Digital Monitoring of Agitation for Short-Term Suicide Risk Prediction

短期自杀风险预测的躁动数字监测

• 批准号: 9806314
• 负责人: Kate H. Bentley
• 金额: $ 19.86万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-19 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9806314
• 关键词: Accelerometer; Acute; Address; Adult; Agitation; Alcohol or Other Drugs use; American; Anxiety; Area; Arousal; Attention; Award; Behavioral; Biological Markers; Cause of Death; Cellular Phone; Cessation of life; Clinical; Clinical assessments; Consensus; DSM-V; Data; Data Analyses; Development; Diagnosis; Distal; Ecological momentary assessment; Feeling; Feeling suicidal; Foundations; Funding; Future; Gold; Grant; Guide prevention; Hospitals; Hour; Individual; Inpatients; Institutes; Intervention; Jordan; Knowledge; Left; Machine Learning; Manuscripts; Maps; Massachusetts; Measures; Mentors; Mentorship; Meta-Analysis; Methodology; Methods; Monitor; Motor; Motor Activity; National Institute of Mental Health; Patient Self-Report; Patients; Positioning Attribute; Prevention; Protocols documentation; Public Health; Public Health Schools; Publishing; Reporting; Research; Research Personnel; Research Training; Resolution; Risk; Risk Assessment; Risk Factors; Role; SECTM1 gene; Sampling; Self-Injurious Behavior; Suicide; Suicide attempt; Suicide prevention; Technology; Testing; Time; Training; United States; United States National Institutes of Health; Work; Wrist; base; behavioral study; career; clinical biomarkers; demographics; diaries; digital; handheld mobile device; high risk; improved; multidisciplinary; multilevel analysis; negative affect; novel; patient oriented research; predictive modeling; standard measure; suicidal; suicidal behavior; suicidal morbidity; suicidal risk; suicide rate; wearable device

Suicide is a prevalent and burdensome public health problem that warrants immediate attention. As the tenth leading cause of death in the United States, suicide claims the lives of more than 44,000 Americans each year. There is an urgent need to identify objective and clinically informative markers of imminent risk for suicidal behavior. Agitation, defined in DSM-5 as excessive motor activity associated with a feeling of inner tension, is listed as a warning sign for suicide by leading organizations and in widely used risk assessment protocols. Yet, prior research on the association between agitation and suicide has key methodological limitations (including related to the operationalization of agitation), which has resulted in minimal empirical evidence to support agitation as a proximal risk factor for suicide. Addressing this gap in knowledge has the potential for significant impact, including informing both the clinical assessment of suicide risk and the development of just-in-time interventions for detecting and responding to acute suicide risk. This project will overcome the limitations of prior suicide risk factor research by assessing multiple behavioral (motor activity and vocal features [e.g., volume, speaking rate, pitch]) and subjective components of agitation and suicidal thoughts and behaviors in a sample at elevated risk for suicide over a short, high-risk period. We will test the hypotheses that (1) objectively measured real-time indicators of agitation correlate with both momentary subjective ratings and validated, gold standard measures of agitation, and (2) both subjective and objective indicators of agitation improve prediction of short-term increases in suicide ideation, plan, and attempt above and beyond other distal and proximal risk factors. We propose to collect high-resolution self-report (e.g., ecological momentary assessment) and passive (e.g., accelerometer) data on agitation using smartphones and wearable sensors from psychiatric inpatients admitted for suicide ideation or attempt during inpatient treatment and the four weeks after discharge. Multi- level modeling and machine learning approaches will be implemented to examine (1) associations between objective and subjective real-time indicators of agitation and validated measures of agitation, and (2) the degree to which real-time indicators of agitation predict momentary fluctuations in suicidal ideation and suicide plan and attempt above and beyond other distal and proximal risk factors. The scientific aims of this study map onto the candidate’s training in three primary areas: (1) digital monitoring of high-risk patients, (2) advanced longitudinal multivariate data analysis, and (3) identification of behavioral and vocal biomarkers. The candidate’s training plan includes mentorship from Dr. Matthew Nock (primary mentor), Dr. Jordan Smoller (co- mentor), Dr. Maurizio Fava (co-mentor), and Drs. Rosalind Picard, Evan Kleiman, and Thomas Quatieri (consultants), as well as quantitative coursework at the Harvard School of Public Health and Massachusetts Institute of Technology. This mentored five-year award will propel the candidate to an independent patient- oriented research career focused on using scalable methods to advance suicide prediction and prevention.

自杀是一个普遍且腐烂的公共卫生问题，值得关注。作为第十 自杀的主要死亡原因每年夺走了44,000多名美国人的生命。 迫切需要确定自杀的目标风险的客观和临床信息的标志 行为。在DSM-5中定义为与内部张力相关的过量运动活动的搅动是 由领先组织和广泛使用的风险评估方案列为自杀的警告标志。然而， 关于煽动与自杀之间关联的事先研究具有关键的方法论局限性（包括 与搅动的操作有关），这导致了最少的经验证据 搅动是自杀的近端风险因素。在了解这一差距方面具有重要的潜力 影响，包括告知自杀风险的临床评估和及时的发展 检测和应对急性自杀风险的干预措施。该项目将克服 先前的自杀危险因素研究通过评估多种行为（运动活动和人声特征[例如， 数量，口语速度，音调]）和煽动和自杀思想和行为的主观组成部分 在短期高风险时期内的自杀风险升高。我们将对（1）客观地测试（1）的假设 测量的搅拌的实时指标与瞬时的主观评分和经过验证的金色相关 搅拌的标准测量以及（2）主观和客观的搅拌改进预测指标 自杀想法，计划和尝试的短期增加，超越其他远端和近端风险 因素。我们建议收集高分辨率自我报告（例如，生态时刻评估）和被动 （例如，加速度计）使用智能手机和精神病患者可穿戴传感器的搅拌数据 在住院治疗期间和出院后的四个星期内接受了自杀想法或尝试。多- 水平建模和机器学习方法将实施以检查（1）之间的关联 客观和主观的搅拌和验证措施的实时指标，以及（2） 搅动的实时指标的程度预测自杀思想和自杀的瞬时波动 计划和尝试超越其他远端和近端风险因素。本研究图的科学目的 在三个主要领域的候选人进行培训：（1）对高风险患者的数字监控，（2）高级 纵向多元数据分析以及（3）行为和声音生物标志物的识别。这 候选人的培训计划包括Matthew Nock博士（主要导师），Jordan Smoller博士（共同指导）的培训计划。 ），毛里齐奥·法瓦（Maurizio Fava）博士（联合学）和博士。 Rosalind Picard，Evan Kleiman和Thomas Quatieri （顾问）以及哈佛公共卫生和马萨诸塞州的定量课程 技术研究院。这项修订的五年奖励将推动候选人到独立患者 - 面向研究职业的重点是使用可扩展方法来提高自杀预测和预防。