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: 10514605
• 负责人: Michael F Armey
• 金额: $ 54.47万
• 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/10514605
• 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; Populations at Risk; 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; circadian; cognitive function; cognitive system; daily functioning; digital; follow up assessment; heart rate variability; high risk; improved; indexing; innovation; negative affect; 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; 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.

项目概要/摘要 不幸的是，自杀意念和行为正在美国日益严重的公共卫生问题。 预测自杀的能力仅略高于偶然性，这可能是由于过度依赖 对自杀风险的近端预测因素较弱的远端/横截面风险因素对复杂过程进行建模。 非典型睡眠影响日常功能，与已确定的近期危险因素相结合，可以帮助 拟议的研究以个人为模型，确定自杀风险最大的背景和时间段。 建立在我们团队在睡眠/觉醒周期、心理生理学、深层表型分析和 高危精神病人群自杀脆弱性的多方法、多变量、生态有效模型。 我们将研究非典型睡眠的整体模型如何与已知特征（基线神经认知 表现；例如，更大的冲动倾向、更高的损失敏感性、调节情绪的能力降低）和 状态（随时间变化，发生在 SI/SB 之前几小时到几天；例如，情绪反应的瞬时波动， 冲动；更大的情绪不稳定；更大的孤立倾向），自杀的危险因素，并检查如何 这些因素共同影响自杀意念并赋予未来自杀行为的风险。 招募 200 名自杀高危精神病住院患者，并对睡眠/觉醒进行基线评估 功能和特质风险因素，并使用基于实验室的任务与心理生理学相结合（即事件- 相关电位、心率变异性和皮肤电活动）与与觉醒相关的表型风险过程 基线评估之后将进行为期四个星期的 EMA 和数字表型分析。 结合 SAFTE 衍生的体动记录技术来描述关键的状态风险因素。 出院后 1、3 和 6 个月进行评估，以确定我们的近端风险模型如何 前瞻性预测 SI 和 SB 拟议的研究旨在利用自杀的近端风险来表征。 强化纵向方法并确定自杀风险最大的“窗口”，这可能因人而异 最后，我们将利用这个广泛的数据集来进行强化干预。 建立一个睡眠与自杀关系的模型，强调特质和状态因素的贡献。 这项研究的结果有可能大大增强我们对自杀现象学的理解 现实世界中存在的风险，有可能提高我们预测、预防和干预的能力 传统的和技术增强的心理治疗。