# Passive Mobile Self-Tracking of Mental Health by Veterans with Serious Mental Illness

> **NIH VA I01** · VA GREATER LOS ANGELES HEALTHCARE SYSTEM · 2023 · —

## Abstract

Background: Serious mental illnesses are common, disabling, challenging to treat, and require years
of monitoring with adjustments in treatments. Stress or reduced medication adherence can lead to rapid
worsening in symptoms and functioning with consequences that include relapse, job loss, homelessness,
incarceration, hospitalization and suicide. In usual care, clinician visits are infrequent, with intervals ranging
from monthly to yearly. Communication between patients and clinicians between visits is challenging and often
nonexistent. Patient illness exacerbations and relapses generally occur with little or no clinician awareness in
real time, leaving little opportunity to adjust treatments.
 Significance/Impact: For the large population of Veterans with serious mental illness, tools are
needed that passively monitor their mental health status, allowing them to self-track their behaviors, quickly
detect worsening of mental health, and support prompt assessment and intervention. At least 60% of Veterans
with serious mental illness use a smart phone. These generate data that characterize sociability, activity, and
sleep. Changes in these behaviors are warning signs of relapse. Passive self-tracking could be used to identify
and predict worsening of illness in real time.
 Innovation: Passive mobile sensing is a novel approach to illness self-tracking and monitoring. There
has been relatively little research on passive self-tracking in serious mental illness, with limited analytics
development in this area, and none in VA.
 Specific Aims: This project studies passive mobile sensing with Veterans in treatment for serious
mental illness. Data are used for self-tracking of behaviors and symptoms. While passive mobile sensing has
been feasible, acceptable and safe in patients with serious mental illness, these are studied for the first time in
VA. Analytics are developed that use passive data to predict behaviors and symptoms. This project responds to
the HSR&D priority areas of Mental Health and Healthcare Informatics. The project has these objectives:
 1. Conduct user-centered design of passive mobile self-tracking to support Veterans’ management of
their mental health.
 2. Study the feasibility, acceptability and safety of passive self-tracking of mental health that includes
feedback of mental health status to the Veteran.
 3. Use mobile sensor and phone utilization data to develop individualized estimates of sociability,
activities, and sleep as measured by weekly interviews.
 4. Study the predictive value of using data on sociability, activities, and sleep to identify exacerbations
of psychiatric symptoms.
 Methodology: Activities can be assessed with data on movement, location, and habits. Sociability can
be assessed with data on communication and public interactions. Sleep can be assessed using data on light,
sound, movement, and phone use. Investigators on this project developed “mWellness,” a functional mobile
app that monitors and transmits m...

## Key facts

- **NIH application ID:** 10249984
- **Project number:** 5I01HX003129-02
- **Recipient organization:** VA GREATER LOS ANGELES HEALTHCARE SYSTEM
- **Principal Investigator:** Alexander S. Young
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2023
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2020-10-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10249984

## Citation

> US National Institutes of Health, RePORTER application 10249984, Passive Mobile Self-Tracking of Mental Health by Veterans with Serious Mental Illness (5I01HX003129-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10249984. Licensed CC0.

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