Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study
Physical activity has not been objectively measured in prospective cohorts with sufficiently large numbers to reliably detect associations with multiple health outcomes. Technological advances now make this possible. We describe the methods used to collect and analyse accelerometer measured physical...
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| Veröffentlicht in: | PloS one 2017-02, Vol.12 (2), p.e0169649-e0169649 |
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| creator | Doherty, Aiden Jackson, Dan Hammerla, Nils Plötz, Thomas Olivier, Patrick Granat, Malcolm H White, Tom van Hees, Vincent T Trenell, Michael I Owen, Christoper G Preece, Stephen J Gillions, Rob Sheard, Simon Peakman, Tim Brage, Soren Wareham, Nicholas J |
| description | Physical activity has not been objectively measured in prospective cohorts with sufficiently large numbers to reliably detect associations with multiple health outcomes. Technological advances now make this possible. We describe the methods used to collect and analyse accelerometer measured physical activity in over 100,000 participants of the UK Biobank study, and report variation by age, sex, day, time of day, and season.
Participants were approached by email to wear a wrist-worn accelerometer for seven days that was posted to them. Physical activity information was extracted from 100Hz raw triaxial acceleration data after calibration, removal of gravity and sensor noise, and identification of wear / non-wear episodes. We report age- and sex-specific wear-time compliance and accelerometer measured physical activity, overall and by hour-of-day, week-weekend day and season.
103,712 datasets were received (44.8% response), with a median wear-time of 6.9 days (IQR:6.5-7.0). 96,600 participants (93.3%) provided valid data for physical activity analyses. Vector magnitude, a proxy for overall physical activity, was 7.5% (2.35mg) lower per decade of age (Cohen's d = 0.9). Women had a higher vector magnitude than men, apart from those aged 45-54yrs. There were major differences in vector magnitude by time of day (d = 0.66). Vector magnitude differences between week and weekend days (d = 0.12 for men, d = 0.09 for women) and between seasons (d = 0.27 for men, d = 0.15 for women) were small.
It is feasible to collect and analyse objective physical activity data in large studies. The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening. This work lays the foundation for studies of physical activity and its health consequences. Our summary variables are part of the UK Biobank dataset and can be used by researchers as exposures, confounding factors or outcome variables in future analyses. |
| doi_str_mv | 10.1371/journal.pone.0169649 |
| format | Article |
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Participants were approached by email to wear a wrist-worn accelerometer for seven days that was posted to them. Physical activity information was extracted from 100Hz raw triaxial acceleration data after calibration, removal of gravity and sensor noise, and identification of wear / non-wear episodes. We report age- and sex-specific wear-time compliance and accelerometer measured physical activity, overall and by hour-of-day, week-weekend day and season.
103,712 datasets were received (44.8% response), with a median wear-time of 6.9 days (IQR:6.5-7.0). 96,600 participants (93.3%) provided valid data for physical activity analyses. Vector magnitude, a proxy for overall physical activity, was 7.5% (2.35mg) lower per decade of age (Cohen's d = 0.9). Women had a higher vector magnitude than men, apart from those aged 45-54yrs. There were major differences in vector magnitude by time of day (d = 0.66). Vector magnitude differences between week and weekend days (d = 0.12 for men, d = 0.09 for women) and between seasons (d = 0.27 for men, d = 0.15 for women) were small.
