# As adolescent substance use declines, internalizing symptoms increase: identifying high-risk substance using groups and the role of social media, parental supervision, and unsupervised time

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $379,773

## Abstract

ABSTRACT
Multiple data sources indicate that adolescent psychopathology, particularly internalizing symptoms, is at
historically unprecedented highs in the United States. These include cognitive (low self-esteem, self-
derogation), affective (depressive affect), and social (loneliness) dimensions of adolescent internalizing
symptoms, which have been rapidly increasing since ~ 2009. Coinciding with these trends has been declines
in adolescent alcohol and other drug (AOD) use (except marijuana), contrary to what would be expected given
the historically strong relationship between AOD use and internalizing symptoms. Declines are also apparent in
high intensity alcohol use (e.g. 10+ drinks per drinking occasion), high-frequency AOD use (although not
marijuana), and simultaneous use of AOD (including marijuana). The strength of the relationship between
internalizing symptoms and AOD use among adolescents is also decreasing; for the first time approaching null
in 2016, which has serious implications for risk factor assessment, prevention and intervention. Divergences
may vary across demographic subgroups, however. Little work has estimated why these diverging trends are
occurring; the most prominent hypothesis is smartphones and social media. These technologies are
hypothesized to underlie less face-to-face time among adolescents, increase real-time parental monitoring,
and negative feelings such as envy and low self-worth. Such a shift may underlie decreases in AOD use (less
unsupervised time with other adolescents, more parental monitoring) and increases in internalizing symptoms
(negative feelings and self-worth). However, existing literature is not based on nationally-representative data,
and the relationship with AOD use has not been investigated. The present study will utilize the Monitoring the
Future (MTF) cross-sectional surveys of ~45,000 adolescents per year. MTF includes a breadth measures
related to internalizing symptoms, substance use, adolescent interaction, as well as a diverse array of potential
confounders. We will address three aims: (1) Examine time trends in the relationship between internalizing
symptoms (low self-esteem, self-derogation, depressive affect, loneliness) with any, high-intensity, high
frequency AOD use, testing the magnitude of the relationship across time and by race, sex, and SES; (2)
Examine time trends in the relationship between internalizing symptoms and simultaneous use of AOD (e.g.,
alcohol and marijuana use, alcohol and opioid use, others), testing the magnitude of the relationship across
time and by subgroups; (3) Test the extent to which social media use, parental supervision, and unsupervised
time with friends are associated with internalizing symptoms, AOD use, simultaneous use of AOD, and whether
the magnitude of these associations explains changes in trends over time. Methods to address these aims will
include time-varying effect modeling as well as parallel process growth models. This study will pro...

## Key facts

- **NIH application ID:** 9949666
- **Project number:** 5R01DA048853-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Katherine M. Keyes
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $379,773
- **Award type:** 5
- **Project period:** 2019-06-15 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9949666

## Citation

> US National Institutes of Health, RePORTER application 9949666, As adolescent substance use declines, internalizing symptoms increase: identifying high-risk substance using groups and the role of social media, parental supervision, and unsupervised time (5R01DA048853-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9949666. Licensed CC0.

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