Proposal of a new conceptual gait model for patients with Parkinson’s disease based on factor analysis

Abstract

Background Gait impairment is a risk factor for falls in patients with Parkinson’s disease (PD). Gait can be conveniently assessed by electronic walkways, but there is need to select which spatiotemporal gait variables are useful for assessing gait in PD. Existing models for gait variables developed in healthy subjects and patients with PD show some methodological shortcomings in their validation through exploratory factor analysis (EFA), and were never confirmed by confirmatory factor analysis (CFA). The aims of this study were (1) to create a new model of gait for PD through EFA, (2) to analyze the factorial structure of our new model and compare it with existing models through CFA. Results From the 37 variables initially considered in 250 patients with PD, 10 did not show good-to-excellent reliability and were eliminated, while further 19 were eliminated after correlation matrix and Kaiser–Meyer–Olkin measure. The remaining eight variables underwent EFA and three factors emerged: pace/rhythm, variability, and asymmetry. Structural validity of our new model was then examined with CFA, using the structural equation modeling. After some modifications, suggested by the Modification Indices, we obtained a final model that showed an excellent fit. In contrast, when the structure of previous models of gait was analyzed, no model achieved convergence with our sample of patients. Conclusions Our model for spatiotemporal gait variables of patients with PD is the first to be developed through an accurate EFA and confirmed by CFA. It contains eight gait variables divided into three factors and shows an excellent fit. Reasons for the non-convergence of other models could be their inclusion of highly inter-correlated or low-reliability variables or could be possibly due to the fact that they did not use more recent methods for determining the number of factors to extract.

Independent and sensitive gait parameters for objective evaluation in knee and hip osteoarthritis using wearable sensors

“…Factor analysis effectively reduced the dimensionality of our dataset from twenty-five gait parameters to four independent domains of gait. Factors reflecting the spatial and temporal aspects of gait speed are consistently reported in literature [32][33][34][35][36] . Other factors related to gait reported in literature are variability 32,33,35,36 , asymmetry 33,35,36 , postural control 33 , and trunk motion 34 .…”

Highly challenging balance and gait training for individuals with Parkinson’s disease improves pace, rhythm and variability domains of gait – A secondary analysis from a randomized controlled trial

“…This could be related to the reduction in muscle power, loss of distal proprioception and vision seen with age [157,158]. In addition, it has been suggested that step length is controlled by the cortico-basal ganglia circuit [159], and there is evidence that gait speed is specifically linked to the frontoparietal control network, which is critically involved in executive function [160]. Decreased executive function has been associated with decreased gait speed due to decreased stride length, together with increased stride length variability in elderly with mild cognitive impairment [161].…”

Atrophy of the Nucleus Basalis of Meynert predicts the progression of gait variability in Parkinson’s disease

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