Use of Multimodal Technology to Identify Digital Correlates of Violence Among Inpatients With Serious Mental Illness: A Pilot Study.

Psychiatr Serv

Dr. Ben-Zeev and Ms. Brian are with the Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle. Dr. Scherer is with the Department of Biomedical Data Science and the Department of Community and Family Medicine, and Dr. Mistler is with the Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire. Dr. Mistler is also with the Department of Psychiatry, New Hampshire Hospital, Concord. Dr. Campbell and Mr. Wang are with the Department of Computer Science, Dartmouth College, Hanover, New Hampshire.

Published: October 2017

Objective: The study examined multimodal technologies to identify correlates of violence among inpatients with serious mental illness.

Methods: Twenty-eight high-risk inpatients were provided with smartphones adapted for data collection. Participants recorded their thoughts and behaviors by using self-report software. Sensors embedded in each device (microphone and accelerometers) and throughout the inpatient unit (Bluetooth beacons) captured patients' activity and location.

Results: Self-reported delusions were associated with violent ideation (odds ratio [OR]=3.08), damaging property (OR=8.24), and physical aggression (OR=12.39). Alcohol and cigarette cravings were associated with violent ideation (OR=5.20 and OR=6.08, respectively), damaging property (OR=3.71 and OR=4.26, respectively), threatening others (OR=3.62 and OR=3.04, respectively), and physical aggression (OR=6.26, and OR=8.02, respectively). Drug cravings were associated with violent ideation (OR=2.76) and damaging property (OR=5.09). Decreased variability in physical activity and noisy ward conditions were associated with violent ideation (OR=.71 and OR=2.82, respectively).

Conclusions: Identifiable digital correlates may serve as indicators of increased risk of violence.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5891222PMC
http://dx.doi.org/10.1176/appi.ps.201700077DOI Listing

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