Digital discharge management interventions had high acceptance (mean 3.99/5) among 259 CVD patients; effort expectancy, performance expectancy, and social influence were key predictors.
Patients with cardiovascular disease show high acceptance of digital discharge management interventions, which is strongly influenced by effort expectancy, performance expectancy, and social influence.
Absolute Event Rate: 0% vs 0%
Summary: Background: Cardiovascular diseases (CVD) are the leading cause of mortality worldwide. Unplanned rehospitalisation rates after discharge remain high, reflecting the chronic nature of CVD and the frequent need for complex, multidisciplinary follow-up care. Digital interventions may provide a valuable complement to traditional discharge management in addressing these challenges. Patients and methods: This study aims to assess the acceptance of digital discharge management interventions (DDMI) and to investigate the underlying factors influencing acceptance among patients with CVD following inpatient treatment. A cross-sectional survey-based study was conducted from June to October 2024 with N = 259 patients with CVD following inpatient treatment. Sociodemographic, medical, mental health, and e-Health-related data were assessed. Acceptance of DDMI and its underlying factors were assessed using a modified model of the Unified Theory of Acceptance of Use of Technology (UTAUT). Results: The overall acceptance of DDMI was high (M = 3.99, SD = 0.92, range = 1–5). The extended UTAUT model explained 62.7% of the variance in acceptance, with male gender (β = −0.36, p < .001) and the UTAUT predictors effort expectancy (β = 0.40, p < .001), performance expectancy (β = 0.24, p < .001), and social influence (β = 0.20, p < .001) being significant predictors. Conclusions: These findings indicate a generally high acceptance of DDMI among patients with CVD following inpatient treatment. To develop and implement such interventions, key drivers and barriers such as effort expectancy, performance expectancy, and social influence should be addressed. Furthermore, such interventions should be tailored to patients’ specific needs.
Mellis et al. (Fri,) reported a other. Digital discharge management interventions had high acceptance (mean 3.99/5) among 259 CVD patients; effort expectancy, performance expectancy, and social influence were key predictors.