Department of Mechanical Engineering, University of Utah, Salt Lake, UT, USA.
Saf Health Work. 2011 Sep;2(3):236-42. doi: 10.5491/SHAW.2011.2.3.236. Epub 2011 Sep 30.
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.
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.
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.
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.
基于(1)起点和终点的静态背部压缩力值和(2)提升速度,确定预测静态和动态峰值背部压缩力的可行性。
10 名男性受试者以三种不同速度(慢、中、快)和两种不同负荷(轻、重)进行对称中矢状面从地板到肩部、地板到腰部和腰部到肩部的提升。记录二维运动学和动力学数据。采用线性回归分析,基于起点和终点平均(SODA)背部压缩力,建立预测方程、预测的可预测性程度和静态及动态峰值背部压缩力的显著性。
SODA 可高度预测静态和动态峰值背部压缩力,R² 值范围为 0.830 至 0.947。对于动态峰值预测方程,慢和快提升速度之间的斜率有显著差异(p<0.05)。静态预测回归线的斜率明显大于 1,且截距值为正,具有统计学意义。
SODA 低估了静态和动态峰值背部压缩力值。峰值值可高度预测,可通过在提升任务的起点和终点进行背部压缩力评估来快速确定。这对于增强工作场所的工作设计和分析以及对于无法对每个提升任务进行全面分析的大规模研究非常有价值。