# Detecting Adolescent Suicidality Biometric Signals and Dynamic Variability with Wearable Technology

> **NIH NIH K23** · OREGON HEALTH & SCIENCE UNIVERSITY · 2024 · $190,717

## Abstract

PROJECT SUMMARY
Suicide rates have exponentially increased, and it is now the 2nd leading cause of death in adolescence,
accounting for over 1.2 million annual emergency department (ED) visits. After an ED visit or attempt, up to
20% of adolescents will have a second attempt within 12 months, and almost half will have a repeat ED visit.
My long-term goal is to be an independent, federally-funded physician-scientist with a research program in
adolescent suicidality. This proposal's overall objectives are to investigate physiologic parameters and
biometric data from wearable technology that is temporally related to suicidal behavior and develop a
personalized, predictive tool that can improve outpatient identification of adolescent patients with suicidality
before a crisis develops requiring an ED visit. The central hypothesis is that biometric data can continuously
monitor and allow for early identification/intervention of escalating suicidal symptoms. The rationale is that
improved outpatient monitoring through wearable technology can create a more objective platform to provide
the ability to identify a worsening condition and utilize patient-specific just-in-time therapeutics developed with
mental health providers for suicidal adolescents. To attain the overall objectives, I will pursue the following
specific aims: (i) To evaluate whether HRV, combined with patient-specific risk factors, can be used to detect
dynamic changes in suicide severity among a prospective cohort of acutely suicidal adolescents, (ii) To utilize
machine learning to determine whether there is a temporal relationship/signature in the raw PPG signal before
or immediately after changes in suicide severity reporting combined with patient-specific risk factors to develop
a prediction tool for suicidality risk. These aims will be accomplished in three years through a prospective
observational study enrolling acutely suicidal adolescents in the ED and an inpatient psychiatric unit. In
addition, the following career development aims will be accomplished to position myself as an independent
physician-scientist following completion of this K23: (i) formal training in clinical suicidality assessment and
monitoring outside the ED, including developmental psychology/youth suicidology; (ii) Machine learning and
data analysis techniques for large longitudinal datasets integral to clinical research translation from digital
monitors; (iii) K-to-R transition that will include the methodology for clinical trials and adaptive design. This
proposal is significant because it aligns with the NIMH mission of improved preventative research, assessing
mental health trajectories over time, and research with an extensive public health reach. The research
proposed in this application is innovative because researchers can use this platform in future studies as a new
physiologic approach to adolescent suicidality. Ultimately, such knowledge can offer unique opportunities for
early detection, just-in-tim...

## Key facts

- **NIH application ID:** 10869986
- **Project number:** 5K23MH131802-02
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** David Clark Sheridan
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $190,717
- **Award type:** 5
- **Project period:** 2023-06-16 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10869986

## Citation

> US National Institutes of Health, RePORTER application 10869986, Detecting Adolescent Suicidality Biometric Signals and Dynamic Variability with Wearable Technology (5K23MH131802-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10869986. Licensed CC0.

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