing insulation (IREQ, ISO 11079) and wind chill index-based guidance for cold weather safety and survival.
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http://dx.doi.org/10.1007/s00484-021-02113-0DOI Listing
April 2021

Effects of modern military backpack loads on walking speed and cardiometabolic responses of US Army Soldiers.

Appl Ergon 2021 Feb 27;94:103395. Epub 2021 Feb 27.

US Army Research Institute of Environmental Medicine (USARIEM), 10 General Greene Avenue, Natick, MA, 01760, USA. Electronic address:

Introduction: Military leaders must understand how modern military equipment loads affect trade-offs between movement speed and physiological strain to optimize pacing strategies.

Purpose: To evaluate the effects of load carried in a recently developed military backpack on the walking speed and cardiometabolic responses of dismounted warfighters.

Methods: Fifteen soldiers (1 woman, 14 men; age, 22 ± 2 years; height, 173 ± 7 cm; body mass (BM), 73 ± 10 kg) completed incremental walking tests with four external load conditions (0, 22, 44, or 66% BM) using the US Army's newest backpack: the Modular Lightweight Load-Carrying Equipment 4000 (MOLLE 4000). Oxygen uptake (V̇O) and heart rate (HR) were evaluated relative to maximal values (V̇O and HR respectively). Testing ceased when participants completed the highest tested speed (1.97 m s), exceeded a respiratory exchange ratio (RER) of 1.00, or reached volitional exhaustion.

Results: Peak speed significantly decreased (p < 0.03) with successively heavier loads (0% BM, 1.95 ± 0.06 m s; 22% BM, 1.87 ± 0.10 m s; 44% BM, 1.69 ± 0.13 m s; 66% BM, 1.48 ± 0.13 m s). Peak V̇O was significantly lower (p < 0.01) with 0% BM (47 ± 5% V̇O) than each load (22% BM, 58 ± 8% V̇O; 44% BM, 63 ± 10% V̇O; 66% BM, 61 ± 11% V̇O). Peak HR was significantly lower (p < 0.01) with 0% BM (71 ± 5% HR) versus each load (22% BM, 83 ± 6% HR; 44% BM, 87 ± 6% HR; 66% BM, 88 ± 6% HR).

Conclusion: Overburdened warfighters suffer severe impairments in walking speed even when carrying recently developed military load carriage equipment. Our results suggest that the relative work intensity of heavy load carriage may be better described when expressed relative to HR versus V̇O.
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http://dx.doi.org/10.1016/j.apergo.2021.103395DOI Listing
February 2021

Formulae for calculating body surface area in modern U.S. Army Soldiers.

J Therm Biol 2020 Aug 30;92:102650. Epub 2020 Jun 30.

Biophysics and Biomedical Modeling Division, U.S. Army Research Institute of Environmental Medicine (USARIEM), 10 General Greene Avenue, Bldg 42, Natick, MA, 01760, USA. Electronic address:

Purpose: Body surface area (BSA) is an important measurement for many thermophysiological, pharmaceutical, toxicological, environmental, and military applications. Unfortunately, BSA is difficult to quantify, and existing prediction methods are not optimized for contemporary populations.

Methods: The present study analyzed data body measurements from 5603 male and female participants of a US Army Anthropometric Survey to determine optimal methods for estimating BSA in modern US Army Soldiers. This data included 94 individual body measurements as well as three dimensional (3D) whole body scans for each participant. We used this data to assess and compared 15 existing equations to the measured data. We also derived best fitting nonlinear regression models for estimating BSA from different combinations of sex, height, and weight and iteratively included the remaining 91 measurements to determine which combinations resulted in the highest goodness-of-fit.

Results: We found that inclusion of armspan measurements as a third body dimension maximized the model goodness-of-fit.

Conclusion: Some of the existing formulae provide reasonable estimates of 3D-scanner derived BSA; while our new formulae derived from this study allows for more accurate estimates of BSA using one or more common input variables.
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http://dx.doi.org/10.1016/j.jtherbio.2020.102650DOI Listing
August 2020

Response.

Med Sci Sports Exerc 2019 07;51(7):1567

U.S. Army Research Institute of Environmental Medicine Natick, MA.

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http://dx.doi.org/10.1249/MSS.0000000000001918DOI Listing
July 2019

Estimating Energy Expenditure during Level, Uphill, and Downhill Walking.

