Study | Participants | Study design | Statistical methods | Results | Level of evidence | Cut-off |
---|---|---|---|---|---|---|
Chorba et al. 2010 [31] | 38 NCAA Div. II female collegiate football, volleyball and basketball players | Prospective cohort | A Fisher's exact test with a one-tailed p value of < 0.05 Correlation and regression analyses | Predisposed females can be identified by using a functional movement screening tool | 2b |  ≤ 14 |
Kodesh et al. 2015 [46] | 158 female soldiers of Combat Fitness Instructor Course of the Israel Defense Forces | Prospective cohort | Fisher Exact Test to evaluate association of the FMS scores and incidence of injuries in high/low risk groups | FMS is defective in predicting injury predisposed female soldiers | 2b |  ≤ 14 |
Knapik et al. 2015 [43] | 275 females of Coast Guard cadets | Prospective cohort | Chi-square statistics risk ratio (RR) and 95% confidence interval (95% CI) 2 × 2 contingency tables (assessing Sensitivity and specificity) The Youden’s Index (to determine the FMS total score cut point that optimized sensitivity and specificity) | Functional movement screening demonstrated moderate prognostic accuracy for determining injury risk among female Coast Guard cadets | 2b |  ≤ 15 |
Clay et al. 2016 [28] | 37 Division. I female collegiate rowers and coxswains | Prospective cohort | 1.Chi-square to determine significant associations between FMS group and history of injury 2.Fisher’s Exact tests to see if any cells in the 2 × 2 contingency tables were less than 10 | No statistically significant evidence for prediction of time loss injury was observed | 2b |  ≤ 14 |
Walbright et al. 2017 [42] | 35 female collegiate volleyball and basketball players | Prospective cohort | 1.ROC analyses 2.Assessment of the area under curve 3.Two sided Z test to determine differences | No adequate validity to predict lower quarter injury risk was reported | 2b |  ≤ 14 |
Armstrong et al. 2018 [40] | 64 female university rugby union players | Prospective cohort | 1.ROC analyses 2.Linear regression, multiple linear regression and stepwise multiple hierarchical linear regression analyses | Individual components of the FMS are a better predictor of injury than FMS composite score | 2b |  ≤ 14 to ≤ 16 |
Landis et al. 2018 [41] | 187 collegiate female football, volleyball and basketball players | Prospective cohort | 1. Univariate analyses 2. Independent samples t-test to compare mean data of the groups | The FMS can be used to identify athletes at an increased risk of sustaining a non-contact ACL or Lower Extremity injury | 3a |  ≤ 14 |
Gonzalez et al. 2018 [44] | 31 National Collegiate Athletic Association Division I, female, open-weight rowers | Prospective cohort | 1. Chi-square statistic to determine association of history of LBP and experience LBP during the current season 2. independent-samples t tests 3. multiple regression analysis 4. Receiver operating characteristic (ROC) | The FMS is not recommended for widespread screening of female rowers because the risk ratio was relatively small and had a wide 95% confidence interval | 2b | 11.5to14.5 |
Šiupšinskas et al. 2019 [45] | 169 elite female basketball players | Prospective cohort | 1.Student’s t-test 2.The Mann–Whitney U-test | A combination of functional tests can be used for injury risk evaluation in female basketball players | 2b |  < 15 |
Pfeifer et al. 2019 [47] | 73 female Sport local schools inc. Football, soccer, volleyball, lacrosse | Prospective cohort study | 1.power analysis 2.Independent t-tests 3.logistic regression | A composite FMS score of < 14 or < 15 was associated with an increased risk of sustaining injury (OR = 2.99) | 1b |  < 14 or < 15 |