Abstract

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: 1. Conduct user-centered design of passive mobile self-tracking to support Veterans’ management of their mental health. 2. Study the feasibility, acceptability and safety of passive self-tracking of mental health that includes feedback of mental health status to the Veteran. 3. Use mobile sensor and phone utilization data to develop individualized estimates of sociability, activities, and sleep as measured by weekly interviews. 4. Study the predictive value of using data on sociability, activities, and sleep to identify exacerbations of psychiatric symptoms. 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 “mWellness,” a functional mobile app that monitors and transmits mobile sensor and utilization data. Focus groups and in-lab usability testing will inform further app and intervention development. Mixed methods research will 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 the behaviors and symptoms of patients with serious mental illness. Next Steps/Implementation: Results inform development of a study of the effectiveness and implementation of these methods to improve Veteran assessment, treatment and outcomes. Results are also applicable to other populations with mental health risk and to the use of similar methods in usual care.

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DRA: Mental, Cognitive and Behavioral Disorders

DRE: Treatment - Observational, TRL - Applied/Translational

Keywords: Serious Mental Illness

MeSH Terms: None at this time.