Med Sci Sports Exerc 2019 09;51(9):1954-1960

United States Army Research Institute of Environmental Medicine, Natick, MA.

Introduction: The load carriage decision aid (LCDA) walking equation was developed from literature-aggregated group mean data to calculate standing and level walking energy expenditures in healthy, military-age adults. The LCDA walking equation has not been validated for use in individuals or graded walking.

Purpose: We aimed to validate the LCDA walking equation as a predictor of standing and level walking energy expenditure in individuals and expand to a new graded walking equation for uphill and downhill walking.

Methods: We compiled standing, level walking, and graded walking energy expenditures measured in 95 participants from 11 studies. Walking speeds reached up to 1.96 m·s with grades ranging between -40% and 45%. The LCDA walking equation was validated against the aggregated standing and level walking data. The new LCDA graded walking equation was developed and cross-validated on the graded walking trials. We compared each equation against four reference predictive equations with the standard error of estimation (SEE) as the primary criterion.

Results: The LCDA walking equation accurately estimated standing and level walking energy expenditure (bias, -0.02 ± 0.20 W·kg; SEE, 0.20 W·kg). Addition of the novel grade term resulted in precise estimates of uphill and downhill walking energy expenditure (bias, 0.09 ± 0.40 W·kg; SEE, 0.42 W·kg).

Conclusions: The LCDA walking equation is a valid predictor of standing and walking energy expenditure in healthy, military-age individuals. We developed a novel grade term for estimating both uphill and downhill walking energy expenditure with a single equation. Practitioners can use the new LCDA graded walking equation to calculate energy expenditure during standing as well as walking on level, uphill, and downhill slopes.
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http://dx.doi.org/10.1249/MSS.0000000000002002DOI Listing
September 2019

Heat Strain Decision Aid (HSDA) accurately predicts individual-based core body temperature rise while wearing chemical protective clothing.

Comput Biol Med 2019 04 16;107:131-136. Epub 2019 Feb 16.

Biophysics and Biomedical Modeling Division, U.S. Army Research Institute of Environmental Medicine, 10 General Greene Avenue, Bldg 42, Natick, MA 01760, USA. Electronic address:

Purpose: We examined the accuracy of the Heat Strain Decision Aid (HSDA) as a predictor of core body temperature in healthy individuals wearing chemical protective clothing during laboratory and field exercises in hot and humid conditions.

Methods: The laboratory experiment examined three chemical protective clothing ensembles in eight male volunteers (age 24 ± 6 years; height 178 ± 5 cm; body mass 76.6 ± 8.4 kg) during intermittent treadmill marching in an environmental chamber (air temperature 29.3 ± 0.1 °C; relative humidity 56 ± 1%; wind speed 0.4 ± 0.1 m s). The field experiment examined four different chemical protective clothing ensembles in twenty activity military volunteers (26 ± 5 years; 175 ± 8 cm; 80.2 ± 12.1 kg) during a prolonged road march (26.0 ± 0.5 °C; 55 ± 3%; 4.3 ± 0.7 m s). Predictive accuracy and precision were evaluated by the bias, mean absolute error (MAE), and root mean square error (RMSE). Additionally, accuracy was evaluated using a prediction bias of ±0.27 °C as an acceptable limit and by comparing predictions to observations within the standard deviation (SD) of the observed data.

Results: Core body temperature predictions were accurate for each chemical protective clothing ensemble in laboratory (Bias -0.10 ± 0.36 °C; MAE 0.28 ± 0.24 °C; RMSE 0.37 ± 0.24 °C) and field experiments (Bias 0.23 ± 0.32 °C; MAE 0.30 ± 0.25 °C; RMSE 0.40 ± 0.25 °C). From all modeled data, 72% of all predictions were within one standard deviation of the observed data including 92% of predictions for the laboratory experiment (SD ± 0.64 °C) and 67% for the field experiment (SD ± 0.38 °C). Individual-based predictions showed modest errors outside the SD range with 98% of predictions falling <1 °C; while, 81% of all errors were within 0.5 °C of observed data.

Conclusion: The HSDA acceptably predicts core body temperature when wearing chemical protective clothing during laboratory and field exercises in hot and humid conditions.
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http://dx.doi.org/10.1016/j.compbiomed.2019.02.004DOI Listing
April 2019

Metabolic Costs of Standing and Walking in Healthy Military-Age Adults: A Meta-regression.

