%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e66191
%T Transdiagnostic Compulsivity Traits in Problematic Use of the Internet Among UK Residents: Cross-Sectional Network Analysis Study
%A Liu,Chang
%A Chamberlain,Samuel
%A Ioannidis,Konstantinos
%A Tiego,Jeggan
%A Grant,Jon
%A Yücel,Murat
%A Hellyer,Peter
%A Lochner,Christine
%A Hampshire,Adam
%A Albertella,Lucy
%+ School of Psychological Sciences, Monash University, Level 5, 18 Innovation Walk, Wellington Road, Clayton, 3800, Australia, 61 07 33620222, chang.liu7@monash.edu
%K compulsivity
%K problematic use of the internet
%K network analysis
%K perfectionism
%K reward drive
%K cognitive rigidity
%K transdiagnostic
%K PUI
%K mental health
%K intrapersonal factor
%K cognitive
%K internet use
%K network
%D 2025
%7 26.3.2025
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: The societal and public health costs of problematic use of the internet (PUI) are increasingly recognized as a concern across all age groups, presenting a growing challenge for mental health research. International scientific initiatives have emphasized the need to explore the potential roles of personality features in PUI. Compulsivity is a key personality trait associated with PUI and has been recognized by experts as a critical factor that should be prioritized in PUI research. Given that compulsivity is a multidimensional construct and PUI encompasses diverse symptoms, different underlying mechanisms are likely involved. However, the specific relationships between compulsivity dimensions and PUI symptoms remain unclear, limiting our understanding of compulsivity’s role in PUI. Objective: This study aimed to clarify the unique relationships among different dimensions of compulsivity, namely, perfectionism, reward drive, cognitive rigidity, and symptoms of PUI using a symptom-based network approach. Methods: A regularized partial-correlation network was fitted using a large-scale sample from the United Kingdom. Bridge centrality analysis was conducted to identify bridge nodes within the network. Node predictability analysis was performed to assess the self-determination and controllability of the nodes within the network. Results: The sample comprised 122,345 individuals from the United Kingdom (51.4% female, age: mean 43.7, SD 16.5, range 9-86 years). The analysis identified several strong mechanistic relationships. The strongest positive intracluster edge was between reward drive and PUI4 (financial consequences due to internet use; weight=0.11). Meanwhile, the strongest negative intracluster edge was between perfectionism and PUI4 (financial consequences due to internet use; weight=0.04). Cognitive rigidity showed strong relationships with PUI2 (internet use for distress relief; weight=0.06) and PUI3 (internet use for loneliness or boredom; weight=0.07). Notably, reward drive (bridge expected influence=0.32) and cognitive rigidity (bridge expected influence=0.16) were identified as key bridge nodes, positively associated with PUI symptoms. Meanwhile, perfectionism exhibited a negative association with PUI symptoms (bridge expected influence=–0.05). The network’s overall mean predictability was 0.37, with PUI6 (compulsion, predictability=0.55) showing the highest predictability. Conclusions: The findings reveal distinct relationships between different dimensions of compulsivity and individual PUI symptoms, supporting the importance of choosing targeted interventions based on individual symptom profiles. In addition, the identified bridge nodes, reward drive, and cognitive rigidity may represent promising targets for PUI prevention and intervention and warrant further investigation. 
%M 40137076
%R 10.2196/66191
%U https://www.jmir.org/2025/1/e66191
%U https://doi.org/10.2196/66191
%U http://www.ncbi.nlm.nih.gov/pubmed/40137076