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. Methods 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. Multi-modal behavioral sensing (i.e., physical activity, geospatial 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 nascency. 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.

Automated sensing of daily activity: A new lens into development

“…Additionally, changes in early versus late semester activities successfully predicted students end of year GPA (Wang, Harari, Hao, Zhou, & Campbell, ; Wang, Hartman et al, ). Within the mobile sensing community, there is a growing interest in such comprehensive ecological approaches, with efforts to predict and intervene upon physical and mental health outcomes such as psychosis onset and failures in smoking cessation (Ben‐Zeev, Brian et al, ; Chatterjee et al., ; Insel, ).…”

Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing

“…Rather, the majority of people with schizophrenia fluctuate between full or partial remission and episodes of symptomatic relapse. Psychotic symptoms may change over months, weeks, or even days, and can be affected by both external conditions and internal states [2]. In the case of relapse, patients may find themselves suffering severe hardship if not helped, such as homelessness, incarceration, and victimization.…”

Voice Patterns in Schizophrenia: A systematic Review and Bayesian Meta-Analysis

“…In addition, voice analysis may potentially allow to assess the response to psychosocial or pharmacological treatment over longer periods using objective and quantitative indices, and enhance the capability of clinicians to capture the complex relationship between emotion regulation, expressive behavior, social perception and cognitive and clinical features of the disorder (e.g. Ben-Zeev et al, 2017;Dahlgren et al, 2018;Tahir et al, 2019) . Finally, vocal abnormalities may be related to neuromotor disorders frequently associated with schizophrenia and its neurodevelopmental pathophysiology (Cannizzaro et al, 2005;Konopka and Roberts, 2016;Matsumoto et al, 2013;Walther, 2015;Walther and Strik, 2012), or secondary induced by antipsychotic medications (Peluso et al, 2012;Tenback et al, VOICE IN SCHIZOPHRENIA: REVIEW AND META-ANALYSIS 2010).…”