Liu Hua, Ding Huixian, Xuan Junjie, Gao Xing, Huang Xuejuan
Laboratory of Physical Fitness Monitoring & Chronic Disease Intervention, Wuhan Sports University, Wuhan, 430079, China.
Graduate School, Wuhan Sports University, Wuhan, 430079, China.
Heliyon. 2023 May 20;9(6):e16454. doi: 10.1016/j.heliyon.2023.e16454. eCollection 2023 Jun.
Functional Movement Screen (FMS) is used to evaluate the movement quality of an individual. However, the FMS composite score used to predict sports injuries is currently ambiguous. Further refinement of the FMS scoring method may be required to more accurately predict sports injuries.
To investigate whether FMS scores could accurately predict sports injuries in college students with different levels of physical activity (PA) and sports performance (SP).
One hundred eighty-seven college students aged 18 to 22 were prospectively screened by the FMS test and grouped by the levels of PA and SP. Sports injury occurrences were monitored and collected 12 months later. Spearman's rank coefficients and binary logistic regression were used to identify the risk factors for sports injuries. The receiver operating characteristic (ROC) curve and the total area under the curve (AUC) value were used to determine the optimal FMS cut-off point for sports injuries.
The FMS composite score (sum of the seven FMS tests) exhibited a fair association with sports injuries (r = -0.434, < 0.001). Those with an FMS cut-off point of 17.5 were more likely to acquire sports injuries. The AUC value of the ROC curves was 0.764 (95% CI: 0.618-0.909) in the low PA students, 0.781 (95% CI: 0.729-0.936) in the moderate PA students, and 0.721 (95% CI: 0.613-0.879) in the high PA students. Furthermore, students stratified by SP level showed an AUC value of 0.730 (95% CI 0.607-0.853) in the low SP group and 0.778 (95% CI 0.662-0.894) in the moderate SP group, while it declined to 0.705 (95% CI 0.511-0.800) in the high SP group. The FMS cut-off score successfully identified individuals who reported sports injuries at a higher rate in the low (PA, 84.62%; SP, 90.48%) and moderate (PA, 93.75%; SP, 77.78%) groups than in the high groups (PA, 65.52%; SP, 57.89%).
The FMS composite score could be used to predict sports injuries in college students with an FMS cut-off value of 17.5. Population stratification by the levels of PA and SP seems to influence the predictive accuracy of the FMS.
功能性动作筛查(FMS)用于评估个体的动作质量。然而,目前用于预测运动损伤的FMS综合评分并不明确。可能需要进一步完善FMS评分方法,以更准确地预测运动损伤。
探讨FMS评分能否准确预测不同身体活动(PA)水平和运动表现(SP)水平的大学生的运动损伤情况。
对187名年龄在18至22岁的大学生进行FMS测试前瞻性筛查,并根据PA和SP水平进行分组。12个月后监测并收集运动损伤发生情况。采用Spearman等级系数和二元逻辑回归分析确定运动损伤的危险因素。利用受试者工作特征(ROC)曲线和曲线下总面积(AUC)值确定运动损伤的最佳FMS切点。
FMS综合评分(七项FMS测试得分总和)与运动损伤之间存在中等程度的关联(r = -0.434,P < 0.001)。FMS切点为17.5的个体更易发生运动损伤。低PA组学生ROC曲线的AUC值为0.764(95%CI:0.618 - 0.909),中等PA组学生为0.781(95%CI:0.729 - 0.936),高PA组学生为0.721(95%CI:0.613 - 0.879)。此外,按SP水平分层的学生中,低SP组的AUC值为0.730(95%CI 0.607 - 0.853),中等SP组为0.778(95%CI 0.662 - 0.894),而高SP组则降至0.705(95%CI 0.511 - 0.800)。FMS切点评分成功识别出低(PA,84.62%;SP,90.48%)和中等(PA,93.75%;SP,77.78%)组中报告运动损伤发生率高于高组(PA,65.52%;SP,57.89%)的个体。
FMS综合评分可用于预测大学生的运动损伤,FMS切点值为17.5。按PA和SP水平进行人群分层似乎会影响FMS的预测准确性。