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Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults

Accurate measurement of sedentary behaviour in older adults is informative and relevant. Yet, activities such as sitting are not accurately distinguished from non-sedentary activities (e.g., upright activities), especially in real-world conditions. This study examines the accuracy of a novel algorit...

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Autores principales: Abdul Jabbar, Khalid, Sarvestan, Javad, Zia Ur Rehman, Rana, Lord, Sue, Kerse, Ngaire, Teh, Ruth, Del Din, Silvia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223684/
https://www.ncbi.nlm.nih.gov/pubmed/37430519
http://dx.doi.org/10.3390/s23104605
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author Abdul Jabbar, Khalid
Sarvestan, Javad
Zia Ur Rehman, Rana
Lord, Sue
Kerse, Ngaire
Teh, Ruth
Del Din, Silvia
author_facet Abdul Jabbar, Khalid
Sarvestan, Javad
Zia Ur Rehman, Rana
Lord, Sue
Kerse, Ngaire
Teh, Ruth
Del Din, Silvia
author_sort Abdul Jabbar, Khalid
collection PubMed
description Accurate measurement of sedentary behaviour in older adults is informative and relevant. Yet, activities such as sitting are not accurately distinguished from non-sedentary activities (e.g., upright activities), especially in real-world conditions. This study examines the accuracy of a novel algorithm to identify sitting, lying, and upright activities in community-dwelling older people in real-world conditions. Eighteen older adults wore a single triaxial accelerometer with an onboard triaxial gyroscope on their lower back and performed a range of scripted and non-scripted activities in their homes/retirement villages whilst being videoed. A novel algorithm was developed to identify sitting, lying, and upright activities. The algorithm’s sensitivity, specificity, positive predictive value, and negative predictive value for identifying scripted sitting activities ranged from 76.9% to 94.8%. For scripted lying activities: 70.4% to 95.7%. For scripted upright activities: 75.9% to 93.1%. For non-scripted sitting activities: 92.3% to 99.5%. No non-scripted lying activities were captured. For non-scripted upright activities: 94.3% to 99.5%. The algorithm could, at worst, overestimate or underestimate sedentary behaviour bouts by ±40 s, which is within a 5% error for sedentary behaviour bouts. These results indicate good to excellent agreement for the novel algorithm, providing a valid measure of sedentary behaviour in community-dwelling older adults.
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spelling pubmed-102236842023-05-28 Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults Abdul Jabbar, Khalid Sarvestan, Javad Zia Ur Rehman, Rana Lord, Sue Kerse, Ngaire Teh, Ruth Del Din, Silvia Sensors (Basel) Article Accurate measurement of sedentary behaviour in older adults is informative and relevant. Yet, activities such as sitting are not accurately distinguished from non-sedentary activities (e.g., upright activities), especially in real-world conditions. This study examines the accuracy of a novel algorithm to identify sitting, lying, and upright activities in community-dwelling older people in real-world conditions. Eighteen older adults wore a single triaxial accelerometer with an onboard triaxial gyroscope on their lower back and performed a range of scripted and non-scripted activities in their homes/retirement villages whilst being videoed. A novel algorithm was developed to identify sitting, lying, and upright activities. The algorithm’s sensitivity, specificity, positive predictive value, and negative predictive value for identifying scripted sitting activities ranged from 76.9% to 94.8%. For scripted lying activities: 70.4% to 95.7%. For scripted upright activities: 75.9% to 93.1%. For non-scripted sitting activities: 92.3% to 99.5%. No non-scripted lying activities were captured. For non-scripted upright activities: 94.3% to 99.5%. The algorithm could, at worst, overestimate or underestimate sedentary behaviour bouts by ±40 s, which is within a 5% error for sedentary behaviour bouts. These results indicate good to excellent agreement for the novel algorithm, providing a valid measure of sedentary behaviour in community-dwelling older adults. MDPI 2023-05-09 /pmc/articles/PMC10223684/ /pubmed/37430519 http://dx.doi.org/10.3390/s23104605 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Abdul Jabbar, Khalid
Sarvestan, Javad
Zia Ur Rehman, Rana
Lord, Sue
Kerse, Ngaire
Teh, Ruth
Del Din, Silvia
Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults
title Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults
title_full Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults
title_fullStr Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults
title_full_unstemmed Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults
title_short Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults
title_sort validation of an algorithm for measurement of sedentary behaviour in community-dwelling older adults
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223684/
https://www.ncbi.nlm.nih.gov/pubmed/37430519
http://dx.doi.org/10.3390/s23104605
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