SchizophreniaNCT05023252
Mobile Self-Tracking of Mental Health
A study testing mobile application in people with Schizophrenia and Bipolar Disorder, run by VA Office of Research and Development at 1 site in United States.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Schizophrenia, Bipolar Disorder, Schizoaffective Disorder
- What is being tested?
- mobile application
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 87 participants
- Where
- 1 siteUnited States
- Study dates
- 18 Oct 2021to 22 Jul 2024
- Phase
- Interventional
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- Under age 18
- Has a conservator/legally authorized representative
Trial locations and contacts
1 site in 1 country.
United States1 site
VA Greater Los Angeles Healthcare System, West Los Angeles, CA
West Los Angeles, California, 90073-1003
About this study
Official title: 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.
Read the 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:
- Conduct user-centered design of passive mobile self-tracking to support Veterans' management of their mental health.
- Study the feasibility, acceptability and safety of passive self-tracking of mental health that includes feedback of mental health status to the Veteran.
- Use mobile sensor and phone utilization data to develop individualized estimates of sociability, activities, and sleep as measured by weekly interviews.
- 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.
- Being tested
- mobile application
- Keywords
- Mental health care, Informatics, Predictive Modeling, Bipolar disorder, Psychotic Disorders, Machine learning
- Medical subject headings
- Schizophrenia, Bipolar Disorder, Psychotic Disorders, Stress Disorders, Post-Traumatic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
mobile applicationOther
Also called VetThrive
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.
| Study group | Role | What they receive |
|---|---|---|
| mobile application | Experimental | Participants use a mobile application on their smartphone Receives: Other: mobile application |
What is being measured
The primary outcome is the main question the study is designed to answer.
Feasibility of Passive Self-tracking of Mental Health
Feasibility of passive self-tracking of mental health. The number of participants who completed the study.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 9 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Aug 2021
- Study start18 Oct 2021
- Primary completion22 Jul 2024
- Study completion22 Jul 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for SchizophreniaStudies of the same condition, open and under way.
- More trials run by VA Office of Research and DevelopmentThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- Participants in the trial are enrolled in an intervention group for 9 months
- Masking
- None (open label)
- Enrolment
- 87 participants (actual)
Sponsor and investigators
- Lead sponsor
- VA Office of Research and Development
- Sponsor type
- US federal agency
- Responsible party
- Sponsor
- Investigators
- Alexander Stehle Young, MD MSHS, Principal investigator, VA Greater Los Angeles Healthcare System, West Los Angeles, CA
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05023252
- Sponsor's protocol ID
- IIR 19-392
- Status last verified
- Nov 2025
- First submitted
- 16 Aug 2021
- Last updated
- 19 Nov 2025
- Results posted
- Yes, on ClinicalTrials.gov
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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.
PubMed 35930336Medich 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.
PubMed 37874639Lin 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.
PubMed 38844692Chinchilla 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.
PubMed 39367388
Study at a glance
From the registry record, last updated by the sponsor on 19 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Passive Mobile Self-Tracking of Mental Health by Veterans With Serious Mental Illness
- ClinicalTrials.gov ID
- NCT05023252
- Acronym / sponsor ID
- IIR 19-392
- Keywords
- Mental health care, Informatics, Predictive Modeling, Bipolar disorder, Psychotic Disorders, Machine learning
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 87 participants (actual)
- Lead sponsor
- VA Office of Research and DevelopmentUS federal agency
- Study start
- 18 Oct 2021 (actual)
- Primary completion
- 22 Jul 2024 (actual)
- Study completion
- 22 Jul 2024 (actual)
- First posted
- 26 Aug 2021 (actual)
- Results
- Posted 15 Oct 2025
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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:
- Conduct user-centered design of passive mobile self-tracking to support Veterans' management of their mental health.
- Study the feasibility, acceptability and safety of passive self-tracking of mental health that includes feedback of mental health status to the Veteran.
- Use mobile sensor and phone utilization data to develop individualized estimates of sociability, activities, and sleep as measured by weekly interviews.
- 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 design
- Primary purpose
- Health services research
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- Participants in the trial are enrolled in an intervention group for 9 months
| Arm | Type | Receives |
|---|---|---|
| mobile application | Experimental | Other: mobile applicationParticipants use a mobile application on their smartphone |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (2 listed)
- Under age 18
- Has a conservator/legally authorized representative
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| mobile applicationAlso: VetThriveVetThrive 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 | mobile application |
Primary outcome
Feasibility of Passive Self-tracking of Mental Health
Time frame: 9 months
Feasibility of passive self-tracking of mental health. The number of participants who completed the study.
Estimates of Sociability
Time frame: 9 months
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.
Identify Exacerbations of Psychiatric Symptoms
Time frame: 9 months
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.
Acceptability of Passive Self-tracking of Mental Health
Time frame: 9 months
Acceptability of passive self-tracking of mental health. The number of participants who completed the study.
Safety of Passive Self-tracking of Mental Health
Time frame: 9 months
Safety of passive self-tracking of mental health. The number of participants with a serious adverse event.
Estimates of Activities
Time frame: 9 months
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.
Estimates of Sleep
Time frame: 9 months
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.
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Nov 2025.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- Results publication: PubMed 35930336Young 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.
- Results publication: PubMed 37874639Medich 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.
- Results publication: PubMed 38844692Lin 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.
- Results publication: PubMed 39367388Chinchilla 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Alexander Stehle Young, MD MSHS principal investigator
VA Greater Los Angeles Healthcare System, West Los Angeles, CA
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing mobile application in people with Schizophrenia and Bipolar Disorder, run by VA Office of Research and Development at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 19 Nov 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Yes. Results are posted in the registry (first posted 15 Oct 2025). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Feasibility of Passive Self-tracking of Mental Health (time frame: 9 months). The study lists 7 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05023252, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Nov 2025.