
Matthew Ruder, student in the sMAP CREATE Program and a PhD Candidate in the department of Kinesiology in the Faculty of Science receives MIRA and Labarge Scholarship to study assessing validity and sensitivity of remotely collected wearable sensor data in patients.
Osteoarthritis (OA) is a degenerative joint disease that involves a loss of cartilage and change in bone and soft tissue, accompanied by joint pain, mobility deficits, and a reduced quality of life. This serious disease affects nearly 20% of all adults, and more than 50% of those over the age of 70. The knee is the most commonly affected joint and often undergoes significant changes in the dynamic loading environment and movement patterns during walking gait. While these changes can be monitored using gait analyses to inform disease progression and treatment options for patients, conventional gait analysis systems involve expensive cameras, force plates, and dedicated laboratory space which makes their accessibility limited.
Fortunately, wearable inertial sensors offer an affordable and more broadly deployable alternative to collect gait data on those with knee OA. Moreover, these data can be collected in more ecologically valid, real-world settings outside of the conventional laboratory environment. Consequently, these devices have the unique ability to vastly improve how we diagnose, treat, and track disease progression in older adults with OA in our community.
Unfortunately, despite the increased use of these sensors for gait analysis research, most data collections still occur in highly controlled settings, under the direct supervision of the researcher. Not only do these laboratory-based collections lack real-world relevance, but the challenges of the COVID-19 pandemic have led to significant disruptions in our ability to collect in-person data on older adults. While this has forced many to reevaluate their data collection protocols, it may be the much-needed driver of a paradigm shift in the field of gait biomechanics. Specifically, biomechanics research needs to better utilize the unique advantages of wearable inertial sensors to support more uncontrolled, real-world, and remote data collections that can help researchers and clinicians promote optimal aging and aging in place. Nevertheless, before we can effectively shift conventional in-person biomechanical research towards more remote collections, there is a need to better understand both the quality of data and sensitivity to changes that may be expected in remote collections, as compared to in-person collections.
Additionally, developing state-of-the-art post-processing techniques to improve remote data collections will be imperative to successfully drive the revolutionary shift in this field of research during the COVID-19 pandemic and for years to come.
Therefore, the primary purpose of this study is to examine the validity and sensitivity to change in remote wearable inertial sensor gait data as compared to in-person collected data. This study will involve older adults with knee OA undergoing intra-articular corticosteroid knee injections as a pain-relieving measure. The pain relief that occurs from this injection has been shown to also result in changes in gait, thereby providing a viable model to study the sensitivity to change in these collections. Moreover, the integration of the non-invasive wearable sensor protocols within clinical visits allows for data to be collected during the COVID-19 pandemic.