Spinal injury and pain can often be debilitating, leading to a significant decrease in quality of life. The development of these spinal conditions may be explained by changes spinal loading patterns. Since spinal loading patterns cannot be analyzed in vivo, biomechanical musculoskeletal models have been developed to estimate them. Incorporating muscle parameters such as cross sectional area and moment arms improves the accuracy of musculoskeletal models, but no current resource provides a comprehensive set of muscle parameters for a wide variety of subjects. This study aims to develop a method for estimating trunk muscle parameters from clinically attainable variables such as age, sex, height, weight, trunk width, and trunk depth.
The regression models built in this study drew from in-vivo CT-based cross sectional area and moment arms measurements of an age- and sex- stratified community-based population. The base regression model used the independent variables age, sex, height, and weight, while subsequent models examined the differences when trunk depth or trunk width was incorporated. 27% of cross sectional area regressions were improved with the addition of trunk weight or trunk width; 26.6% of medial lateral moment arm regressions were improved with the addition of trunk width; 50% of anterior posterior moment arm regressions were improved with the addition of trunk depth.
Although the addition of trunk depth or width improved model fit especially in moment arm regressions, the R2 values of regressions were not increased greatly. It is suspected that muscle position as related to distribution of fat may explain the mismatched contribution of trunk measurements to moment arm estimates in different muscles. Further investigation is needed to examine the effects of fat distribution on muscle parameter estimation.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/36539 |
| Date | 12 June 2019 |
| Creators | Huang, Samantha |
| Contributors | McKnight, C. James, Bouxsein, Mary |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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