Competitive and recreational running kinematics examined using principal components analysis

Wenjing Quan, Huiyu Zhou, Datao Xu, Shudong Li*, Julien S. Baker, Yaodong Gu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
17 Downloads (Pure)


Kinematics data are primary biomechanical parameters. A principal component analysis (PCA) of waveforms is a statistical approach used to explore patterns of variability in biomechanical curve datasets. Differences in experienced and recreational runners’ kinematic variables are still unclear. The purpose of the present study was to compare any differences in kinematics parameters for competitive runners and recreational runners using principal component analysis in the sagittal plane, frontal plane and transverse plane. Forty male runners were divided into two groups: twenty competitive runners and twenty recreational runners. A Vicon Motion System (Vicon Metrics Ltd., Oxford, UK) captured three-dimensional kinematics data during running at 3.3 m/s. The principal component analysis was used to determine the dominating variation in this model. Then, the principal component scores retained the first three principal components and were analyzed using independent t-tests. The recreational runners were found to have a smaller dorsiflexion angle, initial dorsiflexion contact angle, ankle inversion, knee adduction, range motion in the frontal knee plane and hip frontal plane. The running kinematics data were influenced by running experience. The findings from the study provide a better understanding of the kinematics variables for competitive and recreational runners. Thus, these findings might have implications for reducing running injury and improving running performance.

Original languageEnglish
Article number1321
Number of pages15
JournalHealthcare (Switzerland)
Issue number10
Early online date3 Oct 2021
Publication statusPublished - 3 Oct 2021


  • joint angle
  • kinematics data
  • long-distance running
  • principal component analysis


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