Effect of impact mechanism and intervention measures on sleep quality of college students addicted to short video: a randomly controlled trial
Feng J, Ni H, Hou Z, Zhao L, Lei X
Frontiers in behavioral neuroscience · 1 cita
Cómo se estudió
- Diseño
- Ensayo controlado aleatorizado (clasificado por nuestro filtro de IA)
- Estudiado en
- Personas
- Resultado principal
- Marcadores de salud y funcionamiento
Quién lo pagó
- Financiación
- Financiación independiente
Según la declaración de financiación del texto completo.
Publicación
- Publicado
- 2026-02-10 · Front Behav Neurosci · vol. 20 · pág. 1714774
- Editorial
- Frontiers Media
- Citado
- 1 cita · más que el 94% de estudios similares · 8.2× el promedio de su campo
- Impacto
- Entre el 10 % más citado de su campo
- Referencias
- 39 trabajos
- Acceso
- Acceso abierto (revista) · CC-BY
- Áreas de investigación
- Sleep and related disorders · Impact of Technology on Adolescents · Digital Media and Philosophy
- Palabras clave
- short video addiction, sleep quality, university students, personalized recommendation algorithms, entertainment content, cognitive behavioral therapy, digital interventions, social support, screen time reduction, mental health
5 autores
- Jiayu Feng🇨🇳Southwest University
- Huiyan Ni🇺🇸Columbia University
- Zongping Hou🇨🇳Southwest University
- Lin Xiu Zhao🇨🇳Xinxiang Central Hospital
- Xu Lei🇨🇳 · autor de correspondenciaSouthwest University
Resumen
El texto original del estudio, en el idioma en que se publicó.
Introduction
The rapid growth of short video platforms has raised concerns about their impact on users' mental health, particularly sleep quality. The aim of this research is to investigate the relationship between short video addiction and sleep quality among college students, examine the differential impacts of algorithm types (personalized vs. community-based) and content types on sleep parameters, and evaluate the effectiveness of a multi-component intervention.
Methods
Sixty college students (aged 18-25) meeting criteria for short video addiction (PSQI ≥5, daily usage ≥2 h) were randomly assigned to personalized algorithm (n = 30) or community-based algorithm (n = 30) groups. Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), actigraphy, and the Short Video Addiction Scale (SVA-S). The intervention combined cognitive behavioral therapy (CBT), digital technology tools (time-window control, brightness adjustment), and a social support system ("Sleep Guardian").
Results
Personalized algorithms significantly worsened sleep quality compared to community-based algorithms (PSQI: 10.4 ± 2.3 vs. 8.7 ± 2.1, p = 0.003, Cohen's d = 0.77). Entertainment content had the most detrimental effects on sleep parameters compared to knowledge and information content (p 2 = 0.23). The multi-component intervention significantly improved sleep quality in both groups, with PSQI scores decreasing by 3.6 points in the personalized algorithm group and 2.8 points in the community-based group (p d = 1.71 and 1.46, respectively). Daily short video usage decreased by 47.1% and 54.3%, respectively.
Conclusion
Short video addiction significantly impacts sleep quality. The combination of personalized algorithms and entertainment content creates particularly detrimental conditions for sleep. A comprehensive intervention incorporating CBT, digital tools, and social support effectively improves sleep quality and reduces addiction symptoms.
Resumen vía Europe PMC. Los derechos de autor siguen siendo de los autores o la editorial (CC BY).
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