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多变量、纵向分析办公环境变化对表面肌电图测量的影响。

Multivariate, longitudinal analysis of the impact of changes in office work environments on surface electromyography measures.

机构信息

Institute for Work and Health, Toronto, ON, Canada.

出版信息

Int Arch Occup Environ Health. 2012 Jul;85(5):493-503. doi: 10.1007/s00420-011-0696-6. Epub 2011 Sep 1.

Abstract

PURPOSE

To detect impacts of changes in work environment and worker-equipment interface variables upon surface electromyography (EMG) measures using multivariate, longitudinal analysis.

METHODS

For 33 office workers, yearly measurements (1999-2001) were taken during normal work. Independent variables were related to work environment (expert-observed equipment dimensions, work organization on questionnaire) and interface (expert-observed postures, self-reported workstation-equipment relative fit i.e. inside or outside guidelines-informed location, and 30 min video-based task analysis). Internal mechanical exposure (EMG) was recorded bilaterally from extensor carpi radialis brevis (ECRB) and upper trapezius sites, each side, also for 30 min. Dependent variables were amplitude probability distribution functions (APDF 50 and 90%) and gaptime for entire record EMG (over all tasks) and task-specific EMG (for four separate tasks). Multivariate mixed models used independent variables to predict EMG measures (4 muscle sites × (1 entire record + 4 task specific) = 20 models total).

RESULTS

Among EMG measures, 9/16 means and 2/16 variances were significantly different across years (p < 0.1). Environment and interface variables explained part of the variation in EMG measures in 13/20 models. The most consistent predictors included: (1) increased monitor distance predicted reduced APDFs and increased gaptimes; (2) wrist extension <20° predicted decreases in left ECRB APDFs; (3) keyboard location within guidelines predicted improvements in all right ECRB EMG measures during keyboarding; and (4) longer task duration predicted higher APDFs and lower gaptimes.

CONCLUSION

Longitudinal analysis with multivariate models can detect the impacts of changes in environment and interface exposures on EMG measures among office workers.

摘要

目的

利用多变量纵向分析检测工作环境和工人-设备界面变量变化对表面肌电图(EMG)测量的影响。

方法

对 33 名办公室工作人员,在正常工作期间进行了每年一次的测量(1999-2001 年)。自变量与工作环境(专家观察的设备尺寸、问卷调查的工作组织)和界面(专家观察的姿势、自我报告的工作站设备相对贴合度,即符合或不符合指南规定的位置,以及 30 分钟基于视频的任务分析)有关。双侧桡侧腕短伸肌(ECRB)和上斜方肌部位记录内部机械暴露(EMG),每侧记录 30 分钟。因变量是整个记录 EMG(所有任务)和特定任务 EMG(四个单独任务)的振幅概率分布函数(APDF50%和 90%)和间隙时间。使用多元混合模型,自变量预测 EMG 指标(4 个肌肉部位×(1 个整体记录+4 个特定任务)= 20 个总模型)。

结果

在 EMG 指标中,16 个平均值中的 9 个和 16 个方差中的 2 个在不同年份之间有显著差异(p<0.1)。环境和界面变量在 20 个模型中的 13 个模型中解释了 EMG 指标的部分变化。最一致的预测因素包括:(1)监视器距离增加预测 APDF 降低和间隙时间增加;(2)腕关节伸展<20°预测左侧 ECRB APDF 降低;(3)键盘位置在指南内预测键盘输入时所有右侧 ECRB EMG 指标的改善;(4)任务持续时间延长预测 APDF 升高和间隙时间缩短。

结论

使用多元模型的纵向分析可以检测工作环境和界面暴露变化对办公室工作人员 EMG 指标的影响。

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