A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns Article Swipe
Susanne Jauhiainen
,
Andrew J. Pohl
,
Sami Äyrämö
,
Jukka‐Pekka Kauppi
,
Reed Ferber
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1111/sms.13624
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1111/sms.13624
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), were 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.
Related Topics
Concepts
Homogeneous
Gait
Cluster (spacecraft)
Physical medicine and rehabilitation
Hierarchical clustering
Kinematics
Population
Analysis of variance
Rehabilitation
Medicine
Gait analysis
Physical therapy
Cluster analysis
Statistics
Internal medicine
Mathematics
Computer science
Environmental health
Combinatorics
Classical mechanics
Physics
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/sms.13624
- OA Status
- green
- Cited By
- 57
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2997769356
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W2997769356Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/sms.13624Digital Object Identifier
- Title
-
A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patternsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-04Full publication date if available
- Authors
-
Susanne Jauhiainen, Andrew J. Pohl, Sami Äyrämö, Jukka‐Pekka Kauppi, Reed FerberList of authors in order
- Landing page
-
https://doi.org/10.1111/sms.13624Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://urn.fi/URN:NBN:fi:jyu-202001071051Direct OA link when available
- Concepts
-
Homogeneous, Gait, Cluster (spacecraft), Physical medicine and rehabilitation, Hierarchical clustering, Kinematics, Population, Analysis of variance, Rehabilitation, Medicine, Gait analysis, Physical therapy, Cluster analysis, Statistics, Internal medicine, Mathematics, Computer science, Environmental health, Combinatorics, Classical mechanics, Physics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
57Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 15, 2023: 6, 2022: 15, 2021: 8Per-year citation counts (last 5 years)
- References (count)
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54Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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