Back pain is common in general population. Also, occupational back pain represents significant morbidity and cost to industry. Researchers reported a 17.6% 12-month prevalence of back pain, responsible for 149.1 million lost workdays, where 65% of the cases were attributed to occupational activities (Guo et al, 1995). A research indicates that workers’ compensation claims for the low back represent a disproportionate share of the costs, with low back claims ranging from 16% to 19% of all claims, but constituting 30% to 41% of the total costs (Webster and Snook, 1994). Total costs of occupationally related low back pain has been estimated from $50 billion to $100 billion for direct medical and indirect costs in the U.S. (Frymoyer and Cats-Baril, 1991).
Researchers use biomechanical spine models to understand spine pain causes and risk factors. Actually, the assessment of spinal loads, stability and subsequently the estimate of injury risk in most of tasks is possible only through biomechanical models. There are a lot of spine model (e.g. Granata and Wilson, 2001; Daggfeldt and Thorstensson, 2003; Arjamand and Shirazi-Adl, 2006) which developed to answer a particular research question especially about spinal loads in different levels. While detailed biomechanical models are more accurate and reliable but they do not use by ergonomists. Since their complexity make them usefulness. So regression based models and equations have built to solve this drawback (Waters et al,1993 (NIOSH); American Conference of Governmental Industrial Hygienists, 2005 (ACGIH TLV); Snook and Ciriello, 1991; University of Michigan, 2001 (3DSSPP); Merryweather et al, 2009 (HCBCF); McGill et al., 1996; Fathallah et al,1999; Arjmand et al, 2011). With the exception of 3DSSPP and HCBCF, all of them don’t consider weight and height (Russell et al, 2005). Even 3DSSPP and HCBCF do not account for variations in muscle PCSAs and moment arms as body weight and height. i.e. these models use constant muscle geometry for different weight and height.
The moment arm length and PCSA of muscles are important biomechanical parameters for estimating low back pain. Marras et al. (2001) quantified trunk muscle cross-sectional areas of male and female spine muscles. Jorgensen et al. (2001) used linear regression techniques to develop prediction equations for the moment arms for trunk muscles. Seo et al. (2003) found significant correlation between erector spinae and rectus abdominis geometry and personal factors (height, weight and age). They offered regression equations for the moment arms and cross sectional areas of erctor spinae and rectus abdominis. However only few studies have tried to quantified muscle geometry as a function of height and weight, but it is important to investigate the effects of personal factors on muscle geometry and subsequently spinal loads.
The purpose of this study is to establish a predictive equation that relate spinal loads at the L4-L5 level to...