Med Sci Sports Exerc 2019 02;51(2):346-351

U.S. Army Research Institute of Environmental Medicine (USARIEM), Natick, MA.

Introduction: The Load Carriage Decision Aid (LCDA) is a U.S. Army planning tool that predicts physiological responses of soldiers during different dismounted troop scenarios. We aimed to develop an equation that calculates standing and walking metabolic rates in healthy military-age adults for the LCDA using a meta-regression.

Methods: We searched for studies that measured the energetic cost of standing and treadmill walking in healthy men and women via indirect calorimetry. We used mixed effects meta-regression to determine an optimal equation to calculate standing and walking metabolic rates as a function of walking speed (S, m·s). The optimal equation was used to determine the economical speed at which the metabolic cost per distance walked is minimized. The estimation precision of the new LCDA walking equation was compared with that of seven reference predictive equations.

Results: The meta-regression included 48 studies. The optimal equation for calculating normal standing and walking metabolic rates (W·kg) was 1.44 + 1.94S + 0.24S. The economical speed for level walking was 1.39 m·s (~ 3.1 mph). The LCDA walking equation was more precise across all walking speeds (bias ± SD, 0.01 ± 0.33 W·kg) than the reference predictive equations.

Conclusion: Practitioners can use the new LCDA walking equation to calculate energy expenditure during standing and walking at speeds <2 m·s in healthy, military-age adults. The LCDA walking equation avoids the errors estimated by other equations at lower and higher walking speeds.
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http://dx.doi.org/10.1249/MSS.0000000000001779DOI Listing
February 2019

Terrain coefficients for predicting energy costs of walking over snow.

Appl Ergon 2019 Jan 15;74:48-54. Epub 2018 Aug 15.

Oak Ridge Institute for Science and Education (ORISE), 1299 Bethel Valley Rd, Oak Ridge, TN, 37830, USA; Biophysics and Biomedical Modeling Division, United States Army Research Institute of Environmental Medicine, Natick, MA, 01760, USA.

Background: Predicting the energy costs of human travel over snow can be of significant value to the military and other agencies planning work efforts when snow is present. The ability to quantify, and predict, those costs can help planners determine if snow will be a factor in the execution of dismounted tasks and operations. To adjust predictive models for the effect of terrain, and more specifically for surface conditions, on energy costs, terrain coefficients (ƞ) have been developed. The physiological demands of foot travel over snow have been studied previously, and there are well established methods of predicting metabolic costs of locomotion. By applying knowledge gained from prior studies of the effects of terrain and snow, and by leveraging those existing dismounted locomotion models, this paper seeks to outline the steps in developing an improved terrain coefficient (ƞ) for snow to be used in predictive modeling.

Methods: Using published data, methods, and a well-informed understanding of the physical elements of terrain, e.g., characterization of snow sinkage (z), this study made adjustments to ƞ-values specific to snow.

Results: This review of published metabolic cost methods suggest that an improved ƞ-value could be developed for use with the Pandolf equation, where z = depth (h)*(1 - (snow density (ρ)/1.186)) and ƞ = 0.0005z + 0.0001z + 0.1072z + 1.2604.

Conclusion: While the complexity of variables related to characteristics of snow, speed of movement, and individuals confound efforts to develop a simple, predictive model, this paper provides data-driven improvements to models that are used to predict the energy costs of dismounted movements over snow.
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http://dx.doi.org/10.1016/j.apergo.2018.08.017DOI Listing
January 2019

Cardiorespiratory responses to heavy military load carriage over complex terrain.

Appl Ergon 2018 Nov 24;73:194-198. Epub 2018 Jul 24.

US Army Research Institute of Environmental Medicine (USARIEM), 10 General Green Ave, Natick, MA, 01760, USA.

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 Banister's 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.
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http://dx.doi.org/10.1016/j.apergo.2018.07.010DOI Listing
November 2018

Complex Terrain Load Carriage Energy Expenditure Estimation Using Global Positioning System Devices.

Med Sci Sports Exerc 2018 Oct;50(10):2145-2149

United States Army Research Institute of Environmental Medicine, Natick, MA.

Introduction: Military load carriage can cause extreme energy expenditure (EE) that is difficult to estimate due to complex terrain grades and surfaces. Global Positioning System (GPS) devices capture rapid changes in walking speed and terrain but the delayed respiratory response to movement is problematic. We investigated the accuracy using GPS data in three different equations to estimate EE during complex terrain load carriage.

