Functional Movement Screen (FMS) scores did not successfully predict injury in adolescents (AUC 0.54-0.56), and machine learning models provided only marginal improvements (AUC 0.58-0.62).
Cohort (n=556)
Can machine learning combined with the Functional Movement Screen (FMS) and demographic/physical predictors accurately predict injuries in adolescents?
The Functional Movement Screen, even when combined with machine learning and other predictors, does not accurately predict injury risk in athletic or nonathletic adolescents.
Estimación del efecto: AUC 0.54 to 0.62
ABSTRACT: Karuc, J, Mišigoj-Duraković, M, Šarlija, M, Marković, G, Hadžić, V, Trošt-Bobić, T, and Sorić, M. Can injuries be predicted by functional movement screen in adolescents? The application of machine learning. J Strength Cond Res 35(4): 910-919, 2021-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 Naïve 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.
Karuc et al. (Thu,) conducted a cohort in Injury (n=556). Functional Movement Screen (FMS) and Machine Learning was evaluated on Injury prediction accuracy (Area Under the Curve) (AUC 0.54 to 0.62). Functional Movement Screen (FMS) scores did not successfully predict injury in adolescents (AUC 0.54-0.56), and machine learning models provided only marginal improvements (AUC 0.58-0.62).