Detection of the Anaerobic Threshold in Endurance Sports: Validation of a New Method Using Correlation Properties of Heart Rate Variability

Abstract

Past attempts to define an anaerobic threshold (AnT) have relied upon gas exchange kinetics, lactate testing and field-based evaluations. DFA a1, an index of heart rate (HR) variability (HRV) fractal correlation properties, has been shown to decrease with exercise intensity. The intent of this study is to investigate whether the AnT derived from gas exchange is associated with the transition from a correlated to uncorrelated random HRV pattern signified by a DFA a1 value of 0.5. HRV and gas exchange data were obtained from 15 participants during an incremental treadmill run. Comparison of the HR reached at the second ventilatory threshold (VT2) was made to the HR reached at a DFA a1 value of 0.5 (HRVT2). Based on Bland–Altman analysis and linear regression, there was strong agreement between VT2 and HRVT2 measured by HR (r = 0.78, p < 0.001). Mean VT2 was reached at a HR of 174 (±12) bpm compared to mean HRVT2 at a HR of 171 (±16) bpm. In summary, the HR associated with a DFA a1 value of 0.5 on an incremental treadmill ramp was closely related to that of the HR at the VT2 derived from gas exchange analysis. A distinct numerical value of DFA a1 representing an uncorrelated, random interbeat pattern appears to be associated with the VT2 and shows potential as a noninvasive marker for training intensity distribution and performance status.

Analysis of Fractal Correlation Properties of Heart Rate Variability during an Initial Session of Eccentric Cycling

“…A DFA a1 of 0.75 has been associated with intensities near the VT1 during incremental tests while cycling with conventional, concentric contractions or running [ 11 , 15 ]. One participant had a value below 0.5, which corresponds to exercise intensities above the anaerobic threshold [ 12 ]. This same participant showed the largest HR rise during ECC T45 of nearly 50 bpm.…”

“…Even under optimal circumstances, prescribing training intensity targets based on predetermined formulas can be problematic [ 28 , 29 ]. Leveraging the well-established relationship of DFA a1 to exercise intensity levels [ 10 , 11 , 12 ] may therefore provide needed exercise prescription guidance. This could be in the form of estimating an ECC related aerobic threshold, as well as prevention of excessive exercise intensities.…”

“…In addition, they require a dedicated exercise ramp to failure for standardization to HR, VO 2 , or power. Recently, a nonlinear HRV index, the short-term exponent alpha 1 of detrended fluctuation analysis (DFA a1) based on fractal correlation properties, has been shown to decline with increasing exercise loads throughout all intensity domains providing information about physiological transitions corresponding to the aerobic and anaerobic threshold (e.g., as represented by the first and second ventilatory thresholds) [ 10 , 11 , 12 ]. Fractal behavior in relation to HRV can be described as degrees of self-similarity between RR interval variations over different time scales [ 13 ].…”

“…At low exercise intensity, DFA a1 values are usually in a well correlated fractal range near or above 1.0. As exercise intensity rises, DFA a1 passes 0.75 at the aerobic threshold and continues to drop to an uncorrelated random pattern (0.5) near the anaerobic threshold, finally reaching below 0.5, signifying an anticorrelated range at the very highest work rates [ 10 , 11 , 12 ]. The anticorrelated state can be viewed as a protective feedback and stabilizing mechanism, where interactions and/or coordination of subsystems fail before the entire system fails [ 13 , 14 ].…”

Respiratory Activity during Exercise: A Feasibility Study on Transition Point Estimation Using Impedance Pneumography

“…Hence, the constant search for another method for threshold detection, to make the measurements even more non-invasive: a method that would make the procedure simple, easily accessible, and more convenient for the subject, especially for patients with respiratory or cardiological limitations; a method that would be reliable, as well, of course. Several methods that utilize electromyography recordings or heart rate variability analysis based on electrocardiography signals have been presented [ 15 , 16 , 17 , 18 , 19 ]. However, those approaches seems very indirect; therefore, they should be treated rather as illustratory.…”