Chronic Heart FailureSMART-CARENCT06909682
AI-Based Monitoring System for Chronic Heart Failure With Advanced Wearable and Mini-Invasive Devices
An observational study of Chronic Heart Failure and Cardiovascular Diseases, run by University of Salerno at 1 site in Italy.
Trial 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). Read more about this study
- Who is it for?
- Ages 19 years and older
- Conditions the study is for
- Chronic Heart Failure, Cardiovascular Diseases, Heart Failure With Reduced Ejection Fraction (HFrEF)
- What is being tested?
- Intervention Group (Device Group - AI-Based Remote Monitoring) and Standard Clinical Follow-Up
- Type of treatment
- Medical device
- How many people
- 205 participants
- Where
- 1 siteItaly
- Study dates
- 1 Aug 2025to 2 Feb 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 19 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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)
Show all 16Show fewer
- 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)
Who cannot join
None listed.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Italy1 site, 1 recruiting
- Recruiting
Hospital University San Giovanni di Dio and Ruggi d'Aragona
Salerno
Alessia Bramanti, Associate Professor
About this study
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
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:
Read more
- 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.
Read the 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.
- Conditions
- Chronic Heart Failure, Cardiovascular Diseases, Heart Failure With Reduced Ejection Fraction (HFrEF), Heart Failure With Preserved Ejection Fraction (HFPEF), Congestive Heart Failure Chronic
- Being tested
- Intervention Group (Device Group - AI-Based Remote Monitoring), Standard Clinical Follow-Up
- Keywords
- Wearable Medical Devices, Remote Patient Monitoring, Artificial Intelligence in Cardiology, Smart Wearables for Healthcare, Observational Study in Heart Failure, AI-Based Predictive Modeling, Telemedicine in Heart Failure, Non-Invasive Health Monitoring, Personalized Medicine for CHF, Quality of Life Improvement in Heart Failure, Heart Failure Readmission Prevention, Telehealth in Chronic Disease Management, Digital Health Solutions for Heart Disease
- Medical subject headings
- Cardiovascular Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Intervention Group (Device Group - AI-Based Remote Monitoring)Device
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-UpOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Device: Intervention Group (Device Group - AI-Based Remote Monitoring) | |
| 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. Receives: Other: Standard Clinical Follow-Up |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 6 months from participant enrollment.
Secondary outcomes (15)
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.
Measured: Baseline, 3 months, and 6 months
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.
Measured: 6 months
Change in Biochemical Parameters
Biochemical Parameters:
Change in B-type Natriuretic Peptide (BNP) Levels (picograms per milliliter)
Measured: 3 and 6 months from participant enrollment
Change in Biochemical Parameters
Biochemical Parameters:
Change in N-terminal pro-BNP (NT-proBNP) Levels (picograms per milliliter)
Measured: 3 and 6 months from participant enrollment
Change in Biochemical Parameters
Biochemical Parameters:
Change in Serum Creatinine (milligrams per deciliter)
Measured: 3 and 6 months from participant enrollment
Change in Biochemical Parameters
Biochemical Parameters:
Change in Estimated Glomerular Filtration Rate (eGFR) (milliliters per minute per 1.73 square meters)
Measured: 3 and 6 months from participant enrollment
Change in Biochemical Parameters
Biochemical Parameters:
Change in Serum Sodium Levels (millimoles per liter)
Measured: 3 and 6 months from participant enrollment
Change in Biochemical Parameters
Biochemical Parameters:
Change in Serum Potassium Levels (millimoles per liter)
Measured: 3 and 6 months from participant enrollment
Change in Biochemical Parameters
Biochemical Parameters:
Change in Serum Chloride Levels (millimoles per liter)
Measured: 3 and 6 months from participant enrollment
Change in Functional ECG-Derived Parameters
ECG-Derived Parameters:
Change in Heart Rate Variability (HRV) (milliseconds)
Measured: 3 and 6 months from participant enrollment
Change in Functional ECG-Derived Parameters
ECG-Derived Parameters:
Change in Respiratory Rate (breaths per minute)
Measured: 3 and 6 months from participant enrollment
Change in Functional Echocardiographic derived Parameters
Echocardiographic Parameters:
Change in Left Ventricular Ejection Fraction (LVEF) (percent)
Measured: 3 and 6 months from participant enrollment
Change in Functional Echocardiographic derived Parameters
Echocardiographic Parameters:
Change in Diastolic Function - E/A Ratio (unitless)
Measured: 3 and 6 months from participant enrollment
Change in Functional Echocardiographic derived Parameters
Echocardiographic Parameters:
Change in Left Ventricular End-Diastolic Volume (milliliters)
Measured: 3 and 6 months from participant enrollment
Change in Functional Echocardiographic derived Parameters
Echocardiographic Parameters:
Change in Left Ventricular End-Systolic Volume (milliliters)
Measured: 3 and 6 months from participant enrollment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Apr 2025
- Study start1 Aug 2025
- Primary completion1 Aug 2026 (estimated)
- Study completion2 Feb 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Chronic Heart FailureStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 205 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Salerno
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Alessia Bramanti, University of Salerno
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06909682
- Sponsor's protocol ID
- D43C22002120006
- Acronym
- SMART-CARE
- Status last verified
- Mar 2026
- First submitted
- 14 Mar 2025
- Last updated
- 11 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No
Publications
Tang WH, Francis GS, Morrow DA, Newby LK, Cannon CP, Jesse RL, Storrow AB, Christenson RH, Apple FS, Ravkilde J, Wu AH; National Academy of Clinical Biochemistry Laboratory Medicine. National Academy of Clinical Biochemistry Laboratory Medicine practice guidelines: Clinical utilization of cardiac biomarker testing in heart failure. Circulation. 2007 Jul 31;116(5):e99-109. doi: 10.1161/CIRCULATIONAHA.107.185267. Epub 2007 Jul 14. No abstract available.
