Caccese Jaclyn B, Kaminski Thomas W
Dept of Kinesiology and Applied Physiology, University of Delaware, Newark, DE.
J Sport Rehabil. 2016 May;25(2):133-6. doi: 10.1123/jsr.2014-0281. Epub 2014 Oct 30.
The Balance Error Scoring System (BESS) is the current standard for assessing postural stability in concussed athletes on the sideline. However, research has questioned the objectivity and validity of the BESS, suggesting that while certain subcategories of the BESS have sufficient reliability to be used in evaluation of postural stability, the total score is not reliable, demonstrating limited interrater and intrarater reliability. Recently, a computerized BESS test was developed to automate scoring.
To compare computer-derived BESS scores with those taken from 3 trained human scorers.
Interrater reliability study.
Athletic training room.
NCAA Division I student athletes (53 male, 58 female; 19 ± 2 y, 168 ± 41 cm, 69 ± 4 kg).
Subjects were asked to perform the BESS while standing on the Tekscan (Boston, MA) MobileMat® BESS. The MobileMat BESS software displayed an error score at the end of each trial. Simultaneously, errors were recorded by 3 separate examiners. Errors were counted using the standard BESS scoring criteria.
The number of BESS errors was computed for the 6 stances from the software and each of the 3 human scorers. Interclass correlation coefficients (ICCs) were used to compare errors for each stance scored by the MobileMat BESS software with each of 3 raters individually. The ICC values were converted to Fisher Z scores, averaged, and converted back into ICC values.
The double-leg, single-leg, and tandem-firm stances resulted in good agreement with human scorers (ICC = .999, .731, and .648). All foam stances resulted in fair agreement.
Our results suggest that the MobileMat BESS is suitable for identifying BESS errors involving each of the 6 stances of the BESS protocol. Because the MobileMat BESS scores consistently and reliably, this system can be used with confidence by clinicians as an effective alternative to scoring the BESS.
平衡误差评分系统（BESS）是目前在边线对脑震荡运动员进行姿势稳定性评估的标准。然而，研究对BESS的客观性和有效性提出了质疑，表明虽然BESS的某些子类别具有足够的可靠性可用于姿势稳定性评估，但总分不可靠，表现出评分者间和评分者内的可靠性有限。最近，开发了一种计算机化的BESS测试来实现评分自动化。
比较计算机得出的BESS分数与3名训练有素的人工评分者给出的分数。
评分者间可靠性研究。
运动训练室。
美国大学体育协会（NCAA）一级学生运动员（53名男性，58名女性；年龄19±2岁，身高168±41厘米，体重69±4千克）。
要求受试者站在Tekscan（马萨诸塞州波士顿）的MobileMat® BESS上进行BESS测试。MobileMat BESS软件在每次测试结束时显示一个误差分数。同时，由3名独立的检查人员记录误差。使用标准的BESS评分标准计算误差。
计算软件和3名人工评分者各自针对6种姿势得出的BESS误差数量。组内相关系数（ICC）用于比较MobileMat BESS软件针对每种姿势得出的误差与3名评分者各自得出的误差。将ICC值转换为费舍尔Z分数，求平均值，然后再转换回ICC值。
双腿、单腿和串联稳固姿势与人工评分者的结果一致性良好（ICC分别为0.999、0.731和0.648）。所有泡沫姿势的一致性一般。
我们的结果表明，MobileMat BESS适用于识别BESS协议6种姿势中每种姿势的BESS误差。由于MobileMat BESS评分一致且可靠，临床医生可以放心地将该系统用作BESS评分的有效替代方法。
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