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• W4311500340 abstract "Objective: To investigate the sleep quality of video operators in Shenzhen, and explore the relationship between sleep quality and occupational stress and different work and life habits. Methods: In December 2020, a cluster sampling method was used to investigate 791 video operators in Shenzhen from June to December 2020 who were engaged in printing, design, IT and other industries. The Pittsburgh Sleep Quality Index Scale was used to investigate the sleep quality of video operators, and the Job Content Questionnaire was used to investigate the occupational stress of video operators, The Pay Return Imbalance Questionnaire was used to investigate the pay return imbalance of video operators. The measurement data conforming to the normal distribution shall be expressed by mean±standard deviation, and t-test, analysis of variance and linear correlation analysis shall be adopted according to the type of independent variable. Those that do not conform to the normal distribution are described by the median M (Q(1), Q(3)), and two sample Wilcoxon test is used according to the binary data of independent variable type. Kruskal Wallis test was used for multi classification data, and Spearman rank correlation was used for single factor analysis for ordinal classification data. The counting data were analyzed by chi square test or Fisher exact probability method. Logistic regression was used for multivariate analysis. Results: the pittsburgh sleep quality index was 4.76±2.86. 499 of them had high sleep quality. 292 people had low sleep quality, accounting for 36.91% (292/791). Compared with the low sleep quality group, the high sleep quality group had lower work requirement scores (13.48±1.77), higher autonomy scores (24.08±3.33), higher social support scores (23.95±3.08), lower pay scores (16.11±2.63), higher return scores (31.11±3.65), and lower internal input scores (14.98±2.55). There were statistically significant differences between the two scales in each dimension index group (P<0.05). Multi factor unconditional logistic regression analysis showed that high education level (OR=1.57, 95%CI=1.26~1.98, P<0.05), occupational stress (OR=1.69, 95%CI=1.21~2.36, P<0.05), and high pay and low return (OR=1.41, 95%CI=1.01~1.96, P<0.05) were the main influencing factors of sleep quality. Conclusion: The occurrence of occupational stress in video operators is a risk factor for low sleep quality, which should be paid enough attention.目的： 调查深圳市视屏作业人员睡眠质量情况，探索睡眠质量与职业紧张情况和不同工作生活习惯之间的关系。 方法： 于2020年12月，采用整群抽样法，对2020年6-12月深圳市从事印刷、设计、IT等行业的791名视屏作业人员进行调查，用《匹兹堡睡眠质量指数量表》调查视屏作业人员的睡眠质量，用《工作内容问卷》调查视屏作业人员的职业紧张程度，用《付出-回报失衡问卷》调查视屏作业人员的付出-回报失衡程度。计量资料符合正态分布的用均数±标准差表示，根据自变量类型采用t检验、方差分析和线性相关分析。不符合正态分布的用中位数M（Q(1)，Q(3)）进行描述，根据自变量类型二分类资料采用Two-sample Wilcoxon检验。多分类资料采用Kruskal-Wallis检验，有序分类资料采用Spearman秩相关进行单因素分析。计数资料采用卡方检验或Fisher确切概率法进行分析。采用logistic回归进行多因素分析。 结果： 研究对象的匹兹堡睡眠质量指数为4.76±2.86。其中高睡眠质量499人。低睡眠质量292人，占36.91%（292/791）。与低睡眠质量者比较，高睡眠质量者的工作要求得分更低（13.48±1.77），自主程度得分更高（24.08±3.33），社会支持更高（23.95±3.08），付出得分更低（16.11±2.63），回报得分更高（31.11±3.65），内在投入得分更低（14.98±2.55）两个量表的各维度指标组间差异均有统计学意义（P<0.05）。多因素非条件logistic回归分析显示，文化程度高（OR=1.57，95%CI=1.26~1.98，P<0.05）、存在职业紧张（OR=1.69，95%CI=1.21~2.36，P<0.05）、存在高付出低回报（OR=1.41，95%CI=1.01~1.96，P<0.05）是睡眠质量的主要影响因素。 结论： 视屏作业人员职业紧张的发生是低睡眠质量的危险因素，应引起足够的重视。." @default.
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• W4311500340 date "2022-11-20" @default.
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• W4311500340 title "[The study of relationship between occupational stress and sleep quality of video display terminals]." @default.
• W4311500340 doi "https://doi.org/10.3760/cma.j.cn121094-20210629-00316" @default.
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