A system identification approach for improving behavioral interventions based on Social Cognitive Theory

Martín, César A.; Deshpande, Sunil; Hekler, Eric B.; Rivera, Daniel E.

doi:10.1109/acc.2015.7172261

Cited by 23 publications

(18 citation statements)

References 12 publications

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“…Methods from system identification are used extensively in practical engineering settings as a means for obtaining dynamic models that can be used in optimization strategies, such as model predictive control, to develop frameworks that support dynamic decision making, such as selection of a particular intervention option for a particular just-in-time state. 43, 44 Comprehensive system identification methodologies provide guidance regarding experimental design, model structure selection, parameter estimation for defining the dynamics, and validation of these idiographic models (e.g., a system identification experiment for physical activity 45, 46 ). This type of system identification experiment provides great opportunities for the empirical study of multidimensional generalization spaces.…”

Section: Experimental Designs and Analytic Strategiesmentioning

confidence: 99%

Advancing Models and Theories for Digital Behavior Change Interventions

Hekler

Michie

Pavel

et al. 2016

American Journal of Preventive Medicine

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154

111

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To be suitable for informing digital behavior change interventions, theories and models of behavior change need to capture individual variation and changes over time. The aim of this paper is to provide recommendations for development of models and theories that are informed by, and can inform, digital behavior change interventions based on discussions by international experts, including behavioral, computer, and health scientists and engineers. The proposed framework stipulates the use of a state-space representation to define when, where, for whom, and in what state for that person, an intervention will produce a targeted effect. The “state” is that of the individual based on multiple variables that define the “space” when a mechanism of action may produce the effect. A state-space representation can be used to help guide theorizing and identify crossdisciplinary methodologic strategies for improving measurement, experimental design, and analysis that can feasibly match the complexity of real-world behavior change via digital behavior change interventions.

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Section: Experimental Designs and Analytic Strategiesmentioning

confidence: 99%

Advancing Models and Theories for Digital Behavior Change Interventions

Hekler

Michie

Pavel

et al. 2016

American Journal of Preventive Medicine

Self Cite

154

111

View full text Add to dashboard Cite

show abstract

“…In recent years, we assisted the first attempts of developing computational models based on self-efficacy theory in order to promote PA [29,30]. Self-efficacy theory is particularly suitable to be modeled because of its nature that is explicitly dynamic (i.e., it takes into account time-varying information such as individual achievements, self-efficacy beliefs and expectations) and, thus, permits adapting the intervention to the individual over the course of the intervention itself [12].…”

Section: Introductionmentioning

confidence: 99%

“…For these purposes the ACT-R-DStress exploited the computational neurocognitive architecture that characterizes the ACT-R theory's simulation environment [32]. Differently, Martin et al [29] developed a dynamical model of social cognitive theory adopting principles from control system engineering with a focus on system identification methodologies. Specifically, system identification compares what happens in different states and contexts of the person over time to what was predicted by a precise mathematical model of a given theory.…”

Section: Introductionmentioning

confidence: 99%

Improving Physical Activity mHealth Interventions: Development of a Computational Model of Self-Efficacy Theory to Define Adaptive Goals for Exercise Promotion

Baretta

Sartori

Greco

et al. 2019

Advances in Human-Computer Interaction

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The practice of regular physical exercise is a protective factor against noncommunicable diseases and premature mortality. In spite of that, large part of the population does not meet physical activity guidelines and many individuals live a sedentary life. Recent technological progresses and the widespread adoption of mobile technology, such as smartphone and wearables, have opened the way to the development of digital behaviour change interventions targeting physical activity promotion. Such interventions would greatly benefit from the inclusion of computational models framed on behaviour change theories and model-based reasoning. However, research on these topics is still at its infancy. The current paper presents a smartphone application and wearable device system called Muoviti! that targets physical activity promotion among adults not meeting the recommended physical activity guidelines. Specifically, we propose a computational model of behaviour change, grounded on the social cognitive theory of self-efficacy. The purpose of the computational model is to dynamically integrate information referring to individuals’ self-efficacy beliefs and physical activity behaviour in order to define exercising goals that adapt to individuals’ changes over time. The paper presents (i) the theoretical constructs that informed the development of the computational model, (ii) an overview of Muoviti! describing the system dynamics, the graphical user interface, the adopted measures and the intervention design, and (iii) the computational model based on Dynamic Decision Network. We conclude by presenting early results from an experimental study.

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“…In the work of Martín et al (2014) a comprehensive dynamical model of SCT based on a fluid analogy was proposed. In Martín et al (2015), a JITAI based on the SCT model was presented for the physical inactivity problem; the intervention is focused on one of the internal loops within the SCT model that represents a "behaviorist" articulation of the determinants of behavior (Ferster (1970)). This is depicted in Fig.…”

Section: Description Of the Behavioral Interventionmentioning

confidence: 99%

“…In the work of Martín et al (2015) a first step for the design of a JITAI for this problem was described, using a semiphysical system identification procedure to estimate the SCT model parameters. The proposed experiment consists of two stages: an initial informative experiment to obtain a model that contains preliminary information about the dynamics of the system, and the optimized experiment that is based on the first and shapes the behavioral outcomes to a desired pattern.…”

Section: Introductionmentioning

confidence: 99%

Design of Informative Identification Experiments for Behavioral Interventions

2015

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This paper describes the design of informative system identification experiments for behavioral interventions based on Social Cognitive Theory (SCT); prior work has shown that these can be expressed as MIMO dynamical systems. The design methodology relies on identification test monitoring to determine the shortest experimental duration resulting in a model estimate with a prescribed level of accuracy. This is accomplished by generating statistical uncertainty regions that are the result of nonparametric transfer function estimates using periodic (and orthogonal in frequency) multisinusoidal inputs. The procedure is illustrated with an example of a simulated behavioral intervention for promoting physical activity.

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A system identification approach for improving behavioral interventions based on Social Cognitive Theory

Cited by 23 publications

References 12 publications

Advancing Models and Theories for Digital Behavior Change Interventions

Advancing Models and Theories for Digital Behavior Change Interventions

Improving Physical Activity mHealth Interventions: Development of a Computational Model of Self-Efficacy Theory to Define Adaptive Goals for Exercise Promotion

Design of Informative Identification Experiments for Behavioral Interventions

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