Assessing Short-Video Dependence for e-Mental Health: Development and Validation Study of the Short-Video Dependence Scale
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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 (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.
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Existing scales related to problematic short video use primarily measure symptoms and criteria of addiction, such as withdrawal, tolerance, and loss of control.
“Although scales addressing short video addiction16, problematic short-video use18, and short video dependence32 are prevalent in the literature, these instruments primarily measure the symptoms and criteria of addiction as applied to short video overuse, such as withdrawal, tolerance, and loss of control.”
From The binge scrolling scale measures excessive scrolling through a validated three-factor structure (2025) · cites this paper as [32]
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Cites
Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance
Cheung, G. W., & Rensvold, R. B. (2002) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1207/s15328007sem0902_5 · Cited by 16,280 · see citing works
Internet Addiction: The Emergence of a New Clinical Disorder
YOUNG, K. S. (1998) · CyberPsychology & Behavior · doi:10.1089/cpb.1998.1.237 · Cited by 5,513 · see citing works
Applied multivariate statistics for the social sciences
Stevens, J. P., & Stevens, J. P. (2001) · doi:10.4324/9781410604491 · Cited by 4,993 · see citing works
Motivational, emotional, and behavioral correlates of fear of missing out
Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013) · Computers in Human Behavior · doi:10.1016/j.chb.2013.02.014 · Cited by 3,932 · see citing works
The Perceived Competence Scale for Children
Harter, S. (1982) · Child Development · doi:10.2307/1129640 · Cited by 3,639 · see citing works
A “components” model of addiction within a biopsychosocial framework
Griffiths, M. (2005) · Journal of Substance Use · doi:10.1080/14659890500114359 · Cited by 3,088 · see citing works
The Work and Social Adjustment Scale: a simple measure of impairment in functioning
Mundt, J. C., Marks, I. M., Shear, M. K., & Greist, J. M. (2002) · British Journal of Psychiatry · doi:10.1192/bjp.180.5.461 · Cited by 2,515 · see citing works
Spurk, D., Hirschi, A., Wang, M., Valero, D., & Kauffeld, S. (2020) · Journal of Vocational Behavior · doi:10.1016/j.jvb.2020.103445 · Cited by 2,119 · see citing works
Development of a Facebook Addiction Scale
Andreassen, C. S., Torsheim, T., Brunborg, G. S., & Pallesen, S. (2012) · Psychological Reports · doi:10.2466/02.09.18.pr0.110.2.501-517 · Cited by 1,884 · see citing works
Flexible Coding of In-depth Interviews: A Twenty-first-century Approach
Deterding, N. M., & Waters, M. C. (2021) · Sociological Methods & Research · doi:10.1177/0049124118799377 · Cited by 1,626 · see citing works
Latent Class and Latent Transition Analysis
Collins, L. M., & Lanza, S. T. (2009) · Wiley series in probability and statistics · doi:10.1002/9780470567333 · Cited by 1,557 · see citing works
The pursuit of quality in grounded theory
Charmaz, K., & Thornberg, R. (2021) · Qualitative Research in Psychology · doi:10.1080/14780887.2020.1780357 · Cited by 1,221 · see citing works
Grounded Theory: An Exploration of Process and Procedure
Walker, D., & Myrick, F. (2006) · Qualitative Health Research · doi:10.1177/1049732305285972 · Cited by 1,146 · see citing works
The compulsive internet use scale (CIUS): some psychometric properties
Meerkerk, G.-J., Van Den Eijnden, R. J. J. M., Vermulst, A. A., & Garretsen, H. F. L. (2009) · CyberPsychology & Behavior · doi:10.1089/cpb.2008.0181 · Cited by 989 · see citing works
The social media disorder scale
van den Eijnden, R. J. J. M., Lemmens, J. S., & Valkenburg, P. M. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.03.038 · Cited by 948 · see citing works
Qualitative interviews in psychology: problems and possibilities
Potter, J., & Hepburn, A. (2005) · Qualitative Research in Psychology · doi:10.1191/1478088705qp045oa · Cited by 823 · see citing works
The Internet Gaming Disorder Scale.
Lemmens, J. S., Valkenburg, P. M., & Gentile, D. A. (2015) · Psychological Assessment · doi:10.1037/pas0000062 · Cited by 742 · see citing works
Leung, L. (2008) · Journal of Children and Media · doi:10.1080/17482790802078565 · Cited by 733 · see citing works
Paradigms, praxis, problems, and promise: Grounded theory in counseling psychology research.
