Abstract
The pursuit of healthy ageing has seen a dramatic increase in the number of older adults engaging in physical activity programs such as running. However, the incidence of running-related injuries among these runners has also increased representing a significant problem considering the inactivity associated with injury. The aim of the present thesis was to address two major problems for clinical researchers: (1) understanding the effects of biological ageing on musculoskeletal function and running biomechanics; (2) understanding the effects of exercise recommended to counteract the effects of ageing. Two studies were conducted to investigate the age-related adaptations in running biomechanics and their association with musculoskeletal function using traditional and an emergent data analysis technique. The first study involved a cross-sectional investigation using traditional statistics and we found an overall reduction in muscle strength and flexibility along with atypical running biomechanics for older runners compared to their younger counterparts. The second study involved a machine learning technique that demonstrated a promising ability to predict age-group membership based only on gait kinematic variables. When a more comprehensive and representative number of gait variables were tested using the same machine learning approach, the results were similar. The final experiment was a randomized controlled trial (RCT) to determine the effects of exercise on musculoskeletal function and running biomechanics in older runners. An innovative scoring approach was developed that demonstrated running biomechanical patterns were not changed following an 8-week stretching or strengthening protocol.
