Digital Monitoring of Agitation for Short-Term Suicide Risk Prediction

自杀是一个普遍而沉重的公共卫生问题，值得立即关注。作为第十个 自杀是美国的主要死因，每年夺走44000多名美国人的生命。 迫切需要确定自杀危险的客观和临床信息性标志。 行为。激动，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方案列为自杀的警告信号。然而， 以前关于激动和自杀之间的联系的研究在方法论上有关键的局限性(包括 与鼓动的可操作性有关)，这导致支持的经验证据很少 情绪激动是自杀的近端危险因素。解决这一知识差距的问题有可能显著 影响，包括告知自杀风险的临床评估和及时的发展 发现和应对急性自杀风险的干预措施。该项目将克服以下限制 先前通过评估多种行为(运动活动和声音特征)进行的自杀风险因素研究[例如， 音量、语速、音调])以及情绪激动、自杀念头和行为的主观成分 在短期、高风险时期内自杀风险较高的样本。我们将检验以下假设：(1)客观 测量的实时骚动指标与瞬时主观评级和经过验证的金牌相关联 激荡的标准衡量标准，以及(2)激荡的主观和客观指标都提高了预测能力 短期内自杀意念、计划和企图的增加超过了其他远端和近端风险 各种因素。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 (例如，加速计)来自精神科住院患者的使用智能手机和可穿戴传感器的激动数据 在住院治疗期间和出院后四周内因有自杀念头或企图自杀而入院。多个- 将采用层级建模和机器学习方法来检查(1) 鼓动的客观和主观实时指标和有效的鼓动措施，以及(2) 焦虑的实时指标预测自杀意念和自杀的瞬间波动的程度 计划和尝试超越其他远端和近端风险因素。这项研究的科学目标是 关于候选人在三个主要领域的培训：(1)对高危患者的数字监测，(2)高级 纵向多变量数据分析，以及(3)行为和发声生物标志物的识别。这个 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(联合导师)的指导 Mentor)、Maurizio Fava博士(共同导师)以及Rosalind Picard博士、Evan Kleiman博士和Thomas Quatieri博士 (顾问)，以及哈佛公共卫生学院和马萨诸塞州的量化课程 理工学院。这项为期五年的指导奖将促使候选人成为一名独立的患者- 以研究为导向的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.