Detecting Adolescent Suicidality Biometric Signals and Dynamic Variability with Wearable Technology

利用可穿戴技术检测青少年自杀生物特征信号和动态变异性

• 批准号: 10731651
• 负责人: David Clark Sheridan
• 金额: $ 19.14万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-16 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10731651
• 关键词: Accident and Emergency department; Accounting; Acute; Admission activity; Adolescence; Adolescent; Affect; Age; Ambulatory Monitoring; Arousal; Autonomic nervous system; Biometry; Cause of Death; Childhood; Clinical; Clinical Research; Clinical Trials; Data; Data Analyses; Data Set; Devices; Disease; Early Diagnosis; Early Intervention; Early identification; Ecological momentary assessment; Emergency department visit; Enrollment; Family; Frequencies; Funding; Future; Gender; Goals; Health Personnel; Healthcare Systems; Hospitals; Impairment; Inpatients; Intervention; Knowledge; Lead; Life; Machine Learning; Measures; Mental Health; Mental disorders; Mentored Clinical Scientist Development Program; Methodology; Methods; Mining; Mission; Monitor; Moods; National Institute of Mental Health; Observational Study; Outpatients; Patient Self-Report; Patients; Photoplethysmography; Physicians; Physiological; Positioning Attribute; Preventive; Preventive measure; Prospective cohort; Psyche structure; Public Health; Publishing; Recording of previous events; Reporting; Research; Research Personnel; Resources; Risk; Risk Factors; Scientist; Services; Severities; Signal Transduction; Suicide; Suicide attempt; Symptoms; System; Techniques; Therapeutic; Therapeutic Intervention; Time; Time trend; Training; Translational Research; Triage; United States; Visit; Wrist; Youth; adolescent patient; adolescent suicide; career development; clinical predictors; cost; design; developmental psychology; digital monitoring; heart rate monitor; heart rate variability; improved; innovation; longitudinal dataset; machine learning model; novel; pediatric patients; predictive tools; programs; prospective; social; suicidal; suicidal adolescent; suicidal behavior; suicidal risk; suicide rate; wearable device; wrist motion

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.

项目摘要 自杀率已成倍增加，现在是青春期的第二大死亡原因， 核算超过120万的年急诊科（ED）访问。 ED参观或尝试后， 20％的青少年将在12个月内进行第二次尝试，而几乎一半的青少年将重复访问。 我的长期目标是成为一个由联邦政府资助的独立医师科学家，并通过一项研究计划 青少年自杀。该提案的总体目标是研究生理参数和 可穿戴技术的生物识别数据与自杀行为有关，并发展 个性化的预测工具，可以改善自杀性青少年患者的门诊鉴定 在危机发生之前，需要进行ED访问。中心假设是生物识别数据可以连续 监测并允许早期识别/干预自杀症状。理由是 通过可穿戴技术改进的门诊监测可以创建一个更客观的平台来提供 识别恶化状况并利用患者特异性及时治疗的能力 自杀青少年的心理健康提供者。为了实现总体目标，我将追求以下 具体目的：（i）评估HRV是否与患者特异性风险因素相结合来检测 急性自杀青少年队列中自杀严重程度的动态变化，（ii） 机器学习以确定原始PPG信号中是否存在时间关系/签名 或在自杀严重程度报告变化后立即结合患者特异性风险因素发展 自杀风险的预测工具。这些目标将在三年内通过潜在的 观察性研究在ED和住院精神病学单位中急性自杀青少年。在 此外，将实现以下职业发展目标，以将自己定位为独立 该K23完成后的医师科学家：（i）临床自杀评估和 在急诊外的监测，包括发展心理学/青年自杀术； （ii）机器学习和 大型纵向数据集的数据分析技术是数字临床研究翻译不可或缺的 监视器； （iii）K-to-R转型，其中包括用于临床试验和适应性设计的方法。这 提案很重要，因为它与改进预防性研究的NIMH使命一致，评估 随着时间的流逝，心理健康轨迹，并具有广泛的公共卫生研究。研究 在本应用程序中提出的是创新的，因为研究人员可以在未来的研究中使用该平台作为新的 青少年自杀的生理方法。最终，这种知识可以为 早期检测，即时干预措施和向EDS展示的120万自杀青少年 全国。