Prediction of Peak Back Compressive Forces as a Function of Lifting Speed and Compressive Forces at Lift Origin and Destination: A Pilot Study.
- Author:
Kasey O GREENLAND
1
;
Andrew S MERRYWEATHER
;
Donald S BLOSWICK
Author Information
1. Department of Mechanical Engineering, University of Utah, Salt Lake, UT, USA. bloswick@eng.utah.edu
- Publication Type:Original Article
- Keywords:
Lifting;
Biomechanics;
Linear models;
Workplace;
Risk assessment
- MeSH:
Biomechanics;
Humans;
Kinetics;
Lifting;
Linear Models;
Male;
Pilot Projects;
Risk Assessment
- From:Safety and Health at Work
2011;2(3):236-242
- CountryRepublic of Korea
- Language:English
-
Abstract:
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 R2 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.
