Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD?

Among the tools proposed to assess the athlete's “fatigue,” the analysis of heart rate variability (HRV) provides an indirect evaluation of the settings of autonomic control of heart activity. HRV analysis is performed through assessment of time-domain indices, the square root of the mean of th...

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Main Authors:	Schmitt, Laurent, Regnard, Jacques, Millet, Grégoire P.
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4652221/

id	pubmed-4652221
recordtype	oai_dc
spelling	pubmed-46522212015-12-03 Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD? Schmitt, Laurent Regnard, Jacques Millet, Grégoire P. Physiology Among the tools proposed to assess the athlete's “fatigue,” the analysis of heart rate variability (HRV) provides an indirect evaluation of the settings of autonomic control of heart activity. HRV analysis is performed through assessment of time-domain indices, the square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals (RMSSD) measured during short (5 min) recordings in supine position upon awakening in the morning and particularly the logarithm of RMSSD (LnRMSSD) has been proposed as the most useful resting HRV indicator. However, if RMSSD can help the practitioner to identify a global “fatigue” level, it does not allow discriminating different types of fatigue. Recent results using spectral HRV analysis highlighted firstly that HRV profiles assessed in supine and standing positions are independent and complementary; and secondly that using these postural profiles allows the clustering of distinct sub-categories of “fatigue.” Since, cardiovascular control settings are different in standing and lying posture, using the HRV figures of both postures to cluster fatigue state embeds information on the dynamics of control responses. Such, HRV spectral analysis appears more sensitive and enlightening than time-domain HRV indices. The wealthier information provided by this spectral analysis should improve the monitoring of the adaptive training-recovery process in athletes. Frontiers Media S.A. 2015-11-19 /pmc/articles/PMC4652221/ /pubmed/26635629 http://dx.doi.org/10.3389/fphys.2015.00343 Text en Copyright © 2015 Schmitt, Regnard and Millet. http://creativecommons.org/licenses/by/4.0/ 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) or licensor 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.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Schmitt, Laurent Regnard, Jacques Millet, Grégoire P.
spellingShingle	Schmitt, Laurent Regnard, Jacques Millet, Grégoire P. Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD?
author_facet	Schmitt, Laurent Regnard, Jacques Millet, Grégoire P.
author_sort	Schmitt, Laurent
title	Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD?
title_short	Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD?
title_full	Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD?
title_fullStr	Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD?
title_full_unstemmed	Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD?
title_sort	monitoring fatigue status with hrv measures in elite athletes: an avenue beyond rmssd?
description	Among the tools proposed to assess the athlete's “fatigue,” the analysis of heart rate variability (HRV) provides an indirect evaluation of the settings of autonomic control of heart activity. HRV analysis is performed through assessment of time-domain indices, the square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals (RMSSD) measured during short (5 min) recordings in supine position upon awakening in the morning and particularly the logarithm of RMSSD (LnRMSSD) has been proposed as the most useful resting HRV indicator. However, if RMSSD can help the practitioner to identify a global “fatigue” level, it does not allow discriminating different types of fatigue. Recent results using spectral HRV analysis highlighted firstly that HRV profiles assessed in supine and standing positions are independent and complementary; and secondly that using these postural profiles allows the clustering of distinct sub-categories of “fatigue.” Since, cardiovascular control settings are different in standing and lying posture, using the HRV figures of both postures to cluster fatigue state embeds information on the dynamics of control responses. Such, HRV spectral analysis appears more sensitive and enlightening than time-domain HRV indices. The wealthier information provided by this spectral analysis should improve the monitoring of the adaptive training-recovery process in athletes.
publisher	Frontiers Media S.A.
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4652221/
_version_	1613503604980711424

Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD?

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