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      Effects of bench press technique variations on musculoskeletal shoulder loads and potential injury risk

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          Abstract

          While shoulder injuries resulting from the bench press exercise are commonly reported, no biomechanical evidence for lowering injury risk is currently available. Therefore, the aim of the present study was to compare musculoskeletal shoulder loads and potential injury risk during several bench press variations. Ten experienced strength athletes performed 21 technical variations of the barbell bench press, including variations in grip width of 1,1.5 and 2 bi-acromial widths (BAW), shoulder abduction angles of 45°, 70° and 90°, and scapula poses including neutral, retracted, and released conditions. Motions and forces were recorded by an opto-electronic measurement system and an instrumented barbell. An OpenSim musculoskeletal shoulder model was employed to estimate joint reaction forces in the glenohumeral and acromioclavicular joints. Time-series of joint reaction forces were compared between techniques by statistical non-parametric mapping. Results showed that narrower grip widths of < 1.5 BAW decreased acromioclavicular compression ( p < 0.05), which may decrease the risk for distal clavicular osteolysis. Moreover, scapula retraction, as well as a grip width of < 1.5 BAW ( p < 0.05), decreased glenohumeral posterior shear force components and rotator cuff activity and may decrease the risk for glenohumeral instability and rotator cuff injuries. Furthermore, results showed that mediolaterally exerted barbell force components varied considerably between athletes and largely affected shoulder reaction forces. It can be concluded that the grip width, scapula pose and mediolateral exerted barbell forces during the bench press influence musculoskeletal shoulder loads and the potential injury risk. Results of this study can contribute to safer bench press training guidelines.

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          Most cited references30

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          OpenSim: open-source software to create and analyze dynamic simulations of movement.

          Dynamic simulations of movement allow one to study neuromuscular coordination, analyze athletic performance, and estimate internal loading of the musculoskeletal system. Simulations can also be used to identify the sources of pathological movement and establish a scientific basis for treatment planning. We have developed a freely available, open-source software system (OpenSim) that lets users develop models of musculoskeletal structures and create dynamic simulations of a wide variety of movements. We are using this system to simulate the dynamics of individuals with pathological gait and to explore the biomechanical effects of treatments. OpenSim provides a platform on which the biomechanics community can build a library of simulations that can be exchanged, tested, analyzed, and improved through a multi-institutional collaboration. Developing software that enables a concerted effort from many investigators poses technical and sociological challenges. Meeting those challenges will accelerate the discovery of principles that govern movement control and improve treatments for individuals with movement pathologies.
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            OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement

            Movement is fundamental to human and animal life, emerging through interaction of complex neural, muscular, and skeletal systems. Study of movement draws from and contributes to diverse fields, including biology, neuroscience, mechanics, and robotics. OpenSim unites methods from these fields to create fast and accurate simulations of movement, enabling two fundamental tasks. First, the software can calculate variables that are difficult to measure experimentally, such as the forces generated by muscles and the stretch and recoil of tendons during movement. Second, OpenSim can predict novel movements from models of motor control, such as kinematic adaptations of human gait during loaded or inclined walking. Changes in musculoskeletal dynamics following surgery or due to human–device interaction can also be simulated; these simulations have played a vital role in several applications, including the design of implantable mechanical devices to improve human grasping in individuals with paralysis. OpenSim is an extensible and user-friendly software package built on decades of knowledge about computational modeling and simulation of biomechanical systems. OpenSim’s design enables computational scientists to create new state-of-the-art software tools and empowers others to use these tools in research and clinical applications. OpenSim supports a large and growing community of biomechanics and rehabilitation researchers, facilitating exchange of models and simulations for reproducing and extending discoveries. Examples, tutorials, documentation, and an active user forum support this community. The OpenSim software is covered by the Apache License 2.0, which permits its use for any purpose including both nonprofit and commercial applications. The source code is freely and anonymously accessible on GitHub, where the community is welcomed to make contributions. Platform-specific installers of OpenSim include a GUI and are available on simtk.org.
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              One-dimensional statistical parametric mapping in Python.

              Statistical parametric mapping (SPM) is a topological methodology for detecting field changes in smooth n-dimensional continua. Many classes of biomechanical data are smooth and contained within discrete bounds and as such are well suited to SPM analyses. The current paper accompanies release of 'SPM1D', a free and open-source Python package for conducting SPM analyses on a set of registered 1D curves. Three example applications are presented: (i) kinematics, (ii) ground reaction forces and (iii) contact pressure distribution in probabilistic finite element modelling. In addition to offering a high-level interface to a variety of common statistical tests like t tests, regression and ANOVA, SPM1D also emphasises fundamental concepts of SPM theory through stand-alone example scripts. Source code and documentation are available at: www.tpataky.net/spm1d/.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/1912635/overviewRole: Role: Role: Role: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/2669038/overviewRole: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/597211/overviewRole: Role: Role: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/731457/overviewRole: Role: Role:
                Role: Role: Role: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/1304172/overviewRole: Role: Role: Role: Role: Role: Role:
                Journal
                Front Physiol
                Front Physiol
                Front. Physiol.
                Frontiers in Physiology
                Frontiers Media S.A.
                1664-042X
                21 June 2024
                2024
                : 15
                : 1393235
                Affiliations
                [1] 1 Faculty of Behavioral and Movement Sciences , Department of Human Movement Sciences , Amsterdam Movement Sciences , Vrije Universiteit Amsterdam , Amsterdam, Netherlands
                [2] 2 Department of Biomechanical Engineering , Delft University of Technology , Delft, Netherlands
                [3] 3 Department of Cognitive Robotics , Delft University of Technology , Delft, Netherlands
                Author notes

                Edited by: Gil Serrancolí, Universitat Politecnica de Catalunya, Spain

                Reviewed by: Daniel Garcia-Vallejo, Sevilla University, Spain

                Andreas Lipphaus, Biomechanics Research Group Ruhr University Bochum, Germany

                *Correspondence: L. Noteboom, lisa-noteboom@ 123456hotmail.com
                Article
                1393235
                10.3389/fphys.2024.1393235
                11224528
                501bf9c9-9cc1-4b6d-a6db-af1a0441e69a
                Copyright © 2024 Noteboom, Belli, Hoozemans, Seth, Veeger and Van Der Helm.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 28 February 2024
                : 04 June 2024
                Funding
                The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by the Dutch Research Council (NWO), Domain Applied and Engineering Sciences, grant number P16–28 Project 4, and by the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation, through grants 2020-218896 and 2022-252796.
                Categories
                Physiology
                Original Research
                Custom metadata
                Computational Physiology and Medicine

                Anatomy & Physiology
                injury prevention,biomechanics,musculoskeletal model,shoulder,rotator cuff,glenohumeral joint,strength training,bench press

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