Prediction of Core Body Temperature from Multiple Variables

This paper aims to improve the prediction of rectal temperature (T_re) from insulated skin temperature (T_is) and micro-climate temperature (T_mc) previously reported (Richmond et al., Insulated skin temperature as a measure of core body temperature for individuals wearing CBRN protective clothing. Physiol Meas 2013; 34:1531-43.) using additional physiological and/or environmental variables, under several clothing and climatic conditions. Twelve male (25.8±5.1 years; 73.6±11.5kg; 178±6cm) and nine female (24.2±5.1 years; 62.4±11.5kg; 169±3cm) volunteers completed six trials, each consisting of two 40-min periods of treadmill walking separated by a 20-min rest, wearing permeable or impermeable clothing, under neutral (25°C, 50%), moderate (35°C, 35%), and hot (40°C, 25%) conditions, with and without solar radiation (600W m^-2). Participants were measured for heart rate (HR) (Polar, Finland), skin temperature (T_s) at 11 sites, T_is (Grant, Cambridge, UK), and breathing rate (f) (Hidalgo, Cambridge, UK). T_mc and relative humidity were measured within the clothing. T_re was monitored as the 'gold standard' measure of T_c for industrial or military applications using a 10cm flexible probe (Grant, Cambridge, UK). A stepwise multiple regression analysis was run to determine which of 30 variables (T_is, T_s at 11 sites, HR, f, T_mc, temperature, and humidity inside the clothing front and back, body mass, age, body fat, sex, clothing, Thermal comfort, sensation and perception, and sweat rate) were the strongest on which to base the model. Using a bootstrap methodology to develop the equation, the best model in terms of practicality and validity included T_is, T_mc, HR, and 'work' (0 = rest; 1 = exercise), predicting T_re with a standard error of the estimate of 0.27°C and adjusted r² of 0.86. The sensitivity and specificity for predicting individuals who reached 39°C was 97 and 85%, respectively. Insulated skin temperature was the most important individual parameter for the prediction of T_re. This paper provides novel information about the viability of predicting T_c under a wide range of conditions, using predictors which can practically be measured in a field environment.