Comparison of human responses to prototype and standard uniforms using three different human simulation models: HSDA, Scenario_J and Simulink2NM

Berglund, Larry G.; Yokota, Masao

doi:10.1037/e456352006-001

Cited by 4 publications

(9 citation statements)

References 1 publication

(2 reference statements)

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“…A skin temperature of 36°C (SHAP 36 ) is most often selected. This is a good approximation under heat stress conditions and in accordance with progenitor papers (Givoni and Goldman 1972) of the SHAP algorithm and the purposes of models that followed (Berglund and Yokota 2005;Pandolf et al 1986), but its accuracy when generalized across cooler conditions when skin temperatures are lower (Departments of the Army and Air Force 2003;IOM 2005;Montain et al 1999) remains unknown.…”

Section: Introductionsupporting

confidence: 53%

“…The algorithm is the same for both SHAP and SHAP 36 . What differs between them is that SHAP 36 sets T sk to 36°C and P sk to 44 mmHg (Berglund and Yokota 2005;Givoni and Goldman 1972), while SHAP uses actual (measured) values of T sk (Shapiro et al 1982).…”

Section: Measurements and Calculationsmentioning

confidence: 99%

“…These sweat prediction algorithms, though not exclusive, are likely the most accurate and widely used (Cheuvront et al 2002; Departments of the Army and Air Force 2003; Doherty et al 2006;IOM 2005;Montain et al 1999;Pandolf et al 1986;Shapiro et al 1982Shapiro et al , 1995CASCOM 1999). The exponential equation (SHAP) (Shapiro et al 1982), embedded within generations of broader empirical sweat prediction models (Berglund and Yokota 2005), resolves the interaction between the requirement for evaporative cooling (E req ) and the maximum evaporative capacity of the environment (E max ). The SHAP equation, first published in the European Journal of Applied Physiology in 1982, was derived from a matrix of laboratory experiments that included a range of environmental conditions (ambient temperature 20-54°C, and relative humidity 10-90%), clothing configurations (insulation or clo = 0.74-1.50), and metabolic intensities (~50-250 W/ m 2 ) of 2 h duration.…”

Section: Introductionmentioning

confidence: 99%

“…These include cooler environments and extrapolation to more extended physical activity (12 h) with implications for 24 h fluid needs. SHAP is also sometimes modified from the original method when incorporated into more complex models (Berglund and Yokota 2005;Pandolf et al 1986) so that an estimated skin temperature is used to compute E req and E max , rather than actual values. The algorithm then requires that only physical activity, environment, and clothing be known (or estimated) to predict sweating rate.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the limits to accurate sweat loss prediction during prolonged exercise

et al. 2007

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Sweat prediction equations are often used outside their boundaries to estimate fluid requirements and generate guidance. The limitations associated with these generalized predictions have not been characterized. The purposes of this study were to: (1) evaluate the accuracy of a widely used sweat prediction equation (SHAP) when widening it's boundaries to include cooler environments (2 h) and very prolonged exercise (8 h), (2) determine the independent impact of holding skin temperature constant (SHAP36), and (3) describe how adjustments for non-sweat losses (NSL) and clothing saturation dynamics affect prediction accuracy. Water balance was measured in 39 volunteers during 15 trials that included intermittent treadmill walking for 2 h (300-600 W, 15-30 degrees C; n=21) or 8 h (300-420 W, 20-40 degrees C; n=18). Equation accuracy was assessed by comparing actual and predicted sweating rates (211 observations) using least-squares regression. Mean and 95% confidence intervals for group differences were compared against a zone of indifference (+/-0.125 l/h). Sweating rate variance accounted for by SHAP and SHAP36 was always high (r2>0.70), while the standard error of the estimate was small and uniform around the line of best fit. SHAP errors were >0.125 l/h during 2 and 8 h of exercise. SHAP36 errors were <0.125 l/h for 2 h conditions but were higher at 8 h in three of the six warmest trials. Adjustments for NSL and clothing saturation dynamics help explain SHAP errors at 2 and 8 h, respectively. These results provide a basis for future development of accurate algorithms with broader utility.

