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Monitoring Training Adaptation and Recovery Status in Athletes Using Heart Rate Variability via Mobile Devices: A

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Heart rate variability (HRV) monitoring, particularly using the root mean square of successive differences (RMSSD), offers valuable insights into athlete recovery and training adaptation. Routine daily measurements and weekly averages are superior for tracking physiological changes.

Keywords:
HRVathlete performanceautonomic modulationsport sciencewearable device

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Area of Science:

  • Sports Science
  • Physiology
  • Autonomic Nervous System Research

Background:

  • Heart rate variability (HRV) is a key non-invasive biomarker for assessing autonomic nervous system function.
  • HRV provides insights into physiological adaptation, stress, and recovery in athletes.
  • The root mean square of successive differences (RMSSD) is a reliable HRV metric linked to parasympathetic activity.

Purpose of the Study:

  • To review methodological considerations for using HRV in athletic populations.
  • To highlight the benefits of routine HRV measurements for monitoring training and recovery.
  • To discuss practical aspects of HRV device selection, data recording, and athlete compliance.

Main Methods:

  • Review of existing literature on HRV in sports science.
  • Analysis of RMSSD as a primary metric for field-based monitoring.
  • Discussion of data analysis strategies including weekly averages and coefficient of variation (CV).

Main Results:

  • Routine, near-daily HRV measurements are more effective than isolated assessments for tracking adaptations.
  • Weekly averages and CV are useful for capturing chronic changes and acute perturbations.
  • RMSSD is a practical measure but has limitations regarding parasympathetic focus and confounders.

Conclusions:

  • Evidence-based recommendations are provided for sport scientists and practitioners using HRV.
  • Integrating HRV with other markers and machine learning can optimize training and recovery.
  • Optimizing HRV application requires careful consideration of methodology, device selection, and data interpretation.