Mobile Measures of Threat Sensitivity for Cross-sectional and Longitudinal Assessment

项目摘要 对威胁信息的认知信息处理的差异与几乎每一个 严重的精神疾病，从焦虑到精神病再到创伤后应激障碍。然而，客观的，以业绩为基础 此类威胁敏感度指标往往(1)不可靠，(2)难以衡量，以及(3)专为 评估表型性状的差异，而不是随时间变化的特征。自我报告 另一方面，与威胁处理相关的经历(如焦虑、高度警惕、恐惧)倾向于 在个人内部，波动在分钟、小时和几天内。缺乏简短、敏感、可靠和 因此，威胁敏感度的可重复测量代表着我们在理解如何 威胁敏感度与心理健康有关，无论是个体之间还是随着时间的推移。该计划的总体目标 拟议的项目是开发和验证一系列优化和可推广的措施，以 评估人员之间和人员内部在威胁敏感度方面的差异，用于高吞吐量或高吞吐量 频率评估设计。拟议的工作围绕三个具体目标组织。目标1是 在最初的25项措施的基础上，确定并优化一组5项可靠的候选措施。我们将选择 并使用高吞吐量开发基于迭代测试开发方法的措施 在N=75,000名参与者中进行评估，以确定参数、刺激和管理特征 使我们能够满足预先指定的各种设备类型的心理测量和验证基准(请参见 研究战略)。达到这些基准的措施随后将延续到两个大规模的 EMA验证研究：(1)从McLean医院的急性精神病护理环境中招募的样本 (2)多样化的偏向于布朗克斯的社区样本(目标3，N=400)。成功 该项目的完成将产生一套高质量的衡量标准，这些衡量标准可用于研究 高通量和高频评估研究设计(例如EMA)，特别是 旨在刻画威胁处理的时间动态或认知中与状态相关的差异 和症状。这项拟议的研究具有重要意义，因为它将(1)解决我们能力上的一个关键差距 在现场测试环境中客观测量对威胁的认知信息处理，以及(2)提供 用于跟踪威胁敏感度随时间变化的强大工具。拟议的研究在以下方面具有创新性 它(A)采用迭代测试开发方法，使我们能够快速选择和优化 潜在的考生，在自然环境中管理，和(B)允许我们捕捉到这两个 受试者之间和受试者内部的可变性，作为确定有助于 鉴于我们在精神障碍方面的专业知识，我们的团队在实现这些目标方面处于独特的地位 开发和评估移动认知评估、心理测量学、恐惧和威胁信息 在大群组和全国范围内传播认知措施的处理和基础设施。

Project Summary Differences in cognitive information processing of threat information have been linked with almost every major psychiatric illness, from anxiety to psychosis to PTSD. And yet, objective, performance-based measures of such threat sensitivity tend to be (1) unreliable, (2) difficult to scale, and (3) designed for assessing differences in phenotypic traits rather than characteristics that vary over time. Self-reported experiences linked with threat processing (e.g. anxiety, hypervigilance, fear), on the other hand, tend to fluctuate over minutes, hours, and days within individuals. The lack of brief, sensitive, reliable, and repeatable measures of threat sensitivity thus represents a major gap in our ability to understand how threat sensitivity is related to mental health, between individuals and over time. The overall goal of the proposed project is to develop and validate a battery of optimized and generalizable measures for assessing between and within-person differences in threat sensitivity, for use in high throughput or high frequency assessment designs. The proposed work is organized around three specific aims. Aim 1 is to identify and optimize a set of 5 reliable candidate measures, based on an initial set of 25. We will select and develop measures based on an iterative test development approach, using high-throughput assessment in N = 75,000 participants to identify parameters, stimuli, and administration characteristics that allow us to meet pre-specified psychometric and validation benchmarks across device types (see Research Strategy). Measures that achieve these benchmarks will then be carried over to two large-scale EMA validation studies: (1) a sample recruited from an acute psychiatric care setting at McLean Hospital (Aim 2, N = 200) and (2) a diverse Bronx-biased community sample (Aim 3, N = 400). Successful completion of this project will yield a set of high-quality measures that are validated for research using both high throughput and high frequency assessment study designs (e.g. EMA), particularly research that seeks to characterize the temporal dynamics of threat processing or state-related differences in cognition and symptoms. The proposed research is significant because it will (1) address a critical gap in our ability to objectively measure cognitive information processing of threat in field test settings, and (2) provide robust tools for tracking changes in threat sensitivity over time. The proposed research is innovative in that it (a) employs an iterative test development approach that will allow us to rapidly select and optimize potential test candidates, administered in naturalistic environments, and (b) allows us to capture both between and within-subject variability as potential avenues for identifying mechanisms that contribute to mental disorders Our team is uniquely positioned to accomplish these aims, given our expertise in developing and evaluating mobile cognitive assessments, psychometrics, fear and threat information processing, and infrastructure for disseminating cognitive measures in large cohorts and nationally.