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Table 2 Summary of the included articles

From: Evaluation of the Functional Movement Screen (FMS) in Identifying Active Females Who are Prone to Injury. A Systematic Review

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