Detection of Ventilatory Thresholds using Near-Infrared Spectroscopy with a Polynomial Regression Model

Abstract Whether near-infrared spectroscopy (NIRS) is a convenient and accurate method of determining first and second ventilatory thresholds (VT1 and VT2) using raw data remains unknown. This study investigated the reliability and validity of VT1 and VT2 determined by NIRS skeletal muscle hemodynamic raw data via a polynomial regression model. A total of 100 male students were recruited and performed maximal cycling exercises while their cardiopulmonary and NIRS muscle hemodynamic data were measured. The criterion validity of VT1VET and VT2VET were determined using a traditional V-slope and ventilatory efficiency. Statistical significance was set at α =. 05. There was high reproducibility of VT1NIRS and VT2NIRS determined by a NIRS polynomial regression model during exercise (VT1NIRS, r = 0.94; VT2NIRS, r=0.93). There were high correlations of VT1VET vs VT1NIRS (r = 0.93, p