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.

项目摘要 随着生态瞬时评估（EMA）的日益普及，如每日乳制品和 经验抽样测量，行为科学家能够更好地调查人内 动态模式（即，变量之间的关系）潜在的症状，行为和生活 事件这一努力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， 准确评估感兴趣的结构。通过测量模型，我们的意思是观察到的模式 变量与潜在构造相关。例如，抑郁症可以被认为是一种潜在的结构， 心理学家经常试图测量。个体可能与（观察到的）症状有关 在给定的时间点，他们的整体（潜在）抑郁水平。对一个人来说，悲伤的症状， 随着时间的推移，绝望和易怒的感觉可能是抑郁的最佳衡量标准，而对于 另一个，也许悲伤，快感缺乏和疲劳是抑郁症的症状。 个人进行个性化评估将使该领域更接近个性化治疗 通过更好地量化这些有点抽象的结构来制定计划。目前的标准是强制所有个人 有相同的测量模型，但该领域正在迅速走向采用个性化 评估的衡量模型。重要的是，可用的方法有许多问题， 可靠的个性化测量模型。首先，一些方法（如简单地使用观测变量） 忽略测量误差的现实。这导致了潜在的感兴趣结构之间的效应偏差 并且可能导致关于个体过程的不准确推断。二、观察次数 对于给定个体获得的测量值通常太小而不能达到个人特定的测量模型。三是 目前的方法需要满足多元正态性的假设;这在许多方法中通常看不到。 生态瞬时评估数据的形式。第四，有许多方法可以达到个人- 当模型被错误指定时级别模型不能很好地执行（即，观察到的关系模式 症状和潜在结构是不正确的）。这就避免了在试图达到 以探索性的方式构建模型，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易于使用的工具， 个性化的测量模型。这可以通过在井内建立探索方法来实现- 这是一种可以理解的估计方法，具有许多期望的特性。测量误差为 考虑到这一点，即使时间点（观测值）的数量小于 变量的数量，多元正态性将不是一个必要的假设，错误的规定将不会 影响可靠的个性化测量模型的识别。