Health Behavior Clinical Trial
Official title:
Control Systems Engineering for Counteracting Notification Fatigue: An Examination of Health Behavior Change.
Verified date | October 2023 |
Source | University of California, San Diego |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Interventional |
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.
Status | Completed |
Enrollment | 50 |
Est. completion date | May 15, 2023 |
Est. primary completion date | May 15, 2023 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 21 Years and older |
Eligibility | Inclusion Criteria: - inactive: engaging in less than 60 min/week of self-reported moderate intensity physical activity - adults: aged 21 or older - own a smartphone that can run HeartSteps (iOS or Android) Exclusion Criteria: - not proficient in English, or - indicate medical problems that preclude physical activity as defined using physical activity readiness questionnaire (PAR-Q) |
Country | Name | City | State |
---|---|---|---|
United States | University of California San Diego | San Diego | California |
Lead Sponsor | Collaborator |
---|---|
University of California, San Diego | National Library of Medicine (NLM) |
United States,
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. — View Citation
Korinek EV, Phatak SS, Martin CA, Freigoun MT, Rivera DE, Adams MA, Klasnja P, Buman MP, Hekler EB. Adaptive step goals and rewards: a longitudinal growth model of daily steps for a smartphone-based walking intervention. J Behav Med. 2018 Feb;41(1):74-86. doi: 10.1007/s10865-017-9878-3. Epub 2017 Sep 16. — View Citation
Phatak SS, Freigoun MT, Martin CA, Rivera DE, Korinek EV, Adams MA, Buman MP, Klasnja P, Hekler EB. Modeling individual differences: A case study of the application of system identification for personalizing a physical activity intervention. J Biomed Inform. 2018 Mar;79:82-97. doi: 10.1016/j.jbi.2018.01.010. Epub 2018 Feb 1. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
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. | Everyday from baseline to the end of study (for 270 days) | |
Secondary | Behavioral Intentions | After each intervention notification (i.e., suggested daily step goals and bout notifications), participants will be asked to respond on their intentions to achieve the suggested target (no, maybe, yes). | Everyday from baseline to the end of study (for 270 days) | |
Secondary | Intervention adherence | Adherence will be gleaned from the Fitbit and from smartphone app in terms of the degree to which persons engage with the Fitbit, use of the app, reads prompts, and responds to prompts. | Everyday from baseline to the end of study (for 270 days) | |
Secondary | Psychological mediators/process variables | self-efficacy, motivation, behavioral outcomes, and internal cues to action will be measured as key process variables hypothesized to influence a person's drive and capacity to engage in walking. These will be measured daily using ecological momentary assessment questions asked within the smartphone app. | Everyday from baseline to the end of study (for 270 days) | |
Secondary | Environmental context | Barriers, facilitators, schedule, and other factors that might support or hinder a person's capacity to walk will be measured daily using ecological momentary assessment. | Everyday from baseline to the end of study (for 270 days) | |
Secondary | Min/week moderate-to-vigorous physical activity | This will be measured continuously for the duration of the study using the Fitbit Versa, a wrist-worn, consumer-level activity tracker. | Everyday from baseline to the end of study (for 270 days) |
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