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1 TNO Human Factors, Soesterberg 3769ZG, The Netherlands; and 2 Human Thermal Environments Laboratory, Loughborough University, Loughborough LE11 3TU, United Kingdom
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 O2 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°C, 20% relative humidity (rh)], warm-humid [35°C,
80% rh], and cool [21°C, 50% rh] environments}, were used for
validation. The individualized model provides an improved prediction
[mean core temperature error,
0.21
0.07°C (P < 0.001); mean squared error, 0.40
0.16°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.
heat strain; body temperature; exercise; body fat; fitness
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