Methods: Twelve active duty military personnel (age, 20 ± 3 yr; height, 174 ± 8 cm; body mass, 85 ± 13 kg) hiked a complex terrain trail on multiple visits under different external load conditions. Energy expenditure was estimated by inputting GPS data into three different equations: the Pandolf-Santee equation, a recent GPS-based equation from de Müllenheim et al.; and the Minimum Mechanics model. Minute-by-minute EE estimates were exponentially smoothed using smoothing factors between 0.05 and 0.95 and compared with mobile metabolic sensor EE measurements.

Results: The Pandolf-Santee equation had no significant estimation bias (-2 ± 12 W; P = 0.89). Significant biases were detected for the de Müllenheim equation (38 ± 13 W; P = 0.004) and the Minimum Mechanics model (-101 ± 7 W; P < 0.001).

Conclusions: Energy expenditure can be accurately estimated from GPS data using the Pandolf-Santee equation. Applying a basic exponential smoothing factor of 0.5 to GPS data enables more precise tracking of EE during non-steady-state exercise.
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http://dx.doi.org/10.1249/MSS.0000000000001669DOI Listing
October 2018

Metabolic Costs of Military Load Carriage over Complex Terrain.

Mil Med 2018 09;183(9-10):e357-e362

United States Army Research Institute of Environmental Medicine (USARIEM), 10 General Greene Avenue, Natick, MA.

Introduction: Dismounted military operations often involve prolonged load carriage over complex terrain, which can result in excessive metabolic costs that can directly impair soldiers' performance. Although estimating these demands is a critical interest for mission planning purposes, it is unclear whether existing estimation equations developed from controlled laboratory- and field-based studies accurately account for energy costs of traveling over complex terrain. This study investigated the accuracy of the following equations for military populations when applied to data collected over complex terrain with two different levels of load carriage: American College of Sports Medicine (2002), Givoni and Goldman (1971), Jobe and White (2009), Minetti et al (2002), Pandolf et al (1977), and Santee et al (2003).

Materials And Methods: Nine active duty military personnel (age 21 ± 3 yr; height 1.72 ± 0.07 m; body mass 83.4 ± 12.9 kg; VO2 max 47.8 ± 3.9 mL/kg/min) were monitored during load carriage (with loads equal to 30% and 45% of body mass) over a 10-km mixed terrain course on two separate test days. The course was divided into four 2.5-km laps of 40 segments based on distance, grade, and/or surface factors. Timing gates and radio-frequency identification cards (SportIdent; Scarborough Orienteering, Huntington Beach, CA) were used to record completion times for each course segment. Breath-by-breath measures of energy expenditure were collected using portable oxygen exchange devices (COSMED Sri., Rome, Italy) and compared model estimates.

Results: The Santee et al equation performed best, demonstrating the smallest estimation bias (-13 ± 87 W) and lowest root mean square error (99 W).

Conclusion: Current predictive equations underestimate the metabolic cost of load carriage by military personnel over complex terrain. Applying the Santee et al correction factor to the Pandolf et al equation may be the most suitable approach for estimating metabolic demands in these circumstances. However, this work also outlines the need for improvements to these methods, new method development and validation, or the use of a multi-model approach to account for mixed terrain.
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http://dx.doi.org/10.1093/milmed/usx099DOI Listing
September 2018

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

Comput Biol Med 2018 08 18;99:1-6. Epub 2018 May 18.

Biophysics and Biomedical Modeling Division, U.S. Army Research Institute of Environmental Medicine, 10 General Greene Avenue, Natick, MA, 01760-5007, USA. Electronic address:

Core body temperature (T) 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 (T), heat flux (HF), and heart rate (HR) to accurately estimate T 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 T and T, 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 T, HF, and HR but, using only two of these measures provided similar accuracy.
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http://dx.doi.org/10.1016/j.compbiomed.2018.05.021DOI Listing
August 2018

Talk to the Hand: U.S. Army Biophysical Testing.

Mil Med 2017 07;182(7):e1702-e1705

Biophysics and Biomedical Modeling Division, U.S. Army Research Institute of Environmental Medicine, 10 General Greene Avenue, Building 42, Natick, MA 01760-5007.

