中文体育类核心期刊

中国人文社会科学期刊AMI综合评价(A刊)核心期刊

《中文社会科学引文索引》(CSSCI)来源期刊

美国《剑桥科学文摘》(CSA)收录期刊

中国高校百佳科技期刊

功能性动作筛查预测运动损伤的可行性——一项前瞻性队列研究的meta分析

Feasibility of Functional Movement Screen in Predicting Sports Injury: A Meta Analysis of Prospective Cohort Study

  • 摘要:
      目的  运用meta分析的方法系统性回顾功能性动作筛查(functional movement screen,FMS)对运动损伤的预测效果。
      方法  运用STATA 15.0软件的MIDAS模块对纳入的21篇中英文文献的27组数据的真阳性数、假阳性数、真阴性数、假阴性数进行提取,对灵敏度、特异度的合并效应值进行估计,并利用汇总受试者操作特征(summary receiver operating characteristic,sROC)曲线的曲线下面积(area under curve,AUC)和Fagan列线图估计FMS的诊断准确度。采用单因素meta回归对异质性来源进行分析,通过Deeks'漏斗图不对称试验检验发表偏倚。
      结果  灵敏度的合并效应值为0.4095%置信区间(95%CI)0.32~0.48,特异度的合并效应值为0.80(95%CI 0.74~0.85),灵敏度和特异度Q检验的P < 0.01,表明均存在一定异质性。正、负概率比的汇总预测试概率为50%,得出的正、负测试概率分别为67%和43%,AUC值为0.68(95%CI 0.63~0.72),均说明诊断准确性一般。对于灵敏度,文献类型和损伤风险阈值可能是异质性来源(P < 0.05);对于特异度,样本量、观察周期、文献质量、研究对象可能是异质性来源(P < 0.05)。Deeks'漏斗图显示纳入的21篇文献P=0.06 < 0.1,说明可能存在发表偏倚。
      结论  FMS综合评分与运动损伤之间关联强度的证据水平不足以支持其可以直接作为运动损伤的预测工具。

     

    Abstract:
      Objective  Meta-analysis was used to systematically review the predictive effect of functional movement screen(FMS) on sports injury.
      Methods  The MIDAS module of STATA 15.0 software was used to estimate sensitivity and specificity of 21 Chinese and Enghish literatures as well as 27 groups of data including true positive, false positive, true negative and false negative values, and to aggregate the summary receiver operating characteristic(sROC), area under curve (AUC) and Fagan nomogram to estimate the diagnostic accuracy of FMS. The sources of heterogeneity were analyzed by meta-regression analysis.Deeks' funnel diagram asymmetry test was used to test publication bias.
      Results  The combined value of sensitivity is 0.40 (95% CI 0.32-0.48), and the combined effect value of specificity is 0.80 (95% CI 0.74-0.85). The P values of sensitivity and specificity Q test are less than 0.01, indicating some heterogeneity. The aggregate predictive test probability of positive/negative likelihood ratio (LR) is 50%, and the positive/negative test probability 67% and 43% respectively, the AUC 0.68 (95% CI 0.63-0.72), all indicating a general diagnostic accuracy. For sensitivity, literature type and injury risk thresholds may be sources of heterogeneity (P < 0.05). For specificity, sample size, observation period, literature quality, and study object may be sources of heterogeneity (P < 0.05). Deeks' funnel plot showed that P=0.06 < 0.1 for the 21 included literatures, indicating the possible existence of publication bias.
      Conclusion  The evidence level of correlation strength between FMS comprehensive score and subsequent injury is insufficient to support the idea that FMS comprehensive score can be directly used as a tool for sports injury prediction.

     

/

返回文章
返回