2026 Volume 47 Pages 80-90
Continuous monitoring of core body temperature （Tcore） is essential for preventing heatstroke, yet traditional methods for measuring and predicting Tcore encounter challenges in practical applications. This study aimed to develop a novel Tcore prediction model integrating variables such as forearm skin temperature （Tforearm） and physiological （heart rate, exercise duration）， environmental （ambient temperature, humidity）， and individual parameters （gender, body mass index）．From 12 young adult participants, a predictive model for ear canal temperature （Tear） as an index of Tcore was formulated through multiple regression analysis of all variables in a controlled hot environment with an ambient temperature of 35℃ and relative humidity of 60%, during multi-stage treadmill exercise. The concordance between measured and predicted Tear in both experiments was assessed by calculating the intraclass correlation coefficient （ICC2,1） and bias using Bland-Altman plot analysis. A preliminary multiple regression model was established, and by refining the model around the inflection point where thermoregulatory responses became significant （Tear = 37.5℃）， prediction accuracy was enhanced （ICC2,1 = 0.968, P < 0.001; Bias = 0.001）．Prediction accuracy varied among participants, with ICC2,1 ranging from 0.861 to 0.995 （all P < 0.001） and bias ranging from -0.136-0.097. These findings suggest that the prediction model incorporating Tforearm and other relevant parameters may be valid for real-time Tcore monitoring.
