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      Comparability of accelerometer signal aggregation metrics across placements and dominant wrist cut points for the assessment of physical activity in adults

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          Abstract

          Large epidemiological studies that use accelerometers for physical behavior and sleep assessment differ in the location of the accelerometer attachment and the signal aggregation metric chosen. This study aimed to assess the comparability of acceleration metrics between commonly-used body-attachment locations for 24 hours, waking and sleeping hours, and to test comparability of PA cut points between dominant and non-dominant wrist. Forty-five young adults (23 women, 18–41 years) were included and GT3X + accelerometers (ActiGraph, Pensacola, FL, USA) were placed on their right hip, dominant, and non-dominant wrist for 7 days. We derived Euclidean Norm Minus One g (ENMO), Low-pass filtered ENMO (LFENMO), Mean Amplitude Deviation (MAD) and ActiGraph activity counts over 5-second epochs from the raw accelerations. Metric values were compared using a correlation analysis, and by plotting the differences by time of the day. Cut points for the dominant wrist were derived using Lin’s concordance correlation coefficient optimization in a grid of possible thresholds, using the non-dominant wrist estimates as reference. They were cross-validated in a separate sample (N = 36, 10 women, 22–30 years). Shared variances between pairs of acceleration metrics varied across sites and metric pairs (range in r 2: 0.19–0.97, all p < 0.01), suggesting that some sites and metrics are associated, and others are not. We observed higher metric values in dominant vs. non-dominant wrist, thus, we developed cut points for dominant wrist based on ENMO to classify sedentary time (<50 m g), light PA (50–110 m g), moderate PA (110–440 m g) and vigorous PA (≥440 m g). Our findings suggest differences between dominant and non-dominant wrist, and we proposed new cut points to attenuate these differences. ENMO and LFENMO were the most similar metrics, and they showed good comparability with MAD. However, counts were not comparable with ENMO, LFENMO and MAD.

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          GGIR: A Research Community–Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data

          Recent technological advances have transformed the research on physical activity initially based on questionnaire data to the most recent objective data from accelerometers. The shift to availability of raw accelerations has increased measurement accuracy, transparency, and the potential for data harmonization. However, it has also shifted the need for considerable processing expertise to the researcher. Many users do not have this expertise. The R package GGIR has been made available to all as a tool to convermulti-day high resolution raw accelerometer data from wearable movement sensors into meaningful evidence-based outcomes and insightful reports for the study of human daily physical activity and sleep. This paper aims to provide a one-stop overview of GGIR package, the papers underpinning the theory of GGIR, and how research contributes to the continued growth of the GGIR package. The package includes a range of literature-supported methods to clean the data and provide day-by-day, as well as full recording, weekly, weekend, and weekday estimates of physical activity and sleep parameters. In addition, the package also comes with a shell function that enables the user to process a set of input files and produce csv summary reports with a single function call, ideal for users less proficient in R. GGIR has been used in over 90 peer-reviewed scientific publications to date. The evolution of GGIR over time and widespread use across a range of research areas highlights the importance of open source software development for the research community and advancing methods in physical behavior research.
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            ActiGraph GT3X+ cut-points for identifying sedentary behaviour in older adults in free-living environments.

            To determine the ActiGraph GT3X+ cut-points with the highest accuracy for estimating time spent in sedentary behaviour in older adults in free-living environments. ActivPAL(3)™ was used as the reference standard. Cross-sectional study. 37 participants (13 males and 24 females, 73.5 ± 7.3 years old) wore an ActiGraph GT3X+ and an ActivPAL(3)™ for 7 consecutive days. For ActivPAL(3)™, variables were created based on posture. For ActiGraph GT3X+, sedentary behaviour was defined as (1) vector magnitude and (2) vertical axis counts for 1-s, 15-s and 1-min epochs, with cut-points for 1-s epochs of <1 to <10 counts, for 15-s epochs of <1 to <100 counts and for 1-min epochs of <1 to <400 counts. For each of the ActiGraph GT3X+ cut-points, area under the receiver operating characteristic curve (area under the curve), sensitivity, specificity, and percentage correctly classified were calculated. Bias and 95% limits of agreement were calculated using the Bland-Altman method. The highest areas under the curve were obtained for the vector magnitude cut-points: <1 count/s, <70 counts/15-s, and <200 counts/min; and for the vertical axis cut-points: <1 count/s, <10 counts/15-s and <25 counts/min. Mean biases ranged from -4.29 to 124.28 min/day. The 95% limits of agreement for these cut-points were ± 2 h suggesting great inter-individual variation. The results suggest that cut-points are dependent on unit of analyses (i.e. epoch length and axes); cut-points for a given epoch length and axis cannot simply be extrapolated to other epoch lengths. Limitations regarding inter-individual variability and misclassification of standing activity as sitting/lying must be considered. Copyright © 2013 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
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              Validation of Cut-Points for Evaluating the Intensity of Physical Activity with Accelerometry-Based Mean Amplitude Deviation (MAD)

