# Testing Scalable, Single-Session Interventions for Adolescent Depression in the context of COVID-19

> **NIH NIH DP5** · STATE UNIVERSITY NEW YORK STONY BROOK · 2020 · $392,813

## Abstract

Project Summary/Abstract
States and localities nationwide are taking unprecedented steps to reduce public health threats posed by
COVID-19, including school closures affecting >50 million youth. The pandemic has also caused families
extreme financial hardship, sudden unemployment, and distress. This combination of collective trauma, social
isolation, and economic recession drastically increases risk for adolescent major depression (MD): already the
lead cause of disability in youth. However, youth MD treatments face problems of potency and accessibility. Up
to 65% of youth receiving MD treatment fail to respond, partly due to MD’s heterogeneity: an MD diagnosis
reflects >1400 possible symptom combinations, highlighting the need for treatments matched to personal
need. Treatment accessibility issues are similarly severe. Before the pandemic, <50% of youth with MD
accessed any treatment at all; newfound financial strain will further preclude families’ capacity to afford care for
their children. It is thus critical to identify effective, scalable strategies to buffer against youth MD in the context
of COVID-19, along with strategies to match such interventions with youth most likely to benefit. This project
will integrate machine learning approaches and large-scale SSI research to rapidly test potent, accessible
strategies for reducing adolescent MD during COVID-19. Via the largest-ever SSI trial (N=1,200 youth with
elevated MD symptoms, ages 12-16), Aim 1 is to test whether (1) evidence-based SSIs improve proximal
targets (e.g., hopelessness and perceived agency, which has predicted longer-term SSI response) and 3-
month clinical outcomes (MD severity) during the COVID-19 pandemic, and (2) whether SSIs targeting
cognitive versus behavioral MD symptoms are most impactful in this context. In a fully-online trial, youths
recruited from across the U.S. will be randomized to 1 of 3 self-administered SSIs: a behavioral activation SSI,
targeting behavioral MD symptoms (anhedonia; activity withdrawal); an SSI teaching growth mindset, the belief
that personal traits are malleable, targeting cognitive MD symptoms (e.g. hopelessness); or a control SSI. Per
baseline, post-SSI, and 3-month follow-up data, we will test each SSI’s relative benefits, versus the control, in
the context of COVID-19. Results will reveal whether SSIs targeting behavioral versus cognitive symptoms
differentially reduce overall MD severity in this context. Aim 2 is to test whether (and, if so, which of) SSIs can
impact COVID-19 specific trauma and anxiety symptoms, informing whether novel, COVID-19-tailored
supports may be needed to reduce pandemic-specific mental health sequelae. Aim 3 is to test person-level
and contextual predictors of SSI response, via machine-learning techniques, regardless of overall intervention
effects observed. Given MD’s heterogeneity, we will test whether baseline symptoms (e.g., having more severe
cognitive or behavioral MD symptoms) predict response to SSIs...

## Key facts

- **NIH application ID:** 10164526
- **Project number:** 3DP5OD028123-02S2
- **Recipient organization:** STATE UNIVERSITY NEW YORK STONY BROOK
- **Principal Investigator:** Jessica Lee Schleider
- **Activity code:** DP5 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $392,813
- **Award type:** 3
- **Project period:** 2019-09-16 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10164526

## Citation

> US National Institutes of Health, RePORTER application 10164526, Testing Scalable, Single-Session Interventions for Adolescent Depression in the context of COVID-19 (3DP5OD028123-02S2). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10164526. Licensed CC0.

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