Assessing Short-Video Dependence for e-Mental Health: Development and Validation Study of the Short-Video Dependence Scale.

IF 5.8 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research Pub Date : 2025-03-04 DOI:10.2196/66341

AnHang Jiang, Shuang Li, HuaBin Wang, HaoSen Ni, HongAn Chen, JunHong Dai, XueFeng Xu, Mei Li, Guang-Heng Dong

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引用次数: 0

Abstract

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 (r_CIAS=0.61 and r_DSM-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.

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来源期刊

Journal of Medical Internet Research 医学-卫生保健

CiteScore

14.40

自引率

5.40%

发文量

654

审稿时长

1 months

期刊介绍： The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.