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<p>The increased prevalence of loneliness and depression among adolescent and young adults is attributed to social media use, academic pressures, and future worries.&#xa0;This study aimed to model the relationship between loneliness and depressive symptoms in adolescents and investigate their dynamic interplay using cross-lagged panel analysis.&#xa0;A total of 649 secondary school students participated in our study. This study evaluated loneliness and depressive symptoms using an online questionnaire.&#xa0;This study constructed a network model to identify the core and bridge symptoms of loneliness using network analysis and employed a cross-lagged panel network model to investigate the dynamic relationship between loneliness and depressive symptoms.&#xa0;At both survey time points, the bridge symptom was U11 (T1: Strength = 1.68; T2: Strength = 1.46). The symptom with the highest influence on others was U20 (OEI = 1.04), while the symptom most influenced by others was U14 (IEI = 0.73).&#xa0;Timely interventions for adolescent loneliness can prevent depression and mitigate its severity in affected individuals.</p>

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The Relationship between Loneliness and Depression Symptoms in Adolescents: Network Analysis and Cross-lag Panel Model

  • 期刊论文

  • 发表日期：

    2025年10月15日

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  • Weiwei Lan,
  • Xinyuan Duan,
  • Siping Lin,
  • Yueting Huang,
  • Zhicheng Zhong,
  • Xinling Liang,
  • Jianglu Zhao,
  • Yunfei Cai,
  • Hong Cai,
  • Li Su

摘要

The increased prevalence of loneliness and depression among adolescent and young adults is attributed to social media use, academic pressures, and future worries. This study aimed to model the relationship between loneliness and depressive symptoms in adolescents and investigate their dynamic interplay using cross-lagged panel analysis. A total of 649 secondary school students participated in our study. This study evaluated loneliness and depressive symptoms using an online questionnaire. This study constructed a network model to identify the core and bridge symptoms of loneliness using network analysis and employed a cross-lagged panel network model to investigate the dynamic relationship between loneliness and depressive symptoms. At both survey time points, the bridge symptom was U11 (T1: Strength = 1.68; T2: Strength = 1.46). The symptom with the highest influence on others was U20 (OEI = 1.04), while the symptom most influenced by others was U14 (IEI = 0.73). Timely interventions for adolescent loneliness can prevent depression and mitigate its severity in affected individuals.