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修订后的 NIOSH 举重方程预测与手动举重相关的腰痛风险的功效:一项为期一年的前瞻性研究。

Efficacy of the revised NIOSH lifting equation to predict risk of low-back pain associated with manual lifting: a one-year prospective study.

出版信息

Hum Factors. 2014 Feb;56(1):73-85. doi: 10.1177/0018720813513608.

Abstract

OBJECTIVE

The objective was to evaluate the efficacy of the Revised National Institute for Occupational Safety and Health (NIOSH) lifting equation (RNLE) to predict risk of low-back pain (LBP).

BACKGROUND

In 1993, NIOSH published the RNLE as a risk assessment method for LBP associated with manual lifting. To date, there has been little research evaluating the RNLE as a predictor of the risk of LBP using a prospective design.

METHODS

A total of 78 healthy industrial workers' baseline LBP risk exposures and self-reported LBP at one-year follow-up were investigated. The composite lifting index (CLI), the outcome measure of the RNLE for analyzing multiple lifting tasks, was used as the main risk predictor. The risk was estimated using the mean and maximum CLI variables at baseline and self-reported LBP during the follow-up. Odds ratios (ORs) were calculated using a logistic regression analysis adjusted for covariates that included personal factors, physical activities outside of work, job factors, and work-related psychosocial characteristics.

RESULTS

The one-year self-reported LBP incidence was 32.1%. After controlling for history of prior LBP, supervisory support, and job strain, the categorical mean and maximum CLI above 2 had a significant relationship (OR = 5.1-6.5) with self-reported LBP, as compared with the CLI below or equal to I. The correlation between the continuous CLI variables and LBP was unclear.

CONCLUSIONS

The CLI > 2 threshold may be useful for predicting self-reported LBP. Research with a larger sample size is needed to clarify the exposure-response relationship between the CLI and LBP.

摘要

目的

评估修订后的美国国立职业安全卫生研究所(NIOSH)举重方程(RNLE)预测下背痛(LBP)风险的功效。

背景

1993 年,NIOSH 发布了 RNLE,作为与手动举重相关的 LBP 风险评估方法。迄今为止,使用前瞻性设计评估 RNLE 作为 LBP 风险预测指标的研究甚少。

方法

共调查了 78 名健康工业工人的基线 LBP 风险暴露和一年随访时的自我报告 LBP。将综合举重指数(CLI)作为主要风险预测因子,该指数是分析多项举重任务的 RNLE 的结果测量指标。使用基线时的平均和最大 CLI 变量以及随访期间的自我报告 LBP 来估计风险。使用逻辑回归分析计算调整了个人因素、工作外体力活动、工作因素和与工作相关的心理社会特征等协变量后的比值比(OR)。

结果

一年的自我报告 LBP 发病率为 32.1%。在控制既往 LBP 病史、监督支持和工作压力后,高于 2 的分类平均和最大 CLI 与自我报告的 LBP 显著相关(OR=5.1-6.5),而 CLI 低于或等于 1 的则不相关。连续 CLI 变量与 LBP 之间的相关性不明确。

结论

CLI>2 的阈值可能有助于预测自我报告的 LBP。需要更大的样本量来阐明 CLI 与 LBP 之间的暴露-反应关系。

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本文引用的文献

1
Development of Human Posture Simulation Method for Assessing Posture Angles and Spinal Loads.
Hum Factors Ergon Manuf. 2015 Jan;25(1):123-136. doi: 10.1002/hfm.20534.
3
Personal and workplace psychosocial risk factors for carpal tunnel syndrome: a pooled study cohort.
Occup Environ Med. 2013 Aug;70(8):529-37. doi: 10.1136/oemed-2013-101365. Epub 2013 May 3.
4
Biomechanical, psychosocial and individual risk factors predicting low back functional impairment among furniture distribution employees.
Clin Biomech (Bristol). 2012 Feb;27(2):117-23. doi: 10.1016/j.clinbiomech.2011.09.002. Epub 2011 Sep 28.
7
Quantitative biomechanical workplace exposure measures: distribution centers.
J Electromyogr Kinesiol. 2010 Oct;20(5):813-22. doi: 10.1016/j.jelekin.2010.03.006. Epub 2010 Apr 18.
8
Quantitative dynamic measures of physical exposure predict low back functional impairment.
Spine (Phila Pa 1976). 2010 Apr 15;35(8):914-23. doi: 10.1097/BRS.0b013e3181ce1201.
10
A validation of a posture matching approach for the determination of 3D cumulative back loads.
Appl Ergon. 2008 Mar;39(2):199-208. doi: 10.1016/j.apergo.2007.05.004. Epub 2007 Jun 27.

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