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Clustering Accelerometer Activity Patterns from the UK Biobank Cohort
Many researchers are beginning to adopt the use of wrist-worn accelerometers to objectively measure personal activity levels. Data from these devices are often used to summarise such activity in terms of averages, variances, exceedances, and patterns within a profile. In this study, we report the de...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709415/ https://www.ncbi.nlm.nih.gov/pubmed/34960314 http://dx.doi.org/10.3390/s21248220 |
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author | Clark, Stephen Lomax, Nik Morris, Michelle Pontin, Francesca Birkin, Mark |
author_facet | Clark, Stephen Lomax, Nik Morris, Michelle Pontin, Francesca Birkin, Mark |
author_sort | Clark, Stephen |
collection | PubMed |
description | Many researchers are beginning to adopt the use of wrist-worn accelerometers to objectively measure personal activity levels. Data from these devices are often used to summarise such activity in terms of averages, variances, exceedances, and patterns within a profile. In this study, we report the development of a clustering utilising the whole activity profile. This was achieved using the robust clustering technique of k-medoids applied to an extensive data set of over 90,000 activity profiles, collected as part of the UK Biobank study. We identified nine distinct activity profiles in these data, which captured both the pattern of activity throughout a week and the intensity of the activity: “Active 9 to 5”, “Active”, “Morning Movers”, “Get up and Active”, “Live for the Weekend”, “Moderates”, “Leisurely 9 to 5”, “Sedate” and “Inactive”. These patterns are differentiated by sociodemographic, socioeconomic, and health and circadian rhythm data collected by UK Biobank. The utility of these findings are that they sit alongside existing summary measures of physical activity to provide a way to typify distinct activity patterns that may help to explain other health and morbidity outcomes, e.g., BMI or COVID-19. This research will be returned to the UK Biobank for other researchers to use. |
format | Online Article Text |
id | pubmed-8709415 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87094152021-12-25 Clustering Accelerometer Activity Patterns from the UK Biobank Cohort Clark, Stephen Lomax, Nik Morris, Michelle Pontin, Francesca Birkin, Mark Sensors (Basel) Article Many researchers are beginning to adopt the use of wrist-worn accelerometers to objectively measure personal activity levels. Data from these devices are often used to summarise such activity in terms of averages, variances, exceedances, and patterns within a profile. In this study, we report the development of a clustering utilising the whole activity profile. This was achieved using the robust clustering technique of k-medoids applied to an extensive data set of over 90,000 activity profiles, collected as part of the UK Biobank study. We identified nine distinct activity profiles in these data, which captured both the pattern of activity throughout a week and the intensity of the activity: “Active 9 to 5”, “Active”, “Morning Movers”, “Get up and Active”, “Live for the Weekend”, “Moderates”, “Leisurely 9 to 5”, “Sedate” and “Inactive”. These patterns are differentiated by sociodemographic, socioeconomic, and health and circadian rhythm data collected by UK Biobank. The utility of these findings are that they sit alongside existing summary measures of physical activity to provide a way to typify distinct activity patterns that may help to explain other health and morbidity outcomes, e.g., BMI or COVID-19. This research will be returned to the UK Biobank for other researchers to use. MDPI 2021-12-09 /pmc/articles/PMC8709415/ /pubmed/34960314 http://dx.doi.org/10.3390/s21248220 Text en © 2021 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 Clark, Stephen Lomax, Nik Morris, Michelle Pontin, Francesca Birkin, Mark Clustering Accelerometer Activity Patterns from the UK Biobank Cohort |
title | Clustering Accelerometer Activity Patterns from the UK Biobank Cohort |
title_full | Clustering Accelerometer Activity Patterns from the UK Biobank Cohort |
title_fullStr | Clustering Accelerometer Activity Patterns from the UK Biobank Cohort |
title_full_unstemmed | Clustering Accelerometer Activity Patterns from the UK Biobank Cohort |
title_short | Clustering Accelerometer Activity Patterns from the UK Biobank Cohort |
title_sort | clustering accelerometer activity patterns from the uk biobank cohort |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709415/ https://www.ncbi.nlm.nih.gov/pubmed/34960314 http://dx.doi.org/10.3390/s21248220 |
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