Use of Thermoregulatory Models to Evaluate Heat Stress in Industrial Environments.

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.

Modeling the Metabolic Costs of Heavy Military Backpacking.

INTRODUCTION: Existing predictive equations underestimate the metabolic costs of heavy military load carriage. Metabolic costs are specific to each type of military equipment, and backpack loads often impose the most sustained burden on the dismounted warfighter. PURPOSE: This study aimed to develop and validate an equation for estimating metabolic rates during heavy backpacking for the US Army Load Carriage Decision Aid (LCDA), an integrated software mission planning tool. METHODS: Thirty healthy, active military-age adults (3 women, 27 men; age, 25 ± 7 yr; height, 1.74 ± 0.07 m; body mass, 77 ± 15 kg) walked for 6-21 min while carrying backpacks loaded up to 66% body mass at speeds between 0.45 and 1.97 m·s-1. A new predictive model, the LCDA backpacking equation, was developed on metabolic rate data calculated from indirect calorimetry. Model estimation performance was evaluated internally by k-fold cross-validation and externally against seven historical reference data sets. We tested if the 90% confidence interval of the mean paired difference was within equivalence limits equal to 10% of the measured metabolic rate. Estimation accuracy and level of agreement were also evaluated by the bias and concordance correlation coefficient (CCC), respectively. RESULTS: Estimates from the LCDA backpacking equation were statistically equivalent (P < 0.01) to metabolic rates measured in the current study (bias, -0.01 ± 0.62 W·kg-1; CCC, 0.965) and from the seven independent data sets (bias, -0.08 ± 0.59 W·kg-1; CCC, 0.926). CONCLUSIONS: The newly derived LCDA backpacking equation provides close estimates of steady-state metabolic energy expenditure during heavy load carriage. These advances enable further optimization of thermal-work strain monitoring, sports nutrition, and hydration strategies.

Comparison of two mathematical models for predicted human thermal responses to hot and humid environments.

PURPOSE: We compared the accuracy and design of two thermoregulatory models, the US Army's empirically designed Heat Strain Decision Aid (HSDA) and the rationally based Health Risk Prediction (HRP) for predicting human thermal responses during exercise in hot and humid conditions and wearing chemical protective clothing. METHODS: Accuracy of the HSDA and HRP model predictions of core body and skin temperature (Tc, Ts) were compared to each other and relative to measured outcomes from 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-1) wearing three separate chemical protective ensembles. Model accuracies and precisions were evaluated by the bias, mean absolute error (MAE), and root mean square error (RMSE) compared to observed data mean ± SD and the calculated limits of agreement (LoA). RESULTS: Average predictions of Tc were comparable and acceptable for each method, HSDA (Bias 0.02 °C; MAE 0.18 °C; RMSE 0.21 °C) and HRP (Bias 0.10 °C; MAE 0.25 °C; RMSE 0.34 °C). The HRP averaged predictions for Ts were within an acceptable agreement to observed values (Bias 1.01 °C; MAE 1.01 °C; RMSE 1.11 °C). CONCLUSION: Both HSDA and HRP acceptably predict Tc and HRP acceptably predicts Ts when wearing chemical protective clothing during exercise in hot and humid conditions.