Deep learning models developed to predict knee joint kinematics are usually trained on inertial measurement unit (IMU) data from healthy people and only for the activity of walking. Yet, people with knee osteoarthritis have difficulties with other activities and there are a lack of studies using IMU training data from this population. Our objective was to conduct a proof-of-concept study to determine the feasibility of using IMU training data from people with knee osteoarthritis performing multiple clinically important activities to predict knee joint sagittal plane kinematics using a deep learning approach. We trained a bidirectional long short-term memory model on IMU data from 17 participants with knee osteoarthritis to estimate knee joint flexion kinematics for phases of walking, transitioning to and from a chair, and negotiating stairs. We tested two models, a double-leg model (four IMUs) and a single-leg model (two IMUs). The single-leg model demonstrated less prediction error compared to the double-leg model. Across the different activity phases, RMSE (SD) ranged from 7.04° (2.6) to 11.78° (6.04), MAE (SD) from 5.99° (2.34) to 10.37° (5.44), and Pearson’s R from 0.85 to 0.99 using leave-one-subject-out cross-validation. This study demonstrates the feasibility of using IMU training data from people who have knee osteoarthritis for the prediction of kinematics for multiple clinically relevant activities.

Clinicians lack objective means for monitoring if their knee osteoarthritis patients are improving outside of the clinic (e.g., at home). Previous human activity recognition (HAR) models using wearable sensor data have only used data from healthy people and such models are typically imprecise for people who have medical conditions affecting movement. HAR models designed for people with knee osteoarthritis have classified rehabilitation exercises but not the clinically relevant activities of transitioning from a chair, negotiating stairs and walking, which are commonly monitored for improvement during therapy for this condition. Therefore, it is unknown if a HAR model trained on data from people who have knee osteoarthritis can be accurate in classifying these three clinically relevant activities. Therefore, we collected inertial measurement unit (IMU) data from 18 participants with knee osteoarthritis and trained convolutional neural network models to identify chair, stairs and walking activities, and phases. The model accuracy was 85% at the first level of classification (activity), 89–97% at the second (direction of movement) and 60–67% at the third level (phase). This study is the first proof-of-concept that an accurate HAR system can be developed using IMU data from people with knee osteoarthritis to classify activities and phases of activities.

Purpose To examine the applicability and process of change of Cognitive Functional Therapy (CFT) in the management of pain and disability in people with knee osteoarthritis who were offered knee replacement surgery and had risk factors for poor response to surgery.Methods Single-case experimental design with a mixed-methods, repeated measures approach was used to investigate the process of change through CFT in four participants. Qualitative interviews investigated beliefs, behaviours and coping responses, and self-reported measures assessed pain, disability, psychological factors, and function at 25 timepoints. Study registered with the Australian and New Zealand Clinical Trials Registry (ACTRN12619001491156).Results Qualitative data indicate that CFT promoted helpful changes in all participants, with two responses observed. One reflected a clear shift to a biopsychosocial conceptualisation of osteoarthritis, behavioural re-engagement and the view that a knee replacement was no longer necessary. The other response reflected a mixed conceptualisation with dissonant beliefs about osteoarthritis and its management. Psychological and social factors were identified as potential treatment barriers. Overall, quantitative measures supported the qualitative findings.Conclusion The process of change varies between and within individuals over time. Psychological and social barriers to treatment have implications for future intervention studies for the management of knee osteoarthritis.