A predictive model combining heart rate, core body temperature, and forehead lactate excretion rate accurately estimated blood lactate during graded exercise with a conditional R² of 0.939.
Observational (n=31)
No
Does a minimally invasive model using heart rate, core body temperature, and sweat-derived indices accurately estimate blood lactate concentration during incremental exercise in healthy adult males?
Combined cardiovascular, thermoregulatory, and sweat-derived measures enable accurate, minimally invasive estimation of blood lactate during graded exercise, supporting wearable-based metabolic monitoring.
Effect estimate: Conditional R² 0.939
p-value: p=<0.001
Blood lactate concentration (BLa) is a key marker of metabolic stress, but invasive sampling limits real-time monitoring. We developed a minimally invasive model to estimate BLa during incremental exercise using heart rate (HR), core body temperature (CBT), and sweat-derived indices. Thirty-one healthy adult males performed a graded treadmill test. HR and CBT were monitored continuously. Sweat was sampled from the forehead, chest, and back to quantify sweat lactate concentration (La⁻sw) and lactate excretion rate (LER = La⁻sw × sweat rate). Linear mixed-effects models (LMMs) were fitted with log-transformed BLa (LogBLa) and participant-level random effects. BLa increased with exercise intensity (p < 0.001), accompanied by increases in HR, CBT and LER (both p < 0.001). LMMs combining HR, CBT, and sweat indices showed strong performance for LogBLa. The best model (HR + CBT+forehead LER) achieved conditional R²=0.939 and RMSE = 0.229 (log units), and forehead-based models outperformed chest and back. Combined cardiovascular, thermoregulatory, and sweat-derived measures enable accurate, minimally invasive estimation of BLa during graded exercise, supporting wearable-based metabolic monitoring and individualized exercise prescription.
Lee et al. (Tue,) conducted a observational in Healthy adults (n=31). Incremental exercise test with physiological monitoring was evaluated on Prediction of log-transformed blood lactate concentration (Log[BLa]) (Conditional R² 0.939, p=<0.001). A predictive model combining heart rate, core body temperature, and forehead lactate excretion rate accurately estimated blood lactate during graded exercise with a conditional R² of 0.939.