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

NCT06909682 | Recruiting FDA Device

• 1 other identifier: D43C22002120006
• Study Type: observational
• Target: 205
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Cardiovascular Diseases; Heart Failure With Preserved Ejection Fraction (HFPEF); Congestive Heart Failure Chronic; Chronic Heart Failure; Heart Failure With Reduced Ejection Fraction (HFrEF)

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

Outcome Measures

Primary Outcomes (1)

• 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.

  6 months from participant enrollment.

Secondary Outcomes (15)

• Change in Quality of Life

  Baseline, 3 months, and 6 months

• Adverse Effects of CHF Therapy

  6 months

• Change in Biochemical Parameters

  3 and 6 months from participant enrollment

• Change in Biochemical Parameters

  3 and 6 months from participant enrollment

• Change in Biochemical Parameters

  3 and 6 months from participant enrollment

Study Arms (2)

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)

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

Interventions

Standard Clinical Follow-Up OTHER

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.

Control Group (Non-Device Group - Standard Clinical Follow-Up)

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.

Intervention Group (Device Group - AI-Based Remote Monitoring)

Eligibility Criteria

• Age: 19 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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

You may not qualify if:

• 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)

Sponsors & Collaborators

• University of Salerno: lead

Study Sites (1)

Hospital University San Giovanni di Dio and Ruggi d'Aragona

Salerno, Italy

RECRUITING

Related Publications (5)

• 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.

  PMID: 17630410 RESULT

• Cleland 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.

  PMID: 15753115 RESULT

• Bousquet 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.

  PMID: 21745417 RESULT

• Keijser 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.

  PMID: 27925142 RESULT

• Ciccarelli 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.

  PMID: 41451381 DERIVED

Biospecimen

Retention: SAMPLES WITHOUT DNA

The SMART-CARE study will collect and retain the following biospecimens from participants for clinical and research purposes: Types of Samples Retained: Blood Samples * Plasma and Serum - For analysis of biochemical markers related to chronic heart failure (e.g., BNP, NT-proBNP, creatinine, electrolytes, glucose, lipid profile). * Urine Samples - To assess kidney function and electrolyte balance. All biospecimens will be pseudo-anonymized and stored securely following ethical guidelines (EU GDPR 2016/679) and institutional biobank policies. Participants will provide specific informed consent for sample retention and potential future res

MeSH Terms

Conditions

Cardiovascular Diseases

Central Study Contacts

Alessia Bramanti, Electronic Engineering

CONTACT

+393483809181; abramanti@unisa.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor

Study Record Dates

• First Submitted: March 14, 2025
• First Posted: April 3, 2025
• Study Start: August 1, 2025
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): February 2, 2027
• Last Updated: March 11, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)