Using statistical parametric mapping to assess the association of duty factor and step frequency on running kinetic.
Details
Serval ID
serval:BIB_D3A27C3B9C38
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Using statistical parametric mapping to assess the association of duty factor and step frequency on running kinetic.
Journal
Frontiers in physiology
ISSN
1664-042X (Print)
ISSN-L
1664-042X
Publication state
Published
Issued date
2022
Peer-reviewed
Oui
Volume
13
Pages
1044363
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Publication Status: epublish
Abstract
Duty factor (DF) and step frequency (SF) were previously defined as the key running pattern determinants. Hence, this study aimed to investigate the association of DF and SF on 1) the vertical and fore-aft ground reaction force signals using statistical parametric mapping; 2) the force related variables (peaks, loading rates, impulses); and 3) the spring-mass characteristics of the lower limb, assessed by computing the force-length relationship and leg stiffness, for treadmill runs at several endurance running speeds. One hundred and fifteen runners ran at 9, 11, and 13 km/h. Force data (1000 Hz) and whole-body three-dimensional kinematics (200 Hz) were acquired by an instrumented treadmill and optoelectronic system, respectively. Both lower DF and SF led to larger vertical and fore-aft ground reaction force fluctuations, but to a lower extent for SF than for DF. Besides, the linearity of the force-length relationship during the leg compression decreased with increasing DF or with decreasing SF but did not change during the leg decompression. These findings showed that the lower the DF and the higher the SF, the more the runner relies on the optimization of the spring-mass model, whereas the higher the DF and the lower the SF, the more the runner promotes forward propulsion.
Keywords
Physiology (medical), Physiology, biomechanics, ground reaction force, leg stiffness, running pattern, spring-mass model
Pubmed
Open Access
Yes
Create date
06/12/2022 10:27
Last modification date
05/01/2023 6:48