High-density surface electromyography allows to identify risk conditions and people with and without low back pain during fatiguing frequency-dependent lifting activities

IF 2 4区 医学 Q3 NEUROSCIENCES
Tiwana Varrecchia , Alberto Ranavolo , Giorgia Chini , Alessandro Marco De Nunzio , Francesco Draicchio , Eduardo Martinez-Valdes , Deborah Falla , Silvia Conforto
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Abstract

Low back pain (LBP) is a leading cause of disability in the workplace, often caused by manually lifting of heavy loads. Instrumental-based assessment tools are used to quantitatively assess the biomechanical risk of lifting activities. This study aims to verify that, during the execution of fatiguing frequency-dependent lifting, high-density surface electromyography (HDsEMG) allows the discrimination of healthy controls (HC) versus people with LBP and biomechanical risk levels. Fifteen HC and eight people with LBP performed three lifting tasks with a progressively increasing lifting index, each lasting 15 min. Erector spinae (ES) activity was recorded using HDsEMG and amplitude parameters were calculated to characterize the spatial distribution of muscle activity. LBP group showed a less ES activity than HC (lower root mean square across the grid and of the activation region) and an involvement of the same muscular area across the task (lower coefficient of variation of the center of gravity of muscle activity). The results indicate the usefulness of HDsEMG parameters to classify risk levels for both HC and LBP groups and to determine differences between them. The findings suggest that the use of HDsEMG could expand the capabilities of existing instrumental-based tools for biomechanical risk classification during lifting activities.

高密度表面肌电图可以识别疲劳频率相关的举重活动中的风险状况以及有或没有腰痛的人。
腰痛(LBP)是工作场所残疾的主要原因,通常由手动举起重物引起。基于仪器的评估工具用于定量评估举重活动的生物力学风险。本研究旨在验证,在执行疲劳频率依赖性举重过程中,高密度表面肌电图(HDsEMG)可以区分健康对照组(HC)与患有LBP和生物力学风险水平的人。15名HC和8名LBP患者执行了三项提升任务,提升指数逐渐增加,每次持续15分钟。使用HDsEMG记录勃起棘(ES)活动,并计算振幅参数来表征肌肉活动的空间分布。LBP组的ES活性低于HC(整个网格和激活区域的均方根较低),并且在整个任务中涉及相同的肌肉区域(肌肉活动重心的变化系数较低)。结果表明,HDsEMG参数对HC和LBP组的风险水平进行分类并确定它们之间的差异是有用的。研究结果表明,HDsEMG的使用可以扩展现有基于工具的工具在举重活动中进行生物力学风险分类的能力。
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来源期刊
CiteScore
4.70
自引率
8.00%
发文量
70
审稿时长
74 days
期刊介绍: Journal of Electromyography & Kinesiology is the primary source for outstanding original articles on the study of human movement from muscle contraction via its motor units and sensory system to integrated motion through mechanical and electrical detection techniques. As the official publication of the International Society of Electrophysiology and Kinesiology, the journal is dedicated to publishing the best work in all areas of electromyography and kinesiology, including: control of movement, muscle fatigue, muscle and nerve properties, joint biomechanics and electrical stimulation. Applications in rehabilitation, sports & exercise, motion analysis, ergonomics, alternative & complimentary medicine, measures of human performance and technical articles on electromyographic signal processing are welcome.
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