Possibilities and limitations of the polar RS800 in measuring heart rate variability at rest

Abstract

A growing trend among clinical studies is the use of heart rate monitors (HRMs) for assessment of heart rate variability (HRV). These instruments offer a convenient alternative to traditional electrocardiographs (ECGs) for recording and processing of R-R data. Reports on the validity of such systems are, however, conflicting. This study aimed to assess the validity of a commercial HRM on a large study sample, with emphasis on gender and age. Simultaneous recordings of R-R intervals were conducted with the Polar RS800 HRM and a 3-lead ECG on 341 individuals. Data editing was performed with individually designated software for each instrument. Agreement on SDNN, RMSSD, and HF- and LF power was assessed with intraclass correlations (ICCs), standard errors of measurement (SEMs) and Bland and Altman plots. The HRM was not able to identify 18 observations with non-sinus beats. For men, agreement between instruments ranged from good to excellent (ICC ≥ 0.8) on all HRV measures, and SEMs were generally small. For women the results were weaker, with unacceptable agreement between instruments on SDNN. Women over 60 years did not reach a critical ICC value of 0.75 on any of the HRV measures. Bland and Altman plots demonstrated that the RS800 generally overestimated HRV, and that uncertainty increased with higher values. Since the Polar system did not identify errors satisfactorily, or return valid values of HRV for certain groups, it is concluded that, whenever possible, traditional ECGs should be used for both gathering and editing of HRV data.

Reliability of heart rate variability threshold and parasympathetic reactivation after a submaximal exercise test

“…The R-R interval series was obtained by the model RS800CX Polar cardiac monitor ® at a sampling frequency of 1,000 HZ 19,20 . Then, each series was transferred to a microcomputer for offline data processing and analysis of R-R interval variability, employing a Kubios HRV analyzer (Matlab TM version 2.0 beta, Kuopio, Finland).…”

The effect of short-term intermittent hypoxic exposure on heart rate variability in a sedentary population

“…Data were then visually inspected and any remaining 'spikes' in the data were removed and replaced with the next appropriate RR interval. The detection of RR intervals using similar Polar heart rate technology has been validated against 12-lead ECGs (19) however, there is uncertainty regarding the ability of the PPT5's error correction function to detect non-sinus originating beats which can result in inflated results (29). Therefore, all data were screened for outliers using the Median Absolute Deviation (30), and participants with greater or less than 3 × the population standard deviation as estimated by the median (17) were excluded from the data.…”

Assessment of Heart Rate Variability during an Endurance Mountain Trail Race by Multi-Scale Entropy Analysis

“…Several non-linear HRV methods such as fractal scaling analysis, higher order spectra analysis, multi-scale entropy analysis, power law analysis, complexity analysis, symbolic dynamics analysis and heart rate turbulence analysis have been studied for various diseases [7][8][9][10][11][12][13][14][15]. It must be taken into account that while the collection of heart rate (HR) was initially only possible with expensive laboratory-based electrocardiograph recorders, the recent availability of specifically designed portable recorders has substantially boosted the use of HRV monitoring [16,17].…”