目的：建立作业人员工作过程中的动态、静态姿势负荷综合分析评价方法并进行信效度分析，探索作业人员动态静态姿势负荷水平与肌肉骨骼疾患患病情况的关系。方法：选择电子配件组装、轨道客车制造等装配作业人员共844人作为研究对象，依据《中国肌肉骨骼问卷》中的工作姿势相关条目建立作业人员工作过程动态、静态姿势负荷综合分析评价方法，并采用Cronbach系数、聚类分析、因子分析等方法对姿势负荷综合分析评价方法的信效度进行分析，采用非条件Logistic回归模型探索姿势负荷与肌肉骨骼疾患患病情况之间的关系。结果：该评价方法的信效度分析显示：（1）内在一致性评价总体Cronbach系数达0.934，分半信度Spearman-Brown系数0.881，前后相关系数0.787；（2）聚类分析结果提示同一身体部位动态、静态工作姿势距离最短；因子分析共提取2个公因子，累计方差贡献率达65.604%；协方差分析显示负荷得分在不同工种之间差异存在统计学意义（P<0.05）。多因素Logistic回归模型显示，饮酒（OR=2.141，95%CI 1.337~3.428）和肥胖（OR=3.408，95%CI 1.629~7.130）是肌肉骨骼疾患的危险因素，作业人员动静态姿势负荷水平越高肌肉骨骼疾患患病风险也越高（OR=1.035，95%CI 1.022~1.048），不同工种、性别、年龄人群的患病风险也存在差异，女性更易患病（OR=2.626，95%CI 1.414~4.879），30~40岁职业人群患病风险高于30 岁以下者（OR=1.909，95%CI 1.237~2.946）。结论：作业人员动态、静态姿势负荷综合分析评价方法在装配作业人群中应用可靠有效，姿势负荷与肌肉骨骼疾患之间存在关联。

Objective： To form a new assessment method to evaluate postural workload comprehensively analyzing the dynamic and static postural workload for workers during their work process to analyze the reliability and validity, and to study the relation between workers’ postural workload and work-related musculoskeletal disorders (WMSDs). Methods: In the study, 844 workers from electronic and railway vehicle manufacturing factories were selected as subjects investigated by using the China Musculoskeletal Questionnaire (CMQ) to form the postural workload comprehensive assessment method. The Cronbach’s α, cluster analysis and factor analysis were used to assess the reliability and validity of the new assessment method. Non-conditional Logistic regression was used to analyze the relation between workers’ postural workload and WMSDs. Results: Reliability of the assessment method for postural workload: internal consistency analysis results showed that Cronbach’s α was 0.934 and the results of split-half reliability indicated that Spearman-Brown coefficient was 0.881 and the correlation coefficient between the first part and the second was 0.787. Validity of the assessment method for postural workload: the results of cluster analysis indicated that square Euclidean distance between dynamic and static postural workload assessment in the same part or work posture was the shortest. The results of factor analysis showed that 2 components were extracted and the cumulative percentage of variance achieved 65.604%. The postural workload score of the different occupational workers showed significant difference (P<0.05) by covariance analysis. The results of non-conditional Logistic regression indicated that alcohol intake (OR = 2.141, 95%CI 1.337－3.428) and obesity (OR = 3.408, 95%CI 1.629－7.130) were risk factors for WMSDs. The risk for WMSDs would rise as workers’ postural workload rose (OR = 1.035, 95%CI 1.022－1.048). There was significant different risk for WMSDs in the different groups of workers distinguished by work type, gender and age. Female workers exhibited a higher prevalence for WMSDs (OR = 2.626, 95%CI 1.414－4.879) and workers between 30－40 years of age (OR=1.909, 95%CI 1.237－2.946) as compared with those under 30. Conclusion: This method for comprehensively assess-ing postural workload is reliable and effective when used in assembling workers, and there is certain relation between the postural workload and WMSDs.