Background: Many people are unaware of the science underlying the biophysical properties of Soldier clothing and personal protective equipment, yet there is a well-refined biomedical methodology initiated by Army physiologists in World War II. This involves a methodical progression of systematic material testing technologies, computer modeling, and human testing that enables more efficient development and rapid evaluation of new concepts for Soldier health and performance. Sophisticated manikins that sweat and move are a central part of this testing continuum. This report briefly summarizes the evolution and use of one specialized form of the manikin technologies, the thermal hand model, and its use in research on Soldier hand-wear items that sustain dexterity and protect the hand in extreme environments.

Methods: Thermal manikin testing methodologies were developed to provide an efficient and consistent analytical tool for the rapid evaluation of new clothing concepts. These methods have been upgraded since the original World War II and Korean War eras to include articulation and sweating capabilities, as characterized and illustrated in this article. The earlier "retired" versions of thermal hand models have now been transferred to the National Museum of Health and Science.

Findings: The biophysical values from manikin testing are critical inputs to the U.S. Army Research Institute of Environmental Medicine mathematical models that provide predictions of soldier comfort, duration of exposure before loss of manual dexterity, and time to significant risk of freezing (skin temperature <-1°C) and nonfreezing cold injuries (skin temperature <5°C). The greater thickness of better insulated handwear reduces dexterity and also increases surface area which makes added insulation increasingly less effective in retaining heat. Measurements of both thermal resistance (insulation) and evaporative resistance (permeability) collectively characterize the biophysical properties and enable mathematical modeling of the human thermophysiological responses. This information can help guide the hand-wear development and selection process which often requires trade-offs between factors such as material, cost, and sizing.

Impact: Soldier hands provide fine motor dexterity in tactical functions, ranging from pulling a trigger to pulling a parachute ripcord; thus, protecting hand function is critical to soldier readiness. Also, the importance of protection against nonbattle cold injuries was highlighted during World War II in northern Europe, in the Aleutian Islands, and later in Korea. The U.S. Army has been on the forefront of the biophysical analysis of clothing including gloves since environmental research was established at the Armored Medical Research Laboratory and Climatic Research Laboratory during World War II. U.S. Army Research Institute of Environmental Medicine does not make the equipment but works with their Natick Soldier Research, Development, and Engineering Center partners to make the equipment better.
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http://dx.doi.org/10.7205/MILMED-D-16-00156DOI Listing
July 2017

Heat strain imposed by personal protective ensembles: quantitative analysis using a thermoregulation model.

Int J Biometeorol 2016 Jul 5;60(7):1065-74. Epub 2015 Dec 5.

Biophysics and Biomedical Modeling Division, US Army Research Institute of Environmental Medicine, 10 General Greene Avenue, Natick, MA, 01760, USA.

The objective of this paper is to study the effects of personal protective equipment (PPE) and specific PPE layers, defined as thermal/evaporative resistances and the mass, on heat strain during physical activity. A stepwise thermal manikin testing and modeling approach was used to analyze a PPE ensemble with four layers: uniform, ballistic protection, chemical protective clothing, and mask and gloves. The PPE was tested on a thermal manikin, starting with the uniform, then adding an additional layer in each step. Wearing PPE increases the metabolic rates [Formula: see text], thus [Formula: see text] were adjusted according to the mass of each of four configurations. A human thermoregulatory model was used to predict endurance time for each configuration at fixed [Formula: see text] and at its mass adjusted [Formula: see text]. Reductions in endurance time due to resistances, and due to mass, were separately determined using predicted results. Fractional contributions of PPE's thermal/evaporative resistances by layer show that the ballistic protection and the chemical protective clothing layers contribute about 20 %, respectively. Wearing the ballistic protection over the uniform reduced endurance time from 146 to 75 min, with 31 min of the decrement due to the additional resistances of the ballistic protection, and 40 min due to increased [Formula: see text] associated with the additional mass. Effects of mass on heat strain are of a similar magnitude relative to effects of increased resistances. Reducing resistances and mass can both significantly alleviate heat strain.
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http://dx.doi.org/10.1007/s00484-015-1100-0DOI Listing
July 2016

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

Med Sci Sports Exerc 2015 Sep;47(9):1958-64

1U.S. Army Research Institute of Environmental Medicine, Natick, MA; and 2Boston Athletic Association, Boston, MA.

