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Allen NB, Nelson BW, Brent D, Auerbach RP. J. Affect. Disord. 2019; 250: 163-169.


Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York City, United States; Division of Clinical Developmental Neuroscience, Sackler Institute, New York City, United States.


(Copyright © 2019, Elsevier Publishing)






BACKGROUND: Suicide is one of the leading causes of death among adolescents, and developing effective methods to improve short-term prediction of suicidal thoughts and behaviors (STBs) is critical. Currently, the most robust predictors of STBs are demographic or clinical indicators that have relatively weak predictive value. However, there is an emerging literature on short-term prediction of suicide risk that has identified a number of promising candidates, including (but not limited to) rapid escalation of: (a) emotional distress, (b) social dysfunction (e.g., bullying, rejection), and (c) sleep disturbance. However, these prior studies are limited in two critical ways. First, they rely almost entirely on self-report. Second, most studies have not focused on assessment of these risk factors using intensive longitudinal assessment techniques that are able to capture the dynamics of changes in risk states at the individual level.

METHOD: In this paper we explore how to capitalize on recent developments in real-time monitoring methods and computational analysis in order to address these fundamental problems.

RESULTS: We now have the capacity to use: (a) smartphone, wearable computing, and smart home technology to conduct intensive longitudinal assessments monitoring of putative risk factors with minimal participant burden and (b) modern computational techniques to develop predictive algorithms for STBs. Current research and theory on short-term risk processes for STBs, combined with the emergent capabilities of new technologies, suggest that this is an important research agenda for the future. LIMITATIONS: Although these approaches have enormous potential to create new knowledge, the current empirical literature is limited. Moreover, passive monitoring of risk for STBs raises complex ethical issues that will need to be resolved before large scale clinical applications are feasible.

CONCLUSIONS: Smartphone, wearable, and smart home technology may provide one point of access that might facilitate both early identification and intervention implementation, and thus, represents a key area for future STB research.

Copyright © 2019 Elsevier B.V. All rights reserved.

Language: en


Machine learning; Smart home technology; Smart phones; Suicide prediction; Suicide prevention; Wearable computing


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