He Yang, Yang Tianqi, Guo Qingjun, Wu Shengjun, Liu Wei, Xu Tao
School of Psychology, Shanghai Normal University, Shanghai, 200234, People's Republic of China.
Department of Military Medical Psychology, Air Force Military Medical University, Xi'an, 710032, People's Republic of China.
Psychol Res Behav Manag. 2025 Mar 13;18:607-618. doi: 10.2147/PRBM.S507074. eCollection 2025.
A complex interplay exists between anxiety and sleep quality. However, there is a scarcity of network analysis studies examining this relationship, particularly among college students. Previous research has relied on sum scores from scales, which fails to capture the nuanced, symptom-level associations between anxiety and sleep quality. This limitation impedes a comprehensive understanding of their interactions. Thus, the objective of this study was to address this research gap by employing network analysis to explore symptom-level associations between anxiety and sleep quality within a college student population.
Network analysis was conducted to explore the association between anxiety and sleep quality among college students and identify bridge items of anxiety and sleep quality. Anxiety was assessed via the Self-Rating Anxiety Scale (SAS), and sleep quality was assessed via the Pittsburgh Sleep Quality Index (PSQI).
The network structure revealed 47 significant associations between anxiety and sleep quality. "Subjective sleep quality", "daytime dysfunction", "panic", "dizziness", "fatigue" and "sleep disorder" had higher EI values in the network. "fatigue" and "daytime dysfunction" had the highest BEI values in their respective communities.
From a network analysis perspective, this study identified complex pathways of pathological correlations between anxiety and sleep quality among college students. It also identified "subjective sleep quality", "daytime dysfunction", "panic", and "dizziness", "fatigue" and 'sleep disturbance' may be potential targets for intervention in anxiety-sleep disorder comorbidity. In the future, psychologists and medical professionals may adopt appropriate interventions based on the centrality index and bridging centrality indicators identified in this study to effectively reduce the comorbidity of anxiety and sleep disorders in college students.
焦虑与睡眠质量之间存在复杂的相互作用。然而，缺乏关于这种关系的网络分析研究，尤其是在大学生中。以往的研究依赖于量表的总分，这无法捕捉焦虑与睡眠质量之间细微的症状水平关联。这一局限性阻碍了对它们相互作用的全面理解。因此，本研究的目的是通过采用网络分析来探索大学生群体中焦虑与睡眠质量之间的症状水平关联，以填补这一研究空白。
进行网络分析以探索大学生焦虑与睡眠质量之间的关联，并确定焦虑和睡眠质量的桥梁项目。通过自评焦虑量表（SAS）评估焦虑，通过匹兹堡睡眠质量指数（PSQI）评估睡眠质量。
网络结构显示焦虑与睡眠质量之间有47个显著关联。“主观睡眠质量”“日间功能障碍”“惊恐”“头晕”“疲劳”和“睡眠障碍”在网络中具有较高的EI值。“疲劳”和“日间功能障碍”在各自的群落中具有最高的BEI值。
从网络分析的角度来看，本研究确定了大学生焦虑与睡眠质量之间病理相关性的复杂途径。还确定了“主观睡眠质量”“日间功能障碍”“惊恐”以及“头晕”“疲劳”和“睡眠障碍”可能是焦虑 - 睡眠障碍共病干预的潜在靶点。未来，心理学家和医学专业人员可根据本研究确定的中心性指数和桥接中心性指标采取适当干预措施，以有效降低大学生焦虑与睡眠障碍的共病率。
