Estimation of physiological exercise thresholds based on dynamical correlation properties of heart rate variability

Abstract

Aerobic and anaerobic thresholds of the three-zone exercise model are often used to evaluate the exercise intensity and optimize the training load. Conventionally, these thresholds are derived from the respiratory gas exchange or blood lactate concentration measurements. Here, we introduce and validate a computational method based on the RR interval (RRI) dynamics of the heart rate (HR) measurement, which enables a simple, yet reasonably accurate estimation of both metabolic thresholds. The method utilizes a newly developed dynamical detrended fluctuation analysis (DDFA) to assess the real-time changes in the dynamical correlations of the RR intervals during exercise. The training intensity is shown to be in direct correspondence with the time- and scale-dependent changes in the DDFA scaling exponent. These changes are further used in the definition of an individual measure to estimate the aerobic and anaerobic threshold. The results for 15 volunteers who participated in a cyclo-ergometer test are compared to the benchmark lactate thresholds, as well as to the ventilatory threshods and alternative HR-based estimates based on the maximal HR and the conventional detrended fluctuation analysis (DFA). Our method provides the best overall agreement with the lactate thresholds and provides a promising, cost-effective alternative to conventional protocols, which could be easily integrated in wearable devices. However, detailed statistical analysis reveals the particular strengths and weaknessess of each method with respect to the agreement and consistency with the thresholds-thus underlining the need for further studies with more data.

Keywords: aerobic threshold; anaerobic threshold; detrended fluctuation analysis; heart rate variability; time series analysis; wearable health technology.

FIGURE 1

Illustration of the determination of the thresholds DDFAT₁ and DDFAT₂ (cyan vertical lines) compared to the lactate thresholds (black vertical lines) for subject 3 during the exercise. The results are plotted as a function of binned HR in (A) and as a function of time in (B).

FIGURE 2

Aggregate plot of the DDFA scaling exponents for all 15 subjects as a function of the heart rate. The mean values of the aerobic and anaerobic lactate thresholds are shown as vertical dashed lines, and the box plots show the distributions of the threshold values.

FIGURE 3

(A) Comparison of differences between LT₁ (x-axis) and LT₂ (y-axis) for each subject with different threshold estimation methods. (B) Box plot of the summed absolute differences from both LT₁ and LT₂.

FIGURE 4

Bland–Altman plots of the differences to lactate for each method for (A) threshold 1 (B) threshold 2. The solid lines correspond to the mean (dark) and 95% limits of agreement (light) of the distributions.