scholarly journals Effect of Fatigue on Functional Movement Screening Performance in Dancers

2018 ◽  
Vol 33 (3) ◽  
pp. 213-219 ◽  
Author(s):  
Ross Armstrong ◽  
Christopher M Brogden ◽  
Debbie Milner ◽  
Debbie Norris ◽  
Matt Greig
Keyword(s):  
Injury Risk ◽  
Perceived Exertion ◽  
Neuromuscular Control ◽  
Functional Movement ◽  
Screening Performance ◽  
Movement Screening ◽  
And Performance ◽  
High Level ◽  
Deep Squat ◽  
Functional Movement Screening

OBJECTIVE: Dance is associated with a high risk of injury, with fatigue identified as a contributing factor. Functional movement screening (FMS) has been used to identify alterations in normal movement which may contribute to injury risk, though this test is not normally performed in a fatigued state. The aim of this study was to determine whether fatigue induced by the dance aerobic fitness test (DAFT) results in changes in FMS scores with implications for performance and injury risk. METHODS: Forty-one university dancers completed the FMS before and immediately after completion of the DAFT. Rate of perceived exertion and heart rate were quantified as measures of fatigue. RESULTS: Post-DAFT, the mean FMS composite score (15.39±1.86) was significantly less (p≤0.01) than the pre-exercise score (16.83±1.83). Element-specific analysis revealed that the deep squat, non-dominant lunge, and dominant inline lunge scores were all significantly impaired post-DAFT (all p≤0.01). CONCLUSION: The identification of changes in quality of movement in a fatigued state suggests that movement screening should also be performed post-exercise to enhance screening for injury risk. The influence of dance-specific fatigue was FMS element-specific. Specifically, the deep squat and inline lunge were most susceptible to fatigue, with implications for injury risk and performance and reflective of the high level of neuromuscular control required.

Functional Movement Screening as a Predictor of Mechanical Loading and Performance in Dancers

2018 ◽  
Vol 22 (4) ◽  
pp. 203-208 ◽  
Author(s):  
Ross Armstrong ◽  
Christopher Michael Brogden ◽  
Debbie Milner ◽  
Debbie Norris ◽  
Matt Greig
Keyword(s):  
Mechanical Loading ◽  
Functional Movement ◽  
Movement Screening ◽  
And Performance ◽  
Functional Movement Screening

scholarly journals Asymmetry during Functional Movement Screening and injury risk in junior football players: A replication study

2018 ◽  
Vol 28 (3) ◽  
pp. 1281-1287 ◽  
Author(s):  
S. Chalmers ◽  
T. A. Debenedictis ◽  
A. Zacharia ◽  
S. Townsley ◽  
C. Gleeson ◽  
...  
Keyword(s):  
Injury Risk ◽  
Replication Study ◽  
Football Players ◽  
Functional Movement ◽  
Movement Screening ◽  
Functional Movement Screening

scholarly journals Big Data and Deep Learning Model for FMS Score Prediction of Aerobics Athletes

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Wenying Xiong ◽  
Dongqin Huang ◽  
Wei Xu
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Injury Risk ◽  
Risk Model ◽  
Sports Injuries ◽  
Sports Injury ◽  
Positive Role ◽  
Functional Movement ◽  
Movement Screening ◽  
Functional Movement Screening

In recent years, competitive aerobics has developed rapidly in my country, and the corresponding sports injury risks have gradually increased. A number of studies have shown that due to the characteristics of aerobics itself, difficult movement requirements, fast-paced music accompaniment and coherent coordinated movements, athletes will suffer sports injuries if they are not paying attention. Therefore, discovering the causes of athletes’ injuries in time and preventing them in time is crucial for improving athletes’ skill level and prolonging sports life. Through the functional movement screening (FMS) test, understanding young aerobics athletes’ insufficiency in trunk stability, joint flexibility, muscle extension, and core strength can further help athletes reduce the risk of sports injuries. Therefore, this article proposes a novel sports injury risk model based on big data technology and deep learning, which can effectively predict the risk of sports injury and can play a positive role in improving the quality of athletes’ movements and prolonging their sports life.

