ArticleResuscitation plus2022
Smartwatch based automatic detection of out-of-hospital cardiac arrest: Study rationale and protocol of the
Article in Resuscitation plus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A machine learning model to detect falls mimicking cardiac arrest-related collapse based on wrist-derived accelerometry: the DETECT-2 study.European heart journal. Digital health · 2026Article
- First automated detection of a cardiac arrest using a commercially available smartwatch: a case report.Resuscitation plus · 2026Article
- A digital witness for cardiac arrest: are wearables ready for real-world use?Resuscitation plus · 2026Article
- Out-of-hospital cardiac arrest detection by a wearable: the first real-life case.Resuscitation plus · 2026Article
- Patient and physician perspectives on smartwatch-based out-of-hospital cardiac arrest detection.European heart journal. Digital health · 2025Article
- Integrating smartwatch-based out-of-hospital cardiac arrest detection into resuscitation systems: a focus group study of community responder perspectives.Resuscitation plus · 2025Article
- Design of the DETECT project: automated cardiac arrest detection and activation of the emergency medical chain integrated into a wristband.Resuscitation plus · 2025Article
- Cardiac Arrest: Can Technology Be the Solution?Journal of clinical medicine · 2025Article
- Detecting cardiac states with wearable photoplethysmograms and implications for out-of-hospital cardiac arrest detection.Scientific reports · 2024Article
- Ethics of Wearable-Based Out-of-Hospital Cardiac Arrest Detection.Circulation. Arrhythmia and electrophysiology · 2024Review
- Investigating Users' Attitudes Toward Automated Smartwatch Cardiac Arrest Detection: Cross-Sectional Survey Study.JMIR human factors · 2024Article
- Sex Differences in Out-of-Hospital Cardiac Arrest Survival Trends.Journal of the American Heart Association · 2024Article
- Wolf Creek XVII part 3: Automated cardiac arrest diagnosis.Resuscitation plus · 2023Review
- Sudden cardiac arrest: Limitations in risk-stratification and treatment, and the potential for digital technologies and artificial intelligence to improve prediction and outcomes.Progress in cardiovascular diseasesReview
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality. Immediate detection and treatment are of paramount importance for survival and good quality of life. The first link in the 'chain of survival' after OHCA - the early recognition and alerting of emergency medical services - is at the same time the weakest link as it entirely depends on witnesses. About one half of OHCA cases are unwitnessed, and victims of unwitnessed OHCA have virtually no chance of survival with good neurologic outcome. Also in case of a witnessed cardiac arrest, alerting of emergency medical services is often delayed for several minutes. Therefore, a technological solution to automatically detect cardiac arrests and to instantly trigger an emergency response has the potential to save thousands of lives per year and to greatly improve neurologic recovery and quality of life in survivors. The HEART-SAFE consortium, consisting of two academic centres and three companies in the Netherlands, collaborates to develop and implement a technical solution to reliably detect OHCA based on sensor signals derived from commercially available smartwatches using artificial intelligence. In this manuscript, we describe the rationale, the envisioned solution, as well as a protocol outline of the work packages involved in the development of the technology.
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Registered trials
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