Short-term prediction of suicidal thoughts and behaviors in adolescents: Can recent developments in technology and computational science provide a breakthrough?
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 relativ...
| Published in: | Journal of Affective Disorders Vol. 250; pp. 163 - 170 |
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| Main Authors: | , , , |
| Format: | research Journal Article |
| Published: |
Elsevier B.V.
May2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135792438&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135792438 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01650327 3M9 jtl: Journal of Affective Disorders issn: 01650327 maglogo: N pubinfo: dt: May2019 vid: 250 pid: 1004 pub: Elsevier B.V. artinfo: ui: 135792438 135792438 NLM30856493 135792438 10.1016/j.jad.2019.03.044 NLM30856493 135792438 ppf: 163 ppct: 7 formats: tig: atl: Short-term prediction of suicidal thoughts and behaviors in adolescents: Can recent developments in technology and computational science provide a breakthrough? aug: au: Allen, Nicholas B. Nelson, Benjamin W. Brent, David Auerbach, Randy P. affil: Department of Psychology, University of Oregon, Eugene, Oregon, United States sug: subj: Suicidal Ideation Suicide, Attempted Psychosocial Factors Self-Injurious Behavior Psychosocial Factors Suicide Prevention Models, Theoretical Risk Factors Suicide Psychosocial Factors Bullying Psychosocial Factors Adolescence Risk Assessment Funding Source Human Adolescent: 13-18 years ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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