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. 2025 Dec 10:7:1719562.
doi: 10.3389/fdgth.2025.1719562. eCollection 2025.

Artificial intelligence-based remote monitoring for chronic heart failure: design and rationale of the SMART-CARE study

Michele Ciccarelli  1 , Alessia Bramanti  1 , Albino Carrizzo  1   2 , Marina Garofano  1 , Valeria Visco  1 , Carmine Izzo  1 , Maria Rosaria Rusciano  1 , Gennaro Galasso  1   2 , Francesco Loria  3 , Giorgia Bruno  1 , Carmine Vecchione  1   2
Affiliations

Artificial intelligence-based remote monitoring for chronic heart failure: design and rationale of the SMART-CARE study

Michele Ciccarelli et al. Front Digit Health. .

Abstract

Introduction: Chronic heart failure (CHF) is associated with frequent hospitalizations, poor quality of life, and high healthcare costs. Despite therapeutic progress, early recognition of clinical deterioration remains difficult. The SMART-CARE study investigates whether artificial intelligence (AI)-enabled remote monitoring using CE-certified wearable devices can reduce hospital admissions and improve patient outcomes in CHF.

Methods: SMART-CARE is a prospective, multicenter, observational cohort study enrolling 300 adult patients with CHF (HFrEF, HFmrEF, or HFpEF) across three Italian tertiary centers. Participants are assigned to an intervention group, using wrist-worn, chest-worn, and multiparametric CE-certified wearable devices for six months, or to a control group receiving standard CHF care. Physiological data (e.g., SpO₂, HRV, respiratory rate, skin temperature, sleep metrics) are continuously collected and analyzed in real time through AI algorithms to generate alerts for early clinical intervention. The primary endpoint is a ≥20% reduction in hospital admissions over six months. Secondary outcomes include changes in quality of life (Kansas City Cardiomyopathy Questionnaire), biomarkers (BNP, NT-proBNP, renal function, electrolytes), echocardiographic indices (LVEF, LV volumes), and safety events.

Results: We hypothesize that AI-driven remote monitoring will significantly reduce hospitalizations, improve quality of life, and favorably impact biochemical and echocardiographic parameters compared to standard care.

Conclusion: SMART-CARE is designed to evaluate the clinical utility of multimodal wearable devices integrated with AI algorithms in CHF management. If successful, this approach may transform traditional care by enabling earlier detection of decompensation, optimizing resource utilization, and supporting the scalability of remote monitoring in chronic disease management.

Clinical trial registration: ClinicalTrials.gov, identifier NCT06909682.

Keywords: artificial intelligence; chronic heart failure; digital health; remote monitoring; wearable devices.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer MSM declared a past co-authorship with the author(s) VV to the handling editor.

Figures

Figure 1
Figure 1
Flow diagram of the study design.
Figure 2
Figure 2
Overview of the SMART-CARE telemedicine platform architecture. Wearable and multiparametric devices (EmbracePlus, Movesense/Polar H10, additional IoT sensors) transmit data via SDK/API REST to the telemedicine platform, which integrates with electronic health records, generates research datasets, and allows interoperability with external modules for advanced analytics.
Figure 3
Figure 3
SMART-CARE emergency management pathway. The right panel summarizes cross-cutting safety measures (technical contingency, training and support, data security) that are active across all phases of the clinical workflow.
See this image and copyright information in PMC

References

    1. Holman HR. The relation of the chronic disease epidemic to the health care crisis. ACR Open Rheumatol. (2020) 2(3):167–73. 10.1002/acr2.11114 - DOI - PMC - PubMed
    1. Piovani D, Nikolopoulos GK, Bonovas S. Non-Communicable diseases: the invisible epidemic. J Clin Med. (2022) 11:5939. 10.3390/jcm11195939 - DOI - PMC - PubMed
    1. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet. (2020) 396(10258):1204–22. 10.1016/S0140-6736(20)30925-9 - DOI - PMC - PubMed
    1. Murtagh S, McCombe G, Broughan J, Carroll Á, Casey M, Harrold Á, et al. Integrating primary and secondary care to enhance chronic disease management: a scoping review. Int J Integr Care. (2021) 21(1):4. 10.5334/ijic.5508 - DOI - PMC - PubMed
    1. McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M, et al. 2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. (2021) 42(36):3599–726. 10.1093/eurheartj/ehab368 - DOI - PubMed

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