Fassinger, R. E. (2005) · Journal of Counseling Psychology · doi:10.1037/0022-0167.52.2.156 · Cited by 643 · see citing works
Evaluating Model Fit of Measurement Models in Confirmatory Factor Analysis
Goretzko, D., Siemund, K., & Sterner, P. (2023) · Educational and Psychological Measurement · doi:10.1177/00131644231163813 · Cited by 577 · see citing works
On the psychology of TikTok use: a first glimpse from empirical findings
Montag, C., Yang, H., & Elhai, J. D. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.641673 · Cited by 559 · see citing works
Hanaysha, J. R. (2022) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2022.100102 · Cited by 548 · see citing works
Internet addiction in students: Prevalence and risk factors
Kuss, D. J., Griffiths, M. D., & Binder, J. F. (2013) · Computers in Human Behavior · doi:10.1016/j.chb.2012.12.024 · Cited by 540 · see citing works
Structural Equation Modeling with AMOS
(2013) · SAGE Publications Ltd eBooks · doi:10.4135/9781526402257.n4 · Cited by 484 · see citing works
Measuring problem video game playing in adolescents
Tejeiro, R. A., & Bersabé, R. M. (2002) · Addiction · doi:10.1046/j.1360-0443.2002.00218.x · Cited by 473 · see citing works
Exploring short-form video application addiction: socio-technical and attachment perspectives
Zhang, X., Wu, Y., & Liu, S. (2019) · Telematics and Informatics · doi:10.1016/j.tele.2019.101243 · Cited by 469 · see citing works
Epidemiology of Internet Behaviors and Addiction Among Adolescents in Six Asian Countries
Mak, K.-K., Lai, C.-M., Watanabe, H., Kim, D.-I., Bahar, N., Ramos, M. S., Young, K., Ho, R. C. M., Aum, N.-R., & Cheng, C. (2014) · Cyberpsychology, Behavior, and Social Networking · doi:10.1089/cyber.2014.0139 · Cited by 456 · see citing works
A meta-analysis of the effects of teacher personality on teacher effectiveness and burnout
Kim, L. E., Jörg, V., & Klassen, R. M. (2019) · Educational Psychology Review · doi:10.1007/s10648-018-9458-2 · Cited by 406 · see citing works
Conducting in-depth interviews with and without voice recorders: a comparative analysis
Rutakumwa, R., Mugisha, J. O., Bernays, S., Kabunga, E., Tumwekwase, G., Mbonye, M., & Seeley, J. (2020) · Qualitative Research · doi:10.1177/1468794119884806 · Cited by 378 · see citing works
Hoyle, R. H. (2000) · Elsevier eBooks · doi:10.1016/b978-012691360-6/50017-3 · Cited by 346 · see citing works
Stein, C. M., Morris, N. J., & Nock, N. L. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-555-8_27 · Cited by 271 · see citing works
Domoff, S. E., Harrison, K., Gearhardt, A. N., Gentile, D. A., Lumeng, J. C., & Miller, A. L. (2019) · Psychology of Popular Media Culture · doi:10.1037/ppm0000163 · Cited by 264 · see citing works
Qin, Y., Omar, B., & Musetti, A. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.932805 · Cited by 237 · see citing works
Ye, J.-H., Wu, Y.-T., Wu, Y.-F., Chen, M.-Y., & Ye, J.-N. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.847672 · Cited by 221 · cited 1× · see citing works
“Short-video apps also raised public concerns about their potential negative consequences, such as distraction, poor time management, and mental health problems [1,4,5].”
