{"title":"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","authors":"Ying Huang, Ruobing Zheng, Xiaxin Xiong, Yanping Chen, Wanqing Zheng, Rongmao Lin","doi":"10.1177/13591053241274472","DOIUrl":null,"url":null,"abstract":"<p><p>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: <i>severe insomnia without daytime dysfunction</i> (8.8%), <i>good sleepers</i> (39.6%), <i>mild insomnia</i> (41.7%), and <i>moderate to severe insomnia</i> (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.</p>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The heterogeneity of insomnia symptoms for emerging workers in the digital economy: Latent profile and network analysis.\",\"authors\":\"Ying Huang, Ruobing Zheng, Xiaxin Xiong, Yanping Chen, Wanqing Zheng, Rongmao Lin\",\"doi\":\"10.1177/13591053241274472\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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: <i>severe insomnia without daytime dysfunction</i> (8.8%), <i>good sleepers</i> (39.6%), <i>mild insomnia</i> (41.7%), and <i>moderate to severe insomnia</i> (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.</p>\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1177/13591053241274472\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1177/13591053241274472","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
The heterogeneity of insomnia symptoms for emerging workers in the digital economy: Latent profile and network analysis.
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.