Predictors of fall risk in older adults using the G-STRIDE inertial sensor: an observational multicenter case-control study.

Indices de calidad: JCR, FACTOR DE IMPACTO 4.1 SCIE 23/54 T2 Q2 SSCI 9/37 T1 Q1 D1

Detalles Bibliográficos
Autores: Neira‐Álvarez, Marta, Rodríguez-Sánchez, Cristina, Huertas-Hoyas, Elisabet, García-Villamil-Neira, Guillermo, Espinoza-Cerda, M Teresa, Pérez-Delgado, Laura, Reina-Robles, Elena, Bartolomé-Martín, Irene, Ruiz-Ruiz, Luisa, Jimenez-Ruiz, Antonio R
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Rey Juan Carlos
Repositorio:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
OAI Identifier:oai:burjcdigital.urjc.es:10115/27485
Acceso en línea:https://hdl.handle.net/10115/27485
Access Level:acceso abierto
Palabra clave:Elderly
Falls
Frailty
Gait analysis
IMU
Telemedicine
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spelling Predictors of fall risk in older adults using the G-STRIDE inertial sensor: an observational multicenter case-control study.Neira‐Álvarez, MartaRodríguez-Sánchez, CristinaHuertas-Hoyas, ElisabetGarcía-Villamil-Neira, GuillermoEspinoza-Cerda, M TeresaPérez-Delgado, LauraReina-Robles, ElenaBartolomé-Martín, IreneRuiz-Ruiz, LuisaJimenez-Ruiz, Antonio RElderlyFallsFrailtyGait analysisIMUTelemedicineIndices de calidad: JCR, FACTOR DE IMPACTO 4.1 SCIE 23/54 T2 Q2 SSCI 9/37 T1 Q1 D1Background: There are a lot of tools to use for fall assessment, but there is not yet one that predicts the risk of falls in the elderly. This study aims to evaluate the use of the G-STRIDE prototype in the analysis of fall risk, defining the cut-off points to predict the risk of falling and developing a predictive model that allows discriminating between subjects with and without fall risks and those at risk of future falls. Methods: An observational, multicenter case-control study was conducted with older people coming from two different public hospitals and three different nursing homes. We gathered clinical variables ( Short Physical Performance Battery (SPPB), Standardized Frailty Criteria, Speed 4 m walk, Falls Efficacy Scale-International (FES-I), Time-Up Go Test, and Global Deterioration Scale (GDS)) and measured gait kinematics using an inertial measure unit (IMU). We performed a logistic regression model using a training set of observations (70% of the participants) to predict the probability of falls. Results: A total of 163 participants were included, 86 people with gait and balance disorders or falls and 77 without falls; 67,8% were females, with a mean age of 82,63 ± 6,01 years. G-STRIDE made it possible to measure gait parameters under normal living conditions. There are 46 cut-off values of conventional clinical parameters and those estimated with the G-STRIDE solution. A logistic regression mixed model, with four conventional and 2 kinematic variables allows us to identify people at risk of falls showing good predictive value with AUC of 77,6% (sensitivity 0,773 y specificity 0,780). In addition, we could predict the fallers in the test group (30% observations not in the model) with similar performance to conventional methods. Conclusions: The G-STRIDE IMU device allows to predict the risk of falls using a mixed model with an accuracy of 0,776 with similar performance to conventional model. This approach allows better precision, low cost and less infrastructures for an early intervention and prevention of future falls.BMC202320232023info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10115/27485reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlosinstname:Universidad Rey Juan CarlosInglésAttribution 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:burjcdigital.urjc.es:10115/274852026-06-24T12:48:17Z
dc.title.none.fl_str_mv Predictors of fall risk in older adults using the G-STRIDE inertial sensor: an observational multicenter case-control study.
title Predictors of fall risk in older adults using the G-STRIDE inertial sensor: an observational multicenter case-control study.
spellingShingle Predictors of fall risk in older adults using the G-STRIDE inertial sensor: an observational multicenter case-control study.
Neira‐Álvarez, Marta
Elderly
Falls
Frailty
Gait analysis
IMU
Telemedicine
title_short Predictors of fall risk in older adults using the G-STRIDE inertial sensor: an observational multicenter case-control study.
title_full Predictors of fall risk in older adults using the G-STRIDE inertial sensor: an observational multicenter case-control study.
title_fullStr Predictors of fall risk in older adults using the G-STRIDE inertial sensor: an observational multicenter case-control study.
title_full_unstemmed Predictors of fall risk in older adults using the G-STRIDE inertial sensor: an observational multicenter case-control study.
title_sort Predictors of fall risk in older adults using the G-STRIDE inertial sensor: an observational multicenter case-control study.
dc.creator.none.fl_str_mv Neira‐Álvarez, Marta
Rodríguez-Sánchez, Cristina
Huertas-Hoyas, Elisabet
García-Villamil-Neira, Guillermo
Espinoza-Cerda, M Teresa
Pérez-Delgado, Laura
Reina-Robles, Elena
Bartolomé-Martín, Irene
Ruiz-Ruiz, Luisa
Jimenez-Ruiz, Antonio R
author Neira‐Álvarez, Marta
author_facet Neira‐Álvarez, Marta
Rodríguez-Sánchez, Cristina
Huertas-Hoyas, Elisabet
García-Villamil-Neira, Guillermo
Espinoza-Cerda, M Teresa
Pérez-Delgado, Laura
Reina-Robles, Elena
Bartolomé-Martín, Irene
Ruiz-Ruiz, Luisa
Jimenez-Ruiz, Antonio R
author_role author
author2 Rodríguez-Sánchez, Cristina
Huertas-Hoyas, Elisabet
García-Villamil-Neira, Guillermo
Espinoza-Cerda, M Teresa
Pérez-Delgado, Laura
Reina-Robles, Elena
Bartolomé-Martín, Irene
Ruiz-Ruiz, Luisa
Jimenez-Ruiz, Antonio R
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Elderly
Falls
Frailty
Gait analysis
IMU
Telemedicine
topic Elderly
Falls
Frailty
Gait analysis
IMU
Telemedicine
description Indices de calidad: JCR, FACTOR DE IMPACTO 4.1 SCIE 23/54 T2 Q2 SSCI 9/37 T1 Q1 D1
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10115/27485
url https://hdl.handle.net/10115/27485
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv BMC
publisher.none.fl_str_mv BMC
dc.source.none.fl_str_mv reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
instname:Universidad Rey Juan Carlos
instname_str Universidad Rey Juan Carlos
reponame_str BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
collection BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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