Using the Health Belief Model to Predict Self-Care Behaviors Among Patients With Cardiovascular Disease Post COVID-19 Pandemic: A Perspective From the United Arab Emirates

Abstract

Introduction Data on how the health belief model constructs might predict the self-care behavior of patients with cardiovascular disease (CVD) post-coronavirus disease (COVID-19) 2019 pandemic are scarce. Objective This study determines the predictors influencing patients’ intention to adhere to self-care for CVD in the United Arab Emirates after the COVID-19 pandemic. Methods A descriptive cross-sectional design was used. A total of 222 patients with CVD were purposively selected. Three scales were used: Health Beliefs Related to Cardiovascular Disease , Physical Activity Measurement , and Behavioral Intention Measurement . Various socio-demographic and clinical characteristics and the participants’ health belief components were considered potential factors in the multivariate analysis to identify the independent predictors of the intention of self-care behaviors. Results The participants had a high level of perceived CVD risk (M = 4.02, SD = 0.714) and high level of perceived benefits regarding adopting healthy behavior (M = 4.30, SD = 0.817). The multiple linear regression revealed that not performing regular sweating exercises (β = 0.230), not receiving smoking cessation instructions (β = 0.214, p = .005), being sufficiently active (β = 0.304), and having no history of heart surgery (β = 0.155) were the independent predictors of low intention scores. The perceived benefits and perceived cues to the action of the Health Belief Model (HBM) were significant independent predictors of behavior intention and were responsible for a 22% increase in the participants’ intention variances . Conclusions In a post-COVID CVD, this investigation delineated perceived benefits and cues to action derived from the HBM as the most robust prognosticators of behavioral intention (accounting for 47% of the variance), superseding sociodemographic and clinical parameters (explaining 25% of the variance). These results advocate for tailored interventions accentuating individual advantages and explicit prompts for behavioral modifications within this demographic.