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Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment

BACKGROUND: Frailty is a highly recognized geriatric syndrome resulting in decline in reserve across multiple physiological systems. Impaired physical function is one of the major indicators of frailty. The goal of this study was to evaluate an algorithm that discriminates between frailty groups (no...

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Autores principales: Pradeep Kumar, Danya, Toosizadeh, Nima, Mohler, Jane, Ehsani, Hossein, Mannier, Cassidy, Laksari, Kaveh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7203790/
https://www.ncbi.nlm.nih.gov/pubmed/32375700
http://dx.doi.org/10.1186/s12877-020-01572-1
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author Pradeep Kumar, Danya
Toosizadeh, Nima
Mohler, Jane
Ehsani, Hossein
Mannier, Cassidy
Laksari, Kaveh
author_facet Pradeep Kumar, Danya
Toosizadeh, Nima
Mohler, Jane
Ehsani, Hossein
Mannier, Cassidy
Laksari, Kaveh
author_sort Pradeep Kumar, Danya
collection PubMed
description BACKGROUND: Frailty is a highly recognized geriatric syndrome resulting in decline in reserve across multiple physiological systems. Impaired physical function is one of the major indicators of frailty. The goal of this study was to evaluate an algorithm that discriminates between frailty groups (non-frail and pre-frail/frail) based on gait performance parameters derived from unsupervised daily physical activity (DPA). METHODS: DPA was acquired for 48 h from older adults (≥65 years) using a tri-axial accelerometer motion-sensor. Continuous bouts of walking for 20s, 30s, 40s, 50s and 60s without pauses were identified from acceleration data. These were then used to extract qualitative measures (gait variability, gait asymmetry, and gait irregularity) and quantitative measures (total continuous walking duration and maximum number of continuous steps) to characterize gait performance. Association between frailty and gait performance parameters was assessed using multinomial logistic models with frailty as the dependent variable, and gait performance parameters along with demographic parameters as independent variables. RESULTS: One hundred twenty-six older adults (44 non-frail, 60 pre-frail, and 22 frail, based on the Fried index) were recruited. Step- and stride-times, frequency domain gait variability, and continuous walking quantitative measures were significantly different between non-frail and pre-frail/frail groups (p < 0.05). Among the five different durations (20s, 30s, 40s, 50s and 60s), gait performance parameters extracted from 60s continuous walks provided the best frailty assessment results. Using the 60s gait performance parameters in the logistic model, pre-frail/frail group (vs. non-frail) was identified with 76.8% sensitivity and 80% specificity. DISCUSSION: Everyday walking characteristics were found to be associated with frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the early stages of frailty. In-home gait assessment offers an opportunity to screen for and monitor frailty. TRIAL REGISTRATION: The clinical trial was retrospectively registered on June 18th, 2013 with ClinicalTrials.gov, identifier NCT01880229.
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spelling pubmed-72037902020-05-09 Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment Pradeep Kumar, Danya Toosizadeh, Nima Mohler, Jane Ehsani, Hossein Mannier, Cassidy Laksari, Kaveh BMC Geriatr Research Article BACKGROUND: Frailty is a highly recognized geriatric syndrome resulting in decline in reserve across multiple physiological systems. Impaired physical function is one of the major indicators of frailty. The goal of this study was to evaluate an algorithm that discriminates between frailty groups (non-frail and pre-frail/frail) based on gait performance parameters derived from unsupervised daily physical activity (DPA). METHODS: DPA was acquired for 48 h from older adults (≥65 years) using a tri-axial accelerometer motion-sensor. Continuous bouts of walking for 20s, 30s, 40s, 50s and 60s without pauses were identified from acceleration data. These were then used to extract qualitative measures (gait variability, gait asymmetry, and gait irregularity) and quantitative measures (total continuous walking duration and maximum number of continuous steps) to characterize gait performance. Association between frailty and gait performance parameters was assessed using multinomial logistic models with frailty as the dependent variable, and gait performance parameters along with demographic parameters as independent variables. RESULTS: One hundred twenty-six older adults (44 non-frail, 60 pre-frail, and 22 frail, based on the Fried index) were recruited. Step- and stride-times, frequency domain gait variability, and continuous walking quantitative measures were significantly different between non-frail and pre-frail/frail groups (p < 0.05). Among the five different durations (20s, 30s, 40s, 50s and 60s), gait performance parameters extracted from 60s continuous walks provided the best frailty assessment results. Using the 60s gait performance parameters in the logistic model, pre-frail/frail group (vs. non-frail) was identified with 76.8% sensitivity and 80% specificity. DISCUSSION: Everyday walking characteristics were found to be associated with frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the early stages of frailty. In-home gait assessment offers an opportunity to screen for and monitor frailty. TRIAL REGISTRATION: The clinical trial was retrospectively registered on June 18th, 2013 with ClinicalTrials.gov, identifier NCT01880229. BioMed Central 2020-05-06 /pmc/articles/PMC7203790/ /pubmed/32375700 http://dx.doi.org/10.1186/s12877-020-01572-1 Text en © The Author(s) 2020 Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Pradeep Kumar, Danya
Toosizadeh, Nima
Mohler, Jane
Ehsani, Hossein
Mannier, Cassidy
Laksari, Kaveh
Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment
title Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment
title_full Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment
title_fullStr Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment
title_full_unstemmed Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment
title_short Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment
title_sort sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7203790/
https://www.ncbi.nlm.nih.gov/pubmed/32375700
http://dx.doi.org/10.1186/s12877-020-01572-1
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