AUTHOR=A Li-Ya , Chen Meng-Yi , Jiang Yuan-Yuan , Huang Hui-Ting , Liu Shou , Feng Yuan , Zhang Xiao-Li , Su Zhaohui , Cheung Teris , Ng Chee H. , Xiang Yu-Tao , Wang Gang 

TITLE=Internet addiction and its association with quality of life in college students: a network perspective

JOURNAL=Frontiers in Psychiatry

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1555372

DOI=10.3389/fpsyt.2025.1555372

ISSN=1664-0640

ABSTRACT=BackgroundInternet addiction (IA), especially in young people, has gained increasing attention in recent years. This study examined the prevalence of IA and its associated factors, relationship with quality of life (QoL) and network structure among college students.MethodsA cross-sectional study was conducted between September and December 2023 in China. Internet addiction symptoms were assessed using the Internet Addiction Test (IAT). Univariate and multivariate analyses were performed to explore the correlates of IA. The relationship between IA and QoL was examined using analysis of covariance (ANCOVA). The Expected Influence (EI) centrality index was used in the network model to characterize the structure of IA symptoms.ResultsA total of 6,514 college students were included. The prevalence of IA was 27.9% [95% confidence interval (CI): 26.8%-29.0%]. A binary logistic regression analysis indicated that living in urban areas (OR=1.135, P=0.032), being in senior grade (OR=1.396, P=0.017), and having current drinking (OR=1.431, P<0.001) were associated with increased risk of IA, while having a major in health (OR=0.796, P<0.001), good health status (OR=0.516, P<0.001), good economic status (OR=0.607, P<0.001), and regular physical exercise (OR=0.727, P<0.001) were associated with reduced risk of IA. ANCOVA revealed that college students with IA had lower QoL score (F (1, 6514) = 128.167, P < 0.001). The most central (influential) symptoms were “Academic efficiency declines” (IAT8, EI value=1.10), “Request an extension for longer time” (IAT16, EI value=1.10) and “Neglect chores to spend more time online” (IAT2, EI value=1.00) in the network model of IA symptoms. The symptom “Form new relationships with online users” (IAT4) had the strongest direct positive relationship with QoL, while “Sleep loss” (IAT14) and “Prefer the excitement online to the time with others” (IAT3) had the strongest direct negative relationship with QoL.ConclusionInternet addiction was common among Chinese college students. Interventions targeting the most central symptoms and those closely associated with QoL should be developed to address IA in college students and improve the QoL of those with IA in this population.