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AI-Based Monitoring System for Chronic Heart Failure With Advanced Wearable and Mini-Invasive Devices
Sponsor: University of Salerno
Summary
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.
Official title: Smart Monitoring and Analysis System Based on Artificial Intelligence for Patients With Chronic Heart Failure Using Advanced Mini-Invasive and Wearable Medical Devices
Key Details
Gender
All
Age Range
19 Years - Any
Study Type
OBSERVATIONAL
Enrollment
205
Start Date
2025-08-01
Completion Date
2027-02-02
Last Updated
2026-03-11
Healthy Volunteers
No
Conditions
Interventions
Intervention Group (Device Group - AI-Based Remote Monitoring)
This 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
Participants 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.
Locations (1)
Hospital University San Giovanni di Dio and Ruggi d'Aragona
Salerno, Italy