Network analysis of autism traits and problematic mobile phone use and their associations with depression among Chinese college students

The current study employed network analysis to examine the relationship between symptoms from factor level about autism traits and problematic mobile phone use (PMPU) and to explore their associations with depression. We measured the above three variables in 949 college students in China with Autism...

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Bibliographic Details
Main Authors: Gang Liu, Ya Liu, Zongping Chen, Siyuan Zhou, Lingfei Ma
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Psychiatry
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Online Access:https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1521453/full
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Summary:The current study employed network analysis to examine the relationship between symptoms from factor level about autism traits and problematic mobile phone use (PMPU) and to explore their associations with depression. We measured the above three variables in 949 college students in China with Autism Spectrum Quotient (AQ), Smartphone Addiction Scale (SAS), Center for Epidemiological Studies Depression Scale (CES-D). Central and bridge symptoms were pinpointed through the examination of centrality index. In the AQ and PMPU network, results revealed that WD (“Withdrawal”), COR (“Cyberspace-oriented relationship”) and OU (“Overuse”) emerged as the core symptoms. AS (“Attention switching”), CO (“Communication”) and COR (“Cyberspace-oriented relationship”) were the most symptoms bridging the AQ and PMPU communities, suggesting that these symptoms could serve as focal points for interventions aimed at college students with concurrent autism traits and PMPU. SK (“Social skills”), COR (“Cyberspace-oriented relationship”), CO (“Communication”), and DLD (“Daily-life disturbance”) were most strongly associated with depression. In addition, future research should consider various measurement tools and methods to investigate the location of AD (“Attention to detail”), because AD was an isolated symptom in the flow network of depression.
ISSN:1664-0640