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BACKGROUND

Just-in-time adaptive interventions (JITAIs) are designed to provide support when individuals are receptive and can respond beneficially to the prompt. The notion of a just-in-time (JIT) state is critical for JITAIs. To date, JIT states have been formulated either in a largely data-driven way or based on theory alone. There is a need for an approach that enables rigorous theory testing and optimization of the JIT state concept.

OBJECTIVE

The purpose of this system ID experiment was to investigate JIT states empirically and enable the empirical optimization of a JITAI intended to increase physical activity (steps/d).

METHODS

We recruited physically inactive English-speaking adults aged ≥25 years who owned smartphones. Participants wore a Fitbit Versa 3 and used the study app for 270 days. The JustWalk JITAI project uses system ID methods to study JIT states. Specifically, provision of support systematically varied across different theoretically plausible operationalizations of JIT states to enable a more rigorous and systematic study of the concept. We experimentally varied 2 intervention components: notifications delivered up to 4 times per day designed to increase a person's steps within the next 3 hours and suggested daily step goals. Notifications to walk were experimentally provided across varied operationalizations of JIT states accounting for need (ie, whether daily step goals were previously met or not), opportunity (ie, whether the next 3 h were a time window during which a person had previously walked), and receptivity (ie, a person previously walked after receiving notifications). Suggested daily step goals varied systematically within a range related to a person's baseline level of steps per day (eg, 4000) until they met clinically meaningful targets (eg, averaging 8000 steps/d as the lower threshold across a cycle). A series of system ID estimation approaches will be used to analyze the data and obtain control-oriented dynamical models to study JIT states. The estimated models from all approaches will be contrasted, with the ultimate goal of guiding rigorous, replicable, empirical formulation and study of JIT states to inform a future JITAI.

RESULTS

As is common in system ID, we conducted a series of simulation studies to formulate the experiment. The results of our simulation studies illustrated the plausibility of this approach for generating informative and unique data for studying JIT states. The study began enrolling participants in June 2022, with a final enrollment of 48 participants. Data collection concluded in April 2023. Upon completion of the analyses, the results of this study are expected to be submitted for publication in the fourth quarter of 2023.

CONCLUSIONS

This study will be the first empirical investigation of JIT states that uses system ID methods to inform the optimization of a scalable JITAI for physical activity.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05273437; https://clinicaltrials.gov/ct2/show/NCT05273437.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52161.

背景

即时自适应干预（JITAIs）旨在在个体易于接受并能对提示做出有益反应时提供支持。即时（JIT）状态的概念对JITAIs至关重要。迄今为止，JIT状态要么主要以数据驱动的方式制定，要么仅基于理论。需要一种能够严格进行理论检验并优化JIT状态概念的方法。

目的

本系统识别实验的目的是通过实证研究JIT状态，并对旨在增加身体活动（步数/天）的JITAI进行实证优化。

方法

我们招募了年龄≥25岁、讲英语、身体不活跃且拥有智能手机的成年人。参与者佩戴Fitbit Versa 3并使用研究应用程序270天。JustWalk JITAI项目使用系统识别方法来研究JIT状态。具体而言，支持的提供在JIT状态的不同理论上合理的操作化之间系统地变化，以便对该概念进行更严格和系统的研究。我们通过实验改变了2个干预组件：每天最多发送4次旨在在接下来3小时内增加步数的通知，以及建议的每日步数目标。关于步行的通知在考虑需求（即每日步数目标之前是否达成）、机会（即接下来3小时是否是此人之前步行的时间窗口）和接受度（即此人在收到通知后之前是否步行）的JIT状态的不同操作化中进行了实验性提供。建议的每日步数目标在与个人每日步数基线水平相关的范围内（例如4000）系统地变化，直到他们达到具有临床意义的目标（例如，在一个周期内平均每天8000步作为下限阈值）。将使用一系列系统识别估计方法来分析数据并获得面向控制的动态模型以研究JIT状态。所有方法估计的模型将进行对比，最终目标是指导对JIT状态进行严格、可重复的实证制定和研究，以为未来的JITAI提供信息。