Trunk muscle forces and spinal loads during heavy deadlift: Effects of personalization, muscle wrapping, muscle lever arm, and lumbopelvic rhythm

Author:

Ramirez Vanessa12ORCID,Ghezelbash Farshid3,Shirazi‐Adl Aboulfazl3,Bazrgari Babak24

Affiliation:

1. Military Performance Division U.S. Army Research Institute of Environmental Medicine Natick Massachusetts USA

2. Department of Kinesiology and Health Promotion University of Kentucky Lexington Kentucky USA

3. Division of Applied Mechanics, Department of Mechanical Engineering Ecole Polytechnique Montréal Québec Canada

4. F. Joseph Halcomb III, M.D. Department of Biomedical Engineering University of Kentucky Lexington Kentucky USA

Abstract

AbstractHeavy deadlift is used as a physical fitness screening tool in the U.S. Army. Despite the relevance of such a screening tool to military tasks performed by Service Members, the biomechanical impact of heavy deadlift and its risk of low‐back injury remain unknown. A kinematics‐driven musculoskeletal model of spine was implemented to investigate biomechanics of the lower back in a volunteer (23 years old, height of 1.82 m, and body mass of 98.8 kg) during a 68 kg deadlift. In search of protective mechanisms, effects of model personalization and variations in trunk musculature and lumbopelvic rhythm were also investigated. The net moment, compression and shear forces at the L5‐S1 reached peaks of 684 Nm, 17.2 and 4.2 kN, respectively. Geometrical personalization and changes in lumbopelvic rhythm had the least effects on predictions while increases in muscle moment arms (40%) had the largest effects that caused, respectively, 32% and 36% decrease in the maximum compressive and shearing forces. Initiating wrapping of back muscles at farther distances from the spine had opposing effects on spinal loads; peak compression at the L5‐S1 decreased by 12% whereas shear increased by 19%. Despite mechanisms considered, spinal loads during heavy deadlift exceed the existing evidence concerning the threshold of injury for spinal segments, suggesting the vulnerability to injury. Chronic exposure to such high‐spinal loads may lead to (micro) fractures, degeneration, pathoanatomical changes and finally low‐back pain.

Publisher

Wiley

Subject

Applied Mathematics,Computational Theory and Mathematics,Molecular Biology,Modeling and Simulation,Biomedical Engineering,Software

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1. Machine learning applications in spine biomechanics;Journal of Biomechanics;2024-02

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