The heterogeneity of insomnia symptoms for emerging workers in the digital economy: Latent profile and network analysis.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Ying Huang, Ruobing Zheng, Xiaxin Xiong, Yanping Chen, Wanqing Zheng, Rongmao Lin
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Abstract

Although insomnia symptoms is a common public health issue, few studies pay attention to insomnia symptoms among emerging workers in the digital economy. In this study, a total of 1093 emerging workers were recruited. Latent profile analysis was used to investigate the heterogeneity profiles and the relationship between job characteristics and these profiles. Additionally, core symptoms of insomnia were explored through network analysis. Latent profile analysis identified four insomnia profiles: severe insomnia without daytime dysfunction (8.8%), good sleepers (39.6%), mild insomnia (41.7%), and moderate to severe insomnia (9.9%). Job characteristics (e.g. daily working duration, intensity, and performance measurement system) significantly affected the profiles. Network analysis revealed that four profiles had similar network structures, but the edge and strength were varied. The implication for preventing and intervening insomnia symptoms for emerging workers in the digital economy has been discussed.

数字经济时代新兴工作者失眠症状的异质性:潜在特征和网络分析
尽管失眠症状是一个常见的公共健康问题,但很少有研究关注数字经济时代新兴工作者的失眠症状。本研究共招募了 1093 名新兴工作者。研究采用潜特征分析法调查了异质性特征以及工作特征与这些特征之间的关系。此外,还通过网络分析探讨了失眠的核心症状。潜特征分析确定了四种失眠特征:无日间功能障碍的重度失眠(8.8%)、睡眠良好者(39.6%)、轻度失眠(41.7%)和中度至重度失眠(9.9%)。工作特征(如每日工作时间、强度和绩效衡量系统)对这些特征有显著影响。网络分析显示,四种情况具有相似的网络结构,但边缘和强度各不相同。讨论了预防和干预数字经济时代新兴工作者失眠症状的意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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