# Advancing Real-Time Suicide Risk Detection Through the Digital Phenotyping Smartphone Application Screenomics

> **NIH NIH R21** · UNIVERSITY OF NOTRE DAME · 2022 · $234,750

## Abstract

PROJECT SUMMARY/ABSTRACT
 Suicide is a leading public health problem, accounting for over 45,000 deaths in 2017 alone 1. With suicide
rates continuing to rise 2, and the prediction of suicidal thoughts and behaviors (STBs) remaining stagnant 3, there
is a need to shift the focus from identifying who is at risk to when individuals are at risk for suicide. Studies
utilizing ecological momentary assessment to collect data at several intervals per day have demonstrated that
suicidal ideation and STB risk factors change rapidly across the course of the day 4; yet, there is a need to improve
the granularity of assessment to improve identification of real-time risk elevation. To enable reliable detection of
STBs within a relatively short window of time (e.g., minutes) will require technologically innovative methodologies
that can continuously capture the dynamic nature of suicide risk.
 We propose the use of a novel form of digital phenotyping, termed Screenomics 5-6, that captures screenshots
from participant’s phones every five seconds. These data can then be utilized to indirectly identify STBs in real-
time (via generated and viewed text), as well as prospectively predict STBs via individual engagement in
produced and consumed social interactions (via application usage, text messages, and social media text), which
have knowns links to STBs 7. Among 80 individuals with past-month STBs, two primary aims will be investigated.
Aim 1 is to demonstrate that text collected through smartphone use (i.e., web browser, text messages) can serve
as an accurate proxy for the direct assessment of STBs. Aim 2 will identify prospective, short-term STB risk
associated with produced and consumed social interactions not demonstrated via direct assessment.
 The research team (Co-PIs: Ammerman, Jacobucci; Co-I: Jiang; Consultants: Kleiman, Ram, Robinson,
Reeves, Bourgeois, Liu) has access to world-class expertise, with extensive experience in EMA data collection
in high-risk samples, machine learning for predicting suicide, collecting and modeling continuous data streams,
including screenshot data, and ethical and privacy practices unique to technological innovations.
 To meaningfully reduce suicide rates, a more nuanced understanding of STBs and associated risk factors in
real-time is required. Screenomics provides near continuous monitoring, allowing for a closer approximation of
the true associations between risk factors and STBs. Indeed, there is a need to identify near-term risk factors
prior to STB occurrences to successfully deliver an intervention and prevent STBs. These findings will lay the
groundwork necessary for utilizing passive data in STB detection and intervention. Given the grave personal and
societal cost of suicide, this work has important public health implications.

## Key facts

- **NIH application ID:** 10428874
- **Project number:** 1R21MH129688-01
- **Recipient organization:** UNIVERSITY OF NOTRE DAME
- **Principal Investigator:** Brooke A Ammerman
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $234,750
- **Award type:** 1
- **Project period:** 2022-03-04 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10428874

## Citation

> US National Institutes of Health, RePORTER application 10428874, Advancing Real-Time Suicide Risk Detection Through the Digital Phenotyping Smartphone Application Screenomics (1R21MH129688-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10428874. Licensed CC0.

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