Roy S H, De Luca C J, Snyder-Mackler L, Emley M S, Crenshaw R L, Lyons J P
NeuroMuscular Research Center, Boston University, MA 02215.
Med Sci Sports Exerc. 1990 Aug;22(4):463-9.
The purpose of this study was to determine whether surface electromyography (EMG) from the erector spinae muscles could correctly identify individuals with low back pain without a population of elite athletes. A similar technique had previously been successful in identifying low back pain patients within a non-athletic population. A Back Analysis System was used to compute the median frequency of the EMG power density spectrum to monitor metabolic changes in back muscles associated with muscle fatigue. Twenty-three members of a men's collegiate varsity crew team consisting of port (N = 13) and starboard (N = 10) rowers were tested in a laboratory during a fatigue-inducing isometric contraction sustained at a relatively high, constant force. Six of the rowers tested were further classified as having low back pain. A brief test contraction was repeated at a fixed interval following the fatiguing contraction to monitor recovery. A two-group discriminant analysis procedure correctly classified 100% of the rowers with low back pain and 93% of the rowers without back pain on the basis of the median frequency data. The median frequency parameters related to recovery were the best discriminators of back pain. A similar analysis correctly classified 100% of the port rowers and 100% of the starboard rowers on the basis of their spectral parameters. The best discriminating variables in this instance were the median frequency parameters relating to both fatigability and recovery. Results from this study demonstrate that low back pain and asymmetrical muscle function in rowers can be assessed on the basis of EMG spectral analysis.
本研究的目的是确定竖脊肌表面肌电图（EMG）能否在没有精英运动员群体的情况下正确识别出患有腰痛的个体。此前，一种类似的技术已成功在非运动员群体中识别出腰痛患者。使用背部分析系统计算EMG功率密度谱的中位频率，以监测与肌肉疲劳相关的背部肌肉代谢变化。在实验室中，对一支由23名男子大学代表队队员组成的赛艇队进行了测试，这些队员包括左舷（N = 13）和右舷（N = 10）划手，测试过程中让他们在相对较高的恒定力下进行诱发疲劳的等长收缩。所测试的划手中有6人被进一步归类为患有腰痛。在疲劳收缩后，以固定间隔重复进行一次简短的测试收缩，以监测恢复情况。基于中位频率数据，两组判别分析程序正确地将100%的腰痛划手和93%的无腰痛划手进行了分类。与恢复相关的中位频率参数是腰痛的最佳判别指标。类似的分析基于光谱参数正确地将100%的左舷划手和100%的右舷划手进行了分类。在这种情况下，最佳判别变量是与易疲劳性和恢复相关的中位频率参数。本研究结果表明，划手的腰痛和不对称肌肉功能可基于EMG光谱分析进行评估。
