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      • Record: found
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      Real-Time Estimation of Aerobic Threshold and Exercise Intensity Distribution Using Fractal Correlation Properties of Heart Rate Variability: A Single-Case Field Application in a Former Olympic Triathlete

      brief-report

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          Abstract

          A non-linear heart rate variability (HRV) index based on fractal correlation properties called alpha1 of Detrended Fluctuation Analysis (DFA-alpha1), has been shown to change with endurance exercise intensity. Its unique advantage is that it provides information about current absolute exercise intensity without prior lactate or gas exchange testing. Therefore, real-time assessment of this metric during field conditions using a wearable monitoring device could directly provide a valuable exercise intensity distribution without prior laboratory testing for different applied field settings in endurance sports. Until of late no mobile based product could display DFA-alpha1 in real-time using off the shelf consumer products. Recently an app designed for iOS and Android devices, HRV Logger, was updated to assess DFA-alpha1 in real-time. This brief research report illustrates the potential merits of real-time monitoring of this metric for the purposes of aerobic threshold (AT) estimation and exercise intensity demarcation between low (zone 1) and moderate (zone 2) in a former Olympic triathlete. In a single-case feasibility study, three practically relevant scenarios were successfully evaluated in cycling, (1) estimation of a HRV threshold (HRVT) as an adequate proxy for AT using Kubios HRV software via a typical cycling stage test, (2) estimation of the HRVT during real-time monitoring using a cycling 6 min stage test, (3) a simulated 1 h training ride with enforcement of low intensity boundaries and real-time HRVT confirmation. This single-case field evaluation illustrates the potential of an easy-to-use and low cost real-time estimation of the aerobic threshold and exercise intensity distribution using fractal correlation properties of HRV. Furthermore, this approach may enhance the translation of science into endurance sports practice for future real-world settings.

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          Kubios HRV--heart rate variability analysis software.

          Mika P. Tarvainen, Juha-Pekka Niskanen, Jukka A. Lipponen … (2014)
          Kubios HRV is an advanced and easy to use software for heart rate variability (HRV) analysis. The software supports several input data formats for electrocardiogram (ECG) data and beat-to-beat RR interval data. It includes an adaptive QRS detection algorithm and tools for artifact correction, trend removal and analysis sample selection. The software computes all the commonly used time-domain and frequency-domain HRV parameters and several nonlinear parameters. There are several adjustable analysis settings through which the analysis methods can be optimized for different data. The ECG derived respiratory frequency is also computed, which is important for reliable interpretation of the analysis results. The analysis results can be saved as an ASCII text file (easy to import into MS Excel or SPSS), Matlab MAT-file, or as a PDF report. The software is easy to use through its compact graphical user interface. The software is available free of charge for Windows and Linux operating systems at http://kubios.uef.fi. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
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            Monitoring Athlete Training Loads: Consensus Statement.

            Pitre Bourdon, Marco Cardinale, Andrew Murray … (2017)
            Monitoring the load placed on athletes in both training and competition has become a very hot topic in sport science. Both scientists and coaches routinely monitor training loads using multidisciplinary approaches, and the pursuit of the best methodologies to capture and interpret data has produced an exponential increase in empirical and applied research. Indeed, the field has developed with such speed in recent years that it has given rise to industries aimed at developing new and novel paradigms to allow us to precisely quantify the internal and external loads placed on athletes and to help protect them from injury and ill health. In February 2016, a conference on "Monitoring Athlete Training Loads-The Hows and the Whys" was convened in Doha, Qatar, which brought together experts from around the world to share their applied research and contemporary practices in this rapidly growing field and also to investigate where it may branch to in the future. This consensus statement brings together the key findings and recommendations from this conference in a shared conceptual framework for use by coaches, sport-science and -medicine staff, and other related professionals who have an interest in monitoring athlete training loads and serves to provide an outline on what athlete-load monitoring is and how it is being applied in research and practice, why load monitoring is important and what the underlying rationale and prospective goals of monitoring are, and where athlete-load monitoring is heading in the future.
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              Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series.

