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Clarifying the Relations Among Youth Technology Use, Substance Use and Mental Health

Funding Source

NIDA, R21DA057535

Project Period

9/15/22 – 8/31/24

Principal Investigator

Jacob T Borodovsky, PhD (CTBH and Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth)

Other Project Staff

Co-Investigator: Lisa Marsch, PhD, CTBH Director, Geisel School of Medicine at Dartmouth; Consultant: Lindsay Squeglia, PhD, Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina; Consultant: Louise Mewton, PhD, Centre for Healthy Brain Ageing, University of New South Wales - Sydney

Project Summary

From 2009 to 2019, depression and suicidality increased nearly 40% among US high school students. However, during the same period, adolescent use of most substances (e.g., alcohol, prescription drugs, inhalants, ecstasy, cocaine, and several more) decreased by 20-40%. Similar trends are being reported in almost all industrialized Western European and Australasian countries. Importantly, substance use and mental health problems remain strongly and positively correlated with each other – suggesting that other factors must be driving these diverging youth trends. Digital technologies are a potential explanation because they have significantly altered youths’ environment and can impact youths’ behavior, health, and functioning. However, the relations among youth technology use, substance use, and mental health are unclear due to the field’s overreliance on cross-sectional data and inaccurate measures of technology use (e.g., measuring general “screen time” rather than measuring when, where, and for how long youth use specific devices or platforms). Furthermore, associations between technology use and substance use or mental health are likely connected to a multitude of other biopsychosocial factors (e.g., biological sex, peer and parental behaviors) that must also be considered. This exploratory study will produce a new, empirically-derived model of adolescent digital technology use, substance use, and mental health by capitalizing on the unique combination of comprehensive, gold-standard assessments available Adolescent Brain Cognitive Development (ABCD) study (n=11,875). Analyses will be conducted using longitudinal data on youth followed from ages 9/10 to 14/15. In AIM 1, we will use Group-based multi-trajectory modeling to identify an optimal model of developmental subgroups of longitudinal technology use patterns. In AIM 2, we will determine if Aim 1 technology subgroups differ in their relation to longitudinal patterns of substance use and mental health problems. In AIM 3, we will test the robustness of the associations observed in the Aim 2 model by examining the statistical impact of including five domains of biopsychosocial covariates in the model: (1) Biological Sex, (2) Parents (e.g., monitoring of child’s activities), (3) Peers (e.g., peer substance use), (4) Environment/Context (e.g., family income) (5) Comorbidity of mental health and substance use.

Public Health Relevance

Within the last decade, adolescent substance use has declined substantially in the US, but adolescent mental health problems have increased. It is possible that digital technologies are playing a role in driving these changes, but further evaluation within the context of relevant biopsychosocial variables (e.g., parents, peers, etc.) is needed. By capitalizing on the unique combination of comprehensive assessments available in the longitudinal Adolescent Brain Cognitive Development study (n=11,875), this exploratory study will develop a new, empirically derived model of adolescent digital technology use, substance use, and mental health. The resulting model will quantify the magnitude and direction of interrelations among key variables and identify the environmental conditions that meaningfully alter probabilities of adolescent outcomes. These results will help re-orient the field in two ways: (1) address methodological bottlenecks (e.g., assess different technology types, use longitudinal rather than cross-sectional data) and (2) identify core etiological cascades of effects which the field can use to advance the content and timing of new screening and prevention programs.