mHealth Estimate-based Algorithms Signaling Upcoming Recurrence of Episodes in Bipolar Disorders - Trial NCT06204705

Timeline & Enrollment

Primary Outcome

Impaired Social Participation,Modified Hamilton Rating Scale for Depression,Young Mania Rating Scale,PROMIS Ability to Participate in Social Roles and Activities

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. 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.

ICD-10 Classifications