Capturing the Structure and Dynamics of Suicidal Thinking

捕捉自杀想法的结构和动态

• 批准号: 10669593
• 负责人: Daniel Coppersmith
• 金额: $ 3.39万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10669593
• 关键词: Address; Adult; Affective; Behavior; Behavioral; Biology; Cause of Death; Cellular Phone; Clinical; Cognitive; Complex; Computer Models; Data; Dimensions; Discipline; Economics; Event; Feeling suicidal; Funding; Future; Goals; Life; Light; Longitudinal Studies; Measurement; Measures; Meta-Analysis; Methodology; Modeling; National Institute of Mental Health; Participant; Pathway interactions; Persons; Physics; Prevention; Probability; Process; Psychiatry; Psychology; Public Health; Questionnaires; Reporting; Research; Research Personnel; Statistical Models; Structure; Suicide; Suicide attempt; Suicide prevention; Survival Analysis; Technology; Testing; Time; Training; Treatment Efficacy; United States; Update; Work; adaptive intervention; career; dynamic system; high risk; improved; innovation; insight; interest; lens; markov model; predictive modeling; programs; real time monitoring; skills; statistics; suicidal; suicidal behavior; suicidal morbidity; suicidal risk; suicide model; suicide rate; theories; years of life lost

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.

项目总结/摘要 自杀是一个毁灭性的公共卫生问题。美国每年有4万多人死于自杀 它是导致寿命损失的第四大因素。自杀率并没有随着时间的推移而改变， 自杀的预测并没有随着时间的推移而改善，干预措施的有效性也没有随着时间的推移而改变。 为了提高对自杀的理解、预测和预防，迫切需要精确的 概念化、操作化和描述自杀现象。自杀念头是一种前因 自杀行为的核心部分。自杀念头的许多特征，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来观察如何 自杀念头在日常生活中不断出现，并不断发现自杀念头似乎在内心起起伏伏- 人们随着时间的推移。鉴于越来越多的证据表明自杀想法的可变性，理论家们 开始争论自杀应该通过动力系统的透镜来看待。列入这些 理论是这样一个概念，即随着时间的推移，人们会经历多种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，但还没有实证研究进行过测试， 确认自杀状态。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空白： 结合计算建模和动态实时数据来捕捉自杀状态。项目目标1 是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，一种形式的 计算模型，识别隐藏的离散状态的动态数据，将被应用于多个实时 从一项由NIMH资助的正在进行的自杀想法密集纵向研究中获得的自杀想法指标 和行为（N = 300）。目的2是捕捉自杀状态的持续时间。自杀的时间动态 当参与者在状态之间转换时， 参与者停留在给定的状态。目的3是测试哪些自杀状态可以预测近期的自杀风险。 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 以及对每种自杀状态的自杀企图的精确事件时间预测。拟议的研究 最大的潜在影响是提供自杀想法的基础信息， 这些信息可以用于未来的及时适应性干预措施，以预防自杀。拟议 这项研究还促进了一项研究计划，其中包括两个主要的NIMH优先事项的自杀预防和 计算精神病学如果成功，拟议的项目将促进对自杀的理解。 这种想法有一天可以改善对自杀行为的预测和预防。

项目成果

Mapping the timescale of suicidal thinking.

• DOI: 10.1073/pnas.2215434120
• 发表时间: 2023-04-25
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Coppersmith, Daniel D. L.;Ryan, Oisin;Fortgang, Rebecca G.;Millner, Alexander J.;Kleiman, Evan M.;Nock, Matthew K.
• 通讯作者: Nock, Matthew K.