Sensor-Based Gait Retraining Lowers Knee Adduction Moment and Improves Symptoms in Patients with Knee Osteoarthritis: A Randomized Controlled Trial.

The present study compared the effect between walking exercise and a newly developed sensor-based gait retraining on the peaks of knee adduction moment (KAM), knee adduction angular impulse (KAAI), knee flexion moment (KFM) and symptoms and functions in patients with early medial knee osteoarthritis (OA). Eligible participants (n = 71) with early medial knee OA (Kellgren-Lawrence grade I or II) were randomized to either walking exercise or gait retraining group. Knee loading-related parameters including KAM, KAAI and KFM were measured before and after 6-week gait retraining. We also examined clinical outcomes including visual analog pain scale (VASP) and Knee Injury and Osteoarthritis Outcome Score (KOOS) at each time point. After gait retraining, KAM1 and VASP were significantly reduced (both Ps < 0.001) and KOOS significantly improved (p = 0.004) in the gait retraining group, while these parameters remained similar in the walking exercise group (Ps ≥ 0.448). However, KAM2, KAAI and KFM did not change in both groups across time (Ps ≥ 0.120). A six-week sensor-based gait retraining, compared with walking exercise, was an effective intervention to lower medial knee loading, relieve knee pain and improve symptoms for patients with early medial knee OA.

How do training experience and geographical origin of a runner affect running biomechanics?

BACKGROUND: Several studies compared African runners with runners from other places with difference ethnicities to identify biomechanical factors that may contribute to their extraordinary running performance. However, most studies only assessed runners at the elite level. Whether the performance difference was a result of nature or nurture remains unclear. RESEARCH QUESTIONS: This case study aimed to assess the effect of geographical origin and the effect of training on running biomechanics. METHODS: We recruited twenty male runners from two regions (Asian and Africa) at two performance levels (elite and recreational), and asked them to run on an instrumented treadmill at 12 km∙h-1. We measured running kinetics and kinematics parameters, and focused on the parameters that have been shown associated with running performance. We used Friedman test to compare the effect of geographical origin and training on running biomechanics. RESULTS: Compared to recreational runners, elite runners applied higher amount of ground reaction force in both vertical and anterior-posterior directions (P <  0.05, Cohen's d = 1.63-2.03), together with a longer aerial time (P =  0.039, Cohen's d = 1.11). On the other hand, African runners expressed higher vertical stiffness than Asian runners (P =  0.027, Cohen's d = 0.98). However, the increased vertical stiffness in African runners did not lead to a higher vertical loading rate (P >  0.555, Cohen's d < 0.3), which could be a result of a lower footstrike angle during landing (P =  0.012, Cohen's d = 1.36). SIGNIFICANCE: For elite runners, the higher amount of ground reaction force might facilitate a longer aerial time, but could also lead to higher amount of mechanical energy loss. African runners expressed higher vertical stiffness and higher step rate, which might lead to a lower CoM vertical displacement, and furthermore reduce mechanical energy loss.

Biomechanical effects following footstrike pattern modification using wearable sensors.

OBJECTIVES: This study sought to examine the biomechanical effects of an in-field sensor-based gait retraining program targeting footstrike pattern modification during level running, uphill running and downhill running. DESIGN: Quasi-experimental design. METHODS: Sixteen habitual rearfoot strikers were recruited. All participants underwent a baseline evaluation on an instrumented treadmill at their preferred running speeds on three slope settings. Participants were then instructed to modify their footstrike pattern from rearfoot to non-rearfoot strike with real-time audio biofeedback in an 8-session in-field gait retraining program. A reassessment was conducted to evaluate the post-training biomechanical effects. Footstrike pattern, footstrike angle, vertical instantaneous loading rate (VILR), stride length, cadence, and knee flexion angle at initial contact were measured and compared. RESULTS: No significant interaction was found between training and slope conditions for all tested variables. Significant main effects were observed for gait retraining (p-values≤0.02) and slopes (p-values≤0.01). After gait retraining, 75% of the participants modified their footstrike pattern during level running, but effects of footstrike pattern modification were inconsistent between slopes. During level running, participants exhibited a smaller footstrike angle (p≤0.01), reduced VILR (p≤0.01) and a larger knee flexion angle (p=0.01). Similar effects were found during uphill running, together with a shorter stride length (p=0.01) and an increased cadence (p≤0.01). However, during downhill running, no significant change in VILR was found (p=0.16), despite differences found in other biomechanical measurements (p-values=0.02-0.05). CONCLUSION: An 8-session in-field gait retraining program was effective in modifying footstrike pattern among runners, but discrepancies in VILR, stride length and cadence were found between slope conditions.

Real-Time Estimation of Knee Adduction Moment for Gait Retraining in Patients With Knee Osteoarthritis.

Previous clinical studies have reported that gait retraining is an effective non-invasive intervention for patients with medial compartment knee osteoarthritis. These gait retraining programs often target a reduction in the knee adduction moment (KAM), which is a commonly used surrogate marker to estimate the loading in the medial compartment of the tibiofemoral joint. However, conventional evaluation of KAM requires complex and costly equipment for motion capture and force measurement. Gait retraining programs, therefore, are usually confined to a laboratory environment. In this study, machine learning techniques were applied to estimate KAM during walking with data collected from two low-cost wearable sensors. When compared to the traditional laboratory-based measurement, our mobile solution using artificial neural network (ANN) and XGBoost achieved an excellent agreement with R2 of 0.956 and 0.947 respectively. With the implementation of a real-time audio feedback system, the present algorithm may provide a viable solution for gait retraining outside laboratory. Clinical treatment strategies can be developed using the continuous feedback provided by our system.

Walking with head-mounted virtual and augmented reality devices: Effects on position control and gait biomechanics.

What was once a science fiction fantasy, virtual reality (VR) technology has evolved and come a long way. Together with augmented reality (AR) technology, these simulations of an alternative environment have been incorporated into rehabilitation treatments. The introduction of head-mounted displays has made VR/AR devices more intuitive and compact, and no longer limited to upper-limb rehabilitation. However, there is still limited evidence supporting the use of VR and AR technology during locomotion, especially regarding the safety and efficacy relating to walking biomechanics. Therefore, the objective of this study is to explore the limitations of such technology through gait analysis. In this study, thirteen participants walked on a treadmill in normal, virtual and augmented versions of the laboratory environment. A series of spatiotemporal parameters and lower-limb joint angles were compared between conditions. The center of pressure (CoP) ellipse area (95% confidence ellipse) was significantly different between conditions (p = 0.002). Pairwise comparisons indicated a significantly greater CoP ellipse area for both the AR (p = 0.002) and VR (p = 0.005) conditions when compared to the normal laboratory condition. Furthermore, there was a significant difference in stride length (p<0.001) and cadence (p<0.001) between conditions. No statistically significant difference was found in the hip, knee and ankle joint kinematics between the three conditions (p>0.082), except for maximum ankle plantarflexion (p = 0.001). These differences in CoP ellipse area indicate that users of head-mounted VR/AR devices had difficulty maintaining a stable position on the treadmill. Also, differences in the gait parameters suggest that users walked with an unusual gait pattern which could potentially affect the effectiveness of gait rehabilitation treatments. Based on these results, position guidance in the form of feedback and the use of specialized treadmills should be considered when using head-mounted VR/AR devices.