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 citas
Cómo se estudió
- Diseño
- Estudio transversal (indexado por PubMed)
- Estudiado en
- Personas
- Resultado principal
- Marcadores de salud y funcionamiento
Quién lo pagó
- Financiación
- Financiación independiente
- Gobierno
- National Natural Science Foundation of China
- Gobierno
- National Postdoctoral Program for Innovative Talents
- Gobierno
- National Key Research and Development Program of China
- Subvenciones
- National Natural Science Foundation of China (82001313); National Postdoctoral Program for Innovative Talents (GZC20231941)
Según 3 financiadores declarados y la declaración de financiación del texto completo.
Publicación
- Publicado
- 2026-03-31 · BMC Psychiatry · vol. 26 · número 1
- Editorial
- BioMed Central
- Citado
- 1 cita · más que el 96% de estudios similares · 8.9× el promedio de su campo
- Impacto
- Entre el 10 % más citado de su campo
- Referencias
- 64 trabajos
- Acceso
- Acceso abierto (revista) · CC-BY-NC-ND
- Áreas de investigación
- Child and Adolescent Psychosocial and Emotional Development · Digital Mental Health Interventions · Bullying, Victimization, and Aggression
- Palabras clave
- 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 autores
- Zhihan Jiang🇨🇳Tongji University
- Tiejun Kang🇨🇳Tongji University; Northwest Normal University
- Heng Wu🇨🇳 · autor de correspondenciaTongji University
Resumen
El texto original del estudio, en el idioma en que se publicó.
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.
Resumen vía Europe PMC. Los derechos de autor siguen siendo de los autores o la editorial (CC BY-NC-ND).
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