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.
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
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
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.
Case-only
Prospective
Not listed
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.
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.
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.
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.
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.
VA Clinicians Focus Group Themes
Focus groups of 8 VA clinicians who provide care to Veterans with bipolar disorders will be analyzed using a rapid qualitative analysis method to derive themes related to facilitators and inhibitors to clinical implementation of the mHealth methods and the study's machine learning algorithms in future clinical care of Veterans with bipolar disorders.
VA Administrators Focus Group Themes
Focus group of 8 VA administrators who oversee care for Veterans with bipolar disorders will be analyzed using a rapid qualitative analysis method to derive themes related to facilitators and inhibitors to clinical implementation of the mHealth methods and the study's machine learning algorithms in future clinical care of Veterans with bipolar disorders.
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VA Office of Research and Development
Lead sponsor
National Center for PTSD
Collaborator
University of Wisconsin, Madison
Collaborator
