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Estimation of Runners' Lactate Threshold Heart Rate and Speed by Heart Rate Variability.
Changda Lu1, Siyu Lu2, Yiwei Wu1
1School of Sport Science, Beijing Sport University, Beijing, China.
Heart rate variability (HRV) metrics show promise for non-invasively predicting lactate threshold in runners. While some HRV thresholds underestimated lactate threshold heart rate, others correlated moderately with lactate threshold speed, suggesting potential for training optimization.
Area of Science:
- Exercise Physiology
- Autonomic Nervous System Function
- Sports Science
Background:
- Accurate training intensity measurement is key for endurance performance.
- Lactate threshold (LT) is a critical performance indicator but requires invasive methods.
- Heart rate variability (HRV) offers a non-invasive approach to assess autonomic nervous system activity.
Purpose of the Study:
- To evaluate the validity of various HRV metrics in predicting LT heart rate and speed.
- To compare HRV thresholds against LT during graded exercise tests (indoor and outdoor).
- To identify potential non-invasive predictors for optimizing endurance training.
Main Methods:
- 19 recreational runners performed indoor and outdoor graded exercise tests.
- LT heart rate and speed were determined using standard methods.
- HRV thresholds (HRVT1-HRVT6) were calculated using SD1, SD2, DFA exponents, SDNN, and RMSSD via the Dmax method.
Main Results:
- Indoor tests: HRV thresholds often underestimated LT heart rate; most HRV thresholds differed significantly from LT.
- Indoor tests: Moderate correlations found for SDNN (HRVT5HR, r=0.66) and DFA exponents (HRVT3speed, r=0.70) with LT speed.
- Outdoor tests: Most HRV thresholds showed no significant difference from LT; moderate correlations (r=0.54-0.7) observed for HRV speed thresholds with LT speed.
Conclusions:
- HRV thresholds and LT may represent distinct physiological markers.
- Certain HRV metrics, especially DFA exponents, show potential as non-invasive predictors of LT in recreational runners.
- HRV offers a promising, non-invasive tool for monitoring training intensity and optimizing performance.
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