Wearable sensors to predict improvement following an exercise intervention in patients with knee osteoarthritis

Journal of NeuroEngineering and Rehabilitation, Sep 2017

Muscle strengthening exercises consistently demonstrate improvements in the pain and function of adults with knee osteoarthritis, but individual response rates can vary greatly. Identifying individuals who are more likely to respond is important in developing more efficient rehabilitation programs for knee osteoarthritis. Therefore, the purpose of this study was to determine if pre-intervention multi-sensor accelerometer data (e.g., back, thigh, shank, foot accelerometers) and patient reported outcome measures (e.g., pain, symptoms, function, quality of life) can retrospectively predict post-intervention response to a 6-week hip strengthening exercise intervention in a knee OA cohort. Thirty-nine adults with knee osteoarthritis completed a 6-week hip strengthening exercise intervention and were sub-grouped as Non-Responders, Low-Responders, or High-Responders following the intervention based on their change in patient reported outcome measures. Pre-intervention multi-sensor accelerometer data recorded at the back, thigh, shank, and foot and Knee Injury and Osteoarthritis Outcome Score subscale data were used as potential predictors of response in a discriminant analysis of principal components. The thigh was the single best placement for classifying responder sub-groups (74.4%). Overall, the best combination of sensors was the back, thigh, and shank (81.7%), but a simplified two sensor solution using the back and thigh was not significantly different (80.0%; p = 0.27). While three sensors were best able to identify responders, a simplified two sensor array at the back and thigh may be the most ideal configuration to provide clinicians with an efficient and relatively unobtrusive way to use to optimize treatment.

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Wearable sensors to predict improvement following an exercise intervention in patients with knee osteoarthritis

Kobsar et al. Journal of NeuroEngineering and Rehabilitation Wearable sensors to predict improvement following an exercise intervention in patients with knee osteoarthritis Dylan Kobsar 0 Sean T. Osis 0 3 Jeffrey E. Boyd 2 Blayne A. Hettinga 0 Reed Ferber 0 1 3 0 Faculty of Kinesiology, University of Calgary , 2500 University Dr NW, Calgary, AB T2N 1N4 , Canada 1 Faculty of Nursing, University of Calgary , Calgary, AB , Canada 2 Department of Computer Science, University of Calgary , Calgary, AB , Canada 3 Running Injury Clinic , Calgary, AB , Canada Background: Muscle strengthening exercises consistently demonstrate improvements in the pain and function of adults with knee osteoarthritis, but individual response rates can vary greatly. Identifying individuals who are more likely to respond is important in developing more efficient rehabilitation programs for knee osteoarthritis. Therefore, the purpose of this study was to determine if pre-intervention multi-sensor accelerometer data (e.g., back, thigh, shank, foot accelerometers) and patient reported outcome measures (e.g., pain, symptoms, function, quality of life) can retrospectively predict post-intervention response to a 6-week hip strengthening exercise intervention in a knee OA cohort. Methods: Thirty-nine adults with knee osteoarthritis completed a 6-week hip strengthening exercise intervention and were sub-grouped as Non-Responders, Low-Responders, or High-Responders following the intervention based on their change in patient reported outcome measures. Pre-intervention multi-sensor accelerometer data recorded at the back, thigh, shank, and foot and Knee Injury and Osteoarthritis Outcome Score subscale data were used as potential predictors of response in a discriminant analysis of principal components. Results: The thigh was the single best placement for classifying responder sub-groups (74.4%). Overall, the best combination of sensors was the back, thigh, and shank (81.7%), but a simplified two sensor solution using the back and thigh was not significantly different (80.0%; p = 0.27). Conclusions: While three sensors were best able to identify responders, a simplified two sensor array at the back and thigh may be the most ideal configuration to provide clinicians with an efficient and relatively unobtrusive way to use to optimize treatment. Accelerometers; Wearable sensors; Gait analysis; Biomechanics; Knee osteoarthritis; Rehabilitation; Machine learning Background Osteoarthritis (OA) is the most common musculoskeletal disease and will be diagnosed in nearly half of all people at some point in their life [ 1 ]. Specifically, knee OA accounts for more mobility disabilities in people over the age of 65 than any other medical condition [ 2 ]. While there is no known cure, muscle strengthening exercises are considered a mainstay in the management of knee OA [ 3 ]. Many patients report improvements in pain and function with muscle strengthening interventions, but the extent to which these improvements occur can greatly vary between individuals [ 4 ]. This heterogeneity in treatment response has led to a recent shift towards personalized medicine and the identification of those patients who respond to treatment (responders). There is growing evidence supporting the need to predict outcomes to exercise interventions in knee OA and identify responders using baseline data. Previous research has demonstrated various clinical measures, including pain and function, can be predictive of response to exercise in OA [ 5, 6 ]. However, the association between subjective clinical measures and an individual’s response to treatment appears to be limited and inconsistent [7]. Preliminary findings have also suggested that biomechanical factors, such as varus thrust [ 8 ] and knee stability [ 9 ], may be predictive of response to exercise interventions in OA. Moreover, the integration of both objective biomechanical data and subjective clinical measures may further improve clinical prediction models. For example, previous research by our group [ 10 ] demonstrated that a unique combination of subjective patient reported outcome (PRO) measures and objective gait kinematic data collected from a threedimensional (3D) motion capture system could successfully predict treatment outcome for knee OA patients with a classification accuracy of 85%. Specifically, individuals with knee OA whose baseline, pre-treatment data included low self-reported function, in combination with atypical hip frontal plane kinematics, responded best to the hip strengthening 6-week intervention. These findings demonstrated that baseline PRO measures and objective biomechanical gait data can effectively predict an individual’s treatment response to an exercise intervention. However, many clinicians do not have access to 3D motion capture technology necessary to collect biomechanical gait data. Wearable sensors may effectively bridge this gap by providing a clinically accessible (...truncated)


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Dylan Kobsar, Sean T. Osis, Jeffrey E. Boyd, Blayne A. Hettinga, Reed Ferber. Wearable sensors to predict improvement following an exercise intervention in patients with knee osteoarthritis, Journal of NeuroEngineering and Rehabilitation, 2017, pp. 94,