Heart Rate Is a Better Predictor of Cardiorespiratory Fitness Than Heart Rate Variability in Overweight/Obese Children: The ActiveBrains Project

Abstract

Cardiac autonomic function can be quantified through mean heart rate (HR) or heart rate variability (HRV). Numerous studies have supported the utility of different HRV parameters as indicators of cardiorespiratory fitness (CRF). However, HR has recently shown to be a stronger predictor of CRF than HRV in healthy young adults, yet these findings need to be replicated, in other age groups such as children. Therefore, this study aimed: (1) to study the associations between indicators of cardiac autonomic function (HR, standard and corrected HRV parameters) and CRF in overweight/obese children; and (2) to test which of the two indicators (i.e., HR or HRV) is a stronger predictor of CRF. This study used cross-sectional baseline data of 107 overweight/obese children (10.03 ± 1.13 years, 58% boys) from the ActiveBrains project. Cardiac autonomic indicators were measured with Polar RS800CX ® . CRF was assessed using a gas analyzer while performing a maximal incremental treadmill test. Correlations and stepwise linear regressions were performed. Mean HR and standard HRV parameters (i.e., pNN50, RMSSD, and SDNN) were associated with CRF (r coefficients ranging from -0.333 to 0.268; all p ≤ 0.05). The association of HR with CRF persisted after adjusting for sex, peak height velocity (PHV), adiposity moderate-to-vigorous physical activity, energy intake and circadian-related variable intradaily variability of activity patterns whilst for HRV parameters (i.e., pNN50, RMSSD, and SDNN) disappeared. Stepwise linear regression models entering HR and all HRV parameters showed that mean HR was the strongest predictor of CRF (β = -0.333, R 2 = 0.111, p < 0.001). Standard and corrected HRV parameters did not provide additional value to the coefficient of determination (all p > 0.05). Our findings suggest that HR is the strongest indicator of CRF. It seems that quantification of HRV parameters in time and frequency domain do not add relevant clinical information about the cardiovascular health status (as measured by CRF) in overweight/obese children beyond the information already provided by the simple measure of HR.

Resting heart rate as a biomarker for tracking change in cardiorespiratory fitness of UK adults: The Fenland Study

“…Previous detailed studies of the relationship between RHR and CRF have typically been small and focused on identifying electrophysiological predictors of exercise performance (29–32). There are, however, few large-scale studies that have explored this relationship with findings similar to those reported here.…”

Short-term heart rate variability in resting conditions: methodological considerations

“…In 1 st place, it should be noted that not all the parameters can be used "appropriately" when the data is derived from short-term recordings [1]. In Table 1, we introduced the most used HRV derived parameters using data from short-term recordings while the subject is resting [1,16]. The HRV derived parameters in the frequency-domain (Table 1) allow researchers to determine the cyclic fluctuations of the R-R intervals [2,15].…”

“…Lastly, and before performing any statistical analysis, we should consider that the HRV derived parameters are not normally distributed [52]. Therefore, the HRV derived parameters are commonly transformed using the natural logarithm [71], although other transformations or "normalization" procedures have been proposed (e.g., log10, normal scores) [16,72]. These methodological aspects that have been aforementioned should be deeply studied to determine its "real" impact on the different HRV derived parameters, and its validity and reproducibility, to further establish standardized recommendations among the scientific community and general HRV users.…”

“…In such cases, the prognostic value of HRV can be improved by strengthening the relationship between HRV and HR, and it has indeed been shown that multiplying the HRV derived parameters by the mean RR interval increases the ability of HRV to predict mortality in men, where HR was a strong risk factor for cardiac death [77]. Some studies employed the correction method approach [78] to investigate the associations of HRV derived parameters with CRF [16] and body composition measures [79] in children with overweight/obesity. Interestingly, after correcting the HRV derived parameters by HR (i.e., HRV derived parameters divided by average R-R interval) all the associations observed between HRV derived parameters, CRF, and body composition disappeared [16,79].…”

“…Some studies employed the correction method approach [78] to investigate the associations of HRV derived parameters with CRF [16] and body composition measures [79] in children with overweight/obesity. Interestingly, after correcting the HRV derived parameters by HR (i.e., HRV derived parameters divided by average R-R interval) all the associations observed between HRV derived parameters, CRF, and body composition disappeared [16,79]. Similarly, Grant et al [80], concluded similar results addressing the associations between HRV derived parameters corrected by HR (i.e., HRV derived parameters divided by average R-R interval) and CRF in healthy young adults, being HR stronger associated with CRF compared to any HRV derived parameter.…”