Robust Predictors of Mania and Psychosis

躁狂症和精神病的稳健预测因子

• 批准号: 9755521
• 负责人: JUSTIN T BAKER
• 金额: $ 74.02万
• 依托单位: MCLEAN HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-03 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9755521
• 关键词: Acoustics; Affect; Affective; Behavior; Behavioral; Biological; Bipolar Disorder; Brain; Brain Diseases; Categories; Cellular Phone; Chronic; Chronic Disease; Clinical; Clinical Data; Cognitive; Computer Vision Systems; Dangerousness; Data; Data Analytics; Data Collection; Diagnosis; Diagnostic; Early Intervention; Economic Burden; Energy Metabolism; Environment; Event; Face; Functional disorder; Funding Opportunities; Future; Gestures; Goals; Health behavior; Hospitalization; Human; Individual; Intervention; Investigation; Knowledge; Lead; Life; Link; Machine Learning; Manic; Maps; Measures; Medical Records; Methodology; Methods; Modeling; Nature; Neurobiology; Participant; Patient Self-Report; Patients; Perception; Persons; Pharmaceutical Preparations; Physical environment; Process; Prospective Studies; Protocols documentation; Psychotic Disorders; Public Health; Reporting; Reproducibility; Risk; Signal Transduction; Sleep; Social Environment; Symptoms; Technology; Testing; Time; Translating; U-Series Cooperative Agreements; Visual; Voice; Work; Wrist; actigraphy; analytical method; base; built environment; case control; cohort; design; digital; disability; early onset; effective therapy; energy balance; experience; gaze; individual patient; longitudinal analysis; longitudinal design; mental state; mortality; new technology; phenotypic data; predictive modeling; predictive test; prospective; psychotic symptoms; scaffold; sensor; severe mental illness; social; study population; targeted treatment; theories; time use; wearable device; young adult

PROJECT SUMMARY/ABSTRACT The purpose of the new funding opportunity announcement, RFA-OD-17-004 for Intensive Longitudinal Analysis of Health Behaviors: Leveraging New Technologies To Understand Health Behaviors (U01), is to establish a cooperative agreement network to collaboratively study factors that influence key health behaviors in the dynamic environment of individuals, using intensive longitudinal data collection and analytic methods. Importantly, progress has been slow and frustrating in translating knowledge of the brain to new and more effective treatments for human brain diseases such as severe mental disorders. In fact, severe mental disorders, which include psychotic disorders, are brain diseases that are not only devastating because they result in severe disruptions that occur early in life, but, for many, the course of illness is progressive, leading to chronic debilitation and early mortality. Thus the need to accelerate knowledge about the factors that trigger (or increase or decrease the likelihood) of manic and psychotic episodes, and to translate this knowledge to more effective treatment interventions, is critical. The primary goal of the proposed “Robust Predictors of Mania and Psychosis” is to identify biological, environmental, and social factors that trigger dangerous mental states, particularly mania and psychosis, in individuals known to be at risk for these conditions. The eventual goal of this work is to provide quantifiable and predictable information that can be used to scaffold biological observations and tailor intervention strategies to maximize efficacy at the individual level. We first develop models to predict conventional clinical measures specific to psychosis and mania using (1) digital, low- to-minimal burden interactions through smartphones and wearables (Aim 1), and (2) measures extracted from face and voice during in-person clinical interactions (Aim 2), work which leverages existing data we have already collected. We will next collect one hundred person-years of pseudo-continuous multivariate behavioral data from one hundred individuals with a psychotic disorder, to further test and validate our early observations in a wider array of individuals with affective and non-affective psychotic disorders, who are likely to experience illness fluctuations within a one-year timeframe, employing several strategies to optimize participant engagement (Aim 3). We will also perform, as a representative example, a study comparing sleep, energy expenditure, and mania symptoms over time, using data obtained in the first three aims, to quantify how the relationship between energy expenditure and energy perception varies across our study population in ways that could have important consequences for health behaviors (Aim 4). The main goals of this project are thus to acquire high quality, temporally dense behavioral, cognitive, and clinical data on an important cohort of young adult patients, not only to facilitate future investigations linking these behavioral change points to neurobiological processes but also as a precursor to more effective, targeted therapeutics, such as real-time interventions that could be delivered based on dynamic factors in an individual's environment.

项目总结/摘要 新的融资机会公告RFA-OD-17-004的目的是强化 健康行为的纵向分析：利用新技术了解健康行为 (U01)是建立一个合作协议网络，以合作研究影响关键健康的因素， 行为在动态环境中的个人，使用密集的纵向数据收集和分析 方法.重要的是，在将大脑的知识转化为新的、 更有效的治疗人类大脑疾病，如严重的精神障碍。事实上，严重的精神 包括精神障碍在内的大脑疾病不仅是毁灭性的，因为它们 导致生命早期发生的严重破坏，但对许多人来说，疾病的过程是渐进的，导致 慢性衰弱和早期死亡。因此，有必要加快了解触发因素， (or增加或减少躁狂和精神病发作的可能性），并将这些知识转化为 更有效的治疗措施至关重要。建议的“稳健预测”的主要目标是 躁狂症和精神病”是确定生物，环境和社会因素，触发危险的 精神状态，特别是躁狂症和精神病，在已知有这些条件的风险的个人。的 这项工作的最终目标是提供可量化和可预测的信息， 生物学观察和定制干预策略，以最大限度地提高个人水平的功效。我们首先 开发模型，以预测特定于精神病和躁狂症的常规临床指标，使用（1）数字，低- 通过智能手机和可穿戴设备（目标1）进行最小负担的交互，以及（2）从 面对面临床互动中的面部和语音（目标2），利用我们现有的数据 已经收集。接下来，我们将收集100人-年的伪连续多变量行为 来自100名精神障碍患者的数据，以进一步测试和验证我们早期的观察结果。 在更广泛的情感性和非情感性精神障碍患者中， 疾病波动在一年的时间范围内，采用几种策略，以优化参与者 参与（目标3）。作为一个代表性的例子，我们还将进行一项研究， 支出，躁狂症状随着时间的推移，使用前三个目标中获得的数据，以量化 能量消耗和能量感知之间的关系在我们的研究人群中各不相同， 可能对健康行为产生重要影响（目标4）。该项目的主要目标是 获得高质量，时间密集的行为，认知和临床数据的一个重要队列的年轻人， 成年患者，不仅有利于未来的调查，将这些行为变化点， 神经生物学过程，但也作为更有效的，有针对性的治疗，如实时 可以根据个人环境中的动态因素进行干预。