{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T16:23:52Z","timestamp":1787847832562,"version":"build-2784847793"},"reference-count":25,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"2","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:sec>\n                    <jats:title>Background and Aims:<\/jats:title>\n                    <jats:p>Respiratory depression is a rare but serious complication during opioid administration. Therefore, early detection of signs of deterioration is paramount. The current standard of care of using manual intermittent respiratory rate (RR) measurement is labour intensive and inefficient. We evaluated a wireless sensor monitor, Aingeal (Renew Health Ltd, Ireland), to continuously monitor RR, heart rate (HR) and temperature compared to standard clinical measurements.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Patients who underwent major gynaecological operations and received postoperative opioid patient-controlled analgesia were recruited. Patients were connected to the sensor monitor via a central station software platform. The primary outcome was comparison of RR between sensor and nursing monitoring, with secondary outcomes being HR and temperature between two methods. Feedback from patients and healthcare providers was also collected. Bland-Altman analyses were used to compare the vital signs recorded in sensor against those in patient's electronic record.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>A total of 1121 hours of vital signs data were analysed. Bias for RR was -0.90 (95% confidence interval (CI): -9.39, 7.60) breaths\/min between nursing and averaged sensor readings. Bias for heart rate was -1.12 (95% CI: -26.27, 24.03) and bias for temperature was 1.45 (95% CI: -5.67, 2.76) between the two methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>There is satisfactory agreement of RR measurements, as well as HR and temperature measurements, by the wireless sensor monitor with standard clinical intermittent monitoring with overall good user experience.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/ija.ija_1262_20","type":"journal-article","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T10:24:19Z","timestamp":1612952659000},"page":"146-152","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":8,"title":["Use of wireless respiratory rate sensor monitoring during opioid patient-controlled analgesia after gynaecological surgery"],"prefix":"10.4103","volume":"65","author":[{"given":"Shang-Ming","family":"Cheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason Ju In","family":"Chan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chin Wen","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enhong","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rehena","family":"Sultana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ban Leong","family":"Sng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"R1-10-20240902","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1097\/ALN.0000000000000564","article-title":"Postoperative opioid-induced respiratory depression: A closed claims analysis","volume":"122","author":"Lee","year":"2015","journal-title":"Anesthesiology"},{"key":"R2-10-20240902","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s10877-019-00357-1","article-title":"An easy and accurate respiratory rate monitor is necessary","volume":"34","author":"Marjanovic","year":"2020","journal-title":"J Clin Monit Comput"},{"key":"R3-10-20240902","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.jcrc.2018.07.011","article-title":"Evaluation of the efficacy of the National Early Warning Score in predicting in-hospital mortality via the risk stratification","volume":"47","author":"Lee","year":"2018","journal-title":"J Crit Care"},{"key":"R4-10-20240902","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1016\/j.resuscitation.2014.01.013","article-title":"The impact of the use of the Early Warning Score (EWS) on patient outcomes: A systematic review","volume":"85","author":"Alam","year":"2014","journal-title":"Resuscitation"},{"key":"R5-10-20240902","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1213\/ANE.0000000000004788","article-title":"Prediction of opioid-induced respiratory depression on inpatient wards using continuous capnography and oximetry: An international prospective, observational trial","volume":"131","author":"Khanna","year":"2020","journal-title":"Anesth Analg"},{"key":"R6-10-20240902","first-page":"6711","article-title":"Demonstrating the accuracy of an in-hospital ambulatory patient monitoring solution in measuring respiratory rate","volume":"2013","author":"Donnelly","year":"2013","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"R7-10-20240902","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1136\/bmjqs-2017-006671","article-title":"Is everyone really breathing 20 times a minute? 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