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

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

PLoS One

Centre for Human and Applied Physiology, Faculty of Health and Behavioural Sciences, University of Wollongong, Wollongong, Australia.

Published: July 2017

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4933391PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0158418PLOS

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