CrossCheck: Integrating... to identify digital indicators of psychotic relapse
Ben-Zeev, D. et al (2017). CrossCheck: Integrating self-report.... Psychiatric Rehabilitation Journal, 40(3), 266–275.
Full title: CrossCheck: Integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse
Abstract
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. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
Summary
Combining EMA, mobile sensing, and mobile use to look at psychotic relapse (hospitalization)
Features
EMA
Wednesday and Friday
10 item self-report measure from 0 to 3
focus on symptoms of psychois and functioning (table 1)
Final composite score of -15 to 15
Behavioral sensing
physical activity:
activity rating every 10s when the user is moving or every 30s if the device is stationary
geospatial:
GPS every 10min,
distance traveled, standard deviation of distances, duration of time spent at the primary location, maximum displacement from the primary location, and location entropy (calculation detail not given)
speech frequency and duration
every 3 min to capture ambient sound
speech frequency and duration
device use
telecommunication:
number and duration of incoming and outgoing phone calls and messages
app use
three broad category: social media, activity, and entertainment
total number of apps used per day
phone check
Number of unlocks and total duration of unlock per day
Result
Exploratory analysis on 5 different patients.
Patient 2: change in time spent at primary location precede before hospitalization
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