It is feasible to collect and analyse objective physical activity data in large studies. The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening. This work lays the foundation for studies of physical activity and its health consequences. Our summary variables are part of the UK Biobank dataset and can be used by researchers as exposures, confounding factors or outcome variables in future analyses.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0169649</identifier><identifier>PMID: 28146576</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accelerometers ; Accelerometry ; Age ; Aged ; Analysis ; Biological Specimen Banks ; Biology and Life Sciences ; Calibration ; Computer and Information Sciences ; Data processing ; Engineering and Technology ; Epidemiology ; Exercise ; Feasibility studies ; Female ; Gravitation ; Health aspects ; Health sciences ; Humans ; Male ; Measurement ; Medicine and Health Sciences ; Men ; Middle Aged ; People and Places ; Physical activity ; Physical Sciences ; Population ; Population health ; Public Health Surveillance ; Research and Analysis Methods ; Seasons ; Sex ; Studies ; Time Factors ; Time of use ; United Kingdom ; Wear ; Wrist ; Wrist Joint - physiology</subject><ispartof>PloS one, 2017-02, Vol.12 (2), p.e0169649-e0169649</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Doherty et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2017 Doherty et al 2017 Doherty et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c791t-4a0093c11384e1dd8c820504a0d03cfaf8435235123ea8c6e50b8fcadca91bcf3</citedby><cites>FETCH-LOGICAL-c791t-4a0093c11384e1dd8c820504a0d03cfaf8435235123ea8c6e50b8fcadca91bcf3</cites><orcidid>0000-0003-1422-2993 ; 0000-0001-8456-0803 ; 0000-0003-2841-7580 ; 0000-0002-8925-8280 ; 0000-0002-1243-7563 ; 0000-0003-1840-0451 ; 0000-0003-0914-8370</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5287488/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5287488/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28146576$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Doherty, Aiden</creatorcontrib><creatorcontrib>Jackson, Dan</creatorcontrib><creatorcontrib>Hammerla, Nils</creatorcontrib><creatorcontrib>Plötz, Thomas</creatorcontrib><creatorcontrib>Olivier, Patrick</creatorcontrib><creatorcontrib>Granat, Malcolm H</creatorcontrib><creatorcontrib>White, Tom</creatorcontrib><creatorcontrib>van Hees, Vincent T</creatorcontrib><creatorcontrib>Trenell, Michael I</creatorcontrib><creatorcontrib>Owen, Christoper G</creatorcontrib><creatorcontrib>Preece, Stephen J</creatorcontrib><creatorcontrib>Gillions, Rob</creatorcontrib><creatorcontrib>Sheard, Simon</creatorcontrib><creatorcontrib>Peakman, Tim</creatorcontrib><creatorcontrib>Brage, Soren</creatorcontrib><creatorcontrib>Wareham, Nicholas J</creatorcontrib><title>Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Physical activity has not been objectively measured in prospective cohorts with sufficiently large numbers to reliably detect associations with multiple health outcomes. Technological advances now make this possible. We describe the methods used to collect and analyse accelerometer measured physical activity in over 100,000 participants of the UK Biobank study, and report variation by age, sex, day, time of day, and season.
Participants were approached by email to wear a wrist-worn accelerometer for seven days that was posted to them. Physical activity information was extracted from 100Hz raw triaxial acceleration data after calibration, removal of gravity and sensor noise, and identification of wear / non-wear episodes. We report age- and sex-specific wear-time compliance and accelerometer measured physical activity, overall and by hour-of-day, week-weekend day and season.
103,712 datasets were received (44.8% response), with a median wear-time of 6.9 days (IQR:6.5-7.0). 96,600 participants (93.3%) provided valid data for physical activity analyses. Vector magnitude, a proxy for overall physical activity, was 7.5% (2.35mg) lower per decade of age (Cohen's d = 0.9). Women had a higher vector magnitude than men, apart from those aged 45-54yrs. There were major differences in vector magnitude by time of day (d = 0.66). Vector magnitude differences between week and weekend days (d = 0.12 for men, d = 0.09 for women) and between seasons (d = 0.27 for men, d = 0.15 for women) were small.
It is feasible to collect and analyse objective physical activity data in large studies. The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening. This work lays the foundation for studies of physical activity and its health consequences. Our summary variables are part of the UK Biobank dataset and can be used by researchers as exposures, confounding factors or outcome variables in future analyses.</description><subject>Accelerometers</subject><subject>Accelerometry</subject><subject>Age</subject><subject>Aged</subject><subject>Analysis</subject><subject>Biological Specimen Banks</subject><subject>Biology and Life Sciences</subject><subject>Calibration</subject><subject>Computer and Information Sciences</subject><subject>Data processing</subject><subject>Engineering and Technology</subject><subject>Epidemiology</subject><subject>Exercise</subject><subject>Feasibility studies</subject><subject>Female</subject><subject>Gravitation</subject><subject>Health aspects</subject><subject>Health sciences</subject><subject>Humans</subject><subject>Male</subject><subject>Measurement</subject><subject>Medicine and Health Sciences</subject><subject>Men</subject><subject>Middle Aged</subject><subject>People and Places</subject><subject>Physical activity</subject><subject>Physical Sciences</subject><subject>Population</subject><subject>Population health</subject><subject>Public Health Surveillance</subject><subject>Research and Analysis Methods</subject><subject>Seasons</subject><subject>Sex</subject><subject>Studies</subject><subject>Time Factors</subject><subject>Time of use</subject><subject>United Kingdom</subject><subject>Wear</subject><subject>Wrist</subject><subject>Wrist Joint - 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Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study</title><author>Doherty, Aiden ; Jackson, Dan ; Hammerla, Nils ; Plötz, Thomas ; Olivier, Patrick ; Granat, Malcolm H ; White, Tom ; van Hees, Vincent T ; Trenell, Michael I ; Owen, Christoper G ; Preece, Stephen J ; Gillions, Rob ; Sheard, Simon ; Peakman, Tim ; Brage, Soren ; Wareham, Nicholas J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c791t-4a0093c11384e1dd8c820504a0d03cfaf8435235123ea8c6e50b8fcadca91bcf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Accelerometers</topic><topic>Accelerometry</topic><topic>Age</topic><topic>Aged</topic><topic>Analysis</topic><topic>Biological Specimen Banks</topic><topic>Biology and Life Sciences</topic><topic>Calibration</topic><topic>Computer and Information Sciences</topic><topic>Data processing</topic><topic>Engineering 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Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2017-02-01</date><risdate>2017</risdate><volume>12</volume><issue>2</issue><spage>e0169649</spage><epage>e0169649</epage><pages>e0169649-e0169649</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Physical activity has not been objectively measured in prospective cohorts with sufficiently large numbers to reliably detect associations with multiple health outcomes. Technological advances now make this possible. We describe the methods used to collect and analyse accelerometer measured physical activity in over 100,000 participants of the UK Biobank study, and report variation by age, sex, day, time of day, and season.
