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      Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study

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          Abstract

          Background

          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.

          Methods

          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.

          Results

          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% (2.35m g) 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.

          Conclusions

          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.

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          Most cited references4

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          Validation of the GENEA Accelerometer.

          The study aims were: 1) to assess the technical reliability and validity of the GENEA using a mechanical shaker; 2) to perform a GENEA value calibration to develop thresholds for sedentary and light-, moderate-, and vigorous-intensity physical activity; and 3) to compare the intensity classification of the GENEA with two widely used accelerometers. A total of 47 GENEA accelerometers were attached to a shaker and vertically accelerated, generating 15 conditions of varying acceleration and/or frequency. Reliability was calculated using SD and intrainstrument and interinstrument coefficients of variation, whereas validity was assessed using Pearson correlation with the shaker acceleration as the criterion. Next, 60 adults wore a GENEA on each wrist and on the waist (alongside an ActiGraph and RT3 accelerometer) while completing 10-12 activity tasks. A portable metabolic gas analyzer provided the criterion measure of physical activity. Analyses involved the use of Pearson correlations to establish criterion and concurrent validity and receiver operating characteristic curves to establish intensity cut points. The GENEA demonstrated excellent technical reliability (CVintra = 1.4%, CVinter = 2.1%) and validity (r = 0.98, P < 0.001) using the mechanical shaker. The GENEA demonstrated excellent criterion validity using VO2 as the criterion (left wrist, r = 0.86; right wrist, r = 0.83; waist, r = 0.87), on par with the waist-worn ActiGraph and RT3. The GENEA demonstrated excellent concurrent validity compared with the ActiGraph (r = 0.92) and the RT3 (r = 0.97). The waist-worn GENEA had the greatest classification accuracy (area under the receiver operating characteristic curve (AUC) = 0.95), followed by the left (AUC = 0.93) and then the right wrist (AUC = 0.90). The accuracy of the waist-worn GENEA was virtually identical with that of the ActiGraph (AUC = 0.94) and RT3 (AUC = 0.95). The GENEA is a reliable and valid measurement tool capable of classifying the intensity of physical activity in adults.
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            The descriptive epidemiology of accelerometer-measured physical activity in older adults

            Background Objectively measured physical activity between older individuals and between populations has been poorly described. We aimed to describe and compare the variation in accelerometry data in older UK (EPIC-Norfolk) and American (NHANES) adults. Methods Physical activity was measured by uniaxial accelerometry in 4,052 UK (49–91 years) and 3459 US older adults (49–85 years). We summarized physical activity as volume (average counts/minute), its underlying intensity distribution, and as time spent 809 counts/minute is used 18.7 % of people reached the 30 min/day threshold. By comparison, 2.5 % and 9.5 % of American older adults accumulated activity at these levels, respectively. Conclusion As assessed by objectively measured physical activity, the majority of older adults in this UK study did not meet current activity guidelines. Older adults in the UK were more active overall, but also spent more time being sedentary than US adults. Electronic supplementary material The online version of this article (doi:10.1186/s12966-015-0316-z) contains supplementary material, which is available to authorized users.
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              Waist-Worn Actigraphy: Population-Referenced Percentiles for Total Activity Counts in U.S. Adults.

              Accelerometer-derived total activity count is a measure of total physical activity (PA) volume. The purpose of this study was to develop age- and gender-specific percentiles for daily total activity counts (TAC), minutes of moderate-to-vigorous physical activity (MVPA), and minutes of light physical activity (LPA) in U.S. adults.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                1 February 2017
                2017
                : 12
                : 2
                : e0169649
                Affiliations
                [1 ]Big Data Institute, Nuffield Department of Population Health, BHF Centre of Research Excellence, University of Oxford, Oxford, United Kingdom
                [2 ]Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
                [3 ]Open Lab, Newcastle University, Newcastle, United Kingdom
                [4 ]School of Health Sciences, University of Salford, Manchester, United Kingdom
                [5 ]MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
                [6 ]MoveLab, Institute of Cellular Medicine, Newcastle University, Newcastle, United Kingdom
                [7 ]Population Health Research Institute, St George’s University of London, London, United Kingdom
                [8 ]UK Biobank, Stockport, United Kingdom
                Vanderbilt University, UNITED STATES
                Author notes

                Competing Interests: DJ is a director of Axivity Ltd who manufactured the accelerometer used in our study. PO has previously been a director of Axivity Ltd. NH has previously consulted for Axivity Ltd. The partners of DJ and PO own shares in Axivity. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                • Conceptualization: AD PO T. Peakman SB NJW.

