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| ID | Type | Description | Link |
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| 5R01LM013107 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
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| National Library of Medicine (NLM) | NIH |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| System Identification | Experimental | All participants in the study will go through a system identification experiment everyday for 270 days. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| System identification experiment for physical activity | Behavioral | The system identification experiment in Just Walk JITAI study has two key components that are the focus of the system identification experiment: daily adaptive step goal recommendations and within-day suggestions to either plan a bout of walking or to inspire reflection and, by extension, an increased urge to go for a walk. |
| Measure | Description | Time Frame |
|---|---|---|
| Steps/Day | This will be measured continuously for the duration of the study via a Fitbit Versa, a wrist-worn, consumer-level activity tracker. | Everyday from baseline to the end of study (for 270 days) |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Eric Hekler, PhD | University of California, San Diego | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of California San Diego | San Diego | California | 92093 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 29954725 | Background | 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. | |
| 29409750 | Background | 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. |
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| ID | Title | Description |
|---|---|---|
| FG000 | System Identification | All participants in the study will go through a system identification experiment everyday for 270 days. System identification experiment for physical activity: The system identification experiment in Just Walk JITAI study has two key components that are the focus of the system identification experiment: walking suggestions and daily step goals. To achieve the desired dynamics on the timescale of interest, we used 2 input signals, one for each of the 2 components. Although 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 ID experiment. The primary goal of a system ID experiment is to estimate and validate dynamical computational models that are validated based on their ability to predict the 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 timescales and orthogonally, that is, statistically independent of each other. |
| Title | Milestones | Reasons Not Completed | |||||
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| Overall Study |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Jan 12, 2022 |
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See study description for details. All participants will receive all intervention elements, with provision varying across time experimentally using procedures appropriate for system identification.
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| 28918547 | Background | 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. |
| 37751237 | Derived | Park J, Kim M, El Mistiri M, Kha R, Banerjee S, Gotzian L, Chevance G, Rivera DE, Klasnja P, Hekler E. 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. JMIR Res Protoc. 2023 Sep 26;12:e52161. doi: 10.2196/52161. |
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| NOT COMPLETED |
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| ID | Title | Description |
|---|---|---|
| BG000 | System Identification | All participants in the study will go through a system identification experiment everyday for 270 days. System identification experiment for physical activity: The system identification experiment in Just Walk JITAI study has two key components that are the focus of the system identification experiment: walking suggestions and daily step goals. To achieve the desired dynamics on the timescale of interest, we used 2 input signals, one for each of the 2 components. Although 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 ID experiment. The primary goal of a system ID experiment is to estimate and validate dynamical computational models that are validated based on their ability to predict the 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 timescales and orthogonally, that is, statistically independent of each other. |
| Units | Counts |
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| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes | ||||||||||||
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| Age, Categorical | Count of Participants | Participants |
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| Age, Continuous | Mean | Standard Deviation | years |
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| Sex: Female, Male | There were 3 missing data for this measure. | Count of Participants | Participants |
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| Ethnicity (NIH/OMB) | There were 3 missing data for this measure. | Count of Participants | Participants |
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| Race (NIH/OMB) | There were 4 missing data for this measure. | Count of Participants | Participants |
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| Region of Enrollment | Number | participants |
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| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Primary | Steps/Day | This will be measured continuously for the duration of the study via a Fitbit Versa, a wrist-worn, consumer-level activity tracker. | Posted | Number | Steps/day | Everyday from baseline to the end of study (for 270 days) |
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The adverse event data were collected from the enrollment period to the end of intervention period which was about 10 months.
Walking, even if it is a voluntary activity, may produce light-headedness, fatigue, or chest discomfort, especially under severe weather. There is also a risk of a cardiac event, such as a heart attack. This risk is less than 1 occurrence in 12,000 maximal exercise tests on healthy participants, and approximately 1-2 occurrences in 10,000 maximal tests on higher risk participants. The risks involved in this study are no more than minimal.
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
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| EG000 | System Identification | In the intervention phase, all app features were delivered, including the 2 push intervention components (ie, 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 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 on the app accessing other features. | 0 | 50 | 0 | 50 | 0 | 50 |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Dr. Eric Hekler | University of California San Diego | 8584299370 | ehekler@health.ucsd.edu |
| Oct 15, 2024 |
| Prot_SAP_002.pdf |
| ICF | No | No | Yes | Informed Consent Form | Jan 12, 2022 | Oct 5, 2023 | ICF_001.pdf |
| ID | Term |
|---|---|
| D057185 | Sedentary Behavior |
| D015438 | Health Behavior |
| ID | Term |
|---|---|
| D001519 | Behavior |
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| ID | Term |
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| D015444 | Exercise |
| ID | Term |
|---|---|
| D009043 | Motor Activity |
| D009068 | Movement |
| D009142 | Musculoskeletal Physiological Phenomena |
| D055687 | Musculoskeletal and Neural Physiological Phenomena |
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| >=65 years |
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| Male |
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| Not Hispanic or Latino |
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| Unknown or Not Reported |
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| Native Hawaiian or Other Pacific Islander |
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| Black or African American |
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| White |
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| More than one race |
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| Unknown or Not Reported |
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We conducted a Bayesian Regression for all available participants, then visualized the results. (N x 16 table, N: number of participants). |
The decision point was the unit of analysis. For the Bayesian Regression, we used Markov Chain Monte Carlo (MCMC). Four sampling chains were used when performing Bayesian modeling. The number of estimation samples and target acceptance rate were gradually increased until numerical stability was achieved. The number of estimation samples was increased by multiplied by 2, as advised by previous literature, depending on the ratio of convergence diagnostics R̂ > 1.1. We used 100 steps increase during 3 hours as the effect threshold value. We assumed that there is an effect only if the estimated effect is more than 80% probable (i.e., the credibility interval is over 100 steps/3 hours) and the Maximum A Posteriori Point (MAP) of the effect is more than 100 steps/3 hours. Otherwise, there is no effect. Among the models with effects, we clipped to 1000 steps/3hours as a maximum if the MAP was greater than 1000 steps/3 hours. |