Practices and applications of heart rate variability monitoring in endurance athletes

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Bibliographische Detailangaben
Deutscher übersetzter Titel:Praktiken und Anwendungsbereiche des Monitorings der Herzfrequenzvariabilität bei Ausdauersportlern
Autor:Lundstrom, Christopher J.; Foreman, Nicholas A.; Biltz, George
Erschienen in:International journal of sports medicine
Veröffentlicht:44 (2023), 1, S. 9-19, Lit.
Format: Literatur (SPOLIT)
Publikationstyp: Zeitschriftenartikel
Medienart: Elektronische Ressource (online) Gedruckte Ressource
Sprache:Englisch
ISSN:0172-4622, 1439-3964
DOI:10.1055/a-1864-9726
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Erfassungsnummer:PU202302001017
Quelle:BISp

Abstract des Autors

Heart rate variability reflects fluctuations in the changes in consecutive heartbeats, providing insight into cardiac autonomic function and overall physiological state. Endurance athletes typically demonstrate better cardiac autonomic function than non-athletes, with lower resting heart rates and greater variability. The availability and use of heart rate variability metrics has increased in the broader population and may be particularly useful to endurance athletes. The purpose of this review is to characterize current practices and applications of heart rate variability analysis in endurance athletes. Important considerations for heart rate variability analysis will be discussed, including analysis techniques, monitoring tools, the importance of stationarity of data, body position, timing and duration of the recording window, average heart rate, and sex and age differences. Key factors affecting resting heart rate variability will be discussed, including exercise intensity, duration, modality, overall training load, and lifestyle factors. Training applications will be explored, including heart rate variability-guided training and the identification and monitoring of maladaptive states such as overtraining. Lastly, we will examine some alternative uses of heart rate variability, including during exercise, post-exercise, and for physiological forecasting and predicting performance.