Latent Class Models in Diagnostic Studies When There is No Reference Standard--A Systematic Review
van Smeden, M., Naaktgeboren, C. A., Reitsma, J. B., Moons, K. G. M., & de Groot, J. A. H. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kwt286 · Cited by 217 · see citing works
Jo, Y. S., Bhang, S., Choi, J. S., Lee, H., Lee, S., & Kweon, Y. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8070945 · Cited by 214 · see citing works
Luo, T., Qin, L., Cheng, L., Wang, S., Zhu, Z., Xu, J., Chen, H., Liu, Q., Hu, M., Tong, J., et al. (2021) · Journal of Behavioral Addictions · doi:10.1556/2006.2021.00025 · Cited by 170 · see citing works
Self-citationMOBILE PHONE DEPENDENCE AND HEALTH-RELATED LIFESTYLE OF UNIVERSITY STUDENTS
Toda, M., Monden, K., Kubo, K., & Morimoto, K. (2006) · Social Behavior and Personality An International Journal · doi:10.2224/sbp.2006.34.10.1277 · Cited by 166 · see citing works
Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations
Sürücü, L., Yıkılmaz, İ., & Maşlakçı, A. (2024) · Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi · doi:10.37989/gumussagbil.1183271 · Cited by 163 · see citing works
Montoya, A. K., & Edwards, M. C. (2020) · Educational and Psychological Measurement · doi:10.1177/0013164420942899 · Cited by 153 · see citing works
How to Read and Really Use an Item Analysis
McGahee, T. W., & Ball, J. (2009) · Nurse Educator · doi:10.1097/nne.0b013e3181aaba94 · Cited by 126 · see citing works
Shen, Y., Wang, L., Huang, C., Guo, J., Leon, S. A. D., Lu, J., Luo, X., & Zhang, X. Y. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.10.054 · Cited by 84 · see citing works
Bertocchi, F., de Oliveira, A. C., Lucchetti, G., & Lucchetti, A. L. G. (2022) · Journal of Medical Systems · doi:10.1007/s10916-022-01839-7 · Cited by 72 · see citing works
Development of a Problematic Mobile Phone Use Scale for Turkish Adolescents
Güzeller, C. O., & Coşguner, T. (2012) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2011.0210 · Cited by 71 · see citing works
Weber, W., Reinhardt, A., & Rossmann, C. (2020) · Journal of Medical Internet Research · doi:10.2196/15099 · Cited by 67 · see citing works
Bai, C., Chen, X., & Han, K. (2020) · Children and Youth Services Review · doi:10.1016/j.childyouth.2020.105406 · Cited by 66 · see citing works
Yang, J., Ti, Y., & Ye, Y. (2022) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2021.0323 · Cited by 63 · see citing works
Li, J., Wu, A. M. S., Feng, L.-F., Deng, Y., Li, J., Yu-xia, C., Mai, J.-C., Mo, P. K. H., & Lau, J. T. F. (2020) · Journal of Behavioral Addictions · doi:10.1556/2006.2020.00047 · Cited by 61 · see citing works
Social Networking Addiction Scale
Shahnawaz, M., & Rehman, U. (2020) · Cogent Psychology · doi:10.1080/23311908.2020.1832032 · Cited by 61 · see citing works
Social Interactions in Online Gaming
Griffiths, M. D., Hussain, Z., Grüsser, S. M., Thalemann, R., Cole, H., Davies, M. N. O., & Chappell, D. (2011) · International Journal of Game-Based Learning · doi:10.4018/ijgbl.2011100103 · Cited by 50 · see citing works
Peng, P., Chen, Z., Ren, S., Liu, Y., He, R., Liang, Y., Tan, Y., Tang, J., Chen, X., & Liao, Y. (2023) · BMC Psychiatry · doi:10.1186/s12888-023-05170-4 · Cited by 44 · see citing works
Internet use disorders: What's new and what's not?. •
Griffiths, M. D. (2020) · Journal of Behavioral Addictions · doi:10.1556/2006.2020.00072 · Cited by 27 · see citing works
Define and characterize the anhedonia in major depressive disorder: An explorative study
Lin, J., Su, Y., Rizvi, S. J., Jagoda, J., Li, J., Wu, Y., Dai, Y., Zhang, Y., Kennedy, S. H., & Si, T. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.06.082 · Cited by 26 · see citing works
Validity of Polygraph Techniques and Decision Methods
Raskin, D. C., & Kircher, J. C. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-394433-7.00003-8 · Cited by 22 · see citing works
Identifying psychiatric suicides for research purposes
Evenson, R. C., Cho, D. W., & Holland, R. (1988) · Journal of Clinical Psychology · doi:10.1002/1097-4679(198811)44:6 · Cited by 13 · see citing works
Petrović, Z. K., Peraica, T., Blažev, M., & Kozarić‐Kovačić, D. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1227182 · Cited by 5 · see citing works
(2004) · PsycTESTS Dataset · doi:10.1037/t04058-000 · Cited by 4 · see citing works
Blandon, J. A., Puerta-Cortés, D. X., Carmona, A., Ivern, J., Vilca, L. W., Carbonell, X., & Caycho‐Rodríguez, T. (2024) · International Journal of Mental Health and Addiction · doi:10.1007/s11469-024-01376-z · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- [2]Agrawal E Going Viral: An Analysis of Advertising of Technology Products on TikTok arXiv 2025-02-05 https://arxiv.org/abs/2402.00010 · arXiv
