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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10223684 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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