PubMed 17630410Cleland JG, Daubert JC, Erdmann E, Freemantle N, Gras D, Kappenberger L, Tavazzi L; Cardiac Resynchronization-Heart Failure (CARE-HF) Study Investigators. The effect of cardiac resynchronization on morbidity and mortality in heart failure. N Engl J Med. 2005 Apr 14;352(15):1539-49. doi: 10.1056/NEJMoa050496. Epub 2005 Mar 7.
PubMed 15753115Bousquet J, Anto JM, Sterk PJ, Adcock IM, Chung KF, Roca J, Agusti A, Brightling C, Cambon-Thomsen A, Cesario A, Abdelhak S, Antonarakis SE, Avignon A, Ballabio A, Baraldi E, Baranov A, Bieber T, Bockaert J, Brahmachari S, Brambilla C, Bringer J, Dauzat M, Ernberg I, Fabbri L, Froguel P, Galas D, Gojobori T, Hunter P, Jorgensen C, Kauffmann F, Kourilsky P, Kowalski ML, Lancet D, Pen CL, Mallet J, Mayosi B, Mercier J, Metspalu A, Nadeau JH, Ninot G, Noble D, Ozturk M, Palkonen S, Prefaut C, Rabe K, Renard E, Roberts RG, Samolinski B, Schunemann HJ, Simon HU, Soares MB, Superti-Furga G, Tegner J, Verjovski-Almeida S, Wellstead P, Wolkenhauer O, Wouters E, Balling R, Brookes AJ, Charron D, Pison C, Chen Z, Hood L, Auffray C. Systems medicine and integrated care to combat chronic noncommunicable diseases. Genome Med. 2011 Jul 6;3(7):43. doi: 10.1186/gm259.
PubMed 21745417Keijser W, de Manuel-Keenoy E, d'Angelantonio M, Stafylas P, Hobson P, Apuzzo G, Hurtado M, Oates J, Bousquet J, Senn A. DG Connect Funded Projects on Information and Communication Technologies (ICT) for Old Age People: Beyond Silos, CareWell and SmartCare. J Nutr Health Aging. 2016;20(10):1024-1033. doi: 10.1007/s12603-016-0804-0.
PubMed 27925142Ciccarelli M, Bramanti A, Carrizzo A, Garofano M, Visco V, Izzo C, Rusciano MR, Galasso G, Loria F, Bruno G, Vecchione C. Artificial intelligence-based remote monitoring for chronic heart failure: design and rationale of the SMART-CARE study. Front Digit Health. 2025 Dec 10;7:1719562. doi: 10.3389/fdgth.2025.1719562. eCollection 2025.
PubMed 41451381
Study at a glance
From the registry record, last updated by the sponsor on 11 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT06909682
- Acronym / sponsor ID
- SMART-CARE / D43C22002120006
- Conditions
- Chronic Heart Failure, Cardiovascular Diseases, Heart Failure With Reduced Ejection Fraction (HFrEF), Heart Failure With Preserved Ejection Fraction (HFPEF), Congestive Heart Failure Chronic
- Keywords
- Wearable Medical Devices, Remote Patient Monitoring, Artificial Intelligence in Cardiology, Smart Wearables for Healthcare, Observational Study in Heart Failure, AI-Based Predictive Modeling, Telemedicine in Heart Failure, Non-Invasive Health Monitoring
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 205 participants (estimated)
- Lead sponsor
- University of SalernoUniversity, hospital or other
- Study start
- 1 Aug 2025 (actual)
- Primary completion
- 1 Aug 2026 (estimated)
- Study completion
- 2 Feb 2027 (estimated)
- First posted
- 3 Apr 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Intervention Group (Device Group - AI-Based Remote Monitoring) | – | Device: 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. |
| Control Group (Non-Device Group - Standard Clinical Follow-Up) | – | Other: Standard Clinical Follow-UpParticipants 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. |
Who may qualify
- Ages
- 19 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (16 listed)
- 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:
- NYHA Functional Class I, II, or III
Key exclusion criteria (0 listed)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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. | Device | Intervention Group (Device Group - AI-Based Remote Monitoring) |
| Standard Clinical Follow-UpParticipants 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. | Other | Control Group (Non-Device Group - Standard Clinical Follow-Up) |
Primary outcome
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.
