Literature DB >> 30986588

Unsupervised gait retraining using a wireless pressure-detecting shoe insole.

Jade He1, Kevin Lippmann2, Najia Shakoor3, Christopher Ferrigno4, Markus A Wimmer5.   

Abstract

BACKGROUND: The knee adduction moment (KAM) is a surrogate measure of mediolateral distribution of loads across the knee joint and is correlated with progression and severity of knee osteoarthritis (OA). Existing biomechanical approaches for unloading the arthritic medial knee compartment vary in their effectiveness in reducing KAM. This study employed a completely wireless, pressure-detecting shoe insole capable of generating auditory feedback via a smartphone. RESEARCH QUESTION: To investigate whether auditory cues from a smartphone can prompt subjects to adjust their gait pattern and reduce KAM.
METHODS: Nineteen healthy subjects underwent gait training inside the lab (Phase 1) and received auditory cues during mid- and terminal stance to medialize their foot COP (center-of-pressure). This initial training period was continued unsupervised while walking around campus (Phase 2).
RESULTS: After Phase 1, subjects reduced their KAM by 20.6% (p =  0. 001), a finding similar to a previous study that used a wired, lab-based insole system. After further unsupervised training outside the lab during Phase 2, subjects were able to execute the newly learned gait pattern without auditory feedback still showing a KAM reduction of 17.2% (p <  0.001). Although, speed at Phase 2 was lower than at baseline (p =  0.013), this reduction had little effect on KAM (r = 0.297, p =  0.216). In addition, the adduction angular impulse was reduced (p =  0.001), despite the slower speed. SIGNIFICANCE: Together, these results suggest that the wireless insole is a promising tool for gait retraining to lower the KAM and will be implemented in a home-based clinical trial of gait retraining for subjects with knee OA.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Feedback; Gait modification; Knee adduction moment; Osteoarthritis; Wireless sensor insole

Mesh:

Year:  2019        PMID: 30986588     DOI: 10.1016/j.gaitpost.2019.03.021

Source DB:  PubMed          Journal:  Gait Posture        ISSN: 0966-6362            Impact factor:   2.840


  5 in total

Review 1.  How New Technology Is Improving Physical Therapy.

Authors:  Johnny G Owens; Michelle R Rauzi; Andrew Kittelson; Jeremy Graber; Michael J Bade; Julia Johnson; Dustin Nabhan
Journal:  Curr Rev Musculoskelet Med       Date:  2020-04

2.  Loudness affects motion: asymmetric volume of auditory feedback results in asymmetric gait in healthy young adults.

Authors:  Julia Reh; Gerd Schmitz; Tong-Hun Hwang; Alfred O Effenberg
Journal:  BMC Musculoskelet Disord       Date:  2022-06-17       Impact factor: 2.562

Review 3.  Smart Technology and Orthopaedic Surgery: Current Concepts Regarding the Impact of Smartphones and Wearable Technology on Our Patients and Practice.

Authors:  Neil V Shah; Richard Gold; Qurratul-Ain Dar; Bassel G Diebo; Carl B Paulino; Qais Naziri
Journal:  Curr Rev Musculoskelet Med       Date:  2021-11-03

4.  Empirical Study on Human Movement Classification Using Insole Footwear Sensor System and Machine Learning.

Authors:  Wolfe Anderson; Zachary Choffin; Nathan Jeong; Michael Callihan; Seongcheol Jeong; Edward Sazonov
Journal:  Sensors (Basel)       Date:  2022-04-02       Impact factor: 3.576

Review 5.  Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications.

Authors:  Andrei Drăgulinescu; Ana-Maria Drăgulinescu; Gabriela Zincă; Doina Bucur; Valentin Feieș; Dumitru-Marius Neagu
Journal:  Sensors (Basel)       Date:  2020-08-02       Impact factor: 3.576

  5 in total

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