Heart Rate Variability as a Means of Assessing Workload Effects and Recovery During Exercise Training

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Heart rate variability (HRV) has gained considerable attention in the last decades, especially among sports scientists and strength and conditioning coaches to monitor training response in team and individual sports. The increasing use of HRV can be attributed to its potential benefits in optimizing performance, monitoring recovery, and ensuring the holistic well-being of athletes (i.e., training and non-training-related factors). The HRV recording provides objective data on the activity of the autonomic nervous system (ANS), which consists of the balance between the sympathetic and parasympathetic nervous systems. Notably, lower values of vagally-related HRV metrics, such as LnRMSSD, may suggest that an athlete is under heightened stress or has not fully recovered from the previous exertion. As such, daily HRV measurements (i.e., at least 3–5 recordings per week) can be used to guide coaches to manage training loads in order to mitigate the accumulation of undesirable fatigue/stress levels. With the advent of wearable technology and accessible mobile applications, measuring HRV has become easier and non-invasive to implement in practical exercise settings. Through this chapter we will see how to measure, analyse, and interpretate daily HRV recordings in a practical setting, aiming to provide more accurate and detailed insights for coaches in order to monitor athletes’ stress-recovery process.

Original languageEnglish
Title of host publicationFundamentals of Recovery, Regeneration, and Adaptation to Exercise Stress
Subtitle of host publicationAn Integrated Approach
PublisherSpringer Science + Business Media
Pages303-316
Number of pages14
ISBN (Electronic)9783031442704
ISBN (Print)9783031442698
DOIs
Publication statusPublished - 1 Jan 2025

Keywords

  • Autonomic nervous system
  • Cardiovascular system
  • Load monitoring
  • Overload
  • Recovery
  • Stress

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