JMIR Publications

ABSTRACT

Background:

Just-in-time adaptive interventions (JITAIs) are interventions 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 a critical to JITAIs. To date, JIT states are 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 identification experiment is: 1) to investigate JIT states empirically and 2) to enable the empirical optimization of a JITAI intended to increase physical activity (steps/day).

Methods:

We recruited English-speaking adults aged 25+ who are physically inactive and own smartphones. Participants wore a Fitbit Versa 3 and used the study app for 270 days. The Just Walk JITAI project uses system identification methods to study JIT states. Specifically, provision of support was systematically varied across different theoretically plausible operationalizations of JIT states to enable more rigorous and systematic study of the concept. We experimentally varied two intervention components: 1) notifications delivered up to 4 times per day designed to increase a person’s steps within the next 3 hours; and 2) suggested daily step goals. Notifications to walk within the next 3 hours were experimentally provided (or not) across varied operationalizations of JIT states accounting for: need (i.e., whether daily step goals were previously met or not), opportunity (i.e., if the next three hours is a time window when a person previously walked), and receptivity (i.e., previously walked after receiving notifications). Suggested daily step goals varied systematically within a range referenced to a person’s baseline level of steps/day (e.g., 4,000 steps) until they met clinically meaningful targets (e.g., averaging 8,000 steps/day across a cycle, as the lower threshold). A series of system identification 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 ultimately goal of guiding rigorous, replicable, empirical formulation and study of JIT states, to inform a future JITAI.

Results:

As is common in system identification, we conducted a series of simulation studies to formulate the experiment. Results of our simulation studies illustrated the plausibility of 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 Fall/Winter 2023.

Conclusions:

This study will be the first empirical investigation of JIT states that use system identification methods to inform the optimization of a scalable JITAI for physical activity. Clinical Trial: ClinicalTrials.gov NCT05273437