Lower limb sagittal gait kinematics can be predicted based on walking speed, gender, age and BMI.

Moissenet, F; Leboeuf, F; Armand, S

Abstract

Clinical gait analysis attempts to provide, in a pathological context, an objective record that quantifies the magnitude of deviations from normal gait. However, the identification of deviations is highly dependent with the characteristics of the normative database used. In particular, a mismatch between patient characteristics and an asymptomatic population database in terms of walking speed, demographic and anthropometric parameters may lead to misinterpretation during the clinical process. Rather than developing a new normative data repository that may require considerable of resources and time, this study aims to assess a method for predicting lower limb sagittal kinematics using multiple regression models based on walking speed, gender, age and BMI as predictors. With this approach, we were able to predict kinematics with an error within 1 standard deviation of the mean of the original waveforms recorded on fifty-four participants. Furthermore, the proposed approach allowed us to estimate the relative contribution to angular variations of each predictor, independently from the others. It appeared that a mismatch in walking speed, but also age, sex and BMI may lead to errors higher than 5° on lower limb sagittal kinematics and should thus be taken into account before any clinical interpretation.

Citation

Moissenet, F., Leboeuf, F., & Armand, S. (2019). Lower limb sagittal gait kinematics can be predicted based on walking speed, gender, age and BMI. Scientific reports, 9(1), 9510. https://doi.org/10.1038/s41598-019-45397-4

• Journal Article Type: Article
• Acceptance Date: Jun 6, 2019
• Online Publication Date: Jul 2, 2019
• Publication Date: Jul 2, 2019
• Deposit Date: Oct 24, 2019
• Publicly Available Date: Oct 24, 2019
• Journal: Scientific Reports
• Print ISSN: 2045-2322
• Publisher: Nature Publishing Group
• Volume: 9
• Issue: 1
• Pages: 9510
• DOI: https://doi.org/10.1038/s41598-019-45397-4
• Publisher URL: https://doi.org/10.1038/s41598-019-45397-4
• Related Public URLs: https://www.nature.com/srep/
• Additional Information: Additional Information : ** From Europe PMC via Jisc Publications Router ** Licence for this article: cc by **Journal IDs: essn 2045-2322; issn 2045-2322; nlmid 101563288 **Article IDs: pmcid: PMC6606631; pmid: 31267006 **History: published_online 02-07-2019; published 01-07-2019; Funders : Swiss National Science Foundation; Projects : CRSII5_177179