Differential relationships between problematic internet use and sleep problems

A network analysis

Bibliographic Data

ID21579505
AuthorsXinyu Wang (0009-0005-8004-6336, Tianjin Medical University), Yimiao Li (Tianjin Medical University), Yi-miao Li (0009-0008-1348-7360, Tianjin Medical University), Yong Liu (0000-0001-6741-4863, Tianjin Medical University), Nan Zhang (0000-0002-8032-275X, Department of Ophthalmology, Peking University People’s Hospital), Yixiao Ding (Shihezi University), Siyuan Fan (0000-0003-3922-8216, School of Public Health, Imperial College London), Zheng Tian (0000-0001-8204-539X, Capital Medical University), Yibo Wu (0000-0001-9607-313X, Zhejiang University, corresponding author), Lu Wang (0000-0002-9250-3988, Tianjin Medical University, corresponding author), Lan Wang (0000-0001-6843-476X, Tianjin Medical University)
Year2026
Pages1-19
Publication date2026-03-18
Open AccessNo
TypeARTICLE
VenuePsychology & Health (JOURNAL)
Journal identifiersISSN: 0887-0446 • E-ISSN: 1476-8321
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/08870446.2026.2643687
PMID41848289
OpenAlexW7138946168
LanguageEN
References cited42

OBJECTIVE: To construct symptom networks for problematic internet use (PIU) and sleep disturbances across gender and age groups in adults, identifying core symptoms and bridging symptoms to inform targeted interventions. METHODS: = 27,394). Problematic internet use (PIU) was assessed using the Problematic Internet Use Questionnaire-Short Form (PIUQ-SF6), and sleep quality with the Brief Pittsburgh Sleep Quality Index (B-PSQI). Regularised partial correlation networks were employed to estimate symptom relationships. Centrality and bridge centrality analyses identified core symptoms and bridging connectors. Network Comparison Tests (NCT) assessed structural differences across gender and age groups. RESULTS: In the network of relationships between PIU and B-PSQI across gender groups, both males and females exhibited the strongest positive correlation edges between B-PSQI-4 (sleep duration) and B-PSQI-5 (sleep disturbance). The most central node in each network differed: PIUQ-3 (inability to reduce use) was central in the female network, whereas PIUQ-6 (emotional dependence) was central in the male network. In the network of relationships between PIU and B-PSQI across age groups, the strongest positive correlation edge, PIUQ-2 (deprivation stress)-PIUQ-6, was identified for those aged 18-59 years old, and in which PIUQ-6 was identified as the central node. In contrast, individuals aged 60 and above exhibited the strongest positive correlation on edges B-PSQ-I4-B-PSQ-I5, with PIUQ-1 (sacrifice sleep) identified as the central node. Across gender and age groups, bedtime procrastination was identified as a bridging variable in the network. NCTs revealed that the strength of the network was significantly stronger in males, and that there were significant differences in network structure across gender and age groups. CONCLUSION: Tailored interventions are essential to extend sleep duration and curb pathological smartphone use across demographic groups, with emotional regulation strategies recommended for men, enhanced time management for women, and screen time reduction for older adults vulnerable to sleep problems

Psychological intervention · Sleep patterns · The Internet · Digital Mental Health Interventions · Impact of Technology on Adolescents · Sleep and related disorders

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