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Practices and Applications of Heart Rate Variability Monitoring in Endurance Athletes
Abstract 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 ra...
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Published in: | International journal of sports medicine 2023-01, Vol.44 (1), p.9-19 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Abstract
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. |
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ISSN: | 0172-4622 1439-3964 |
DOI: | 10.1055/a-1864-9726 |