Evidence map›Paper›PMID 40103686›Full record

ArticleAmerican journal of preventive cardiology2025

Individualized prediction of atrial fibrillation onset risk based on lifelogs.

Takehiro Kimura, Masahiro Jinzaki, Hiroshi Miyama, Kenji Hashimoto, Terumasa Yamashita, Yoshinori Katsumata, Seiji Takatsuki, Keiichi Fukuda, Masaki Ieda

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of preventive cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Takehiro KimuraDepartment of Cardiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo, Japan.
Masahiro JinzakiDepartment of Radiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo, Japan.
Hiroshi MiyamaDepartment of Cardiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo, Japan.
Kenji HashimotoDepartment of Cardiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo, Japan.
Terumasa YamashitaDepartment of Cardiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo, Japan.
Yoshinori KatsumataDepartment of Cardiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo, Japan.
Seiji TakatsukiDepartment of Cardiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo, Japan.
Keiichi FukudaDepartment of Cardiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo, Japan.
Masaki IedaDepartment of Cardiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objective: The Apple Watch alerts users to irregular heart rhythms and potential atrial fibrillation (AF), but delays in obtaining electrocardiograms (ECGs) after notifications can impede accurate disease diagnosis. We aimed to predict personalized AF risk using continuous Apple Watch lifelog data to facilitate timely ECG acquisition. We conducted two analyses: Keio and national. In the Keio analysis, AF patients underwent continuous 2-week Holter ECG monitoring, and a machine-learning model combining gradient-boosting decision trees and deep learning was developed. The national analysis recruited Apple Watch users across Japan to assess the model; data and survey responses were collected for seven days via a dedicated iPhone app. Results: A total of 100 subjects (age: 63.9 ± 12.4 years, AF burden: 37.7 %) participated in the Keio analysis, while 8,935 subjects participated in the national analysis. Significant differences in Apple Watch data, including pulse rate ( Conclusions: Apple Watch-derived lifelogs enabled individualized AF onset risk assessment and the development of a machine-learning model for optimizing ECG timing for early AF detection.

Indexed as

Apple watchAtrial fibrillationEarly detectionIrregular rhythm notificationMachine-learning

Identifiers

PMID40103686
PMCPMC11914761

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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.