Functional Movement Screening and injury risk in elite adolescent rugby league players

2019 ◽  
Vol 14 (4) ◽  
pp. 498-506
Author(s):  
Cameron S Dyer ◽  
Robin Callister ◽  
Colin E Sanctuary ◽  
Suzanne J Snodgrass
Keyword(s):  
Injury Risk ◽  
Rugby League ◽  
Injury Incidence ◽  
Functional Movement Screen ◽  
Functional Movement ◽  
Incidence Data ◽  
Movement Screening ◽  
The Mean ◽  
Functional Movement Screening ◽  
And Training

Research is limited as to whether Functional Movement Screen scores relate to non-contact injury risk in rugby league players. This cohort study investigates whether the Functional Movement Screen score predicts non-contact injuries in elite adolescent rugby league players. Australian adolescent rugby league players ( n = 52; mean age 16.0 ± 1.0 years) from one club participated in this study. Functional Movement Screen scores, height, and mass were collected at the beginning of the preseason. Training, match exposure, and injury incidence data (non-contact match and training injuries with three levels of severity) were recorded for each individual athlete throughout the season. Linear and logistic regression analyses were conducted to investigate the association between Functional Movement Screen score (continuous score, ≤ 14 or > 14, and three subscores) and injury risk, whilst controlling for exposure time. The mean Functional Movement Screen score for the sample was 13.4 (95% CI: 11.0–14.0). A total of 72 non-contact injuries were recorded (incidence rate: 18.7 per 1000 exposure hours; 95% CI: 11.6–24.8). There were no statistically significant associations between non-contact injury and Functional Movement Screen score for any of the analyses conducted. Our results suggest that the Functional Movement Screen does not reflect non-contact injury risk in elite adolescent rugby league players. Further research should investigate whether a more sport-specific movement screen in the preseason can more effectively predict injury risk in this population.

Functional Movement Screening Performance and Association With Key Health Markers in Older Adults

2019 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Stephen W. Farrell ◽  
Andjelka Pavlovic ◽  
Carolyn E. Barlow ◽  
David Leonard ◽  
Joseph R. DeFina ◽  
...  
Keyword(s):  
Older Adults ◽  
Functional Movement ◽  
Screening Performance ◽  
Movement Screening ◽  
Functional Movement Screening

Functional Movement Screening Performance of Brazilian Jiu-Jitsu Athletes From Brazil

2016 ◽  
Vol 30 (8) ◽  
pp. 2341-2347 ◽  
Author(s):  
Fabrício Boscolo Del Vecchio ◽  
Denis Foster Gondim ◽  
Antonio Carlos Pereira Arruda
Keyword(s):  
Functional Movement ◽  
Screening Performance ◽  
Movement Screening ◽  
Functional Movement Screening

scholarly journals Dynamic Movement Assessment and Functional Movement Screening for injury prediction: a systematic review

2018 ◽  
Vol 25 (3) ◽  
pp. 352-361
Author(s):  
Priscila dos Santos Bunn ◽  
Elirez Bezerra da Silva
Keyword(s):  
Systematic Review ◽  
Injury Risk ◽  
Physical Activities ◽  
Musculoskeletal Injuries ◽  
Functional Movement ◽  
Injury Prediction ◽  
Dynamic Movement ◽  
Movement Screening ◽  
Movement Assessment ◽  
Functional Movement Screening

ABSTRACT Dynamic Movement AssessmentTM (DMATM) and Functional Movement ScreeningTM (FMSTM) are tools to predict the risk of musculoskeletal injuries in individuals who practice physical activities. This systematic review aimed to evaluate the association of DMATM and FMSTM with the risk of musculoskeletal injuries, in different physical activities, categorizing by analysis. A research without language or time filters was carried out in November 2016 in MEDLINE, Google Scholar, SciELO, SCOPUS, SPORTDiscus, CINAHL and BVS databases using the keywords: “injury prediction”, “injury risk”, “sensitivity”, “specificity”, “functional movement screening”, and “dynamic movement assessment”. Prospective studies that analyzed the association between DMATM and FMSTM with the risk of musculoskeletal injuries in physical activities were included. The data extracted from the studies were: participant’s profile, sample size, injury’s classification criteria, follow-up time, and the results presented, subdivided by the type of statistical analysis. The risk of bias was performed with Newcastle-Ottawa Scale for cohort studies. No study with DMATM was found. A total of 20 FMSTM studies analyzing one or more of the following indicators were included: diagnostic accuracy (PPV, NPV and AUC), odds ratios (OR) or relative risk (RR). FMSTM showed a sensitivity=12 to 99%; specificity=38 to 97%; PPV=25 to 91%; NPV=28 to 85%; AUC=0.42 to 0.68; OR=0.53 to 54.5; and RR=0.16-5.44. The FMSTM has proven to be a predictor of musculoskeletal injuries. However, due to methodological limitations, its indiscriminate usage should be avoided.

Sign in / Sign up

Export Citation Format

Share Document