              Purpose Our recent study of three accelerometer brands in various ambulatory activities showed that the mean amplitude deviation (MAD) of the resultant acceleration signal performed best in separating different intensity levels and provided excellent agreement between the three devices. The objective of this study was to derive a regression model that estimates oxygen consumption (VO2) from MAD values and validate the MAD-based cut-points for light, moderate and vigorous locomotion against VO2 within a wide range of speeds. Methods 29 participants performed a pace-conducted non-stop test on a 200 m long indoor track. The initial speed was 0.6 m/s and it was increased by 0.4 m/s every 2.5 minutes until volitional exhaustion. The participants could freely decide whether they preferred to walk or run. During the test they carried a hip-mounted tri-axial accelerometer and mobile metabolic analyzer. The MAD was calculated from the raw acceleration data and compared to directly measured incident VO2. Cut-point between light and moderate activity was set to 3.0 metabolic equivalent (MET, 1 MET = 3.5 ml · kg-1 · min-1) and between moderate and vigorous activity to 6.0 MET as per standard use. Results The MAD and VO2 showed a very strong association. Within individuals, the range of r values was from 0.927 to 0.991 providing the mean r = 0.969. The optimal MAD cut-point for 3.0 MET was 91 mg (milligravity) and 414 mg for 6.0 MET. Conclusion The present study showed that the MAD is a valid method in terms of the VO2 within a wide range of ambulatory activities from slow walking to fast running. Being a device-independent trait, the MAD facilitates directly comparable, accurate results on the intensity of physical activity with all accelerometers providing tri-axial raw data.
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                Author and article information

                Contributors
                jairohm@ugr.es
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                3 December 2019
                3 December 2019
                2019
                : 9
                : 18235
                Affiliations
                [1 ]ISNI 0000000121678994, GRID grid.4489.1, PROFITH “PROmoting FITness and Health through physical activity” Research Group, , Sport and Health University Research Institute (iMUDS), Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Ctra. Alfacar s/n, ; 18011 Granada, Spain
                [2 ]Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK
                [3 ]NIHR Leicester Biomedical Research Centre, Leicester, UK
                [4 ]ISNI 0000 0000 8994 5086, GRID grid.1026.5, Alliance for Research in Exercise, Nutrition and Activity (ARENA), Sansom Institute for Health Research, Division of Health Sciences, , University of South Australia, ; Adelaide, Australia
                [5 ]ISNI 0000 0004 1937 0626, GRID grid.4714.6, Department of Biosciences and Nutrition, , Karolinska Institutet, ; Huddinge, Sweden
                [6 ]ISNI 0000 0000 9372 4913, GRID grid.419475.a, Laboratory of Epidemiology and Population Science, , National Institute on Aging, ; Bethesda, MD USA
                [7 ]ISNI 0000 0001 2173 3359, GRID grid.261112.7, Center for Cognitive and Brain Health, Department of Psychology, , Northeastern University, ; Boston, MA USA
                [8 ]ISNI 0000 0000 8567 2092, GRID grid.412285.8, Department of Sport Medicine, , Norwegian School of Sport Sciences, ; Oslo, Norway
                [9 ]GRID grid.454309.f, Netherlands eScience Center, ; Amsterdam, The Netherlands
                [10 ]ISNI 0000 0001 2162 9922, GRID grid.5640.7, Department of Medical and Health Sciences, Linköping University, ; Linköping, Sweden
                Author information
                http://orcid.org/0000-0003-0366-6935
                http://orcid.org/0000-0002-1463-697X
                http://orcid.org/0000-0003-0723-876X
                Article
                54267
                10.1038/s41598-019-54267-y
                6890686
                31796778
                5cd10349-35f4-47ce-ae4a-66b52ab85000
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 20 March 2019
                : 10 November 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100003176, Ministerio de Educación, Cultura y Deporte (Ministry of Education, Culture and Sports, Spain);
                Award ID: FPU15/02645
                Award ID: FPU16/02760
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100003329, Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness);
                Award ID: BES-2014-068829
                Award ID: DEP2013-47540
                Award ID: RYC-2011-09011
                Award ID: DEP2016-79512-R
                Award Recipient :
                Funded by: NIHR Leicester Biomedical Research Centre Collaboration for leadership in Applied Health Research and Care
                Funded by: FundRef https://doi.org/10.13039/100008062, Fundación Alicia Koplowitz (Alicia Koplowitz Foundation);
                Funded by: FundRef https://doi.org/10.13039/501100005416, Norges Forskningsråd (Research Council of Norway);
                Award ID: 249932/F20
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100006393, Universidad de Granada (University of Granada);
                Funded by: FundRef https://doi.org/10.13039/501100002878, Consejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía (Ministry of Economy, Innovation, Science and Employment, Government of Andalucia);
                Award ID: SOMM17/6107/UGR
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010686, EC | EU Framework Programme for Research and Innovation H2020 | H2020 European Institute of Innovation and Technology (H2020 The European Institute of Innovation and Technology);
                Award ID: 667302
                Award Recipient :
                Funded by: EXERNET Research Network on Exercise and Health in Special Populations
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                occupational health,epidemiology
                Uncategorized
                occupational health, epidemiology

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