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Published on: June 5, 2019
Detailed heart rate variability analysis in athletes.
Orsolya Kiss1, Nóra Sydó1, Péter Vargha1
1Semmelweis University Heart and Vascular Center, 1122, Varosmajor str. 68., Budapest, Hungary.
Elite athletes exhibit distinct heart rate variability (HRV) compared to non-athletes, with training influencing autonomic regulation. This study establishes athletic normal HRV values for elite and masters athletes.
Area of Science:
- Sports Medicine and Physiology
- Cardiovascular Health
- Autonomic Nervous System Function
Background:
- Heart rate variability (HRV) analysis is a key tool for assessing cardiovascular health.
- Previous HRV research predominantly focused on pathological conditions, neglecting athletic populations.
- Understanding HRV in athletes is crucial for performance optimization and health monitoring.
Purpose of the Study:
- To investigate HRV differences across various training intensities and sports in healthy athletes.
- To compare HRV parameters between elite athletes, masters athletes, and non-athletic controls.
- To establish normative HRV values specific to athletic populations.
Main Methods:
- Time-domain HRV analysis was conducted using Holter monitoring (21.3 ± 3.0 hours).
- The study included 138 athletes (elite and masters) and 100 healthy non-athletic controls.
- Data analysis focused on key HRV parameters like SDNN, SDNN Index, pNN50, and RMSSD.
Main Results:
- Elite athletes demonstrated significantly higher HRV parameters (SDNN, SDNN Index, pNN50, RMSSD) compared to controls (p < 0.001).
- Masters athletes showed higher HRV than controls, with no significant difference between elite and masters athletes.
- Specific sports modalities (canoeing-kayaking, cycling) showed variations in HRV compared to running.
Conclusions:
- Elite athletes possess significantly different autonomic regulation, as indicated by HRV, compared to healthy controls.
- HRV in athletes is influenced by sport type and performance level, not age or sex.
- This study provides essential athletic normal HRV values, highlighting potential applications in training optimization and overtraining detection.
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