                • Data curation: AD DJ NH TW.

                • Formal analysis: AD SB NJW.

                • Funding acquisition: AD T. Plötz PO T. Peakman SB NJW.

                • Investigation: AD DJ T. Plötz PO MG TW VTvH MIT CGO SJP RG SS T. Peakman SB NJW.

                • Methodology: AD DJ NH T. Plötz PO MG TW VTvH MIT CGO SJP SB NJW.

                • Project administration: RG SS T. Peakman.

                • Resources: AD PO RG SS T. Peakman.

                • Software: AD DJ NH TW.

                • Supervision: NJW.

                • Validation: AD DJ NH TW VTvH SB.

                • Visualization: AD.

                • Writing – original draft: AD SB NJW.

                • Writing – review & editing: AD DJ T. Plötz PO MG TW VTvH MIT CGO SJP RG SS T. Peakman SB NJW.

                ‡ These authors are joint last authors

                Author information
                http://orcid.org/0000-0003-1840-0451
                http://orcid.org/0000-0002-1243-7563
                http://orcid.org/0000-0003-2841-7580
                http://orcid.org/0000-0001-8456-0803
                http://orcid.org/0000-0003-0914-8370
                http://orcid.org/0000-0002-8925-8280
                http://orcid.org/0000-0003-1422-2993
                Article
                PONE-D-16-26249
                10.1371/journal.pone.0169649
                5287488
                28146576
                c37dd256-b119-4a2d-85ed-14b68c3844c5
                © 2017 Doherty et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 11 July 2016
                : 20 December 2016
                Page count
                Figures: 7, Tables: 1, Pages: 14
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Funded by: British Heart Foundation (GB)
                Award ID: RE/13/1/30181
                Award Recipient :
                Funded by: Li Ka Shing Foundation (HK)
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MC_UU_12015/1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MC_UU_12015/3
                Award Recipient :
                Funded by: Research Councils UK (GB)
                Award ID: EP/G066019/1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000266, Engineering and Physical Sciences Research Council;
                Award ID: EP/L016176/1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000272, National Institute for Health Research;
                Award ID: SRF-2011-04-017
                Award Recipient :
                The UK Biobank Activity Project and the collection of activity data from participants was funded by the Wellcome Trust ( https://wellcome.ac.uk/) and the Medical Research Council ( http://www.mrc.ac.uk/). The analysis was supported by the British Heart Foundation Centre of Research Excellence at Oxford ( http://www.cardioscience.ox.ac.uk/bhf-centre-of-research-excellence) [grant number RE/13/1/30181 to AD], the Li Ka Shing Foundation ( http://www.lksf.org/) [to AD], the UK Medical Research Council ( http://www.mrc.ac.uk/) [grant numbers MC_UU_12015/1 and MC_UU_12015/3 to NW and SB], the RCUK Digital Economy Research Hub on Social Inclusion through the Digital Economy (SiDE) ( http://www.rcuk.ac.uk/) [EP/G066019/1 to NH], the EPSRC Centre for Doctoral Training in Digital Civics ( https://www.epsrc.ac.uk/)[EP/L016176/1 to DJ], and the National Institute for Health Research ( http://www.nihr.ac.uk/) [SRF-2011-04-017 to MIT]. The MRC and Wellcome Trust played a key role in the decision to establish UK Biobank, and the accelerometer data collection. No funding bodies had any role in the analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Public and Occupational Health
                Physical Activity
                Engineering and Technology
                Electronics
                Accelerometers
                People and Places
                Population Groupings
                Age Groups
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Limbs (Anatomy)
                Arms
                Wrist
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Limbs (Anatomy)
                Arms
                Wrist
                Computer and Information Sciences
                Information Technology
                Data Processing
                Physical Sciences
                Physics
                Classical Mechanics
                Acceleration
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Analysis of Variance
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Analysis of Variance
                Biology and Life Sciences
                Biochemistry
                Bioenergetics
                Custom metadata
                The summary variables that we have constructed are now part of the UK Biobank dataset at http://biobank.ctsu.ox.ac.uk/crystal/label.cgi?id=1008. Furthermore, our analysis is freely available and hosted as an open source software project at https://github.com/activityMonitoring/biobankAccelerometerAnalysis

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