Runners with patellofemoral pain demonstrate sub-groups of pelvic acceleration profiles using hierarchical cluster analysis: an exploratory cross-sectional study
| Autor: | Sean T. Osis, Ricky Watari, Reed Ferber, Angkoon Phinyomark |
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| Rok vydání: | 2018 |
| Předmět: |
Adult
Male medicine.medical_specialty lcsh:Diseases of the musculoskeletal system Sports medicine Principal component analysis Pain Context (language use) Patellofemoral pain Kinematics Accelerometer Running 03 medical and health sciences Acceleration Cluster analysis Deep Learning 0302 clinical medicine Physical medicine and rehabilitation Rheumatology Pelvic acceleration medicine Humans Biomechanics Orthopedics and Sports Medicine Pelvic Bones business.industry 030229 sport sciences Running kinematics Cross-Sectional Studies medicine.anatomical_structure Patellofemoral Pain Syndrome Gait analysis Female lcsh:RC925-935 Ankle business 030217 neurology & neurosurgery Research Article |
| Zdroj: | BMC Musculoskeletal Disorders, Vol 19, Iss 1, Pp 1-10 (2018) BMC Musculoskeletal Disorders |
| ISSN: | 1471-2474 |
| DOI: | 10.1186/s12891-018-2045-3 |
| Popis: | Background Previous studies have suggested that distinct and homogenous sub-groups of gait patterns exist among runners with patellofemoral pain (PFP), based on gait analysis. However, acquisition of 3D kinematic data using optical systems is time consuming and prone to marker placement errors. In contrast, axial segment acceleration data can represent an overall running pattern, being easy to acquire and not influenced by marker placement error. Therefore, the purpose of this study was to determine if pelvic acceleration patterns during running could be used to classify PFP patients into homogeneous sub-groups. A secondary purpose was to analyze lower limb kinematic data to investigate the practical implications of clustering these subjects based on 3D pelvic acceleration data. Methods A hierarchical cluster analysis was used to determine sub-groups of similar running profiles among 110 PFP subjects, separately for males (n = 44) and females (n = 66), using pelvic acceleration data (reduced with principal component analysis) during treadmill running acquired with optical motion capture system. In a secondary analysis, peak joint angles were compared between clusters (α = 0.05) to provide clinical context and deeper understanding of variables that separated clusters. Results The results reveal two distinct running gait sub-groups (C1 and C2) for female subjects and no sub-groups were identified for males. Two pelvic acceleration components were different between clusters (PC1 and PC5; p |
| Databáze: | OpenAIRE |
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