%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e66341
%T Assessing Short-Video Dependence for e-Mental Health: Development and Validation Study of the Short-Video Dependence Scale
%A Jiang,AnHang
%A Li,Shuang
%A Wang,HuaBin
%A Ni,HaoSen
%A Chen,HongAn
%A Dai,JunHong
%A Xu,XueFeng
%A Li,Mei
%A Dong,Guang-Heng
%+ Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, No. 2318, Yuhangtang Rd, Yuhang District, Hangzhou, 311121, China, 86 15968849698, maylee530@126.com
%K short-video dependence
%K problematic short-video use
%K cutoff point
%K scale development
%K mental health
%K short video
%K internet addiction
%K latent profile analysis
%K exploratory factor analysis
%K confirmatory factor analysis
%D 2025
%7 4.3.2025
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: Short-video dependence (SVD) has become a significant mental health issue around the world. The lack of scientific tools to assess SVD hampers further advancement in this area. Objective: This study aims to develop and validate a scientific tool to measure SVD levels, ensuring a scientifically determined cutoff point. Methods: We initially interviewed 115 highly engaged short-video users aged 15 to 63 years. Based on the summary of the interview and references to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for behavioral addictions, we proposed the first version of the short-video dependence scale (SVDS). We then screened the items through item analysis (second version) and extracted common factors using exploratory factor analysis (third version) and confirmatory factor analysis (final version). Convergent validity was tested with other scales (Chinese Internet Addiction Scale [CIAS] and DSM-5). Finally, we tested the validity of the final version in 16,038 subjects and set the diagnostic cutoff point through latent profile analysis and receiver operating characteristic curve analysis. Results: The final version of the SVDS contained 20 items and 4 dimensions, which showed strong structural validity (Kaiser-Meyer-Olkin value=0.94) and internal consistency (Cronbach α=.93), and good convergent validity (rCIAS=0.61 and rDSM-5=0.68), sensitivity (0.77, 0.83, 0.87, and 0.62 for each of the 4 dimensions), and specificity (0.75, 0.87, 0.80, and 0.79 for each of the 4 dimensions). Additionally, an SVDS score of 58 was determined as the best cutoff score, and latent profile analysis identified a 5-class model for SVD. Conclusions: We developed a tool to measure SVD levels and established a threshold to differentiate dependent users from highly engaged nondependent users. The findings provide opportunities for further research on the impacts of short-video use. 
%M 40053762
%R 10.2196/66341
%U https://www.jmir.org/2025/1/e66341
%U https://doi.org/10.2196/66341
%U http://www.ncbi.nlm.nih.gov/pubmed/40053762