Measuring the Impact of Resistance Training on Core Muscle Effective Lever Arms and Improving Biomechanical Modeling
Type of DegreePhD Dissertation
Industrial and Systems Engineering
Restriction TypeAuburn University Users
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Low back pain (LBP) is one of the most common types of musculoskeletal disorder (MSD) in the U.S.; resulting in pain and discomfort as well as reducing the efficiency and productivity of workplaces. It presents a major economic burden for both industries and individuals. Although LBP is a multifactorial problem, major root causes commonly originate from work-related issues such as MMH tasks. Evaluating jobs and estimating relative risks have always been a challenge for safety practitioners. Biomechanical models have been widely employed to estimate the capabilities of the spine and its relevant structures to develop LBP mitigation strategies. Accurate measurement of low back geometry is an important factor for biomechanical models and can improve the risk estimation capability of such models. Improving trunk muscle strength can help to reduce spinal loading and increase torso stability, therefore helping to reduce LBP. Muscle cross-sectional area (CSA) is the most common and effective predictor of muscle force capability. The erector spinae muscle (ESM) group is generally used to estimate the force capacity or strength of low back muscles. Many studies have demonstrated that exercise, especially resistance training (weightlifting) and core stability exercises, can strengthen the back muscles and theoretically increase lifting capacity and therefore reduce subsequent risk for low back disorders. The objectives of this dissertation were: 1) to quantify trunk muscle response to resistance training; 2) to develop regression models to predict muscle CSA and lever arms; 3) to estimate low back muscle capability by calculating muscle force per unit area; 4) to investigate the reliability of the MRI-derived measurements; and 5) to improve the risk estimation capability of an existing biomechanical tool by incorporating personal factors and torso flexion angle. The results of this dissertation indicate that performing a comprehensive, whole-body exercise routine can significantly increase low back muscle CSAs, however; their effective lever arm lengths were largely unaffected by such training. Muscle CSAs and lever arm measurements can be predicted using simple subject demographics. The morphometric data of the lower back muscles can be obtained reliably via MRI, and these measurements can subsequently improve biomechanical modeling. Accounting for individual differences in low back geometry allows for more precise ergonomic risk evaluation.