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.

项目摘要/摘要 神经性厌食症（AN）是一种严重的精神疾病，任何精神病患者的死亡率最高， 自杀是死亡的第二大原因。尽管自杀率极高，但自杀的危险因素 在这种高风险人群中的构想（SI）和行为/尝试（SA）尚不清楚。而有 威胁反应性，压力反应，过度呼应，情绪失调和躁动的证据有助于 为了自杀的风险，这些过程之间的动态关系尚未在 全面，暂时的基础。我们过去工作开发的科学前提是应用程序 构想与行为和网络理论将使动态纵向相互作用识别 在核心维度（例如，唤醒，威胁），症状以及个人之间和内部的SI/SA。 我们的研究目标是（1）确定同时发生的症状和维度风险相互作用 在人之间和内部，（2）区分哪些风险因素可以预测SI与SA，（3）测试这些风险因素是否存在 预测SI/SA的发作。这些目标最终将确定在新型预防中应针对哪些因素 和治疗工作。我们将使用多个分析方法，结合新颖的，尖端 自杀和网络科学的进步。我们将收集有关自杀行为的密集实时数据， 使用移动技术的焦虑，过度宣布，情绪调节和煽动 情绪调节（通过心率的变异性）和唤醒的心理生理评估（通过 表征表征睡眠效果周期过度和加速度的电肌活动），从 230个患者诊断为AN/非典型A（AAN）。在1个月，6个月和一年的随访中，我们将 测试个人风险因素是否预测SI/SA。我们预计在我们的学习期间，有35-58名参与者将拥有SA。 具体目的是（1）测试哪些症状和尺寸在时间和彼此之间保持 合并症SI/SA和症状，（2）开发个性化网络模型，以识别哪种自杀和 功能可以预测个人内部的SI/SA和探索性目标（3），以测试是否存在差异 和aan。拟议的研究使用了高度创新的方法，结合了密集的纵向数据 收集方法，通过可穿戴传感器技术测量生理数据，以及新的进步 网络科学要回答以前无法解决的问题，以指出哪些个人风险因素 有助于自杀结果。拟议的研究具有临床影响。如果我们确定模式 这些数据有助于自杀风险，为整个领域提供个性化医学模型 精神病学，并提供新颖的干预靶标，以预防和治疗谱系疾病。 此外，我们开发的算法都可以在（a）临床友好的软件中使用，以识别治疗 在可穿戴警报设备中防止SI/SA和（b）的目标，该设备可能会在发生之前破坏SA。