Association of problematic short-video use with adolescent depression: a cross-sectional study and machine learning identification model construction
Jiang Z, Kang T, Wu H
BMC psychiatry · 0 citations
How it was studied
- Design
- Cross-sectional study (indexed by PubMed)
- Studied in
- People
- Main outcome
- Health markers and function
Who paid for it
- Funding
- Independent funding
- Government
- National Natural Science Foundation of China
- Government
- National Postdoctoral Program for Innovative Talents
- Government
- National Key Research and Development Program of China
- Grants
- National Natural Science Foundation of China (82001313); National Postdoctoral Program for Innovative Talents (GZC20231941)
Based on 3 listed funder(s) and full-text disclosure statement.
Publication
- Published
- 2026-03-31 · BMC Psychiatry · vol. 26 · issue 1
- Publisher
- BioMed Central
- Cited
- 1 citation · more than 96% of similar papers · 8.9× the field average
- Impact
- Top 10% most cited in its field
- References
- 64 works
- Access
- Open access (journal) · CC-BY-NC-ND
- Research areas
- Child and Adolescent Psychosocial and Emotional Development · Digital Mental Health Interventions · Bullying, Victimization, and Aggression
- Keywords
- adolescent depression, short video platforms, depression screening, model interpretability, emotion regulation, non-suicidal self-injury, sleep quality, nonlinear dose-response relationship
- MeSH
- humans, cross-sectional studies, reproducibility of results, depression, adolescent, female, male, machine learning, internet addiction disorder, random forest, boosting machine learning algorithms, predictive learning models, classification algorithms
3 authors
From CN
- Zhihan JiangTongji University
- Tiejun KangTongji University; Northwest Normal University
- Heng Wu · correspondingTongji University
Abstract
BACKGROUND: Problematic short-video use (PSVU) is prevalent among adolescents and is increasingly recognized for its association with adverse mental health outcomes. This cross-sectional study aims to explore the association between PSVU and depression using SFPSVUS, and to develop an interpretable machine learning (ML) model for identifying adolescent depression. METHODS: A cross-sectional analysis was conducted on data from 4000 adolescent participants. Multivariate Logistic Regression and restricted cubic spline (RCS) methods were used to assess the association between PSVU and depression. Feature variables were identified via Boruta feature selection and validated using Shapley Additive Interpretation (SHAP) methods. Nine ML models, including XGBoost, support vector machine (SVM), and random forest (RF), were developed to identify adolescents with depression. RESULTS: A total of 3816 adolescents were included. The SFPSVUS demonstrated acceptable reliability (Cronbach’s α = 0.70) in the current sample. Multivariate Logistic Regression analysis revealed a significant positive correlation between PSVU and depression. RCS analysis indicated a significant nonlinear association between PSVU and adolescent depression. Six variables selected via Boruta feature selection were used to develop nine ML models, with the RF model demonstrating superior performance in identifying depression (AUC = 0.863) in the test set. PSVU emerged as an important feature in the model, alongside emotion regulation, non-suicidal self-injury (NSSI) and sleep quality. CONCLUSIONS: PSVU shows a significant positive association with adolescent depression and serves as an important feature in ML identification models. CLINICAL TRIAL NUMBER: Not applicable.
Abstract via Europe PMC. Copyright remains with the authors or publisher (CC BY-NC-ND).
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