Marathon runners are prone to femoral stress fractures due to the high magnitudes and frequencies of lower extremity loads during training. Female runners tend to have a greater incidence of stress fracture compared to male runners. Sex-specific differences in body structure, joint pressure, and muscle activation patterns that influence bone remodeling may cause this observed difference in stress fracture occurrence. The goal of this thesis was to develop a finite element model of the femur during marathon training, then determine if marathon training affected bone properties of male and female runners differently. To achieve this goal, a finite element femur model was integrated with a bone remodeling algorithm. Sex-specific muscle and joint pressure loads corresponding to baseline activity and marathon training were applied to the finite element femur model. Axial strain, density, damage, and remodeling activity were quantified at regions predicted to be at high risk of stress fracture. The major results of this analysis predicted that marathon training increased bone damage at all regions of interest in both males and females, especially at the inferior neck. The model predicted that the superior neck, trochanter, and proximal diaphysis were more severely weakened in females than males after marathon training. While this model cannot directly quantify femoral stress fracture risk, it may be used to predict regions of bone weakness in male and female marathon runners. Future work may be done to improve accuracy of this model by using sex-specific femur geometry and bone remodeling parameters specific to male and female marathon runners. This model may be useful in future applications to study effectiveness of injury preventive methods, such as gait retraining, in reducing bone damage.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3733 |
| Date | 01 November 2020 |
| Creators | Lin, Clara |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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