# 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

Junghwan Park, Meelim Kim, Mohamed El Mistiri, Rachael Kha, Sarasij Banerjee, Lisa Gotzian, Guillaume Chevance, Daniel E Rivera, Predrag Klasnja, Eric Hekler

► To cite this version:

Junghwan Park, Meelim Kim, Mohamed El Mistiri, Rachael Kha, Sarasij Banerjee, et al. 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. 2023. [https://doi.org/10.55516/ijcai.2023-58149](https://doi.org/10.55516/ijcai.2023-58149)

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# Advancing Understanding of Just-In-Time States for Supporting Physical Activity: The Just Walk Just-In-Time Adaptive Intervention System Identification Study Protocol

Junghwan Park, Meelim Kim, Mohamed El Mistiri, Rachael Kha, Sarasij Banerjee, Lisa Gotzian, Guillaume Chevance, Daniel E Rivera, Predrag Klasnja, Eric Hekler

Submitted to: JMIR Research Protocols
on: August 26, 2023

**Disclaimer:** © The authors. All rights reserved. This is a privileged document currently under peer-review/community review. Authors have provided JMIR Publications with an exclusive license to publish this preprint on its website for review purposes only. While the final peer-reviewed paper may be licensed under a CC BY license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.
Table of Contents

<table><tr><td>Original Manuscript</td><td>5</td></tr><tr><td>Supplementary Files</td><td>37</td></tr><tr><td>Figures</td><td>38</td></tr><tr><td>Figure 1</td><td>39</td></tr><tr><td>Figure 2</td><td>40</td></tr><tr><td>Figure 3</td><td>41</td></tr><tr><td>Figure 4</td><td>42</td></tr><tr><td>Figure 5</td><td>43</td></tr><tr><td>Figure 6</td><td>44</td></tr><tr><td>Figure 7</td><td>45</td></tr><tr><td>Figure 8</td><td>46</td></tr><tr><td>Multimedia Appendixes</td><td>47</td></tr><tr><td>Multimedia Appendix 1</td><td>48</td></tr><tr><td>Multimedia Appendix 2</td><td>48</td></tr><tr><td>Multimedia Appendix 3</td><td>48</td></tr><tr><td>TOC/Feature image for homepages</td><td>49</td></tr><tr><td>TOC/Feature image for homepage 0</td><td>50</td></tr></table>
# Advancing Understanding of Just-In-Time States for Supporting Physical Activity: The Just Walk Just-In-Time Adaptive Intervention System Identification Study Protocol

Junghwan Park<fnref n="1" />, <fnref n="2" />, <fnref n="3" />, <fnref n="4" /> BS; Meelim Kim<fnref n="1" />, <fnref n="2" />, <fnref n="3" />, <fnref n="5" /> PhD; Mohamed El Mistiri<fnref n="6" /> BS, MS; Rachael Kha<fnref n="7" /> MS; Sarasij Banerjee<fnref n="6" /> BTECH; Lisa Gotzian<fnref n="8" />; Guillaume Chevance<fnref n="9" /> PhD; Daniel E Rivera<fnref n="6" /> PhD; Predrag Klasnja<fnref n="10" /> PhD; Eric Hekler<fnref n="1" />, <fnref n="2" />, <fnref n="3" /> PhD

**Corresponding Author:**

Junghwan Park BS
Herbert Wertheim School of Public Health and Human Longevity Science
University of California, San Diego
9500 Gilman Dr
La Jolla
US

## Abstract

**Background:** Just-in-time adaptive interventions (JITAIs) are developed to provide support when it is most needed and when users are most receptive. However, the notion of a just-in-time (JIT) state has remained mostly theoretical; there has been little work to gather empirical evidence for what makes a state “just-in-time.” The Just Walk JITAI project uses system identification methods from engineering to systematically vary provision of support across time and context to empirically assess how need, opportunity, and receptivity—three key aspects of the theoretical formulation of JIT states—influence the effectiveness of intervention provision.

**Objective:** The purpose of this research is to investigate JIT states empirically and 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. Building on our prior work, including our previously developed mobile health apps (Just Walk and HeartSteps), we conducted a system identification experiment to investigate and optimize two intervention components: 1) notifications delivered up to 4 times 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 goals. Specifically, 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 based on a previously published machine learning algorithm), and receptivity (i.e., the person received 6 or fewer messages in the last 72 hours or previously walked after receiving a recent notification). The second intervention component is daily steps goals. 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 meet clinically meaningful targets (e.g., averaging 8,000 steps/day across a cycle, as the lower threshold).

**Results:** The study began enrolling participants in June 2022, with a final enrollment of 48 participants. Data collection

<footnote n="1">¹Herbert Wertheim School of Public Health and Human Longevity Science University of California, San Diego La Jolla US</footnote>

<footnote n="2">²Center for Wireless & Population Health Systems, Calit2's Qualcomm Institute University of California, San Diego La Jolla US</footnote>

<footnote n="3">³The Design Lab University of California, San Diego La Jolla US</footnote>

<footnote n="4">⁴Ministry of Health and Welfare Korean National Government Sejong KR</footnote>

<footnote n="5">⁵Department of Preventive Medicine College of Medicine Yonsei University Seoul KR</footnote>

<footnote n="6">⁶Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, and Energy Arizona State University Tempe US</footnote>

<footnote n="7">⁷Institute for Data, Systems, And Society (IDSS) Massachusetts Institute of Technology Cambridge US</footnote>

<footnote n="8">⁸Lufthansa Industry Solutions Lufthansa Norderstedt DE</footnote>

<footnote n="9">⁹ISGlobal Barcelona ES</footnote>

<footnote n="10">¹⁰School of Information University of Michigan Ann Arbor US</footnote>
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 optimization of a scalable JITAI for physical activity. Clinical Trial: ClinicalTrials.gov NCT05273437

(JMIR Preprints 26/08/2023:52161)

DOI: https://doi.org/10.2196/preprints.52161

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Original Manuscript
Junghwan Park
Herbert Wertheim School of Public Health and Human Longevity Science
University of California, San Diego
San Diego, CA
United States
jup014@ucsd.edu

8/25/23

Dear Editorial team,

I am writing to submit our manuscript entitled "Advancing Understanding of Just-In-Time States for Supporting Physical Activity: The *Just Walk* Just-In-Time Adaptive Intervention System Identification Study Protocol" for consideration for publication in the *Journal of Medical Internet Research - Research Protocols*.

This protocol is for a System Identification experimental study designed to optimize a just-in-time adaptive intervention (JITAI) to promote physical activity. It was funded by the National Library of Medicine (R01LM013107), and the study ended in April 2023.

System identification is a suite of methods commonly used in control systems engineering. As the name implies, the core focus of system identification is to seek to estimate dynamical system models that can describe and predict the actions and responses of a focal system. Within this paper, we report, in detail, on our research protocol for two key reasons. First, this unique system identification experiment provides a valuable, hypothesis-driven approach to studying complex dynamic phenomena such as "just-in-time" states, which we operationalize as moments when a person has a need for support, has an opportunity to act favorably if they received the support, and would be receptive to receiving support. We have developed this protocol to make explicit our goals, methods, and overall approach, to then support future research activities. Second, this study design protocol, we argue, could be a valuable addition to the social and behavioral sciences toolkit on methods that could be used to study mechanisms of action and other complex dynamic processes. Based on these two points, we do hope JMIR Research Protocols will seriously consider this work.

Please note this paper is not being considered elsewhere for publication.

