Clinical trial NCT05273437: The JustWalk JITAI Study: A System Identification Experiment to Understand Just-in-Time States of Physical Activity
Official title: Control Systems Engineering for Counteracting Notification Fatigue: An Examination of Health Behavior Change.
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Physical Inactivity
Health Behavior
Healthy Lifestyle
Lead sponsor: University of California, San Diego
Sponsor class: OTHER
Collaborators: name: National Library of Medicine (NLM)
First posted: 2022-03-10
Enrollment: 50
Sex: ALL
Minimum age (years): 21
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: See study description for details. All participants will receive all intervention elements, with provision varying across time experimentally using procedures appropriate for system identification.
Primary purpose: TREATMENT

Brief summary: The goal of this system identification experiment is to estimate and validate dynamical computational models that can be used in a future a multi-timescale model-predictive controller. System identification is an experimental approach used in control systems engineering, which uses random and pseudo-random signal designs to experimentally manipulate independent variables, with the goal of producing dynamical models that can meaningfully predict individual responses to varying provision of support. A system identification is single subject/N-of-1 experimental design, whereby each person is their own control. This 9-month system identification experiment will experimentally vary daily suggested step goals and provision of notifications meant to inspire bouts of walking during different plausible just-in-time states. Results of this system identification experiment will then enable the development a future multi-timescale model-predictive controller-driven just-in-time adaptive intervention (JITAI) intended to increase steps/day. The system identification experiment will be conducted among N=50 inactive, adults aged 21 or over who have no preexisting conditions that preclude them from engaging in an exercise program, as determined using the physical activity readiness questionnaire.

Detailed description: N=50 English-speaking adults aged 21+ who are physically inactive (self-reported engagement in less than 60 minutes/week of moderate-intensity activity) and own a smartphone (iPhone or Android) will be recruited. Participants will be provided with and asked to wear a Fitbit Versa 3 and use the study app, JustWalk, for 270 days.

A system identification experiment, which is a single subject/N-of-1 experimental protocol used in control systems engineering, will be conducted. This study is designed to empirically optimize dynamical models that can be used within a future model-predictive controller-driven just-in-time adaptive intervention (JITAI). This system identification experiment will include two experimentally manipulated components: 1) notifications delivered up to 4 time per day designed to increase a person's steps within the next 3 hours via either increased awareness of the urge to walk or via bout planning; and 2) adaptive daily step goal suggestions. Both components will be experimentally manipulated using procedures appropriate for system identification. Specifically, notifications prompting planning of short walks within the next 3 hours will be experimentally provided or not across variations of need (i.e., whether daily step goals were previously met), opportunity (i.e., the next three hours is a time window when a person previously walked), and receptivity (i.e., person received fewer than 6 messages in the last 72 hours and walked after notifications were sent). This enables experimental manipulation of varying "just-in-time" states, thus providing valuable data for guiding future predictions about when, where, and for whom a bout notification would produce the desired effects compared to not. Thus, this is a hypothesis-driven approach to better understanding issues of notification fatigue by seeking to provide notifications only when said notifications are needed, when a person has the opportunity to act on them, and is receptive to receiving support. In addition, suggested daily step goals will also be varied systematically across time. A suggested step goal will vary between a person's median steps/day, calculated from the person's previous activity measured via Fitbit, up to 3,000 steps above their median reference. The goals will continue to get progressively more difficult if a person meets their suggested step goals. The system will stop increasing suggested step goals if a person achieves a median of 12,000 steps/day as their reference. During the study, participants will wear a Fitbit for the duration to measure PA and also fill out ecological momentary assessment surveys of psychological constructs hypothesized to be key variables for the targeted dynamical computational models.

After study completion, dynamical modeling analyses appropriate for system identification will be conducted for each participant (see references for more details on the types of analyses that will be conducted). The goal is to estimate and validate the dynamical computational models, with a particular benchmark used on the degree to which a dynamical model can predict, prospectively, each person's future steps/day and response to a particular bout notification. Results from this dynamical systems modeling will then enable the development of a multi-timescale model-predictive controller driven JITAI designed to provide support for increasing walking among healthy adults, which can then be tested in a future clinical trial.

Eligibility criteria: Inclusion Criteria:

* inactive: engaging in less than 60 min/week of self-reported moderate intensity physical activity
* adults: aged 21 or older
* own a smartphone that can run HeartSteps (iOS or Android)

Exclusion Criteria:

* not proficient in English, or
* indicate medical problems that preclude physical activity as defined using physical activity readiness questionnaire (PAR-Q)

Primary outcomes:
measure: Steps/Day
timeFrame: Everyday from baseline to the end of study (for 270 days)
Secondary outcomes:
Other outcomes:
Locations:
city: San Diego
country: United States
facility: University of California San Diego
state: California
Study officials:
affiliation: University of California, San Diego
name: Eric Hekler, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Hekler EB, Rivera DE, Martin CA, Phatak SS, Freigoun MT, Korinek E, Klasnja P, Adams MA, Buman MP. Tutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions. J Med Internet Res. 2018 Jun 28;20(6):e214. doi: 10.2196/jmir.8622.
citation: Phatak SS, Freigoun MT, Martin CA, Rivera DE, Korinek EV, Adams MA, Buman MP, Klasnja P, Hekler EB. Modeling individual differences: A case study of the application of system identification for personalizing a physical activity intervention. J Biomed Inform. 2018 Mar;79:82-97. doi: 10.1016/j.jbi.2018.01.010. Epub 2018 Feb 1.
citation: Korinek EV, Phatak SS, Martin CA, Freigoun MT, Rivera DE, Adams MA, Klasnja P, Buman MP, Hekler EB. Adaptive step goals and rewards: a longitudinal growth model of daily steps for a smartphone-based walking intervention. J Behav Med. 2018 Feb;41(1):74-86. doi: 10.1007/s10865-017-9878-3. Epub 2017 Sep 16.