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.
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http://dx.doi.org/10.1249/MSS.0000000000000624DOI Listing
September 2015

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

Mil Med 2013 Aug;178(8):926-33

Biophysics and Biomedical Modeling Division, U.S. Army Institute of Environmental Medicine, 42 Kansas Street, Natick, MA 01760, USA.

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 Liljegren's classification. Results indicate Liljegren is an acceptable alternative to direct WBGT measurement, but verification under other environmental conditions is needed.
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http://dx.doi.org/10.7205/MILMED-D-13-00117DOI Listing
August 2013

Relationship between core temperature, skin temperature, and heat flux during exercise in heat.

Eur J Appl Physiol 2013 Sep 18;113(9):2381-9. Epub 2013 Jun 18.

Biophysics and Biomedical Modeling Division, US Army Research Institute of Environmental Medicine, 42 Kansas Street, Natick, MA 01760-5007, USA.

Purpose: This paper investigates the relationship between core temperature (T c), skin temperature (T s) and heat flux (HF) during exercise in hot conditions.

Method: Nine test volunteers, wearing an Army Combat Uniform and body armor, participated in three sessions at 25 °C/50 % relative humidity (RH); 35 °C/70 % RH; and 42 °C/20 % RH. Each session consisted of two 1-h treadmill walks at ~350 W and ~540 W intensity. T s and HF from six sites on the forehead, sternum, pectoralis, left rib cage, left scapula, and left thigh, and T c (i.e., core temperature pill used as a suppository) were measured. Multiple linear regressions were conducted to derive algorithms that estimate T c from T s and HF at each site. A simple model was developed to simulate influences of thermal conductivity and thickness of the local body tissues on the relationship between T c, T s, and HF.

Results: Coefficient of determination (R (2)) ranged from 0.30 to 0.88, varying with locations and conditions. Good sites for T c measurement at surface were the sternum, and a combination of the sternum, scapula, and rib sites. The combination of T s and HF measured at the sternum explained ~75 % or more of variance in observed T c in hot environments. The forehead was found unsuitable for exercise in heat due to sweating and evaporative heat loss. The derived algorithms are likely applicable only for the same ensemble or ensembles with similar thermal and vapor resistances.

Conclusion: Algorithms for T c measurement are location-specific and their accuracy is dependent, to a large degree, on sensor placement.
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http://dx.doi.org/10.1007/s00421-013-2674-zDOI Listing
September 2013

Applications of real-time thermoregulatory models to occupational heat stress: validation with military and civilian field studies.

J Strength Cond Res 2012 Jul;26 Suppl 2:S37-44

Biophysics and Biomedical Modeling Division, US Army Research Institute of Environmental Medicine, Natick, Massachusetts, USA.

A real-time thermoregulatory model using noninvasive measurements as inputs was developed for predicting physiological responses of individuals working long hours. The purpose of the model is to reduce heat-related injuries and illness by predicting the physiological effects of thermal stress on individuals while working. The model was originally validated mainly by using data from controlled laboratory studies. This study expands the validation of the model with field data from 26 test volunteers, including US Marines, Australian soldiers, and US wildland fire fighters (WLFF). These data encompass a range of environmental conditions (air temperature: 19-30° C; relative humidity: 25-63%) and clothing (i.e., battle dress uniform, chemical-biological protective garment, WLFF protective gear), while performing diverse activities (e.g., marksmanship, marching, extinguishing fires, and digging). The predicted core temperatures (Tc), calculated using environmental, anthropometric, clothing, and heart rate measures collected in the field as model inputs, were compared with subjects' Tc collected with ingested telemetry temperature pills. Root mean standard deviation (RMSD) values, used for goodness of fit comparisons, indicated that overall, the model predictions were in close agreement with the measured values (grand mean of RMSD: 0.15-0.38° C). Although the field data showed more individual variability in the physiological data relative to more controlled laboratory studies, this study showed that the performance of the model was adequate.
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http://dx.doi.org/10.1519/JSC.0b013e31825ceba4DOI Listing
July 2012

Sweat loss prediction using a multi-model approach.

Int J Biometeorol 2011 Jul 4;55(4):501-8. Epub 2010 Oct 4.

Biophysics and Biomedical Modeling Division, US Army Research Institute of Environmental Medicine, Natick, MA 01760, USA.

