# MAPS: Mobile Assessment for the Prediction of Suicide

> **NIH NIH U01** · UNIVERSITY OF OREGON · 2020 · $714,133

## Abstract

Project Summary
 Suicide is the second leading cause of death among adolescents. In addition to deaths, 16% of
adolescents report seriously considering suicide each year, and 8% make one or more attempts. Despite these
alarming statistics, little is known about factors that confer imminent risk for suicide. Thus, developing effective
methods to improve short-term prediction of suicidal thoughts and behaviors (STBs) is critical.
 Currently, our most robust predictors of STBs are demographic or clinical indicators that have relatively
weak predictive value. However, there is an emerging literature on short-term prediction of suicide risk that has
identified a number of promising candidates, including rapid escalation of: (a) emotional distress, (b) social
dysfunction (i.e., bullying, rejection), and (c) sleep disturbance. Yet, prior studies are limited in two critical ways.
First, they rely almost entirely on self-report. Second, most studies have not focused on assessment of these
risk factors using intensive longitudinal assessment techniques that are able to capture the dynamics of
changes in risk states. These are fundamental limitations. While suicidal ideation may precede an attempt by
years, socio-emotional changes preceding a suicide attempt often occurs within the time span of minutes to
hours. This study will capitalize on recent developments in real-time monitoring methods that harness
adolescents' naturalistic use of smartphone technology. Specifically, we now have the capacity to use: (a)
smartphone technology to conduct intensive longitudinal assessments monitoring putative risk factors with
minimal participant burden and (b) modern computational techniques to develop predictive algorithms for STBs.
 The project will include high-risk adolescents (n = 200) aged 13-18 years recruited from outpatient and
inpatient clinics: (a) recent suicide attempters with current ideation (n = 70), (b) current suicide ideators with no
attempt history (n = 70), and (c) a psychiatric control group with no STB history (n = 60). Effortless Assessment
of Risk States (EARS) will be used to continuously measure variables relevant to key risk domains—emotional
distress, social dysfunction, and sleep disturbance—through passive monitoring of participants' smartphone
use. First, we will test between-group differences in risk factors during an initial 2-week period, and determine
the extent to which risk factors derived from mobile phones improves discrimination over self-reported
indicators. Second, we will use statistical techniques to test whether the risk factors improve short-term
prediction of STBs (e.g., suicide attempts, hospitalization) during the 6-month follow-up period above and
beyond clinical assessments. Third, computational machine learning techniques—based on a priori and
learned features—will develop predictive models that utilize the full range of intensive longitudinal data
collected by the active and passive monitoring methods to predict gro...

## Key facts

- **NIH application ID:** 9982129
- **Project number:** 5U01MH116923-03
- **Recipient organization:** UNIVERSITY OF OREGON
- **Principal Investigator:** NICHOLAS B ALLEN
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $714,133
- **Award type:** 5
- **Project period:** 2018-09-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9982129

## Citation

> US National Institutes of Health, RePORTER application 9982129, MAPS: Mobile Assessment for the Prediction of Suicide (5U01MH116923-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9982129. Licensed CC0.

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