Evidence mapPaperPMID 41343137Full record

ReviewCurrent heart failure reports2025

Non-Invasive Remote Monitoring in Heart Failure: Towards Wearable Devices and Artificial Intelligence Solutions : Short Title: Remote Monitoring and Wearable Devices in Heart Failure.

Camila S Pizarro, Bas B S Schots, Mark J Schuuring, Pim van der Harst, René van Es, Marish I F J Oerlemans

Abstract readReview
In one paragraph

Review in Current heart failure reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Remote monitoring and outcomes in heart failure: 5-year study.Journal of cardiovascular medicine (Hagerstown, Md.) · 2026
    Article
  2. Review
  3. Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Camila S PizarroDepartment of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.ORCID http://orcid.org/0009-0006-0279-8871
Bas B S SchotsDepartment of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.ORCID http://orcid.org/0009-0009-0291-7940
Mark J SchuuringDepartment of Biomedical Signals and Systems, University of Twente, Enschede, The Netherlands.ORCID http://orcid.org/0000-0002-2843-1852
Pim van der HarstDepartment of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
René van EsDepartment of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.ORCID http://orcid.org/0000-0001-9950-4388
Marish I F J OerlemansDepartment of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands. m.i.f.oerlemans-4@umcutrecht.nl.ORCID http://orcid.org/0000-0003-3166-518X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewThis review examines the potential benefits of non-invasive remote monitoring in patients with heart failure (HF), focusing on early detection of clinical deterioration and reducing hospitalizations. Key questions addressed include: Can remote monitoring prevent hospitalisations in patients with HF? Does it improve quality of life and promote self-care? Is it cost-effective? Can artificial intelligence (AI) facilitate its implementation? RECENT

findingsMonitoring with non-wearable and wearable devices reduces hospitalizations by detecting early signs of deterioration and enhancing self-care behaviour. While the initial investment can be high, the long-term cost-effectiveness is supported by reduced hospitalisations. AI is increasingly integrated into monitoring systems, enhancing predictive accuracy and personalized care. Remote monitoring reduces mortality and hospitalisations in patients with HF, with benefits in cost-effectiveness, and the potential to optimize care delivery by integrating AI. Future research should focus on identifying monitoring strategies for specific HF populations, such as patients with advanced HF.

Indexed as

Artificial IntelligenceHeart FailureWearable Electronic DevicesHumansMonitoring, PhysiologicQuality of LifeTelemedicineArtificial intelligenceHeart failureNon-invasiveRemote monitoringWearable devices

Identifiers

PMID41343137
PMCPMC12678519

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.