Many thanks,

Junghwan Park
Protocols

# Advancing Understanding of Just-In-Time States for Supporting Physical Activity: The *Just Walk* Just-In-Time Adaptive Intervention System Identification Study Protocol

Junghwan Park<sup>1,2,3,4</sup>, Meelim Kim<sup>1,2,3,5</sup>, Mohamed El Mistiri<sup>6</sup>, Rachael Kha<sup>6</sup>, Sarasij Banerjee<sup>6</sup>, Lisa Gotzian<sup>7</sup>, Guillaume Chevance<sup>8</sup>, Daniel E. Rivera<sup>6</sup>, Predrag Klasnja<sup>9</sup>, Eric Hekler<sup>1,2,3</sup>

<sup>1</sup>Herbert Wertheim School of Public Health and Human Longevity Science (HWSPH), UC San Diego, San Diego, CA 92093 USA

<sup>2</sup>Center for Wireless & Population Health Systems, Calit2's Qualcomm Institute, UC San Diego, San Diego, CA 92093 USA

<sup>3</sup>The Design Lab, UC San Diego, San Diego, CA 92093 USA

<sup>4</sup>Ministry of Health and Welfare, Sejong 30113 Republic of Korea

<sup>5</sup>Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, 03722 Republic of Korea

<sup>6</sup>Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85287 USA.

<sup>7</sup>Lufthansa Industry Solutions

<sup>8</sup>ISGlobal, Barcelona, Spain

<sup>9</sup>School of Information, University of Michigan, Ann Arbor, MI 48109 USA
**Abstract**

**Background:** Just-in-time adaptive interventions (JITAIs) are developed to provide support when it is most needed and when users are most receptive. However, the notion of a just-in-time (JIT) state has remained mostly theoretical; there has been little work to gather empirical evidence for what makes a state "just-in-time." The *Just Walk JITAI* project uses system identification methods from engineering to systematically vary the provision of support across time and context to empirically assess how need, opportunity, and receptivity—three key aspects of the theoretical formulation of JIT states—influence the effectiveness of intervention provision.

**Objective:** The purpose of this research is to investigate JIT states empirically and 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. Building on our prior work, including our previously developed mobile health apps (*Just Walk* and *HeartSteps*), we conducted a system identification experiment to investigate and optimize two intervention components: 1) notifications delivered up to 4 times 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 goals. Specifically, 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 based on a previously published machine learning algorithm), and receptivity (i.e., the person received 6 or fewer messages in the last 72 hours or previously walked after receiving a recent notification). The second intervention component is daily steps goals. 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 meet clinically meaningful targets (e.g., averaging 8,000 steps/day across a cycle, as the lower threshold).

**Results:** 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.

**TRIAL REGISTRATION:** ClinicalTrials.gov NCT05273437

**Keywords:** Just-in-time Adaptive Intervention; JITAI; JIT; Walking; Physical Activity; Needs; Opportunity; Receptivity

**Introduction**

There is great interest in the promise of just-in-time adaptive interventions (JITAIs) to support behavioral medicine. A JITAI is a behavioral intervention that is designed to 1) provide interventions and support during "just-in-time states," defined as times when a person would have a *need for support*, an *opportunity to act* in accordance with the support and *be receptive* to receiving support [1] and 2) adapt, over time, to a person's changing needs, with the use of adaptation algorithms that both strive towards enabling a person to meet clinically meaningful behavioral targets (e.g., national recommendations for a given behavior) while accounting for the person's current capabilities and constraints. While there is a lot of interest in this type of intervention, more work is needed to advance the understanding of foundational concepts implied by JITAIs, particularly the JIT state. The JIT state concept is inherently context-
dependent, dynamic, and likely will manifest differently for different people over time. Given
this complexity, much of the work on JITAIs has focused on either creating interventions that
are theory-driven, in terms of specifying JIT states according to a priori decision rules, or
through more data-driven approaches, such as reinforcement learning. An important gap is the
lack of conceptual understanding of JIT states, which could be achieved by conducting rigorous
theory-testing protocols designed to test dynamic hypotheses about JIT states.

The purpose of this paper is to describe a research protocol for an NIH-funded Smart and Connected Health study (R01LM013107) explicitly designed to produce rigorous empirical evidence to study JIT states in the context of a physical activity JITAI. The structure of the paper is as follows: First, background information is provided about JITAIs needed to understand the motivation for our system identification protocol. Next, a description of the system identification experimental protocol is provided, including the specific goals for the project, experimental design procedures, measurement approach, and analysis plan. Lastly, a discussion and implications of this work are offered in terms of future research on JITAIs.

Improving Understanding of “Just-In-Time States” within a Digital Health
PA Intervention

Convincing evidence indicates physical activity (PA) is valuable for reducing the risk of colon, breast, endometrial, lung, and pancreatic cancer [2,3], cardiovascular disease [4], and improving glycemic control [5]. With an aging population, step interventions could help prevent chronic diseases, reduce healthcare costs, and improve functional life years and quality of life [2–18]. The clinical guidelines for steps suggest 8,000 steps/day for adults [19,20], but only 1/3 of this group meets guidelines [21–31]. Across PA interventions for adults (e.g., human-delivered, digital), results show increases of 496 steps/day achieved above baseline levels of 5,000 steps/day, and even high-impact interventions peak at 1,363 steps/day above baseline; both result in activity that is still below guidelines. Even among interventions that produce an effect, maintenance is rarely measured, and when it is, it is not achieved for many participants [32–35]. Our long-term goal is to create a model predictive controller-driven JITAI to increase walking that, we hypothesize, will be more effective than current physical activity interventions at supporting individuals in achieving and maintaining national guideline recommendations of at least 8,000 steps/day averaged across a week [19,20].

While there are many possible algorithmic approaches to do this, such as reinforcement
learning [36] or recommender systems [37], this research effort is focused on the use of a
model-predictive controller approach [38]. A model-predictive controller is an adaptation
algorithm that uses time series data from an N-of-1 unit [39,40] to support decisions over time
in dynamic, often complex situations, such as dynamically providing support to a person to
increase their physical activity. As the name implies, a central feature is a computational
dynamical model, which is a series of mathematical equations that encode prior domain
knowledge and include parameters that are estimated from data derived from each N-of-1 unit
(i.e., a person in this context), thus enabling the controller to account for individual differences
in predictions. These computational models, much like weather or climate forecasting models,
able rigorous simulations of a person's likely responses to different types of support
provided both now and in the future. For example, the model could be used to simulate a
person's response to the provision of a notification meant to nudge a person to walk within the
next 3 hours. The model would produce predictions on the likelihood that a person will walk
after receiving the notification at each moment. In addition, the model can be used to simulate
the potential synergistic or antagonistic effects that might happen because of different
decisions that could be made. For example, using the model, predictions could be made on the
potential diminishing effects of providing notifications over time due to habituation or growing
annoyance, particularly if notifications are sent when a person does not need them.

As this description implies, model-predictive controller-driven JITAIs are complex and, thus, are difficult to create using theory alone, which, historically, was the dominant way in which adaptive behavioral interventions were developed [41,42]. Instead, JITAIs require robust experimentation that enables empirical optimization of its elements, particularly the generation of the computational dynamical models that the controller utilizes to run simulations and, by extension, make dynamic decisions. As described in previous work [43], empirical estimation and validation of dynamical models occur through system identification (ID).

The system ID study described in this paper has two complementary but distinct aims. First, it aims to gather empirical evidence for the concept of JIT states. By varying whether an intervention is provided when the person is thought to be in a state of need, when they have an opportunity to walk, when they are thought to be receptive, or combinations of these three states, the experiment collected initial evidence for which aspects of the JIT state are most important for supporting the effectiveness of just-in-time interventions, and whether this changes over time.

Second, the experiment is designed to collect data needed to optimize a digital health intervention, *JustWalk JITAI*. The goal is to estimate and validate dynamical models that can be used to construct a model-predictive controller that can make decisions on the provision of support in given moments in support of achieving and sustaining clinically meaningful physical activity targets. Prior work was used as a foundation to achieve these aims, particularly a dynamic model of the Social Cognitive Theory (SCT) that encapsulates domain knowledge about behavioral processes that influence physical activity [44–46]. The SCT models were refined with the newly collected data both to help us better understand JIT states and to develop models that can be incorporated into a multi-timescale model-predictive controller.

