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

冲动作为自杀结果的近端风险因素的数字监测

• 批准号: 10642521
• 负责人: Rebecca Gwen Fortgang
• 金额: $ 19.56万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-15 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10642521
• 关键词: Accident and Emergency department; Adult; Affect; Award; Behavior; Behavioral; Biological Markers; Cause of Death; Clinical; Data; Data Analyses; Data Set; Decision Making; Detection; Development; Diagnostic; Distal; Ecological momentary assessment; Elements; Environment; Fatigue; Feeling hopeless; Feeling suicidal; Funding; Future; General Hospitals; Goals; Grain; Hospitalization; Impulsive Behavior; Impulsivity; Individual; Intervention; Investigation; Jordan; Knowledge; Left; Life; Link; Machine Learning; Manuscripts; Maps; Massachusetts; Measures; Mentors; Mentorship; Methods; Modeling; National Institute of Mental Health; Outcome; Participant; Patient Monitoring; Patient Self-Report; Patients; Pattern; Phenotype; Positioning Attribute; Predictive Analytics; Prevention; Process; Psychiatric therapeutic procedure; Psychiatry; Public Health; Publishing; Research; Research Priority; Research Proposals; Resources; Risk; Risk Factors; Sampling; Science; Sleep Deprivation; Statistical Models; Subgroup; Suicide; Suicide attempt; Suicide prevention; Telephone; Testing; Time; Training; Work; career development; clinical decision-making; digital; digital monitoring; experience; germine; high risk; improved; inferential statistics; medical schools; mobile computing; multidisciplinary; multimodality; network models; novel; programs; real time monitoring; screening; skills; suicidal; suicidal behavior; suicidal risk; therapy development; time interval; trait; trait impulsivity

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.

背景：自杀是死亡的主要原因，但预防自杀的进展已被放缓 关于迫在眉睫的风险预测因素的知识差距是近端风险的理想选择 因素：它是已知的经诊断远端风险因素，它随着个人而随着时间的流逝而波动，它是一个 但是，可修改的干预目标。 实时，高风险期间的状态冲动性 - 必要步骤测试（a）冲动性是否会降低 能够实时抵抗自杀冲动，（b）该多面构造的哪些组成部分是相关的 自杀风险以及何时以及（c）paterns对个体还是亚组有不同 对冲动性的组成部分和 自杀的两个样本中的自杀率涉及数字的辅助数据分析 监测对急诊科的个人的研究，以自杀思想来分析现实 冲动性，自杀冲动和抵抗自杀冲动的能力之间的时间关联。 国家冲动性超出了AIM 2的特征冲动的影响。 研究140个人住院的自杀念头，以评估国家冲动的多次组成部分 使用自我报告，移动任务和被动电话数据，我们将测试与自杀的特定关联 敦促和实时抵抗它们的能力。 这些数据的个性化模型，结合了下属统计（网络建模）和 预测分析（机器学习）。 经诊断过程，为开发检测和国际策略奠定了基础 针对最佳时间尺度的冲动性要素。 和环境：该提案的研究目的和候选人的职业发展将得到支持 通过马萨诸塞州综合医院/哈佛医学院的许多资源作为正式 （T1）对有自杀风险高风险的患者进行数字监控的培训和指导，（T2）高级多变量 纵向数据分析，（T3）数字表型和（T4）以相互作用为中心的R01进行 提交。 和预测分析； 自杀状态的实时监控和数字表型专家； 专家企业和个性化统计建模； 统计网络科学和移动任务评估的先驱Laura Germine博士。 具有高级培训和技能的候选人，启动独立研究计划所需的技能 关于使用移动技术来提高对冲动决策和自杀的理解。

项目成果

Validation of an ICD-code-based case definition for psychotic illness across three health systems.

跨三个卫生系统验证基于 ICD 代码的精神病病例定义。

• DOI: 10.1101/2024.02.28.24303443
• 发表时间: 2024
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Deo,AnthonyJ;Castro,VictorM;Baker,Ashley;Carroll,Devon;Gonzalez-Heydrich,Joseph;Henderson,DavidC;Holt,DaphneJ;Hook,Kimberly;Karmacharya,Rakesh;Roffman,JoshuaL;Madsen,EmilyM;Song,Eugene;Adams,WilliamG;Camacho,Luisa;Gasman
• 通讯作者: Gasman