Longitudinal Personalized Dynamics Among Anorexia Nervosa Symptoms, Core Dimensions, and Physiology Predicting Suicide Risk

神经性厌食症症状、核心维度和预测自杀风险的生理学之间的纵向个性化动态

• 批准号: 10731597
• 负责人: Cheri Alicia Levinson
• 金额: $ 78.21万
• 依托单位: UNIVERSITY OF LOUISVILLE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10731597
• 关键词: Acceleration; Accelerometer; Adult; Affect; Agitation; Algorithms; Anorexia Nervosa; Anxiety; Arousal; Behavioral; Cause of Death; Clinical; Computer software; Data; Data Collection; Devices; Diagnosis; Diagnostic; Dimensions; Distal; Eating Disorders; Ecological momentary assessment; Feeling suicidal; Female; Friends; General Population; Goals; Hour; Individual; Intervention; Interview; Life; Link; Measurement; Mediator; Mental Health; Mental disorders; Methodology; Methods; Modeling; Movement; Negative Valence; Outcome; Participant; Pathology; Pathway Analysis; Pathway interactions; Pattern; Persons; Physiological; Physiology; Population; Precision Medicine Initiative; Precision therapeutics; Predictive Factor; Prevention; Prevention program; Process; Psychiatry; Psychophysiology; Reporting; Research; Resistance; Risk; Risk Factors; Science; Shapes; Skin; Sleep Wake Cycle; Sleep disturbances; Suicide; Suicide attempt; Suicide prevention; Symptoms; Testing; Thinking; Time; United States National Institutes of Health; Variant; Weight; Work; biological adaptation to stress; comorbidity; dynamic system; emotion dysregulation; emotion regulation; follow-up; handheld mobile device; heart rate variability; high risk; high risk population; ideation; improved; indexing; individualized prevention; innovation; mobile computing; mobile sensing; mortality; network models; novel; personalized medicine; prediction algorithm; prevent; psychologic; public health relevance; restrictive eating; severe mental illness; suicidal; suicidal behavior; suicidal morbidity; suicidal risk; suicide rate; theories; user-friendly; wearable device; wearable sensor technology

PROJECT SUMMARY/ABSTRACT Anorexia nervosa (AN) is a severe mental illness with the highest mortality rate of any psychiatric disorder, with suicide as the second leading cause of death. Despite extremely high rates of suicide, risk factors for suicidal ideation (SI) and behaviors/attempts (SA) in this high-risk population are not well understood. While there is evidence that threat reactivity, stress-response, over-arousal, emotion dysregulation, and agitation contribute to suicide risk, the dynamic relations among these processes have not been characterized on a comprehensive, momentary basis. Our scientific premise, developed from our past work, is that the application of ideation-to-action and network theories will enable the identification of dynamic longitudinal interactions among core dimensions (e.g., arousal, threat), AN symptoms, and SI/SA both between and within individuals. Our study goals are to (1) identify symptom and dimension risk interactions of co-occurring AN and SI/SA between and within persons, (2) differentiate which risk factors predict SI vs SA and (3) test if these risk factors predict onset of SI/SA. These goals will ultimately identify which factors should be targeted in novel prevention and treatment efforts. We will use a multiple units of analysis approach, combined with novel, cutting-edge advances in suicide and network science. We will collect intensive real-time data on AN and suicide behaviors, anxiety, over-arousal, emotion regulation, and agitation using mobile technology, as well as psychophysiological assessment of emotion regulation (via heart-rate variability) and arousal (via electrodermal activity characterizing over-arousal and acceleration characterizing the sleep-wake cycle), from 230 individuals with a diagnosis of AN/Atypical AN (AAN). At 1-month, 6-month, and one year follow-up we will test if individual risk factors predict SI/SA. We expect 35-58 participants will have SA across our study period. Specific aims are to (1) test which symptoms and dimensions across time and between-persons maintain comorbid SI/SA and AN symptoms, (2) develop personalized network models to identify which suicide and AN features predict SI/SA within individuals and an exploratory aim (3) to test if there are differences between AN and AAN. The proposed research uses highly innovative methods, combining intensive longitudinal data collection methods, measurement of physiological data via wearable sensor technology, and novel advances in network science to answer previously unresolvable questions pinpointing which individual risk factors contribute to suicide outcomes. The proposed research has clinical impact. If we identify patterns that contribute to suicide risk, these data will provide a model of personalized medicine for the entire field of psychiatry, as well as providing novel intervention targets to prevent and treat AN spectrum illnesses. Additionally, the algorithms we develop can be used in both (a) clinician friendly software to identify treatment targets to prevent SI/SA and (b) in wearable alert devices that can disrupt SA before it occurs.

项目概要/摘要 神经性厌食症（AN）是一种严重的精神疾病，死亡率是所有精神疾病中最高的， 自杀成为第二大死因。尽管自杀率极高，但自杀的危险因素 这个高风险人群的观念（SI）和行为/尝试（SA）尚不清楚。虽然有 有证据表明威胁反应、压力反应、过度兴奋、情绪失调和激动会导致 对于自杀风险，这些过程之间的动态关系尚未得到表征 全面、暂时的基础。我们的科学前提是从我们过去的工作中发展而来的 想法到行动和网络理论将能够识别动态纵向相互作用 核心维度（例如，唤醒、威胁）、AN 症状以及个体之间和个体内部的 SI/SA。 我们的研究目标是 (1) 识别症状并衡量同时发生的 AN 和 SI/SA 的风险相互作用 人与人之间和人内部，(2) 区分哪些风险因素预测 SI 与 SA，以及 (3) 测试这些风险因素是否 预测 SI/SA 的发作。这些目标将最终确定新型预防措施应针对哪些因素 和治疗努力。我们将使用多单元分析方法，并结合新颖、前沿的方法 自杀和网络科学的进展。我们将收集有关 AN 和自杀行为的密集实时数据， 使用移动技术来缓解焦虑、过度兴奋、情绪调节和激动，以及 情绪调节（通过心率变异性）和唤醒（通过心率变异性）的心理生理学评估 表征过度唤醒的皮肤电活动和表征睡眠-觉醒周期的加速）， 230 名诊断为 AN/非典型 AN (AAN) 的个体。在 1 个月、6 个月和一年的随访中，我们将 测试个体风险因素是否可以预测 SI/SA。我们预计在我们的研究期间将有 35-58 名参与者拥有 SA。 具体目标是 (1) 测试哪些症状和维度在不同时间和不同人之间保持不变 共病 SI/SA 和 AN 症状，(2) 开发个性化网络模型来识别哪种自杀和 AN 特征预测个体内的 SI/SA 和探索性目标 (3) 来测试 AN 之间是否存在差异 和 AAN。拟议的研究采用高度创新的方法，结合密集的纵向数据 收集方法、通过可穿戴传感器技术测量生理数据以及在 网络科学可以回答以前无法解决的问题，查明哪些个人风险因素 有助于自杀结果。拟议的研究具有临床影响。如果我们识别出以下模式 有助于自杀风险，这些数据将为整个领域提供个性化医疗模型 精神病学，以及提供新的干预目标来预防和治疗 AN 谱系疾病。 此外，我们开发的算法可用于（a）临床医生友好的软件来识别治疗 防止 SI/SA 的目标以及 (b) 可在 SA 发生之前扰乱 SA 的可穿戴警报设备。