Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0199215
Title: Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values
Authors: Mikos V. 
Yen S.-C. 
Tay A. 
Heng C.-H. 
Chung C.L.H.
Liew S.H.X.
Tan D.M.L. 
Au W.L. 
Issue Date: 2018
Publisher: Public Library of Science
Citation: Mikos V., Yen S.-C., Tay A., Heng C.-H., Chung C.L.H., Liew S.H.X., Tan D.M.L., Au W.L. (2018). Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values. PLoS ONE 13 (6) : e0199215. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0199215
Abstract: Background Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by anthropometry and such differences among subjects impede accurate assessment. De-correlation of anthropometry from gait parameters and mobility measures is therefore desirable. Methods 87 (42 male) healthy subjects varying form 21 to 84 years of age were assessed on gait parameters (cadence, ankle velocity, stride time, stride length) and mobility measures (the 3-meter/7-meter Timed Up-and-Go, 10-meter Walk Test). Multiple linear regression models were derived for each gait parameter and mobility measure, with anthropometric measurements (age, height, body mass, gender) and self-selected walking speed as independent variables. The resulting models were used to normalize the gait parameters and mobility measures. The normalization’s capability in de-correlating data and reducing data dispersion were evaluated. Results Gait parameters were predominantly influenced by height and walking speed, while mobility measures were affected by age and walking speed. Normalization de-correlated data from anthropometric measurements from |rs| < 0.74 to |rs| < 0.23, and reduced data dispersion by up to 69%. Conclusion Normalization of gait parameters and mobility measures through linear regression models augment the capability to compare subjects with varying anthropometric measurements. © 2018 Mikos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Source Title: PLoS ONE
URI: http://scholarbank.nus.edu.sg/handle/10635/152568
ISSN: 19326203
DOI: 10.1371/journal.pone.0199215
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