Weber V; Johannes Gutenberg University, Mainz, Germany.
Lopez DA; Ochmann DT; Zentgraf S; Nagele M; Neuberger EWI;
Schomer E; Simon P; Hillen B

Scientific Reports. 16(1), 2026 Mar 28.
Headings added by Dr Older

Background 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.
Aims This study aimed to examine the
reproducibility and physiological relevance of specific skin temperature
(TSK) metrics processed automatically using deep learning-assisted IRT
during running, and to synchronize these metrics with cardiopulmonary and
thermoregulatory parameters.
Methods 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 (VO2), core temperature (TCORE), heart rate
(HR) and different TSK metrics, including non-vessel (TNV), cutaneous
arterial perforator (TP), and superficial vein patterns, were continuously
measured.
Results All TSK metrics displayed consistent temporal dynamics aligned
with external load, but their absolute temperature levels differed
systematically. During intermittent running and recovery, TP exhibited
robust correlations with HR and VO2 (r = – 0.63 to – 0.9, p < 0.001), and
TP entropy showed consistent associations with TCORE during the warm-up (r
= 0.59-0.83, p < 0.001). This indicates uniform response patterns across
the cohort. In contrast, TNV demonstrated heterogeneous correlations with
TCORE, depending on individual exercise capacity. A strong inverse
correlation was identified between TNV and vIAT (r = – 0.74 to – 0.88, p
<= 0.009) and individuals with higher vIAT demonstrated greater TCORE-TNV
gradients during running. Measurements of TNV 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.
Conclusions Deep learning-assisted IRT
provides reproducible, physiologically consistent metrics across repeated
exercise sessions, regardless of the day or prior load. Distinct TSK
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