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Adam Culiver, J. Craig Garrison, Kalyssa M. Creed, John E. Conway, Shiho Goto and Sherry Werner

collection. Kinematic variables used for analysis in the current study included stride length, time to maximal humerus velocity, time to maximal thorax velocity, and time from SFC to maximal knee flexion. All kinematic data were analyzed using a 9-camera Qualisys (Göteborg, Sweden) camera system sampling at

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Kym J. Williams, Dale W. Chapman, Elissa J. Phillips and Nick Ball

different force–time and velocity–time contractile characteristics. 10 Interestingly, approximately 40% of a muscle fiber’s contractile characteristics (eg, lengths, pennation angles, contractile shortening velocity) are defined by training background, with heredity-based characteristics (eg, fiber typing

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Zachary M. Gillen, Lacey E. Jahn, Marni E. Shoemaker, Brianna D. McKay, Alegra I. Mendez, Nicholas A. Bohannon and Joel T. Cramer

derivative of the force–time tracing during the concentric phase. Velocity–time tracings were calculated by taking the integral of force divided by mass. Power–time tracings were then calculated by multiplying the force–time curve by the velocity–time curve. PP was taken as the highest value during the

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Kevin M. Carroll, Jake R. Bernards, Caleb D. Bazyler, Christopher B. Taber, Charles A. Stuart, Brad H. DeWeese, Kimitake Sato and Michael H. Stone

obtain a velocity-time trace. Peak power was the maximal value obtained from the product of the velocity-time and force-time trace, and was allometrically scaled to account for differences in body mass. The mean of the 2 best trials within a 2-cm difference in JH was used for analysis. Additional trials

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Matt R. Cross, Farhan Tinwala, Seth Lenetsky, Scott R. Brown, Matt Brughelli, Jean-Benoit Morin and Pierre Samozino

horizontal direction over the course of 2 sprints, with (black line) and without (gray line) accounting for the constant variable of friction force, applied to the instantaneous velocity–time data of athletes sprinting with 2 different loading protocols: (A) 20% body-mass load (chosen to be representative of

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Mark G.L. Sayers and Stephen Bishop

Exerc. 2012 ; 44 ( 2 ), 313 – 322 . PubMed doi: 10.1249/MSS.0b013e31822d757a 21775909 10.1249/MSS.0b013e31822d757a 16. Cormie P , McBride JM , McCaulley GO . Power-time, force-time, and velocity-time curve analysis of the countermovement jump: impact of training . J Strength Cond Res

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Paige E. Rice, Herman van Werkhoven, Edward K. Merritt and Jeffrey M. McBride

were used to obtain a velocity–time curve from force plate data, which allowed for determination of power from the product of force and velocity. Peak power was determined as the maximal power transferred during the concentric phase and expressed relative to body mass. All hopping data were collected

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Kajetan J. Słomka, Slobodan Jaric, Grzegorz Sobota, Ryszard Litkowycz, Tomasz Skowronek, Marian Rzepko and Grzegorz Juras

-time, force-time, and velocity-time curve analysis during the jump squat: Impact of load . Journal of Applied Biomechanics, 24 ( 2 ), 112 – 120 . PubMed ID: 18579903 doi:10.1123/jab.24.2.112 10.1123/jab.24.2.112 Cronin , J. , & Sleivert , G. ( 2005 ). Challenges in understanding the influence of

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Seiichiro Takei, Kuniaki Hirayama and Junichi Okada

peak force, peak velocity, and peak power values were the highest during the propulsion phase of the force–time, velocity–time, and power–time data, respectively. The force at peak power and velocity at peak power were the values when peak power was expressed. To obtain those relative values, we

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Dean Norris, David Joyce, Jason Siegler, James Clock and Ric Lovell

-time, force-time, and velocity-time curve analysis of the countermovement jump: impact of training . J Strength Cond Res . 2009 ; 23 ( 1 ): 177 – 186 . PubMed ID: 19077740 doi:10.1519/JSC.0b013e3181889324 10.1519/JSC.0b013e3181889324 19077740 14. Gathercole R , Sporer B , Stellingwerff T , Sleivert