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


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          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.

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          Physical fitness in childhood and adolescence: a powerful marker of health.

          This review aims to summarize the latest developments with regard to physical fitness and several health outcomes in young people. The literature reviewed suggests that (1) cardiorespiratory fitness levels are associated with total and abdominal adiposity; (2) both cardiorespiratory and muscular fitness are shown to be associated with established and emerging cardiovascular disease risk factors; (3) improvements in muscular fitness and speed/agility, rather than cardiorespiratory fitness, seem to have a positive effect on skeletal health; (4) both cardiorespiratory and muscular fitness enhancements are recommended in pediatric cancer patients/survivors in order to attenuate fatigue and improve their quality of life; and (5) improvements in cardiorespiratory fitness have positive effects on depression, anxiety, mood status and self-esteem, and seem also to be associated with a higher academic performance. In conclusion, health promotion policies and physical activity programs should be designed to improve cardiorespiratory fitness, but also two other physical fitness components such us muscular fitness and speed/agility. Schools may play an important role by identifying children with low physical fitness and by promoting positive health behaviors such as encouraging children to be active, with special emphasis on the intensity of the activity.
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            Extended international (IOTF) body mass index cut-offs for thinness, overweight and obesity : Extended international BMI cut-offs

            The international (International Obesity Task Force; IOTF) body mass index (BMI) cut-offs are widely used to assess the prevalence of child overweight, obesity and thinness. Based on data from six countries fitted by the LMS method, they link BMI values at 18 years (16, 17, 18.5, 25 and 30 kg m(-2)) to child centiles, which are averaged across the countries. Unlike other BMI references, e.g. the World Health Organization (WHO) standard, these cut-offs cannot be expressed as centiles (e.g. 85th). To address this, we averaged the previously unpublished L, M and S curves for the six countries, and used them to derive new cut-offs defined in terms of the centiles at 18 years corresponding to each BMI value. These new cut-offs were compared with the originals, and with the WHO standard and reference, by measuring their prevalence rates based on US and Chinese data. The new cut-offs were virtually identical to the originals, giving prevalence rates differing by < 0.2% on average. The discrepancies were smaller for overweight and obesity than for thinness. The international and WHO prevalences were systematically different before/after age 5. Defining the international cut-offs in terms of the underlying LMS curves has several benefits. New cut-offs are easy to derive (e.g. BMI 35 for morbid obesity), and they can be expressed as BMI centiles (e.g. boys obesity = 98.9th centile), allowing them to be compared with other BMI references. For WHO, median BMI is relatively low in early life and high at older ages, probably due to its method of construction. © 2012 The Authors. Pediatric Obesity © 2012 International Association for the Study of Obesity.
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              Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society.

              Following the publication of the Task Force document on heart rate variability (HRV) in 1996, a number of articles have been published to describe new HRV methodologies and their application in different physiological and clinical studies. This document presents a critical review of the new methods. A particular attention has been paid to methodologies that have not been reported in the 1996 standardization document but have been more recently tested in sufficiently sized populations. The following methods were considered: Long-range correlation and fractal analysis; Short-term complexity; Entropy and regularity; and Nonlinear dynamical systems and chaotic behaviour. For each of these methods, technical aspects, clinical achievements, and suggestions for clinical application were reviewed. While the novel approaches have contributed in the technical understanding of the signal character of HRV, their success in developing new clinical tools, such as those for the identification of high-risk patients, has been rather limited. Available results obtained in selected populations of patients by specialized laboratories are nevertheless of interest but new prospective studies are needed. The investigation of new parameters, descriptive of the complex regulation mechanisms of heart rate, has to be encouraged because not all information in the HRV signal is captured by traditional methods. The new technologies thus could provide after proper validation, additional physiological, and clinical meaning. Multidisciplinary dialogue and specialized courses in the combination of clinical cardiology and complex signal processing methods seem warranted for further advances in studies of cardiac oscillations and in the understanding normal and abnormal cardiac control processes.

                Author and article information

                Front Physiol
                Front Physiol
                Front. Physiol.
                Frontiers in Physiology
                Frontiers Media S.A.
                07 May 2019
                : 10
                : 510
                [1] 1PROFITH “PROmoting FITness and Health Through Physical Activity” Research Group, Department of Physical and Sports Education, Faculty of Sport Sciences, University of Granada , Granada, Spain
                [2] 2Department of Rehabilitation Sciences, KU Leuven – University of Leuven , Leuven, Belgium
                [3] 3Center for Cognitive and Brain Health, Department of Psychology, Northeastern University , Boston, MA, United States
                [4] 4IRyS Group, School of Physical Education, Pontificia Universidad Católica de Valparaíso , Valparaíso, Chile
                [5] 5Andalusian Centre of Sport Medicine (CAMD), Junta de Andalucía , Granada, Spain
                [6] 6Department of Medical Physiology, School of Medicine, University of Granada , Granada, Spain
                [7] 7Department of Public Health, Faculty of Medicine and Health Sciences, Ghent University , Ghent, Belgium
                [8] 8Faculty of Physical Education and Physiotherapy, Opole University of Technology , Opole, Poland
                [9] 9Department of Cardiology, University Hospital in Opole, University of Opole , Opole, Poland
                [10] 10Department of Biosciences and Nutrition, Karolinska Institutet , Huddinge, Sweden
                Author notes

                Edited by: Gary Iwamoto, University of Illinois at Urbana–Champaign, United States

                Reviewed by: Fiorenzo Moscatelli, University of Foggia, Italy; Zhaowei Kong, University of Macau, China; J. A. Taylor, Harvard Medical School, United States

                *Correspondence: Abel Plaza-Florido, abeladrian@ 123456ugr.es

                This article was submitted to Exercise Physiology, a section of the journal Frontiers in Physiology

                Copyright © 2019 Plaza-Florido, Migueles, Mora-Gonzalez, Molina-Garcia, Rodriguez-Ayllon, Cadenas-Sanchez, Esteban-Cornejo, Solis-Urra, de Teresa, Gutiérrez, Michels, Sacha and Ortega.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 23 October 2018
                : 11 April 2019
                Page count
                Figures: 2, Tables: 3, Equations: 0, References: 58, Pages: 11, Words: 0
                Original Research

                Anatomy & Physiology
                parasympathetic,sympathetic,heart rate variability,treadmill,adiposity,youth
                Anatomy & Physiology
                parasympathetic, sympathetic, heart rate variability, treadmill, adiposity, youth


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