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; 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.

项目摘要/摘要 新的资助机会公告的目的，RFA-OD-17-004 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，以协作研究影响关键健康的因素 使用密集的纵向数据收集和分析的个人动态环境中的行为 方法。重要的是，在将大脑知识转化为新的和 对人脑疾病（例如严重精神疾病）的更有效治疗。实际上，严重的精神障碍 包括精神病在内的疾病是脑部疾病，不仅是毁灭性的，因为它们 导致生命早期发生的严重破坏，但是对于许多人来说，疾病的进步过程是渐进的，导致 长期衰弱和早期死亡率。需要加速有关触发因素的知识 （或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 更有效的治疗干预措施至关重要。拟议的“强大预测指标的主要目标 躁狂和精神病”是为了确定触发危险的生物学，环境和社会因素 已知有可能患有这些疾病风险的个体，精神状态，尤其是躁狂症和精神病。 这项工作的最终目标是提供可量化且可预测的信息，可用于脚手架 生物学观察和裁缝干预策略，以最大程度地提高个人级别的效率。我们首先 开发模型，使用（1）数字，低 - 通过智能手机和可穿戴设备（AIM 1）和（2）提取的措施 面对面临床互动期间的面部和声音（AIM 2），利用我们拥有的现有数据的工作 已经收集了。接下来，我们将收集一百年的伪连续多元行为 来自一百个患有精神病的人的数据，以进一步测试和验证我们的早期观察 在各种各样的情感和非情感精神病患者中，他们可能会经历 疾病在一年的时间内发生，采用多种策略来优化参与者 参与（目标3）。作为代表性的例子，我们还将进行比较睡眠，能量的研究 使用前三个目的获得的数据，支出和躁狂症随着时间的流逝，以量化 能源消耗与我们研究人群之间的能量感知变化之间的关系，以 可能对健康行为产生重要的后果（AIM 4）。因此，该项目的主要目标是 在重要的年轻队列上获取高质量，暂时密集的行为，认知和临床数据 成年患者，不仅为了促进将这些行为改变点与 神经生物学过程，也是更有效，有针对性疗法的前体，例如实时 可以根据个人环境中的动态因素进行的干预措施。