An interventional study of mobile application in Schizophrenia, Bipolar Disorder and Schizoaffective Disorder, sponsored by VA Office of Research and Development. Completed at 1 site in United States. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2025-11-19.
Sponsored by VA Office of Research and Development · Not applicable, Interventional, and Health services research
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.
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:
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.
3,471 studies on the registry are indexed under Schizophrenia; 471 are open to participants now.
This study's enrollment of 87 is above the median of 70 across 2,871 interventional studies indexed under Schizophrenia.
Browse Schizophrenia studies →VA Office of Research and Development is the lead sponsor of 1,733 studies on the registry; 396 are open to participants now.
Of its 206 completed or terminated interventional studies of FDA-regulated products, 180 (87%) have results posted.
Counted across the registry records on this site, refreshed daily.
Exclusion Criteria:
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.
Also known as: VetThrive
Feasibility of Passive Self-tracking of Mental Health
Feasibility of passive self-tracking of mental health. The number of participants who completed the study.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 9 months
Recruitment was conducted at the Greater Los Angeles VA 10/18/2021 - 6/24/2024.
| Milestone | mobile application |
|---|---|
| Started | 87 |
| Completed | 58 |
| Not completed | 29 |
Feasibility of passive self-tracking of mental health. The number of participants who completed the study.
| Participants | mobile application |
|---|---|
| Feasibility of Passive Self-tracking of Mental Health | 58 |
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.
| Participants | mobile application |
|---|---|
| Estimates of Sociability | 73 |
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.
| Participants | mobile application |
|---|---|
| Identify Exacerbations of Psychiatric Symptoms | 85 |
Acceptability of passive self-tracking of mental health. The number of participants who completed the study.
| Participants | mobile application |
|---|---|
| Acceptability of Passive Self-tracking of Mental Health | 58 |
Safety of passive self-tracking of mental health. The number of participants with a serious adverse event.
| Participants | mobile application |
|---|---|
| Safety of Passive Self-tracking of Mental Health | 0 |
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.
| Participants | mobile application |
|---|---|
| Estimates of Activities | 85 |
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.
| Participants | mobile application |
|---|---|
| Estimates of Sleep | 85 |
Collected over Adverse event data were collected during participant enrollment. The enrollment period for participants was 9 months.. Non-serious events are listed at a 0% frequency threshold.
| Group | Deaths | Serious | Other |
|---|---|---|---|
| mobile application | 1/87 (1.1%) | 0/87 (0%) | 0/87 (0%) |
| Age, Continuous(years) | Mobile Application |
|---|---|
| Mean | 53.7 ± 13.6 |
| Sex: Female, Male(Participants) | Mobile Application |
|---|---|
| Female | 16 |
| Male | 71 |
| Ethnicity (NIH/OMB)(Participants) | Mobile Application |
|---|---|
| Hispanic or Latino | 22 |
| Not Hispanic or Latino | 64 |
| Unknown or Not Reported | 1 |
| Race (NIH/OMB)(Participants) | Mobile Application |
|---|---|
| American Indian or Alaska Native | 2 |
| Asian | 7 |
| Native Hawaiian or Other Pacific Islander | 1 |
| Black or African American | 38 |
| White | 29 |
| More than one race | 3 |
| Unknown or Not Reported | 7 |
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