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Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values
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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6005486/ https://www.ncbi.nlm.nih.gov/pubmed/29912992 http://dx.doi.org/10.1371/journal.pone.0199215 |
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author | Mikos, Val Yen, Shih-Cheng Tay, Arthur Heng, Chun-Huat Chung, Chloe Lau Ha Liew, Sylvia Hui Xin Tan, Dawn May Leng Au, Wing Lok |
author_facet | Mikos, Val Yen, Shih-Cheng Tay, Arthur Heng, Chun-Huat Chung, Chloe Lau Ha Liew, Sylvia Hui Xin Tan, Dawn May Leng Au, Wing Lok |
author_sort | Mikos, Val |
collection | PubMed |
description | 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 |r(s)| < 0.74 to |r(s)| < 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. |
format | Online Article Text |
id | pubmed-6005486 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-60054862018-06-25 Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values Mikos, Val Yen, Shih-Cheng Tay, Arthur Heng, Chun-Huat Chung, Chloe Lau Ha Liew, Sylvia Hui Xin Tan, Dawn May Leng Au, Wing Lok PLoS One Research Article 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 |r(s)| < 0.74 to |r(s)| < 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. Public Library of Science 2018-06-18 /pmc/articles/PMC6005486/ /pubmed/29912992 http://dx.doi.org/10.1371/journal.pone.0199215 Text en © 2018 Mikos et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Mikos, Val Yen, Shih-Cheng Tay, Arthur Heng, Chun-Huat Chung, Chloe Lau Ha Liew, Sylvia Hui Xin Tan, Dawn May Leng Au, Wing Lok Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values |
title | Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values |
title_full | Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values |
title_fullStr | Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values |
title_full_unstemmed | Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values |
title_short | Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values |
title_sort | regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6005486/ https://www.ncbi.nlm.nih.gov/pubmed/29912992 http://dx.doi.org/10.1371/journal.pone.0199215 |
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