# Social Media Use, Sleep, and Suicidality in Adolescents

> **NIH NIH R01** · BROWN UNIVERSITY · 2024 · $923,457

## Abstract

Project Summary
Adolescent suicide rates have increased over the past two decades, with the recent COVID-19 exacerbating
pre-pandemic risk factors including increases in online social messaging. We propose that online social
messaging for adolescents serves as a “social zeitgeber” (an environmental “time giver” that synchronizes
diurnal rhythm) and that late night online social messaging contributes to delays in sleep onset. Over time,
delays in sleep onset in combination with inability to phase shift the wake time for adolescents, contributes to
impairments in cognitive and emotional capacity for managing daily stress, resulting in overall vulnerability
throughout the day as well as a shift of vulnerability to the evenings when research has shown increases in
adolescent suicidal thoughts and behaviors. The present research longitudinal research will examine these
processes in the real world using a combination of (1) text extraction of online social messaging during the
intensive sampling period, (2) ecological momentary assessment (EMA), and (3) wearable technology in
combination with predictive algorithms. Findings will inform interventions that can range from ecological
momentary interventions to adaptations to interventions such as Interpersonal and Social Rhythm Therapy.

## Key facts

- **NIH application ID:** 10924021
- **Project number:** 5R01MH135499-02
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** NICOLE R NUGENT
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $923,457
- **Award type:** 5
- **Project period:** 2023-09-07 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10924021

## Citation

> US National Institutes of Health, RePORTER application 10924021, Social Media Use, Sleep, and Suicidality in Adolescents (5R01MH135499-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10924021. Licensed CC0.

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