Digital Monitoring of Impulsivity as a Proximal Risk Factor for Suicidal Outcomes

背景：自杀是死亡的主要原因，但自杀预防的进展已经放缓， 关于迫在眉睫的风险预测因素的知识存在重大差距。冲动是近端风险的理想候选人 因素：这是一个已知的transdiagnosis远端风险因素，它随着时间的推移在个体内波动，它是一个 可修改的干预目标然而，现有的自杀研究还没有检查多个组成部分， 高风险时期的实时，状态冲动-测试的必要步骤（a）冲动是否减少 在真实的时间内抵抗自杀冲动的能力，（B）这个多方面结构的哪些组成部分是相关的 与自杀风险和时间，以及（c）模式是否因个体或亚组而异。研究：我们建议 一个细粒度的，深入的纵向调查之间的联系组成部分的冲动和 在两个自杀高风险样本中发现自杀冲动。目标1将涉及数字化的二级数据分析， 对有自杀念头的急诊科就诊者进行监测研究，以分析真实的- 冲动、自杀冲动和抵抗自杀冲动的能力之间的时间关联。我们将测试 状态冲动的预测性超过了特质冲动的影响。在目标2中，我们将进行数字监测， 对140名因自杀念头住院的人进行的研究，以评估状态冲动的多个组成部分 使用自我报告，移动的任务，和被动电话数据，我们将测试与自杀的具体联系， 在真实的时间里抵抗它们的欲望和能力。在目标3中，我们将比较组级、子组级和 使用推理统计（网络建模）和 预测分析（机器学习）。这项工作将使我们能够大大提高对一个关键的理解， 转诊断过程，为制定检测和干预战略奠定基础 在最佳的时间尺度上针对冲动的特定元素。候选人的职业发展，目标， 与环境：本方案的研究目标和候选人的职业发展将得到支持 通过马萨诸塞州总医院/哈佛医学院以及正式的 培训和指导（T1）自杀高危患者的数字监测，（T2）高级多变量 纵向数据分析，（T3）数字表型分析，以及（T4）准备干预重点R 01 成绩.导师团队包括导师Jordan Smoller博士，精准精神病学的领先专家 和预测分析;共同导师马修·诺克博士，自杀研究的领导者;埃文·克莱曼博士， 自杀状态的实时监测和数字表型分析专家;顾问艾丹·赖特博士， 多层次和个性化统计建模专家; JP Onnela博士，数字表型分析和 统计网络科学; Laura Germine博士，移动的任务评估的先驱。该奖项将提供 具有高级培训和必要技能的候选人，以启动独立的研究计划，重点是 关于使用移动的技术来促进对冲动决策和自杀的理解。

Background: Suicide is a leading cause of death, but progress in suicide prevention has been slowed by critical gaps in knowledge about predictors of imminent risk. Impulsivity is an ideal candidate for a proximal risk factor: it is a known transdiagnostic distal risk factor, it fluctuates over time within individuals, and it is a modifiable target for intervention. Existing suicide research, however, has not examined multiple components of real-time, state impulsivity over high-risk periods — a necessary step to test (a) whether impulsivity reduces ability to resist suicidal urges in real time, (b) which components of this multi-faceted construct are associated with suicide risk and when, and (c) whether patterns differ for individuals or subgroups. Research: We propose a fine-grained, intensive longitudinal investigation of associations between components of impulsivity and suicidal urges in two samples at high risk for suicide. Aim 1 will involve secondary data analysis of a digital monitoring study of individuals presenting to an emergency department with suicidal thoughts to analyze real- time associations between impulsivity, suicidal urges, and ability to resist suicidal urges. We will test whether state impulsivity is predictive beyond the effect of trait impulsivity. In Aim 2, we will conduct a digital monitoring study of 140 individuals hospitalized for suicidal thoughts to assess multiple components of state impulsivity using self-report, mobile tasks, and passive phone data, and we will test specific associations with suicidal urges and ability to resist them in real time. In Aim 3, we will compare group-level, subgroup-level, and personalized models of these data using a combination of inferential statistics (network modeling) and predictive analytics (machine learning). This work will allow us to dramatically improve understanding of a key transdiagnostic process, laying the groundwork for development of detection and intervention strategies targeted at specific elements of impulsivity at an optimal timescale. Candidate’s Career Development, Goals, and Environment: This proposal’s research aims and the candidate’s career development will be supported by the many resources available at Massachusetts General Hospital/Harvard Medical School as well as formal training and mentorship in (T1) digital monitoring of patients at high risk for suicide, (T2) advanced multivariate longitudinal data analysis, (T3) digital phenotyping, and (T4) preparing for an intervention-focused R01 submission. The mentorship team includes Mentor Dr. Jordan Smoller, leading expert in precision psychiatry and predictive analytics; Co-Mentors Dr. Matthew Nock, leader in the study of suicide; and Dr. Evan Kleiman, expert in real-time monitoring and digital phenotyping of suicidal states; and Consultants Dr. Aidan Wright, expert in multilevel and personalized statistical modeling; Dr. JP Onnela, leader in digital phenotyping and statistical network science; and Dr. Laura Germine, pioneer in mobile task assessment. This award will provide the candidate with advanced training and skills necessary to launch an independent research program focused on using mobile technology to advance understanding of impulsive decision-making and suicide.