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<p>This cross-sectional study examined the role of sleep quality and its individual components in the association between social network addiction and depressive symptoms among 746 university students (20.81 ± 3.99 years; 68.9% female). Validated questionnaires were used to assess social network addiction (Social Networks Addiction-6 brief version), depressive symptoms (Beck Depression Inventory-II), and sleep quality (Pittsburgh Sleep Quality Index). Sociodemographic, body composition, and lifestyle data were assessed as covariates. Statistical analysis included multivariate linear regression,&#xa0;logistic regression and structural equation models adjusted for major confounders. The results showed that sleep quality accounted for 23.9% of the total association between social network addiction and depressive symptoms (indirect association = 0.104, 95% CI: 0.027–0.184, <i>p-</i>value &lt; 0.05). Among sleep quality components, daytime dysfunction due to inadequate sleep accounted for 31.2%, whereas sleep latency and use of sleep medication accounted for 10.1% and 11.3%, respectively (<i>p</i>-value &lt; 0.05 for all). Future prospective studies should examine whether interventions targeting sleep quality may represent a potential strategy for reducing depressive symptoms associated with SNA in this population.</p>

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The role of sleep in the association between social network addiction and depressive symptoms in university students: a cross-sectional study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月1日

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  • Tomás Olivo-Martins-de-Passos,
  • Arthur E. Mesas,
  • Nuria Beneit,
  • Valentina Díaz-Goñi,
  • Fernando Peral-Martínez,
  • Shkelzen Cekrezi,
  • José Ignacio Recio-Rodríguez,
  • Vicente Martínez-Vizcaíno,
  • Bruno Bizzozero-Peroni,
  • Estela Jiménez-López

摘要

This cross-sectional study examined the role of sleep quality and its individual components in the association between social network addiction and depressive symptoms among 746 university students (20.81 ± 3.99 years; 68.9% female). Validated questionnaires were used to assess social network addiction (Social Networks Addiction-6 brief version), depressive symptoms (Beck Depression Inventory-II), and sleep quality (Pittsburgh Sleep Quality Index). Sociodemographic, body composition, and lifestyle data were assessed as covariates. Statistical analysis included multivariate linear regression, logistic regression and structural equation models adjusted for major confounders. The results showed that sleep quality accounted for 23.9% of the total association between social network addiction and depressive symptoms (indirect association = 0.104, 95% CI: 0.027–0.184, p-value < 0.05). Among sleep quality components, daytime dysfunction due to inadequate sleep accounted for 31.2%, whereas sleep latency and use of sleep medication accounted for 10.1% and 11.3%, respectively (p-value < 0.05 for all). Future prospective studies should examine whether interventions targeting sleep quality may represent a potential strategy for reducing depressive symptoms associated with SNA in this population.