# Methods

## Overview

### Aims

The aim of this study is to conduct a system identification experiment to empirically assess conceptual elements of a JIT state and to estimate and validate dynamical computational models relevant to JIT states. This work is being conducted to inform the development of a future model-predictive controller-driven JITAI. We have three broad hypotheses: 1) walking bout planning prompts that are provided when the system determines that individuals meet all three conditions of a JIT state—have a need, have an opportunity to walk, and are receptive to intervention notifications—will be more effective than when such prompts are provided when only some or none of those conditions are met; 2) idiographic computational models (i.e., models developed by and for individual participants) can be produced that are effective at predicting contexts in which suggestions to go for a walk will be effective, and how such suggestions and adaptive step goals combine to support a person in achieving, both daily step goals and sustained engagement in steps/day; and 3) nomothetic analyses (i.e., insights gleaned from data aggregated across participants) will reveal meaningful clusters for different types of contextual patterns and trajectories of change across participants. These clusters will enable the selection of initial dynamical model parameters and, by extension, the development of a generic semi-physical model that can be used as a starting point for new participants in a future model-predictive controller-driven JITAI. In aggregate, these results will also be used to empirically test the added value of prior domain knowledge, as encapsulated in prior computational models, for improving model prediction and response, with a basic
AutoRegressive model with eXternal input (ARX) as a reference model that only accounts for prior domain knowledge in the form of variable selection but not the structure of their relationships.

## Study Design Overview

Building on prior work, including the mobile health app, *HeartSteps* (which was relabeled *JustWalk JITAI* for this study to continue on the control systems side of JITAI development) [47,48], we conducted a system identification experiment designed to study the theoretical concept of just-in-time states as a tool for fostering behavior change. The system ID experiment focused on two key intervention components: 1) notifications delivered up to 4 times 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 goals. Both types of notifications prompting short walks within the next 3 hours were 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 had an opportunity to walk based on their prior step data), and *receptivity* (i.e., the person received fewer than 6 messages in the last 72 hours and walked after notifications sent). In addition, suggested daily step goals also varied systematically across time, rooted in a person's baseline levels of steps/day (e.g., 4,000 steps) and gradually increasing until they meet clinically meaningful targets (at least 8,000 steps/day on average). Participants wore a Fitbit Versa 3 and used the study app for 270 days.

## Technology

### Wearable Sensor

The Fitbit Versa 3 is a wrist-worn, watch-style activity tracker that records participants' steps and minutes of Moderate or Vigorous Physical Activity (MVPA; "active minutes" in the language used by Fitbit) that the tracker detects based on accelerometer and heart-rate data. The Fitbit tracker records step and activity data, automatically synchronizes with the Fitbit server, and pushes to the *JustWalk JITAI* servers using Fitbit's subscription API. The participants were recommended to set the Fitbit to use one of the market-available watch faces with the following features: 1) always-visible information about the current step count, daily step goal, and progress towards meeting the goal; and 2) positive reinforcement (in the form of a fireworks display and vibrations) when the daily step goal is met. The list of watch faces that met these requirements was provided by staff.

### Mobile App

The *JustWalk JITAI* application contained: 1) "pull" components that participants could access at any time by opening the *JustWalk JITAI* app; 2) "push" components that were sent to the participants as app notifications based on system-based rules. These were our key experimental manipulations and are described in the study design section below; and 3) ecological momentary assessment (EMA) questions (described in the measurement section). The *JustWalk JITAI* app consisted of three *pull components*, which it drew from the *HeartSteps* app [48]. These were accessible through tabs along the bottom of the application screen (Figure 1, left): (1) **Dashboard**: The dashboard was the home screen of the application and was displayed whenever a participant entered the application or finished interacting with a *JustWalk JITAI* notification. The dashboard implemented three behavior-change techniques (BCTs) [49,50]: self-monitoring, feedback on goal progress, and reminders of activity plans. At the center of the dashboard, a participant's progress toward their daily step goal were shown as feedback. If participants created an activity plan for the day, the dashboard also displayed this plan for participants. (2) **Planning**: Through the planning tab (Figure 1, center), participants could identify when they would plan to exercise that week. The planning tool was designed to operationalize the BCT implementation intentions [51,52] by enabling participants to identify when they will be active, for how long, and identify a specific activity to engage in.
(3) **Activity log** (Figure 1, right): Participants could see an activity summary for the last two weeks, including steps walked and distance covered each day, as well as the types of activities that the Fitbit tracker detected automatically or the user logged manually (e.g., running, hiking, walking, yoga). In addition, the activity log displayed Fitbit-derived active minutes of each day. Finally, the tab displayed the participant's all-time statistics—hours of active time, total distance walked, total counts of activities (detected or manually logged) and the total number of steps recorded since the user started using the JustWalk JITAI system. These all-time statistics were intended to provide longer time-frame feedback on what the participant accomplished over the duration of the study.

<figure><img src="image_2.png" /><figcaption>**Figure 1.** App screenshots. (left) App dashboard. (center) Planning tab. (right) Activity log tab.</figcaption></figure>

# Participant Procedures

## Ethics Review and Pre-trial Registration

The study was approved by the UCSD institutional review board (IRB protocol number: 800132) and was pre-registered on clinicaltrials.gov (NCT05273437).

## Recruitment

Participants were recruited nationally using a non-contact approach. Participants were recruited mainly through university mailing lists and word-of-mouth. The targeted number of participants was 50, with a final fully enrolled sample of 48, as two participants never showed up to pre-intervention meetings.

## Eligibility

Inclusion criteria were participants who: 1) were aged 25+; 2) were inactive, defined as engaging in less than 60 min/week of self-reported moderate intensity PA; 3) own either an iPhone with iOS 11 or above or an Android phone with Android 5.1 or above; 4) stated a commitment to follow study protocols, including regularly carrying a mobile phone, using the *JustWalk JITAI* application, answering phone-based questionnaires, and wearing the Fitbit Versa 3 activity tracker at least 8 hours a day; 5) fluent in English.
Exclusion criteria were participants who: 1) were incapable of giving informed consent or 2) had a psychiatric disorder that limited their ability to follow study protocol, including psychosis and dementia.

## Screening, Informed Consent, and On-boarding meeting

All participant interactions occurred remotely. Recruitment materials directed participants to the study website, which included a contact entry form. Upon completion, participants were automatically sent a link to an eligibility screener, which asked about age, physical activity levels (as reported using the Global Physical Activity Questionnaire (GPAQ) [53], and the revised Physical Activity Readiness Questionnaire (revised PAR-Q) [54]. Staff reviewed survey responses to confirm eligibility.

Eligible participants were offered time slots for an informed consent meeting. Ineligible participants were informed of their ineligibility.

During the informed consent meeting, the following activities took place: 1) The study was described in detail via a guided read-through of the consent form; 2) participants were provided with a list of reasons to and reasons not to take part in the study; 3) participants were invited to develop a list of their own pros and cons for taking part in the study; and 4) participants were provided with a time to ask any questions they had. If participants verbally agreed, study staff asked them to sign the consent form via DocuSign.

Consented participants were asked for a mailing address to send a Fitbit. Participants were also sent instructions on how to set up the Fitbit and the app via email. Once receipt of delivery of the Fitbit was received by staff, a follow-up email was sent to participants, inviting them to pick a time slot for the pre-intervention meeting.

During the on-boarding virtual meeting, participants were instructed on the following topics: 1) instructions on how to install and use the Fitbit and study app; 2) information about the 10-day baseline; 3) information about what to expect after the 10-day baseline period; 4) participants were directed to complete a baseline survey; and 5) instructional videos, with corresponding notes, about how to use and maintain the Fitbit (e.g., strategies to keep it charged, reminders to clean it to reduce skin irritation). All meetings between the participants and the staff occurred via Zoom, and the interview occurred within 2 weeks after the time slots were sent.

