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Article Title

ASSOCIATIONS BETWEEN TWO ATHLETE MONITORING SYSTEMS USED TO QUANTIFY EXTERNAL TRAINING LOADS IN BASKETBALL PLAYERS

Abstract

Keldon M. Peak1,2, Aaron D. Heishman1,2, Ryan M. Miller1, Eduardo D.S. Freitas1, Brady S. Brown1,2, and Michael G. Bemben FACSM1 1Neuromuscular Laboratory, Department of Health and Exercise Science, University of Oklahoma, Norman, Oklahoma; 2Basketball Strength and Performance, Department of Athletics, University of Oklahoma, Norman, Oklahoma

Quantifying external training load (eTL), referred to as the biomechanical load during training, is becoming increasingly popular for team sport in an effort to manage fatigue, optimize performance, and guide return-to-play protocols following injury. During indoor team sport play, eTL can be measured via Inertial Measurement Units (IMUs) which incorporate accelerometers, gyroscopes, and a magnetometer to characterize an athlete’s movement signature, while Indoor Positioning Systems (IPS) are also common, which use Ultra-wideband (UWB) to detect player positioning and their subsequent movements. PURPOSE: The purpose of this study was to assess the association between a commercially available IMU and IPS used to monitor eTL in team sport. METHODS:A retrospective analysis was performed on 13 elite male NCAA Division 1 basketball players from three practices during the off-season training phase. A Pearson’s correlation was used to examine the association between the Distance traveled during practice captured by IPS system compared to Player Load (PL), Player Load per Minute (PL/Min), 2-Dimensional Player Load (PL2D), 1-Dimensional Player Load Forward (PL1D-FWD), Side (PL1D-SIDE), and Up (PL1D-UP) captured from the Catapult Sport IMU. RESULTS: There were significant (p ≤0.001) positive correlations between Distance and PL (r=0.891), PL/Min (r=0.891), PL2D(r=0.863), PL1D-FWD(r=0.799), PL1D-SIDE(r=0.879), and PL1D-UP(r=0.887) during Practice 1. Practice 2 revealed significant (p ≤0.001) positive correlations between Distance and PL (r=0.947), PL/Min (r=0.947), PL2D(r=0.901), PL1D-FWD(r=0.819), PL1D-SIDE(r=0.944), and PL1D-UP(r=0.972), while Practice 3 also displayed significant (p ≤0.001) positive correlations between Distance and PL (r=0.858), PL/Min (r=0.872), PL2D(r=0.809), PL1D-FWD(r=0.810), PL1D-SIDE(r=0.761), and PL1D-UP(r=0.891). CONCLUSION: These data suggest a strong association between parameters captured by the two systems used to monitor eTL, however coaches and performance practitioners should be aware that each system may potentially provide unique information used to monitor and track eTL of athletes during basketball play.

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