AI-driven wearable monitoring for chronic heart failure
Smart Monitoring and Analysis System Based on Artificial Intelligence for Patients With Chronic Heart Failure Using Advanced Mini-Invasive and Wearable Medical Devices
This project will try an AI-driven wearable and mini‑invasive device to see if continuous home monitoring reduces hospital visits and improves health for adults with chronic heart failure.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 205 (estimated) |
| Ages | 19 Years and up |
| Sex | All |
| Sponsor | University of Salerno Academic / other |
| Locations | 1 site (Salerno) |
| Trial ID | NCT06909682 on ClinicalTrials.gov |
What this trial studies
SMART-CARE is a prospective, multicenter observational comparison of two parallel cohorts: patients managed with standard clinical follow-up versus patients wearing a CE‑certified EmbracePlus device with AI analytics for six months. The wearable continuously captures physiological signals (SpO2, HRV, electrodermal activity, respiratory rate, skin temperature, actigraphy-based sleep and fatigue metrics) and transmits them to a central platform for automated analysis. Investigators will compare rates of emergency visits and hospitalizations and changes in functional, biochemical, and instrumental parameters between groups. The design aims to determine whether AI-driven continuous monitoring can detect deterioration earlier and guide timely care compared with routine follow-up.
Who should consider this trial
Good fit: Adults (≥18 years) with chronic heart failure for at least 6 months, NYHA class I–III, stable on optimized heart failure therapy for ≥1 month, any LVEF category, and with at least one CHF-related visit or hospitalization in the prior 12 months.
Not a fit: Patients with NYHA class IV, those expected to receive a heart transplant or VAD within 6 months, those with severe renal impairment (eGFR <30 mL/min/1.73 m2), or individuals who cannot wear or operate the device are unlikely to benefit from this monitoring approach.
Why it matters
Potential benefit: If successful, the approach could reduce emergency visits and hospitalizations by enabling earlier detection of worsening and more timely treatment adjustments.
How similar studies have performed: Previous telemonitoring and implantable‑device trials have shown mixed but sometimes encouraging reductions in hospitalizations, while continuous AI-driven wearable monitoring remains relatively new and less tested.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria * 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 * Any left ventricular ejection fraction (LVEF) classification, including: * Heart Failure with Reduced Ejection Fraction (HFrEF) * Heart Failure with Mid-Range Ejection Fraction (HFmrEF) * Heart Failure with Preserved Ejection Fraction (HFpEF) * NYHA Functional Class I, II, or III * History of at least one hospital admission or outpatient visit in the past 12 months requiring intravenous (IV) diuretics, vasodilators, or inotropes for CHF exacerbation * Ability to provide written informed consent or availability of a legally authorized representative Exclusion Criteria * 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 * Presence of skin conditions or allergies preventing prolonged use of a wearable device * Inability to comply with study procedures (e.g., cognitive impairment, significant psychiatric disorders)
Where this trial is running
Salerno
- Hospital University San Giovanni di Dio and Ruggi d'Aragona — Salerno, Italy (Recruiting)
Study contacts
- Study coordinator: Alessia Bramanti, Electronic Engineering
- Email: abramanti@unisa.it
- Phone: +393483809181
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.