A system identification approach for improving behavioral interventions based on Social Cognitive Theory
Abstract
Mobile and wireless health (mHealth) interventions offer the opportunity for applying control engineering and system identification concepts in behavioral change settings. Social Cognitive Theory provides a recognized theoretical framework that can be applied to explain changes in behavior over time. Based on earlier work describing a dynamical model of this theory, a semi-physical system identification approach is developed in this paper for interventions associated with improving physical activity. An initial informative experiment that relies on prior knowledge from similar interventions is first designed to obtain basic insights regarding the dynamics of the system. Based on these results a second, optimized experiment is developed which solves a constrained optimization problem to find the intervention component profiles needed to mirror a desired behavioral pattern and to provide sufficient information that allows a more precise estimation of the parameters. A simulation study is presented to illustrate the design procedure.
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Randomised controlled trials have repeatedly overturned assumptions drawn from observational or non-randomised studies.
“The importance of undertaking a RCT, and not relying solely on formative studies is evidenced by the fact that RCTs have repeatedly overturned assumptions drawn from observational or non-randomised studies (e.g.50, 51).”
From Evaluating Digital Health Interventions (2016) · cites this paper as [50]
The micro-randomized trial can be used to test proximal main effects of components over time, time-varying moderation, and, with variations in study design based on system identification, the development of idiographic computational models.
“It can be used to test proximal main effects of components over time, time-varying moderation [16], and with variations in study design based on strategies from system identification, the development of idiographic computational models [17, 18].”
From Agile science: creating useful products for behavior change in the real world (2016) · cites this paper as [17]
The micro-randomized trial is a sequential factorial design for modeling effectiveness of treatment components over time.
“The micro-randomized trial is a sequential factorial design for modeling effectiveness of treatment components over time.”
From Agile science: creating useful products for behavior change in the real world (2016) · cites this paper as [17]
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Evaluating Digital Health Interventions
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- Authors
- Martin, C. A., Deshpande, S., Hekler, E. B., & Rivera, D. E.
- Affiliations
- Arizona State University (US)
- Venue
- 2015 American Control Conference (ACC)
- Publisher
- IEEE
- Published
- 2015-07
- Pages
- 5878-5883
- ISSN
- 0743-1619 (ISSN-L)
- OpenAlex
- W1528555758
- Cited by
- 23
- References
- 18
- Citations by year
- 2024: 1 · 2023: 4 · 2022: 1 · 2020: 2 · 2019: 1 · 2018: 5 · 2017: 3 · 2016: 5 · 2015: 1
- FWCI
- 6.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the ... American Control Conference/Proceedings of the American Control Conference (publishedVersion)
- Funders
- National Science Foundation
- Arizona State University
- Virginia G. Piper Charitable Trust
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Keywords
- system identification, behavioral interventions, social cognitive theory, mobile health, physical activity, intervention design, constrained optimization, parameter estimation, behavior change
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