Wearable-based automated cardiac arrest detection and alerting: A systematic review

Out-of-hospital cardiac arrest (OHCA) remains a leading cause of death worldwide, with survival strongly dependent on rapid recognition and timely emergency care. Wearables capable of automated cardiac arrest detection and alerting may reduce delays to treatment, particularly in unwitnessed arrests.Therefore, this review aims to systematically evaluate the existing evidence on the performance of wearable sensors and algorithms designed for OHCA detection.

A systematic search was performed using PubMed, Scopus, Web of Science, Embase, the Cochrane Libraries, forward citation and backward citation to identify all eligible studies up to 22 September 2026. Studies describing non-invasive, intended wearable systems designed to detect OHCA and reporting on their detection performance were included. The methodology and report of the present review are based on recommendations of the PRISMA statement and Cochrane Collaboration. Primary outcome is the sensitivity for cardiac arrest detection. Secondary outcomes include false positives cardiac arrest alerts and specificity. Studies were evaluated using the QUADAS-2 tool.

From 6,344 records screened, 13 publications were included with the number of participants ranging from 18 to 1,423. Wristbands were the most prevalent device type (n=9). Nine studies used photoplethysmography as primary sensor type, with accelerometry as additive sensor modality (n=2). Other sensors used were mechanical plethysmography (n=1), electrocardiography (n=1), sound-based (n=1) and a nonlinear resonant circuit (n=1). Sensitivity ranged between 43.8 and 100%, with ten studies reporting a sensitivity of ≥ 90%. False positives ranged between multiple per day and one per twenty-one user-years.

Wearable devices have demonstrated promising sensitivity for detecting cardiac arrest. However, (external) validation and real-life data are scarce, and the sensitivity-false alarm trade-off requires further investigation.

OSF, doi: https://doi.org/10.17605/OSF.IO/AEPF8.