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<p>This study aimed to investigate the trajectories of internalizing and externalizing problems following childhood potentially traumatic events (PTEs) and analyse a comprehensive set of baseline variables (PTEs, individual, environmental) to elucidate their predictive role as contributors to different mental health trajectories. The sample consisted of 4,141 participants (<i>M</i> = 9.48, <i>SD</i> = 0.51&#xa0;years at baseline; 48.7% girls; 72.1% White) from the <i>Adolescent Brain Cognitive Development</i> study who had experienced at least one PTE. Participants’ mental health problems were assessed using the <i>Brief Problem Monitor</i> self-report form. <i>Latent Growth Mixture Modelling</i> was used to identify trajectories of youth´s internalizing and externalizing problems across the six assessments. Machine learning was utilized to investigate 37 predictors of different trajectories. Three distinct trajectories were identified for both internalizing and externalizing problems: “Resilient”, “Mild” and “Moderate chronic”. Predictors of the “Moderate chronic” versus “Resilient” trajectories were identified using machine learning. The three most important predictors of the internalizing problems trajectory were: behavioural inhibition, caregiver internalizing problems, and female gender, whereas predictors of the externalizing problems trajectory were family conflicts, screentime, and a low level of prosocial behaviour. The findings can help characterize individual variation in mental health trajectories following childhood PTEs and provide potential targets for intervention to foster mental health.</p>

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Predicting mental health trajectories after potentially traumatic events: a machine learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月1日

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  • Dunja Tutus,
  • Tanmay Nayyar,
  • Jörg M. Fegert,
  • Ann-Christin Haag

摘要

This study aimed to investigate the trajectories of internalizing and externalizing problems following childhood potentially traumatic events (PTEs) and analyse a comprehensive set of baseline variables (PTEs, individual, environmental) to elucidate their predictive role as contributors to different mental health trajectories. The sample consisted of 4,141 participants (M = 9.48, SD = 0.51 years at baseline; 48.7% girls; 72.1% White) from the Adolescent Brain Cognitive Development study who had experienced at least one PTE. Participants’ mental health problems were assessed using the Brief Problem Monitor self-report form. Latent Growth Mixture Modelling was used to identify trajectories of youth´s internalizing and externalizing problems across the six assessments. Machine learning was utilized to investigate 37 predictors of different trajectories. Three distinct trajectories were identified for both internalizing and externalizing problems: “Resilient”, “Mild” and “Moderate chronic”. Predictors of the “Moderate chronic” versus “Resilient” trajectories were identified using machine learning. The three most important predictors of the internalizing problems trajectory were: behavioural inhibition, caregiver internalizing problems, and female gender, whereas predictors of the externalizing problems trajectory were family conflicts, screentime, and a low level of prosocial behaviour. The findings can help characterize individual variation in mental health trajectories following childhood PTEs and provide potential targets for intervention to foster mental health.