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Cited by 29Mobile Health and mHealth ApplicationsPhysical Activity and HealthDigital Mental Health Interventions

Advancing Understanding of Just-in-Time States for Supporting Physical Activity (Project JustWalk JITAI): Protocol for a System ID Study of Just-in-Time Adaptive Interventions

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Park, Junghwan, Kim, Meelim, El Mistiri, Mohamed, Kha, Rachael, Banerjee, Sarasij, Gotzian, Lisa, Chevance, Guillaume, Rivera, Daniel E +2 more·2023·JMIR Research Protocols
DOI 10.2196/52161PMC PMC10565629

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Abstract

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

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  • The system uses context-triggered alerts only when risk thresholds are crossed, minimizing unnecessary interruptions to prevent notification fatigue.

    “To prevent notification fatigue a known barrier to long-term engagement in mHealth programs the system uses contexttriggered alerts only when risk thresholds are crossed, minimizing unnecessary interruptions (Keshavjee et al., 2022; Park et al., 2023).”

    From SiKurang: Development and Field Evaluation of an Offline-First mHealth System for Community-Based Stunting Risk Detection and Counselling in Low-Connectivity Primary Care Settings (2026) · cites this paper as Park et al., 2023

    The Methods section describes notification timing and need, opportunity, and receptivity conditions, and discusses the burden and habituation that frequent notifications may cause. But the Results simulations say notifications were also sent on fully randomized days when the need condition was not met. The paper therefore does not support the claim that alerts occur only when risk thresholds are crossed; no page numbers were provided in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Authors
Park, J., Kim, M., El Mistiri, M., Kha, R., Banerjee, S., Gotzian, L., Chevance, G., Rivera, D. E., Klasnja, P., & Hekler, E.
Affiliations
  • Yonsei University (KR)
  • Massachusetts Institute of Technology (US)
  • Arizona State University (US)
  • Lufthansa (Germany) (DE)
  • Barcelona Institute for Global Health (ES)
  • University of Michigan (US)
  • University of California San Diego (US)
ORCID
Venue
JMIR Research Protocols
Publisher
JMIR Publications Inc.
Published
2023-09-26
Volume
12
Pages
e52161
ISSN
1929-0748 (ISSN-L)
DOI
10.2196/52161
PMC
PMC10565629
OpenAlex
W4386293202
Cited by
29
References
78
Citations by year
2026: 11 · 2025: 9 · 2024: 9
FWCI
10.27
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Research Protocols (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
U.S. National Library of Medicine (R01LM013107)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Medicine › Physiology › Physical Activity and Health
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Keywords
just-in-time adaptive interventions, physical activity, step count, system identification, dynamic modeling, wearable activity trackers, mobile notifications, physical inactivity, theory testing, simulation study, control-oriented model, intervention optimization

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