Evidence map›Paper›PMID 42553162›Full record

ReviewFrontiers in cardiovascular medicine2026

Wearable devices in atrial fibrillation: screening, monitoring, and management.

Wen Chen, Xinyi Lin, Ping Jia, Zhenwei Liu

Abstract readReview
In one paragraph

Review in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Wen Chen *University of Electronic Science and Technology of China, Chengdu, China.
Xinyi Lin *University of Electronic Science and Technology of China, Chengdu, China.
Ping JiaSichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Zhenwei LiuSichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Atrial fibrillation (AF) remains the most prevalent cardiac arrhythmia worldwide, contributing substantially to the risk of ischemic stroke and heart failure. Brief monitoring windows inherently limit traditional episodic screening strategies and frequently fail to detect paroxysmal or asymptomatic occurrences. Wearable devices integrating multimodal sensors-such as photoplethysmography (PPG) and single-lead electrocardiography (ECG)-with artificial intelligence now enable continuous, real-world AF screening. However, the widespread consumer adoption of these technologies has precipitated a novel clinical dilemma. A marked increase in the detection of subclinical atrial fibrillation (SCAF) introduces considerable uncertainty regarding the appropriate threshold for initiating oral anticoagulation. In this review, we evaluate the underlying technical principles of wearable sensors and examine their integration into clinical practice across community screening, postoperative monitoring, and long-term patient management. We critically analyze emerging clinical challenges-focusing on the detection-vs.-intervention paradox emphasized by recent landmark outcome trials-and propose actionable strategies for future implementation. Successful clinical translation will require standardized computational algorithms, validated hardware accuracy, semantic electronic health record (EHR) interoperability, and the continuous quantification of AF burden to tailor personalized antithrombotic therapy.

Indexed as

atrial fibrillationdigital healthelectrocardiographyphotoplethysmographyremote monitoringscreeningwearable devices

Identifiers

PMID42553162
PMCPMC13433222

What OpenQuestion holds

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Registered trials

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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.