## Incentives

Participants received the following incentives: 1) Fitbit Versa 3 (received at study enrollment, $229 value); 2) $25 gift cards provided to them up to 3 times ($75 total) if they completed at least 80% of daily EMA items within each 3-month period; and 3) $25 gift cards if they attended in an optional post-intervention interview.

## Study Timeline

During the 10-day baseline, participants were asked to engage in their normal level of steps or physical activity and to always wear the Fitbit except while charging. No interventions were provided, and no Ecological Momentary Assessment (EMA) questions were asked during the baseline period. When participants opened the app, 10 circles were shown, designating the number of days they have met the minimal wear-time requirements (i.e., 8 hours/day). If the participant did not wear the Fitbit for at least 8 hours a day, the circles did not fill up. Once all 10 circles were filled, the app automatically transitioned into the intervention phase, displaying a dashboard (see below).

In the intervention phase, all app features were delivered, including the two push intervention
components (i.e., walking prompts and adaptive suggested step goals) and the daily EMA questions. The participants also gained access to other parts of the *JustWalk JITAI* app, such as activity logs and planning support. Participants were asked to interact with the app whenever it sent them notifications and were told that they could open the app at any time if they wanted to access pull components, and they found them useful. Total interaction time with the app from push interventions and EMA notifications does not exceed 10 minutes each day, but participants may choose to spend more time in the app accessing other features. The interactions participants were prompted to do occurred in response to four types of notifications: 1) daily step goal notifications; 2) walking suggestion notifications; 3) prompts to complete daily EMA battery; and 4) experience sampling prompts (i.e., if Fitbit detects an activity) to complete EMA items throughout the day and in relation to the notifications to either increase the urge to walk or plan. (For the details about EMA, see the Measures section below.)

# Interventions & System ID Experimental Design

## System identification overview

The current study used a system identification approach to manipulate two intervention components experimentally: *walking suggestions* and *daily step goals*. To achieve the desired dynamics on the time scale of interest, we used two input signals, one for each of the two components. While our study design enables traditional statistical analyses to examine the impact of intervention components on behavioral outcomes, that is not the primary focus of a system identification experiment. The primary goal of a system identification experiment is to estimate and validate dynamical computational models that are validated on their ability to predict future responses of each individual's behavior across time. These aims are achieved by having different intervention components—*suggestions* to walk in the next 3 hours and *adaptive goal setting*—*delivered* at different time scales and delivered orthogonally; that is, statistically independent of each other. Our approach is analogous to a within-person factorial experiment (and, indeed, can be treated as such with all relevant nomothetic statistics used on the developed dataset, which the team plans to do as secondary analyses). The critical difference is that, in system identification studies, the designed input signals achieve statistical independence through orthogonality as verified in the frequency domain. Orthogonality enables separately studying and estimating the dynamics and impact of each intervention component. One can think of frequencies as different repeating rhythms across time, such as the notion of a minute-by-minute frequency, daily frequency, or weekly frequency. The study was designed to ensure that intervention signals are disambiguated across time (via delivering within different frequencies). This enables rigorous independent study of dynamical responses for both intervention components within the same experiment and, indeed, within each person, both proximally (e.g., immediate responses following intervention delivery) and distally (e.g., continued or delayed effects up to several days out of any one notification)

Specifically, both signals are designed to follow the guidelines presented in [55], in which equation (1) is highlighted to define the effective frequency range of the input signal based on a *priori* knowledge of the dominant system time constant.

**Equation 1.** Effective frequency range of the input signal based on a priori knowledge.

$$ \omega_i = \frac{1}{\beta_s \tau_{dom}^H} \le \omega \le \frac{\alpha_s}{\tau_{dom}^L} = \omega^i $$

$\tau_{dom}^L$ and $\tau_{dom}^H$ represent the higher and lower bounds for the estimated dominant time constant of the system, meaning the range in which signals relevant to walking would occur naturally. $\alpha_s$ and $\beta_s$ dictate the input signal's content of high and low frequency, respectively. Orthogonality of our intervention components was confirmed via the use of cross-
correlation analysis (the appropriate approach for testing orthogonality via frequency domains) to the designed input signals.

## Sample Size Considerations

The number of participants has little impact on the power estimate for system ID studies because system ID approaches mostly use dynamic models that take into account individual-level changes over time [43]. Instead, different methods, such as study length and validation analysis, can be used to establish the equivalent notion of “power” [56]. A multisine signal’s “cycle”, or predetermined time interval, serves as the foundation for the power calculation [57]. By dividing the cycles into estimation and validation datasets, this kind of design maximizes the signal-to-noise ratio and aids in the evaluation of model fits.

Previous research has demonstrated that 3 independently excited harmonics per cycle can achieve a sufficient excitation to deliver dynamically meaningful data in relation to daily frequencies. This can be done with 3 sinusoids per cycle, resulting in a cycle that lasts at least 12 days [58]. Estimating and comprehending the multi-timescale dynamics of behavior change are the goals of this effort. A total of 9 excited harmonics were revealed by simulations to be necessary to provide persistence of excitation across relevant frequencies [59]. The result is that each cycle lasts 26 days. From this, the final study length was set at 10 cycles (260 days).

## Intervention Design Overview

<table><caption>**Table 1.** Summary of the intervention elements.</caption><thead><tr><th>Intervention Components</th><th>Description</th></tr></thead><tbody><tr><td>Wearable sensor</td><td><ul><li>Fitbit Versa 3</li></ul></td></tr><tr><td>Mobile app</td><td><ul><li>HeartSteps [47] (renamed to "JustWalk JITAI")</li></ul></td></tr><tr><td>Walking notifications</td><td><ul><li>Pushed up to 4 times a day starting at the participant-set time (e.g., 7a, 8a), with 3-hour gaps between each possible notification (e.g., 7a, 10a, 1p, 4p as possible decision points for sending notifications).</li><li>Randomly chosen from a library of 50 messages that included 24 messages meant to inspire participants to plan a time when they would walk in the next 3 hours, or 26 messages meant to invite participants to become aware of internal urges that could inspire them to walk.</li><li>Used 4 JIT definitions: (1) full JIT [N+O+R], (2) N+R, (3) N+O, and (4) random, with each element defined as such:<ul><li><strong>Need (N):</strong> on-track to meet the daily step goal, accounting for time of day when assessed (e.g., 50% of steps accrued halfway in a person's day, using their self-selected start time as a reference and assuming 12-hour windows),</li><li><strong>Opportunity (O):</strong> next 3-hour time window was predicted to have an 80% likelihood that someone could take steps using a previously published algorithm [60], and</li><li><strong>Receptivity (R):</strong> participant has received less than 6 notifications and responded (i.e., walked) to at least 50% of notifications sent to them within the previous 72 hour</li></ul></li></ul></td></tr></tbody></table>
<table><tr><td>Adaptive step goals</td><td><ul><li>Each morning, participants were provided with a suggested daily step goal.<ul><li>The notification also included a single-item EMA, whether it is helpful or not.</li></ul></li><li>Daily Step goals were calculated using the following procedures:<ul><li>For the first cycle, median steps/day was used as a personalized reference to guide adaptive step goal suggestions. For cycle 1, a participant's personalized reference [i.e., median steps/day] was calculated from their 10-day baseline period, excluding non-wear days.</li><li>For all subsequent cycles, their personalized reference (i.e., median steps/day) was calculated from the previous 26-day cycle (e.g., cycle 2's median steps/day was calculated using all steps/day data from cycle 1, excluding non-wear days).</li><li>Participants were provided a step goal that ranged between their personalized reference (median steps/day) up to personalized reference+4,000 steps.</li><li>A multisine signal design (described below) that ranged from 0 (personalized reference) to 1 (personalized reference+4,000 steps) is used. For example, if a person's median steps/day during the baseline period was 5,000 steps/day, they would receive step goal suggestions between 5,000 steps/day and 9,000 steps/day.</li><li>Maximum step goals were set at 12,000 steps/day, and minimum step goals were 2,000 steps/day.</li></ul></li></ul></td></tr></table>

