Capturing the Structure and Dynamics of Suicidal Thinking

项目摘要/摘要 自杀是一个毁灭性的公共卫生问题。在美国，每年有超过4万人死于自杀 它是造成寿命损失年数的第四大因素。自杀率并没有随着时间的推移而变化， 自杀的预测并没有随着时间的推移而改善，干预的效果也没有随着时间的推移而改变。 为了提高对自杀的认识、预测和预防，迫切需要准确的 自杀现象的概念化、操作化和描述。自杀念头是一种先天因素 自杀行为和自杀途径的中心部分。自杀念头的许多特征，例如 它们的持续时间在很大程度上是未知的。初步的描述性工作已经使用智能手机观察到 自杀念头在日常生活中展开，并不断发现自杀念头似乎在-- 随着时间的推移，人们。鉴于越来越多的证据表明自杀想法是多变的，理论家们 开始主张应该从动力系统的角度来看待自杀。包括在这些文件中 理论认为，随着时间的推移，人们会经历多个离散的自杀风险状态。 尽管自杀状态具有强大的理论和临床意义，但还没有经验性的研究证明 有效的自杀状态。拟议的项目旨在通过以下方式解决自杀研究中的这一重大差距 结合计算模型和动态实时数据来捕捉自杀状态。该项目的目标1 是为了确定人内自杀状态的数量。为了实现这一目标，隐马尔可夫模型是一种 识别动态数据中隐藏的离散状态的计算模型将应用于多个实时 来自美国国立卫生研究院资助的自杀念头密集纵向研究的自杀念头测量 和行为(N=300)。目标2是捕捉自杀状态的持续时间。自杀的时间动力学 国家的运作将取决于参与者何时在国家之间过渡以及平均多长时间 参与者保持在给定的状态。目的3是测试哪些自杀状态预示着近期的糖尿病风险 自杀行为。将应用现有的事件-时间预测模型框架来生成可解释的 以及每种自杀状态下自杀未遂的准确事件时间预测。这项拟议研究的 最大的潜在影响是提供关于自杀想法的基本信息，并产生自杀倾向 这些国家可用于未来的及时适应性自杀预防干预措施。建议数 研究还促进了一项研究计划，该计划包括NIMH的两个主要优先事项：自杀预防和 计算精神病学。如果成功，拟议的项目将促进对自杀的理解 认为有一天可以提高对自杀行为的预测和预防。

Project Summary/Abstract Suicide is a devastating public health problem. Over 40,000 people die by suicide each year in the United States and it is the fourth leading contributor to years of life lost. The suicide rate has not changed over time, prediction of suicide has not improved over time, and the efficacy of interventions has not changed over time. In order to improve the understanding, prediction, and prevention of suicide, there is an urgent need for precise conceptualizing, operationalizing, and describing of suicidal phenomena. Suicidal thoughts are an antecedent of suicidal behavior and a central part of the pathway to suicide. Many features of suicidal thoughts, such as their duration, are largely unknown. Preliminary descriptive work has used smartphones to observe how suicidal thoughts unfold in daily life and consistently found that suicidal thoughts seem to ebb and flow within- people over time. In light of the accumulating evidence of the variability of suicidal thinking, theorists are beginning to argue that suicide should be viewed through the lens of dynamical systems. Included in these theories is the notion that there are multiple discrete states of suicide risk that people move through over time. Despite the powerful theoretical and clinical implications of suicidal states, no empirical work has tested and validated suicidal states. The proposed project aims to address this major gap in suicide research by combining computational modeling and dynamic real-time data to capture suicidal states. Aim 1 of the project is to identify the number of within-person suicidal states. To achieve this aim, Hidden Markov Models, a form of computational model that identifies hidden discrete states in dynamic data, will be applied to multiple real-time measures of suicidal thinking from an ongoing NIMH-funded intensive longitudinal study of suicidal thoughts and behaviors (N = 300). Aim 2 is to capture the duration of suicidal states. The temporal dynamics of suicidal states will be operationalized as when participants transition between states and on average how long participants stay in a given state. Aim 3 is to test which suicidal states are predictive of near-term risk of suicidal behavior. An existing event-time prediction model framework will be applied to generate interpretable and precise event-time predictions of suicide attempts for each type of suicidal state. The proposed study’s greatest potential impacts are to provide foundational information on suicidal thinking and to generate suicidal states that could be used in future Just-in-Time-Adaptive Interventions for suicide prevention. The proposed study also promotes a program of research that includes two major NIMH priorities of suicide prevention and computational psychiatry. If successful the proposed project would advance the understanding of suicidal thinking which could one day improve the prediction and prevention of suicidal behavior.