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Intra- and Interrater Reliability of Short-Term Measurement of Heart Rate Variability on Rest in Individuals Post-COVID-19.

Lucivalda Viegas de AlmeidaAldair Darlan Santos-de-AraújoRodrigo Costa CutrimRudys Rodolfo de Jesus TavarezAudrey Borghi SilvaFábio Henrique Ferreira PereiraAndré Pontes-SilvaAdriana Sousa RêgoDaniel Santos RochaRenan Shida MarinhoAlmir Vieira Dibai-FilhoDaniela Bassi Dibai
Published in: International journal of environmental research and public health (2022)
Individuals affected by COVID-19 have an alteration in autonomic balance, associated with impaired cardiac parasympathetic modulation and, consequently, a decrease in heart rate variability (HRV). This study examines the inter- and intrarater reliability of HRV) parameters derived from short-term recordings in individuals post-COVID. Sixty-nine participants of both genders post-COVID were included. The RR interval, the time elapsed between two successive R-waves of the QRS signal on the electrocardiogram (RRi), were recorded during a 10 min period in a supine position using a portable heart rate monitor (Polar ® V800 model). The data were transferred into Kubios ® HRV standard analysis software and analyzed within the stable sessions containing 256 sequential RRi. The intraclass correlation coefficient (ICC) ranged from 0.920 to 1.000 according to the intrarater analysis by Researcher 01 and 0.959 to 0.999 according to the intrarater by Researcher 02. The interrater ICC ranged from 0.912 to 0.998. The coefficient of variation was up to 9.23 for Researcher 01 intrarater analysis, 6.96 for Researcher 02 intrarater analysis and 8.83 for interrater analysis. The measurement of HRV in post-COVID-19 individuals is reliable and presents a small amount of error inherent to the method, supporting its use in the clinical environment and in scientific research.
Keyphrases
  • heart rate variability
  • heart rate
  • coronavirus disease
  • sars cov
  • blood pressure
  • machine learning
  • computed tomography
  • magnetic resonance imaging
  • respiratory syndrome coronavirus
  • data analysis
  • deep learning