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Updated: May 25, 2025

Muscle Imbalances: Testing and Training Functional Eccentric Hamstring Strength in Athletic Populations
Published on: May 1, 2018
Estimation of maximum lower limb muscle strength from vertical jumps
Chuan-Fang Hou1, Chin-Wei Hsu2, Philip X Fuchs1
1Department of Physical Education and Sport Sciences, National Taiwan Normal University, Taipei, Taiwan.
Abstract:
Determining the one-repetition maximum (1RM) is crucial for organizing training loads, but it also is time-consuming, physically demanding, and poses a risk of injury. Vertical jumps are a less demanding and well-established method to test the ability of the lower limbs to generate great forces over a short time, which may allow for the estimation of 1RM in squatting. The purpose of this study was to develop a model for estimating 1RM back squat from ground reaction forces during vertical jumps. Thirteen healthy participants completed a 1RM back squat test, countermovement jumps, and squat jumps. Five kinematic and kinetic variables (e.g., peak and mean power, relative net impulse, jump height, and peak kinetic energy during various phases) were derived from ground reaction forces collected via a Kistler force plate (1000 Hz). Five out of 5 variables correlated with 1RM in countermovement jump and squat jump (ICC = .96-.98, r = .88-.95, p < .001 and ICC = .97-.99, r = .76-.90, p < .05, respectively). The most accurate stepwise regression model (adjusted R2 = .90, SEE = 13.24 kg, mean error = 7.4% of mean 1RMm, p < .001) estimated 1RM back squat based on peak kinetic energy during countermovement jumps. Estimation errors ranged from 7.4% to 10.7% of mean measured 1RM, with no differences between estimated and measured values (d < 0.01, p = .96-1.00). Estimating 1RM via jump tests may offer a practical alternative to traditional methods, reducing injury risks, testing intervals, and effort. Our study proposes a new possible approach for estimating 1RM back squat from jump forces, providing coaches and sports professionals with a more efficient tool to monitor and adjust training loads.
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