Resting Heart Rate Variability Can Predict Track and Field Sprint Performance
Abstract
The purpose of this study was to train numerous predictive models to investigate if resting heart rate variability (HRV) can predict elite track and field sprint performance. Dataset encompassed fifteen male Division I NCAA track and field student-athletes who monitored HRV 24 hours prior to competition throughout their competitive careers, accruing 182 total samples. Ensemble machine learning techniques (bagging, boosting, and stacking) were trained from athlete’s HRV to forecast their individual competition performance. Bagging random forest algorithm resulted in mean accuracy of 75.0 ± 8.0%, stochastic gradient boosting achieved 81.2 ± 6.8% mean accuracy, and stacking meta-classifier achieved mean accuracy of 85.6 ± 4.8%. DC Potential, RMSSD, and SDSD were the most predictive readiness indicators towards sprint performance. Estimated functional relationships suggest DC Potential between 25-35 mV and log-transformed RMSSD around 4.5 ms indicates a physiological status that may promote optimal sprint performance.
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Halson SL. Monitoring training load to understand fatigue in athletes. Sports Medicine 2014; 44:139-147.
Buchheit, M. Monitoring training status with HR measures: do all roads lead to Rome? Frontiers in Physiology 2014: 73:1-19.
Kiviniemi AM, Hautala AJ, Kinnunen H, et al. Endurance training guided individually by daily heart rate variability measurements. European Journal of Applied Physiology 2007; 101:743-751.
Buchheit M, Voss SC, Nybo L, et al. Physiological and performance adaptations to an in-season soccer camp in the heat: Associations with heart rate and heart rate variability. Scandinavian Journal of Medicine and Science in Sports 2011; 21:477-485.
Buchheit M, Mendez-Villanueva A, Quod MJ, et al. Determinants of the variability of heart rate measures during a competitive period in young soccer players. European Journal of Applied Physiology 2010; 109:869-878.
Boullosa DA, Abreu L, Nakamura FY, et al. (2013). Cardiac autonomic adaptations in elite Spanish soccer players during preseason. International Journal of Sports Physiology and Performance 2013; 8:400-409.
Morris CW. The effect of fluid periodization on athletic performance outcomes in American football players. [Dissertation]. University of Kentucky; 2015.
Ilyukhina VA. The omega potential: A quantitative parameter of the state of brain structures and organism. I. physiological significance of the omega potential when recorded from deep structures and from the scalp. Human Physiology 1982; 8(3):221-226.
Ilyukhina VA, Sychev A, Shcherbakova N, et al. The omega-potential: A quantitative parameter of the state of brain structures and of the individual: II. Possibilities and limitations of the use of the omega-potiential for rapid assessment of the state of the individual. Human Physiology 1982; 8(5):328-339.
De’arth G, Fabricius KE. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 2010; 81:3178-3192.
Schapire R. The strength of weak learnability. Machine Learning 1990; 2:197-227.
Dietterich TG. Ensemble methods in machine learning. In: Multiple Classifier Systems. Heidelberg: Springer-Verlag; 2000.
Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability. Standards of measurements, physiological interpretation, and clinical use. European Heart Journal 1996; 17:354-381.
Breiman L. Bagging predictors. Machine Learning 1996; 24(2):123-140.
Breiman L. Random forests. Machine Learning 2001; 45(1):5-32.
Friedman J. Greedy function approximation: a gradient boosting machine. The Annals of Statistics 2001; 29(5):1189-1232.
Friedman J. Stochastic gradient boosting. Computational Statistics & Data Analysis 2002; 38(4):367-378.
Wolpert D. Stacked generalization. Neural Networks 1992; 5(2):241-259.
Quinlan JR. C4.5: programs for machine learning. San Mateo: Morgan Kaufmann Publishers; 1993.
Hastie T, Buja A, Tibshirani R. Penalized discriminant analysis. The Annals of Statistics 1995; 23(1):73-102.
Ripley BD. Pattern Recognition and Neural Networks. United Kingdom: Cambridge; 1996.
Cortes C, Vapnik V. Support-vector network. Machine Learning 1995; 20(3):273-297.
Park MY, Hastie T. Penalized Logistic Regression for Detecting Gene Interactions. Biostatistics 2008; 9(1):30-50.
Cohen J. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 1960; 20(1):37–46.
Mohammed S, Angell LC. Personality heterogeneity in teams: Which differences make a difference for team performance? Small Group Research 2003; 34:651-677.
Horwitz SK, Horwitz IB. The effects of team diversity on team outcomes: A meta-analytic review on team demography. Journal of Management 2007; 33:967-1015.
D’Ascenzi F, Alvino F, Natali BM, et al. Pre-competitive assessment of heart rate variability in elite female athletes during play-offs. Clinical Physiology & Functional Imaging 2014; 34(4):230-236.
Fronso S, Delia G, Robazza C, et al. Relationship between performance and heart rate variability in amateur basketball players during playoffs. Sport Science & Health 2012; 8:45.
Cipryan L, Stejskal P, Bartáková O, et al. Autonomic nervous system observation through to use of spectral analysis of heart rate variability in ice hockey players. Acta Universitatis Palackianae Olomucnesis 2007; 37(4):17-21.
Porges SW. Heart rate variability: An autonomic correlate of reaction time performance. Bulletin of the Psychonomic Society 1973; 1(4):270-272.
Speckmann E, Elger CE, Gorji A. Neurophysiologic Basis of EEG and DC Potentials. In: Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 6th eds. Philadelphia: Lippincott Williams & Wilkins; 2011.
Caspers H, Speckmann E, Lehmenkühler A. Electrogenesis of cortical DC potentials. In: Motivation Factor and Sensory Processes of the Brain: Electrical Potentials, Behavior and Clinical Use. Progress in Brain Research. Vol 54. New York: Elsevier; 1980.
Burke D. Clinical uses of H reflexes of upper and lower limb muscles, Clinical Neurophysiology Practice 2016; 1:9-17.
Ross A, Leveritt M, Riek S. Neural influences on sprint running. Sports Medicine 2001; 31(6):409-425.
Landis JR. The measurement of observer agreement for categorical data. Biometrics 1977 33(1):159-174.
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