Neural Control of Movement Laboratory, Menzies Health Institute Queensland, Griffith University, Gold Coast, Queensland, Australia.
The Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia.
Exp Physiol. 2023 Mar;108(3):465-479. doi: 10.1113/EP090981. Epub 2023 Feb 10.
What is the central question of this study? What is the predictive relationship between self-reported scales to quantify perceptions of fatigue during exercise and gold standard measures used to quantify the development of neuromuscular fatigue? What is the main finding and its importance? No scale was determined to be substantively more effective than another. However, the number of ongoing contractions performed was shown to be a better predictor of fatigue in the motor system than any of the subjective scales.
The purpose of this study was to determine the relationship between transcranial magnetic stimulation (TMS) measures of performance fatigability and commonly used scales that quantify perceptions of fatigue during exercise. Twenty healthy participants (age 23 ± 3 years, 10 female) performed 10 submaximal isometric elbow flexions at 20% maximal voluntary contraction (MVC) for 2 min, separated by 45 s of rest. Biceps brachii muscle electromyography and elbow flexion torque responses to single-pulse TMS were obtained at the end of each contraction to assess central factors of performance fatigability. A rating of perceived exertion (RPE) scale, Omnibus Resistance scale, Likert scale, Rating of Fatigue scale and a visual analogue scale (VAS) were used to assess perceptions of fatigue at the end of each contraction. The RPE (root mean square error (RMSE) = 0.144) and Rating of Fatigue (RMSE = 0.145) scales were the best predictors of decline in MVC torque, whereas the Likert (RMSE= 0.266) and RPE (RMSE= 0.268) scales were the best predictors of electromyographic amplitude. Although the Likert (RMSE = 7.6) and Rating of Fatigue (RMSE = 7.6) scales were the best predictors of voluntary muscle activation of any scale, the number of contractions performed during the protocol was a better predictor (RMSE = 7.3). The ability of the scales to predict TMS measures of performance fatigability were in general similar. Interestingly, the number of contractions performed was a better predictor of TMS measures than the scales themselves.
本研究的核心问题是什么?自我报告的量表与用于量化神经肌肉疲劳发展的金标准测量之间的预测关系是什么?主要发现及其重要性是什么?没有一个量表被确定在实质上比另一个更有效。然而,与任何主观量表相比,进行的连续收缩次数被证明是运动系统疲劳的更好预测指标。
本研究的目的是确定经颅磁刺激(TMS)测量的运动疲劳能力与常用的量化运动中疲劳感的量表之间的关系。20 名健康参与者(年龄 23±3 岁,10 名女性)以 20%最大自主收缩(MVC)进行 10 次 2 分钟的次最大等长肘部弯曲,每次收缩之间休息 45 秒。在每次收缩结束时,获取肱二头肌肌电图和肘部弯曲扭矩对单次 TMS 脉冲的反应,以评估运动疲劳能力的中枢因素。使用感觉用力评分(RPE)量表、综合阻力量表、李克特量表、疲劳评分量表和视觉模拟量表(VAS)在每次收缩结束时评估疲劳感。RPE(均方根误差(RMSE)=0.144)和疲劳评分(RMSE=0.145)量表是 MVC 扭矩下降的最佳预测指标,而李克特(RMSE=0.266)和 RPE(RMSE=0.268)量表是肌电图幅度的最佳预测指标。虽然李克特(RMSE=7.6)和疲劳评分(RMSE=7.6)量表是任何量表中预测自愿肌肉激活的最佳量表,但在协议期间进行的收缩次数是更好的预测指标(RMSE=7.3)。量表预测 TMS 运动疲劳能力的能力总体上相似。有趣的是,进行的收缩次数比量表本身更能预测 TMS 测量值。