CrossCheck: Integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse

Dror Ben-Zeev, Rachel Brian, Rui Wang, Weichen Wang, Andrew T. Campbell, Min S. H. Aung, Michael Merrill, Vincent W. S. Tseng, Tanzeem Choudhury, Marta Hauser, John M. Kane, Emily A. Scherer

Research output: Contribution to journalArticlepeer-review

115 Citations (Scopus)


Objective: This purpose of this study was to describe and demonstrate CrossCheck, a multimodal data collection system designed to aid in continuous remote monitoring and identification of subjective and objective indicators of psychotic relapse. Method: Individuals with schizophrenia-spectrum disorders received a smartphone with the monitoring system installed along with unlimited data plan for 12 months. Participants were instructed to carry the device with them and to complete brief self-reports multiple times a week. Multimodal behavioral sensing (i.e., physical activity, geospatials activity, speech frequency, and duration) and device use data (i.e., call and text activity, app use) were captured automatically. Five individuals who experienced psychiatric hospitalization were selected and described for instructive purposes. Results: Participants had unique digital indicators of their psychotic relapse. For some, self-reports provided clear and potentially actionable description of symptom exacerbation prior to hospitalization. Others had behavioral sensing data trends (e.g., shifts in geolocation patterns, declines in physical activity) or device use patterns (e.g., increased nighttime app use, discontinuation of all smartphone use) that reflected the changes they experienced more effectively. Conclusion: Advancements in mobile technology are enabling collection of an abundance of information that until recently was largely inaccessible to clinical research and practice. However, remote monitoring and relapse detection is in its nascence. Development and evaluation of innovative data management, modeling, and signal-detection techniques that can identify changes within an individual over time (i.e., unique relapse signatures) will be essential if we are to capitalize on these data to improve treatment and prevention.
Original languageEnglish
Pages (from-to)266-275
JournalPsychiatric Rehabilitation Journal
Issue number3
Publication statusPublished - 3 Apr 2017

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