The goal of this observational, multicenter study is to evaluate whether AI-driven remote monitoring using a mini-invasive wearable device can improve clinical outcomes in adult patients (≥18 years) with chronic heart failure (CHF).
The main questions it aims to answer are:
* Can continuous remote monitoring reduce hospital admissions (emergency visits and hospitalizations) by 20% compared to standard care? * Does wearable-based remote monitoring improve functional, biochemical, and instrumental parameters in CHF patients? Researchers will compare patients using the wearable device (intervention group) to those receiving standard clinical follow-up (control group) to assess whether AI-driven monitoring leads to fewer hospitalizations, better disease management, and improved quality of life.
Participants will:
* Wear the EmbracePlus (Empatica Inc.) device continuously for six months (intervention group only). * Have their biometric data (SpO₂, HRV, EDA, respiratory rate, temperature, sleep quality) monitored remotely. * Receive automated alerts and teleconsultations if abnormal physiological changes are detected. * Attend scheduled follow-up visits (remote and in-person) for clinical evaluation and treatment adjustments.
The study aims to provide real-world evidence on whether integrating wearable health technology with AI analytics can enhance CHF management and improve patient outcomes.
Age ≥ 18 years (adults of any sex)
Confirmed diagnosis of chronic heart failure (CHF) for at least 6 months prior to screening
Stable on optimized heart failure therapy for at least one month before enrollment
Heart Failure with Reduced Ejection Fraction (HFrEF)
NYHA Functional Class IV or anticipated heart transplant or ventricular assist device (VAD) implantation within 6 months of screening
Severe renal impairment (eGFR < 30 mL/min/1.73 m²) or dialysis dependence
Terminal comorbidities (e.g., advanced cancer, end-stage pulmonary disease) significantly limiting life expectancy
Pregnancy
The SMART-CARE study will enroll adult patients (≥18 years) with chronic heart failure (CHF) from specialized cardiology and geriatrics clinics. Participants will have NYHA Class I-III heart failure and varying left ventricular ejection fraction (LVEF) classifications (HFrEF, HFmrEF, HFpEF). Patients will be recruited from tertiary hospitals and research centers, including: AOU "San Giovanni di Dio Ruggi d'Aragona" (Cardiology \& Cardiac Rehab) IRCCS Neurolesi "Bonino Pulejo" (Cardiology Unit) IRCCS Fondazione Casa Sollievo della Sofferenza (Geriatrics Unit) The study will compare: Intervention Group: AI-based remote monitoring with a wearable device. Control Group: Standard CHF management with routine follow-ups. Participants will be clinically stable but at risk of hospitalization, and those with NYHA Class IV, severe renal impairment, or terminal illnesses will be excluded.
Cohort
Prospective
Intervention Group (Device Group - AI-Based Remote Monitoring)
DeviceThis intervention utilizes a mini-invasive wearable device for continuous remote monitoring of chronic heart failure (CHF) patients. Unlike traditional telemonitoring, it integrates AI-driven predictive analytics to track oxygen saturation (SpO₂), heart rate variability (HRV), electrodermal activity (EDA), temperature, respiratory rate, and sleep quality in real time. The system generates automated alerts for healthcare providers, enabling early detection of CHF exacerbation and proactive intervention through teleconsultations, medication adjustments, or in-person evaluations. Data is securely transmitted to a cloud-based platform, allowing continuous risk assessment and personalized care adjustments. This approach aims to reduce unnecessary hospitalizations, enhance patient monitoring, and optimize heart failure management through advanced AI-based digital health technology.
Standard Clinical Follow-Up
Other interventionParticipants in this group will receive standard chronic heart failure (CHF) management according to current clinical guidelines. Their follow-up will consist of scheduled in-person visits every three months, during which they will undergo routine laboratory tests (including BNP, NT-proBNP, renal function, and electrolytes), as well as echocardiography and ECG evaluations. Treatment adjustments will be made based on clinical assessments and reported symptoms. Unlike the intervention group, these participants will not use a wearable device, and their condition will be monitored exclusively through traditional hospital visits and self-reported health status.
Change in Hospital Admissions with AI-Based Remote Monitoring
The study aims to determine whether AI-based remote monitoring using a wearable device leads to a 20% reduction in hospital admissions (including emergency department visits and hospitalizations) compared to standard clinical follow-up in patients with chronic heart failure (CHF). The intervention group will use a mini-invasive wearable device for continuous physiological monitoring, while the control group will receive standard CHF management without remote monitoring. Hospital admission rates will be analyzed to assess the effectiveness of early AI-driven detection and intervention.
Change in Quality of Life
Quality of life (QoL) will be measured using the Kansas City Cardiomyopathy Questionnaire (KCCQ) Overall Summary Score, a validated instrument specifically designed to assess symptom burden, functional status, social limitations, and quality of life in patients with chronic heart failure (CHF). The score ranges from 0 to 100, where higher scores indicate better health status and quality of life. The study will evaluate whether patients in the AI-based remote monitoring group report higher KCCQ scores compared to the control group. Unit of Measure: KCCQ score (0-100 scale) Time Frame: Baseline, 3 months, and 6 months Interpretation: Higher scores indicate better outcomes.
Adverse Effects of CHF Therapy
The study will analyze whether continuous AI-driven monitoring helps in reducing adverse effects related to CHF treatments, such as: Hypotension (low blood pressure episodes due to overuse of diuretics or vasodilators) Bradyarrhythmias (slow heart rate linked to beta-blockers or other heart failure medications) By detecting early physiological changes, the wearable device may enable timely adjustments in medication dosages, reducing complications and therapy-related hospitalizations.
Change in Biochemical Parameters
Biochemical Parameters: Change in B-type Natriuretic Peptide (BNP) Levels (picograms per milliliter)
Change in Biochemical Parameters
Biochemical Parameters: Change in N-terminal pro-BNP (NT-proBNP) Levels (picograms per milliliter)
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