Detection of ventilatory thresholds using near-infrared spectroscopy with a polynomial regression model

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 VT_1VET and VT_2VET were determined using a traditional V-slope and ventilatory efficiency. Statistical significance was set at α = . 05. There was high reproducibility of VT_1NIRS and VT_2NIRS determined by a NIRS polynomial regression model during exercise (VT_1NIRS, r = 0.94; VT_2NIRS, r = 0.93). There were high correlations of VT_1VET vs VT_1NIRS (r = 0.93, p < .05) and VT_2VET vs VT_2NIRS (r = 0.94, p < .05). The oxygen consumption (VO₂) between VT_1VET and VT_1NIRS or VT_2VET and VT_2NIRS was not significantly different. NIRS raw data are reliable and valid for determining VT₁ and VT₂ in healthy males using a polynomial regression model. Skeletal muscle raw oxygenation and deoxygenation status reflects more realistic causes and timing of VT₁ and VT₂.