mHealth Estimate-based Algorithms Signaling Upcoming Recurrence of Episodes in Bipolar Disorders (MEASURE-BD)

Minneapolis Va Medical Center, Minneapolis MN

Investigators

Abstract

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, our 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. We will explore whether adding more burdensome daily self-report and voice diaries’ speech analysis features improves our 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. Aims 1 and 2 will be accomplished using a smartphone app that continuously and passively monitors location/GPS data, along with a daily self-report of mood/symptoms and daily voice diaries using separate apps, to predict bipolar symptoms and social participation assessed during biweekly videoconference interviews in a sample of 200 Veterans with BD receiving care at the Minneapolis VA Health Care System. mHealth data from days prior to the biweekly interviews will be used as features in a small number of candidate machine learning models (e.g., Random Forest, Lasso, XGBoost, Support Vector Machine) with outcome measures being biweekly interview assessments of bipolar symptoms and social participation. The machine learning models were chosen to be complementary for being parametric versus non-parametric, linear versus non-linear, and of varying degrees of flexibility and model complexity. For Aim 3, two focus groups—one of 8 VA mental health providers and one of 8 VA administrators—representing diverse disciplines will use guided discussion questions to elicit feedback about implementation of mHealth-based algorithms in future clinical care of Veterans with BD.