Participants were approached by email to wear a wrist-worn accelerometer for seven days that was posted to them. Physical activity information was extracted from 100Hz raw triaxial acceleration data after calibration, removal of gravity and sensor noise, and identification of wear / non-wear episodes. We report age- and sex-specific wear-time compliance and accelerometer measured physical activity, overall and by hour-of-day, week-weekend day and season.
103,712 datasets were received (44.8% response), with a median wear-time of 6.9 days (IQR:6.5-7.0). 96,600 participants (93.3%) provided valid data for physical activity analyses. Vector magnitude, a proxy for overall physical activity, was 7.5% (2.35mg) lower per decade of age (Cohen's d = 0.9). Women had a higher vector magnitude than men, apart from those aged 45-54yrs. There were major differences in vector magnitude by time of day (d = 0.66). Vector magnitude differences between week and weekend days (d = 0.12 for men, d = 0.09 for women) and between seasons (d = 0.27 for men, d = 0.15 for women) were small.
It is feasible to collect and analyse objective physical activity data in large studies. The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening. This work lays the foundation for studies of physical activity and its health consequences. Our summary variables are part of the UK Biobank dataset and can be used by researchers as exposures, confounding factors or outcome variables in future analyses.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28146576</pmid><doi>10.1371/journal.pone.0169649</doi><tpages>e0169649</tpages><orcidid>https://orcid.org/0000-0003-1422-2993</orcidid><orcidid>https://orcid.org/0000-0001-8456-0803</orcidid><orcidid>https://orcid.org/0000-0003-2841-7580</orcidid><orcidid>https://orcid.org/0000-0002-8925-8280</orcidid><orcidid>https://orcid.org/0000-0002-1243-7563</orcidid><orcidid>https://orcid.org/0000-0003-1840-0451</orcidid><orcidid>https://orcid.org/0000-0003-0914-8370</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2017-02, Vol.12 (2), p.e0169649-e0169649 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1863565103 |
| source | Public Library of Science (PLoS) Journals Open Access; MEDLINE; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Accelerometers Accelerometry Age Aged Analysis Biological Specimen Banks Biology and Life Sciences Calibration Computer and Information Sciences Data processing Engineering and Technology Epidemiology Exercise Feasibility studies Female Gravitation Health aspects Health sciences Humans Male Measurement Medicine and Health Sciences Men Middle Aged People and Places Physical activity Physical Sciences Population Population health Public Health Surveillance Research and Analysis Methods Seasons Sex Studies Time Factors Time of use United Kingdom Wear Wrist Wrist Joint - physiology |
| title | Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-05T04%3A23%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Large%20Scale%20Population%20Assessment%20of%20Physical%20Activity%20Using%20Wrist%20Worn%20Accelerometers:%20The%20UK%20Biobank%20Study&rft.jtitle=PloS%20one&rft.au=Doherty,%20Aiden&rft.date=2017-02-01&rft.volume=12&rft.issue=2&rft.spage=e0169649&rft.epage=e0169649&rft.pages=e0169649-e0169649&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0169649&rft_dat=%3Cgale_plos_%3EA481039921%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1863565103&rft_id=info:pmid/28146576&rft_galeid=A481039921&rft_doaj_id=oai_doaj_org_article_ccd18cb32c254941b0dca48a1b08c10e&rfr_iscdi=true |