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Applying different mathematical variability methods to identify older fallers and non-fallers using gait variability data

dc.contributor.authorMarques, Nise Ribeiro [UNESP]
dc.contributor.authorHallal, Camilla Zamfolini
dc.contributor.authorSpinoso, Deborah Hebling [UNESP]
dc.contributor.authorMorcelli, Mary Hellen [UNESP]
dc.contributor.authorCrozara, Luciano Fernandes
dc.contributor.authorGonçalves, Mauro [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUFU
dc.contributor.institutionFAMEMA
dc.date.accessioned2018-12-11T17:03:15Z
dc.date.available2018-12-11T17:03:15Z
dc.date.issued2017-06-01
dc.description.abstractBackground: The clinical assessment of gait variability may be a particularly powerful tool in the screening of older adults at risk of falling. Measurement of gait variability is important in the assessment of fall risk, but the variability metrics used to evaluate gait timing have not yet been adequately studied. Objectives: The aims of this study were (1) to identify the best mathematical method of gait variability analysis to discriminate older fallers and non-fallers and (2) to identify the best temporal, kinematic parameter of gait to discriminate between older fallers and non-fallers. Methods: Thirty-five physically active volunteers participated in this study including 16 older women fallers (69.6 ± 8.1 years) and 19 older women non-fallers (66.1 ± 6.2 years). Volunteers were instructed to walk for 3 min on the treadmill to record the temporal kinematic gait parameters including stance time, swing time and stride time by four footswitches sensors placed under the volunteers’ feet. Data analysis used 40 consecutive gait cycles. Six statistical methods were used to determine the variability of the stance time, swing time and stride time. These included: (1) standard deviation of all the time intervals; (2) standard deviation of the means of these intervals taken every five strides; (3) mean of the standard deviations of the intervals determined every five strides; (4) root-mean-square of the differences between intervals; (5) coefficient of variation calculated as the standard deviation of the intervals divided by the mean of the intervals; and (6) a geometric method calculated based on the construction of a histogram of the intervals. Results: The standard deviation of 40 consecutive gait cycles was the most sensitive (100 %) and specificity (100 %) parameter to discriminate older fallers and non-fallers. Conclusion: The standard deviation of stance time is the kinematic gait variability parameter that demonstrated the best ability to discriminate older fallers from non-fallers. Protocol number of Brazilian Registry of Clinical Trialsen
dc.description.affiliationDepartment of Physical Therapy and Occupational Therapy São Paulo State University UNESP
dc.description.affiliationDepartment of Physical Education and Physical Therapy Uberlândia Federal University UFU
dc.description.affiliationFaculty of Medicine of Marilia FAMEMA
dc.description.affiliationDepartment of Physical Education São Paulo State University UNESP
dc.description.affiliationDepartamento de Fisioterapia e Terapia Ocupacional UNESP, Avenida Hygino Muzzi Filho, 737
dc.description.affiliationUnespDepartment of Physical Therapy and Occupational Therapy São Paulo State University UNESP
dc.description.affiliationUnespDepartment of Physical Education São Paulo State University UNESP
dc.description.affiliationUnespDepartamento de Fisioterapia e Terapia Ocupacional UNESP, Avenida Hygino Muzzi Filho, 737
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 11/11639-7
dc.format.extent473-481
dc.identifierhttp://dx.doi.org/10.1007/s40520-016-0592-8
dc.identifier.citationAging Clinical and Experimental Research, v. 29, n. 3, p. 473-481, 2017.
dc.identifier.doi10.1007/s40520-016-0592-8
dc.identifier.file2-s2.0-84973131537.pdf
dc.identifier.issn1720-8319
dc.identifier.issn1594-0667
dc.identifier.lattes3023304896722902
dc.identifier.scopus2-s2.0-84973131537
dc.identifier.urihttp://hdl.handle.net/11449/173041
dc.language.isoeng
dc.relation.ispartofAging Clinical and Experimental Research
dc.relation.ispartofsjr0,670
dc.relation.ispartofsjr0,670
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectAging
dc.subjectFalls risk
dc.subjectKinematics
dc.titleApplying different mathematical variability methods to identify older fallers and non-fallers using gait variability dataen
dc.typeArtigo
dspace.entity.typePublication
unesp.author.lattes3023304896722902
unesp.campusUniversidade Estadual Paulista (UNESP), Faculdade de Filosofia e Ciências, Maríliapt
unesp.departmentFisioterapia e Terapia Ocupacional - FFCpt

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