AI-Based Monitoring System for Chronic Heart Failure With Advanced Wearable and Mini-Invasive Devices (SMART-CARE)

March 9, 2026 updated by: Alessia Bramanti, University of Salerno

Smart Monitoring and Analysis System Based on Artificial Intelligence for Patients With Chronic Heart Failure Using Advanced Mini-Invasive and Wearable Medical Devices

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Chronic Heart Failure (CHF) is a multifactorial syndrome characterized by high rates of hospitalization, morbidity, and mortality. Despite advances in pharmacological and device-based therapies, early identification of clinical deterioration remains a major challenge. Traditional follow-up models, based primarily on intermittent in-person evaluations, are often inadequate in capturing subclinical changes that precede acute decompensation.

The SMART-CARE (System of Monitoring and Analysis based on Artificial Intelligence for Chronic Heart Failure Patients with Mini-Invasive and Wearable Medical Devices) study aims to assess whether continuous remote monitoring using a CE (Conformité Européenne)-certified wearable device (EmbracePlus by Empatica Inc.) integrated with AI (Artificial Intelligence) analytics can improve the management of CHF patients. The study adopts a prospective, multicenter, observational design with two parallel cohorts: patients managed with standard care versus patients equipped with the wearable device for six months.

The wearable device captures a range of physiological signals-including peripheral capillary oxygen saturation (SpO₂), heart rate variability (HRV), electrodermal activity (EDA), skin conductance level (SCL), respiratory rate, peripheral skin temperature, pulse rate, fatigue detection, and sleep metrics via actigraphy-and transmits them in real time to a centralized digital platform. AI algorithms analyze these data continuously, triggering alerts in the event of abnormal trends. When alerts are generated, patients undergo teleconsultation, with possible treatment adjustments or in-person follow-up as clinically indicated.

The study is designed to generate real-world evidence on whether AI-enhanced monitoring can reduce unplanned hospital admissions by at least 20% over a six-month follow-up, compared to standard care. Secondary endpoints include improvements in cardiac function (evaluated through echocardiographic parameters), neurohormonal biomarkers such as B-type Natriuretic Peptide (BNP) and Atrial Natriuretic Peptide (ANP), exercise tolerance assessed by the Six-Minute Walk Test (6MWT), quality of life measured by the Kansas City Cardiomyopathy Questionnaire (KCCQ), and incidence of therapy-related adverse events (e.g., hypotension, bradyarrhythmias).

In addition to evaluating clinical efficacy, the study supports the development of a predictive multimarker model. Data collected through the SMART-CARE platform-including clinical history, biochemical markers, imaging data, and continuous sensor-derived variables-will be used by collaborating academic centers to train AI algorithms capable of forecasting CHF progression and tailoring individualized interventions.

All data are pseudonymized in compliance with the General Data Protection Regulation (GDPR, Regulation EU 2016/679). The study does not interfere with ongoing medical treatments and adheres to Good Clinical Practice (GCP) and the ethical principles of the Declaration of Helsinki. Patients provide written informed consent prior to enrollment.

The SMART-CARE initiative reflects a broader goal: integrating telemedicine, wearable health technology, and AI-based predictive modeling into a seamless care pathway that promotes proactive CHF management and enables personalized, data-driven therapeutic decisions.

Study Type

Observational

Enrollment (Estimated)

205

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

  • Name: Alessia Bramanti, Electronic Engineering
  • Phone Number: +393483809181
  • Email: abramanti@unisa.it

Study Locations

  • Italy
      • Salerno, Italy
        • Recruiting
        • Hospital University San Giovanni di Dio and Ruggi d'Aragona
        • Contact:
          • Alessia Bramanti, Associate Professor

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

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.

Description

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)

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Intervention Group (Device Group - AI-Based Remote Monitoring)
Participants in this group will wear the EmbracePlus mini-invasive device for continuous remote monitoring over a six-month period. The device tracks key physiological parameters, including oxygen saturation (SpO₂), heart rate variability (HRV), electrodermal activity (EDA), temperature, respiratory rate, and sleep quality. Data is transmitted to a centralized AI-driven platform, which analyzes trends and detects early signs of heart failure worsening. If significant abnormalities are identified, the system triggers automated alerts, prompting teleconsultations or in-person evaluations as needed to ensure timely clinical intervention.
Device: 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.
Control Group (Non-Device Group - 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.
Other: 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Change in Hospital Admissions with AI-Based Remote Monitoring
Time Frame: 6 months from participant enrollment.
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.
6 months from participant enrollment.

