Literature DB >> 31629340

Autoregressive Modeling as Diagnostic Tool to Identify Postanterior Cruciate Ligament Reconstruction Limb Asymmetry.

Kristin D Morgan1.   

Abstract

Between-limb deficits in vertical ground reaction force (vGRF) production continue to remain years after anterior cruciate ligament rehabilitation, resulting in altered dynamic stability. However, the challenge is in identifying ways to assess this between-limb stability. This study implemented second-order autoregressive [AR(2)] modeling and its stationarity triangle to both quantitatively and visually delineate differences in dynamic stability from peak vGRF data in controls and post-anterior cruciate ligament reconstruction (ACLR) individuals during running. It was hypothesized that post-ACLR individuals would exhibit less dynamic stability than the controls, and that they would reside in a different location on the stationarity triangle, thus denoting differences in stability. The results presented supported the hypothesis that post-ACLR individuals exhibited significantly less dynamic stability than their control counterparts based on their model coefficients (AR1 P < .01; AR2 P = .02). These findings suggested that the post-ACLR individuals adopted a similar running pattern, possibly due to muscle weakness asymmetry, which was less dynamically stable and potentially places them at greater risk for injury. The ability of this approach to both quantitatively and visually delineate differences between these 2 groups indicates its potential as a return-to-sport decision tool.

Entities:  

Keywords:  dynamic stability; ground reaction force; running; time series

Year:  2019        PMID: 31629340     DOI: 10.1123/jab.2018-0414

Source DB:  PubMed          Journal:  J Appl Biomech        ISSN: 1065-8483            Impact factor:   1.833


  2 in total

1.  Autoregressive modeling to assess stride time pattern stability in individuals with Huntington's disease.

Authors:  Helia Mahzoun Alzakerin; Yannis Halkiadakis; Kristin D Morgan
Journal:  BMC Neurol       Date:  2019-12-09       Impact factor: 2.474

2.  Characterizing gait pattern dynamics during symmetric and asymmetric walking using autoregressive modeling.

Authors:  Helia Mahzoun Alzakerin; Yannis Halkiadakis; Kristin D Morgan
Journal:  PLoS One       Date:  2020-12-03       Impact factor: 3.240

  2 in total

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