Lower extremity muscle patterns and frontal plane biomechanics are altered in the contralateral knee of adults with osteoarthritis compared to asymptomatic adults.

PURPOSE: To compare knee joint muscle activity during gait between the contralateral limb of individuals with knee osteoarthritis (OA) and an asymptomatic older adult group. A secondary objective was to compare frontal and sagittal plane moment and sagittal plane motion features between groups. SCOPE: 84 individuals with moderate knee OA (61 ± 6 years, 43 % female, BMI 29.2 ± 5.7 kg/m2), and 45 asymptomatic older adults (61 ± 7 years, 49 % female, BMI 25.0 ± 3.4 kg/m2) participated. Participants walked at a self-selected pace on a dual belt treadmill. Surface electromyograms of the quadriceps, hamstrings, and gastrocnemius, segment motions and ground reaction forces were recorded. Principal component analyses identified amplitude and temporal electromyogram features. Sagittal plane motion and net external sagittal and frontal plane moments were calculated. Analysis of Variance models using Bonferroni corrections determined between and within group differences in these gait features. CONCLUSIONS: The contralateral knee showed prolonged lateral hamstring activation and altered temporal features of the gastrocnemius and greater knee adduction moments compared to asymptomatic adults. Group, muscle, or interaction effects were not found for the quadriceps. These findings highlight the importance of exploring the implications of contralateral knee function of individuals with moderate knee OA, particularly considering the altered antagonist muscle activations, and heightened frontal plane moments.

My Approach to Failed Rotator Cuff Repair.

Failed rotator cuff repairs pose several challenges due to the high incidence rate, complexity, and range of symptoms. We propose an overview for assessing and treating failed rotator cuff repairs. For active young patients, attempt revision repair with patch augmentation if possible. When anatomic revision is not viable, but muscle is retained, consider partial repair with interposition bridging. Isolated, irreparable supraspinatus tears may benefit from superior capsule reconstruction. Tendon transfer is suitable for patients with significant atrophy and multiple irreparable cuff tears. Low-demand elderly patients or those with substantial glenohumeral arthritis may consider reverse total shoulder arthroplasty if conservative management fails. There are a variety of reported outcomes in the literature but long-term studies with larger cohorts are needed to improve the management of failed rotator cuff repair.

Towards the Use of 2D Video-Based Markerless Motion Capture to Measure and Parameterize Movement During Functional Capacity Evaluation.

Purpose The objective of this study was to determine the agreement of kinematic parameters calculated from motion data collected via a 2D video-based pose-estimation (markerless motion capture) approach and a laboratory-based 3D motion capture approach during a floor-to-waist height functional lifting test. Method Twenty healthy participants each performed three floor-to-waist height lifts. Participants' lifts were captured simultaneously using 2D video (camcorder) in the sagittal plane and 3D motion capture (Vicon, Oxford, UK). The three lifts were representative of a perceived light, medium, and heavy load. Post-collection, video data were processed through a pose-estimation software (i.e., markerless motion capture). Motion data from 3D motion capture and video-based markerless motion capture were each used to calculate objective measures of interest relevant to a functional capacity evaluation (i.e., posture, balance, distance of the load from the body, and coordination). Bland-Altman analyses were used to calculate agreement between the two methods. Results Bland-Altman analysis revealed that mean differences ranged from 1.9° to 22.1° for posture and coordination-based metrics calculated using markerless and 3D motion capture, respectively. Limits of agreement for most posture and coordination measures were approximately + 20°. Conclusions 2D video-based pose estimation offers a strategy to objectively measure movement and subsequently calculated metrics of interest within an FCE context and setting, but at present the agreement between metrics calculated using 2D video-based methods and 3D motion capture is insufficient. Therefore, continued effort is required to improve the accuracy of 2D-video based pose estimation prior to inclusion into functional testing paradigms.

Exploring the Application of Pattern Recognition and Machine Learning for Identifying Movement Phenotypes During Deep Squat and Hurdle Step Movements.

BACKGROUND: Movement screens are increasingly used in sport and rehabilitation to evaluate movement competency. However, common screens are often evaluated using subjective visual detection of a priori prescribed discrete movement features (e.g., spine angle at maximum squat depth) and may not account for whole-body movement coordination, or associations between different discrete features. OBJECTIVE: To apply pattern recognition and machine learning techniques to identify whole-body movement pattern phenotypes during the performance of exemplar functional movement screening tasks; the deep squat and hurdle step. Additionally, we also aimed to compare how discrete kinematic measures, commonly used to score movement competency, differed between emergent groups identified via pattern recognition and machine learning. METHODS: Principal component analysis (PCA) was applied to 3-dimensional (3D) trajectory data from participant's deep squat (DS) and hurdle step performance, identifying emerging features that describe orthogonal modes of inter-trial variance in the data. A gaussian mixture model (GMM) was fit and used to cluster the principal component scores as an unsupervised machine learning approach to identify emergent movement phenotypes. Between group features were analyzed using a one-way ANOVA to determine if the objective classifications were significantly different from one another. RESULTS: Three clusters (i.e., phenotypes) emerged for the DS and right hurdle step (RHS) and 4 phenotypes emerged for the left hurdle step (LHS). Selected discrete points commonly used to score DS and hurdle step movements were different between emergent groups. In regard to the select discrete kinematic measures, 4 out of 5, 7 out of 7 and 4 out of 7, demonstrated a main effect (p < 0.05) between phenotypes for the DS, RHS, and LHS respectively. CONCLUSION: Findings support that whole-body movement analysis, pattern recognition and machine learning techniques can objectively identify movement behavior phenotypes without the need to a priori prescribe movement features. However, we also highlight important considerations that can influence outcomes when using machine learning for this purpose.