The effect of lifting speed on cumulative and peak biomechanical loading for symmetric lifting tasks.

BACKGROUND: To determine the influence of lifting speed and type on peak and cumulative back compressive force (BCF) and shoulder moment (SM) loads during symmetric lifting. Another aim of the study was to compare static and dynamic lifting models. METHODS: Ten male participants performed a floor-to-shoulder, floor-to-waist, and waist-to-shoulder lift at three different speeds [slow (0.34 m/s), medium (0.44 m/s), and fast (0.64 m/s)], and with two different loads [light (2.25 kg) and heavy (9 kg)]. Two-dimensional kinematics and kinetics were determined. A three-way repeated measures analysis of variance was used to calculate peak and cumulative loading of BCF and SM for light and heavy loads. RESULTS: Peak BCF was significantly different between slow and fast lifting speeds (p < 0.001), with a mean difference of 20% between fast and slow lifts. The cumulative loading of BCF and SM was significantly different between fast and slow lifting speeds (p < 0.001), with mean differences ≥80%. CONCLUSION: Based on peak values, BCF is highest for fast speeds, but the BCF cumulative loading is highest for slow speeds, with the largest difference between fast and slow lifts. This may imply that a slow lifting speed is at least as hazardous as a fast lifting speed. It is important to consider the duration of lift when determining risks for back and shoulder injuries due to lifting and that peak values alone are likely not sufficient.

Prediction of peak back compressive forces as a function of lifting speed and compressive forces at lift origin and destination - a pilot study.

OBJECTIVES: To determine the feasibility of predicting static and dynamic peak back-compressive forces based on (1) static back compressive force values at the lift origin and destination and (2) lifting speed. METHODS: Ten male subjects performed symmetric mid-sagittal floor-to-shoulder, floor-to-waist, and waist-to-shoulder lifts at three different speeds (slow, medium, and fast), and with two different loads (light and heavy). Two-dimensional kinematics and kinetics were captured. Linear regression analyses were used to develop prediction equations, the amount of predictability, and significance for static and dynamic peak back-compressive forces based on a static origin and destination average (SODA) back-compressive force. RESULTS: Static and dynamic peak back-compressive forces were highly predicted by the SODA, with R(2) values ranging from 0.830 to 0.947. Slopes were significantly different between slow and fast lifting speeds (p < 0.05) for the dynamic peak prediction equations. The slope of the regression line for static prediction was significantly greater than one with a significant positive intercept value. CONCLUSION: SODA under-predict both static and dynamic peak back-compressive force values. Peak values are highly predictable and could be readily determined using back-compressive force assessments at the origin and destination of a lifting task. This could be valuable for enhancing job design and analysis in the workplace and for large-scale studies where a full analysis of each lifting task is not feasible.