Evidence map›Paper›PMID 41001270›Full record

ArticleCJC open2025

From Wrist to Precision: Enhanced Atrial Flutter Detection with Modified Smartwatch Single-Lead Electrocardiogram Placement.

Yuval Avidan, Amir Aker, Razi Khoury, Sameha Zahra, Nissan Ben Dov, Jorge E Schliamser, Asaf Danon

Abstract read
In one paragraph

Article in CJC open, 2025. 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

7 authors.

Yuval AvidanDepartment of Cardiology, Lady Davis Carmel Medical Center, Haifa, Israel.
Amir AkerDepartment of Cardiology, Lady Davis Carmel Medical Center, Haifa, Israel.
Razi KhouryDepartment of Cardiology, Lady Davis Carmel Medical Center, Haifa, Israel.
Sameha ZahraDepartment of Internal Medicine, Lady Davis Carmel Medical Center, Haifa, Israel.
Nissan Ben DovDepartment of Cardiology, Lady Davis Carmel Medical Center, Haifa, Israel.
Jorge E SchliamserDepartment of Cardiology, Lady Davis Carmel Medical Center, Haifa, Israel.
Asaf DanonDepartment of Cardiology, Lady Davis Carmel Medical Center, Haifa, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Smartwatches, such as the Apple Watch (AW), are well-established tools for detecting atrial fibrillation (AF). We hypothesize that atrial flutter (AFL) is frequently misdiagnosed using traditional single-lead electrocardiogram configurations and that modified device positioning could substantially improve diagnostic accuracy. Methods: Standard smartwatch lead-I (AW-I) recordings were obtained from 75 patients, including 25 with AFL, 25 with AF, and 25 with sinus rhythm. Additionally, modified lead-II (AW-II) recordings were collected for all AFL cases, resulting in a total of 100 tracings. Twenty blinded physicians from 4 different specialties independently analyzed all recordings. Results: Physicians' ability to detect AFL using the AW-I lead was poor, with only 11.6% of cases correctly identified ( Conclusions: The standard lead-I configuration used by smartwatches is prone to misclassifying AF as AFL, particularly in the setting of variable conduction. A simple modification to lead-II positioning significantly enhances diagnostic accuracy. This adjustment may be especially valuable during post-pulmonary vein isolation surveillance and in broader clinical scenarios in which precise rhythm identification can influence therapeutic decision-making.

Indexed as

atrial fibrillationatrial fluttermodified configurationSmartwatch

Identifiers

PMID41001270
PMCPMC12457205

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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.