IDEAS home Printed from https://ideas.repec.org/a/taf/gcmbxx/v17y2014i14p1602-1616.html
   My bibliography  Save this article

The application of musculoskeletal modeling to investigate gender bias in non-contact ACL injury rate during single-leg landings

Author

Listed:
  • Nicholas Ali
  • Michael Skipper Andersen
  • John Rasmussen
  • D. Gordon E. Robertson
  • Gholamreza Rouhi

Abstract

The central tenet of this study was to develop, validate and apply various individualised 3D musculoskeletal models of the human body for application to single-leg landings over increasing vertical heights and horizontal distances. While contributing to an understanding of whether gender differences explain the higher rate of non-contact anterior cruciate ligament (ACL) injuries among females, this study also correlated various musculoskeletal variables significantly impacted by gender, height and/or distance and their interactions with two ACL injury-risk predictor variables; peak vertical ground reaction force (VGRF) and peak proximal tibia anterior shear force (PTASF). Kinematic, kinetic and electromyography data of three male and three female subjects were measured. Results revealed no significant gender differences in the musculoskeletal variables tested except peak VGRF (p = 0.039) and hip axial compressive force (p = 0.032). The quadriceps and the gastrocnemius muscle forces had significant correlations with peak PTASF (r = 0.85, p < 0.05 and r = − 0.88, p < 0.05, respectively). Furthermore, hamstring muscle force was significantly correlated with peak VGRF (r = − 0.90, p < 0.05). The ankle flexion angle was significantly correlated with peak PTASF (r = − 0.82, p < 0.05). Our findings indicate that compared to males, females did not exhibit significantly different muscle forces, or ankle, knee and hip flexion angles during single-leg landings that would explain the gender bias in non-contact ACL injury rate. Our results also suggest that higher quadriceps muscle force increases the risk, while higher hamstring and gastrocnemius muscle forces as well as ankle flexion angle reduce the risk of non-contact ACL injury.

Suggested Citation

  • Nicholas Ali & Michael Skipper Andersen & John Rasmussen & D. Gordon E. Robertson & Gholamreza Rouhi, 2014. "The application of musculoskeletal modeling to investigate gender bias in non-contact ACL injury rate during single-leg landings," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 17(14), pages 1602-1616, October.
  • Handle: RePEc:taf:gcmbxx:v:17:y:2014:i:14:p:1602-1616
    DOI: 10.1080/10255842.2012.758718
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10255842.2012.758718
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/10255842.2012.758718?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. M.S. Andersen & M. Damsgaard & B. MacWilliams & J. Rasmussen, 2010. "A computationally efficient optimisation-based method for parameter identification of kinematically determinate and over-determinate biomechanical systems," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 13(2), pages 171-183.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mark Tröster & Sarah Budde & Christophe Maufroy & Michael Skipper Andersen & John Rasmussen & Urs Schneider & Thomas Bauernhansl, 2022. "Biomechanical Analysis of Stoop and Free-Style Squat Lifting and Lowering with a Generic Back-Support Exoskeleton Model," IJERPH, MDPI, vol. 19(15), pages 1-16, July.
    2. Vincent Richard & Giuliano Lamberto & Tung-Wu Lu & Aurelio Cappozzo & Raphaël Dumas, 2016. "Knee Kinematics Estimation Using Multi-Body Optimisation Embedding a Knee Joint Stiffness Matrix: A Feasibility Study," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-18, June.
    3. Kevin Ball & Thomas Greiner, 2012. "A procedure to refine joint kinematic assessments: Functional Alignment," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 15(5), pages 487-500.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:gcmbxx:v:17:y:2014:i:14:p:1602-1616. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/gcmb .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.