DATASETS HEATSHIELD project Loughborough University, averaged per condition and individual sessions, and files showing the statistical analyses.

The purpose of this study was to investigate which climate/heat indices perform best in predicting heat-induced loss of physical work capacity (PWC-loss). Integrating data from earlier studies, data from 982 exposures (75 conditions) exercising at a fixed cardiovascular load of 130b.min-1, in varying temperatures (15-50°C), humidities (20-80%), solar radiation (0-800W.m-2), wind (0.2-3.5m.s-1) and two clothing levels, were used to model the predictive power of ambient temperature, Universal Thermal Climate Index (UTCI), Wet Bulb Globe Temperature (WBGT), Modified Equivalent Temperature (mPET), Heat Index, Apparent Temperature (AT), and Wet Bulb Temperature (Twb) for the calculation of PWC-loss, skin temperature (Tskin) and core-to-skin temperature gradient, and Thermal perception( TSV) in the heat. R2, RMSD and Akaike stats were used indicating model performance.Indices not including wind/radiation in their calculation (Ta, Heat Index, AT, Twb) struggled to provide consistent predictions across variables. For PWC-loss and TSV, UTCI and WBGT had the highest predictive power. For Tskin, and core-to-skin temperature gradient, the physiological models UTCI and mPET worked best in semi-nude conditions, but clothed, AT, WBGT and UTCI worked best. For all index predictions, Ta, vapor pressure and Twb were shown to be the worst heat strain predictors. While UTCI and WBGT had similar model performance using the full dataset, WBGT did not work appropriately in windy, hot-dry, conditions where WBGT predicted lower strain due to wind, whereas the empirical data, UTCI and mPET indicated that wind in fact increased the overall level of thermal strain. The findings of the current study highlight the advantages of using a physiological model-based index like UTCI when evaluating heat stress in dynamic thermal environments.

本研究旨在探究哪些气候/热指数在预测由高温引起的工作能力损失（PWC损失）方面表现最为出色。通过整合先前研究的数据，包括在固定的心血管负荷130b.min-1下，于不同温度（15-50°C）、湿度（20-80%）、太阳辐射（0-800W.m-2）、风速（0.2-3.5m.s-1）以及两种着装水平下进行的982次（75种条件）暴露实验数据，本研究对环境温度、通用热气候指数（UTCI）、湿球黑球温度（WBGT）、修正等效温度（mPET）、热指数、感觉温度（AT）和湿球温度（Twb）在计算PWC损失、皮肤温度（Tskin）和核心与皮肤温度梯度以及热感知（TSV）方面的预测能力进行了建模。R2、均方根误差（RMSD）和赤池信息量（Akaike stats）被用于评估模型性能。在计算中未包含风速/辐射的指数（Ta、热指数、AT、Twb）在变量间提供一致的预测方面存在困难。对于PWC损失和TSV，UTCI和WBGT显示出最高的预测能力。对于Tskin和核心与皮肤温度梯度，在半裸条件下，基于生理模型的UTCI和mPET表现最佳，而在着装条件下，AT、WBGT和UTCI则更为合适。在所有指数预测中，Ta、水汽压和Twb被发现是预测热应激最差的热应激指标。尽管UTCI和WBGT在使用完整数据集时模型性能相似，但在有风、高温干燥条件下，WBGT未能适当地工作，因为WBGT预测的风速会导致较低的热应激，而经验数据、UTCI和mPET表明风速实际上增加了整体的热应激水平。本研究的结果突出了在动态热环境中评估热应激时，使用基于生理模型构建的指数如UTCI的优势。