Passive Mobile Self-tracking of Mental Health by Veterans With Serious Mental Illness: Protocol for a User-Centered Design and Prospective Cohort Study

Abstract

Background Serious mental illnesses (SMI) are common, disabling, and challenging to treat, requiring years of monitoring and treatment adjustments. Stress or reduced medication adherence can lead to rapid worsening of symptoms and behaviors. Illness exacerbations and relapses generally occur with little or no clinician awareness in real time, leaving limited opportunity to modify treatments. Previous research suggests that passive mobile sensing may be beneficial for individuals with SMI by helping them monitor mental health status and behaviors, and quickly detect worsening mental health for prompt assessment and intervention. However, there is too little research on its feasibility and acceptability and the extent to which passive data can predict changes in behaviors or symptoms. Objective The aim of this research is to study the feasibility, acceptability, and safety of passive mobile sensing for tracking behaviors and symptoms of patients in treatment for SMI, as well as developing analytics that use passive data to predict changes in behaviors and symptoms. Methods A mobile app monitors and transmits passive mobile sensor and phone utilization data, which is used to track activity, sociability, and sleep in patients with SMI. The study consists of a user-centered design phase and a mobile sensing phase. In the design phase, focus groups, interviews, and usability testing inform further app development. In the mobile sensing phase, passive mobile sensing occurs with participants engaging in weekly assessments for 9 months. Three- and nine-month interviews study the perceptions of passive mobile sensing and ease of app use. Clinician interviews before and after the mobile sensing phase study the usefulness and feasibility of app utilization in clinical care. Predictive analytic models are built, trained, and selected, and make use of machine learning methods. Models use sensor and phone utilization data to predict behavioral changes and symptoms. Results The study started in October 2020. It has received institutional review board approval. The user-centered design phase, consisting of focus groups, usability testing, and preintervention clinician interviews, was completed in June 2021. Recruitment and enrollment for the mobile sensing phase began in October 2021. Conclusions Findings may inform the development of passive sensing apps and self-tracking in patients with SMI, and integration into care to improve assessment, treatment, and patient outcomes. Trial Registration ClinicalTrials.gov NCT05023252; https://clinicaltrials.gov/ct2/show/NCT05023252 International Registered Report Identifier (IRRID) DERR1-10.2196/39010

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“…This setting of large, unlabeled data sets with sparse supervision appears frequently in the field of digital health care. Notable examples include passive mobile sensing studies for mental health and well-being [11][12][13][14][15][16][17][18][19][20], digital therapeutics for children with autism spectrum disorder that record videos of the child [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37], and passive brain sensors for brain-computer interfaces [38][39][40][41][42][43][44][45][46]. As such, this study protocol can be considered as one of the first tests of a broader emerging paradigm in precision health.…”

Personalized Machine Learning using Passive Sensing and Ecological Momentary Assessments for Meth Users in Hawaii: A Research Protocol

“…This setting of large unlabeled datasets with sparse supervision appears frequently in the field of digital healthcare. Notable examples include passive mobile sensing studies for mental health and wellbeing [11][12][13][14][15][16][17][18][19][20], digital therapeutics for children with autism spectrum disorder which record video of the child [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37], and passive brain sensors for brain-computer interfaces [38][39][40][41][42][43][44][45][46]. As such, this research protocol can be considered as one of the first tests of a broader emerging paradigm in precision health.…”