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Prediction of ACL-tear by lower limbs muscle strength and flexibility: a prospective cohort study in 95 female soccer players.

Antonio CejudoJosé Manuel Armada-ZarcoFrancisco AyalaPilar Sainz de Baranda
Published in: Research in sports medicine (Print) (2023)
The aims of the study were to build models using logistic regression analysis of flexibility and strength tests to prospectively predict risk factors for anterior cruciate ligament tear (ACL-tear) in female soccer (FS) players, and to determine training cut-off for risk factors of the predictive model built. A prospective cohort study of 95 female players (aged 14-33 years) was conducted. Age, anthropometric data, soccer history, lower limb range of motion (ROM) and hip maximal isometric strength (MIS) were measured. At the prospective follow-up after 12 months, 7.4% of the players had developed an ACL-tear. The model showed a significant relationship (χ 2 (93) = 30.531, p  < 0.001) between the ACL-tear and the predictor variables (leg length, HAD-NH [hip adduction] MIS, asymmetric ROM [ankle dorsiflexion with knee extended (AD-KE) and with knee flexed (AD-KF), and HE (hip extension)], hip ROM [HIR (internal rotation) and HAB (abduction)]). The Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) for model fit were 30.24 and 51.79, respectively. The value R 2 showed good model fit, 76.5% for Nagelkerke´s R 2 , 71.4% for McFadden´s R 2 and 67.5% for Tjur´s R 2 . For the screening test, cut-off for leg length of ≥0.40 m, for HIR ROM of ≤44º and for asymmetry of HE ROM of ≥5° were set, which have an acceptable (AUC ≥ 0.755) discriminatory ability for the development of ACL-tear.
Keyphrases
  • anterior cruciate ligament
  • lower limb
  • risk factors
  • total hip arthroplasty
  • healthcare
  • machine learning
  • big data
  • high resolution
  • knee osteoarthritis
  • artificial intelligence
  • high school
  • perovskite solar cells