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Change in Quality of Life
Time Frame: Baseline, 3 months, and 6 months

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.
Baseline, 3 months, and 6 months
Adverse Effects of CHF Therapy
Time Frame: 6 months

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.
6 months
Change in Biochemical Parameters
Time Frame: 3 and 6 months from participant enrollment

Biochemical Parameters:

Change in B-type Natriuretic Peptide (BNP) Levels (picograms per milliliter)
3 and 6 months from participant enrollment
Change in Biochemical Parameters
Time Frame: 3 and 6 months from participant enrollment

Biochemical Parameters:

Change in N-terminal pro-BNP (NT-proBNP) Levels (picograms per milliliter)
3 and 6 months from participant enrollment
Change in Biochemical Parameters
Time Frame: 3 and 6 months from participant enrollment

Biochemical Parameters:

Change in Serum Creatinine (milligrams per deciliter)
3 and 6 months from participant enrollment
Change in Biochemical Parameters
Time Frame: 3 and 6 months from participant enrollment

Biochemical Parameters:

Change in Estimated Glomerular Filtration Rate (eGFR) (milliliters per minute per 1.73 square meters)
3 and 6 months from participant enrollment
Change in Biochemical Parameters
Time Frame: 3 and 6 months from participant enrollment

Biochemical Parameters:

Change in Serum Sodium Levels (millimoles per liter)
3 and 6 months from participant enrollment
Change in Biochemical Parameters
Time Frame: 3 and 6 months from participant enrollment

Biochemical Parameters:

Change in Serum Potassium Levels (millimoles per liter)
3 and 6 months from participant enrollment
Change in Biochemical Parameters
Time Frame: 3 and 6 months from participant enrollment

Biochemical Parameters:

Change in Serum Chloride Levels (millimoles per liter)
3 and 6 months from participant enrollment
Change in Functional ECG-Derived Parameters
Time Frame: 3 and 6 months from participant enrollment

ECG-Derived Parameters:

Change in Heart Rate Variability (HRV) (milliseconds)
3 and 6 months from participant enrollment
Change in Functional ECG-Derived Parameters
Time Frame: 3 and 6 months from participant enrollment

ECG-Derived Parameters:

Change in Respiratory Rate (breaths per minute)
3 and 6 months from participant enrollment
Change in Functional Echocardiographic derived Parameters
Time Frame: 3 and 6 months from participant enrollment

Echocardiographic Parameters:

Change in Left Ventricular Ejection Fraction (LVEF) (percent)
3 and 6 months from participant enrollment
Change in Functional Echocardiographic derived Parameters
Time Frame: 3 and 6 months from participant enrollment

Echocardiographic Parameters:

Change in Diastolic Function - E/A Ratio (unitless)
3 and 6 months from participant enrollment
Change in Functional Echocardiographic derived Parameters
Time Frame: 3 and 6 months from participant enrollment

Echocardiographic Parameters:

Change in Left Ventricular End-Diastolic Volume (milliliters)
3 and 6 months from participant enrollment
Change in Functional Echocardiographic derived Parameters
Time Frame: 3 and 6 months from participant enrollment

Echocardiographic Parameters:

Change in Left Ventricular End-Systolic Volume (milliliters)
3 and 6 months from participant enrollment

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University of Salerno

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 1, 2025

Primary Completion (Estimated)

August 1, 2026

Study Completion (Estimated)

February 2, 2027

Study Registration Dates

First Submitted

March 14, 2025

First Submitted That Met QC Criteria

April 1, 2025

First Posted (Actual)

April 3, 2025

Study Record Updates

Last Update Posted (Actual)

March 11, 2026

Last Update Submitted That Met QC Criteria

March 9, 2026

Last Verified

March 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • D43C22002120006

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

Yes

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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