Dornsife Center for Self-Report Science & Center for Economic and Social Research, University of Southern California, 635 Downey Way, Los Angeles, CA, 90089-3332, USA.
Department of Psychology, University of Southern California, Los Angeles, CA, USA.
Behav Res Methods. 2024 Feb;56(2):765-783. doi: 10.3758/s13428-023-02083-8. Epub 2023 Feb 25.
Interest in just-in-time adaptive interventions (JITAI) has rapidly increased in recent years. One core challenge for JITAI is the efficient and precise measurement of tailoring variables that are used to inform the timing of momentary intervention delivery. Ecological momentary assessment (EMA) is often used for this purpose, even though EMA in its traditional form was not designed specifically to facilitate momentary interventions. In this article, we introduce just-in-time adaptive EMA (JITA-EMA) as a strategy to reduce participant response burden and decrease measurement error when EMA is used as a tailoring variable in JITAI. JITA-EMA builds on computerized adaptive testing methods developed for purposes of classification (computerized classification testing, CCT), and applies them to the classification of momentary states within individuals. The goal of JITA-EMA is to administer a small and informative selection of EMA questions needed to accurately classify an individual's current state at each measurement occasion. After illustrating the basic components of JITA-EMA (adaptively choosing the initial and subsequent items to administer, adaptively stopping item administration, accommodating dynamically tailored classification cutoffs), we present two simulation studies that explored the performance of JITA-EMA, using the example of momentary fatigue states. Compared with conventional EMA item selection methods that administered a fixed set of questions at each moment, JITA-EMA yielded more accurate momentary classification with fewer questions administered. Our results suggest that JITA-EMA has the potential to enhance some approaches to mobile health interventions by facilitating efficient and precise identification of momentary states that may inform intervention tailoring.
近年来，即时自适应干预（JITAI）的研究兴趣迅速增加。JITAI 的一个核心挑战是高效、精确地测量用于告知即时干预传递时机的定制变量。生态瞬时评估（EMA）通常用于此目的，尽管传统形式的 EMA 并非专门为促进即时干预而设计。在本文中，我们介绍了即时自适应 EMA（JITA-EMA），作为一种策略，可以减少参与者的响应负担，并减少 EMA 用作 JITAI 中的定制变量时的测量误差。JITA-EMA 基于为分类目的开发的计算机自适应测试方法（计算机分类测试，CCT），并将其应用于个体内部瞬时状态的分类。JITA-EMA 的目标是在每个测量时刻管理一小部分信息丰富的 EMA 问题，以准确分类个体的当前状态。在说明了 JITA-EMA 的基本组件（自适应选择要管理的初始和后续项目、自适应停止项目管理、适应动态定制的分类截止值）之后，我们进行了两项模拟研究，以使用瞬时疲劳状态的示例探索 JITA-EMA 的性能。与在每个时刻管理固定问题集的传统 EMA 项目选择方法相比，JITA-EMA 可以用更少的问题管理更准确的瞬时分类。我们的结果表明，JITA-EMA 有可能通过促进对可能影响干预定制的瞬时状态的有效和精确识别，来增强某些移动健康干预方法。
