A1 Journal article (refereed)
A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns (2020)


Jauhiainen, Susanne; Pohl, Andrew J.; Äyrämö, Sami; Kauppi, Jukka-Pekka; Ferber, Reed (2020). A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns. Scandinavian Journal of Medicine and Science in Sports, 30 (4), 732-740. DOI: 10.1111/sms.13624


JYU authors or editors


Publication details

All authors or editors: Jauhiainen, Susanne; Pohl, Andrew J.; Äyrämö, Sami; Kauppi, Jukka-Pekka; Ferber, Reed

Journal or series: Scandinavian Journal of Medicine and Science in Sports

ISSN: 0905-7188

eISSN: 1600-0838

Publication year: 2020

Volume: 30

Issue number: 4

Pages range: 732-740

Publisher: Wiley-Blackwell

Publication country: United Kingdom

Publication language: English

DOI: http://doi.org/10.1111/sms.13624

Open Access: Publication channel is not openly available

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/67110


Abstract

Previous studies have suggested that runners can be subgrouped based on homogeneous gait patterns, however, no previous study has assessed the presence of such subgroups in a population of individuals across a wide variety of injuries. Therefore, the purpose of this study was to assess whether distinct subgroups with homogeneous running patterns can be identified among a large group of injured and healthy runners and whether identified subgroups are associated with specific injury location. Three‐dimensional kinematic data from 291 injured and healthy runners, representing both sexes and a wide range of ages (10‐66 years) was clustered using hierarchical cluster analysis. Cluster analysis revealed five distinct subgroups from the data. Kinematic differences between the subgroups were compared using one‐way analysis of variance (ANOVA). Against our hypothesis, runners with the same injury types did not cluster together, but the distribution of different injuries within subgroups was similar across the entire sample. These results suggest that homogeneous gait patterns exist independent of injury location and that it is important to consider these underlying patterns when planning injury prevention or rehabilitation strategies.


Keywords: running; sports injuries; kinematics; machine learning


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2020

Preliminary JUFO rating: 2


Last updated on 2020-18-08 at 13:37