Can Injuries Be Predicted by Functional Movement Screen in Adolescents?: The Application of Machine Learning

• Josip Karuc ^[1] ; Marjeta Durakovic ^[1] ; Marko Sarlija ^[1] ; Goran Markovic ^[1] ; Vedran Hadzic ^[2] ; Tatjana Trost-Bobic ^[1] ; Maroje Soric ^[1]
  1. [1] University of Zagreb

     University of Zagreb

     Croacia

     
  2. [2] University of Ljubljana

     University of Ljubljana

     Eslovenia

     
• Localización: Journal of strength and conditioning research: the research journal of the NSCA, ISSN 1064-8011, Vol. 35, Nº. 4, 2021, págs. 910-919
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • This study used machine learning (ML) to predict injuries among adolescents by functional movement testing. This research is a part of the CRO-PALS study conducted in a representative sample of adolescents and analyses for this study are based on nonathletic (n = 364) and athletic (n = 192) subgroups of the cohort (16-17 years). Sex, age, body mass index (BMI), body fatness, moderate-to-vigorous physical activity (MVPA), training hours per week, Functional Movement Screen (FMS), and socioeconomic status were assessed at baseline. A year later, data on injury occurrence were collected. The optimal cut-point of the total FMS score for predicting injury was calculated using receiver operating characteristic curve. These predictors were included in ML analyses with calculated metrics: area under the curve (AUC), sensitivity, specificity, and odds ratio (95% confidence interval [CI]). Receiver operating characteristic curve analyses with associated criterium of total FMS score >12 showed AUC of 0.54 (95% CI: 0.48-0.59) and 0.56 (95% CI: 0.47-0.63), for the nonathletic and athletic youth, respectively. However, in the nonathletic subgroup, ML showed that the Naive Bayes exhibited highest AUC (0.58), whereas in the athletic group, logistic regression was demonstrated as the model with the best predictive accuracy (AUC: 0.62). In both subgroups, with given predictors: sex, age, BMI, body fat percentage, MVPA, training hours per week, socioeconomic status, and total FMS score, ML can give a more accurate prediction then FMS alone. Results indicate that nonathletic boys who have lower-body fat could be more prone to suffer from injury incidence, whereas among athletic subjects, boys who spend more time training are at a higher risk of being injured. Conclusively, total FMS cut-off scores for each subgroup did not successfully discriminate those who suffered from those who did not suffer from injury, and, therefore, our research does not support FMS as an injury prediction tool.