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Supplemental pages Havenith et al 2024 JAP.docx (4.54 MB)
DATASET
D1 DATASET FINAL HEATSHIELD averaged per condition Data for UTCI paper JAP update march 2024.xlsx (70.93 kB)
DATASET
D2 DATASET FINAL HEATSHIELD individual sessions March 2024 PWC6.xlsx (2.18 MB)
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D3 supplemental material 1 hour PWCloss analyses.xlsx (127.77 kB)
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D4 supplemental file full day analyses.xlsx (517.31 kB)
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D5 supplemental file Tskin and TSV models versus climate indices.xlsx (34.36 kB)
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D6 individual experiments data sheets.zip (518.95 MB)
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DATASETS HEATSHIELD project Loughborough University, averaged per condition and individual sessions, and files showing the statistical analyses.

Version 2 2024-06-25, 09:15
Version 1 2024-05-01, 09:02
dataset
posted on 2024-06-25, 09:15 authored by George HavenithGeorge Havenith, Josh Foster, James Smallcombe, Simon HodderSimon Hodder

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.

Funding

‘HEAT-SHIELD’, European Union’s Horizon 2020 research and innovation program under grant agreement no. 668786.

History

School

  • Design and Creative Arts

Department

  • Design