dc.contributor.author | Harbo, Erlend | |
dc.contributor.author | Fuglerud, Silje Skeide | |
dc.contributor.author | Skjaervold, Nils Kristian | |
dc.date.accessioned | 2020-08-19T07:20:28Z | |
dc.date.available | 2020-08-19T07:20:28Z | |
dc.date.created | 2020-06-09T14:34:36Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Critical Care. 2020, 24 (1), 283-283. | en_US |
dc.identifier.issn | 1364-8535 | |
dc.identifier.uri | https://hdl.handle.net/11250/2672897 | |
dc.description.abstract | The prognostication of neurological outcome in
sedated ICU patients is challenging. Multiple clinical scoring schemes and examinations are used,
where different motoric responses are important input variables. Accelerometery is a well-known technology widely applied in different fields of research
and everyday electronic products. In medical research, accelerometers have been used in longitudinal epidemiological studies of physical activity and
health as well as in ICU studies on the topic of
activity, sleep and agitation monitoring. Similarly,
accelerometric information could be a candidate to
improve future neurological prognostication schemes. To
the best of our knowledge, including a systematic review
from 2015 [1], there are no published articles on automatic motion registration from ICU patients in connection to neurologic outcome prognostication | en_US |
dc.language.iso | eng | en_US |
dc.publisher | BMC | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Visualisation of Limb Movements by Accelerometers in Sedated Patients | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.pagenumber | 283-283 | en_US |
dc.source.volume | 24 | en_US |
dc.source.journal | Critical Care | en_US |
dc.source.issue | 1 | en_US |
dc.identifier.doi | 10.1186/s13054-020-02975-7 | |
dc.identifier.cristin | 1814585 | |
dc.description.localcode | © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |