Bipolar DisorderMEASURE-BDNCT06204705
mHealth Estimate-based Algorithms Signaling Upcoming Recurrence of Episodes in Bipolar Disorders
An observational study of Bipolar Disorder, run by VA Office of Research and Development at 1 site in United States.
Trial summary
Veterans with bipolar disorders (BD) experience recurrent and seemingly unpredictable periods of severe impairments in psychosocial functioning, such as participation in social roles and activities. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Bipolar Disorder
- How many people
- 200 participants
- Where
- 1 siteUnited States
- Study dates
- 10 Sep 2024to 30 Sep 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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
Study population: Veterans with bipolar disorders who receive care at the Minneapolis VA Health Care System. VA providers and administrators who provide care to Veterans with bipolar disorders at the Minneapolis VA Health Care System.
Who may be able to join
- Veteran participants will have a confirmed primary diagnosis of a Bipolar I Disorder, Bipolar II Disorder or Other Specified Bipolar Disorder (i.e., those with major depressive episodes and hypomania that meets all episode criteria but for duration) based on the clinical Interview for DSM-5-Research Version (SCID-5-RV), medical chart review and consensus procedure directed by the PI
- All Veteran participants will endorse presence of at least one bipolar episode in the last 12 months based on the interview and/or medical chart information
- All Veteran participants will own a smartphone capable of running all study apps
- All participants will be age 18 years or older
- All participants will be fluent in English
- All Veteran participants will be able to demonstrate capacity for consent (see below) and have no active court-appointed legal guardianship precluding ability to provide consent
- Focus group participants will be active Minneapolis VAHCS providers and administrators who are either actively engaged in care for Veterans with BD or involved in administrative roles overseeing mental health care of Veterans within Minneapolis VAHCS
Who cannot join
- Presence of a major neurocognitive disorder or neurological disorder, such as Alzheimer's dementia, vascular dementia, Parkinson's disease, etc.
- Impaired global cognition (MoCA score < 20 for in-person assessment, or equivalent score on "blind" MoCA for virtual assessments)
- Presence of physical conditions preventing use of smartphone apps Lack of capacity to provide informed consent
- Age < 18 years
- No exclusion for focus group participants as their VA status employment will be taken to indicate age of majority, intact global cognition, etc.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Minneapolis VA Health Care System, Minneapolis, MN
Minneapolis, Minnesota, 55417-2309
Snezana Urosevic, PhD, 612-467-3897, Snezana.Urosevic@va.gov
Snezana Urosevic, PhD
About this study
Official title: mHealth Estimate-based Algorithms Signaling Upcoming Recurrence of Episodes in Bipolar Disorders (MEASURE-BD)
Veterans with bipolar disorders (BD) experience recurrent and seemingly unpredictable periods of severe impairments in psychosocial functioning, such as participation in social roles and activities. Many effective treatments for BD emphasize early detection of bipolar episodes, in order to make necessary treatment adjustments and prevent psychosocial impairments associated with acute mood episodes. Unfortunately, acute mood episodes in BD are also associated with a decrease in a patient's insight into their own symptoms, which can prevent one's ability to self-report first signs of symptoms and functional declines. Moreover, routine care visits for BD are typically too infrequent to capture and effectively monitor day-to-day changes in a patient's mood and functioning.
Objective, low-effort, and continuous methods of tracking symptoms and social participation of Veterans with BD in real-time and in-situ are needed to provide early (i.e., days in advance) warning signs of acute bipolar episodes and functional declines, which in turn would enable well-timed interventions to prevent poor psychosocial outcomes. mHealth refers to the use of mobile and wireless devices as part of patient care and offers many potential opportunities for early detection of and intervention for acute mood states in this population. However, these mHealth approaches have not been investigated in Veterans with BD. In a Small Projects in Rehabilitation Research (SPiRE)-funded pilot study, the investigator team established high feasibility and acceptability of one such innovative passive mHealth approach using a smartphone program, or an app, in a small sample of Veterans with BD to track their smartphone's GPS/location. The pilot study used a priori location context ratings of visited places (e.g., a priori ratings on types of activities usually engaged in at a frequently visited location) to derive unobtrusive measures of social participation (e.g., time spent at work-related locations). The goal of this Merit Review proposal is to establish reliable and valid machine-learning algorithms using the same types of mHealth data to prospectively (days in advance) detect declines in social participation and prospective onset of mania and depression in Veterans with BD. This proposal has three aims:
Read more
Aim 1. To establish a machine learning algorithm using GPS/location data for predicting prospective declines in social participation in Veterans with BD.
Aim 2. To establish machine learning algorithms using GPS/location data for predicting prospective acute BD clinical states. The investigators will explore whether adding more burdensome daily self-report and voice diaries' speech analysis features improves the models' precision using statistical indices of prediction precision or accuracy.
Aim 3. To explore clinical implementation of the mHealth-based algorithms in treatment of BD. Focus groups of VA providers and administrators will assess feasibility of algorithms' implementation in clinical care.
Read the detailed description
Veterans with bipolar disorders (BD) experience recurrent and seemingly unpredictable periods of severe impairments in psychosocial functioning, which lead to poor outcomes over their lifetime, such as incarceration, homelessness, and death by suicide. Studies support a link between greater severity and frequency of BD symptoms and worse psychosocial functioning. Veterans with BD often drop out of care at times when treatment would be most beneficial for preventing deterioration in psychosocial functioning-when new manic and depressive episodes onset. Thus, despite the availability of evidence-based treatments, BD is among the leading causes of disability worldwide.
Effective tools for prospectively detecting manic and depressive episode onset could provide clinicians with the opportunity to intervene more efficiently and prevent poor psychosocial outcomes and loss of life. Unsurprisingly, psychotherapeutic interventions often focus on teaching patients mood-monitoring techniques for episode relapse prevention. However, these self-report techniques require insight and high patient effort, which may be lacking during acute BD episodes. Real-world measures of both BD symptoms and social functioning in Veterans with BD that are objective and do not require high insight or high effort are missing. Thus, passive mHealth methods that are feasible and acceptable to Veterans with BD and effective in prospectively detecting onsets of both mania and depression could prevent psychosocial functioning declines by ensuring evidence-based care is provided at the times of greatest need.
The overarching goal of this Merit Award project is to establish reliable and valid machine-learning algorithms using mHealth data to prospectively detect declines in social participation and prospective onset of mania and depression in Veterans with BD. The study's specific aims are:
Aim #1. To establish a machine learning algorithm using GPS/location data for predicting prospective declines in social participation in Veterans with BD. The investigators will provide novel, real-world GPS-based machine learning models that predict days in advance changes in social participation in Veterans. Based on pilot data, the investigators expect GPS data predictors/features to include time spent at residence, work, and daily routine locations.
Aim #2. To establish machine learning algorithms using GPS/location data for predicting prospective acute BD clinical states. The investigators will explore whether adding more burdensome daily self-report and voice dairy features improves the models' accuracy using positive prediction and other statistical indices. The investigators predict passive GPS/location data alone will provide accurate prediction of prospective changes in BD symptoms.
Aim #3. To explore clinical implementation of the mHealth-based algorithms in treatment of BD. Focus groups of VA providers and administrators will assess feasibility of algorithms' implementation in clinical care.
