Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians
The aim of this study was to explore the impact of motion generated by ambulance patient management on the performance of two lightweight physiologic sensors. Two physiologic sensors were applied to pre-hospital patients. The first was the Contec Medical Systems CMS50FW finger pulse oximeter, monito...
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| Veröffentlicht in: | Journal of clinical monitoring and computing 2016-02, Vol.30 (1), p.23-32 |
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| creator | Mort, Alasdair J. Fitzpatrick, David Wilson, Philip M. J. Mellish, Chris Schneider, Anne |
| description | The aim of this study was to explore the impact of motion generated by ambulance patient management on the performance of two lightweight physiologic sensors. Two physiologic sensors were applied to pre-hospital patients. The first was the Contec Medical Systems CMS50FW finger pulse oximeter, monitoring heart rate (HR) and blood oxygen saturation (SpO2). The second was the RESpeck respiratory rate (RR) sensor, which was wireless-enabled with a Bluetooth
®
Low Energy protocol. Sensor data were recorded from 16 pre-hospital patients, who were monitored for 21.2 ± 9.8 min, on average. Some form of error was identified on almost every HR and SpO
2
trace. However, the mean proportion of each trace exhibiting error was |
| doi_str_mv | 10.1007/s10877-015-9673-z |
| format | Article |
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®
Low Energy protocol. Sensor data were recorded from 16 pre-hospital patients, who were monitored for 21.2 ± 9.8 min, on average. Some form of error was identified on almost every HR and SpO
2
trace. However, the mean proportion of each trace exhibiting error was <10 % (range <1–50 % for individual patients). There appeared to be no overt impact of the gross motion associated with road ambulance transit on the incidence of HR or SpO
2
error. The RESpeck RR sensor delivered an average of 4.2 (±2.2) validated breaths per minute, but did not produce any validated breaths during the gross motion of ambulance transit as its pre-defined motion threshold was exceeded. However, this was many more data points than could be achieved using traditional manual assessment of RR. Error was identified on a majority of pre-hospital physiologic signals, which emphasised the need to ensure consistent sensor attachment in this unstable and unpredictable environment, and in developing intelligent methods of screening out such error.</description><identifier>ISSN: 1387-1307</identifier><identifier>EISSN: 1573-2614</identifier><identifier>DOI: 10.1007/s10877-015-9673-z</identifier><identifier>PMID: 25804608</identifier><identifier>CODEN: JCMCFG</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Accelerometry - instrumentation ; Adult ; Aged ; Aged, 80 and over ; Ambulances ; Anesthesiology ; Critical Care Medicine ; Electrocardiography - instrumentation ; Emergency Medical Services ; Equipment Design ; Equipment Failure Analysis ; Error detection ; Errors ; Health Sciences ; Humans ; Intensive ; Lightweight ; Male ; Medicine ; Medicine & Public Health ; Middle Aged ; Miniaturization ; Monitoring, Ambulatory - instrumentation ; Motion ; Original Research ; Oximetry - instrumentation ; Patients ; Reproducibility of Results ; Respiratory Function Tests - instrumentation ; Respiratory Rate ; Sensitivity and Specificity ; Sensors ; Statistics for Life Sciences ; Transducers ; Transit ; Weight reduction ; Wireless Technology - instrumentation</subject><ispartof>Journal of clinical monitoring and computing, 2016-02, Vol.30 (1), p.23-32</ispartof><rights>The Author(s) 2015</rights><rights>Springer Science+Business Media Dordrecht 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c621t-654d7ac9ebce7190a02ec3b637b7f19c81049a67c44c618ba9cbfeb60cec80243</citedby><cites>FETCH-LOGICAL-c621t-654d7ac9ebce7190a02ec3b637b7f19c81049a67c44c618ba9cbfeb60cec80243</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10877-015-9673-z$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10877-015-9673-z$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,315,781,785,886,27926,27927,41490,42559,51321</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25804608$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mort, Alasdair J.</creatorcontrib><creatorcontrib>Fitzpatrick, David</creatorcontrib><creatorcontrib>Wilson, Philip M. J.</creatorcontrib><creatorcontrib>Mellish, Chris</creatorcontrib><creatorcontrib>Schneider, Anne</creatorcontrib><title>Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians</title><title>Journal of clinical monitoring and computing</title><addtitle>J Clin Monit Comput</addtitle><addtitle>J Clin Monit Comput</addtitle><description>The aim of this study was to explore the impact of motion generated by ambulance patient management on the performance of two lightweight physiologic sensors. Two physiologic sensors were applied to pre-hospital patients. The first was the Contec Medical Systems CMS50FW finger pulse oximeter, monitoring heart rate (HR) and blood oxygen saturation (SpO2). The second was the RESpeck respiratory rate (RR) sensor, which was wireless-enabled with a Bluetooth
®
Low Energy protocol. Sensor data were recorded from 16 pre-hospital patients, who were monitored for 21.2 ± 9.8 min, on average. Some form of error was identified on almost every HR and SpO
2
trace. However, the mean proportion of each trace exhibiting error was <10 % (range <1–50 % for individual patients). There appeared to be no overt impact of the gross motion associated with road ambulance transit on the incidence of HR or SpO
2
