Literature DB >> 28501342

Speed, age, sex, and body mass index provide a rigorous basis for comparing the kinematic and kinetic profiles of the lower extremity during walking.

E F Chehab1, T P Andriacchi2, J Favre3.   

Abstract

The increased use of gait analysis has raised the need for a better understanding of how walking speed and demographic variations influence asymptomatic gait. Previous analyses mainly reported relationships between subsets of gait features and demographic measures, rendering it difficult to assess whether gait features are affected by walking speed or other demographic measures. The purpose of this study was to conduct a comprehensive analysis of the kinematic and kinetic profiles during ambulation that tests for the effect of walking speed in parallel to the effects of age, sex, and body mass index. This was accomplished by recruiting a population of 121 asymptomatic subjects and analyzing characteristic 3-dimensional kinematic and kinetic features at the ankle, knee, hip, and pelvis during walking trials at slow, normal, and fast speeds. Mixed effects linear regression models were used to identify how each of 78 discrete gait features is affected by variations in walking speed, age, sex, and body mass index. As expected, nearly every feature was associated with variations in walking speed. Several features were also affected by variations in demographic measures, including age affecting sagittal-plane knee kinematics, body mass index affecting sagittal-plane pelvis and hip kinematics, body mass index affecting frontal-plane knee kinematics and kinetics, and sex affecting frontal-plane kinematics at the pelvis, hip, and knee. These results could aid in the design of future studies, as well as clarify how walking speed, age, sex, and body mass index may act as potential confounders in studies with small populations or in populations with insufficient demographic variations for thorough statistical analyses.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Demographics; Gait analysis; Kinematics; Kinetics; Walking speed

Mesh:

Year:  2017        PMID: 28501342     DOI: 10.1016/j.jbiomech.2017.04.014

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  16 in total

1.  Abnormal Joint Loading During Gait in Persons With Hip Osteoarthritis Is Associated With Symptoms and Cartilage Lesions.

Authors:  Tzu-Chieh Liao; Michael A Samaan; Tijana Popovic; Jan Neumann; Alan L Zhang; Thomas M Link; Sharmila Majumdar; Richard B Souza
Journal:  J Orthop Sports Phys Ther       Date:  2019-10-14       Impact factor: 4.751

2.  Analysis of Continuously Varying Kinematics for Prosthetic Leg Control Applications.

Authors:  Kyle R Embry; Robert D Gregg
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2021-03-01       Impact factor: 3.802

3.  Biofeedback augmenting lower limb loading alters the underlying temporal structure of gait following anterior cruciate ligament reconstruction.

Authors:  Cortney Armitano-Lago; Brian Pietrosimone; Hope C Davis-Wilson; Alyssa Evans-Pickett; Jason R Franz; Troy Blackburn; Adam W Kiefer
Journal:  Hum Mov Sci       Date:  2020-09-25       Impact factor: 2.397

4.  Loss of Mechanical Ankle Function Is Not Compensated by the Distal Foot Joints in Patients with Ankle Osteoarthritis.

Authors:  Maarten Eerdekens; Kevin Deschamps; Sander Wuite; Giovanni A Matricali
Journal:  Clin Orthop Relat Res       Date:  2021-01-01       Impact factor: 4.755

5.  Three-Dimensional Human Gait Pattern: Reference Data for Young, Active Women Walking with Low, Preferred, and High Speeds.

Authors:  Slawomir Winiarski; Jadwiga Pietraszewska; Bogdan Pietraszewski
Journal:  Biomed Res Int       Date:  2019-01-03       Impact factor: 3.411

6.  A multimodal dataset of human gait at different walking speeds established on injury-free adult participants.

Authors:  Céline Schreiber; Florent Moissenet
Journal:  Sci Data       Date:  2019-07-03       Impact factor: 6.444

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

Authors:  Florent Moissenet; Fabien Leboeuf; Stéphane Armand
Journal:  Sci Rep       Date:  2019-07-02       Impact factor: 4.379

8.  Parametric generation of three-dimensional gait for robot-assisted rehabilitation.

Authors:  Di Shi; Wuxiang Zhang; Xilun Ding; Lei Sun
Journal:  Biol Open       Date:  2020-03-05       Impact factor: 2.422

9.  Decrease in walking speed increases hip moment impulse in the frontal plane during the stance phase.

Authors:  Takuma Inai; Tomoya Takabayashi; Mutsuaki Edama; Masayoshi Kubo
Journal:  PeerJ       Date:  2019-11-19       Impact factor: 2.984

10.  Mechanics of very slow human walking.

Authors:  Amy R Wu; Cole S Simpson; Edwin H F van Asseldonk; Herman van der Kooij; Auke J Ijspeert
Journal:  Sci Rep       Date:  2019-12-02       Impact factor: 4.379

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