Evidence map›Paper›PMID 40354649›Full record

ArticleJMIR public health and surveillance2025

Peer Relationships Are a Direct Cause of the Adolescent Mental Health Crisis: Interpretable Machine Learning Analysis of 2 Large Cohort Studies.

Heiner Stuke, Robert Schlack, Michael Erhart, Anne Kaman, Ulrike Ravens-Sieberer, Christopher Irrgang

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Heiner StukeCentre for Artificial Intelligence in Public Health Research of the Robert Koch-Institute, Nordufer 20, Berlin, 13353, Germany, 49 30 18754 211.ORCID 0000-0003-4011-8946
Robert SchlackDepartment B Epidemiology and Health Monitoring, Robert Koch-Institute, Berlin, Germany.ORCID 0000-0002-3340-0452
Michael ErhartDepartment of Child and Adolescent Psychiatry and Psychotherapy and Psychosomatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.ORCID 0009-0005-4511-2211
Anne KamanDepartment of Child and Adolescent Psychiatry and Psychotherapy and Psychosomatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.ORCID 0000-0001-7498-6229
Ulrike Ravens-SiebererDepartment of Child and Adolescent Psychiatry and Psychotherapy and Psychosomatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.ORCID 0000-0002-2031-095X
Christopher IrrgangCentre for Artificial Intelligence in Public Health Research of the Robert Koch-Institute, Nordufer 20, Berlin, 13353, Germany, 49 30 18754 211.ORCID 0000-0001-8274-1678

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Converging evidence indicates an adolescent mental health crisis in Western societies that has developed and exacerbated over the past decade. The proposed driving factors of this trend include more screen time, physical inactivity, and social isolation, but their causal influence on mental health is insufficiently understood. Objective: The objective of this study is to test whether and based on which predictor variables the development of mental health in adolescents in the last decade can be predicted and to better understand the causal chain of factors at work. Methods: We implemented an interpretable machine learning pipeline based on gradient boosting regression with repeated cross-validation to assess the development of mental health throughout adolescence in members of 2 longitudinal cohort studies, the British Millenium cohort (MC; n=8599) and the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) cohort (n=1212). In total, 144 (MC) and 102 (KiGGS) predictors assessed at the age of around 13.8 years (MC) and 11.6 years (KiGGS) were used to assess mental health at the ages of around 16.7 years (MC) and 16.4 years (KiGGS). Based on these predictive models, we used permutation-based feature importance analyses to identify relevant predictors and predictor domains. Moreover, we performed partial dependence analyses in a causal inference framework to determine the direct effects of physical inactivity, screen time, and peer problems on the development of mental health. Results: The average cross-validated Pearson correlation coefficient (r) between predicted and true mental health in late adolescence was 0.614 (MC) and 0.466 (KiGGS). Feature importance analyses indicated a strong impact of preexisting mental health and weaker impacts of sex (female as a risk factor), physical health (chronic disease as a risk factor), lifestyle, and socioeconomic and family factors (eg, low parental education, income, and mental health as risk factors). Causal inference analyses suggested a strong direct effect of peer relationships, but only a small direct effect of physical inactivity and a very small direct effect of screen time. Conclusions: Mental health development during adolescence can be assessed by a combination of variables from early adolescence. Peer problems represent an important direct cause of mental health development, and their deterioration may contribute to the current mental health crisis.

Indexed as

Machine LearningMental DisordersMental HealthPeer GroupAdolescentChildCohort StudiesFemaleGermanyHumansLongitudinal StudiesMaleUnited Kingdomadolescenceadolescentadolescent healthanalogous analysesassessmentscohort studydepressionepidemiologyhealth crisislongitudinal studymachine learningmental healthPearson correlationpeerphysical inactivitypredictionpublic healthrisk factorsscreen timesocial isolationWestern countries

Identifiers

PMID40354649
PMCPMC12088615

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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.