META-HEART CARE: Integrating the Care–Core–Cure Model with Artificial Intelligence to Enhance Self-Care in Patients with Chronic Heart Failure

Abstract

This study aimed to evaluate the effectiveness of META-HEART CARE, an artificial intelligence–based nursing intervention integrating the Care-Core-Cure model, in improving self-care, blood pressure, and stress levels among patients with chronic heart failure. A quasi-experimental design with a pretest–posttest control group approach was employed. A total of 60 participants were recruited and assigned to an intervention group (n = 30) and a control group (n = 30). The intervention group received an 8 week META-HEART CARE digital education program, while the control group received standard care. Self-care was measured using the Self-Care of Heart Failure Index, blood pressure was assessed using a validated digital sphygmomanometer, and stress levels were evaluated using the Perceived Stress Scale. The results showed a significant improvement in self-care in the intervention group compared to the control group (p < 0.001). In addition, the intervention group demonstrated a significant reduction in both systolic and diastolic blood pressure (p < 0.001) and a significant decrease in stress levels (p < 0.001). These findings indicate that integrating digital education, behavioral support, and artificial intelligence enhances both behavioral and physiological outcomes in chronic heart failure management. This study provides practical implications for nursing practice by introducing an innovative, theory-based digital intervention applicable in clinical and community settings. The novelty of this study lies in the integration of the Care-Core-Cure nursing model with artificial intelligence to develop a comprehensive, adaptive, and patient-centered self-care intervention for chronic heart failure management.