ABSTRACT
Body core temperature (Tc) monitoring is crucial for minimizing heat injury risk. However, validated strategies are invasive and expensive. Although promising, aural canal temperature (Tac) is susceptible to environmental influences. This study investigated whether incorporation of external auricle temperature (Tea) into an ear-based Tc algorithm enhances its accuracy during multiple heat stress conditions. Twenty males (mean ± SD; age = 25 ± 3 years, BMI = 21.7 ± 1.8, body fat = 12 ± 3%, maximal aerobic capacity (VO2max) = 64 ± 7 ml/kg/min) donned an ear-based wearable and performed a passive heating (PAH), running (RUN) and brisk walking trial (WALK). PAH comprised of immersion in hot water (42.0 ± 0.3 °C). RUN (70 ± 3%VO2max) and WALK (50 ± 10%VO2max) were conducted in an environmental chamber (Tdb = 30.0 ± 0.2 °C, RH = 71 ± 2%). Several Tc models, developed using Tac, Tea and heart rate, were validated against gastrointestinal temperature. Inclusion of Tea as a model input improved the accuracy of the ear-based Tc algorithm. Our best performing model (Trf3) displayed good group prediction errors (mean bias error = - 0.02 ± 0.26 °C) but exhibited individual prediction errors (percentage target attainment ± 0.40 °C = 88%) that marginally exceeded our validity criterion. Therefore, Trf3 demonstrates potential utility for group-based Tc monitoring, with additional refinement needed to extend its applicability to personalized heat strain monitoring.
