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Neal K, Williams JR, Alfayyadh A, Capin JJ, Khandha A, Manal K, Snyder-Mackler L, Buchanan TS. Knee joint biomechanics during gait improve from 3 to 6 months after anterior cruciate ligament reconstruction. J Orthop Res 2022; 40:2025-2038. [PMID: 34989019 PMCID: PMC9256843 DOI: 10.1002/jor.25250] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 10/08/2021] [Accepted: 12/19/2021] [Indexed: 02/04/2023]
Abstract
Gait alterations after anterior cruciate ligament reconstruction (ACLR) are commonly reported and have been linked to posttraumatic osteoarthritis development. While knee gait alterations have been studied at several time points after ACLR, little is known about how these biomechanical variables change earlier than 6 months after surgery, nor is much known about how they differ over the entire stance phase of gait. The purpose of this study was to examine knee gait biomechanical variables over their entire movement pattern through stance at both 3 and 6 months after ACLR and to study the progression of interlimb asymmetry between the two postoperative time points. Thirty-five individuals underwent motion analysis during overground walking 3 (3.2 ± 0.5) and 6 (6.4 ± 0.7) months after ACLR. Knee biomechanical variables were compared between limbs and across time points through 100% of stance using statistical parametric mapping; this included a 2 × 2 (Limb × Time) repeated measures analysis of variance and two-tailed t-tests. Smaller knee joint angles, moments, extensor forces, and medial compartment forces were present in the involved versus uninvolved limb. Interlimb asymmetries were present at both time points but were less prevalent at 6 months. The uninvolved limb's biomechanical variables stayed relatively consistent over time, while the involved limb's trended toward that of the uninvolved limb. Statement of Clinical Significance: Interventions to correct asymmetrical gait patterns after ACLR may need to occur early after surgery and may need to focus on multiple parts of stance phase.
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Affiliation(s)
- Kelsey Neal
- Department of Mechanical Engineering, University of Delaware, Newark, DE
| | - Jack R. Williams
- Department of Mechanical Engineering, University of Delaware, Newark, DE
| | | | - Jacob J. Capin
- Biomechanics and Movement Science, University of Delaware, Newark, DE
- Department of Physical Therapy, University of Delaware, Newark, DE
- Physical Therapy Program, Department of Physical Medicine and Rehabilitation, University of Colorado, Aurora, CO
- Eastern Colorado VA Geriatric Research Education and Clinical Center (GRECC), Aurora, CO
- Department of Physical Therapy, Marquette University, Milwaukee, WI
| | - Ashutosh Khandha
- Department of Biomedical Engineering, University of Delaware, Newark, DE
| | - Kurt Manal
- Kinesiology and Applied Physiology, University of Delaware, Newark, DE
| | - Lynn Snyder-Mackler
- Biomechanics and Movement Science, University of Delaware, Newark, DE
- Department of Physical Therapy, University of Delaware, Newark, DE
- Department of Biomedical Engineering, University of Delaware, Newark, DE
| | - Thomas S. Buchanan
- Department of Mechanical Engineering, University of Delaware, Newark, DE
- Biomechanics and Movement Science, University of Delaware, Newark, DE
- Department of Biomedical Engineering, University of Delaware, Newark, DE
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Chandra A, Kar O. Data-driven prognosis: a multi-physics approach verified via balloon burst experiment. Proc Math Phys Eng Sci 2015; 471:20140525. [PMID: 27547071 DOI: 10.1098/rspa.2014.0525] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
A multi-physics formulation for data-driven prognosis (DDP) is developed. Unlike traditional predictive strategies that require controlled offline measurements or 'training' for determination of constitutive parameters to derive the transitional statistics, the proposed DDP algorithm relies solely on in situ measurements. It uses a deterministic mechanics framework, but the stochastic nature of the solution arises naturally from the underlying assumptions regarding the order of the conservation potential as well as the number of dimensions involved. The proposed DDP scheme is capable of predicting onset of instabilities. Because the need for offline testing (or training) is obviated, it can be easily implemented for systems where such a priori testing is difficult or even impossible to conduct. The prognosis capability is demonstrated here via a balloon burst experiment where the instability is predicted using only online visual observations. The DDP scheme never failed to predict the incipient failure, and no false-positives were issued. The DDP algorithm is applicable to other types of datasets. Time horizons of DDP predictions can be adjusted by using memory over different time windows. Thus, a big dataset can be parsed in time to make a range of predictions over varying time horizons.
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Affiliation(s)
- Abhijit Chandra
- Mechanical Engineering , Iowa State University , Ames, IA, USA
| | - Oliva Kar
- Computer Science and Human Computer Interaction , Iowa State University , Ames, IA, USA
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