{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T11:39:17Z","timestamp":1779881957767,"version":"3.53.1"},"reference-count":83,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T00:00:00Z","timestamp":1639008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000269","name":"Economic and Social Research Council","doi-asserted-by":"publisher","award":["ES\/S007164\/1"],"award-info":[{"award-number":["ES\/S007164\/1"]}],"id":[{"id":"10.13039\/501100000269","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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: \u201cActive 9 to 5\u201d, \u201cActive\u201d, \u201cMorning Movers\u201d, \u201cGet up and Active\u201d, \u201cLive for the Weekend\u201d, \u201cModerates\u201d, \u201cLeisurely 9 to 5\u201d, \u201cSedate\u201d and \u201cInactive\u201d. 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.<\/jats:p>","DOI":"10.3390\/s21248220","type":"journal-article","created":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T21:46:58Z","timestamp":1639086418000},"page":"8220","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Clustering Accelerometer Activity Patterns from the UK Biobank Cohort"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4090-6002","authenticated-orcid":false,"given":"Stephen","family":"Clark","sequence":"first","affiliation":[{"name":"Leeds Institute for Data Analytics and School of Geography, University of Leeds, Leeds LS2 9JT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nik","family":"Lomax","sequence":"additional","affiliation":[{"name":"Leeds Institute for Data Analytics and School of Geography, University of Leeds, Leeds LS2 9JT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9325-619X","authenticated-orcid":false,"given":"Michelle","family":"Morris","sequence":"additional","affiliation":[{"name":"Leeds Institute for Data Analytics and School of Medicine, University of Leeds, Leeds LS2 9JT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7143-8718","authenticated-orcid":false,"given":"Francesca","family":"Pontin","sequence":"additional","affiliation":[{"name":"Leeds Institute for Data Analytics and School of Geography, University of Leeds, Leeds LS2 9JT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Birkin","sequence":"additional","affiliation":[{"name":"Leeds Institute for Data Analytics and School of Geography, University of Leeds, Leeds LS2 9JT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/S0140-6736(12)61031-9","article-title":"Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy","volume":"380","author":"Lee","year":"2012","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12889-019-6424-y","article-title":"Associations between social isolation, loneliness, and objective physical activity in older men and women","volume":"19","author":"Schrempft","year":"2019","journal-title":"BMC Public Health"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1136\/bjsports-2021-104080","article-title":"Physical inactivity is associated with a higher risk for severe COVID-19 outcomes: A study in 48,440 adult patients","volume":"55","author":"Sallis","year":"2021","journal-title":"Br. 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