Effects of Hip Bracing on Gait Biomechanics, Pain and Function in Subjects With Mild to Moderate Hip Osteoarthritis

Hip Osteoarthritis (HOA) is a common joint disease with serious impact on the quality of life of the affected persons. Additionally, persons with HOA often show alterations in gait biomechanics. Developing effective conservative treatment strategies is of paramount importance, as joint replacement is only indicated for end-stage HOA. In contrast to knee osteoarthritis, little is known about the effectiveness of hip bracing for the management of HOA. Studies analysing mechanically unloading hip braces partly showed beneficial results. However, methodological limitations of these studies, such as small sample sizes or lack of control groups, limit the applicability of the results. Additionally, mechanically unloading braces might impose restrictions on motion and comfort and thus, might not be suitable for people with only mild or moderate symptoms. The aim of this study was to comprehensively quantify the effects of unilateral HOA as well as functional hip bracing on gait biomechanics, pain, proprioception and functional capacity in people with mild to moderate HOA. Hip and pelvis biomechanics during walking were analysed in 21 subjects with mild to moderate HOA under three bracing conditions: unbraced, immediately after brace application and after 1 week of brace usage. Additionally, pain, hip proprioception and functional capacity were assessed. A matched group of 21 healthy subjects was included as reference. Kinematic and kinetic data were collected using a 16-camera infrared motion capturing system and two force plates. Visual analogue scales, an angle reproduction test and a 6-min walking test were applied to measure pain, hip proprioception and functional capacity, respectively. Subjects with HOA walked slower, with reduced step length, sagittal hip range of motion and peak extension angle and had a reduced functional capacity. After 1 week of brace application step length, walking speed and functional capacity were significantly increased. Additionally, pain perception was significantly lower in the intervention period. These results encourage the application of functional hip braces in the management of mild to moderate HOA. However, as key parameters of HOA gait such as a reduced peak extension angle remained unchanged, the underlying mechanisms remain partly unclear and have to be considered in the future

WHY IS THE LEFT KNEE RATHER PRONE TO INJURY DURING TEAM HANDBALL-SPECIFIC SIDE-CUTTING MANEUVERS TO THE RIGHT?

The purpose of this study was the biomechanical inter-leg evaluation in three team handball-specific side-cutting maneuvers. This should help to gain a better understanding how different movement executions potentially produce harmful demands to one or both knee joints. Therefore, eight competitive handball player performed the three most common side-cutting maneuvers to the right side (side-cutting maneuver was performed with alternating or simultaneous steps) in a game-like setting in a movement laboratory. Movement data were collected with a 3D motion capture system and two linked 3D force plates. The analysis of the side-cutting maneuvers revealed increased vertical and medio-lateral ground reaction force components on the left leg, which initiated the side-cutting maneuver. The peak knee abduction moments in the weight acceptance phase did not differ between the left and the right leg in all three side-cutting maneuvers. However, higher peak knee valgus angles occurred at the left leg, which increased with increasing stance time. The results of this study indicate that during the performance of handball-specific side-cutting maneuvers to the right, the left knee joint has a greater risk to get injured. Consequently, athletes and coaches should place special focus on the movement execution of the cutting initiating leg to reduce the risk of knee injuries. Furthermore, leg explosive strength and core stability should be in major focus in training exercises to prepare the athlete for the demands in such high intensity movements

IDENTIFICATION OF FATIGUE-RELATED KINEMATIC CHANGES IN ELITE RUNNERS USING A SUPPORT VECTOR MACHINE APPROACH

Understanding the kinematic changes underlying fatigue is essential in running biomechanics. The aim of this study was to identify fatigue-related kinematic changes in elite runners using a support vector machine approach. Full-body kinematics of thirteen trained runners were recorded in a non-fatigued and a fatigued state during treadmill running at their individual fatigue-speed. A support vector machine was trained and used to identify kinematic differences between the non-fatigued and fatigued state based on principal component scores. Strides during non-fatigued and fatigued running could be separated with 99.4% classification accuracy. Four upper limb (two shoulder and two elbow), four lower limb (one ankle, two knee and one hip) and two trunk (one thoracic and one lumbar spine) principal component scores were identified as most discriminative kinematic features between non-fatigued and fatigued running. The findings of the study suggest the feasibility of a support vector machine approach to identify subtle fatigue-related kinematic changes in elite runners

Identification of Patients with Similar Gait Compensating Strategies Due to Unilateral Hip Osteoarthritis and the Effect of Total Hip Replacement: A Secondary Analysis

Despite good clinical functional outcome, deficits in gait biomechanics exist 2 years after total hip replacement surgery. The aims of this research were (1) to group patients showing similar gait adaptations to hip osteoarthritis and (2) to investigate the effect of the surgical treatment on gait kinematics and external joint moments. In a secondary analysis, gait data of 51 patients with unilateral hip osteoarthritis were analyzed. A k-means cluster analysis was performed on scores derived via a principal component analysis of the gait kinematics. Preoperative and postoperative datasets were statistically tested between clusters and 46 healthy controls. The first three principal components incorporated hip flexion/extension, pelvic tilt, foot progression angle and thorax tilt. Two clusters were discriminated best by the peak hip extension during terminal stance. Both clusters deviated from healthy controls in spatio-temporal, kinematic and kinetic parameters. The cluster with less hip extension deviated significantly more. The clusters improved postoperatively but differences to healthy controls were still present one year after surgery. A poor preoperative gait pattern in patients with unilateral hip osteoarthritis is associated with worse gait kinematics after total hip replacement. Further research should focus on the identification of patients who can benefit from an adapted or individualized rehabilitation program

Estimation of Knee Joint Forces in Sport Movements Using Wearable Sensors and Machine Learning

Knee joint forces (KJF) are biomechanical measures used to infer the load on knee joint structures. The purpose of this study is to develop an artificial neural network (ANN) that estimates KJF during sport movements, based on data obtained by wearable sensors. Thirteen participants were equipped with two inertial measurement units (IMUs) located on the right leg. Participants performed a variety of movements, including linear motions, changes of direction, and jumps. Biomechanical modelling was carried out to determine KJF. An ANN was trained to model the association between the IMU signals and the KJF time series. The ANN-predicted KJF yielded correlation coefficients that ranged from 0.60 to 0.94 (vertical KJF), 0.64 to 0.90 (anterior–posterior KJF) and 0.25 to 0.60 (medial–lateral KJF). The vertical KJF for moderate running showed the highest correlation (0.94 ± 0.33). The summed vertical KJF and peak vertical KJF differed between calculated and predicted KJF across all movements by an average of 5.7% ± 5.9% and 17.0% ± 13.6%, respectively. The vertical and anterior–posterior KJF values showed good agreement between ANN-predicted outcomes and reference KJF across most movements. This study supports the use of wearable sensors in combination with ANN for estimating joint reactions in sports applications