MS in Public Health (M.S.P.H.)
Degree Granting Department
Thomas E. Bernard, Ph.D.
Steven Mlynarek, Ph.D.
John Smyth, Ph.D.
automotive manufacturing, lifting index, low back pain
Musculoskeletal disorders of the low back are common injuries found in many work environments. Some of the risk factors that workers experience include the weight of object being lifted during a task, frequency, duration, posture, distance the object is lifted, how well the worker can grasp the object, and the degree to which the worker may have to twist and turn their body during a lift. There are many tools that have been developed that are used to assess risk of a worker developing low back pain. The purpose of this study is to determine if the UAW-Ford Ergonomic Surveillance Tool (EST) is a good predictor of low back pain.
The data analyzed in this thesis are from a study done at four automotive manufacturing plants. About 50 interviews were done at each plant to determine if workers had experienced low back pain and other musculoskeletal disorders. To be considered a case job, a worker had to have visited the plant clinic for experiencing low back pain on the job or through an interview of an operator on the job questioning if pain or discomfort affected their work, activities outside of work, or sleep. They were also asked whether or not they sought treatment because of it. Any job associated with an operator who made a clinic visit or exhibited other treatment seeking behavior was considered as a case job. Logistic regression was conducted on this data using the EST and it showed statistical significance when using the maximum lifting index to predict low back pain. The analysis revealed that the maximum lifting index can be used to predict low back pain in workers. A cut point for the maximum LI of 1.3 with a sensitivity of 0.96 and specificity of 0.89 will be good at determining if a job task is at an increased risk of low back pain.
Scholar Commons Citation
Nwafor, Colins, "Using Observations from the UAW-Ford Ergonomic Assessment Tool to Predict Low Back Musculoskeletal Disorders" (2020). Graduate Theses and Dissertations.