PROJECT SUMMARY/ABSTRACT Vertebral fracture is the most common type of osteoporotic fracture. Spine is also the most common site of bone metastasis, leading to pathologic vertebral fractures. While performing activities of daily living is an essential part of healthy aging, pathologic and non-pathologic vertebral fractures can occur during these activities in metastatic or osteoporotic spines. These fractures cause pain and neurologic manifestations, affecting quality of life. Currently, there is no objective clinical technique that can assess bone fracture risks associated with physical activities. To fill the gap, we propose a patient-specific computational technique to quantitatively evaluate spine injury risks associated with physical activities. This novel approach will enable clinicians to reliably recommend safe and individualized activities to elderly populations. To achieve this, we will obtain motions and muscle activity outcomes from an elderly patient cohort using video motion analysis. These data will be used as input for kinematic motion analyses and mechanical testing on cadaveric lumbar spines, to create and validate our computational models. The rationale for this project is that a QCT/FEA process that can mimic physical activities will be able to reduce vertebral fractures and improve quality of life in elderly patient populations. Our long-term goal is to develop reliable computational techniques to enable earlier injury risk predictions in elderly patients with musculoskeletal diseases. Our overall objective, in this application, is to develop a patient-specific quantitative computed tomography-based finite element analysis (QCT/FEA) method that can assess both kinematic motions and fracture characteristics of the spine, to estimate fracture risks of physical activities. To achieve the overall objective, the following three independent specific aims will be accomplished: 1) to obtain lumbar range of motion and muscle response outcomes in an elderly patient population during five physical movements; 2) to perform kinematic testing on cadaveric spines to measure intradiscal pressures –using a novel approach– during physical movements; and also mechanical testing on spine segments to measure intradiscal pressure at fracture; and 3) to develop and validate QCT/FEA models of the lumbar spine to estimate spine injury risks. This research will lead to the development of a computational tool that can assign a risk score associated with physical activities. Further, this work will provide preliminary data for future R01 grant proposals to predict fracture risks associated with physical movements and exercises in patient populations.