Measuring the individual: Personalized latent variable models from ecological momentary assessments

衡量个体：来自生态瞬时评估的个性化潜变量模型

• 批准号: 9906952
• 负责人: Kenneth Bollen
• 金额: $ 22.74万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-04 至 2022-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9906952
• 关键词: Adopted; Anhedonia; Behavior; Behavioral; Behavioral Model; Clinical; Collaborations; Data; Data Collection; Depressed mood; Detection; Devices; Ecological momentary assessment; Emotional; Ensure; Event; Factor Analysis; Fatigue; Feeling; Feeling hopeless; Funding; Happiness; Heterogeneity; Individual; Lead; Life; Measurement; Measures; Mental Depression; Mental Health; Methods; Modeling; Modification; Monte Carlo Method; Normalcy; Patient Self-Report; Pattern; Performance; Persons; Process; Property; Psychologist; Research Personnel; Sampling; Science; Scientist; Series; Source; Stress; Structural Models; Symptoms; Techniques; Time; Work; behavior observation; clinically relevant; depressive symptoms; digital; experience; interest; personalized approach; personalized medicine; physical process; prevent; psychologic; tool; treatment planning

PROJECT SUMMARY With the increasing availability of ecological momentary assessments (EMA) such as daily dairy and experience sampling measurements, behavioral scientists are better able to investigate the within-person dynamic patterns (i.e., relations among variables across time) underlying symptoms, behaviors, and life events. One prominent challenge in this endeavor is the inherent heterogeneity in individual mental health processes. Others have demonstrated that this heterogeneity requires personalized measurement models to accurately assess constructs of interest. By measurement model, we mean the pattern of how observed variables relate to a latent construct. As an example, depression can be thought of as a latent construct that psychologists often seek to measure. Individuals may differ with regards to which (observed) symptoms relate to their overall (latent) depression levels at a given time point. For one person, the symptoms of sadness, feelings of hopelessness, and irritability may be the best measures of depression over time whereas for another, perhaps sadness, anhedonia, and fatigue are the symptoms that indicate depression.Allowing individuals to have personalized assessments will enable the field to get even closer to personalized treatment plans by better quantifying these somewhat abstract constructs.The current standard is to force all individuals to have the same measurement model, but the field is quickly moving towards adopting personalized measurement models for assessments. Critically, the available methods have a number of issues that prevent reliable personalized measurement models. First, some approaches (such as simply using observed variables) ignore the reality of measurement errors. This causes bias in the effects among latent constructs of interest and can lead to inaccurate inferences regarding anindividuals' process. Second, the number of observations obtained for a given individual is often too small to arrive at person-specific measurement models. Third, the current methods require the assumption of multivariate normality to be met; this is typically not seen in many forms of ecological momentary assessment data. Fourth, many available approaches for arriving at individual- level models do not perform well when the model is misspecified (i.e., the pattern of relations among observed symptoms and latent constructs is incorrect). This prevents a considerable hurdle when attempting to arrive at model structures in an exploratory manner where by definition the correct model is unknown in the beginning.Our project, if funded, would provide researchers with an easy-to-use tool for arriving at personalized measurement models. This can be achieved by building an exploratory approach within a well- understood estimation approach that has a number of desirable properties. Measurement errors would be accounted for, the method will work well even when the number of time points (observations) is less than the number of variables, multivariate normality will not be a required assumption, and misspecifications will not influence the identification of a reliable personalized measurement model.

项目总结 随着生态瞬时评估(EMA)的可用性不断增加，如每日乳制品和 通过抽样测量，行为科学家能够更好地研究人的内部 动态模式(即变量之间随时间的关系)是潜在的症状、行为和生命 事件。这一努力中的一个突出挑战是个体心理健康的内在异质性 流程。其他人已经证明，这种异质性需要个性化的测量模型来 准确评估感兴趣的构造。所谓测量模型，我们指的是如何观察到的模式 变量与潜在的结构有关。例如，抑郁可以被认为是一种潜在的结构， 心理学家经常试图测量。每个人可能在哪些(观察到的)症状方面有所不同 他们在给定时间点的总体(潜在)抑郁水平。对于一个人来说，悲伤的症状， 随着时间的推移，无望感和易怒可能是衡量抑郁的最好指标，而对于 另一种可能是悲伤、快感减退和疲劳是抑郁症的症状。 个人进行个性化评估将使现场更接近个性化治疗 通过更好地量化这些有点抽象的结构来制定计划。目前的标准是迫使所有个人 拥有相同的测量模式，但该领域正在迅速转向采用个性化 评估的测量模型。关键的是，可用的方法存在许多问题，这些问题会阻止 可靠的个性化测量模型。首先，一些方法(例如简单地使用观察变量) 忽略测量误差的实际情况。这导致了潜在的兴趣结构之间的影响存在偏差 并可能导致对个人过程的不准确推断。第二，观察的次数 对于特定个人的测量结果往往太小，无法得出特定于个人的测量模型。第三， 目前的方法需要满足多元正态分布的假设；这在许多情况下是不常见的 生态瞬时评估数据的形式。第四，许多可用的方法可以达到个人- 当模型被错误指定时(即，观察到的关系的模式)，水平模型表现不佳 症状和潜在结构不正确)。这就避免了在试图达到 模型结构，其中根据定义，正确的模型在 我们的项目，如果得到资助，将为研究人员提供一个简单易用的工具来达到 个性化测量模型。这可以通过在油井内建立一种探索性方法来实现- 了解具有许多所需属性的评估方法。测量误差将是 考虑到，即使在时间点(观测值)少于 变量数量、多元正态不是必需的假设，错误的规范也不是必需的假设 影响可靠的个性化测量模型的识别。