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

Acute physiological responses to eccentric cycling: a systematic review and meta-analysis

INTRODUCTION

Eccentric cycling (ECCCYC) has attracted considerable interest due to its potential applicability for exercise treatment/training of patients with poor exercise tolerance as well as healthy and trained individuals. Conversely, little is known about the acute physiological responses to this exercise modality, thus challenging its proper prescription. This study aimed to provide precise estimates of the acute physiological responses to ECCCYC in comparison to traditional concentric cycling (CONCYC).

EVIDENCE ACQUISITION

Searches were performed until November 2021 using the PubMed, Embase, and ScienceDirect databases. Studies that examined individuals' cardiorespiratory, metabolic, and perceptual responses to ECCCYC and CONCYC sessions were included. Bayesian multilevel meta-analysis models were used to estimate the population mean difference between acute physiological responses from ECCCYC and CONCYC bouts. Twenty-one studies were included in this review.

EVIDENCE SYNTHESIS

The meta-analyses showed that ECCCYC induced lower cardiorespiratory (i.e., V̇O2, V̇E, and HR), metabolic (i.e., [BLa]), and perceptual (i.e., RPE) responses than CONCYC performed at the same absolute power output, while greater cardiovascular strain (i.e., greater increases in HR, Q, MAP, [norepinephrine], and lower SV) was detected when compared to CONCYC performed at the same V̇O2.

CONCLUSIONS

The prescription of ECCCYC based on workloads used in the CONCYC sessions may be considered safe and, therefore, feasible for the rehabilitation of individuals with poor exercise tolerance. However, the prescription of ECCCYC based on the V̇O2 obtained during CONCYC sessions should be conducted with caution, especially in clinical settings, since there is a high probability of additional cardiovascular overload in this condition.

Fractal Correlation Properties of Heart Rate Variability as a Biomarker for Intensity Distribution and Training Prescription in Endurance Exercise: An Update

While established methods for determining physiologic exercise thresholds and intensity distribution such as gas exchange or lactate testing are appropriate for the laboratory setting, they are not easily obtainable for most participants. Data over the past two years has indicated that the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA a1), a heart rate variability (HRV) index representing the degree of fractal correlation properties of the cardiac beat sequence, shows promise as an alternative for exercise load assessment. Unlike conventional HRV indexes, it possesses a dynamic range throughout all intensity zones and does not require prior calibration with an incremental exercise test. A DFA a1 value of 0.75, reflecting values midway between well correlated fractal patterns and uncorrelated behavior, has been shown to be associated with the aerobic threshold in elite, recreational and cardiac disease populations and termed the heart rate variability threshold (HRVT). Further loss of fractal correlation properties indicative of random beat patterns, signifying an autonomic state of unsustainability (DFA a1 of 0.5), may be associated with that of the anaerobic threshold. There is minimal bias in DFA a1 induced by common artifact correction methods at levels below 3% and negligible change in HRVT even at levels of 6%. DFA a1 has also shown value for exercise load management in situations where standard intensity targets can be skewed such as eccentric cycling. Currently, several web sites and smartphone apps have been developed to track DFA a1 in retrospect or in real-time, making field assessment of physiologic exercise thresholds and internal load assessment practical. Although of value when viewed in isolation, DFA a1 tracking in combination with non-autonomic markers such as power/pace, open intriguing possibilities regarding athlete durability, identification of endurance exercise fatigue and optimization of daily training guidance.