Proposal of a new conceptual gait model for patients with Parkinson's disease based on factor analysis.
Factor analysis
Gait
Model
Parkinson’s disease
Journal
Biomedical engineering online
ISSN: 1475-925X
Titre abrégé: Biomed Eng Online
Pays: England
ID NLM: 101147518
Informations de publication
Date de publication:
03 Jun 2019
03 Jun 2019
Historique:
received:
31
12
2018
accepted:
27
05
2019
entrez:
5
6
2019
pubmed:
5
6
2019
medline:
27
11
2019
Statut:
epublish
Résumé
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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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.
Identifiants
pubmed: 31159825
doi: 10.1186/s12938-019-0689-3
pii: 10.1186/s12938-019-0689-3
pmc: PMC6547597
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
70Commentaires et corrections
Type : ErratumIn
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