A nonlinear model for the characterization and optimization of athletic training and performance

Biomedical Human Kinetics. 2017;9(1):82-93 DOI 10.1515/bhk-2017-0013

 

Journal Homepage

Journal Title: Biomedical Human Kinetics

ISSN: 2080-2234 (Print)

Publisher: Sciendo

Society/Institution: Józef Piłsudski University of Physical Education

LCC Subject Category: Medicine: Internal medicine: Special situations and conditions: Sports medicine | Science: Physiology

Country of publisher: Poland

Language of fulltext: English

Full-text formats available: PDF

 

AUTHORS


Turner James D. (Dynamical Systems Research Laboratory, Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA)

Mazzoleni Michael J. (Dynamical Systems Research Laboratory, Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA)

Little Jared A. (Dynamical Systems Research Laboratory, Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA)

Sequeira Dane (Dynamical Systems Research Laboratory, Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA)

Mann Brian P. (Dynamical Systems Research Laboratory, Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA)

EDITORIAL INFORMATION

Double blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 15 weeks

 

Abstract | Full Text

Study aim: Mathematical models of the relationship between training and performance facilitate the design of training protocols to achieve performance goals. However, current linear models do not account for nonlinear physiological effects such as saturation and over-training. This severely limits their practical applicability, especially for optimizing training strategies. This study describes, analyzes, and applies a new nonlinear model to account for these physiological effects. Material and methods: This study considers the equilibria and step response of the nonlinear differential equation model to show its characteristics and trends, optimizes training protocols using genetic algorithms to maximize performance by applying the model under various realistic constraints, and presents a case study fitting the model to human performance data. Results: The nonlinear model captures the saturation and over-training effects; produces realistic training protocols with training progression, a high-intensity phase, and a taper; and closely fits the experimental performance data. Fitting the model parameters to subsets of the data identifies which parameters have the largest variability but reveals that the performance predictions are relatively consistent. Conclusions: These findings provide a new mathematical foundation for modeling and optimizing athletic training routines subject to an individual’s personal physiology, constraints, and performance goals.