@article{ref1,
title="Longitudinal monitoring of biomechanical and psychological state in collegiate female basketball athletes using principal component analysis",
journal="Translational sports medicine",
year="2024",
author="Keogh, Joshua A. J. and Ruder, Matthew C. and White, Kaylee and Gavrilov, Momchil G. and Phillips, Stuart M. and Heisz, Jennifer J. and Jordan, Matthew J. and Kobsar, Dylan",
volume="2024",
number="",
pages="e7858835-e7858835",
abstract="BACKGROUND: The growth in participation in collegiate athletics has been accompanied by increased sport-related injuries. The complex and multifactorial nature of sports injuries highlights the importance of monitoring athletes prospectively using a novel and integrated biopsychosocial approach, as opposed to contemporary practices that silo these facets of health. <br><br>METHODS: Data collected over two competitive basketball seasons were used in a principal component analysis (PCA) model with the following objectives: (i) investigate whether biomechanical PCs (i.e., on-court and countermovement jump (CMJ) metrics) were correlated with psychological state across a season and (ii) explore whether subject-specific significant fluctuations could be detected using minimum detectable change statistics. Weekly CMJ (force plates) and on-court data (inertial measurement units), as well as psychological state (questionnaire) data, were collected on the female collegiate basketball team for two seasons. <br><br>RESULTS: While some relationships (n = 2) were identified between biomechanical PCs and psychological state metrics, the magnitude of these associations was weak (r = |0.18-0.19|, p < 0.05), and no other overarching associations were identified at the group level. However, post-hoc case study analysis showed subject-specific relationships that highlight the potential utility of red-flagging meaningful fluctuations from normative biomechanical and psychological patterns. <br><br>CONCLUSION: Overall, this work demonstrates the potential of advanced analytical modeling to characterize components of and detect statistically and clinically relevant fluctuations in student-athlete performance, health, and well-being and the need for more tailored and athlete-centered monitoring practices.<p /> <p>Language: en</p>",
language="en",
issn="2573-8488",
doi="10.1155/2024/7858835",
url="http://dx.doi.org/10.1155/2024/7858835"
}