A new multi-model approach (MMA) for sweat loss prediction is proposed to improve prediction accuracy. MMA was computed as the average of sweat loss predicted by two existing thermoregulation models: i.e., the rational model SCENARIO and the empirical model Heat Strain Decision Aid (HSDA). Three independent physiological datasets, a total of 44 trials, were used to compare predictions by MMA, SCENARIO, and HSDA. The observed sweat losses were collected under different combinations of uniform ensembles, environmental conditions (15-40°C, RH 25-75%), and exercise intensities (250-600 W). Root mean square deviation (RMSD), residual plots, and paired t tests were used to compare predictions with observations. Overall, MMA reduced RMSD by 30-39% in comparison with either SCENARIO or HSDA, and increased the prediction accuracy to 66% from 34% or 55%. Of the MMA predictions, 70% fell within the range of mean observed value ± SD, while only 43% of SCENARIO and 50% of HSDA predictions fell within the same range. Paired t tests showed that differences between observations and MMA predictions were not significant, but differences between observations and SCENARIO or HSDA predictions were significantly different for two datasets. Thus, MMA predicted sweat loss more accurately than either of the two single models for the three datasets used. Future work will be to evaluate MMA using additional physiological data to expand the scope of populations and conditions.
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http://dx.doi.org/10.1007/s00484-010-0371-8DOI Listing
July 2011

Reflective inserts to reduce heat strain in body armor: tests with and without irradiance.

Aviat Space Environ Med 2007 Aug;78(8):809-13

U.S. Army Research Institute of Environmental Medicine, Natick, MA 01760-5007, USA.

Background: This study evaluated adding reflective thermal inserts (RTI) to reduce the physiological strain during exercise-heat stress with a radiant load. RTI were used with a U.S. Army desert battle dress uniform, body armor, and helmet.

Methods: Four male volunteers attempted four trials (10 min rest followed by 100 min walking at 1.56 m x s(-1)). All trials were at 40.0 degrees C dry bulb (Tdb), 12.4 degrees C dew point (Tdp), 20% RH, and 1.0 m x s(-1) wind speed. On 2 d, there was supplementary irradiance (+1) with globe temperature (Tbg) = 56.5 degrees C and on 2 d there was no supplementary irradiance (-I) with Tbg approximately Tdb. Trial conditions were: 1) RTI and armor with supplementary irradiance (RA+I); 2) plain armor with supplementary irradiance (PA+I); 3) RTI and armor with no supplementary irradiance (RA-I); and 4) plain armor with no supplementary irradiance (PA-I).

Results: Endurance times were not significantly different among trials. With one exception, armor and helmet interior and exterior surface temperatures were not significantly different between either RA+I and PA+I or RA-I and PA-I. Temperature on the inside of the helmet in RA+I (47.1 +/- 1.4 degrees C) was significantly lower than in PA+I (49.5 +/- 2.6 degrees C). There were no differences for any physiological measure (core temperature, heart rate, mean weighted skin temperature, forehead skin temperature, sweating rate, evaporative cooling, rate of heat storage) between either RA+I and PA+L or RA-I and PA-I.

Conclusions: Results showed no evidence that wearing RTI with body armor and helmet reduces physiological strain during exercise-heat stress with either high or low irradiance.
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August 2007

Energy expenditure in men and women during 54 h of exercise and caloric deprivation.

Med Sci Sports Exerc 2006 May;38(5):894-900

US Army Research Institute of Environmental Medicine, Natick, MA 01760, USA.

Unlabelled: Fifty U.S. Marine recruits (30 men, 20 women) were studied during a physically intense, energy intake-restricted, winter-time 54-h field training exercise (FEX) at Parris Island Marine Corps Recruit Depot. Men and women completed the same physical tasks.

Purpose: To characterize and compare the total energy expenditure (TEE) and core temperature responses in men and women working almost continuously for 2.25 d in an outdoor environment while developing a substantial energy deficit.

Methods: TEE was measured using doubly labeled water (D(2)O(18)). Energy intake was estimated using beverage diaries and collecting ration wrappers saved by each volunteer and adding the known caloric value of each food item consumed. Core temperature was measured using an ingested thermometer pill. Physical activity level (PAL) was calculated by dividing TEE by the calculated basal metabolic rate.

