Is it possible to predict falls in older adults using gait kinematics?
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.clinbiomech.2018.08.006 http://hdl.handle.net/11449/180097 |
Resumo: | Background: Gait kinematic parameters have been reported as an important clinical tool to assess the risk of falls in older adults. However, the ability of these parameters to predict falls in the older population is still unclear. Objective: To identify the ability that gait kinematic parameters present to predict fall in older adults. Methods: Data from 102 older adults, who live in a community setting, were considered for this study. For data collection, older subjects had to walk on a 14 meter-walkway in their preferred gait speed. The incidence of falls was recorded at baseline together with gait kinematics and then every three months during the period of the study. The ability of gait kinematic parameters to predict falls was tested using the ROC curve. Results: Stance time variability, swing time, and stride length presented a sensitivity to predict falls in older adults higher than 70%. Conclusion: Gait kinematic parameters, such as stance variability, swing time, and stride length may predict future falls in older adults. |
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Repositório Institucional da UNESP |
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2946 |
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Is it possible to predict falls in older adults using gait kinematics?AgingBiomechanicsMobilityPhysical therapyBackground: Gait kinematic parameters have been reported as an important clinical tool to assess the risk of falls in older adults. However, the ability of these parameters to predict falls in the older population is still unclear. Objective: To identify the ability that gait kinematic parameters present to predict fall in older adults. Methods: Data from 102 older adults, who live in a community setting, were considered for this study. For data collection, older subjects had to walk on a 14 meter-walkway in their preferred gait speed. The incidence of falls was recorded at baseline together with gait kinematics and then every three months during the period of the study. The ability of gait kinematic parameters to predict falls was tested using the ROC curve. Results: Stance time variability, swing time, and stride length presented a sensitivity to predict falls in older adults higher than 70%. Conclusion: Gait kinematic parameters, such as stance variability, swing time, and stride length may predict future falls in older adults.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Center of Health Sciences Universidade do Sagrado Coração USCDepartment of Physical Therapy and Occupation Therapy Universidade Estadual Paulista UNESPDepartment of Physical Therapy and Occupation Therapy Universidade Estadual Paulista UNESPFAPESP: 2014/07227-3USCUniversidade Estadual Paulista (Unesp)Marques, Nise RibeiroSpinoso, Deborah Hebling [UNESP]Cardoso, Bruna Carvalho [UNESP]Moreno, Vinicius ChristianiniKuroda, Marina HiromiNavega, Marcelo Tavella [UNESP]2018-12-11T17:38:08Z2018-12-11T17:38:08Z2018-11-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article15-18application/pdfhttp://dx.doi.org/10.1016/j.clinbiomech.2018.08.006Clinical Biomechanics, v. 59, p. 15-18.1879-12710268-0033http://hdl.handle.net/11449/18009710.1016/j.clinbiomech.2018.08.0062-s2.0-850513706472-s2.0-85051370647.pdf1153464448003029Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengClinical Biomechanics0,982info:eu-repo/semantics/openAccess2024-08-09T15:17:12Zoai:repositorio.unesp.br:11449/180097Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-09T15:17:12Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Is it possible to predict falls in older adults using gait kinematics? |
title |
Is it possible to predict falls in older adults using gait kinematics? |
spellingShingle |
Is it possible to predict falls in older adults using gait kinematics? Marques, Nise Ribeiro Aging Biomechanics Mobility Physical therapy |
title_short |
Is it possible to predict falls in older adults using gait kinematics? |
title_full |
Is it possible to predict falls in older adults using gait kinematics? |
title_fullStr |
Is it possible to predict falls in older adults using gait kinematics? |
title_full_unstemmed |
Is it possible to predict falls in older adults using gait kinematics? |
title_sort |
Is it possible to predict falls in older adults using gait kinematics? |
author |
Marques, Nise Ribeiro |
author_facet |
Marques, Nise Ribeiro Spinoso, Deborah Hebling [UNESP] Cardoso, Bruna Carvalho [UNESP] Moreno, Vinicius Christianini Kuroda, Marina Hiromi Navega, Marcelo Tavella [UNESP] |
author_role |
author |
author2 |
Spinoso, Deborah Hebling [UNESP] Cardoso, Bruna Carvalho [UNESP] Moreno, Vinicius Christianini Kuroda, Marina Hiromi Navega, Marcelo Tavella [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
USC Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Marques, Nise Ribeiro Spinoso, Deborah Hebling [UNESP] Cardoso, Bruna Carvalho [UNESP] Moreno, Vinicius Christianini Kuroda, Marina Hiromi Navega, Marcelo Tavella [UNESP] |
dc.subject.por.fl_str_mv |
Aging Biomechanics Mobility Physical therapy |
topic |
Aging Biomechanics Mobility Physical therapy |
description |
Background: Gait kinematic parameters have been reported as an important clinical tool to assess the risk of falls in older adults. However, the ability of these parameters to predict falls in the older population is still unclear. Objective: To identify the ability that gait kinematic parameters present to predict fall in older adults. Methods: Data from 102 older adults, who live in a community setting, were considered for this study. For data collection, older subjects had to walk on a 14 meter-walkway in their preferred gait speed. The incidence of falls was recorded at baseline together with gait kinematics and then every three months during the period of the study. The ability of gait kinematic parameters to predict falls was tested using the ROC curve. Results: Stance time variability, swing time, and stride length presented a sensitivity to predict falls in older adults higher than 70%. Conclusion: Gait kinematic parameters, such as stance variability, swing time, and stride length may predict future falls in older adults. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T17:38:08Z 2018-12-11T17:38:08Z 2018-11-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.clinbiomech.2018.08.006 Clinical Biomechanics, v. 59, p. 15-18. 1879-1271 0268-0033 http://hdl.handle.net/11449/180097 10.1016/j.clinbiomech.2018.08.006 2-s2.0-85051370647 2-s2.0-85051370647.pdf 1153464448003029 |
url |
http://dx.doi.org/10.1016/j.clinbiomech.2018.08.006 http://hdl.handle.net/11449/180097 |
identifier_str_mv |
Clinical Biomechanics, v. 59, p. 15-18. 1879-1271 0268-0033 10.1016/j.clinbiomech.2018.08.006 2-s2.0-85051370647 2-s2.0-85051370647.pdf 1153464448003029 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Clinical Biomechanics 0,982 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
15-18 application/pdf |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128152536350720 |