{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T20:08:56Z","timestamp":1784923736509,"version":"3.55.0"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T00:00:00Z","timestamp":1772841600000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016036","name":"Top Sector Life Science & Health","doi-asserted-by":"publisher","award":["R0007420"],"award-info":[{"award-number":["R0007420"]}],"id":[{"id":"10.13039\/100016036","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006209","name":"Radboudumc","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006209","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002996","name":"Dutch Heart Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002996","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Radboud Fonds","award":["SRF2231"],"award-info":[{"award-number":["SRF2231"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>In wearable-based automated cardiac arrest detection technology, photoplethysmography (PPG) is the most commonly used sensor to detect the absence of pulsations. To minimize false-positive cardiac arrest alerts, accelerometry signals are often used for the detection of ongoing movement. We conducted the DETECT-2 study to develop an accelerometer-based machine learning model for the detection of cardiac arrest-related collapse, which is often a first manifestation of cardiac arrest.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>Healthy volunteers simulated cardiac arrest-related collapses through sudden and soft falls without subsequent movement. Accelerometer signals were collected using the CardioWatch wristband; video recordings were made as a reference. An accelerometer-based gradient boosting model (GBM) for fall detection was trained (70%) and tested (30%). The primary endpoint was the sensitivity for the detection of falls; secondary endpoints were false-positive fall alerts. Nineteen participants performed 567 falls. In the training set (n =13; 388 falls), the sensitivity of the GBM was 99.2% (95% confidence interval [CI] 98\u2013100%], with four false positives. In the test set (n = 6; 179 falls), sensitivity was 96.1% (95% CI 92\u201398%), with two false positives. For sudden falls (n = 120) and soft falls (n = 59), sensitivities were 100% (95% CI 96\u2013100%) and 88.1% (95% CI 76\u201395%) in the test set (P &amp;lt; 0.001), respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Using accelerometry data from the CardioWatch, sudden and soft falls that mimic cardiac arrest-related collapse can be accurately detected. The next step in the development of automated cardiac arrest detection is the integration of accelerometer signals into the existing PPG-based model, with the aim of reducing false positives and increasing sensitivity in everyday use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztag043","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T12:38:18Z","timestamp":1772800698000},"source":"Crossref","is-referenced-by-count":0,"title":["A machine learning model to detect falls mimicking cardiac arrest-related collapse based on wrist-derived accelerometry: the DETECT-2 study"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3167-7458","authenticated-orcid":false,"given":"Roos","family":"Edgar","sequence":"first","affiliation":[{"name":"Department of Cardiology, Radboud University Medical Center , Geert Grooteplein Zuid 10, Nijmegen 6525 GA ,","place":["the 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Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8617-7672","authenticated-orcid":false,"given":"Marc A","family":"Brouwer","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Radboud University Medical Center , Geert Grooteplein Zuid 10, Nijmegen 6525 GA ,","place":["the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6572-9466","authenticated-orcid":false,"given":"Rypko J","family":"Beukema","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Radboud University Medical Center , Geert Grooteplein Zuid 10, Nijmegen 6525 GA ,","place":["the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4032-7863","authenticated-orcid":false,"given":"Eelko","family":"Ronner","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Reinier de Graaf Hospital , 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