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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4652221/ https://www.ncbi.nlm.nih.gov/pubmed/26635629 http://dx.doi.org/10.3389/fphys.2015.00343 |
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author | Schmitt, Laurent Regnard, Jacques Millet, Grégoire P. |
author_facet | Schmitt, Laurent Regnard, Jacques Millet, Grégoire P. |
author_sort | Schmitt, Laurent |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-4652221 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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. Front Physiol 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. |
spellingShingle | Physiology Schmitt, Laurent Regnard, Jacques Millet, Grégoire P. Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD? |
title | 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_short | 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? |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4652221/ https://www.ncbi.nlm.nih.gov/pubmed/26635629 http://dx.doi.org/10.3389/fphys.2015.00343 |
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