Osteoarthritis year in review 2020: mechanics.

The mechanical environment of the joint during dynamic activity plays a significant role in osteoarthritis processes. Understanding how the magnitude, pattern and duration of joint-specific loading features contribute to osteoarthritis progression and response to treatment is a topic of on-going relevance. This narrative review synthesizes evidence from recent papers that have contributed to knowledge related to three identified emerging subthemes: 1) the role of the joint mechanical environment in osteoarthritis pathogenesis, 2) joint biomechanics as an outcome to arthroplasty treatment of osteoarthritis, and 3) methodological trends for advancing our knowledge of the role of biomechanics in osteoarthritis. Rather than provide an exhaustive review of a broad area of research, we have focused on evidence this year related to these subthemes. New research this year has indicated significant interest in using biomechanics investigations to understand structural vs clinical progression of osteoarthritis, the role and interaction in the three-dimensional loading environment of the joint, and the contribution of muscle activation and forces to osteoarthritis progression. There is ongoing interest in understanding how patient variability with respect to gait biomechanics influences arthroplasty surgery outcomes, and subgroup analyses have provided evidence for the potential utility in tailored treatment approaches. Finally, we are seeing a growing trend in the application of translational biomechanics tools such as wearable inertial measurement units for improved integration of biomechanics into clinical decision-making and outcomes assessment for osteoarthritis.

Validity of a novel method to measure vertical oscillation during running using a depth camera.

Recent advancements in low-cost depth cameras may provide a clinically accessible alternative to conventional three-dimensional (3D) multi-camera motion capture systems for gait analysis. However, there remains a lack of information on the validity of clinically relevant running gait parameters such as vertical oscillation (VO). The purpose of this study was to assess the validity of measures of VO during running gait using raw depth data, in comparison to a 3D multi-camera motion capture system. Sixteen healthy adults ran on a treadmill at a standard speed of 2.7 m/s. The VO of their running gait was simultaneously collected from raw depth data (Microsoft Kinect v2) and 3D marker data (Vicon multi-camera motion capture system). The agreement between the VO measures obtained from the two systems was assessed using a Bland-Altman plot with 95% limits of agreement (LOA), a Pearson's correlation coefficient (r), and a Lin's concordance correlation coefficient (rc). The depth data from the Kinect v2 demonstrated excellent results across all measures of validity (r = 0.97; rc = 0.97; 95% LOA = -8.0 mm - 8.7 mm), with an average absolute error and percent error of 3.7 (2.1) mm and 4.0 (2.0)%, respectively. The findings of this study have demonstrated the ability of a low cost depth camera and a novel tracking method to accurately measure VO in running gait.