AUTOMATED DETECTION OF OUT-OF-HOSPITAL CARDIAC ARREST USING WEARABLE TECHNOLOGY: A SYSTEMATIC SCOPING REVIEW OF DIAGNOSTIC ACCURACY, REAL-WORLD FEASIBILITY, AND ETHICAL IMPLICATIONS
DOI:
https://doi.org/10.31435/ijitss.2(50).2026.5561Keywords:
Out-of-Hospital Cardiac Arrest, Wearable Technology, Smartwatch, Photoplethysmography, Automated Detection, Chain of SurvivalAbstract
Out-of-hospital cardiac arrest (OHCA) is characterized by an exceptionally low survival rate, primarily due to delayed recognition in unwitnessed cases (Gräsner et al., 2021; Hutton et al., 2022). This scoping review aims to evaluate the diagnostic accuracy, real-world feasibility, and ethical implications of using commercial smartwatches for the automated detection of OHCA (Hutton et al., 2022; Eversdijk et al., 2024). Electronic databases (including PubMed and PMC) were searched for publications spanning 2019–2026, identifying high-quality studies in accordance with the PRISMA-ScR guidelines. The analysis focused on multimodal algorithms combining photoplethysmography (PPG) with triaxial accelerometry (Schober et al., 2022; Shah et al., 2025).
Results indicate that these algorithms achieve a sensitivity of approximately 98% and a specificity of 99% in simulation studies (Schober et al., 2022). The first documented case reports confirm successful automated detections in real-world settings, demonstrating the potential to significantly reduce emergency medical services (EMS) response times (Edgar et al., 2026; Scquizzato et al., 2020; Eversdijk et al., 2024). Major identified barriers include motion artifacts, health disparities resulting from degraded PPG performance in individuals with darker skin tones, and the risk of alert fatigue caused by false-positive calls (Bent et al., 2020; Eversdijk et al., 2024; van der Eerden et al., 2025). Ethical integration necessitates the implementation of "dynamic consent" models to safeguard patient privacy (Kaye et al., 2015; Eversdijk et al., 2024).
In conclusion, wearable devices represent a breakthrough tool capable of acting as "digital witnesses" to significantly strengthen the chain of survival (Schober et al., 2022; Semeraro et al., 2021). However, their widespread implementation requires AI-driven optimization, the mitigation of algorithmic biases, and rigorous integration with emergency dispatch systems (Scquizzato et al., 2020; Shah et al., 2025).
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Copyright (c) 2026 Zofia Sinczak, Aleksandra Zofia Sobol, Stanisław Bajerski, Anna Maria Kowalska, Sonia Zembrzuska, Justyna Maria Wiktorowicz, Martyna Kadłubańska, Aleksandra Anna Kuls, Bogusław Pabis, Nicole Bulińska
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