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RELATIONSHIP BETWEEN COUNTERMOVEMENT JUMP METRICS AND CONCENTRIC DEADLIFT VELOCITY IN COLLEGIATE ATHLETES

Abstract

Velocity Based Training (VBT) is technology that measures concentric velocity and is used to create force-velocity profiles, calculate 1-repetition maximum (1RM), which can then prescribe the appropriate load based on neuromuscular readiness and specific neuromuscular goals. PURPOSE: To correlate an individual's concentric velocity on the trapbar deadlift (TDL) at specific load increments, with countermovement jump (CMJ) performance. METHODS: NCAA Division III athletes (n=21, 20.6 years + 2.29, 173.77 cm + 7.6, 80.2 kg + 16.64) participated in a single testing session. After a standardized warm-up, each athlete performed 3 CMJ on a force plate. Their highest jump height (JH) and its corresponding 6 kinetic metrics were used in the analyses. Participants then performed TDL with a standardized set of loads, which was based on relative percentages of the individual’s body weight: 50%, 75%, 100%, 125%, 150%, 175%, and 200%. Two repetitions were performed at each load and concentric velocity was measured using a barbell sensor. Their average velocity (AV) from the 2 reps at each load was used in the analyses. The trend in incremental load and corresponding AV was also used to calculate 1RM. Correlation coefficients were calculated to examine the relationship between CMJ metrics and TDL performance. Linear regressions were used to calculate significant trends with moderate to high correlations. RESULTS: High correlations were displayed between JH and AV at loads 75%, 100%, 125%, and 150% (0.6 to 0.74). Linear regressions displayed significant trends in JH and AV at those loads, with 125% having the highest R2 value (0.55, p < 0.005). The 125% load also was a significant predictor of reactive strength index-modified (mRSI) with an R2 value of 0.29 (p = 0.01). Finally, 1RM displayed significant predictive qualities to JH (R2 of 0.43, p = 0.001) and jump momentum (JM; R2 of 0.59, p < 0.0001). CONCLUSION: AV in the TDL at a semi-intense load could be used to predict CMJ performance, specifically JH. Using these relationships allows for more in-depth creation of athletic profiles and the crossover of information when using various technology.

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