ArticleBiomedical engineering online2019
A multivariate relationship between the kinematic and clinical parameters of knee osteoarthritis population.
Article in Biomedical engineering online, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 17 citations in OpenAlex.
- Biomechanical markers associations with pain, symptoms, and disability compared to radiographic severity in knee osteoarthritis patients: a secondary analysis from a cluster randomized controlled trial.BMC musculoskeletal disorders · 2022Trial
- A Complete Functional Characterization of Patients with Severe Knee Osteoarthritis in Need of Total Knee Replacement.Journal of functional morphology and kinesiology · 2024Article
- Diagnosing gait disorders based on angular variations of knee and ankle joints utilizing a developed wearable motion sensor.Healthcare technology letters · 2021Article
- Assessing Site Specificity of Osteoarthritic Gait Kinematics with Wearable Sensors and Their Association with Patient Reported Outcome Measures (PROMs): Knee versus Hip Osteoarthritis.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundBiomechanical and clinical parameters contribute very closely to functional evaluations of the knee joint. To better understand knee osteoarthritis joint function, the association between a set of knee biomechanical data and a set of clinical parameters of an osteoarthritis population (OA) is investigated in this study.
methodsThe biomechanical data used here are a set of characteristics derived from 3D knee kinematic patterns: flexion/extension, abduction/adduction, and tibial internal/external rotation measurements, all determined during gait recording. The clinical parameters include a KOOS questionnaire and the patient's demographic characteristics. Canonical correlation analysis (CCA) is used (1) to evaluate the multivariate relationship between biomechanical data and clinical parameter sets, and (2) to cluster the most correlated parameters. Multivariate models were created within the identified clusters to determine the effect of each parameter's subset on the other. The analyses were performed on a large database containing 166 OA patients.
resultsThe CCA results showed meaningful correlations that gave rise to three different clusters. Multivariate linear models were found explaining the subjective clinical parameters by evaluating the biomechanical data contained within each cluster.
conclusionThe results showed that a multivariate analysis of the clinical symptoms and the biomechanical characteristics of knee joint function allowed a better understanding of their relationships.
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