CrossCheck: Integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse
| Autor: | Michael Merrill, M.S.H. Aung, Rachel Brian, Dror Ben-Zeev, Marta Hauser, Weichen Wang, Tanzeem Choudhury, Emily A. Scherer, Rui Wang, John M. Kane, Vincent W.-S. Tseng, Andrew T. Campbell |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Adult
Male Telemedicine Data management Applied psychology MEDLINE Monitoring Ambulatory Motor Activity Health Professions (miscellaneous) Article 03 medical and health sciences Young Adult 0302 clinical medicine Recurrence medicine Humans Speech Mobile technology Medical Informatics Applications Spatial Analysis business.industry Rehabilitation medicine.disease 030227 psychiatry Hospitalization Psychiatry and Mental health Geolocation Identification (information) Psychotic Disorders Schizophrenia Female Smartphone business Mobile device 030217 neurology & neurosurgery Clinical psychology |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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