MAPS: Mobile Assessment for the Prediction of Suicide

MAPS：自杀预测的移动评估

基本信息

批准号：
9982129
负责人：
NICHOLAS B ALLEN
金额：
$ 71.41万
依托单位：
UNIVERSITY OF OREGON
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9982129
关键词：
Academic Medical Centers Acoustics Acute Address Adolescent Behavior Biological Car Phone Cause of Death Cellular Phone Centers for Disease Control and Prevention (U.S.)Cessation of life Clinic Clinical Clinical Management Clinical assessments Communication Computational Technique Computer Models Computing Methodologies Control Groups Data Development Discrimination Disease susceptibility Ecological momentary assessment Emotional Event Facial Expression Failure Feeling suicidal Fibrinogen Functional disorder Geography High School Student Hospitalization Hour Individual Inpatients Interview Language Life Light Literature Loneliness Machine Learning Measures Medical center Meta-Analysis Methodology Methods Modernization Monitor Movement Music Outpatients Participant Patient Self-Report Patients Pattern Predictive Value Process Measure Public Health Questionnaires Recording of previous events Reporting Research Risk Risk Assessment Risk Factors Savings Severities Sleep disturbances Sleeplessness Social Interaction Software Tools Stress Suicide Suicide attempt Techniques Technology Testing Text Messaging Time Universities Variant Voice actigraphy active method adolescent suicide aged base behavior prediction behavioral outcome bullying clinical care diaries emotional distress follow up assessment follow-up high-risk adolescents ideation improved indexing insight instrument language processing longitudinal design mobile computing prediction algorithm predictive modeling programs psychiatric symptom real time monitoring recruit sensor sex signal processing social social deficits social media social relationships statistics success suicidal adolescent suicidal behavior suicidal risk suicide attempter theories tool

项目摘要

Project Summary Suicide is the second leading cause of death among adolescents. In addition to deaths, 16% of adolescents report seriously considering suicide each year, and 8% make one or more attempts. Despite these alarming statistics, little is known about factors that confer imminent risk for suicide. Thus, developing effective methods to improve short-term prediction of suicidal thoughts and behaviors (STBs) is critical. Currently, our 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 rapid escalation of: (a) emotional distress, (b) social dysfunction (i.e., bullying, rejection), and (c) sleep disturbance. Yet, 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. These are fundamental limitations. While suicidal ideation may precede an attempt by years, socio-emotional changes preceding a suicide attempt often occurs within the time span of minutes to hours. This study will capitalize on recent developments in real-time monitoring methods that harness adolescents' naturalistic use of smartphone technology. Specifically, we now have the capacity to use: (a) smartphone technology to conduct intensive longitudinal assessments monitoring putative risk factors with minimal participant burden and (b) modern computational techniques to develop predictive algorithms for STBs. The project will include high-risk adolescents (n = 200) aged 13-18 years recruited from outpatient and inpatient clinics: (a) recent suicide attempters with current ideation (n = 70), (b) current suicide ideators with no attempt history (n = 70), and (c) a psychiatric control group with no STB history (n = 60). Effortless Assessment of Risk States (EARS) will be used to continuously measure variables relevant to key risk domains—emotional distress, social dysfunction, and sleep disturbance—through passive monitoring of participants' smartphone use. First, we will test between-group differences in risk factors during an initial 2-week period, and determine the extent to which risk factors derived from mobile phones improves discrimination over self-reported indicators. Second, we will use statistical techniques to test whether the risk factors improve short-term prediction of STBs (e.g., suicide attempts, hospitalization) during the 6-month follow-up period above and beyond clinical assessments. Third, computational machine learning techniques—based on a priori and learned features—will develop predictive models that utilize the full range of intensive longitudinal data collected by the active and passive monitoring methods to predict group membership and STB outcomes. Ultimately, by leveraging smartphone technology, we aim to improve the short-term STB prediction and provide clinicians and patients with reliable, scalable and actionable tools that will reduce the needless loss of life.

项目概要自杀是青少年除死亡之外的第二大死因。每年都有青少年认真考虑自杀，尽管如此，仍有 8% 的青少年尝试过自杀。令人震惊的统计数据表明，人们对导致自杀风险的因素知之甚少，因此，开发有效的方法。改善自杀想法和行为（STB）短期预测的方法至关重要。目前，我们对 STB 最有力的预测因素是人口统计或临床指标，这些指标具有相对然而，有一些关于自杀风险短期预测的新兴文献。确定了一些有前途的候选者，包括迅速升级的：（a）情绪困扰，（b）社交功能障碍（即欺凌、拒绝）和（c）睡眠障碍然而，先前的研究在两个关键方面受到限制。首先，他们几乎完全依赖于自我报告，其次，大多数研究并没有集中于对这些的评估。使用能够捕捉动态的密集纵向评估技术的风险因素风险状态的变化是根本性的限制，而自杀意念可能先于尝试。数年来，自杀未遂前的社会情绪变化通常发生在几分钟内这项研究将利用实时监测方法的最新发展。具体来说，我们现在有能力使用：(a) 智能手机技术进行密集的纵向评估，监测假定的风险因素最小的负担和（b）现代计算技术来开发机顶盒的预测参与者算法。该项目将包括从门诊和医院招募的 13-18 岁高危青少年 (n = 200)。住院诊所：(a) 最近有自杀念头的自杀者 (n = 70)，(b) 目前有自杀念头的人尝试历史（n = 70），以及（c）没有 STB 历史的精神病对照组（n = 60）。风险状态（EARS）将用于持续测量与关键风险领域相关的变量——情绪痛苦、社交功能障碍和睡眠障碍——通过参与者智能手机的被动监控首先，我们将在最初的两周内测试危险因素的组间差异，并确定。手机带来的风险因素在多大程度上改善了对自我报告的歧视其次，我们将利用统计技术来检验风险因素是否短期内改善。上述 6 个月随访期间的 STB 预测（例如自杀未遂、住院治疗）第三，基于先验和计算的机器学习技术。学习到的特征——将开发利用全方位纵向密集数据的预测模型通过主动和被动监测方法收集来预测群体成员资格和 STB 结果。最终，通过利用智能手机技术，我们的目标是改进短期 STB 预测并提供为战士和患者提供可靠、可扩展且可操作的工具，以减少不必要的生命损失。