              C. Peng, S. Havlin, H. Stanley … (1995)
              The healthy heartbeat is traditionally thought to be regulated according to the classical principle of homeostasis whereby physiologic systems operate to reduce variability and achieve an equilibrium-like state [Physiol. Rev. 9, 399-431 (1929)]. However, recent studies [Phys. Rev. Lett. 70, 1343-1346 (1993); Fractals in Biology and Medicine (Birkhauser-Verlag, Basel, 1994), pp. 55-65] reveal that under normal conditions, beat-to-beat fluctuations in heart rate display the kind of long-range correlations typically exhibited by dynamical systems far from equilibrium [Phys. Rev. Lett. 59, 381-384 (1987)]. In contrast, heart rate time series from patients with severe congestive heart failure show a breakdown of this long-range correlation behavior. We describe a new method--detrended fluctuation analysis (DFA)--for quantifying this correlation property in non-stationary physiological time series. Application of this technique shows evidence for a crossover phenomenon associated with a change in short and long-range scaling exponents. This method may be of use in distinguishing healthy from pathologic data sets based on differences in these scaling properties.
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                Author and article information

                Contributors
                Journal
                Journal ID (nlm-ta): Front Sports Act Living
                Journal ID (iso-abbrev): Front Sports Act Living
                Journal ID (publisher-id): Front. Sports Act. Living
                Title: Frontiers in Sports and Active Living
                Publisher: Frontiers Media S.A.
                ISSN (Electronic): 2624-9367
                Publication date (Electronic): 28 May 2021
                Publication date Collection: 2021
                Volume: 3
                Electronic Location Identifier: 668812
                Affiliations
                [1] 1Faculty of Health Sciences, Department of Performance, Neuroscience, Therapy and Health, MSH Medical School Hamburg, University of Applied Sciences and Medical University , Hamburg, Germany
                [2] 2Dutch Triathlon Federation , Arnhem, Netherlands
                [3] 3Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam , Amsterdam, Netherlands
                [4] 4EA3920 Prognostic Factors and Regulatory Factors of Cardiac and Vascular Pathologies, Exercise Performance Health Innovation (EPHI) Platform, University of Bourgogne Franche-Comté , Besançon, France
                [5] 5Division for Physical Education, National Research Tomsk Polytechnic University , Tomsk, Russia
                [6] 6Center for Sports and Physical Education, Julius-Maximilians-University of Wuerzburg , Wuerzburg, Germany
                [7] 7College of Medicine, University of Central Florida , Orlando, FL, United States
                Author notes

                Edited by: Julien Louis, Liverpool John Moores University, United Kingdom

                Reviewed by: Sarah J. Willis, University of Lausanne, Switzerland; Ari Tapani Nummela, Research Institute for Olympic Sports, Finland

                *Correspondence: Thomas Gronwald thomas.gronwald@ 123456medicalschool-hamburg.de

                This article was submitted to Elite Sports and Performance Enhancement, a section of the journal Frontiers in Sports and Active Living

                Article
                DOI: 10.3389/fspor.2021.668812
                PMC ID: 8193503
                PubMed ID: 34124661
                SO-VID: cf364755-0dc2-4289-9bc9-c0a3b326e800
                Copyright © Copyright © 2021 Gronwald, Berk, Altini, Mourot, Hoos and Rogers.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                Date received : 18 February 2021
                Date accepted : 04 May 2021
                Page count
                Figures: 5, Tables: 0, Equations: 0, References: 38, Pages: 10, Words: 5963
                Categories
                Subject: Sports and Active Living
                Subject: Brief Research Report

                Keywords: hrv,autonomic nervous system,endurance exercise,endurance training,polarized training
                Data availability:
                Keywords: hrv, autonomic nervous system, endurance exercise, endurance training, polarized training

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