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Effect of lokal skin blood flow during light and medium activities on local skin temperature predictions

Author: Vesela, S. · Kingma, B.R.M. · Frijns, A.J.H. · Marken Lichtenbelt, W.D. van
Type:article
Date:2019
Source:Journal of Thermal Biology, 84, 439-450
Identifier: 868489
doi: doi:10.1016/j.jtherbio.2019.07.033
Keywords: Thermoregulation · Local skin blood flow · Mathematical modelling · Local skin temperature · Adult · Body composition · Built environment · Clinical article · Comfort · Controlled study · Energy expenditure · Female · Foot · Gender · Human · Male · Prediction · Simulation · Skin blood flow · Skin temperature · Sport · Thermoregulation · Walking

Abstract

The quality of local skin temperature prediction by thermophysiological models depends on the local skin blood flow (SBF) control functions. These equations were derived for low activity levels (0.8-1 met ) and mostly in sitting or supine position. This study validates and discusses the prediction of foot SBF during activities of 1-3 met in male and females, and the effect on the foot skin temperature prediction (∆ꓔ skin,foot ) using the thermophysiological simulation model ThermoSEM. The SBF at the foot was measured for ten male and ten female human subjects at baseline and during three activities (sitting, walking at 1 km/h, preferred walking around 3 km/h ). Additional measurements included the energy expenditure, local skin temperatures (ꓔ skin,loc ), environmental conditions and body composition. Measured, normalized foot SBF is 2-8 times higher than the simulated SBF during walking sessions. Also, SBF increases are significantly higher in females vs. males (preferred walking: versus 4.8 ±1.5 versus 2.7 ±1.4, P < 0.05). The quality of ∆ꓔ skin,foot using the simulated foot SBF is poor (median deviation is -4.8 C, maximum deviation is -6 C). Using the measured SBF in ThermoSEM results in an improved local skin temperature prediction (new maximum deviation is -3.3 C ). From these data a new SBF model was developed that includes the walking activity level and gender, and improves SBF prediction and ∆ꓔ skin,foot of the thermophysiological model. Accurate SBF and local skin temperature predictions are ben-eficial in optimizing thermal comfort simulations in the built environment, and might also be applied in sport science or patient's temperature management.