Fractal correlation properties of HRV as a noninvasive biomarker to assess the physiological status of triathletes during simulated warm-up sessions at low exercise intensity: a pilot study

Abstract

Background The non-linear index alpha 1 of Detrended Fluctuation Analysis (DFA a1) of heart rate variability, has been shown to be a marker of fatigue during endurance exercise. This report aims to explore its ability to assess the physiological status as a surrogate metric for “readiness to train” while performing simulated warm-up sessions the day after two different exercise sessions. Methods 11 triathletes were recruited to determine the first ventilatory threshold (VT1) during a baseline assessment and to perform 10-min of cycling at 90% of VT1 (simulating a warm-up bout) before (PRE) and within 36 h after (POST) light and heavy running exercise. RR intervals were recorded for DFA a1 analysis along with neuromuscular testing to verify the effects of the performed exercise sessions. In addition to common statistical methods, magnitude-based inferences (MBI) were applied to assess the changes in true score and thus also the practical relevance of the magnitude. Results Rating of perceived exertion for the heavy exercise session showed a significant higher rating as opposed to the light exercise session (p < 0.001, d = 0.89). In regard of MBIs, PRE versus POST comparisons revealed a significant reduced DFA a1 with large effect size after the heavy exercise session (p = 0.001, d = − 1.44) and a 99% chance that this negative change was clinically relevant. Conclusions Despite inter-individual differences, DFA a1 offers potential to assess physiological status and guide athletes in their training as an easy-to-apply monitoring procedure during a standardized warm-up. A regular assessment including individual data history and statistical references for identification of response is recommended. Further data are necessary to confirm the results in a larger and more homogeneous population.

Unveiling the acute neurophysiological responses to strength training: an exploratory study on novices performing weightlifting bouts with different motor learning models

“…From the ECG signal, the R-peaks are detected to derive the RR interval time series. Both the ECG quality [ 65 ] and the quality of the derived RR intervals [ 66 , 67 ] using Polar H10 were previously compared to the gold standard ECG measurement using a 12-channel medical-grade ECG device and found to be excellent.…”

Reliability and validity of a non-linear index of heart rate variability to determine intensity thresholds

“…Moreover, apart from its application in determining intensity domains during incremental tests, HRV can be employed in other contexts due to its sensitivity to homeostatic perturbations such as fatigue or psychological factors ( Blasco-Lafarga et al, 2017 ). In fact, when combined with its ease of periodic use throughout a training program, this capability allows for the adjustment of training intensities on a day-to-day basis based on the athlete’s status, as suggested by previous studies ( Javaloyes et al, 2019 ; Schaffarczyk et al, 2022a ; Van Hooren et al, 2023 ).…”

Fractal correlation properties of heart rate variability as a marker of exercise intensity during incremental and constant-speed treadmill running

“…While DFAα1 is reportedly able to delineate exercise intensity domains, quantify systemic perturbation imposed by a previous bout of exhaustive exercise, and indicate fatigue accumulation during endurance exercise (Gronwald & Hoos, 2020; Rogers & Gronwald, 2022; Rogers, Mourot, et al, 2021; Schaffarczyk et al, 2022), there is limited data to support these suppositions, particularly in running. Accordingly, the purposes of this study were to: i) test whether DFAα1 values of 0.75 and 0.50 coincide with the gas exchange threshold (GET) and respiratory compensation point (RCP), respectively; ii) quantify the DFAα1 response during constant-speed exercise near the maximal lactate steady state (MLSS)—a proxy of the MMSS; and iii) determine the repeatability of DFAα1 at MLSS.…”