Background: Abnormal gait after anterior cruciate ligament reconstruction (ACLR) may contribute to development and/or progression of knee osteoarthritis. Objective: To conduct a systematic review and meta-analysis of knee kinematics and joint moments during walking after ACLR. Methods: We searched seven electronic databases and reference lists of relevant papers, for cross-sectional, human-based observational studies comparing knee joint kinematics and moments during level walking in individuals with ACLR, with the uninjured contralateral knee or healthy individuals as a control. Two independent reviewers appraised methodological quality (modified Downs and Black scale). Where possible, data were pooled by time post-ACLR (RevMan), otherwise narrative synthesis was undertaken. Results: Thirty-four studies were included. Meta-analysis revealed significant sagittal plane deficits in ACLR knees. We found greater knee flexion angles (standardised mean difference: 1.06; 95% CI 0.39 to 1.74) and joint moments (1.61; 0.87 to 2.35) 3 years after ACLR (vs healthy controls) (0.09; −0.63 to 0.81). No transverse plane conclusions could be drawn. Conclusions: Sagittal plane biomechanics, rather than the knee adduction moment, appear to be more relevant post-ACLR. Better understanding of sagittal plane biomechanics is necessary for optimal post-operative recovery, and to potentially prevent early onset and progression of knee OA after ACLR. Trial registration number: PROSPERO systematic review protocol registration number CRD4201400882 2.

Quantitative gait assessment is important in diagnosis and management of Parkinson's disease (PD); however, gait characteristics of a cohort are dispersed by patient physical properties including age, height, body mass, and gender, as well as walking speed, which may limit capacity to discern some pathological features. The aim of this study was twofold. First, to use a multiple regression normalization strategy that accounts for subject age, height, body mass, gender, and self-selected walking speed to identify differences in spatial-temporal gait features between PD patients and controls; and second, to evaluate the effectiveness of machine learning strategies in classifying PD gait after gait normalization. Spatial-temporal gait data during self-selected walking were obtained from 23 PD patients and 26 aged-matched controls. Data were normalized using standard dimensionless equations and multiple regression normalization. Machine learning strategies were then employed to classify PD gait using the raw gait data, data normalized using dimensionless equations, and data normalized using the multiple regression approach. After normalizing data using the dimensionless equations, only stride length, step length, and double support time were significantly different between PD patients and controls (p <; 0.05); however, normalizing data using the multiple regression method revealed significant differences in stride length, cadence, stance time, and double support time. Random Forest resulted in a PD classification accuracy of 92.6% after normalizing gait data using the multiple regression approach, compared to 80.4% (support vector machine) and 86.2% (kernel Fisher discriminant) using raw data and data normalized using dimensionless equations, respectively. Our multiple regression normalization approach will assist in diagnosis and treatment of PD using spatial-temporal gait data.

Le. The purpose of this study was twofold: (1) to measure the instantaneous moment arms of thirteen subregions of major muscles spanning the glenohumeral joint during abduction and flexion of the shoulder after reverse total shoulder arthroplasty and (2) to compare these data with the muscle moment arms previously measured preoperatively in the anatomical shoulders. Methods: Reverse total shoulder arthroplasty was performed on eight entire cadaveric upper extremities. The specimens were mounted onto a dynamic testing apparatus, and the instantaneous abductor/adductor and flexor/extensor moment arms of subregions of the deltoid, latissimus dorsi, pectoralis major, teres major, and subscapularis muscles (a total of thirteen subregions) were measured with use of the tendon excursion method. These muscle moment arms were compared with those measured preoperatively in the anatomical shoulders. Results: Reverse total shoulder arthroplasty resulted in significant increases in the abductor moment arms of the anterior subregion of the deltoid (mean increase = 10.4 mm; 95% confidence interval = 7.5 to 13.3 mm) and the middle subregion of the deltoid (mean increase = 15.5 mm; 95% confidence interval = 10.8 to 20.3 mm) as well as recruitment of the posterior subregion of the deltoid as an abductor. The superior subregion of the pectoralis major (the clavicular fibers) and anterior subregion of the deltoid were the most effective flexors and had a substantial potential to initiate flexion. The adductor and extensor moment arms of the teres major, latissimus dorsi subregions, and inferior and middle subregions of the pectoralis major increased substantially after the arthroplasty. The subscapularis subregions behaved as extensors, abductors, and adductors after the arthroplasty; this was in contrast to their roles in the anatomical shoulder, in which they were mainly flexors and adductors. Conclusions: Reverse total shoulder arthroplasty increases the moment arms of the major abductors, flexors, adductors, and extensors of the glenohumeral joint, thereby reducing muscle effort during common tasks such as lifting and pushing. Clinical Relevance: The results suggest that surgeons should attempt to preserve as much of the pectoralis major and subscapularis as possible during reverse total shoulder arthroplasty as these muscles may contribute substantially to shoulder mobility and stability, respectively....