To accomplish the aims, the study will recruit 200 Veterans with a BD diagnosis who receive care in the Minneapolis VA Health Care System through direct mailings to patients, flyers in the medical center, and referrals by clinicians. The study will use stratified sampling recruitment strategies for enrolling at least 20 Veterans in the age ranges 18-35, 36-45, 46-55, 56-65, and 66 and older. Participants will be followed for 14 weeks using three smartphone apps (i.e., VA mPRO, FollowMee, and Recorder Plus or ASR Voice Recorder). Daily, participants will complete an 8-question assessment of their current symptoms and provide voice data for speech analysis to a fixed prompt about their planned activities for the day. Another app will continuously and passively monitor location using the smartphone GPS features to detect deviations in daily routine. Biweekly, participants will complete a brief phone screen assessing social and community participation, symptoms of mania and depression, and suicidality. mHealth data from days prior to the biweekly interviews will be used as features in a small number of candidate machine learning models with outcome measures being biweekly interview assessments of bipolar symptoms and social participation. Project staff will also hold two focus groups-one of 8 VA mental health providers and one of 8 VA administrators-representing diverse disciplines and use guided discussion questions to elicit feedback about implementation of mHealth-based algorithms in future clinical care of Veterans with BD.
Impact: The study goal is to provide clinical tools for real-time, unobtrusive, and prospective signals about imminent depressive and manic episode relapses in Veterans with BD to their clinicians for more rapid, less costly, and more effective use of existing evidence-based treatments to prevent poor psychosocial functional outcomes. Moreover, the current study will yield objective, low effort, and unobtrusive measures for tracking social participation in-situ and in real-time in both Veterans with BD and other Veteran populations.
- Conditions
- Bipolar Disorder
- Keywords
- Bipolar Disorder, Veterans, Telemedicine, Digital Phenotyping, Machine Learning
- Medical subject headings
- Bipolar Disorder
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Veterans with Bipolar Disorders | Veterans with a diagnosis of a bipolar disorder. | |
| VA clinicians and administrators | VA clinicians and administrators who provide or oversee clinical care of Veterans with bipolar disorders |
What is being measured
The primary outcome is the main question the study is designed to answer.
Impaired Social Participation
To determine presence of impaired social participation, an average of T scores will be calculated from two scales: PROMIS Satisfaction with Participation in Social Roles and PROMIS Ability to Participate in Social Roles and Activities scales. Averaged T scores less than 40 (1 standard deviation below population mean of 50) will be considered as indicators of impaired social participation. The PROMIS scales will be administered biweekly for the duration of 14 weeks of the follow-up.
Measured: Biweekly for 14 weeks
Modified Hamilton Rating Scale for Depression
Modified Hamilton Rating Scale for Depression interviews administered biweekly will assess depression symptoms for each week of the 14-week follow-up to assess presence of clinically significant depression (score of 14 or higher) and/or changes in depression severity.
Measured: Weekly for 14 weeks
Young Mania Rating Scale
Young Mania Rating Scale interviews administered biweekly will assess manic/hypomanic symptoms for each week of the 14-week follow-up period to determine presence of clinically significant hypomania/mania (scores above 12) and changes in hypomania/mania symptom severity.
Measured: Weekly for 14 weeks
PROMIS Ability to Participate in Social Roles and Activities
PROMIS Ability to Participate in Social Roles and Activities is a self-report measure of difficulties with social participation. The raw scores range from 8 to 40 with higher scores indicating greater difficulties with social participation. The raw scores will be transformed into T scores and then average with T score for PROMIS Satisfaction with Participation in Social Roles for each week of the follow-up. These average scores of T score less than 40 will be considered as indicators of impaired social participation during this week.
Measured: Biweekly for 14 weeks
Secondary outcomes (1)
DSI Suicidality Subscale
Depressive Symptom Inventory Suicidality Subscale will assess presence of suicidal ideation and impulses in the prior two weeks during biweekly interviews for the duration of 14-week follow-up period. Exploratory analyses will assess ability to predict changes in suicidality symptoms using machine learning algorithms.
