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
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