error. The RESpeck RR sensor delivered an average of 4.2 (±2.2) validated breaths per minute, but did not produce any validated breaths during the gross motion of ambulance transit as its pre-defined motion threshold was exceeded. However, this was many more data points than could be achieved using traditional manual assessment of RR. Error was identified on a majority of pre-hospital physiologic signals, which emphasised the need to ensure consistent sensor attachment in this unstable and unpredictable environment, and in developing intelligent methods of screening out such error.</description><subject>Accelerometry - instrumentation</subject><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Ambulances</subject><subject>Anesthesiology</subject><subject>Critical Care Medicine</subject><subject>Electrocardiography - instrumentation</subject><subject>Emergency Medical Services</subject><subject>Equipment Design</subject><subject>Equipment Failure Analysis</subject><subject>Error detection</subject><subject>Errors</subject><subject>Health Sciences</subject><subject>Humans</subject><subject>Intensive</subject><subject>Lightweight</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Miniaturization</subject><subject>Monitoring, Ambulatory - instrumentation</subject><subject>Motion</subject><subject>Original Research</subject><subject>Oximetry - instrumentation</subject><subject>Patients</subject><subject>Reproducibility of Results</subject><subject>Respiratory Function Tests - instrumentation</subject><subject>Respiratory Rate</subject><subject>Sensitivity and Specificity</subject><subject>Sensors</subject><subject>Statistics for Life Sciences</subject><subject>Transducers</subject><subject>Transit</subject><subject>Weight reduction</subject><subject>Wireless Technology - instrumentation</subject><issn>1387-1307</issn><issn>1573-2614</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNkU2LFDEQhoMo7jr6A7xIgxcvrfnqJH0RZPELBrzoOSQ11TNZujtt0r3L7K83vbMuqyB4SQrqqbc-XkJeMvqWUarfZUaN1jVlTd0qLeqbR-ScNSXgisnHJRZG10xQfUae5XxJKW2NYE_JGW8MlYqac4LbsD_M17i-1XQ45hD7uA9QZRxzTNWEqYtpcCNgtVtSGPfVlLA-xDyF2fUVuFQS2IcrTLir_LFyg1_6Wx76MAYIbszPyZPO9Rlf3P0b8uPTx-8XX-rtt89fLz5sa1CczbVq5E47aNEDatZSRzmC8EporzvWgmFUtk5pkBIUM9614Dv0igKCoVyKDXl_0p0WP-AOcJyT6-2UwuDS0UYX7J-ZMRzsPl5ZqaXk5XIb8uZOIMWfC-bZDiED9mUhjEu2TLeCS2r-C1W8VaJpVEFf_4VexiWN5RK3lNZMs3V4dqIgxZwTdvdzM2pXv-3Jb1v8tqvf9qbUvHq48H3Fb4MLwE9AnlbzMD1o_U_VX4tluTA</recordid><startdate>20160201</startdate><enddate>20160201</enddate><creator>Mort, Alasdair J.</creator><creator>Fitzpatrick, David</creator><creator>Wilson, Philip M. J.</creator><creator>Mellish, Chris</creator><creator>Schneider, Anne</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7SC</scope><scope>7SP</scope><scope>7U5</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0S</scope><scope>M1P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20160201</creationdate><title>Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians</title><author>Mort, Alasdair J. ; Fitzpatrick, David ; Wilson, Philip M. 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J.</au><au>Mellish, Chris</au><au>Schneider, Anne</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians</atitle><jtitle>Journal of clinical monitoring and computing</jtitle><stitle>J Clin Monit Comput</stitle><addtitle>J Clin Monit Comput</addtitle><date>2016-02-01</date><risdate>2016</risdate><volume>30</volume><issue>1</issue><spage>23</spage><epage>32</epage><pages>23-32</pages><issn>1387-1307</issn><eissn>1573-2614</eissn><coden>JCMCFG</coden><abstract>The aim of this study was to explore the impact of motion generated by ambulance patient management on the performance of two lightweight physiologic sensors. Two physiologic sensors were applied to pre-hospital patients. The first was the Contec Medical Systems CMS50FW finger pulse oximeter, monitoring heart rate (HR) and blood oxygen saturation (SpO2). The second was the RESpeck respiratory rate (RR) sensor, which was wireless-enabled with a Bluetooth
®
Low Energy protocol. Sensor data were recorded from 16 pre-hospital patients, who were monitored for 21.2 ± 9.8 min, on average. Some form of error was identified on almost every HR and SpO
2
trace. However, the mean proportion of each trace exhibiting error was <10 % (range <1–50 % for individual patients). There appeared to be no overt impact of the gross motion associated with road ambulance transit on the incidence of HR or SpO
2
error. The RESpeck RR sensor delivered an average of 4.2 (±2.2) validated breaths per minute, but did not produce any validated breaths during the gross motion of ambulance transit as its pre-defined motion threshold was exceeded. However, this was many more data points than could be achieved using traditional manual assessment of RR. Error was identified on a majority of pre-hospital physiologic signals, which emphasised the need to ensure consistent sensor attachment in this unstable and unpredictable environment, and in developing intelligent methods of screening out such error.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><pmid>25804608</pmid><doi>10.1007/s10877-015-9673-z</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | Journal of clinical monitoring and computing, 2016-02, Vol.30 (1), p.23-32 |
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| subjects | Accelerometry - instrumentation Adult Aged Aged, 80 and over Ambulances Anesthesiology Critical Care Medicine Electrocardiography - instrumentation Emergency Medical Services Equipment Design Equipment Failure Analysis Error detection Errors Health Sciences Humans Intensive Lightweight Male Medicine Medicine & Public Health Middle Aged Miniaturization Monitoring, Ambulatory - instrumentation Motion Original Research Oximetry - instrumentation Patients Reproducibility of Results Respiratory Function Tests - instrumentation Respiratory Rate Sensitivity and Specificity Sensors Statistics for Life Sciences Transducers Transit Weight reduction Wireless Technology - instrumentation |
| title | Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians |
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