15 secondary outcomes
Change in Quality of Life
Time frame: Baseline, 3 months, and 6 months
Adverse Effects of CHF Therapy
Time frame: 6 months
Change in Biochemical Parameters
Time frame: 3 and 6 months from participant enrollment
Change in Biochemical Parameters
Time frame: 3 and 6 months from participant enrollment
Change in Biochemical Parameters
Time frame: 3 and 6 months from participant enrollment
Change in Biochemical Parameters
Time frame: 3 and 6 months from participant enrollment
Change in Biochemical Parameters
Time frame: 3 and 6 months from participant enrollment
Change in Biochemical Parameters
Time frame: 3 and 6 months from participant enrollment
Change in Biochemical Parameters
Time frame: 3 and 6 months from participant enrollment
Change in Functional ECG-Derived Parameters
Time frame: 3 and 6 months from participant enrollment
Change in Functional ECG-Derived Parameters
Time frame: 3 and 6 months from participant enrollment
Change in Functional Echocardiographic derived Parameters
Time frame: 3 and 6 months from participant enrollment
Change in Functional Echocardiographic derived Parameters
Time frame: 3 and 6 months from participant enrollment
Change in Functional Echocardiographic derived Parameters
Time frame: 3 and 6 months from participant enrollment
Change in Functional Echocardiographic derived Parameters
Time frame: 3 and 6 months from participant enrollment
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Mar 2026.
- Results publication: PubMed 17630410Tang WH, Francis GS, Morrow DA, Newby LK, Cannon CP, Jesse RL, Storrow AB, Christenson RH, Apple FS, Ravkilde J, Wu AH; National Academy of Clinical Biochemistry Laboratory Medicine. National Academy of Clinical Biochemistry Laboratory Medicine practice guidelines: Clinical utilization of cardiac biomarker testing in heart failure. Circulation. 2007 Jul 31;116(5):e99-109. doi: 10.1161/CIRCULATIONAHA.107.185267. Epub 2007 Jul 14. No abstract available.
- Results publication: PubMed 15753115Cleland JG, Daubert JC, Erdmann E, Freemantle N, Gras D, Kappenberger L, Tavazzi L; Cardiac Resynchronization-Heart Failure (CARE-HF) Study Investigators. The effect of cardiac resynchronization on morbidity and mortality in heart failure. N Engl J Med. 2005 Apr 14;352(15):1539-49. doi: 10.1056/NEJMoa050496. Epub 2005 Mar 7.
- Results publication: PubMed 21745417Bousquet J, Anto JM, Sterk PJ, Adcock IM, Chung KF, Roca J, Agusti A, Brightling C, Cambon-Thomsen A, Cesario A, Abdelhak S, Antonarakis SE, Avignon A, Ballabio A, Baraldi E, Baranov A, Bieber T, Bockaert J, Brahmachari S, Brambilla C, Bringer J, Dauzat M, Ernberg I, Fabbri L, Froguel P, Galas D, Gojobori T, Hunter P, Jorgensen C, Kauffmann F, Kourilsky P, Kowalski ML, Lancet D, Pen CL, Mallet J, Mayosi B, Mercier J, Metspalu A, Nadeau JH, Ninot G, Noble D, Ozturk M, Palkonen S, Prefaut C, Rabe K, Renard E, Roberts RG, Samolinski B, Schunemann HJ, Simon HU, Soares MB, Superti-Furga G, Tegner J, Verjovski-Almeida S, Wellstead P, Wolkenhauer O, Wouters E, Balling R, Brookes AJ, Charron D, Pison C, Chen Z, Hood L, Auffray C. Systems medicine and integrated care to combat chronic noncommunicable diseases. Genome Med. 2011 Jul 6;3(7):43. doi: 10.1186/gm259.
- Results publication: PubMed 27925142Keijser W, de Manuel-Keenoy E, d'Angelantonio M, Stafylas P, Hobson P, Apuzzo G, Hurtado M, Oates J, Bousquet J, Senn A. DG Connect Funded Projects on Information and Communication Technologies (ICT) for Old Age People: Beyond Silos, CareWell and SmartCare. J Nutr Health Aging. 2016;20(10):1024-1033. doi: 10.1007/s12603-016-0804-0.
- Derived from this study: PubMed 41451381Ciccarelli M, Bramanti A, Carrizzo A, Garofano M, Visco V, Izzo C, Rusciano MR, Galasso G, Loria F, Bruno G, Vecchione C. Artificial intelligence-based remote monitoring for chronic heart failure: design and rationale of the SMART-CARE study. Front Digit Health. 2025 Dec 10;7:1719562. doi: 10.3389/fdgth.2025.1719562. eCollection 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Chronic Heart Failure and Cardiovascular Diseases, run by University of Salerno at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 16 inclusion and 0 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 11 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Alessia Bramanti, Electronic Engineering (+393483809181, abramanti@unisa.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Change in Hospital Admissions with AI-Based Remote Monitoring (time frame: 6 months from participant enrollment.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06909682, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 11 Mar 2026.