Advisor

Babski-Reeves, Kari

Committee Member

Peterson, Daniel

Committee Member

Eksioglu, Burak

Committee Member

Strawderman, Lesley

Committee Member

Burgess, Shane C.

Date of Degree

1-1-2013

Document Type

Dissertation - Open Access

Major

Industrial and Systems Engineering

Degree Name

Doctor of Philosophy

College

College of Engineering

Department

Department of Industrial and Systems Engineering

Abstract

Low back pain (LBP) is the most prevalent work-related musculoskeletal disorder. Occupational risk factors have been studied for current ergonomic prevention strategies; however, other underlying mechanisms may exist since not all workers performing the same task develop the same severity. Previous research has identified personal and psychosocial risk factors that also contribute to LBP. Research quantifying the interactive effects of the various personal, psychosocial and occupational factors is limited, along with research on the effect of risk factor combinations on LBP severity. The objectives of this study were to: 1) study the various factors that are known to be involved in low back pain and analyze interactions, and 2) develop a model to predict low back pain and validate it. In order to address these objectives, 2 studies were conducted. The first study investigated the effects of various personal, genetic, occupational and psychosocial factors on two subjective LBP severity ratings: Oswestry Disability Index (ODI) and a Visual Analog Scale (VAS), and three physician-based ratings: MRI severity, canal stenosis and nerve impingement. Personal and psychosocial factors, in addition to occupational factors, were found to significantly affect the severity ratings. The second study involved building predictive models of LBP severity for each risk factor category as well as a combined risk factor model. Results showed that the combined risk factor models considering interaction effects both within and across risk factor categories were significantly better in predicting severity ratings than the individual models. However, validation conducted using 5 random samples showed inconsistent accuracies. Results obtained may help to develop a more reliable way to predict and, hence, prevent chronic LBP.

URI

https://hdl.handle.net/11668/19282

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