Gao Tingting, Zhou Chengchao, Su Yingying
Department of Social Medicine and Health Management, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
NHC Key Lab of Health Economics and Policy Research (Shandong University), Jinan, Shandong, China.
Depress Anxiety. 2025 Jul 15;2025:7589775. doi: 10.1155/da/7589775. eCollection 2025.
While traditional psychometric approaches, such as latent variable modeling, have primarily focused on the association between bedtime procrastination and anxiety, they often fail to capture symptom-level temporal and directional relationships. Therefore, this study aims to explore the temporal dynamics of symptom-level associations between bedtime procrastination and anxiety, examining both within-person and between-person variations over time in an adolescent population. This study utilized panel data-based network analyses to examine both within-person effects (temporal and contemporaneous networks) and between-person dynamics across 3,296 adolescents. Specifically, we examined symptom-to-symptom associations of bedtime procrastination and anxiety using both cross-sectional and temporal network analyses and assessed the symptom centrality to identify key drivers of symptom dynamics. At the within-person level, the temporal network analysis indicated that restlessness (GAD5) was the most stable and predictive node across time. Additionally, nervousness (GAD1) and going to bed later than intended (BPS1) had the most significant influence on other symptoms in the T1→T2 and T2→T3 networks, respectively. In the contemporaneous network, inability to control worry (GAD2), excessive worry (GAD3), and trouble relaxing (GAD4) were identified as the central symptoms. At the between-person level, positive relationships between specific bedtime procrastination symptoms were consistently observed. Our findings elucidate the potential complex interactions between bedtime procrastination and anxiety symptoms, highlighting central symptoms that vary across temporal and contemporaneous networks. The identification of central symptoms and their dynamic associations within these networks can inform the causal mechanisms underlying bedtime procrastination and anxiety, thereby guiding the design of targeted interventions for adolescents.
虽然传统的心理测量方法,如潜在变量建模,主要关注就寝拖延与焦虑之间的关联,但它们往往无法捕捉症状层面的时间和方向关系。因此,本研究旨在探讨就寝拖延与焦虑之间症状层面关联的时间动态,研究青少年群体中随时间变化的个体内部和个体之间的差异。本研究利用基于面板数据的网络分析来检验个体内部效应(时间和同期网络)以及3296名青少年之间的个体动态。具体而言,我们使用横断面和时间网络分析来检验就寝拖延与焦虑的症状间关联,并评估症状中心性以识别症状动态的关键驱动因素。在个体内部层面,时间网络分析表明,坐立不安(广泛性焦虑障碍量表第5项)是随时间最稳定且最具预测性的节点。此外,紧张(广泛性焦虑障碍量表第1项)和比预期更晚睡觉(就寝拖延量表第1项)分别在T1→T2和T2→T3网络中对其他症状影响最为显著。在同期网络中,无法控制担忧(广泛性焦虑障碍量表第2项)、过度担忧(广泛性焦虑障碍量表第3项)和难以放松(广泛性焦虑障碍量表第4项)被确定为核心症状。在个体之间层面,始终观察到特定就寝拖延症状之间存在正相关关系。我们的研究结果阐明了就寝拖延与焦虑症状之间潜在的复杂相互作用,突出了在时间和同期网络中各不相同的核心症状。识别这些网络中的核心症状及其动态关联可为就寝拖延和焦虑背后的因果机制提供信息,从而指导针对青少年的有针对性干预措施的设计。