Measured: Biweekly for 14 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Jan 2024
- Study start10 Sep 2024
- Primary completion30 Sep 2027 (estimated)
- Study completion30 Sep 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Bipolar DisorderStudies 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
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- VA Office of Research and Development
- Sponsor type
- US federal agency
- Collaborators
- National Center for PTSD, University of Wisconsin, Madison
- Responsible party
- Sponsor
- Investigators
- Snezana Urosevic, PhD, Principal investigator, Minneapolis VA Health Care System, Minneapolis, MN
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06204705
- Sponsor's protocol ID
- D4800-R
- Acronym
- MEASURE-BD
- Status last verified
- Apr 2026
- First submitted
- 18 Dec 2023
- Last updated
- 24 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: PI will store all study research records and data for a minimum of 6 fiscal years post study closure as required by VA data retention policies. De-identified data will be stored for at least 7 years after publication of the project's findings and will be made available for data sharing when requests for sharing are made for scientific purposes (e.g., results validation purposes). When possible, written agreements will be used specifying conditions of data sharing.
Study at a glance
From the registry record, last updated by the sponsor on 24 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- mHealth Estimate-based Algorithms Signaling Upcoming Recurrence of Episodes in Bipolar Disorders (MEASURE-BD)
- ClinicalTrials.gov ID
- NCT06204705
- Acronym / sponsor ID
- MEASURE-BD / D4800-R
- Conditions
- Bipolar Disorder
- Keywords
- Bipolar Disorder, Veterans, Telemedicine, Digital Phenotyping, Machine Learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- VA Office of Research and DevelopmentUS federal agency
- Collaborators
- National Center for PTSD, University of Wisconsin, Madison
- Study start
- 10 Sep 2024 (actual)
- Primary completion
- 30 Sep 2027 (estimated)
- Study completion
- 30 Sep 2027 (estimated)
- First posted
- 12 Jan 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Veterans with bipolar disorders (BD) experience recurrent and seemingly unpredictable periods of severe impairments in psychosocial functioning, such as participation in social roles and activities. Many effective treatments for BD emphasize early detection of bipolar episodes, in order to make necessary treatment adjustments and prevent psychosocial impairments associated with acute mood episodes. Unfortunately, acute mood episodes in BD are also associated with a decrease in a patient's insight into their own symptoms, which can prevent one's ability to self-report first signs of symptoms and functional declines. Moreover, routine care visits for BD are typically too infrequent to capture and effectively monitor day-to-day changes in a patient's mood and functioning.
Objective, low-effort, and continuous methods of tracking symptoms and social participation of Veterans with BD in real-time and in-situ are needed to provide early (i.e., days in advance) warning signs of acute bipolar episodes and functional declines, which in turn would enable well-timed interventions to prevent poor psychosocial outcomes. mHealth refers to the use of mobile and wireless devices as part of patient care and offers many potential opportunities for early detection of and intervention for acute mood states in this population. However, these mHealth approaches have not been investigated in Veterans with BD. In a Small Projects in Rehabilitation Research (SPiRE)-funded pilot study, the investigator team established high feasibility and acceptability of one such innovative passive mHealth approach using a smartphone program, or an app, in a small sample of Veterans with BD to track their smartphone's GPS/location. The pilot study used a priori location context ratings of visited places (e.g., a priori ratings on types of activities usually engaged in at a frequently visited location) to derive unobtrusive measures of social participation (e.g., time spent at work-related locations). The goal of this Merit Review proposal is to establish reliable and valid machine-learning algorithms using the same types of mHealth data to prospectively (days in advance) detect declines in social participation and prospective onset of mania and depression in Veterans with BD. This proposal has three aims:
Aim 1. To establish a machine learning algorithm using GPS/location data for predicting prospective declines in social participation in Veterans with BD.
Aim 2. To establish machine learning algorithms using GPS/location data for predicting prospective acute BD clinical states. The investigators will explore whether adding more burdensome daily self-report and voice diaries' speech analysis features improves the models' precision using statistical indices of prediction precision or accuracy.
Aim 3. To explore clinical implementation of the mHealth-based algorithms in treatment of BD. Focus groups of VA providers and administrators will assess feasibility of algorithms' implementation in clinical care.
