University of North Carolina at Chapel Hill Graduate School
Doi
Abstract
ABSTRACT Darren William Hearn: Combining Individual Kinetic and Kinematic Profiles: A Novel Approach to Injury Prevention(Under the direction of Darin Anthony Padua) Musculoskeletal injury related to fitness and training represent an extraordinary burden to the individual, the workplace, and the healthcare system. Though injury incidence can exceed 79% for some training, our ability to identify those at greatest risk for sustaining injury is limited. The validity of screening techniques appears dependent on the population and have come under recent scrutiny. The purpose of this dissertation was to investigate how demographic, performance, and biomechanical variables, both individually and in combination, correlate with survival time to and hazard of musculoskeletal injury during cadet basic training at the United States Military Academy (USMA). We hypothesized that females, those with a history of injury, greater BMI, lesser cadence, those who move poorly, and those with poor performance on their physical fitness test would exhibit greater hazard of injury during training. Subjects were cadets entering cadet basic training in Summer, 2018. Data were collected using questionnaires, physical fitness tests, kinematic software, and wearable accelerometers. Injury surveillance was conducted over the first 60 days of training. Descriptive statistics and time to event analyses including the derivation of Kaplan Meier curves, Log Rank tests, and Cox proportional hazard regression modeling were used to address the research questions. A total of 595 participants met inclusion criteria and 97 sustained injury during the follow up period. Key observations included that most injuries occurred during weeks three and four of training; greater hazard for musculoskeletal injury was observed in females, those with a history of injury, poor movement and a poor physical fitness test score. However, hazards were unique to the individual based on modifiable and non-modifiable characteristics. Our observations suggest that multivariable risk modeling using survival analysis is an effective means of identifying those at greatest risk for sustaining musculoskeletal injury risk during training. Using carefully selected variables including demographic, movement, and performance variables appears to produce the most precise models. However, model precision is dependent upon individualized factors and care should be taken to understand how the presence of unmodifiable variables influence risk.Doctor of Philosoph
