Literature DB >> 32980589

Biofeedback augmenting lower limb loading alters the underlying temporal structure of gait following anterior cruciate ligament reconstruction.

Cortney Armitano-Lago1, Brian Pietrosimone2, Hope C Davis-Wilson3, Alyssa Evans-Pickett4, Jason R Franz5, Troy Blackburn6, Adam W Kiefer7.   

Abstract

Biofeedback has recently been explored to target deviant lower extremity loading mechanics following anterior cruciate ligament reconstruction (ACLR) to mitigate the development of post traumatic osteoarthritis. The impact this feedback has on the structure of the stride interval dynamics-a barometer of gait system health-however, have yet to be examined. This study was designed to assess how feedback, used to alter lower-extremity loading during gait, affects the structure of stride interval variability by examining long-range stride-to-stride correlations during gait in those with unilateral ACLR. Twelve participants walked under three separate loading conditions: (1) control (i.e., no cue) (2) high loading, and (3) low loading. Baseline vertical ground reaction force (vGRF) data was used to calculate a target 5% change in vGRF for the appropriate loading condition (i.e., high loading was +5% vGRF, low loading was -5% vGRF). The target for the load condition was displayed on a screen along with real-time vGRF values, prescribing changes in stride-to-stride peak vertical ground reaction forces of each limb. From time-series of stride intervals (i.e., duration), we analyzed the mean and standard deviation of stride-to-stride variability and, via detrended fluctuation analysis (i.e., DFA α), temporal persistence for each feedback condition. Both the high and low loading conditions exhibited a change toward more temporally persistent stride intervals (high loading: α =0.92, low loading: α = 0.98) than walking under the control condition (α = 0.78; high vs. control: p = .026, low vs. control: p = .001). Overall, these results indicate that altering lower extremity load changes the temporal persistence of the stride internal dynamics in ACLR individuals, demonstrating the implications of the design of gait training interventions and the influence feedback has on movement strategies.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  DFA; Feedback; Nonlinear; Variability

Mesh:

Year:  2020        PMID: 32980589      PMCID: PMC9100834          DOI: 10.1016/j.humov.2020.102685

Source DB:  PubMed          Journal:  Hum Mov Sci        ISSN: 0167-9457            Impact factor:   2.397


  58 in total

1.  The effect of anterior cruciate ligament reconstruction on lower extremity relative phase dynamics during walking and running.

Authors:  Max J Kurz; Nicholas Stergiou; Ugo H Buzzi; Anastasios D Georgoulis
Journal:  Knee Surg Sports Traumatol Arthrosc       Date:  2004-10-09       Impact factor: 4.342

2.  Automatic detection of gait events using kinematic data.

Authors:  Ciara M O'Connor; Susannah K Thorpe; Mark J O'Malley; Christopher L Vaughan
Journal:  Gait Posture       Date:  2006-07-28       Impact factor: 2.840

3.  Transtibial versus anteromedial portal technique in single-bundle anterior cruciate ligament reconstruction: outcomes of knee joint kinematics during walking.

Authors:  Hongsheng Wang; James E Fleischli; Naiquan Nigel Zheng
Journal:  Am J Sports Med       Date:  2013-06-10       Impact factor: 6.202

4.  Return of normal gait as an outcome measurement in acl reconstructed patients. A systematic review.

Authors:  A Gokeler; A Benjaminse; C F van Eck; K E Webster; L Schot; E Otten
Journal:  Int J Sports Phys Ther       Date:  2013-08

Review 5.  Rehabilitation Principles of the Anterior Cruciate Ligament Reconstructed Knee: Twelve Steps for Successful Progression and Return to Play.

Authors:  Kevin E Wilk; Christopher A Arrigo
Journal:  Clin Sports Med       Date:  2017-01       Impact factor: 2.182

6.  Real-time biofeedback can increase and decrease vertical ground reaction force, knee flexion excursion, and knee extension moment during walking in individuals with anterior cruciate ligament reconstruction.

Authors:  Brittney A Luc-Harkey; Jason R Franz; J Troy Blackburn; Darin A Padua; Anthony C Hackney; Brian Pietrosimone
Journal:  J Biomech       Date:  2018-06-15       Impact factor: 2.712

7.  Inter-limb differences in impulsive loading following anterior cruciate ligament reconstruction in females.

Authors:  J Troy Blackburn; Brian Pietrosimone; Matt S Harkey; Brittney A Luc; Derek N Pamukoff
Journal:  J Biomech       Date:  2016-07-30       Impact factor: 2.712

Review 8.  Progressive Changes in Walking Kinematics and Kinetics After Anterior Cruciate Ligament Injury and Reconstruction: A Review and Meta-Analysis.

Authors:  Lindsay V Slater; Joseph M Hart; Adam R Kelly; Christopher M Kuenze
Journal:  J Athl Train       Date:  2017-09       Impact factor: 2.860

9.  Gait adaptations by patients who have a deficient anterior cruciate ligament.

Authors:  M Berchuck; T P Andriacchi; B R Bach; B Reider
Journal:  J Bone Joint Surg Am       Date:  1990-07       Impact factor: 5.284

10.  Is walking a random walk? Evidence for long-range correlations in stride interval of human gait.

Authors:  J M Hausdorff; C K Peng; Z Ladin; J Y Wei; A L Goldberger
Journal:  J Appl Physiol (1985)       Date:  1995-01
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  3 in total

1.  Sensory substitution for orthopaedic gait rehabilitation: A systematic review and meta-analysis for clinical practice guideline development.

Authors:  Peter Lynch; Patrick Broderick; Kenneth Monaghan
Journal:  Heliyon       Date:  2022-10-08

Review 2.  Review of Real-Time Biomechanical Feedback Systems in Sport and Rehabilitation.

Authors:  Matevž Hribernik; Anton Umek; Sašo Tomažič; Anton Kos
Journal:  Sensors (Basel)       Date:  2022-04-14       Impact factor: 3.847

3.  Gait asymmetries are exacerbated at faster walking speeds in individuals with acute anterior cruciate ligament reconstruction.

Authors:  Steven A Garcia; Scott R Brown; Mary Koje; Chandramouli Krishnan; Riann M Palmieri-Smith
Journal:  J Orthop Res       Date:  2021-06-14       Impact factor: 3.494

  3 in total

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