Detailed description
Veterans with bipolar disorders (BD) experience recurrent and seemingly unpredictable periods of severe impairments in psychosocial functioning, which lead to poor outcomes over their lifetime, such as incarceration, homelessness, and death by suicide. Studies support a link between greater severity and frequency of BD symptoms and worse psychosocial functioning. Veterans with BD often drop out of care at times when treatment would be most beneficial for preventing deterioration in psychosocial functioning-when new manic and depressive episodes onset. Thus, despite the availability of evidence-based treatments, BD is among the leading causes of disability worldwide.
Effective tools for prospectively detecting manic and depressive episode onset could provide clinicians with the opportunity to intervene more efficiently and prevent poor psychosocial outcomes and loss of life. Unsurprisingly, psychotherapeutic interventions often focus on teaching patients mood-monitoring techniques for episode relapse prevention. However, these self-report techniques require insight and high patient effort, which may be lacking during acute BD episodes. Real-world measures of both BD symptoms and social functioning in Veterans with BD that are objective and do not require high insight or high effort are missing. Thus, passive mHealth methods that are feasible and acceptable to Veterans with BD and effective in prospectively detecting onsets of both mania and depression could prevent psychosocial functioning declines by ensuring evidence-based care is provided at the times of greatest need.
The overarching goal of this Merit Award project is to establish reliable and valid machine-learning algorithms using mHealth data to prospectively detect declines in social participation and prospective onset of mania and depression in Veterans with BD. The study's specific aims are:
Aim #1. To establish a machine learning algorithm using GPS/location data for predicting prospective declines in social participation in Veterans with BD. The investigators will provide novel, real-world GPS-based machine learning models that predict days in advance changes in social participation in Veterans. Based on pilot data, the investigators expect GPS data predictors/features to include time spent at residence, work, and daily routine locations.
Aim #2. To establish machine learning algorithms using GPS/location data for predicting prospective acute BD clinical states. The investigators will explore whether adding more burdensome daily self-report and voice dairy features improves the models' accuracy using positive prediction and other statistical indices. The investigators predict passive GPS/location data alone will provide accurate prediction of prospective changes in BD symptoms.
Aim #3. To explore clinical implementation of the mHealth-based algorithms in treatment of BD. Focus groups of VA providers and administrators will assess feasibility of algorithms' implementation in clinical care.
To accomplish the aims, the study will recruit 200 Veterans with a BD diagnosis who receive care in the Minneapolis VA Health Care System through direct mailings to patients, flyers in the medical center, and referrals by clinicians. The study will use stratified sampling recruitment strategies for enrolling at least 20 Veterans in the age ranges 18-35, 36-45, 46-55, 56-65, and 66 and older. Participants will be followed for 14 weeks using three smartphone apps (i.e., VA mPRO, FollowMee, and Recorder Plus or ASR Voice Recorder). Daily, participants will complete an 8-question assessment of their current symptoms and provide voice data for speech analysis to a fixed prompt about their planned activities for the day. Another app will continuously and passively monitor location using the smartphone GPS features to detect deviations in daily routine. Biweekly, participants will complete a brief phone screen assessing social and community participation, symptoms of mania and depression, and suicidality. mHealth data from days prior to the biweekly interviews will be used as features in a small number of candidate machine learning models with outcome measures being biweekly interview assessments of bipolar symptoms and social participation. Project staff will also hold two focus groups-one of 8 VA mental health providers and one of 8 VA administrators-representing diverse disciplines and use guided discussion questions to elicit feedback about implementation of mHealth-based algorithms in future clinical care of Veterans with BD.
Impact: The study goal is to provide clinical tools for real-time, unobtrusive, and prospective signals about imminent depressive and manic episode relapses in Veterans with BD to their clinicians for more rapid, less costly, and more effective use of existing evidence-based treatments to prevent poor psychosocial functional outcomes. Moreover, the current study will yield objective, low effort, and unobtrusive measures for tracking social participation in-situ and in real-time in both Veterans with BD and other Veteran populations.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Veterans with Bipolar Disorders | – | –Veterans with a diagnosis of a bipolar disorder. |
| VA clinicians and administrators | – | –VA clinicians and administrators who provide or oversee clinical care of Veterans with bipolar disorders |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Veterans with bipolar disorders who receive care at the Minneapolis VA Health Care System. VA providers and administrators who provide care to Veterans with bipolar disorders at the Minneapolis VA Health Care System.
