Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( 🔒 ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Need Help

AI-Based Screenomic Analysis of Digital Experiences and bidirectional Links to Psychopathological Symptoms

Award Number: R21MH145218
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 08/15/2026
PERIOD OF PERFORMANCE END DATE: 08/14/2028

Group Awards By:

View Award Description

AI-Based Screenomic Analysis of Digital Experiences and bidirectional Links to Psychopathological Symptoms - Project Summary Adolescent social media use has risen dramatically. This rise has occurred alongside with increasing rates of depression, anxiety, and other psychopathological symptoms. However, current research lacks the understand- ing of which specific online experiences drive mental health risks. This project pioneers the use of AI-based screenomic analysis to transform over 38 million smartphone screenshots from 154 adolescents into structured records of digital experiences, enabling the first scalable, causal study of risky online exposures and their bidirec- tional links to psychopathological symptoms. Towards this objective, we propose the following specific aims: (1) we will develop computational pipelines to detect event-level risky exposures—individual encounters with harm- ful content (e.g., violence, suicide, body image) or risky social interactions (e.g., cyberbullying, peer pressure). Vision-language models (VLMs), fine-tuned and personalized using few-shot adaptation, will integrate visual and textual cues to classify these exposures. These event-level data will be linked to biweekly measures of anxiety and depressive symptoms using Random-Intercept Cross-Lagged Panel Models to assess reciprocal causality. (2) We will move beyond single events to model sequential risky digital exposures—chains of online behaviors that may amplify risk (e.g., influencer viewing → body image discussion → late-night browsing). Multi-image VLMs and sequential pattern mining will identify common trajectories, and Marginal Structural Models will test whether these digital patterns predict symptom escalation, or vice versa. By advancing from broad “screen time” metrics to fine-grained digital biomarkers, this research will establish a mechanistic understanding of how online experiences shape mental health in adolescence. The resulting computational tools and insights will lay the foun- dation for precision monitoring and prevention strategies, informing policy, clinical interventions, and youth digital well-being initiatives.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $403,282 )
2026	2026	REGENTS OF THE UNIVERSITY OF MINNESOTA	2221 UNIVERSITY AVE SE STE 100	MINNEAPOLIS	MN	55414	HENNEPIN	USA	Mental Health Research Grants	000	1	8/17/2026	NEW	$403,282
														Subtotal = $403,282

Grand Total All Awards = $403,282

Top

All Categories

About

Search

Reports

Data Submission

Award Information

AI-Based Screenomic Analysis of Digital Experiences and bidirectional Links to Psychopathological Symptoms

Award Number: R21MH145218

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 08/15/2026

PERIOD OF PERFORMANCE END DATE: 08/14/2028

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer