Yang He,1– 3 Tianqi Yang,2 Qingjun Guo,3 Shengjun Wu,2 Wei Liu,1 Tao Xu3 

1School of Psychology, Shanghai Normal University, Shanghai, 200234, People’s Republic of China; 2Department of Military Medical Psychology, Air Force Military Medical University, Xi’an, 710032, People’s Republic of China; 3Department of Psychology, Second Sanatorium of Air Force Hangzhou Special Recreation Centre, Hangzhou, 310007, People’s Republic of China

Correspondence: Wei Liu; Tao Xu, Email lwei0922@163.com; hangzhou310007@163.com

Background: 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.
Methods: 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).
Results: 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.
Conclusion: 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.

Keywords: network analysis, anxiety, sleep quality, college students, prevention