Key inclusion criteria (7 listed)
- Veteran participants will have a confirmed primary diagnosis of a Bipolar I Disorder, Bipolar II Disorder or Other Specified Bipolar Disorder (i.e., those with major depressive episodes and hypomania that meets all episode criteria but for duration) based on the clinical Interview for DSM-5-Research Version (SCID-5-RV), medical chart review and consensus procedure directed by the PI
- All Veteran participants will endorse presence of at least one bipolar episode in the last 12 months based on the interview and/or medical chart information
- All Veteran participants will own a smartphone capable of running all study apps
- All participants will be age 18 years or older
- All participants will be fluent in English
Key exclusion criteria (5 listed)
- Presence of a major neurocognitive disorder or neurological disorder, such as Alzheimer's dementia, vascular dementia, Parkinson's disease, etc.
- Impaired global cognition (MoCA score < 20 for in-person assessment, or equivalent score on "blind" MoCA for virtual assessments)
- Presence of physical conditions preventing use of smartphone apps Lack of capacity to provide informed consent
- Age < 18 years
- No exclusion for focus group participants as their VA status employment will be taken to indicate age of majority, intact global cognition, etc.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Impaired Social Participation
Time frame: Biweekly for 14 weeks
To determine presence of impaired social participation, an average of T scores will be calculated from two scales: PROMIS Satisfaction with Participation in Social Roles and PROMIS Ability to Participate in Social Roles and Activities scales. Averaged T scores less than 40 (1 standard deviation below population mean of 50) will be considered as indicators of impaired social participation. The PROMIS scales will be administered biweekly for the duration of 14 weeks of the follow-up.
Modified Hamilton Rating Scale for Depression
Time frame: Weekly for 14 weeks
Modified Hamilton Rating Scale for Depression interviews administered biweekly will assess depression symptoms for each week of the 14-week follow-up to assess presence of clinically significant depression (score of 14 or higher) and/or changes in depression severity.
Young Mania Rating Scale
Time frame: Weekly for 14 weeks
Young Mania Rating Scale interviews administered biweekly will assess manic/hypomanic symptoms for each week of the 14-week follow-up period to determine presence of clinically significant hypomania/mania (scores above 12) and changes in hypomania/mania symptom severity.
PROMIS Ability to Participate in Social Roles and Activities
Time frame: Biweekly for 14 weeks
PROMIS Ability to Participate in Social Roles and Activities is a self-report measure of difficulties with social participation. The raw scores range from 8 to 40 with higher scores indicating greater difficulties with social participation. The raw scores will be transformed into T scores and then average with T score for PROMIS Satisfaction with Participation in Social Roles for each week of the follow-up. These average scores of T score less than 40 will be considered as indicators of impaired social participation during this week.
1 secondary outcome
DSI Suicidality Subscale
Time frame: Biweekly for 14 weeks
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Apr 2026.
- Individual participant dataSharing planned: Yes. PI will store all study research records and data for a minimum of 6 fiscal years post study closure as required by VA data retention policies. De-identified data will be stored for at least 7 years after publication of the project's findings and will be made available for data sharing when requests for sharing are made for scientific purposes (e.g., results validation purposes). When possible, written agreements will be used specifying conditions of data sharing.
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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.
Snezana Urosevic, PhD principal investigator
Minneapolis VA Health Care System, Minneapolis, MN
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of 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 7 inclusion and 5 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: looking for participants now, as of the sponsor's last update on 24 Apr 2026. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Snezana Urosevic, PhD ((612) 467-3897, Snezana.Urosevic@va.gov), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Impaired Social Participation (time frame: Biweekly for 14 weeks). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 24 Apr 2026; AzTrials last refreshed its copy on 7 Nov 2026.