INTRODUCTION HRV monitoring, economic and non-invasive, allows the detection of possible symptoms of autonomic imbalance and has been suggested as potential tool to prevent overtraining in competitive sport [2]. Previous studies showed that resting heart rate variability (HRV) measures, indices of autonomic function, are influenced by exercise, both after a single bout [1] and training cycles [2]. To date however specific short- and long-term effects of training on HRV parameters are not completely clear. Our aim is to check the predictability of daily HRV measures time series on the basis of training volume and intensity series. METHODS HRV was recorded in a middle-distance runner (VO2max = 65 ml*Kg-1*min-1; Age:23,2; height: 175 cm; bm:64,5 Kg), at the awakening, for 90 consecutive days, with a telemeter (Polar S810; Polar Electro Oy, Finland), for 5 min in supine and 5 min in upright position. Data were processed through the HRV Analysis Software 1.1 for windows (developed by The Biomedical Signal Analysis Group, Department of Applied Physics, University of Kuopio, Finland, and free available at the website http://venda.uku.fi/research/biosignal). All the commonly used time and frequency domain HRV measures [3] were computed. The athlete trained once a day, in the evening. Volume (min) and intensity (speed %AT) were recorded. For each of HRV indexes a forecasting model was computed treating training volume and intensity separately as independent variables (predictors). Stationary R-squared (R2) was considered to determine the goodness of the model. The bivariate time series analysis was performed through the software SPSS® 14.0 (SPSS Inc, USA) RESULTS Considering training volume as the predictor variable, the upright HR is the better predictable measure (R2=0,505), while for other parameters models are not good (R2<0,3). 4 HRV variables are better foreseeable from the training intensity time series: supine HR (R2=0,581), triangular index in the supine position (R2=0,535), the minimum RR interval after standing up (R2=0,518), and upright HR (R2=0,552). CONCLUSIONS Bivariate time series analysis shows that some HRV measures are more influenced by training than others, and may be forecasted with a sufficient accuracy from training load series data. Training intensity seems a better HRV predictor than volume and it should be considered for the predictions by coaches using HRV as a tool for the detection and prevention of overtraining. 1.JAMES DV et AL.Heart rate variability: response following a single bout of interval training. Int J Sports Med. 23(4):247-51.2002 2.PICHOT V et AL.Relation between heart rate variability and training load in middle-distance runners. Med Sci Sports Exerc.32(10):1729-36.2000 3.TASK FORCE OF THE EUROPEAN SOCIETY OF CARDIOLOGY AND THE NORTH AMERICAN SOCIETY OF PACING AND ELECTROPHYSIOLOGY.Heart rate variability: standards of meaurement, physiological interpretation, and clinical use. Circulation. 93:1043-1065. 1996

Prediction of resting heart rate variability from training load time series

DI MICHELE, ROCCO;MERNI, FRANCO
2006

Abstract

INTRODUCTION HRV monitoring, economic and non-invasive, allows the detection of possible symptoms of autonomic imbalance and has been suggested as potential tool to prevent overtraining in competitive sport [2]. Previous studies showed that resting heart rate variability (HRV) measures, indices of autonomic function, are influenced by exercise, both after a single bout [1] and training cycles [2]. To date however specific short- and long-term effects of training on HRV parameters are not completely clear. Our aim is to check the predictability of daily HRV measures time series on the basis of training volume and intensity series. METHODS HRV was recorded in a middle-distance runner (VO2max = 65 ml*Kg-1*min-1; Age:23,2; height: 175 cm; bm:64,5 Kg), at the awakening, for 90 consecutive days, with a telemeter (Polar S810; Polar Electro Oy, Finland), for 5 min in supine and 5 min in upright position. Data were processed through the HRV Analysis Software 1.1 for windows (developed by The Biomedical Signal Analysis Group, Department of Applied Physics, University of Kuopio, Finland, and free available at the website http://venda.uku.fi/research/biosignal). All the commonly used time and frequency domain HRV measures [3] were computed. The athlete trained once a day, in the evening. Volume (min) and intensity (speed %AT) were recorded. For each of HRV indexes a forecasting model was computed treating training volume and intensity separately as independent variables (predictors). Stationary R-squared (R2) was considered to determine the goodness of the model. The bivariate time series analysis was performed through the software SPSS® 14.0 (SPSS Inc, USA) RESULTS Considering training volume as the predictor variable, the upright HR is the better predictable measure (R2=0,505), while for other parameters models are not good (R2<0,3). 4 HRV variables are better foreseeable from the training intensity time series: supine HR (R2=0,581), triangular index in the supine position (R2=0,535), the minimum RR interval after standing up (R2=0,518), and upright HR (R2=0,552). CONCLUSIONS Bivariate time series analysis shows that some HRV measures are more influenced by training than others, and may be forecasted with a sufficient accuracy from training load series data. Training intensity seems a better HRV predictor than volume and it should be considered for the predictions by coaches using HRV as a tool for the detection and prevention of overtraining. 1.JAMES DV et AL.Heart rate variability: response following a single bout of interval training. Int J Sports Med. 23(4):247-51.2002 2.PICHOT V et AL.Relation between heart rate variability and training load in middle-distance runners. Med Sci Sports Exerc.32(10):1729-36.2000 3.TASK FORCE OF THE EUROPEAN SOCIETY OF CARDIOLOGY AND THE NORTH AMERICAN SOCIETY OF PACING AND ELECTROPHYSIOLOGY.Heart rate variability: standards of meaurement, physiological interpretation, and clinical use. Circulation. 93:1043-1065. 1996
Book of Abstracts
87
Di Michele R.; Merni F.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/34366
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