Abstract Infrared thermography (IRT) has recently gained attention in the field of exercise physiology, due to its ability to monitor thermoregulatory and cardiopulmonary responses non-invasively and in real time during physical exercise. However, the reproducibility of intra-individual measurement and standardization of region-of-interest selection in relation to the acute exercise response remain inconclusive. This study aimed to examine the reproducibility and physiological relevance of specific skin temperature (T SK ) metrics processed automatically using deep learning-assisted IRT during running, and to synchronize these metrics with cardiopulmonary and thermoregulatory parameters. Eleven endurance-trained individuals performed three 46-min running sessions over 2 days, with the same average external load but different intensity distributions. Individual anaerobic threshold velocity (vIAT), previously determined by cardiopulmonary exercise testing, was used to prescribe running intensity. During exercise, oxygen consumption (VO 2 ), core temperature (T CORE ), heart rate (HR) and different T SK metrics, including non-vessel (T NV ), cutaneous arterial perforator (T P ), and superficial vein patterns, were continuously measured. All T SK metrics displayed consistent temporal dynamics aligned with external load, but their absolute temperature levels differed systematically. During intermittent running and recovery, T P exhibited robust correlations with HR and VO 2 ( r = − 0.63 to − 0.9, p < 0.001), and T P entropy showed consistent associations with T CORE during the warm-up ( r = 0.59–0.83, p < 0.001). This indicates uniform response patterns across the cohort. In contrast, T NV demonstrated heterogeneous correlations with T CORE , depending on individual exercise capacity. A strong inverse correlation was identified between ∆T NV and vIAT ( r = − 0.74 to − 0.88, p ≤ 0.009) and individuals with higher vIAT demonstrated greater T CORE -T NV gradients during running. Measurements of ∆T NV demonstrated high reproducibility, with intra-individual ICC(3,1) values of 0.89 for recovery and 0.76 for warm-up, and no statistically significant differences between the three sessions. Deep learning-assisted IRT provides reproducible, physiologically consistent metrics across repeated exercise sessions, regardless of the day or prior load. Distinct T SK metrics capture both uniform and individual-specific thermoregulatory responses. Variability in peripheral temperature regulation is more strongly associated with running velocity at the individual anaerobic threshold than with maximal cardiorespiratory fitness.
Weber et al. (Sat,) studied this question.