**Box 1.** The overview of intervention design. See below for details in depth.
<figure><img src="image_3.png" /><figcaption>**Figure 2.** App Notification Screenshot on the locked screen and background status
*Walking Notifications*</figcaption></figure>

*Overview*

The first component of *JustWalk JITAI* was notifications meant to inspire short (e.g., 10+ minute) walking bouts within 3 hours after receiving the intervention. This component had two variations that targeted different behavioral processes: 1) an invitation for a person to create a micro-implementation intention on when, where, and how to fit in a 10+ minute walk in the next 3 hours; we label these suggestions “bout planning”; and 2) an invitation for a person to become aware of interoceptive experiences and signals (e.g., stiff muscles, lethargy) that may inspire them to go for a walk; we call these messages “cultivating an urge.” Both types of walking notifications were drawn from a library of 50 messages (24 bout planning and 26 cultivating an urge). Walking notifications were provided in the form of push notifications from the *JustWalk JITAI* app. The notification could be sent four times a day (i.e., decision points), starting at the user-defined “start of day” time, which was gathered during the on-boarding processes. Starting from the participant’s self-described “start of the day,” the walking-notification decision points occurred every 3 hours. For example, if a participant’s day starts at 8 am; their decision points would be 8 am, 11 am, 2 pm, and 5 pm. For each participant, on each day of the study, at each of the four decision times, the *JustWalk JITAI* system decided whether or not to send a walking notification based on a system identification procedure described next.

*Operationalization of *Just-In-Time* States*

Just-in-time states were experimentally varied via the use of different rules to define a “just-in-time” state. By nudging participants when they were in “just-in-time” states, the hypothesis is that the effect of the walking notifications should increase while maintaining a low level of burden on participants and, thus, minimizing notification fatigue.
A just-in-time state has been previously conceptualized [1] as a state in which a person is receptive to receiving support (e.g., if a notification is sent, a person would appreciate receiving said notification) and the opportunity to engage in the desired behavior (or vulnerability to a negative behavior). Building on this theoretical formulation, a third theoretical parameter was added: the need for intervention support. For example, if someone is already meeting their daily step goals, they likely do not need additional intervention prompts in order to walk. For the purposes of this study, just-in-time states were operationalized as follows:

* **Need (N):** A person is defined as in a state of need if they did not meet the previous day's step goal (for the first decision point) or if they are not making steady progress toward that day's goal (for all other decision points). Sufficient progress was defined as the goal prorated to the current time of the day:

$$ (\text{daily goal}) \times \frac{(\text{time elapsed since the first decision point})}{12 \text{ hours}} $$

* **Opportunity (O):** A person was deemed in a state of opportunity when they could feasibly walk. To operationalize this, a predictive algorithm described in [60], used a threshold of 80% probability that within the next 3 hours, a person may walk. We used the high threshold of 80% so that even a slight nudge to walk could effectively achieve short-term behavior change. [NOTE: Whether notification is needed at such a high moment of opportunity is one question this study will be able to study retrospectively].

* **Receptivity (R):** A person is deemed to be receptive when they have received 6 or fewer messages in the last 72 hours and have responded (i.e., walked in the following 3 hours) after 50% or more of walking notifications sent in that period.

The operationalizations of these three facets of a just-in-time state allow us both to define a full JIT state—i.e., when need, opportunity, and receptivity are all present—as well as to empirically test how different operationalizations of JIT states (e.g., states when only some of these components are present) influence walking notification effectiveness. Specifically, daily decision rules were tested that embody four different levels of being in a JIT state:

* **Full JIT state:** need, opportunity, and receptivity are present.

* **Partial JIT state** (two forms):

* **N+O:** only need and opportunity are present.

* **N+R:** only need and receptivity are present.

* **Not in a JIT state/random:** notifications are randomized each time at 50% probability.

How and when each of these rules for defining JIT states was varied experimentally is described next.

## Prior Observations and Theoretical Considerations That Guided Our Study Design to Test JIT States

This specific study design was created based both on the data from the original *HeartSteps* trial [47], followed by engaging with prior domain knowledge, including behavioral theory and our previously developed SCT dynamical model [61], to guide the final study design, such that this study could provide robust data for supporting computational model testing.

Concerning prior observations, in the *HeartSteps* trial related to the notifications designed to inspire bouts of walking, three empirical observations guided our understanding of just-in-time states. First, as reported previously [47], notifications had a diminishing proximal impact on the total number of steps taken within the 30-minute window after the notification. These results indicated a theorized diminishing value/burden ratio of the prompts. Namely, a dynamic concept that balances, for each instance, a person's perceived value they receive from an intervention compared to the perception of the level of burden of the intervention. This
dynamic hypothesis, which conformed with the data, was that the value/burden ratio would diminish over time, with initial notifications being perceived as more valuable than burdensome but, by the end of the study, this would shift towards low or negative value in relation to the burden.

Second, it was observed that if fewer than 2 notifications were sent on a given day, even later in the 6-week trial, then the bout notifications resulted in significantly improved steps taken within the 30-minute window after the notification. We interpreted this dynamic observation as representing a hypothesized “auto-recovery” that could take place on a person’s value/burden ratio. One could think of this as analogous to a neuron. Once a neuron fires, if new signals come in, the neuron will not fire again, until it has sufficient time to recover, but this recovery process is automatic. It was hypothesized that a similar dynamic is taking place regarding notifications. Namely, if notifications are sent at a rate that is faster than a person’s auto-recovery rate to the notifications, habituation will set in, and the notifications will be ignored (again, like a neuron not firing). If, in contrast, “sufficient time” has passed for auto-recovery to take place (e.g., like a neuron re-establishing itself as ready for the next signal), then a notification sent would be more likely to be attended and reacted to by a participant. Prior data guided us to a population-based starting point of, on average, 2 notifications in a day, providing sufficient time for auto-recovery. With that said, it was postulated that this auto-recovery may vary between individuals. The study design enables us to study these individual differences in temporal responses.

Last, it was observed that there was a trend in the daily timescale/frequency. Specifically, it was observed that there was an overall trend of increased steps/day over the 6-week intervention period. This third observation was translated into a hypothesized “internalization” process of the knowledge, skills, and practices that the intervention is meant to cultivate. This third dynamic hypothesis is the most critical target for designing an effective JIT intervention. Specifically, the goal is to create a JITAI that would enable a person to develop internalized knowledge, skills, and practices that could be maintained after the cessation of the intervention, while accounting for the likely diminishing value/burden ratio and the need for “recovery” between notifications. This complex, interactive dynamic hypothesis, which postulates three different underlying dynamics that interact together, is what is primarily being studied in this experiment. Most critically, it was hypothesized that “internalization,” observed in the form of increases in steps/day in a time series, would take place more often when notifications/interventions were provided using JIT states compared to times when notifications were offered without taking account JIT states.

## System Identification Experiment Design via Simulation Studies

With these empirical observations as a foundation, prior behavioral literature was reviewed to 1) look for prior domain knowledge that could be used to guide understanding of these dynamics; and 2) support us in better operationalizing the dynamic expectations we hypothesize to be observed, particularly if interventions could be provided consistently with taking account JIT states. A computational model was developed, guided by these empirical observations and building on principles drawn from operant learning and cognitive science, which is described elsewhere [62]. A set of simulations were run to model anticipated responses to receiving PA notifications during positive and negative JIT states. A key focus of the simulation work was to determine if the models could produce the dynamics observed in *HeartSteps*, described above, and to guide the anticipated length of time needed to observe a possible overall step/day increase in steps across days when notifications are repeatedly delivered during positive JIT states. In this context, a positive outcome was operationalized as a person taking at least 1,000+ steps [as a proxy for 10 minutes] within the 3-hour window after
receiving a walking notification. Overall, it was hypothesized that a greater number of positive outcomes when using the full JIT state operationalization (N+O+R) would be observed, with increased overall steps/day occurring across days during those times (accumulative internalization). In contrast, it was hypothesized that a relatively steady steps/day response would occur during times when notifications were sent at random (which was a replication of the original *HeartSteps* study and intentionally not considering JIT states, thus, it was hypothesized that the random signals would replicate the observations from the original study). A set of additional simulations were run based on the social cognitive theory model [61] with results of the simulation reported elsewhere [59] to further refine our study design.

