Limited mobility due to injury or disease affects nearly everyone at some point in life. Osteoarthritis, one common cause of limited mobility, affects one in five adults over age 45. Running is a popular and low-cost recreational activity that can assist in weight control and promote cardiovascular health; however, running injuries plague upwards of 50% of long-distance runners. The prevalence and negative impact of reduced mobility due to injury and joint degeneration will continue to increase as our population ages.
The field lacks the fundamental understanding needed to optimize treatments and prevent limited mobility.
Currently, the field lacks a fundamental understanding of risk factors and mechanisms, which are required to optimize treatment and prevent limited mobility. There are several reasons for this:
- The systems we are studying are incredibly complex. There are many variables at play when it comes to understanding neural control and dysfunction or teasing apart the causes of pathology and implications of rehabilitation.
- Subject numbers are typically small in isolated studies, and thus researchers often cannot collect enough data to achieve statistical significance.
- Researchers have to start from scratch for new studies since there are few tools to enable utilizing and building on existing data, again preventing the collection of enough data to achieve statistical significance.
- The lack of tools for analyzing anything but the highest quality data precludes the use of many data sources.
The Mobilize Center will use an integrative big data approach to enhance risk assessment and rehabilitation.
To enhance risk assessment and treatments for gait rehabilitation, the Mobilize Center will:
- Integrate high-resolution three-dimensional motion measurements made on thousands of individuals with knee osteoarthritis and running injuries with activity monitoring data from low-cost measurements, such as wearable sensors and smartphones, that is available for an even larger number of subjects
- Develop and validate statistical learning classifiers to detect individuals at risk for running injuries or knee osteoarthritis using data from low-cost sensing devices. These methods will also identify the associated predictive variables or risk factors for these conditions.
- Develop a framework to go from statistical risk factors to rehabilitation/intervention using biomechanical modeling. We will begin with a predictive variable identified in the data, apply biomechanical modeling to explain the risk factor, and design an intervention (e.g., a minor alteration in knee kinematics and muscle activity that will reduce the knee joint loading associated with osteoarthritis).
We will initially focus on osteoarthritis and running injuries. The framework can also be used to understand limited mobility and rehabilitation for individuals who have suffered from a stroke, Parkinson’s disease, or limb loss, or individuals at risk for injury due to falls.