Author(s): Havenith G
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Abstract A population-based dynamic model of human thermoregulation was expanded with control equations incorporating the individual person's characteristics (body surface area, mass, fat\%, maximal O(2) uptake, acclimation). These affect both the passive (heat capacity, insulation) and active systems (sweating and skin blood flow function). Model parameters were estimated from literature data. Other data, collected for the study of individual differences (working at relative or absolute workloads in hot-dry [45 degrees C, 20\% relative humidity (rh)], warm-humid [35 degrees C, 80\% rh], and cool [21 degrees C, 50\% rh] environments), were used for validation. The individualized model provides an improved prediction [mean core temperature error, -0.21 --> -0.07 degrees C (P < 0.001); mean squared error, 0.40 --> 0.16 degrees C, (P < 0.001)]. The magnitude of improvement varies substantially with the climate and work type. Relative to an empirical multiple-regression model derived from these specific data sets, the analytical simulation model has between 54 and 89\% of its predictive power, except for the cool climate, in which this ratio is zero. In conclusion, individualization of the model allows improved prediction of heat strain, although a substantial error remains.
This article was published in J Appl Physiol (1985)
and referenced in Journal of Entrepreneurship & Organization Management