Based on the simulation results from both models, a system identification study was devised that experimentally varied the use of different definitions of JIT states but did so in a way that would enable the study of possible accumulation or degradation of the dynamic effects across days. Specifically, 4 days was set as a minimal length of days needed to observe the effects of successive full JIT rules. It was anticipated that stabilization to degradation of effects would start to occur within 1 day of sending non-JIT notifications, based on our simulation studies. With that said, given the highly novel study design and limited robust empirical data to guide this subtle study of dynamics, longer periods were used, particularly of the full JIT state (N+O+R). In other words, the experiment compared decision rules that range from not trying to intervene at a JIT state to trying to intervene at a full JIT state over a sustained period of time that, based on simulation studies, would be sufficiently long to detect accumulative effects if they occurred.

This resulted in a categorical four-level design. To construct this categorical four-level input signal, a Pseudo-Random Binary Sequence (PRBS) was used (for full justification and details, see [59]). This base signal compares JIT to some form of partial JIT or random (i.e., non-JIT) states. To incorporate the exploratory examination of differences between JIT operationalizations, a Random Multi-Level Sequence (RMLS) was superimposed over one of PRBS's binary levels to compare the two incomplete JIT decision rules (N+O, N+R) to the randomly sent walking notifications [59]. Input signal design parameters for the PRBS were chosen as ($\tau_{dom}^{L}=3$ days, $\tau_{dom}^{H}=3.5$ days, $\alpha_{s}=2, \beta_{s}=2$), which was done to cover the frequency range of interest based on the guidelines provided in equation (1). This resulted in a 60-day cycle with $n_{r}=4$ shift registers and switching time $T_{sw}=4$ days (see Figure 3). This 60-day cycle enabled the team to 1) study the hypothesized dynamic, positive accumulative effect on steps within 3 hours of notification times, and steps/day when walking notifications were sent during theoretically defined JIT states; 2) compare these dynamics to the hypothesized dynamic degradation across days when walking notifications are delivered during partial or negative JIT states (i.e., at random); and 3) as an exploratory aim, study if the dynamics vary across different JIT state operationalizations. Four cycles of a 60-day PRBS signal were generated to support both estimation and validation of the dynamical models that operationalized the hypothesized dynamics, which results in a total of a 240-day period, followed by a final period of full JIT state level to match the full study period (260 days) which was constrained by adaptive step goal cycles (26 days × 10 cycles).
<figure><img src="image_4.png" /></figure>

**Figure 3.** The designed decision rules signal for the walking notification component of the intervention in the time domain. Each level represents one of the decision rules. The four signal levels were obtained by superimposing a 3-level RMLS signal on the base PRBS signal. Figure 4 provides a visualization of the spectral power density as it relates to the walking notifications. This visualization provides insights into the degree to which the theorized dynamics will be appropriately excited, enabling the detection of effects if they occur across various frequencies and, thus, the study of the proposed dynamic hypotheses. The results suggest sufficient persistent excitation by the number of harmonics included in the effective frequency range between 0.14 rad/day to 0.67 rad/day. This frequency range, determined by the time constant guidelines in equation (1), assures that the appropriate slow dynamics (i.e., low frequencies) and fast dynamics (i.e., high frequencies) of the system are captured.

<figure><img src="image_5.png" /></figure>

**Figure 4.** Spectral power density of the designed decision rule input signal visualized in Figure 3. It is shown to determine if sufficient excitation across key frequencies is established within the trial.

Adaptive Step Goals Intervention Component

Overview

The adaptive daily step goals component follows a similar structure to a previous system identification experiment, whereby participants were given a specific suggested step goal to target each day [63]. Like this previous design, a key consideration is to design a “cycle,” which is a deterministic, repeatable pattern that defines the provision of intervention options to an individual. Intervention options can be provisioned to mimic randomness via pseudo-random
signal designs that achieve the valuable properties of randomness for causal inference, while still being deterministic and, thus, repeatable. This provides valuable properties for a system identification experiment as it enables a more robust comparison between cycles (for more details see prior work [38]). In this study, the same basic logic of prior work is followed, specifically using a pseudo-random signal design that varied step goals between an "achievable" target up to a plausibly "ambitious" target (how goals are assigned each day is described in greater depth in prior work [59]).

The definition of an “achievable” step goal was personalized to each person, which was labeled a personalized reference, defined as a person’s median steps/day calculated from the previous 26-day cycle period [59]; note that, for the first cycle only, the personal reference was the 10 day-baseline period. Each morning, participants received a notification informing them of their targeted step goal for the day. The updated goal was also available to them in the *JustWalk JITAI* dashboard and was automatically synced by the *JustWalk JITAI* server to the participant’s Fitbit account so that the feedback in the Fitbit app and the participant’s Fitbit tracker always showed the correct goal progress each day. To further facilitate goal pursuit, participants were instructed to install a watch face providing the step goal number and a goal progress bar to enable always-visible goal-progress feedback. Fitbit’s native visual and haptic feedback was used when the participant completed the daily step goals (i.e., fireworks animation and vibrations).

## Experimental Manipulation: Input Signal Design

To define a cycle for this component, a MultiSine (MS) signal was utilized. The input signal design parameters ($τ_{dom}^L=1$ days, $τ_{dom}^H=2$ days, $α_s=2, β_s=2$), as described in equation (1), are chosen based on the results from previous work and the simulation studies we conducted in preparation for staging this system identification experiment [59]. The design parameters result in a cycle length of 26 days, as shown in Figure 5. For each participant, a personalized realization of the MS signal generates the daily goals throughout the 260-day intervention, across 10 cycles, by determining, for each day, the factor by which the 4,000 steps/day range is multiplied and then added to the person’s personalized reference (i.e., median steps/day) as described in Table 1.
<figure><img src="image_6.png" /><figcaption>**Figure 5.** One cycle of the designed multisine (MS) input signal for the Goal Setting component of the intervention, in both time (top) and frequency (bottom) domains. As described in Table 1, the multiplier factor varies between zero and one over time. The spectral power density plot highlights the number of excited harmonics at the frequencies of interest.</figcaption></figure>

The effective frequency range of the signal is related to the design parameters through equation (1), which yields persistence of excitation between $\omega_* \approx 0.25$ rad/day to $\omega^* \approx 2$ rad/day for the designed MS signal, as it is highlighted in the power spectrum of the signal seen in Figure 5. This showcases that the designed input signal for goal setting creates variability in relevant dynamical ranges of interest.

# Measures

## Baseline Survey

The baseline survey includes a) demographic information, including age, height, weight, ethnicity, gender, race, marital status, household size, employment status, and level of education, b) personal characteristics related to the study (i.e., how you spend your time, about your routine and your neighborhood), c) self-perception (i.e., personality [64], perceived stress [65]), and d) life habits [66] and physical activity [67] (i.e., how you feel about physical activity, how you engage in exercise, how much you intend to exercise, and how you notice the effects of exercise).

