Fractal Correlation Properties of Heart Rate Variability: A New Biomarker for Intensity Distribution in Endurance Exercise and Training Prescription?

Abstract

Exercise and training prescription in endurance-type sports has a strong theoretical background with various practical applications based on threshold concepts. Given the challenges and pitfalls of determining individual training zones on the basis of subsystem indicators (e.g., blood lactate concentration, respiratory parameters), the question arises whether there are alternatives for intensity distribution demarcation. Considering that training in a low intensity zone substantially contributes to the performance outcome of endurance athletes and exceeding intensity targets based on a misleading aerobic threshold can lead to negative performance and recovery effects, it would be desirable to find a parameter that could be derived via non-invasive, low cost and commonly available wearable devices. In this regard, analytics conducted from non-linear dynamics of heart rate variability (HRV) have been adapted to gain further insights into the complex cardiovascular regulation during endurance-type exercise. Considering the reciprocal antagonistic behavior and the interaction of the sympathetic and parasympathetic branch of the autonomic nervous system from low to high exercise intensities, it may be promising to use an approach that utilizes information about the regulation quality of the organismic system to determine training-intensity distribution. Detrended fluctuation analysis of HRV and its short-term scaling exponent alpha1 (DFA-alpha1) seems suitable for applied sport-specific settings including exercise from low to high intensities. DFA-alpha1 may be taken as an indicator for exercise prescription and intensity distribution monitoring in endurance-type sports. The present perspective illustrates the potential of DFA-alpha1 for diagnostic and monitoring purposes as a "global" system parameter and proxy for organismic demands.

A New Detection Method Defining the Aerobic Threshold for Endurance Exercise and Training Prescription Based on Fractal Correlation Properties of Heart Rate Variability

“…In a recent perspective review ( Gronwald et al, 2020 ), identification of a low intensity exercise zone based on DFA a1 for the purposes of endurance exercise and training prescription was discussed. The mechanism underlying DFA a1 decline with exercise is felt to be related to autonomic balance and a complex interaction of the two main branches, namely parasympathetic withdrawal, sympathetic intensification as well as other factors ( Gronwald and Hoos, 2020 ).…”

“…Within this framework, DFA a1 has been shown to decline as work rate rises, starting from strongly correlated patterns (value of 1.5) at rates well below the first ventilatory threshold (VT1), transitioning (values of 1.0–0.5) through values representing uncorrelated, less complex white noise behavior at moderate to high work rates, then finally showing anti-correlated behavior at the highest intensities (values of <0.5) ( Gronwald et al, 2019c ; Gronwald and Hoos, 2020 ). Given this relationship, there may be an opportunity to assist athletes in delineating intensity training zones by observing the change in DFA a1 with increasing exercise intensity ( Gronwald et al, 2020 ; Rogers, 2020 ).…”

Influence of Artefact Correction and Recording Device Type on the Practical Application of a Non-Linear Heart Rate Variability Biomarker for Aerobic Threshold Determination

“…The data are then plotted against the size of the window on a log-log scale. The scaling exponent represents the slope of the line, which relates (log) fluctuation to (log) window size [ 6 ]. DFA a1 window width was set to 4 ≤ n ≤ 16 beats.…”

“…This measurement is based on fractal dynamics and self-similarity of the cardiac beat to beat pattern [ 4 ]. DFA a1 has been shown to decline as work rates rise, starting from strongly correlated patterns at levels below the first ventilatory threshold, transitioning through values representing uncorrelated, less complex behavior at moderate to high work rates, then finally showing anticorrelated and random patterns at the highest intensities [ 3 , 5 , 6 ].…”

“… To explore the importance of missed beat artefact on the location of the HRVT. In other words, as a physiologic marker related to the aerobic threshold [ 6 , 7 ], does the calculated HRVT heart rate shift by the presence of artefact? Compare DFA a1 data gathered from a research grade ECG to that obtained from a Polar H7 recording device.…”