Physiologically based thermoregulatory models are useful for deriving predictions of heat strain for pragmatic applications such as planning of continuous exercise/work-rest protocols. The SCENARIO model is an example of a thermoregulatory model that predicts heat strain including body core temperature (Tc) from individual characteristics, physical activity, clothing properties and environmental conditions. This paper presents work to optimize and enhance the SCENARIO model for prediction of Tc during high intensity load carriage tasks under predominantly tropical climate conditions. Data for model optimization (in-sample analysis) and model external validation were derived from four and two load carriage studies respectively. A total of four parameters characterizing metabolic heat production, sweat evaporation and ice ingestion for hydration were identified for model optimization based on physiological reasoning. The accuracy of Tc estimates was evaluated based on bias, root mean square deviation (RMSD), RMSD based on mean values (RMSD-Mean), and standard deviation fall-in percentage (SDP). Under in-sample analysis, the optimized model achieved bias, RMSD, RMSD-Mean and SDP of 0.01°C, 0.39°C, 0.14°C and 99%, respectively. When externally validated against two sets of unseen data, the model achieved comparable bias, RMSD, RMSD-Mean and SDP values of 0.06°C, 0.32°C, 0.13°C, 92% and 0.08°C, 0.39°C, 0.19°C, 92%, respectively. Overall, the results indicate the robustness of the optimized SCENARIO model for predicting the Tc responses during prolonged, high-intensity physical tasks under hot and humid environments. Future work to further validate the model against data beyond the range of the present study’s experimental data and enhancing it for more accurate simulations of other heat strain markers including heart rate is recommended.
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