Results: TEE was higher (P < 0.001) for the men (25.7 MJ.d(-1)) than women (19.8 MJ.d(-1)), but there were no differences between men and women in TEE normalized to body mass (men, 0.35 +/- 0.05 MJ.d(-1).kg(-1); women, 0.34 +/- 0.06 MJ.d(-).kg(-1)), corrected body mass (men, 0.29 +/- 0.04 MJ.d(-1).kg(-1) corrected body mass; women, 0.27 +/- 0.04 MJ.d(-1).kg(-1) corrected body mass), fat-free mass (men, 0.41 +/- 0.07 MJ.d(-1).kg(-1) FFM; women, 0.46 +/- 0.07 MJ.d(-1).kg(-1) FFM), or corrected fat-free mass (men, 0.30 +/- 0.05 MJ.d(-1).kg corrected body mass; women, 0.30 +/- 0.04 0.30 +/- 0.05 MJ.d(-1).kg(-1) corrected body mass). PAL was the same for men (3.4 +/- 0.5) and women (3.3 +/- 0.4). Energy intakes were higher (P < 0.05) in men (6.0 +/- 2.0 MJ.d(-1)) than women (4.8 +/- 1.8 MJ.d(-1)). The average minimum core temperature was 36.0 +/- 0.4 degrees C, and the mean maximum core temperature was 38.5 +/- 0.3 degrees C.

Conclusions: For both men and women, total energy expenditures were among the highest observed for a military FEX. TEE, when normalized or corrected to body mass and fat-free mass, and PAL were the same for men and women.
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http://dx.doi.org/10.1249/01.mss.0000218122.59968.ebDOI Listing
May 2006

Modeling physiological responses to military scenarios: initial core temperature and downhill work.

Aviat Space Environ Med 2005 May;76(5):475-80

Geo-Centers, Inc, Natick, MA 01760, USA.

Introduction: Previous field studies suggested that a thermoregulatory model developed by the U.S. Army Research Institute of Environmental Medicine (USARIEM) needed an adjustment of initial core temperature (Tcr) for individual variation and a metabolic (M) correction during downhill movements. This study evaluated the updated version of the model incorporating these new features using a dataset collected during U.S. Marine Corps marksmanship training at Quantico, VA.

Methods: Individual anthropometrics, physiological, and environmental time series data were obtained from five Marine men. The study focused on the marksmanship training for approximately 2 h, then 30-min marching including uphill and downhill movements in a moderately hot environment (air temperature: approximately 30 degrees C; dew point: approximately 21 degrees C). The predicted and observed heart rate (HR) and Tcr measurements were compared by root mean square deviations (RMSD).

Results: Overall, the current model improved predictions of physiological measures (HR RMSD = 23 bpm, Tcr RMSD = 0.46 degrees C), particularly for marching in the heat (HR RMSD = 21 bpm, Tcr RMSD = 0.32 degrees C). The model under-predicted both HR and Tcr during marksmanship training, indicating that a greater solar effect or non-thermal factors may have required higher M rates during these periods.

Conclusions: Updated features of the model significantly improved physiological predictions. However, accurate M estimates are required for slow movements of subjects under heat stress, such as movements on the firing range. Such improvement should result in more accurate simulations of physiological status and better risk assessment, thereby reducing heat injuries and improving performance of deployed military personnel.
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May 2005

Total energy expenditure estimated using foot-ground contact pedometry.

Diabetes Technol Ther 2004 Feb;6(1):71-81

MCMR-BMD, U.S. Army Research Institute of Environmental Medicine, Natick, Massachusetts 01760-5007, USA.

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.
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http://dx.doi.org/10.1089/152091504322783459DOI Listing
February 2004

Windchill index and military applications.

Authors:
William R Santee

Aviat Space Environ Med 2002 Jul;73(7):699-702

Biophysics and Biomedical Modeling Division, U.S. Army Research Institute of Environmental Medicine, Natick, MA 01760-5007, USA.

A new Windchill Apparent Temperature (WCT) has been introduced to replace the Windchill Index (WCI) and Windchill Equivalent Temperature (WCET) used to quantify cold exposure. From the time of its introduction the WCI has been criticized on scientific grounds. Despite a history of criticism, the WCI and the derived WCET have been adopted by military and civilian organizations to characterize the hazards presented by exposure to cold environments. However, the military has specific needs that differ from those of the civilian population. Thus, additional weather products and devices, including thermoregulatory models, environmental monitors, and personal physiological status monitors, are available to supplement the revised WCT.
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July 2002