Improved screening of fall risk using free-living based accelerometer data

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dc.contributor.authorKelly, D.
dc.contributor.authorCondell, J.
dc.contributor.authorGillespie, J.
dc.contributor.authorMunoz Esquivel, K.
dc.contributor.authorBarton, John
dc.contributor.authorTedesco, Salvatore
dc.contributor.authorNordstrom, A.
dc.contributor.authorÅkerlund Larsson, M.
dc.contributor.authorAlamäki , A.
dc.contributor.funderEngineering and Physical Sciences Research Councilen
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2022-09-02T10:36:40Z
dc.date.available2022-09-02T10:36:40Z
dc.date.issued2022-06-13
dc.date.updated2022-09-02T10:24:48Z
dc.description.abstractFalls are one of the most costly population health issues. Screening of older adults for fall risks can allow for earlier interventions and ultimately lead to better outcomes and reduced public health spending. This work proposes a solution to limitations in existing fall screening techniques by utilizing a hip-based accelerometer worn in free-living conditions. The work proposes techniques to extract fall risk features from periods of free-living ambulatory activity. Analysis of the proposed techniques is conducted and compared with existing screening methods using Functional Tests and Lab-based Gait Analysis. 1705 Older Adults from Umea (Sweden) were assessed. Data consisted of 1 Week of hip worn accelerometer data, gait measurements and performance metrics for 3 functional tests. Retrospective and Prospective fall data were also recorded based on the incidence of falls occurring 12 months before and after the study commencing respectively. Machine learning based experiments show accelerometer based measures perform best when predicting falls. Prospective falls had a sensitivity and specificity of 0.61 and 0.66 respectively while retrospective falls had a sensitivity and specificity of 0.61 and 0.68 respectively.en
dc.description.sponsorshipEuropean Commission (European Union Interreg Northern Periphery and Arctic 2014-2020 program); Engineering and Physical Sciences Research Council (Grant No. EP/T022175/1)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid104116en
dc.identifier.citationKelly, D., Condell, J., Gillespie, J., Munoz Esquivel, K., Barton, J., Tedesco, S., Nordstrom, A., Åkerlund Larsson, M. and Alamäki, A. (2022) 'Improved screening of fall risk using free-living based accelerometer data', Journal of Biomedical Informatics, 131, 104116 (13pp). doi: 10.1016/j.jbi.2022.104116en
dc.identifier.doi10.1016/j.jbi.2022.104116en
dc.identifier.endpage13en
dc.identifier.issn1532-0480
dc.identifier.issn1532-0464
dc.identifier.journaltitleJournal of Biomedical Informaticsen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/13531
dc.identifier.volume131en
dc.language.isoenen
dc.publisherElsevier Inc.en
dc.rights© 2022, the Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectFall risken
dc.subjectAccelerometeren
dc.titleImproved screening of fall risk using free-living based accelerometer dataen
dc.typeArticle (peer-reviewed)en
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