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The theory of reasoned action as parallel constraint satisfaction: towards a dynamic computational model of health behavior.

ABSTRACT: The reasoned action approach, although ubiquitous in health behavior theory (e.g., Theory of Reasoned Action/Planned Behavior), does not adequately address two key dynamical aspects of health behavior: learning and the effect of immediate social context (i.e., social influence). To remedy this, we put forth a computational implementation of the Theory of Reasoned Action (TRA) using artificial-neural networks. Our model re-conceptualized behavioral intention as arising from a dynamic constraint satisfaction mechanism among a set of beliefs. In two simulations, we show that constraint satisfaction can simultaneously incorporate the effects of past experience (via learning) with the effects of immediate social context to yield behavioral intention, i.e., intention is dynamically constructed from both an individual's pre-existing belief structure and the beliefs of others in the individual's social context. In a third simulation, we illustrate the predictive ability of the model with respect to empirically derived behavioral intention. As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior. Furthermore, our approach may inform the development of population-level agent-based models of health behavior that aim to incorporate psychological theory into models of population dynamics.

SUBMITTER: Orr MG

PROVIDER: S-EPMC3643950 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundVaccination against Covid 19 disease was based on rational practice theory. One of the most effective methods to control the COVID-19 pandemic is extensive vaccination coverage in the shortest time. The relevant beliefs and predictors of COVID-19 vaccine and the barriers to and facilitators of receiving COVID-19 vaccine should be identified. Individuals' intention to receive COVID-19 and the effective factors are of an utmost importance. This study aimed to predict intention to receive COVID-19 vaccine in the South of Iran.MethodsThis cross-sectional study was performed over a period of 2 months (May 2021 to July 2021) in 4 southern provinces in Iran. The study population of this study included people over 18 years of age who did not receive the COVID-19 vaccine. The online questionnaire was used to collect data. We recruited participants through a self-selection sampling method and posted the online survey link. The questionnaire had two parts: demographic information and Theory of Reasoned Action (TRA) questions. All statistical calculations and hypotheses tests were performed using SPSS21 and Amos21 software and the significance level was considered 0.05.ResultsA total number of 2556 people participated in this study with a mean age of 37.76 (10.7) of years (Age Range = 18-75). The findings showed that attitudes and subjective norms and the use of social media predict the intention to receive COVID-19 vaccine. SEM showed that attitude (β = 0.596, P < 0.001), subjective norms (β = 0.265, P < 0.001) were significant predictors of vaccination intention. In this study, 78% of people were willing to receive the vaccine when they were officially allowed to.ConclusionAccording to the results of the study, it is suggested to strengthen positive attitudes and subjective norms about the importance of COVID-19 vaccination as well as using social media to inform the community in order increase the intention to vaccinate COVID-19 and increase vaccine coverage.

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The theory of reasoned action as parallel constraint satisfaction: towards a dynamic computational model of health behavior.

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