ReviewJACC. Asia2026
Wearable Electrocardiogram Technologies for the Early Detection of Acute Coronary Syndromes.
Review in JACC. Asia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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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.
Who cites it
0 citing papers in PubMed.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ischemic heart disease remains the leading cause of cardiovascular mortality globally, with acute coronary syndrome (ACS) representing its most critical clinical manifestation. Although the standard 12-lead electrocardiogram is the diagnostic cornerstone for ACS, its utility is limited by the intermittent nature of recording and the requirement for clinical infrastructure, potentially leading to delayed diagnosis or missed atypical acute ischemic events. Advances in sensor technology have enabled the development of wearable electrocardiogram devices capable of continuous, ambulatory monitoring. This review examines the current landscape of wearable technologies for ACS detection, categorizing them into smartwatches, handheld monitors, patch-based systems, and textile-based garments. We evaluate the diagnostic performance of these modalities, highlighting that single-lead devices offer convenience, whereas multilead configurations are essential for the accurate localization of acute ischemia and the reduction of false negatives. Furthermore, we address critical challenges impeding clinical adoption, including signal fidelity amidst motion artifacts, the lack of large-scale validation studies, and challenges in integrating data into clinical workflows. Finally, we discuss future directions, emphasizing the role of multimodal sensing-combining electrophysiology with biochemical or mechanical sensors-and artificial intelligence in ushering in a new era of personalized monitoring for ACS.
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