Artificial Intelligence-Based Remote Monitoring for Chronic Heart Failure: Design and Rationale of the SMART-CARE Study

Michele Ciccarelli¹Alessia Bramanti^1*Albino Carrizzo^1,2Marina Garofano¹Valeria Visco¹Carmine Izzo¹Maria Rosaria Rusciano¹Gennaro Galasso¹FRANCESCO LORIA³Giorgia Bruno¹Carmine Vecchione^1,2

• ¹Universita degli Studi di Salerno Dipartimento di Medicina Chirurgia e Odontoiatria Scuola Medica Salernitana, Baronissi, Italy
• ²NEUROMED, Pozzilli, Italy
• ³Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d'Aragona, Salerno, Italy

Introduction: Chronic heart failure (CHF) is associated with frequent hospitalizations, poor quality of life, and high healthcare costs. Despite therapeutic progress, early recognition of clinical deterioration remains difficult. The SMART-CARE study investigates whether artificial intelligence (AI)-enabled remote monitoring using CE-certified wearable devices can reduce hospital admissions and improve patient outcomes in CHF. Methods: SMART-CARE is a prospective, multicenter, observational cohort study enrolling 300 adult patients with CHF (HFrEF, HFmrEF, or HFpEF) across three Italian tertiary centers. Participants are assigned to an intervention group, using wrist-worn, chest-worn, and multiparametric CE-certified wearable devices for six months, or to a control group receiving standard CHF care. Physiological data (e.g., SpO₂, HRV, respiratory rate, skin temperature, sleep metrics) are continuously collected and analyzed in real time through AI algorithms to generate alerts for early clinical intervention. The primary endpoint is a ≥20% reduction in hospital admissions over six months. Secondary outcomes include changes in quality of life (Kansas City Cardiomyopathy Questionnaire), biomarkers (BNP, NT-proBNP, renal function, electrolytes), echocardiographic indices (LVEF, LV volumes), and safety events. Results: We hypothesize that AI-driven remote monitoring will significantly reduce hospitalizations, improve quality of life, and favorably impact biochemical and echocardiographic parameters compared to standard care. Conclusion: SMART-CARE is designed to evaluate the clinical utility of multimodal wearable devices integrated with AI algorithms in CHF management. If successful, this approach may transform traditional care by enabling earlier detection of decompensation, optimizing resource utilization, and supporting the scalability of remote monitoring in chronic disease management. Trial registration: This study is registered on ClinicalTrials.gov with the identifier NCT06909682, registered on June 14, 2024. This trial was prospectively registered prior to enrollment of the first participant.