2015
DOI: 10.1111/cpf.12331
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Heart rate variability in elite sprinters: effects of gender and body position

Abstract: To describe the heart rate variability (HRV) of high-level sprinters in both the supine and standing positions, the HRV of 7 male (24 ± 6 years; 80 ± 8 kg and 182 ± 7 cm) and 11 female (27 ± 5 years; 61 ± 4 kg; 167 ± 5 cm) high-level Brazilian sprinters was measured in the standing and supine body positions in both the time and frequency domains. The heart rate (HR) and the time (SDNN, RMSSD) and frequency (LF; HF and LF:HF) domains of the HRV were assessed. Natural log-transformations (ln) of all HRV indices … Show more

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Cited by 19 publications
(19 citation statements)
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“…The Kubios software (v.2.2) (Tarvainen, Niskanen, Lipponen, & Ranta‐aho, & Karjalainen, ) was used to analyze the RR intervals data and artifacts that were manually filtered by visual inspection. Ectopic beats and artifacts were automatically detected and replaced by interpolated adjacent beats by applying an adaptive filter to generate normal‐to‐normal (NN) interval time series (as seen in Abad et al, ; Nakamura et al, ; Pereira et al, ; Tarvainen et al, ). HRV was analyzed in time, frequency and Poincare geometric measures domain (see Tarvainen et al, for detailed HRV indices calculation).…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The Kubios software (v.2.2) (Tarvainen, Niskanen, Lipponen, & Ranta‐aho, & Karjalainen, ) was used to analyze the RR intervals data and artifacts that were manually filtered by visual inspection. Ectopic beats and artifacts were automatically detected and replaced by interpolated adjacent beats by applying an adaptive filter to generate normal‐to‐normal (NN) interval time series (as seen in Abad et al, ; Nakamura et al, ; Pereira et al, ; Tarvainen et al, ). HRV was analyzed in time, frequency and Poincare geometric measures domain (see Tarvainen et al, for detailed HRV indices calculation).…”
Section: Methodsmentioning
confidence: 99%
“…Ectopic beats and artifacts were automatically detected and replaced by interpolated adjacent beats by applying an adaptive filter to generate normal-to-normal (NN) interval time series (as seen in Abad et al, 2017;Nakamura et al, 2015;Pereira et al, 2016;Tarvainen et al, 2014 (Brennan, Palaniswami, & Kamen, 2001;Kamen, Krum, & Tonkin, 1996;Tarvainen et al, 2014;Task Force, 1996).…”
Section: Physiologic Recordingmentioning
confidence: 99%
“…The reasons of these discrepancies are unclear, but some aspects should be considered. For example, it has been shown that body posture may change the autonomic and hemodynamic control [42,43], and our subjects were seated while Clancy et al [21] performed tests in a semi-supine position. We decided to perform experiments in a seated position due the fact that it is likely possible that people will conduct tVNS whilst watching TV or other such daily activity (i.e., external validity).…”
Section: Accepted Manuscriptmentioning
confidence: 99%
“…Aerobic training mainly alters parasympathetic indices derived from time or from spectral analyses and these indices may contribute to predict endurance performance (Buchheit et al, 2010). These indices are usually assessed on supine rest since this position is known to maximize parasympathetic nervous system (PNS) activity (Abad et al, 2017;Silva et al, 2017;Young and Leicht, 2011). Among these parasympathetic indices, the Roots Mean Square of the Successive Differences (RMSSD) has been widely used given its robustness for aerobic training monitoring (Buchheit, 2014).…”
Section: Introductionmentioning
confidence: 99%