Literature DB >> 30526232

Real-Time Biofeedback of Performance to Reduce Braking Forces Associated With Running-Related Injury: An Exploratory Study.

Christopher Napier, Christopher L MacLean, Jessica Maurer, Jack E Taunton, Michael A Hunt.   

Abstract

BACKGROUND: The high rate of running-related injury may be associated with increased peak braking forces (PBFs) and vertical loading rates. Gait retraining has been suggested by some experts to be an effective method to reduce loading parameters.
OBJECTIVES: To investigate whether PBF could be decreased following an 8-session gait retraining program among a group of female recreational runners and which self-selected kinematic strategies could achieve this decrease.
METHODS: In this exploratory study, 12 female recreational runners with high PBFs (greater than 0.27 body weight) completed an 8-session gait retraining program with real-time biofeedback of braking forces over the course of a half-marathon training program. Baseline and follow-up kinetics and kinematics were analyzed with a repeated-measures analysis of variance.
RESULTS: There was an average reduction of 15% in PBF (-0.04 body weight; 95% confidence interval [CI]: -0.07, -0.02 body weight; P = .001; effect size, 0.62), accompanied by a 7% increase in step frequency (11.3 steps per minute; 95% CI: 1.8, 20.9 steps per minute; P = .024; effect size, 0.38) and a 6% decrease in step length (-5.5 cm; 95% CI: -9.9, -1.0 cm; P = .020; effect size, 0.40), from baseline to follow-up.
CONCLUSION: The gait retraining program significantly reduced the PBF among a group of female recreational runners. This was achieved through a combination of increased step frequency and decreased step length. Furthermore, the modified gait pattern was incorporated into the runners' natural gait pattern by the completion of the program. Based on these results, the outlined gait retraining program should be further investigated to assess whether it may be an effective injury prevention strategy for recreational runners. This study was registered with ClinicalTrials.gov (NCT03302975). LEVEL OF EVIDENCE: Prevention, level 4. J Orthop Sports Phys Ther 2019;49(3):136-144. Epub 7 Dec 2018. doi:10.2519/jospt.2019.8587.

Entities:  

Keywords:  gait analysis; kinematics; kinetics; prevention (injury); running

Mesh:

Year:  2018        PMID: 30526232     DOI: 10.2519/jospt.2019.8587

Source DB:  PubMed          Journal:  J Orthop Sports Phys Ther        ISSN: 0190-6011            Impact factor:   4.751


  5 in total

1.  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

2.  Estimating Lower Extremity Running Gait Kinematics with a Single Accelerometer: A Deep Learning Approach.

Authors:  Mohsen Gholami; Christopher Napier; Carlo Menon
Journal:  Sensors (Basel)       Date:  2020-05-22       Impact factor: 3.576

3.  Providing low-dimensional feedback of a high-dimensional movement allows for improved performance of a skilled walking task.

Authors:  Kevin A Day; Amy J Bastian
Journal:  Sci Rep       Date:  2019-12-24       Impact factor: 4.996

4.  Relationship between duty factor and external forces in slow recreational runners.

Authors:  Senne Bonnaerens; Pieter Fiers; Samuel Galle; Rud Derie; Peter Aerts; Edward Frederick; Yasunori Kaneko; Wim Derave; Dirk De Clercq; Veerle Segers
Journal:  BMJ Open Sport Exerc Med       Date:  2021-03-03

5.  Wearable Technology to Increase Self-Awareness of Low Back Pain: A Survey of Technology Needs among Health Care Workers.

Authors:  Andrea Ferrone; Christopher Napier; Carlo Menon
Journal:  Sensors (Basel)       Date:  2021-12-16       Impact factor: 3.576

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.