Dynamic Impacts of Sleep Disruption on Ecologically Assessed Affective, Behavioral, and Cognitive Risk Factors for Suicide

睡眠中断对生态评估的自杀情感、行为和认知风险因素的动态影响

• 批准号: 10307605
• 负责人: Michael F Armey
• 金额: $ 55.25万
• 依托单位: BUTLER HOSPITAL (PROVIDENCE, RI)
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10307605
• 关键词: Acute; Affect; Affective; Algorithms; Arousal; Aviation; Behavior; Behavior assessment; Behavioral; Biological Markers; Cellular Phone; Clinical; Cognitive; Complex; Coupled; Data; Data Set; Decision Making; Development; Distal; Ecological momentary assessment; Effectiveness; Emotional; Emotions; Enhancement Technology; Environment; Equation; Event-Related Potentials; Fatigue; Feeling suicidal; Future; Goals; Hospitals; Hour; Impairment; Impulsivity; Individual; Inpatients; Intervention; Interview; Laboratories; Link; Location; Measures; Mental Fatigue; Methods; Mission; Modeling; Motion; Neurocognitive; Patient Self-Report; Performance; Peripheral; Persons; Phase; Phenotype; Population; Process; Psychophysiology; Psychotherapy; Public Health; Research Domain Criteria; Risk; Risk Factors; Sampling; Sleep; Sleep Wake Cycle; Sleep disturbances; Stress; Suicide; System; Time; United States; Variant; actigraphy; base; circadian; cognitive function; cognitive system; daily functioning; digital; follow up assessment; heart rate variability; high risk; improved; indexing; innovation; negative affect; new technology; novel; phenomenological models; poor sleep; predictive modeling; prevent; prospective; recruit; reducing suicide; response; rumination; sensor; sleep pattern; sleep quality; social; social factors; social networking website; suicidal behavior; suicidal risk; suicide model; suicide rate; trait

Project Summary/Abstract Suicidal ideation and behavior are growing public health problems in the United States. Unfortunately, our current ability to predict suicide is only slightly above chance, which may be attributable to an overreliance on distal/cross-sectional risk factors that are weak proximal predictors of suicide risk. Modeling the complex process by which atypical sleep impacts daily functioning in conjunction with established proximal risk factors can aid in identifying contexts and time periods of greatest suicidal risk, modeled at the individual level. The proposed study builds upon our team’s extensive expertise in sleep/wake cycles, psychophysiology, deep phenotyping, and multi-method, multivariate, ecologically valid models of suicide vulnerability in high-risk psychiatric populations. We will examine how a holistic model of atypical sleep relates to known trait (baseline neurocognitive performance; e.g., greater impulsive tendencies, higher loss sensitivity, reduced ability to regulate emotions) and state (time-varying, occurring hours to days before SI/SB; e.g., momentary fluctuations in emotional reactivity, impulsivity; greater emotional lability; greater isolative tendencies), risk factors for suicide, and examine how these factors together proximally influence suicidal ideation and confer risk for future suicidal behavior. We will recruit 200 psychiatric inpatients at high risk for suicide and conduct a baseline assessment of sleep/wake functioning and trait risk factors and use laboratory-based tasks coupled with psychophysiology (i.e., event- related potentials, heart rate variability, and electrodermal activity) to phenotype risk processes linked to arousal and cognitive systems. This baseline assessment will be followed by four weeks of EMA and digital phenotyping coupled with SAFTE-derived actigraphy to characterize key state risk factors. We will conduct follow-up assessments at 1-, 3-, and 6-months post hospital discharge to determine how our proximal model of risk prospectively predicts SI and SB. The proposed study aims to characterize proximal risk for suicide using intensive longitudinal methods and to identify “windows” of greatest risk for suicide, which may vary from person to person, that serve as markers for intensive intervention. Finally, we will leverage this extensive dataset to develop a model of the sleep-suicide relationship emphasizing the contribution of trait and state factors. The results of this study have the potential to greatly enhance our understanding of the phenomenology of suicide risk as it exists in the real world, with the potential to improve our ability to predict, prevent, and intervene using both traditional and technology-enhanced psychotherapies.

项目摘要/摘要 自杀构想和行为正在美国越来越多。不幸的是，我们的当前 预测自杀的能力仅略高于机会，这可能归因于过度依赖 自杀风险近端预测因子较弱的远端/横截面风险因素。建模复杂过程 非典型睡眠与既定的代理风险因素结合使用的日常功能可以帮助您有助于 识别以个人级别建模的最大自杀风险的上下文和时间段。拟议的研究 基于我们团队在睡眠/唤醒周期，心理生理学，深层表型和 高风险精神病种群中自杀脆弱性的多种方法，多变量，生态有效的模型。 我们将研究非典型睡眠的整体模型与已知性状有关（基线神经认知）如何 表现;例如，更大的冲动趋势，更高的损失敏感性，降低了调节情绪的能力）和 状态（随时间变化，发生在SI/SB之前的几小时到几天；例如，情绪反应性的瞬时波动， 冲动更大的情感劳动；更大的隔离倾向），自杀的危险因素，并检查如何 这些因素共同有效地影响了对未来自杀行为的自杀念头和会议风险。我们将 招募200个自杀风险高风险的精神病患者，并对睡眠/唤醒进行基线评估 功能和特征风险因素以及使用基于实验室的任务以及心理生理学（即事件 - 相关电位，心率变异性和电肌活动）与唤醒相关的表型风险过程 和认知系统。该基线评估将进行四个星期的EMA和数字表型 再加上Safte衍生的行为摄影，以表征关键的状态风险因素。我们将进行后续行动 住院后1、3和6个月的评估，以确定我们的近端风险模型 前瞻性预测SI和SB。拟议的研究旨在表征使用自杀的替代风险 密集的纵向方法并确定自杀风险最大的“窗户”，这可能与人有所不同 对人来说，这是密集干预的标志。最后，我们将把这个广泛的数据集利用 开发一种旨在强调特质和状态因素的贡献的睡眠自由关系模型。 这项研究的结果有可能大大增强我们对自杀现象的理解 风险存在于现实世界中，有可能提高我们预测，预防和干预的能力 传统和技术增强的心理治疗。