Institute for Disaster Management and Reconstruction, Sichuan University-The Hong Kong Polytechnic University, Chengdu, China; West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Sleep Med. 2024 Jan;113:76-83. doi: 10.1016/j.sleep.2023.11.020. Epub 2023 Nov 18.
Mental health issues are severe public health problems, inevitably affected by, also affecting, sleep. We used network analysis to estimate the relationship among various aspects of sleep and mental health simultaneously, and identify potential action points for improving sleep and mental health among employees.
We used data from the baseline survey of the Chinese Cohort of Working Adults that recruited 31,105 employees between October 1st and December 31st, 2021. The mental health included anxiety (measured by the Generalized Anxiety Disorder-7), depression (Patient Health Questionnaire-9]), loneliness (Short Loneliness Scale), well-being (Short Scales of Flourishing and Positive and Negative Feelings), and implicit health attitude (Lay Theory of Health Measures). Seven dimensions of sleep were assessed by the Pittsburgh Sleep Quality Index. An undirected network model and two directed network approaches, including Bayesian Directed Acyclic Graphs (DAGs) and Evidence Synthesis for Constructing-DAGs (ESC-DAGs), were applied to investigate associations between variables and identify key variables.
Depression, daytime dysfunction, and well-being were the "bridges" connecting the domains of sleep and mental health in the undirected network, and were in the main pathway connecting most variables in the Bayesian DAG. Anxiety constituted a gateway that activated other sleep and mental health variables, with sleep duration and implicit health attitude forming end points of the pathway. Similar directed pathways were confirmed in the ESC-DAG.
Our network study suggests anxiety, depression, well-being, and daytime dysfunction may be potential action points in preventing the development of poor sleep and mental health outcomes for employees.
心理健康问题是严重的公共卫生问题，不可避免地受到睡眠的影响，也会影响睡眠。我们使用网络分析同时估计睡眠和心理健康各个方面之间的关系，并确定改善员工睡眠和心理健康的潜在切入点。
我们使用了 2021 年 10 月 1 日至 12 月 31 日期间招募的 31105 名员工的中国工作成年人队列研究的基线调查数据。心理健康包括焦虑（用广泛性焦虑障碍 7 项量表测量）、抑郁（患者健康问卷 9 项量表）、孤独（短孤独量表）、幸福感（繁荣和积极与消极感受的短量表）和隐含健康态度（健康理论的措施）。睡眠的七个维度通过匹兹堡睡眠质量指数进行评估。应用无向网络模型和两种有向网络方法，包括贝叶斯有向无环图（DAG）和构建 DAG 的证据综合（ESC-DAG），以调查变量之间的关联，并确定关键变量。
抑郁、日间功能障碍和幸福感是无向网络中连接睡眠和心理健康领域的“桥梁”，也是连接贝叶斯 DAG 中大多数变量的主要途径。焦虑构成了激活其他睡眠和心理健康变量的门户，睡眠持续时间和隐含健康态度构成了途径的终点。ESC-DAG 中也证实了类似的有向途径。
我们的网络研究表明，焦虑、抑郁、幸福感和日间功能障碍可能是预防员工睡眠和心理健康不良结果发展的潜在切入点。
