Preseason multiple biomechanics testing and dimension reduction for injury risk surveillance in elite female soccer athletes: short-communication

Simple item page
dc.contributor.authorDe la Fuente, Carlos
dc.contributor.authorSilvestre, Rony
dc.contributor.authorYanez, Roberto
dc.contributor.authorRoby, Matias
dc.contributor.authorSoldan, Macarena
dc.contributor.authorFerrada, Wilson
dc.contributor.authorCarpes, Felipe P.
dc.date.accessioned2025-01-20T20:21:38Z
dc.date.available2025-01-20T20:21:38Z
dc.date.issued2023
dc.description.abstractBackground Injury risk is regularly assessed during the preseason in susceptible populations like female soccer players. However, multiple outcomes (high-dimensional dataset) derived from multiple testing may make pattern recognition difficult. Thus, dimension reduction and clustering may be useful for improving injury surveillance when results of multiple assessment tools are available. Aim To determine the influence of dimension reduction for pattern recognition followed by clustering on multiple biomechanical injury markers in elite female soccer players during preseason. Methdology We introduced the use of dimension reduction through linear principal component analysis (PCA), non-linear kernel principal component analysis (k-PCA), t-distributed stochastic neighbor embedding (t-sne), and uniform manifold approximation and projection (umap) for injury markers via grid search. Muscle strength, muscle function, jump technique and power, balance, muscle stiffness, exercise tolerance, and running performance were assessed in an elite female soccer team (n = 21) prior to the competitive season. Results As a result, umap facilitated the injury pattern recognition compared to PCA, k-PCA, and t-sne. One of the three patterns was related to a team subgroup with acceptable muscle conditions. In contrast, the other two patterns showed higher injury risk profiles. For our dataset, umap improved injury surveillance through multiple testing characteristics. Conclusion Dimension reduction and clustering techniques present as useful strategies to analyze subgroups of female soccer players who have different risk profiles for injury.
dc.description.funderPontificia Universidad Catolica de Chile
dc.fuente.origenWOS
dc.identifier.doi10.1080/24733938.2022.2075558
dc.identifier.eissn2473-4446
dc.identifier.issn2473-3938
dc.identifier.urihttps://doi.org/10.1080/24733938.2022.2075558
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/92637
dc.identifier.wosidWOS:000798237500001
dc.issue.numero2
dc.language.isoen
dc.pagina.final188
dc.pagina.inicio183
dc.revistaScience and medicine in football
dc.rightsacceso restringido
dc.subjectSports
dc.subjectbiomechanics
dc.subjectmachine learning
dc.subjectfootball
dc.subjectnon-linear reduction
dc.titlePreseason multiple biomechanics testing and dimension reduction for injury risk surveillance in elite female soccer athletes: short-communication
dc.typeartículo
dc.volumen7
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
Files
Collections
Artículos de revistas