Mobile Self-Tracking of Mental Health

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

Serious mental illnesses require years of monitoring and adjustments in treatment. Stress, substance abuse or reduced medication adherence cause rapid worsening of symptoms, with consequences that include job loss, homelessness, suicide, incarceration, and hospitalization. Treatment visits can be infrequent. Illness exacerbations usually occur with no clinician awareness, leaving little opportunity to make treatment adjustments. Tools are needed that quickly detect illness worsening. At least two thirds of Veterans with serious mental illness use a smartphone. These phones generate data that characterize sociability, activity and sleep. Changes in these are warning signs for relapse. Members of this project developed an app that monitors and transmits these mobile data. This project studies passive mobile sensing that allows Veterans to self-track their activities, sociability and sleep; and studies whether this can be used to track symptoms. The project intends to produce a mobile platform that monitors the clinical status of patients, identifies risk for relapse, and allows early intervention.

Study Overview

• Status: Completed
• Conditions: Schizophrenia; Schizoaffective Disorder; Bipolar Disorder; Post-traumatic Stress Disorder
• Intervention / Treatment: Other: mobile application

Detailed Description

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 a functional mobile app that monitors and transmits mobile sensor and utilization data. Focus groups and in-lab usability testing inform further app and intervention development. Mixed methods research study deployment in Veterans who passively self-track their behaviors and psychiatric symptoms. If this project meets intended goals, the VA will have a mobile analytics platform that continuously monitors behaviors and symptoms of patients with serious mental illness.

• Study Type: Interventional
• Enrollment (Actual): 87
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • California
    • West Los Angeles, California, United States, 90073-1003
      • VA Greater Los Angeles Healthcare System, West Los Angeles, CA

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Veteran patient at the Greater Los Angeles Veterans Healthcare Center with a chart diagnosis of serious mental illness, defined as a diagnosis of schizophrenia, schizoaffective disorder, or bipolar disorder
• Risk for symptoms based on having had, during the past year, psychiatric hospitalization, psychiatric emergency care, lived at a crisis program, or more than 6 outpatient visits; and,
• Ownership of a smartphone with a data plan

Exclusion Criteria:

• Under age 18
• Has a conservator/legally authorized representative

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: mobile application; Participants use a mobile application on their smartphone	Other: mobile application; VetThrive is a mobile smartphone application that monitors and transmits mobile sensor and utilization data. This app is deployed in Veteran patients who passively self-track their behaviors and psychiatric symptoms.; Other Names: VetThrive

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Feasibility of Passive Self-tracking of Mental Health	Feasibility of passive self-tracking of mental health. The number of participants who completed the study.	9 months
Estimates of Sociability	Use mobile sensor and phone utilization data to develop individualized estimates of sociability. There was an effort to calculate an estimate of sociability for each participant. Some participants had insufficient data collected to calculate an estimate. The intent is to determine the number of participants for whom this outcome can be estimated. The investigators report here on the number of participants for whom an estimate of sociability could be successfully calculated.	9 months
Identify Exacerbations of Psychiatric Symptoms	Study the predictive value of using data on sociability, activities, and sleep to identify exacerbations of psychiatric symptoms. There was an effort to calculate identify exacerbations of psychiatric symptoms for each participant. Some participants had insufficient data collected to identify exacerbations. The intent is to determine the number of participants for whom this outcome can be estimated. The investigators report here on the number of participants for whom exacerbations of psychiatric symptoms could be successfully calculated.	9 months
Acceptability of Passive Self-tracking of Mental Health	Acceptability of passive self-tracking of mental health. The number of participants who completed the study.	9 months
Safety of Passive Self-tracking of Mental Health	Safety of passive self-tracking of mental health. The number of participants with a serious adverse event.	9 months
Estimates of Activities	Use mobile sensor and phone utilization data to develop individualized estimates of activities. There was an effort to calculate an estimate of activity for each participant. Some participants had insufficient data collected to calculate an estimate. The intent is to determine the number of participants for whom this outcome can be estimated. The investigators report on the number of participants for whom an estimate of activity could be successfully calculated.	9 months
Estimates of Sleep	Use mobile sensor and phone utilization data to develop individualized estimates of sleep. There was an effort to calculate an estimate of sleep for each participant. Some participants had insufficient data collected to calculate an estimate. The intent is to determine the number of participants for whom this outcome can be estimated. The investigators report here on the number of participants for whom an estimate of sleep could be successfully calculated.	9 months

Collaborators and Investigators

• Sponsor: VA Office of Research and Development
• Investigators: Principal Investigator: Alexander Stehle Young, MD MSHS, VA Greater Los Angeles Healthcare System, West Los Angeles, CA

Publications and helpful links

General Publications

• Young AS, Choi A, Cannedy S, Hoffmann L, Levine L, Liang LJ, Medich M, Oberman R, Olmos-Ochoa TT. Passive Mobile Self-tracking of Mental Health by Veterans With Serious Mental Illness: Protocol for a User-Centered Design and Prospective Cohort Study. JMIR Res Protoc. 2022 Aug 5;11(8):e39010. doi: 10.2196/39010.
• Medich M, Cannedy SL, Hoffmann LC, Chinchilla MY, Pila JM, Chassman SA, Calderon RA, Young AS. Clinician and Patient Perspectives on the Use of Passive Mobile Monitoring and Self-Tracking for Patients With Serious Mental Illness: User-Centered Approach. JMIR Hum Factors. 2023 Oct 24;10:e46909. doi: 10.2196/46909.
• Lin Z, Weinberger E, Nori-Sarma A, Chinchilla M, Wellenius GA, Jay J. Daily heat and mortality among people experiencing homelessness in 2 urban US counties, 2015-2022. Am J Epidemiol. 2024 Nov 4;193(11):1576-1582. doi: 10.1093/aje/kwae084.
• Chinchilla M, Lulla A, Agans D, Chassman S, Gabrielian SE, Young AS. Pathways to social integration among homeless-experienced adults with serious mental illness: a qualitative perspective. BMC Health Serv Res. 2024 Oct 4;24(1):1180. doi: 10.1186/s12913-024-11678-6.

Study record dates

Study Major Dates

• Study Start (Actual): October 18, 2021
• Primary Completion (Actual): July 22, 2024
• Study Completion (Actual): July 22, 2024

Study Registration Dates

• First Submitted: August 16, 2021
• First Submitted That Met QC Criteria: August 20, 2021
• First Posted (Actual): August 26, 2021

Study Record Updates

• Last Update Posted (Estimated): November 19, 2025
• Last Update Submitted That Met QC Criteria: November 5, 2025
• Last Verified: November 1, 2025

More Information

Terms related to this study

• Keywords: Bipolar disorder; Psychotic Disorders; Mental health care; Machine learning; Informatics; Predictive Modeling
• Additional Relevant MeSH Terms: Trauma and Stressor Related Disorders; Bipolar and Related Disorders; Schizophrenia Spectrum and Other Psychotic Disorders; Mental Disorders; Mood Disorders; Stress Disorders, Traumatic; Schizophrenia; Psychotic Disorders; Bipolar Disorder; Stress Disorders, Post-Traumatic
• Other Study ID Numbers: IIR 19-392

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No