MAPS: Mobile Assessment for the Prediction of Suicide

MAPS：自杀预测的移动评估

• 批准号: 10228034
• 负责人: NICHOLAS B ALLEN
• 金额: $ 69.92万
• 依托单位: UNIVERSITY OF OREGON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10228034
• 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; smartphone based assessment; social; social deficits; social media; social relationships; statistics; success; suicidal adolescent; suicidal behavior; suicidal risk; suicide attempter; theories; tool

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 自杀是青少年死亡的第二大原因。除了死亡之外，还有 16% 每年都有青少年认真考虑自杀，其中 8% 的人会尝试一次或多次。尽管有这些 统计数据令人震惊，但人们对导致自杀风险迫在眉睫的因素知之甚少。因此，开发有效的 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 预测价值较弱。然而，有一篇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），以及 (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，他们几乎完全依赖自我报告。其次，大多数研究并没有集中于对这些因素的评估。 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。这些是根本性的限制。虽然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 小时。这项研究将利用实时监测方法的最新发展，这些方法利用 青少年自然地使用智能手机技术。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 最小的参与者负担和（b）现代计算技术来开发机顶盒的预测算法。 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）。轻松评估 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 使用。首先，我们将在最初的两周内测试风险因素的组间差异，并确定 源自移动电话的风险因素在多大程度上改善了对自我报告的歧视 指标。其次，我们将使用统计技术来测试风险因素是否会在短期内改善。 上述 6 个月随访期间的 STB 预测（例如自杀未遂、住院治疗） 超出临床评估。第三，计算机器学习技术——基于先验和 学习到的特征——将开发利用全方位密集纵向数据的预测模型 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 为临床医生和患者提供可靠、可扩展且可操作的工具，以减少不必要的生命损失。