Detecting Adolescent Suicidality Biometric Signals and Dynamic Variability with Wearable Technology

项目总结 自杀率呈指数级增长，现在是青春期死亡的第二大原因， 每年急诊科就诊人次超过120万人次。在ED访问或尝试之后，最高可达 20%的青少年将在12个月内进行第二次尝试，几乎一半的青少年将再次去看ED。 我的长期目标是成为一名独立的、由联邦政府资助的内科科学家，并在 青少年自杀。这项提案的总体目标是调查生理参数和 来自可穿戴技术的生物识别数据，与自杀行为在时间上相关，并开发出一种 个性化的预测性工具，可以改善青少年自杀患者的门诊识别 在危机发展到需要急诊室访问之前。中心假设是生物识别数据可以连续 监测并允许及早识别/干预不断升级的自杀症状。理由是 通过可穿戴技术改进的门诊监控可以创建一个更客观的平台来提供 识别病情恶化并利用患者特有的即时治疗方法的能力 有自杀倾向的青少年的心理健康提供者。为实现总体目标，我将开展以下工作 具体目标：(I)评价心率变异性结合患者特有的风险因素是否可用于检测 急性自杀青少年队列中自杀严重程度的动态变化，(Ii)利用 机器学习以确定在之前的原始PPG信号中是否存在时间关系/签名 或在自杀严重程度报告发生变化后立即结合患者特定的危险因素而发展 自杀风险的预测工具。这些目标将在三年内通过远景实现 将自杀倾向严重的青少年纳入急诊室和住院精神病室的观察性研究。在……里面 此外，还将实现以下职业发展目标，将自己定位为独立的 完成本K23课程后的医生-科学家：(I)临床自杀评估和 在教育署外进行监测，包括发展心理学/青少年自杀；。(2)机器学习和 临床研究从数字化转化为整体的大型纵向数据集的数据分析技术 监测；(3)从K到R的过渡，将包括临床试验和适应性设计的方法。这 该提案意义重大，因为它符合NIMH改进预防性研究、评估 随着时间的推移精神健康轨迹，以及具有广泛的公共卫生覆盖范围的研究。这项研究 本申请的提出具有创新性，因为研究人员可以在未来的研究中使用该平台作为新的 青少年自杀的生理学研究。归根结底，这样的知识可以为 早期发现、及时干预和支持120多万名有自杀倾向的青少年到急诊室就诊 全国范围内。

PROJECT SUMMARY Suicide rates have exponentially increased, and it is now the 2nd leading cause of death in adolescence, accounting for over 1.2 million annual emergency department (ED) visits. After an ED visit or attempt, up to 20% of adolescents will have a second attempt within 12 months, and almost half will have a repeat ED visit. My long-term goal is to be an independent, federally-funded physician-scientist with a research program in adolescent suicidality. This proposal's overall objectives are to investigate physiologic parameters and biometric data from wearable technology that is temporally related to suicidal behavior and develop a personalized, predictive tool that can improve outpatient identification of adolescent patients with suicidality before a crisis develops requiring an ED visit. The central hypothesis is that biometric data can continuously monitor and allow for early identification/intervention of escalating suicidal symptoms. The rationale is that improved outpatient monitoring through wearable technology can create a more objective platform to provide the ability to identify a worsening condition and utilize patient-specific just-in-time therapeutics developed with mental health providers for suicidal adolescents. To attain the overall objectives, I will pursue the following specific aims: (i) To evaluate whether HRV, combined with patient-specific risk factors, can be used to detect dynamic changes in suicide severity among a prospective cohort of acutely suicidal adolescents, (ii) To utilize machine learning to determine whether there is a temporal relationship/signature in the raw PPG signal before or immediately after changes in suicide severity reporting combined with patient-specific risk factors to develop a prediction tool for suicidality risk. These aims will be accomplished in three years through a prospective observational study enrolling acutely suicidal adolescents in the ED and an inpatient psychiatric unit. In addition, the following career development aims will be accomplished to position myself as an independent physician-scientist following completion of this K23: (i) formal training in clinical suicidality assessment and monitoring outside the ED, including developmental psychology/youth suicidology; (ii) Machine learning and data analysis techniques for large longitudinal datasets integral to clinical research translation from digital monitors; (iii) K-to-R transition that will include the methodology for clinical trials and adaptive design. This proposal is significant because it aligns with the NIMH mission of improved preventative research, assessing mental health trajectories over time, and research with an extensive public health reach. The research proposed in this application is innovative because researchers can use this platform in future studies as a new physiologic approach to adolescent suicidality. Ultimately, such knowledge can offer unique opportunities for early detection, just-in-time interventions, and support over 1.2 million suicidal adolescents presenting to EDs nationally.