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 (VT(1) and VT(2)) using raw data remains unknown. This study investigated the reliability and validity of VT(1) and VT(2) 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 (VO(2)) 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 VT(1) and VT(2) in healthy males using a polynomial regression model. Skeletal muscle raw oxygenation and deoxygenation status reflects more realistic causes and timing of VT(1) and VT(2).