Date of Award
Spring 2019
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Biomedical Engineering
First Advisor
Cross, Janelle
Second Advisor
Harris, Gerald
Third Advisor
Raasch, William
Abstract
Pitching involves high stresses to the arm that may alter soft tissue responsible for controlling biomechanics. It has been hypothesized that imbalances in strength and flexibility of the dominant shoulder lead to decreased performance and increased injury risk, but it is not fully known what specific pitching biomechanics are altered. There is a critical need to determine correlations between shoulder rotational strength, range of motion and pitching kinetics. Without such knowledge, identifying potential for injury from shoulder imbalances will likely remain difficult and invasive. The goal of this study was to determine correlations between shoulder rotational strength and range of motion and kinetics. Twelve collegiate pitchers participated in this IRB approved study. The clinical measures session tested shoulder rotational range of motion and strength and grip strength. The motion analysis session tested pitching biomechanics. Paired t-tests investigated differences in strength and range of motion between arms. Linear regression was performed to determine correlations between clinical measures, kinetics and pitch velocity. Regression learner neural networks were created to predict pitch velocity and elbow varus torque using clinical measures as inputs. The dominant arm had significantly higher external rotation and total range of motion than the nondominant arm. The nondominant arm normalized external rotation peak torque was significantly greater than the dominant arm at 0˚ external rotation. Correlations were found between elbow varus torque and isometric external/internal rotation ratio, and between shoulder posterior shear force and isokinetic eccentric external rotation/internal rotation ratios. Correlations to velocity included grip strength, concentric external rotation peak torque, isometric internal rotation peak torques, and isometric external rotation peak torques. The neural network accurately predicted velocity, with the standard deviation of the error equal to 2.29 (2.97%). These correlations associate two testing methods to identify injury risk. Increasing external/internal rotation ratios may decrease elbow varus torque and shoulder posterior shear force. Increasing external rotation, internal rotation, and grip strength may lead to velocity gains. Velocity can be predicted using clinical measures and a neural network.