IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0158418.html
   My bibliography  Save this article

Predicting Endurance Time in a Repetitive Lift and Carry Task Using Linear Mixed Models

Author

Listed:
  • Ben Beck
  • Daniel J Ham
  • Stuart A Best
  • Greg L Carstairs
  • Robert J Savage
  • Lahn Straney
  • Joanne N Caldwell

Abstract

Objectives: Repetitive manual handling tasks account for a substantial portion of work-related injuries. However, few studies report endurance time in repetitive manual handling tasks. Consequently, there is little guidance to inform expected work time for repetitive manual handling tasks. We aimed to investigate endurance time and oxygen consumption of a repetitive lift and carry task using linear mixed models. Methods: Fourteen male soldiers (age 22.4 ± 4.5 yrs, height 1.78 ± 0.04 m, body mass 76.3 ± 10.1 kg) conducted four assessment sessions that consisted of one maximal box lifting session and three lift and carry sessions. The relationships between carry mass (range 17.5–37.5 kg) and the duration of carry, and carry mass and oxygen consumption, were assessed using linear mixed models with random effects to account for between-subject variation. Results: Results demonstrated that endurance time was inversely associated with carry mass (R2 = 0.24), with significant individual-level variation (R2 = 0.85). Normalising carry mass to performance in a maximal box lifting test improved the prediction of endurance time (R2 = 0.40). Oxygen consumption presented relative to total mass (body mass, external load and carried mass) was not significantly related to lift and carry mass (β1 = 0.16, SE = 0.10, 95%CI: -0.04, 0.36, p = 0.12), indicating that there was no change in oxygen consumption relative to total mass with increasing lift and carry mass. Conclusion: Practically, these data can be used to guide work-rest schedules and provide insight into methods assessing the physical capacity of workers conducting repetitive manual handling tasks.

Suggested Citation

  • Ben Beck & Daniel J Ham & Stuart A Best & Greg L Carstairs & Robert J Savage & Lahn Straney & Joanne N Caldwell, 2016. "Predicting Endurance Time in a Repetitive Lift and Carry Task Using Linear Mixed Models," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-11, July.
  • Handle: RePEc:plo:pone00:0158418
    DOI: 10.1371/journal.pone.0158418
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0158418
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0158418&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0158418?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
    ---><---

    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:plo:pone00:0158418. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.