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

  • Schmitt L Centre National de Ski Nordique et de Moyenne Montagne, Ecole Nationale des Sports de Montagne Prémanon, France ; Faculty of Biology and Medicine, Institute of Sport Sciences, University of Lausanne Lausanne, Switzerland.
  • Regnard J Unité de Recherche EA3920, Marqueurs Pronostiques et Facteurs de Régulations des Pathologies Cardiaques et Vasculaires, Hôpital Universitaire de Besançon, Université de Franche-Comté Besançon, France.
  • Millet GP Faculty of Biology and Medicine, Institute of Sport Sciences, University of Lausanne Lausanne, Switzerland.
  • 2015-12-05
Published in:
  • Frontiers in physiology. - 2015
RMSSD
fatigue
heart rate variability
monitoring
overreaching
physiologic
English 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.
Language
  • English
Open access status
gold
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Persistent URL
https://folia.unifr.ch/global/documents/158799
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