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Section: Introductionsupporting

confidence: 53%

Section: Measurements and Calculationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of the limits to accurate sweat loss prediction during prolonged exercise

et al. 2007

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“…Numerous metrics are based upon direct physiological responses within humans and animals; however, almost all of them are complicated algorithms (e.g., Moran et al, 2001; Berglund and Yokota, 2005;Gribok et al, 2008;Maloney and Forbes, 2011;Havenith et al, 2011;Gonzalez et al, 2012;Chan et al, 2012). Most metrics require radiation measurements or heart rates, and/or even sweat rates.…”

Section: Physiology Algorithmsmentioning

confidence: 99%

Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4.5

2015

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Abstract. We implement and analyze 13 different metrics (4 moist thermodynamic quantities and 9 heat stress metrics) in the Community Land Model (CLM4.5), the land surface component of the Community Earth System Model (CESM). We call these routines the HumanIndexMod. We limit the algorithms of the HumanIndexMod to meteorological inputs of temperature, moisture, and pressure for their calculation. All metrics assume no direct sunlight exposure. The goal of this project is to implement a common framework for calculating operationally used heat stress metrics, in climate models, offline output, and locally sourced weather data sets, with the intent that the HumanIndexMod may be used with the broadest of applications. The thermodynamic quantities use the latest, most accurate and efficient algorithms available, which in turn are used as inputs to the heat stress metrics. There are three advantages of adding these metrics to CLM4.5: (1) improved moist thermodynamic quantities; (2) quantifying heat stress in every available environment within CLM4.5; and (3) these metrics may be used with human, animal, and industrial applications.We demonstrate the capabilities of the HumanIndexMod in a default configuration simulation using CLM4.5. We output 4× daily temporal resolution globally. We show that the advantage of implementing these routines into CLM4.5 is capturing the nonlinearity of the covariation of temperature and moisture conditions. For example, we show that there are systematic biases of up to 1.5 • C between monthly and ±0.5 • C between 4× daily offline calculations and the online instantaneous calculation, respectively. Additionally, we show that the differences between an inaccurate wet bulb calculation and the improved wet bulb calculation are ±1.5 • C. These differences are important due to human responses to heat stress being nonlinear. Furthermore, we show heat stress has unique regional characteristics. Some metrics have a strong dependency on regionally extreme moisture, while others have a strong dependency on regionally extreme temperature.

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Use of Thermoregulatory Models to Evaluate Heat Stress in Industrial Environments

Yermakova¹,

Potter

Raimundo

et al. 2022

IJERPH

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Heat stress in many industrial workplaces imposes significant risk of injury to individuals. As a means of quantifying these risks, a comparison of four rationally developed thermoregulatory models was conducted. The health-risk prediction (HRP) model, the human thermal regulation model (HuTheReg), the SCENARIO model, and the six-cylinder thermoregulatory model (SCTM) each used the same inputs for an individual, clothing, activity rates, and environment based on previously observed conditions within the Portuguese glass industry. An analysis of model correlations was conducted for predicted temperatures (°C) of brain (TBrain), skin (TSkin), core body (TCore), as well as sweat evaporation rate (ER; Watts). Close agreement was observed between each model (0.81–0.98). Predicted mean ± SD of active phases of exposure for both moderate (TBrain 37.8 ± 0.25, TSkin 36.7 ± 0.49, TCore 37.8 ± 0.45 °C, and ER 207.7 ± 60.4 W) and extreme heat (TBrain 39.1 ± 0.58, TSkin, 38.6 ± 0.71, TCore 38.7 ± 0.65 °C, and ER 468.2 ± 80.2 W) were assessed. This analysis quantifies these heat-risk conditions and provides a platform for comparison of methods to more fully predict heat stress during exposures to hot environments.

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Comparison of human responses to prototype and standard uniforms using three different human simulation models: HSDA, Scenario_J and Simulink2NM

Cited by 4 publications

References 1 publication

Evaluation of the limits to accurate sweat loss prediction during prolonged exercise

Evaluation of the limits to accurate sweat loss prediction during prolonged exercise

Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4.5

Use of Thermoregulatory Models to Evaluate Heat Stress in Industrial Environments

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