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Sports (Basel). 2019 Apr 22;7(4). pii: E93. doi: 10.3390/sports7040093.

Physiological Performance Measures as Indicators of CrossFit® Performance.

Author information

1
Department of Exercise Science, Concordia University Chicago, Riverforest, IL 60305, USA. jdexheimer@apu.edu.
2
Department of Kinesiology, Azusa Pacific University, Azusa, CA 91702, USA. jdexheimer@apu.edu.
3
Division of Biokinesiology & Physical Therapy, University of Southern California, Los Angeles, CA 90033, USA. eschroed@usc.edu.
4
Department of Kinesiology, Point Loma Nazarene University, San Diego, CA 92106, USA. bsawyer@pointloma.edu.
5
Office of Research and Sponsored Projects, Rocky Mountain University of Health Professions, Provo, UT 84606, USA. rpettitt@rmuohp.edu.
6
Department of Kinesiology, Point Loma Nazarene University, San Diego, CA 92106, USA. arnelaguinaldo@pointloma.edu.
7
Department of Exercise Science, Concordia University Chicago, Riverforest, IL 60305, USA. William.Torrence@cuchicago.edu.

Abstract

CrossFit® began as another exercise program to improve physical fitness and has rapidly grown into the "sport of fitness". However, little is understood as to the physiological indicators that determine CrossFit® sport performance. The purpose of this study was to determine which physiological performance measure was the greatest indicator of CrossFit® workout performance. Male (n = 12) and female (n = 5) participants successfully completed a treadmill graded exercise test to measure maximal oxygen uptake (VO2max), a 3-minute all-out running test (3MT) to determine critical speed (CS) and the finite capacity for running speeds above CS (D'), a Wingate anaerobic test (WAnT) to assess anaerobic peak and mean power, the CrossFit® total to measure total body strength, as well as the CrossFit® benchmark workouts: Fran, Grace, and Nancy. It was hypothesized that CS and total body strength would be the greatest indicators of CrossFit® performance. Pearson's r correlations were used to determine the relationship of benchmark performance data and the physiological performance measures. For each benchmark-dependent variable, a stepwise linear regression was created using significant correlative data. For the workout Fran, back squat strength explained 42% of the variance. VO2max explained 68% of the variance for the workout Nancy. Lastly, anaerobic peak power explained 57% of the variance for performance on the CrossFit® total. In conclusion, results demonstrated select physiological performance variables may be used to predict CrossFit® workout performance.

KEYWORDS:

CrossFit® sport performance; D′; VO2max; benchmark performance; critical speed; physiological indicators; strength

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