Evidence map›Paper›PMID 41156292›Full record

ReviewJournal of clinical medicine2025

Non-Invasive Wearable Technology to Predict Heart Failure Decompensation.

Jack Devin, Eden Powell, Dylan McGagh, Tyler Jones, Brian Wang, Pierre Le Page, Andrew J M Lewis, Oliver J Rider, Andrew R J Mitchell, John A Henry

Abstract readReview
In one paragraph

Review in Journal of clinical medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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

10 authors.

Jack DevinCardiology Department, Jersey General Hospital, St Helier, Jersey JE1 3QS, UK.
Eden PowellCardiology Department, Jersey General Hospital, St Helier, Jersey JE1 3QS, UK.ORCID 0009-0002-6535-4160
Dylan McGaghNuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK.
Tyler JonesCardiology Department, Jersey General Hospital, St Helier, Jersey JE1 3QS, UK.ORCID 0000-0003-2585-0434
Brian WangCardiology Department, Jersey General Hospital, St Helier, Jersey JE1 3QS, UK.ORCID 0000-0002-8846-2220
Pierre Le PageCardiology Department, Jersey General Hospital, St Helier, Jersey JE1 3QS, UK.
Andrew J M LewisOxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK.
Oliver J RiderOxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK.
Andrew R J MitchellCardiology Department, Jersey General Hospital, St Helier, Jersey JE1 3QS, UK.ORCID 0000-0002-5386-9645
John A HenryCardiology Department, Jersey General Hospital, St Helier, Jersey JE1 3QS, UK.ORCID 0000-0002-7650-0963

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heart failure (HF) remains a leading cause of recurrent hospitalisations worldwide, largely driven by acute episodes of decompensation. Early identification of impending decompensation could enable timely intervention and potentially prevent costly admissions. Non-invasive wearable devices have emerged as promising tools for continuously monitoring physiological parameters and detecting early signs of deterioration. This review summarises recent advances in wearable technologies designed to predict HF decompensation and appraises their ability to generate clinically useful alerts. It will examine various modalities designed to monitor different aspects of cardiorespiratory physiology that have the potential to detect abnormalities preceding heart failure decompensation. Broadly, these devices either monitor physical activity capacity and cardiac function or monitor changes in pulmonary fluid congestion. We will also cover evidence exploring whether these devices can generate timely alerts for interventions to improve patient outcomes and reduce hospitalisations. However, despite advances in these technologies, challenges remain regarding their accuracy and usability for remote monitoring, as well as concerns with data storage, processing, patient adherence, and integration into existing healthcare workflows. While current limitations exist, previous results warrant further research into this area, with a focus on larger randomised trials, exploring both single- and multi-sensor systems, using artificial intelligence and cost-effectiveness analysis. Overall, non-invasive wearables represent an opportunity to create a more proactive approach to HF management, with the potential to shift the paradigm from reactive treatment to anticipatory care.

Indexed as

decompensationdigital healthheart failuresensorswearables

Identifiers

PMID41156292
PMCPMC12565691

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.