- [11]Li T Liu H Liu X Interdisciplinary Insights into Short Video Addiction: Integrating Psychological and Brain imaging Approaches in a Comprehensive Review Preprints 2024 2025-02-05 https://www.preprints.org/manuscript/202405.0824/v1
- [18]Rutledge P Hogg J Bulck J In-Depth Interviews The International Encyclopedia of Media Psychology 2020 Hoboken, NJ Wiley
- [26]Kırcaburun K Effects of gender and personality differences on twitter addiction among Turkish undergraduates Journal of Education and Practice 2016 7 24 33 42
- [34]Hox JJ Barnes JC Forde DR Confirmatory Factor Analysis The Encyclopedia of Research Methods in Criminology and Criminal Justice 2021 Hoboken, NJ Wiley
Cited by
Sorted by citations
Guo, F., Ding, G., Zhang, Y., & Liu, X. (2025) · JMIR Cancer · doi:10.2196/73455 · Cited by 28 · see citing works
Association Between Short-Form Video Use and Mental Health: Systematic Review and Meta-Analysis
Tang, D., Zhang, X., Gou, P., Feng, J., Hu, R., & Sum, R. K. W. (2026) · Journal of Medical Internet Research · doi:10.2196/82503 · Cited by 14 · see citing works
From active users to passive watchers: Profiles of TikTok engagement and mental health predictors
Chen, J., Yao, N., & Elhai, J. D. (2025) · Addictive Behaviors · doi:10.1016/j.addbeh.2025.108552 · Cited by 5 · see citing works
Zhang, J., Ren, L., Wu, Y., Shi, Y., & Liu, J. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1763608 · Cited by 4 · see citing works
The binge scrolling scale measures excessive scrolling through a validated three-factor structure
Savci, M., Savci, H., Ugur, E., Zincirli, M., & Elhai, J. D. (2025) · Scientific Reports · doi:10.1038/s41598-025-31861-x · Cited by 2 · see citing works
“In addition, social desirability bias has been considered a crucial limitation in studies on problematic short video use 23,30,32 .”
Taming the endless scroll? Short-form videos, digital routines and neurocognitive outcomes in youth
Ebster, M., Lauerer, M., Nagel, E., & Schmidt, S. (2026) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-026-03083-7 · Cited by 3 · see citing works
Liu, J., Ren, L., & Zhang, J. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-8420-8_31
Learning via short videos impairs memory accuracy and reduces brain synchrony
Wei, M., Li, Y., Ni, H., 李正隆, Liu, J., & Dong, G.-H. (2026) · Communications Psychology · doi:10.1038/s44271-026-00476-x · Cited by 1 · see citing works
Self-citationVideos cortos de YouTube sobre anorexia: malas prácticas informativas
Aguado-Guadalupe, G., & Bernaola, I. (2026) · Prisma social · doi:10.65598/rps.6069
ATENÇÃO, USO AUTOMÁTICO E CONSUMO DE VÍDEOS CURTOS EM ADULTOS: Um estudo exploratório
Ramos, J. V. C., & de Almeida, S. S. M. (2026) · Psicologia e Saúde em Debate · doi:10.22289/2446-922x.v12a2a9
Liu, Z., Li, M., Liu, Y., Pan, Y., Liu, W., & Hu, B. (2026) · Nursing Open · doi:10.1002/nop2.70879
Self-citationZhang, L., Gelin, H., & Jiang, X. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1855722
Yang, Y., & Zhang, Y. (2026) · Behavioral Sciences · doi:10.3390/bs16101893
- Authors
- Jiang, A., Li, S., Wang, H., Ni, H., Chen, H., Dai, J., Xu, X., Li, M., & Dong, G.-H.
- Affiliations
- Affiliated Hospital of Hangzhou Normal University (CN)
- Corresponding author
- Li, Mei
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2025-03-04
- Volume
- 27
- Pages
- e66341
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC11920665
- OpenAlex
- W4406923357
- Cited by
- 13
- References
- 44
- Citations by year
- 2026: 10 · 2025: 3
- FWCI
- 22.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Hangzhou Normal University (20JYXK008)
- Natural Science Foundation of Zhejiang Province (LY20C090005)
- Topics
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Social Sciences › Psychology › Clinical Psychology › Gambling Behavior and Treatments
- Social Sciences › Psychology › Clinical Psychology › Sexuality, Behavior, and Technology
- Keywords
- behavioral addiction, e-mental health, scale development, psychometric validation, diagnostic thresholds, latent profile analysis, internet addiction
- MeSH terms
- Internet Addiction Disorder, Adolescent, Adult, Factor Analysis, Statistical, Female, Humans, Male, Mental Health, Middle Aged, Psychometrics, Surveys and Questionnaires, Reproducibility of Results, Behavior, Addictive, Internet, Young Adult
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