Current Evidence of Gait Modification with Real-time Biofeedback to Alter Kinetic, Temporospatial, and Function-Related Outcomes: A Review

Abstract

Background: Gait retraining using real-time biofeedback (RTB) may have positive outcomes in decreasing knee adduction moment (KAM) in healthy individuals and has shown equal likelihood in patients with knee osteoarthritis (OA). Currently, there is no consensus regarding the most effective gait modification strategy, mode of biofeedback or treatment dosage. Objective: The purpose of this review was: i) to assess if gait retraining interventions using RTB are valuable to reduce KAM, pain, and improve function in individuals with knee osteoarthritis, ii) to evaluate the effectiveness of different gait modifications and modes of RTB in reducing KAM in healthy individuals, and iii) to assess the impact of gait retraining interventions with RTB on other variables that may affect clinical outcomes. Methods: Seven electronic databases were searched using five search terms. Studies that utilized any form of gait retraining with RTB to improve one or a combination of the following measures were included: KAM, knee pain, and function. Twelve studies met the inclusion criteria, evaluating eleven distinctive gait modifications and three modes of RTB. Results: All but one study showed positive outcomes. Self-selected and multi-parameter gait modifications showed the greatest reductions in KAM with visual and haptic RTB being more effective than auditory. Conclusions: Current evidence suggests that gait modification using RTB can Positively alter KAM in asymptomatic and symptomatic participants. However, the existing literature is limited and of low quality, with the optimal combination strategies remaining unclear (gait and biofeedback mode). Future studies should employ randomized controlled study designs to compare the effects of different gait modification strategies and biofeedback modes on individuals with knee OA.

Real-Time Prediction of Joint Forces by Motion Capture and Machine Learning

“…As a result, the joint contact forces have been extensively used to assess joint malfunction [28], disease-driven gait alterations [29] and joint replacement design [30,31]. Medial knee force calculation has particularly attracted research efforts due to the high prevalence of medial knee osteoarthritis compared to the lateral compartment [32], thus medial knee contact force (KCF) reduction has been the focus of numerous gait modification regimens [33,34]. However, knowledge of inter-compartmental knee loading patterns and force direction during locomotion will uplift the design of knee rehabilitation programs for lateral osteoarthritis patients as well, and help towards the investigation of the mechanical factors of knee prostheses' loosening phenomenon [35].…”

“…Moreover, standard modeling methods can be rather time-consuming-ranging from several minutes to hours-thus being unsuitable for real-time applications. A representative example involves the assessment and guidance of knee-osteoarthritic patients in self-managing their condition through medial knee offloading while performing daily activities [33,34]. Consequently, new approaches must emerge to facilitate prevention and treatment of major musculoskeletal deficits, integrating sensor-based biomedical technologies coupled with computational models of muscle and bone.…”

Longitudinal Changes in the Total Knee Joint Moment After Anterior Cruciate Ligament Reconstruction Correlate With Cartilage Thickness Changes

“…The associations between changes in KFM and KAM contributions to the TJM and cartilage thickness ratio changes in the ACLR limb provide support for load modifying interventions to alter gait mechanics following ACLR. For example, gait retraining, bracing, lateral wedging, and specialized footwear can reduce the KAM and could be explored in the ACLR population. Furthermore, extended rehabilitation or interventions aiming to improve quadriceps function/KFM after ACLR could be investigated in future work.…”

Comparison of the Spinal Loads Produced by Carrying a Backpack and Carrying a Person Piggyback: Crossfit Training

“…Several gait parameters were investigated, including walking velocity, walking cadence, average step length, right to right stride length, left to left stride length, right foot stance time, single stance time, and double stance time (Eddo, Lindsey, Caswell, & Cortes, 2017). The 0.47 metre force plate width was used as the distance calibration within the plane of the carrier during Dartfish analysis.…”