Evidence map›Paper›PMID 40531519›Full record

ArticleJAMA2025

Addictive Screen Use Trajectories and Suicidal Behaviors, Suicidal Ideation, and Mental Health in US Youths.

Yunyu Xiao, Yuan Meng, Timothy T Brown, Katherine M Keyes, J John Mann

Abstract read
In one paragraph

Article in JAMA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

41 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  7. Intergenerational Co-parenting and Children's Screen Use: A Scoping Review.Clinical child and family psychology review · 2026
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  20. Understanding sleep and smartphone use in diverse adolescents through passive digital monitoring.Journal of research on adolescence : the official journal of the Society for Research on Adolescence · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Yunyu XiaoDepartment of Population Health Sciences, Weill Cornell Medicine/NewYork-Presbyterian, New York, New York.
Yuan MengDepartment of Population Health Sciences, Weill Cornell Medicine/NewYork-Presbyterian, New York, New York.
Timothy T BrownSchool of Public Health, University of California, Berkeley.
Katherine M KeyesColumbia University Mailman School of Public Health, New York, New York.
J John MannDepartments of Psychiatry and Radiology, Irving Medical Center, Columbia University, New York, New York.

Funding

Leveraging Longitudinal Survey and EHR data to Dissect the Impact of COVID-19 related Stressors and Infections on Child Mental Health and Suicide RisksRF1MH134649 · NIMH · WEILL MEDICAL COLL OF CORNELL UNIV · PI XIAO, YUNYU · 2023 to 2023
$3.2M
NIMH NIH HHS RF1 MH134649
6 · The paper itself

Abstract

Importance: Increasing child and adolescent use of social media, video games, and mobile phones has raised concerns about potential links to youth mental health problems. Prior research has largely focused on total screen time rather than longitudinal addictive use trajectories. Objectives: To identify trajectories of addictive use of social media, mobile phones, and video games and to examine their associations with suicidal behaviors and ideation and mental health outcomes among youths. Design, Setting, and Participants: Cohort study analyzing data from baseline through year 4 follow-up in the Adolescent Brain Cognitive Development Study (2016-2022), with population-based samples from 21 US sites. Exposures: Addictive use of social media, mobile phones, and video games using validated child-reported measures from year 2, year 3, and year 4 follow-up surveys. Main Outcomes and Measures: Suicidal behaviors and ideation assessed using child- and parent-reported information via the Kiddie Schedule for Affective Disorders and Schizophrenia. Internalizing and externalizing symptoms were assessed using the parent-reported Child Behavior Checklist. Results: The analytic sample (n = 4285) had a mean age of 10.0 (SD, 0.6) years; 47.9% were female; and 9.9% were Black, 19.4% Hispanic, and 58.7% White. Latent class linear mixed models identified 3 addictive use trajectories for social media and mobile phones and 2 for video games. Nearly one-third of participants had an increasing addictive use trajectory for social media or mobile phones beginning at age 11 years. In adjusted models, increasing addictive use trajectories were associated with higher risks of suicide-related outcomes than low addictive use trajectories (eg, increasing addictive use of social media had a risk ratio of 2.14 [95% CI, 1.61-2.85] for suicidal behaviors). High addictive use trajectories for all screen types were associated with suicide-related outcomes (eg, high-peaking addictive use of social media had a risk ratio of 2.39 [95% CI, 1.66-3.43] for suicidal behaviors). The high video game addictive use trajectory showed the largest relative difference in internalizing symptoms (T score difference, 2.03 [95% CI, 1.45-2.61]), and the increasing social media addictive use trajectory for externalizing symptoms (T score difference, 1.05 [95% CI, 0.54-1.56]), compared with low addictive use trajectories. Total screen time at baseline was not associated with outcomes. Conclusions and Relevance: High or increasing trajectories of addictive use of social media, mobile phones, or video games were common in early adolescents. Both high and increasing addictive screen use trajectories were associated with suicidal behaviors and ideation and worse mental health.

Indexed as

Behavior, AddictiveMental HealthScreen TimeSuicidal IdeationAdolescentAdolescent BehaviorCell PhoneChildChild BehaviorCohort StudiesFemaleFollow-Up StudiesHumansMaleSocial MediaSuicide, Attempted

Identifiers

PMID40531519
PMCPMC12177733

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.