Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

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• dc.contributor.author Kirk, Cameron
• dc.contributor.author Carsin, Anne-Elie
• dc.contributor.author García Aymerich, Judith
• dc.contributor.author Koch, Sarah
• dc.contributor.author Mobilise-D consortium
• dc.date.accessioned 2024-06-20T07:04:37Z
• dc.date.available 2024-06-20T07:04:37Z
• dc.date.issued 2024
• dc.description.abstract This study aimed to validate a wearable device's walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and - 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application.Trial registration: ISRCTN - 12246987.
• dc.description.sponsorship This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No. 820820. This JU receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). SDD, LR, AY were also supported by the Innovative Medicines Initiative 2 Joint Undertaking (IMI2 JU) project IDEA-FAST—Grant Agreement 853981. LA, LR, AY and SDD were also supported by the National Institute for Health Research (NIHR) Newcastle Biomedical Research Centre (BRC) based at The Newcastle upon Tyne Hospital NHS Foundation Trust, Newcastle University and the Cumbria, Northumberland and Tyne and Wear (CNTW) NHS Foundation Trust. LA, LR, AY and SDD were also supported by the NIHR/Wellcome Trust Clinical Research Facility (CRF) infrastructure at Newcastle upon Tyne Hospitals NHS Foundation Trust. This study was also supported by the National Institute for Health Research (NIHR) through the Sheffield Biomedical Research Centre (BRC, Grant Number IS-BRC-1215–20017). ISGlobal acknowledges support from the Spanish Ministry of Science and Innovation through the “Centro de Excelencia Severo Ochoa 2019–2023” Program (CEX2018-000806-S), and from the Generalitat de Catalunya through the CERCA Program. All opinions are those of the authors and not the funders. The content in this publication reflects the authors’ view, and neither IMI nor the European Union, EFPIA, NHS, NIHR, DHSC, or any associated partners are responsible for any use that may be made of the information contained herein.
• dc.format.mimetype application/pdf
• dc.identifier.citation Kirk C, Küderle A, Micó-Amigo ME, Bonci T, Paraschiv-Ionescu A, Ullrich M, et al. Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device. Sci Rep. 2024 Jan 19;14(1):1754. DOI: 10.1038/s41598-024-51766-5
• dc.identifier.doi http://dx.doi.org/10.1038/s41598-024-51766-5
• dc.identifier.issn 2045-2322
• dc.identifier.uri http://hdl.handle.net/10230/60534
• dc.language.iso eng
• dc.publisher Nature Research
• dc.relation.ispartof Sci Rep. 2024 Jan 19;14(1):1754
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/820820
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/853981
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/CEX2018-000806-S
• dc.rights © The Author(s) 2024. 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/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Biomarkers
• dc.subject.keyword Biomedical engineering
• dc.subject.keyword Medical research
• dc.subject.keyword Outcomes research
• dc.subject.keyword Translational research
• dc.title Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion