Arthritis is the leading cause of physical disability in the U.S. and osteoarthritis (OA) is the most prevalent form of arthritis causing disabling pain and loss of mobility for almost 27 million adults (14% of all aged 25 and older), worldwide, OA costs in the US, Canada, UK, France, and Australia account for 1–2.5% of each country’s gross domestic product. Disability and healthcare costs of OA can be drastically reduced by novel patient-specific intervention programs (e.g., exercise) that benefit people with OA by slowing disease progression and delaying disability; however, there is a significant need for evidence-based prescription targets that directly address joint loads during exercise. Cycling rather than other exercise modalities is generally considered an alternative for people with knee OA, but there is a lack of scientific knowledge characterizing and maximizing the rehabilitation benefits (i.e., reduced joint loads) of cycling for people with knee OA.

The long-term goal of this research is to provide a scientific framework for planning, evaluation, and improvement of patient-specific rehabilitation for individuals with knee OA. Our research objective is to investigate how cycling exercise modifications lead to different joint loads in people with knee OA. This study proposes to use a combination of clinical motion analysis, patient-specific musculoskeletal models, muscle-actuated inverse dynamic simulations, and optimization to determine what features and their variations lead to improved joint loads for better exercise prescriptions.