Gait and neuromuscular pattern changes are associated with differences in knee osteoarthritis severity levels

Astephen, Janie L.; Deluzio, Kevin J.; Caldwell, Graham E.; Dunbar, Michael J.; Hubley‐Kozey, Cheryl L.

doi:10.1016/j.jbiomech.2007.10.016

Cited by 241 publications

(201 citation statements)

References 28 publications

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“…In these applications, one PCA is often conducted for each joint/muscle/segment using a subjects × time matrix, where the subjects mode is a collection of subjects from different groups (e.g., Astephen et al, 2008;Chester & Wrigley, 2008;Cochran et al, 1984;Donoghue et al, 2008;Lamoth et al, 2006;Lauer et al, 2005;Lee et al, 2009;McKean et al, 2007). The PCs from these separate analyses are then examined to understand gait patterns of different subject groups, and PC scores (see Section 2.1.1) are often used to quantify between-group differences.…”

Section: Introductionmentioning

confidence: 99%

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

Helwig

Hong

Polk

2012

Human Movement Science

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Gait data are typically collected in multivariate form, so some multivariate analysis is often used to understand interrelationships between observed data. Principal Component Analysis (PCA), a data reduction technique for correlated multivariate data, has been widely applied by gait analysts to investigate patterns of association in gait waveform data (e.g., interrelationships between joint angle waveforms from different subjects and/or joints). Despite its widespread use in gait analysis, PCA is for two-mode data, whereas gait data are often collected in higher-mode form. In this paper, we present the benefits of analyzing gait data via Parallel Factor Analysis (Parafac), which is a component analysis model designed for three-or higher-mode data. Using three-mode joint angle waveform data (subjects × time × joints), we demonstrate Parafac's ability to (a) determine interpretable components revealing the primary interrelationships between lowerlimb joints in healthy gait and (b) identify interpretable components revealing the fundamental differences between normal and perturbed subjects' gait patterns across multiple joints. Our results offer evidence of the complex interconnections that exist between lower-limb joints and limb segments in both normal and abnormal gaits, confirming the need for the simultaneous analysis of multi-joint gait waveform data (especially when studying perturbed gait patterns).ii To my loving and supportive family.iii

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Section: Introductionmentioning

confidence: 99%

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

Helwig

Hong

Polk

2012

Human Movement Science

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“…Gait velocity among individuals with knee arthrosis has been established to be slower than among healthy controls at same age [8,[9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, despite having the same gait velocity, the duration of the support phase of the gait cycle becomes slightly longer among knee OA individuals than healthy controls [12,14,15] Previous investigations have explored TKA and its influence on gait. Kramers-de Quervain et al [16] stated that gait velocity and cadence were significantly improved two years after TKA surgery.…”

Section: Introductionmentioning

confidence: 99%

Effect of total knee replacement surgery and postoperative 12 month home exercise program on gait parameters

et al. 2017

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“…[8] We found one study looking at the link between biomechanical changes at the knee, the hip, and the ankle in 2008. [9] The authors measured the knee and hip moments using an inverse dynamic model after gait analysis with skin markers. They concluded that, with increasing severity of knee OA, knee flexion angles decreased, early stance knee extension moments decreased, stance phase hip internal rotation moments decreased and peak ankle dorsiflexion moments decreased.…”

Section: Introductionmentioning

confidence: 99%

Biomechanical analysis to characterize the impact of knee osteoarthritis on hip, knee, and ankle kinematics

Mezghani

Billard

Ouakrim

et al. 2017

JBEI

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Background: Numerous studies use a biomechanical assessment to evaluate joint function in knee pathologies such as osteoarthritis. However, most of them focus only on the knee and the consequences of the pathology on other lower limb joints are poorly documented. The objective of this study is to analyze the impact of knee osteoarthritis on ipsilateral hip, knee and ankle joint during gait. Methods: Three-dimension (3D) angular kinematic patterns of the three joints were analyzed on 32 patients diagnosed with knee osteoarthritis (OA) and a control group of 15 asymptomatic subjects (AS). Kinematic data was captured during treadmill gait trials at a self-selected comfortable speed. Analysis of covariance (ANCOVA) was performed on selected points of interest from 3D kinematic patterns of the hip, knee and ankle joints to compare both groups. The significance level was set at p = .05. Results: Gait 3D kinematic gait patterns of OA patients revealed significant differences with those of AS subjects at the three joints. Conclusions: Results suggest that patients with knee osteoarthritis also present alterations in hip and ankle kinematic during gait that should be considered when tailoring conservative treatments.

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Gait and neuromuscular pattern changes are associated with differences in knee osteoarthritis severity levels

Cited by 241 publications

References 28 publications

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

Effect of total knee replacement surgery and postoperative 12 month home exercise program on gait parameters

Biomechanical analysis to characterize the impact of knee osteoarthritis on hip, knee, and ankle kinematics

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