The motivating question comes from a gap in the just-in-time adaptive intervention (JITAI) literature: the field leans heavily on the idea of a "just-in-time state" — a moment when someone is both receptive and able to act — yet that notion has typically been defined either purely data-driven or purely from theory, without rigorous empirical testing. The protocol sets out to investigate just-in-time states empirically and to lay the groundwork for optimizing a JITAI that increases physical activity, measured as steps per day.
Methodologically the study is framed as a system-identification experiment rather than a conventional efficacy trial. Two intervention components are manipulated orthogonally across a 270-day participation window: adaptive daily step goals that vary systematically, and notifications (up to four per day) intended to raise step count within the following three hours, delivered across operationalizations of need, opportunity, and receptivity. The experimental variation uses control-engineering input designs — pseudorandom binary sequence and multisine signals — which is the key technique: by injecting these designed perturbations, the responses of each person can be separated and used to fit idiographic (within-person) computational models of intervention dynamics, including a model-predictive-controller framing.
The sample and timeline are concrete: 48 physically inactive, English-speaking adults aged 25 or older, each wearing a Fitbit Versa 3, completing a 10-day baseline followed by a 260-day intervention organized as ten 26-day cycles. Primary outcomes center on steps per day, the controller dynamics, and the idiographic models that predict when intervention will be effective for a given person.
Honest context: this describes the published protocol, with enrollment completed in April 2023; it states the study's design and intended analyses rather than reporting trial outcomes (the outcome findings are carried in Park's dissertation and related papers). The authorship places Park as first author alongside the Hekler and Rivera groups, situating the work at the intersection of behavioral medicine and control systems engineering — the through-line of his doctoral research.