## Continuous Measurement of Activity and Heart Rate

Steps/minute and minute-level heart rate were measured using the Fitbit Versa 3, a wrist-worn, consumer-level activity tracker that uses triaxial accelerometry to measure movement. See the next subsection for the details. MVPA was measured in four ways: 1) automatically triggered objective measurement: Fitbit Versa 3 automatically detects vigorous movement [68] if the activity is sufficiently vigorous and long; 2) manually initiated objective measurement: Fitbit Versa 3 or Fitbit app on the smartphone has an “activity” app to log activity manually; 3) manual logging in the Fitbit app, and 4) manual logging on the study app using the “activity” tab (which was also populated with any reporting in the Fitbit app). The Fitbit assesses steps,
physical activity intensity levels, energy expenditure, start/end time point and type of activity, distance traveled, and number of floors. Prior work shows that Fitbits likely underestimate heart rates and, by extension, total activity, but they do so reliably, thus establishing a meaningful within-person comparison [69], which is the focus of this study.

## Ecological Momentary Assessment (EMA)

Psychological constructs and process variables were asked daily for inclusion in our targeted, dynamic models via EMA conducted at 7 pm local time. The EMA items included concepts of 1) Self-efficacy for walking; 2) Self-efficacy for problem-solving; 3) Positive context for walking; 4) Negative context for walking; 5) Supportive routine; 6) Drive to walk; 7) Relationships supportive of walking; 8) Interoceptive awareness of cues that could inspire walking (e.g., stiffness, fatigue); 9) Negative reinforcement of walking; 10) Behavioral repertoire; and 11) Typical supportiveness for walking. Detailed items are included in multimedia appendix 1. The items from 1 through 6 were asked daily, the items from 7 through 10 were asked every four days to minimize the burden of responding, and item 11 was asked every day for the first week of each month (i.e., 7 times a month).

Two other types of additional EMA question items were sent. Triggered by a completed physical activity, the participants were asked if they were feeling 1) healthy, 2) fatigued, 3) energized, and 4) discomfort. Also, we asked if the participants thought they could meet the daily goal.

## Weather

Daily weather data (current weather and weather forecasts) were gathered from the public weather database API [70]. No actual location GPS data were gathered; instead, self-reported home zip code was used to gather weather data.

## Post-intervention interviews

After the 260-day intervention, participants were asked to fill out a brief post-intervention survey and were also given options to participate in an optional post-intervention interview. During post-intervention interviews, participants were asked about their overall reactions, including both positive and negative aspects of using the app and any suggestions to improve the intervention. Interviews were audio-recorded and transcribed.

The participants were also given a choice to either stop the use of the *JustWalk JITAI* intervention or to continue to use it, in which case the data past the study end date would not be used in analyses.

## Treatment Fidelity Monitoring Procedures

### Mobile App Use Logs

Mobile app usage was recorded, with time stamps, for every page viewed in the *JustWalk JITAI* app, including opening notifications, opening the app, viewing pages within the app, and opening surveys. The one piece of information that could not be logged due to operating system limitations was whether notifications (e.g., walking notifications) were seen without being opened, such as when they automatically expand on the iOS lock screen.

### Monitoring the *JustWalk JITAI* Systems

*JustWalk JITAI* server was automatically monitored every 10 minutes throughout the study period by the separate program to check for five performance and stability targets: 1) web server; 2) database server; 3) security firewall; 4) software framework for the server; and 5) Fitbit API. If the server stopped working or took too long to respond (>3,000ms), the program sent text messages and emails to study staff. The monitoring program was separately monitored by another program to ensure the monitoring was conducted properly. If any data operations failed (e.g., if the Fitbit server was not responding), the study staff were immediately notified via email. If there was an error in Fitbit data sync, when the data connection resumed,
all the missing data were re-fetched to fill up any missing period.

## Data Collection Monitoring

Data collection was monitored by study staff on a weekly basis, with automated visualizations, to ensure there were no technical errors that may have compromised the study.

## Study Adherence

Study adherence was monitored automatically by the *JustWalk JITAI* server. During the pre-intervention meetings, participants were asked to wear the Fitbit a minimum of 8 hours a day, but it was suggested to them to wear the Fitbit all day, even at night. Fitbit devices typically sync with the Fitbit server via the Fitbit phone app every 15 minutes. This synchronization stops if the Fitbit device runs out of battery or is not worn for several days. The *JustWalk JITAI* Server regularly checks whether a participant's device stopped syncing with the Fitbit server, and, if so, it sends an adherence text message to the participant.

As an operationalized protocol, at 15 minutes prior to the first decision point for walking notifications (e.g., 6:45 am local for most participants), if a participant had no Fitbit app synchronization records for 60 minutes (e.g., since 5:45 am local for most participants), the server sent an automated adherence text message including an approximate length of the period for which the updates were missed (e.g., a few hours, a day, or a while) to invite participants to recharge and sync their Fitbit in the Fitbit App.

This approach helped avoid making the mistake of responding too immediately to the problem of data drops caused by accidental battery discharges. Since it can be assumed that people do not carry their Fitbit charger around during the day, sending an immediate “charge it now” message when data updates stop during the day is unlikely to be an effective remedy. Also, given that it only takes about 30 minutes to charge a Fitbit from fully depleted to lasting more than a day, it was assumed that sending these reminders before the start of the day would allow participants a chance to charge their Fitbit.

## Modeling and Data Analysis

### Analyses of System Identification Experiments

A series of system identification estimation approaches will be used to analyze the data and obtain control-oriented dynamical models to study JIT states. General data analysis will start with examining the cross-correlation of the data to verify the hypothesized structure of the system, as operationalized via the computational models described earlier. Nonparametric estimation methods such as correlational analysis (CRA) and spectral analysis (SPA) [71] will be used to obtain preliminary information about the responses of each individual (i.e., time constants, gains, orders). The knowledge gained from the nonparametric estimation methods will be utilized to obtain ideographic models through prediction-error modeling approaches such as AutoRegressive with *eXternal input (ARX)* and output error (OE) [71] estimation and more elaborate grey box methods utilizing SCT model structure. Additionally, Model-on-Demand (MoD) [72,73] estimation will be used to estimate more flexible models that address nonlinearities in the system. The estimated models from all approaches will be contrasted with one another, and the advantages/disadvantages of each will be assessed in order to inform future efforts.

## Results

### Simulations

The input signal design for the two intervention components in this study involved an iterative procedure that relied on a *priori* knowledge and simulation results for different types of anticipated participants to guide the efforts. The simulations were based on a dynamic SCT model derived from a fluid analogy, which provided the means to guide specific conditions for
the JIT states considered in the decision rules to ensure that both the number of notifications sent per day as well as the overall number of notifications sent throughout the intervention were not burdensome for the participants. Furthermore, the simulation framework with diverse scenarios provided insights into the “ambitious yet achievable” range of adaptive goals provided in each goal-setting cycle. A detailed account of the model used in the simulations, technical details of the designed input signals, and simulation results that guided the design are provided in [59]. In this section, results for a hypothetical adherent participant are presented to illustrate the dynamic nature by which the daily goals adapt to the participant’s performance, as well as the effectiveness of the JIT decision rules in limiting the provision of support only to times that are hypothesized to be beneficial.

Figure 6 shows the implementation of the designed daily goals signal in a simulation setting. In this case, the goals in each cycle are adjusted to the performance of the participant in the previous cycle, as described in Table 1. Note that in this case, a hypothetical adherent participant is capable of achieving the daily goals given to them in each cycle; consequently, the median of the participant’s performance increases, which leads to an increase in the goals provided in subsequent cycles. As a result, the daily goals gradually increase over the span of the intervention, from a 2,000 steps/day low in the first two cycles of the intervention (the first 52 days) to a high of 12,000 steps/day in the last five cycles. This simulation result illustrates that the input signal design for this component is working as intended by adapting the daily goals to each participant in a personalized manner, while nudging the participant towards higher levels of physical activity through a combination of “ambitious” and “achievable” goals.

