@Article{ijmhp.2025.064385,
AUTHOR = {Lin Luo, Junfeng Yuan, Yanling Wang, Rui Zhu, Huilin Xu, Siyuan Bi, Zhongge Zhang},
TITLE = {Possible Classifications of Social Network Addiction: A Latent Profile Analysis of Chinese College Students},
JOURNAL = {International Journal of Mental Health Promotion},
VOLUME = {27},
YEAR = {2025},
NUMBER = {6},
PAGES = {863--876},
URL = {http://www.techscience.com/IJMHP/v27n6/62851},
ISSN = {2049-8543},
ABSTRACT = { <b>Objectives:</b> Social Network Addiction (SNA) is becoming increasingly prevalent among college students; however, there remains a lack of consensus regarding the measurement tools and their optimal cutoff score. This study aims to validate the 21-item Social Network Addiction Scale-Chinese (SNAS-C) in its Chinese version and to determine its optimal cutoff score for identifying potential SNA cases within the college student population. <b>Methods:</b> A cross-sectional survey was conducted, recruiting 3387 college students. Latent profile analysis (LPA) and receiver operating characteristic (ROC) curve analysis were employed to establish the optimal cutoff score for the validated 21-item SNAS-C. <b>Results:</b> Three profile models were selected based on multiple statistical criteria, classifying participants into low-risk, moderate-risk, and high-risk groups. The highest-risk group was defined as “positive” for SNA, while the remaining groups were considered “negative”, serving as the reference standard for ROC analysis. The optimal cutoff score was determined to be 72 (sensitivity: 98.2%, specificity: 96.86%), with an overall classification accuracy of 97.0%. The “positive” group reported significantly higher frequency of social network usage, greater digital media dependence scores, and a higher incidence of network addiction. <b>Conclusion:</b> This study identified the optimal cutoff score for the SNAS-C as ≥72, demonstrating high sensitivity, specificity, and diagnostic accuracy. This threshold effectively distinguishes between high-risk and low-risk SNA.},
DOI = {10.32604/ijmhp.2025.064385}
}