Abstract
This study’s purpose was to examine heart rate variability (HRV) and direct current potential (DC) measures’ sensitivity and correlations between changes in the acute recovery and stress scale (ARSS) and the previous day’s training load. Training load, HRV, DC and ARSS data were collected from fourteen professional mixed martial arts athletes (32.6 ± 5.3 years, 174.8 ± 8.8 cm, 79.2 ± 17.5 kg) the following morning after hard, easy and rest days. Sensitivity was expressed as a signal-to-noise ratio (SNR, inter-day typical error (TE) or coefficient of variation (%CV) divided by intra-day TE or %CV). Correlations between HRV, DC and ARSS with training load were also examined. The SNRs for the various HRV and DC measures were acceptable to good (1.02–2.85). There was a 23.1% CV average increase between measures taken between different locations versus the same location. Training load changes were not correlated with HRV/DC but were correlated with ARSS stress variables. Practitioners should be aware of HRV/DC variability; however the daily training signal was greater than the test-retest error in this investigation. Upon awakening, HRV/DC measures appear superior for standardization and planning. HRV and DC measures were less sensitive to the previous day’s training load than ARSS measures.
Highlights
Various monitoring tools are applied in professional sports to help estimate an athlete’s potential risk of injury and readiness to train or compete [1,2]
We have provided the heart rate variability (HRV) and direct current potential (DC) measures’ signal-to-noise ratio (SNR) to aid in this evaluation of sensitivity and overall usefulness of these measures for informing decisions on training availability and optimal training choices
It seems that HRV and DC measures taken upon awakening are superior if able to be practically implemented
Summary
Various monitoring tools are applied in professional sports to help estimate an athlete’s potential risk of injury and readiness to train or compete [1,2]. Reliability measures of athlete monitoring tools can be measured using a test-retest analysis by taking repeated measures from the same individual under as close as possible to identical test conditions [5,6]. This approach can be used to calculate reliability statistics including an intra-class correlation (ICC), an absolute typical error (TE), a standardised typical error (sTE) and a coefficient of variation (%CV).
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