A data mining approach for determining biomechanical adaptations in runners who experienced and recovered from patellofemoral pain syndrome.

Patellofemoral pain biomechanics feature ranking machine learning principal component analysis running

Journal

Journal of sports sciences
ISSN: 1466-447X
Titre abrégé: J Sports Sci
Pays: England
ID NLM: 8405364

Informations de publication

Date de publication:
Nov 2023
Historique:
medline: 18 3 2024
pubmed: 2 2 2024
entrez: 2 2 2024
Statut: ppublish

Résumé

Patellofemoral pain (PFP) is a common musculoskeletal pain disorder experienced by runners. While biomechanics of those with PFP have been extensively studied, methodological considerations may omit important adaptations exhibited by those experiencing and recovered from pain. Instead of

Identifiants

pubmed: 38303115
doi: 10.1080/02640414.2024.2308419
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1971-1982

Auteurs

Ross J Brancati (RJ)

Department of Kinesiology, University of Massachusetts Amherst, Amherst, MA, USA.

Joseph Hamill (J)

Department of Kinesiology, University of Massachusetts Amherst, Amherst, MA, USA.

Carl Jewell (C)

Department of Kinesiology, University of Massachusetts Amherst, Amherst, MA, USA.
Department of Biomechanics, ARCCA, Inc, Penns Park, PA, USA.

Katherine A Boyer (KA)

Department of Kinesiology, University of Massachusetts Amherst, Amherst, MA, USA.

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Classifications MeSH