中国大学生手机成瘾与睡眠障碍症状的网络分析

摘要(中文译文)

本研究旨在考察中国大学生手机成瘾与睡眠障碍之间的共病关系。通过构建共病网络、识别核心症状与桥梁症状，并探索症状之间潜在的方向性关联，本研究拟为针对性干预策略奠定理论基础。采用便利抽样法共招募1842名中国大学生。分别使用手机成瘾量表简版（SAS-SV）和匹兹堡睡眠质量指数（PSQI）评估手机成瘾症状和睡眠障碍症状。数据分析分三步进行。首先，采用高斯图模型（GGM）构建无向共病网络，以识别核心症状和桥梁症状。其次，采用贝叶斯网络方法生成有向无环图（DAGs），以探索症状之间潜在的方向性关联。最后，进行网络比较检验和社区检测分析，以考察共病网络结构中的性别差异。GGM共病网络的连接密度为0.80，全局强度为9.39。在该网络中，PSQI2（入睡潜伏期）、SA2（难以集中注意力）和SA5（无手机时烦躁不安）被识别为核心症状。PSQI2（入睡潜伏期）、PSQI1（主观睡眠质量）和SA9（使用时间超过预期）被识别为桥梁症状。使用DAGs的进一步分析提示，睡眠障碍症状对手机成瘾症状存在统计学方向性。值得注意的是，SA5（无手机时烦躁不安）在DAGs中作为起始节点。最后，网络比较检验表明，GGM网络结构在性别间无显著差异；然而，在症状的社区聚类模式中观察到了明显的性别差异。在大学生中，手机成瘾与睡眠障碍症状相互作用，形成一个结构稳定的共病网络。因此，针对核心症状、桥梁症状和起始节点的干预可有效中断该共病的维持。

Abstract

This study aims to examine the comorbid relationship between smartphone addiction and sleep disorders in Chinese college students. By constructing a comorbidity network, identifying core and bridge symptoms, and exploring potential directional associations among symptoms, this research intends to establish a theoretical foundation for targeted intervention strategies. A total of 1842 Chinese college students were recruited through convenience sampling. The smartphone addiction and sleep disorder symptoms were assessed using the Smartphone Addiction Scale-Short Version (SAS-SV) and the Pittsburgh Sleep Quality Index (PSQI), respectively. The data analysis was conducted in three steps. First, an undirected comorbidity network was constructed using the Gaussian Graphical Model (GGM) to identify core and bridge symptoms. Second, a Bayesian network approach was employed to generate Directed Acyclic Graphs (DAGs) that explored potential directional associations among symptoms. Finally, network comparison tests and community detection analyses were performed to examine gender differences in the comorbidity network structure. The GGM comorbidity network exhibited a connection density of 0.80 and a global strength of 9.39. Within this network, PSQI2 (sleep latency), SA2 (difficulty concentrating), and SA5 (impatience without phone) were identified as core symptoms. PSQI2 (sleep latency), PSQI1 (subjective sleep quality), and SA9 (longer use than intended) were identified as bridge symptoms. Further analysis using the DAGs suggested statistical directionality from sleep disorder symptoms toward smartphone addiction symptoms. Notably, SA5 (impatience without phone) served as an initial node in the DAGs. Finally, network comparison tests indicated no significant differences in the GGM network structure between genders; however, distinct gender differences were observed in the community clustering patterns of symptoms. In college students, smartphone addiction and sleep disorder symptoms interact to form a structurally stable comorbidity network. Consequently, interventions targeting core symptoms, bridge symptoms, and initial node could effectively interrupt the maintenance of this comorbidity.

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