Digital Monitoring of Agitation for Short-Term Suicide Risk Prediction

自杀是一个普遍和沉重的公共卫生问题，值得立即关注。作为第十 自杀是美国人的主要死因，每年有44,000多人死于自杀。 目前迫切需要确定客观和临床信息的标志物迫在眉睫的风险自杀 行为躁动，在DSM-5中定义为与内心紧张感相关的过度运动活动， 被主要组织和广泛使用的风险评估协议列为自杀的警告信号。然而， 先前关于激越与自杀之间关系的研究存在关键的方法学局限性（包括 与鼓动的操作化有关），这导致了最少的经验证据来支持 激动是自杀的近端危险因素。解决这一知识差距有可能产生重大影响， 影响，包括告知自杀风险的临床评估和及时 检测和应对急性自杀风险的干预措施该项目将克服 通过评估多种行为（运动活动和声音特征[例如， 音量，语速，音高]）和主观成分的激动和自杀的想法和行为，在一个 样本在短期内自杀风险高，高风险时期。我们将测试假设（1）客观地 测量的实时指标与瞬时主观评级和验证，黄金 焦虑的标准测量，以及（2）焦虑的主观和客观指标都改善了预测 自杀意念、计划和企图的短期增加高于其他远端和近端风险 因素我们建议收集高分辨率的自我报告（例如，生态瞬时评估）和被动 (e.g.,使用智能手机和可穿戴传感器从精神病住院患者获得的关于躁动的数据 在住院治疗期间和出院后四周内因自杀意念或企图而入院。多- 将实施水平建模和机器学习方法，以检查（1） 躁动的客观和主观实时指标以及躁动的有效测量，以及（2） 焦虑的实时指标预测自杀意念和自杀的瞬时波动的程度 计划和尝试超越其他远端和近端风险因素。本研究地图的科学目的 在三个主要领域的候选人的培训：（1）高风险患者的数字监控，（2）先进的 纵向多变量数据分析，以及（3）行为和声音生物标志物的鉴定。的 候选人的培训计划包括马修·诺克博士（主要导师），乔丹·斯莫勒博士（共同导师）， 导师）、Maurizio Fava博士（共同导师）和Rosalind Picard博士、Evan Kleiman博士和托马斯Quatieri博士 （顾问），以及哈佛公共卫生学院和马萨诸塞州的定量课程 理工学院这个为期五年的指导奖将推动候选人成为一个独立的病人- 导向的研究职业生涯侧重于使用可扩展的方法来推进自杀预测和预防。

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.