Literature DB >> 27485366

Gait Retraining With Real-Time Biofeedback to Reduce Knee Adduction Moment: Systematic Review of Effects and Methods Used.

Rosie Richards1, Josien C van den Noort2, Joost Dekker3, Jaap Harlaar2.   

Abstract

OBJECTIVE: To review the current literature regarding methods and effects of real-time biofeedback used as a method for gait retraining to reduce knee adduction moment (KAM), with intended application for patients with knee osteoarthritis (KOA). DATA SOURCES: Searches were conducted in MEDLINE, Embase, CINAHL, SPORTDiscus, Web of Science, and Cochrane Central Register of Controlled Trials with the keywords gait, feedback, and knee osteoarthritis from inception to May 2015. STUDY SELECTION: Titles and abstracts were screened by 1 individual for studies aiming to reduce KAM. Full-text articles were assessed by 2 individuals against predefined criteria. DATA EXTRACTION: Data were extracted by 1 individual according to a predefined list, including participant demographics and training methods and effects. DATA SYNTHESIS: Electronic searches resulted in 190 potentially eligible studies, from which 12 met all inclusion criteria. Within-group standardized mean differences (SMDs) for reduction of KAM in healthy controls ranged from .44 to 2.47 and from .29 to .37 in patients with KOA. In patients with KOA, improvements were reported in pain and function, with SMDs ranging from .55 to 1.16. Methods of implementation of biofeedback training varied between studies, but in healthy controls increased KAM reduction was noted with implicit, rather than explicit, instructions.
CONCLUSIONS: This review suggests that biofeedback gait training is effective primarily for reducing KAM but also for reducing pain and improving function in patients with KOA. The review was limited by the small number of studies featuring patients with KOA and the lack of controlled studies. The results suggest there is value and a need in further researching biofeedback training for reducing KAM. Future studies should include larger cohorts of patients, long-term follow-up, and controlled trials.
Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Gait; Osteoarthritis, knee; Rehabilitation

Mesh:

Year:  2016        PMID: 27485366     DOI: 10.1016/j.apmr.2016.07.006

Source DB:  PubMed          Journal:  Arch Phys Med Rehabil        ISSN: 0003-9993            Impact factor:   3.966


  15 in total

1.  Subject-specific toe-in or toe-out gait modifications reduce the larger knee adduction moment peak more than a non-personalized approach.

Authors:  Scott D Uhlrich; Amy Silder; Gary S Beaupre; Peter B Shull; Scott L Delp
Journal:  J Biomech       Date:  2017-11-08       Impact factor: 2.712

2.  Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback.

Authors:  Federica Tamburella; Juan C Moreno; Diana Sofía Herrera Valenzuela; Iolanda Pisotta; Marco Iosa; Febo Cincotti; Donatella Mattia; José L Pons; Marco Molinari
Journal:  J Neuroeng Rehabil       Date:  2019-07-23       Impact factor: 4.262

3.  Gait biofeedback training in people with Parkinson's disease: a pilot study.

Authors:  Kate McMaster; Michael H Cole; Daniel Chalkley; Mark W Creaby
Journal:  J Neuroeng Rehabil       Date:  2022-07-16       Impact factor: 5.208

4.  Biofeedback for Gait Retraining Based on Real-Time Estimation of Tibiofemoral Joint Contact Forces.

Authors:  Claudio Pizzolato; Monica Reggiani; David J Saxby; Elena Ceseracciu; Luca Modenese; David G Lloyd
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2017-04-18       Impact factor: 3.802

Review 5.  Efficacy of Biofeedback for Medical Conditions: an Evidence Map.

Authors:  Karli Kondo; Katherine M Noonan; Michele Freeman; Chelsea Ayers; Benjamin J Morasco; Devan Kansagara
Journal:  J Gen Intern Med       Date:  2019-08-14       Impact factor: 5.128

6.  Learning Gait Modifications for Musculoskeletal Rehabilitation: Applying Motor Learning Principles to Improve Research and Clinical Implementation.

Authors:  Jesse M Charlton; Janice J Eng; Linda C Li; Michael A Hunt
Journal:  Phys Ther       Date:  2021-02-04

7.  Validation of wearable visual feedback for retraining foot progression angle using inertial sensors and an augmented reality headset.

Authors:  Angelos Karatsidis; Rosie E Richards; Jason M Konrath; Josien C van den Noort; H Martin Schepers; Giovanni Bellusci; Jaap Harlaar; Peter H Veltink
Journal:  J Neuroeng Rehabil       Date:  2018-08-15       Impact factor: 4.262

8.  The efficacy of electromyographic biofeedback on pain, function, and maximal thickness of vastus medialis oblique muscle in patients with knee osteoarthritis: a randomized clinical trial.

Authors:  Seyed Ahmad Raeissadat; Seyed Mansoor Rayegani; Leyla Sedighipour; Zeynab Bossaghzade; Mohamad Hesam Abdollahzadeh; Rojin Nikray; Fazeleh Mollayi
Journal:  J Pain Res       Date:  2018-11-08       Impact factor: 3.133

9.  A Machine Learning and Wearable Sensor Based Approach to Estimate External Knee Flexion and Adduction Moments During Various Locomotion Tasks.

Authors:  Bernd J Stetter; Frieder C Krafft; Steffen Ringhof; Thorsten Stein; Stefan Sell
Journal:  Front Bioeng Biotechnol       Date:  2020-01-24

Review 10.  Feedback Design in Targeted Exercise Digital Biofeedback Systems for Home Rehabilitation: A Scoping Review.

Authors:  Louise Brennan; Enrique Dorronzoro Zubiete; Brian Caulfield
Journal:  Sensors (Basel)       Date:  2019-12-28       Impact factor: 3.576

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