<figure><img src="image_7.png" /><figcaption>**Figure 6.** Simulation results illustrating the implementation of adaptive daily goals (top) in reaction to the performance of a hypothetical adherent participant in terms of daily step count (bottom). Adapted from [59].</figcaption></figure>

Figure 7 shows the walking notification component of the intervention in the simulated scenario for the hypothetical adherent participant. As seen in the figure, the decision rules work as intended in terms of dictating the nature of the notifications sent to the participant. At the beginning of the intervention, when the participant does not achieve the daily goals (hence, the need condition of the decision rules is met), the number of notifications sent to the participant is high across all the levels of the decision rules. Later in the intervention, as the participant adopts healthier behavior and meets the daily goals, the number of walking notifications sent on a daily basis is significantly lower. Furthermore, note that on days when the receptivity condition is considered, the number of notifications sent follows the notifications budget mentioned in Table 1.

Finally, as the need condition is not met by the participant towards the end of the intervention, walking notifications are only sent on days with fully randomized notifications. This design
allows for comparing the impact of fully randomized notifications on the participant to notifications that are guided by JIT state conditions that should make them more beneficial. From this simulation results, the rate at which notifications are sent (i.e., notifications/decision point) on full JIT state days is the lowest at 0.084, followed by days of need and opportunity (N+O) conditions at 0.148, then need and receptivity (N+R) conditions at 0.176. The highest rate of notifications is observed on days with fully randomized walking notifications at 0.488.

<figure><img src="image_8.png" /><figcaption>**Figure 7.** Simulation results for a hypothetical adherent participant illustrating the expected walking notifications (top) sent based on the designed decision rules signal (bottom). For the sent notifications signal at the top, a zero value implies that no notifications were sent at that decision point, whereas a value of one implies that a notification was sent. Adapted from [59].</figcaption></figure>

## Recruitment

Enrollment began in March 2022 and ended in July 2022. In total, 761 potential participants submitted a letter of interest, and 48 enrolled in the study. See Figure 6 for the CONSORT diagram [74]. The intervention was finished in April 2023. The data were gathered without major incidents. The source code for the server and the app is publicly available in the project's GitHub repository [75].
<figure><img src="image_9.png" /><figcaption>**Figure 8.** CONSORT diagram for the recruitment.</figcaption></figure>

**Discussion**

There is well-documented evidence suggesting that digital health interventions, to date, have not lived up to their intended potential [76–78]. From issues of poor adherence, results that only produce limited effects, and questionable scalability, particularly among those with less access to digital devices, the potential of digital health interventions has not yet translated into real-world impact [76, 77, 78]. One pathway for improving this is to focus on producing evidence directly targeting and seeking to improve on the fundamental shortcomings of digital health interventions [77, 78].

In this study, our primary focus was to rigorously study the notion of just-in-time (JIT) states. To date, JIT states used to formulate intervention decision rules have either been assumed to be correctly defined—typically based on the guidance from behavioral theory—but are not empirically tested, or they derived by using data-driven approaches such as reinforcement learning whereby prior domain knowledge and understanding is under-emphasized, and, instead, there is hope that useful insights about intervention timing will emerge from data collected in intervention studies [80]. While we believe that both of these paths have merit, this study protocol offers a middle ground whereby prior domain knowledge is encapsulated into computational models, which, using simulation studies, can then be used to guide the careful generation of evidence that can test dynamic hypotheses about the nature of JIT states.

This is important because a JIT state, conceptually, is an inherently non-linear causal phenomenon [81]. There is no one causal factor that makes a given moment a JIT state, but, instead, it is a mixture of different factors, such as time of day, a person's current motivational levels, their relationships, recent experiences with the types of support given, and the degree to which support is well-matched to a person's current needs. Such factors combine in a given moment to influence the decision to engage—or not—in the target behavior. This study protocol recognizes the inherent non-linear causal nature of the phenomenon under study and
provides a rigorous approach to gathering the data needed to make progress in the context of
such complexity. By varying whether an intervention is provided when the person is thought to
be in a state of need when they are thought to have an opportunity to walk based on their
personal historical step data, and when they are thought to be receptive, or combinations of
these three, the experiment will collect initial evidence for which aspects of the JIT state are
most important for supporting the effectiveness of JITAIs, and whether this changes over time.
Using this information, particularly when linked with slow dynamic processes of change (i.e.,
daily goal setting), the experiment produces data needed to empirically optimize a digital
health intervention, *JustWalk JITAI*. Specifically, the work will result in individualized,
empirically validated dynamical models that can be used to predict each individual's response
to intervention options offered to them. These individualized/idiographic dynamical models
can be applied in optimal personalized behavioral interventions through sophisticated control
algorithms like model predictive control (MPC) [43,82]. These MPC-driven JITAIs could have
the potential to work more effectively than prior digital health interventions. Thus, this work
could feasibly be a key step in filling the gap between the hope of digital tools and current
realities based on the evidence. While this is all still quite hypothetical, the trial is a critical step
in testing the potential benefits of this overall approach for intervention optimization.

## Acknowledgments

ME, RK, LG, GC, DER, PK, EH did theoretical foundation work and simulations. JP, MK, ME, RK, DER, PK, EH developed the intervention. JP, PK developed the technical systems, including server and mobile apps. Patrick Neggie, Rahul D'Costa, and Nick Reid supported the development and operation of the technical systems. JP, ME, RK, SB conducted recruitment, interview, and worked on the trial logistics (e.g., sending out Fitbits). Michael Higgins and Shadia J. Assi supported the operation of the clinical trial. JP, ME monitored the participants, communicated with the participants if necessary, and executed the adherence protocol. ME, RK, DER developed system identification programs. DER, PK, EH provided guidance at each stage of the study. All authors contributed to the writing of the manuscript. This study is funded by the National Library of Medicine (R01LM013107).

## Conflicts of Interest

JP is an employee of the Korean National Government, the Ministry of Health and Welfare. LG is
an employee of Lufthansa Industry Solutions.
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Supplementary Files
Figures
<figure><img src="image_10.png" /><figcaption>App screenshots. (left) App dashboard. (center) Planning tab. (right) Activity log tab.</figcaption></figure>
App notification screenshot on the locked screen and background status.

<figure><img src="image_11.png" /></figure>
The designed decision rules signal for the walking notification component of the intervention in the time domain. Each level represents one of the decision rules. The four signal levels were obtained by superimposing a 3-level RMLS signal on the base PRBS signal.

<figure><img src="image_12.png" /></figure>
Spectral power density of the designed decision rule input signal visualized in Figure 3. It is shown to determine if sufficient excitation across key frequencies is established within the trial.

<figure><img src="image_13.png" /></figure>
One cycle of the designed multisine (MS) input signal for the Goal Setting component of the intervention, in both time (top) and frequency (bottom) domains. As described in Table 1, the multiplier factor varies between zero and one over time. The spectral power density plot highlights the number of excited harmonics at the frequencies of interest.

<figure><img src="image_14.png" /></figure>
Simulation results illustrating the implementation of adaptive daily goals (top) in reaction to the performance of a hypothetical
adherent participant in terms of daily step count (bottom). Adapted from [59].

<figure><img src="image_15.png" /></figure>
Simulation results for a hypothetical adherent participant illustrating the expected walking notifications (top) sent based on the designed decision rules signal (bottom). For the sent notifications signal at the top, a zero value implies that no notifications were sent at that decision point, whereas a value of one implies that a notification was sent. Adapted from [59].

<figure><img src="image_16.png" /></figure>
<figure><img src="image_17.png" /><figcaption>CONSORT diagram for the recruitment.</figcaption></figure>
# Multimedia Appendixes
Ecological Momentary Assessment Items.

URL: http://asset.jmir.pub/assets/b2b6188144f5d9133eb1157ac98ff2a0.docx

Bout planning notification messages and their classifications into two categories of messages.

URL: http://asset.jmir.pub/assets/7ba505164d8758ce9a7d676e272443bd.docx

NIH peer review report.

URL: http://asset.jmir.pub/assets/cb24e9f7f8e78ccb1ce46d530015be87.pdf
TOC/Feature image for homepages
Walking.

<figure><img src="image_18.png" /></figure>