Abstract

This study aimed to examine the agreement and acceptance of ultra-short-term heart rate (HR) variability (HRVUST) measures during post-exercise recovery in college football players. Twenty-five male college football players (age: 19.80 ± 1.08 years) from the first division of national university championship voluntarily participated in the study. The participants completed both a repeated sprint ability test (RSA) and a Yo-Yo intermittent recovery test level 1 (YYIR1) in a randomized order and separated by 7 days. Electrocardiographic signals (ECG) were recorded in a supine position 10 min before and 30 min after the exercise protocols. The HR and HRV data were analyzed in the time segments of baseline 5~10 min (Baseline), post-exercise 0~5 min (Post 1), post-exercise 5~10 min (Post 2), and post-exercise 25~30 min (Post 3). The natural logarithm of the standard deviation of normal-to-normal intervals (LnSDNN), root mean square of successive normal-to-normal interval differences (LnRMSSD), and LnSDNN:LnRMSSD ratio was compared in the 1st min HRVUST and 5-min criterion (HRVcriterion) of each time segment. The correlation of time-domain HRV variables to 5-min natural logarithm of low frequency power (LnLF) and high frequency power (LnHF), and LF:HF ratio were calculated. The results showed that the HRVUST of LnSDNN, LnRMSSD, and LnSDNN:LnRMSSD ratio showed trivial to small effect sizes (ES) (−0.00~0.49), very large and nearly perfect interclass correlation coefficients (ICC) (0.74~0.95), and relatively small values of bias (RSA: 0.01~−0.12; YYIR1: −0.01~−0.16) to the HRVcriterion in both exercise protocols. In addition, the HRVUST of LnLF, LnHF, and LnLF:LnHF showed trivial to small ES (−0.04~−0.54), small to large ICC (−0.02~0.68), and relatively small values of bias (RSA: −0.02~0.65; YYIR1: 0.03~−0.23) to the HRVcriterion in both exercise protocols. Lastly, the 1-min LnSDNN:LnRMSSD ratio was significantly correlated to the 5-min LnLF:LnHF ratio with moderate~high level (r = 0.43~0.72; p < 0.05) during 30-min post-exercise recovery. The post-exercise 1-min HRV assessment in LnSDNN, LnRMSSD, and LnSDNN:LnRMSSD ratio was acceptable and accurate in the RSA and YYIR1 tests, compared to the 5-min time segment of measurement. The moderate to high correlation coefficient of the HRVUST LnSDNN:LnRMSSD ratio to the HRVcriterion LnLF:LnHF ratio indicated the capacity to facilitate the post-exercise shortening duration of HRV measurement after maximal anaerobic or aerobic shuttle running. Using ultra-short-term record of LnSDNN:LnRMSSD ratio as a surrogate for standard measure of LnLF:LnHF ratio after short-term bouts of maximal intensity field-based shuttle running is warranted.

Highlights

  • Heart rate (HR) variability (HRV) is a physiological process that reflects the biological fluctuation in cardiac activation that is regulated by the autonomic nervous system (ANS)

  • Materials and Methods hypothesized that HRVUST of LnSDNN:LnRMSSD ratio would demonstrate significant correlations to HRVcriterion of LnLF:LnHF ratio during post-exercise recovery in both exercise protocols

  • The results showed a broad range of Pearson correlation coefficient between the HRVUST and time points in either the relatively small values of bias (RSA) test (r = 0.34 moderate, p = 0.09~r = 0.73 very high, p < 0.01) or the Yo-Yo intermittent recovery test level 1 (YYIR1)

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Summary

Introduction

Heart rate (HR) variability (HRV) is a physiological process that reflects the biological fluctuation in cardiac activation that is regulated by the autonomic nervous system (ANS). Heart rate variability assessment requires biosignal recording via non-invasive techniques to detect beat-to-beat intervals of the HR responses in a time series. North American Society of Pacing Electrophysiology recommend a series of 512 R-wave to R-wave intervals (RRI) for HRV data analysis [7]. The standardization of testing procedure requires around 10 min to obtain sufficient number of RRI for time-domain, frequency-domain, and non-liner analyses. This process is very time-consuming and is mainly limited to clinical application; this type of analysis is seldom used in applied sports settings. There is a need for more time-efficient methods for analyzing HRV in applied sports contexts

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