Literature DB >> 29449002

Shoulder joint kinetics and dynamics during underwater forward arm elevation.

Jessy Lauer1, João Paulo Vilas-Boas2, Annie Hélène Rouard3.   

Abstract

Aquatic exercises are widely implemented into rehabilitation programs. However, both evaluating their mechanical demands on the musculoskeletal system and designing protocols to provide progressive loading are difficult tasks. This study reports for the first time shoulder joint kinetics and dynamics during underwater forward arm elevation performed at speeds ranging from 22.5 to 90°/s. Net joint moments projected onto anatomical axes of rotation, joint power, and joint work were calculated in 18 participants through a novel approach coupling numerical fluid flow simulations and inverse dynamics. Joint dynamics was revealed from the 3D angle between the joint moment and angular velocity vectors, identifying three main functions-propulsion, stabilization, and resistance. Speeds <30°/s necessitated little to no power at all, whereas peaks about 0.20 W⋅kg-1 were seen at 90°/s. As speed increased, peak moments were up to 61 × higher at 90 than at 22.5°/s, (1.82 ± 0.12%BW⋅AL vs 0.03 ± 0.01%BW⋅AL, P < 0.038). This was done at the expense of a substantial decrease in the joint moment contribution to joint stability though, which goes against the intuition that greater stabilization is required to protect the shoulder from increasing loads. Slow arm elevations (<30°/s) are advantageous for joint mobility gain at low mechanical solicitation, whereas the intensity at 90°/s is high enough to stimulate muscular endurance improvements. Simple predictive equations of shoulder mechanical loading are provided. They allow for easy design of progressive protocols, either for the postoperative shoulder or the conditioning of athlete targeting very specific intensity regions.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Aquatic rehabilitation; Computational fluid dynamics; Inverse dynamics; Load

Mesh:

Year:  2018        PMID: 29449002     DOI: 10.1016/j.jbiomech.2018.01.043

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  1 in total

1.  Construction of Swimmer's Underwater Posture Training Model Based on Multimodal Neural Network Model.

Authors:  Wei Wen; Tingyu Yang; Yanhao Fu; Siwen Liu
Journal:  Comput Intell Neurosci       Date:  2022-04-11
  1 in total

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