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.

项目摘要/摘要 自杀念头和行为在美国是日益严重的公共卫生问题。不幸的是，我们目前 预测自杀的能力仅略高于概率，这可能是由于过度依赖 远端/横断面风险因素对自杀风险的近端预测能力较弱。对复杂流程进行建模 非典型睡眠对日常功能的影响与已确定的近端风险因素一起有助于 确定自杀风险最大的背景和时间段，在个人层面建模。建议进行的研究 建立在我们团队在睡眠/清醒周期、心理生理学、深度表型和 精神疾病高危人群自杀易感性的多方法、多变量、生态有效模型。 我们将研究非典型睡眠的整体模型与已知特征(基线神经认知)之间的关系 表现；例如，更大的冲动倾向，更高的损失敏感度，调节情绪的能力降低)以及 状态(时变，发生在SI/SB之前的几个小时到几天；例如，情绪反应的瞬时波动， 冲动；更大的情绪波动；更大的孤立性倾向)，自杀的风险因素，并研究如何 这些因素共同影响自杀意念，并增加未来自杀行为的风险。我们会 招募200名自杀风险高的精神科住院患者，并进行睡眠/清醒基线评估 功能和特质风险因素，并使用基于实验室的任务和心理生理学(即，事件- 相关电位、心率变异性和皮肤电活动)与觉醒相关的表型风险过程 和认知系统。这一基线评估之后将进行四周的EMA和数字表型分析 与SAFTE派生的活动相结合，以表征关键的国家风险因素。我们会继续跟进。 出院后1个月、3个月和6个月的评估，以确定我们的近端风险模型 对SI和SB进行前瞻性预测。这项拟议的研究旨在描述自杀的近端风险。 密集的纵向方法，以确定自杀风险最大的“窗口”，这可能因人而异 对个人来说，这是强化干预的标志。最后，我们将利用这个广泛的数据集来 建立一个睡眠-自杀关系模型，强调特质和状态因素的贡献。这个 这项研究的结果有可能极大地提高我们对自杀现象的理解 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 既有传统的，也有科技强化的心理疗法。