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差异几乎与每一个 从焦虑到精神病再到创伤后应激障碍然而，客观的，基于表现的 这种威胁敏感度的测量往往是（1）不可靠的，（2）难以衡量，（3）设计用于 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（例如焦虑、过度警惕、恐惧）往往 在个体内的几分钟、几小时和几天内波动。缺乏简洁、敏感、可靠和 因此，威胁敏感度的可重复测量代表了我们理解如何 威胁敏感度与心理健康有关，在个人之间和随着时间的推移。的总体目标 拟议的项目是开发和验证一组优化和可推广的措施， 评估人与人之间和人与人之间的威胁敏感度差异，用于高通量或高风险环境中。 频率评估设计。拟议的工作围绕三个具体目标展开。目标1： 基于初始的25个候选度量，识别并优化一组5个可靠的候选度量。我们将选择 并基于迭代测试开发方法，使用高吞吐量 在N = 75，000名参与者中进行评估，以确定参数、刺激和给药特征 这使我们能够满足跨设备类型的预先指定的心理测量和验证基准（请参见 研究策略）。达到这些基准的措施将被带到两个大规模的 EMA验证研究：（1）从姆克林医院的急性精神病护理环境中招募的样本 (Aim 2，N = 200）和（2）多样化的布朗克斯偏向社区样本（目标3，N = 400）。成功 该项目的完成将产生一套高质量的措施，这些措施将被验证用于研究， 高通量和高频率评估研究设计（例如EMA），特别是 试图描述威胁处理的时间动态或认知中与状态相关的差异 和症状。这项拟议中的研究意义重大，因为它将（1）解决我们能力的关键差距 客观地测量现场测试环境中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。该研究具有创新性， 它（a）采用迭代测试开发方法，使我们能够快速选择和优化 潜在的测试候选人，在自然环境中管理，和（B）允许我们捕捉两者 受试者间和受试者内变异性作为识别有助于 我们的团队在实现这些目标方面具有独特的优势，因为我们在以下方面拥有专业知识： 开发和评估移动的认知评估、心理测量、恐惧和威胁信息 处理和基础设施，用于在大型队列和全国范围内传播认知测量。

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.