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      Estimating physical activity and sedentary behaviour in a free-living environment: A comparative study between Fitbit Charge 2 and Actigraph GT3X

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Activity trackers such as the Fitbit Charge 2 enable users and researchers to monitor physical activity in daily life, which could be beneficial for changing behaviour. However, the accuracy of the Fitbit Charge 2 in a free-living environment is largely unknown.

          Objective

          To investigate the agreement between Fitbit Charge 2 and ActiGraph GT3X for the estimation of steps, energy expenditure, time in sedentary behaviour, and light and moderate-to-vigorous physical activity under free-living conditions, and further examine to what extent placing the ActiGraph on the wrist as opposed to the hip would affect the findings.

          Methods

          41 adults (n = 10 males, n = 31 females) were asked to wear a Fitbit Charge 2 device and two ActiGraph GT3X devices (one on the hip and one on the wrist) for seven consecutive days and fill out a log of wear times. Agreement was assessed through Bland-Altman plots combined with multilevel analysis.

          Results

          The Fitbit measured 1,492 steps/day more than the hip-worn ActiGraph (limits of agreement [LoA] = -2,250; 5,234), while for sedentary time, it measured 25 min/day less (LoA = -137; 87). Both Bland-Altman plots showed fixed bias. For time in light physical activity, the Fitbit measured 59 min/day more (LoA = -52;169). For time in moderate-to-vigorous physical activity, the Fitbit measured 31 min/day less (LoA = -132; 71) and for activity energy expenditure it measured 408 kcal/day more than the hip-worn ActiGraph (LoA = -385; 1,200). For the two latter outputs, the plots indicated proportional bias. Similar or more pronounced discrepancies, mostly in opposite direction, appeared when comparing to the wrist-worn ActiGraph.

          Conclusion

          Moderate to substantial differences between devices were found for most outputs, which could be due to differences in algorithms. Caution should be taken if replacing one device with another and when comparing results.

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          Most cited references 37

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          Reliability and validity of ten consumer activity trackers

          Background Activity trackers can potentially stimulate users to increase their physical activity behavior. The aim of this study was to examine the reliability and validity of ten consumer activity trackers for measuring step count in both laboratory and free-living conditions. Method Healthy adult volunteers (n = 33) walked twice on a treadmill (4.8 km/h) for 30 min while wearing ten different activity trackers (i.e. Lumoback, Fitbit Flex, Jawbone Up, Nike+ Fuelband SE, Misfit Shine, Withings Pulse, Fitbit Zip, Omron HJ-203, Yamax Digiwalker SW-200 and Moves mobile application). In free-living conditions, 56 volunteers wore the same activity trackers for one working day. Test-retest reliability was analyzed with the Intraclass Correlation Coefficient (ICC). Validity was evaluated by comparing each tracker with the gold standard (Optogait system for laboratory and ActivPAL for free-living conditions), using paired samples t-tests, mean absolute percentage errors, correlations and Bland-Altman plots. Results Test-retest analysis revealed high reliability for most trackers except for the Omron (ICC .14), Moves app (ICC .37) and Nike+ Fuelband (ICC .53). The mean absolute percentage errors of the trackers in laboratory and free-living conditions respectively, were: Lumoback (−0.2, −0.4), Fibit Flex (−5.7, 3.7), Jawbone Up (−1.0, 1.4), Nike+ Fuelband (−18, −24), Misfit Shine (0.2, 1.1), Withings Pulse (−0.5, −7.9), Fitbit Zip (−0.3, 1.2), Omron (2.5, −0.4), Digiwalker (−1.2, −5.9), and Moves app (9.6, −37.6). Bland-Altman plots demonstrated that the limits of agreement varied from 46 steps (Fitbit Zip) to 2422 steps (Nike+ Fuelband) in the laboratory condition, and 866 steps (Fitbit Zip) to 5150 steps (Moves app) in the free-living condition. Conclusion The reliability and validity of most trackers for measuring step count is good. The Fitbit Zip is the most valid whereas the reliability and validity of the Nike+ Fuelband is low.
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            An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer.

            The aim of this investigation was to develop and test two artificial neural networks (ANN) to apply to physical activity data collected with a commonly used uniaxial accelerometer. The first ANN model estimated physical activity metabolic equivalents (METs), and the second ANN identified activity type. Subjects (n = 24 men and 24 women, mean age = 35 yr) completed a menu of activities that included sedentary, light, moderate, and vigorous intensities, and each activity was performed for 10 min. There were three different activity menus, and 20 participants completed each menu. Oxygen consumption (in ml x kg(-1) x min(-1)) was measured continuously, and the average of minutes 4-9 was used to represent the oxygen cost of each activity. To calculate METs, activity oxygen consumption was divided by 3.5 ml x kg(-1) x min(-1) (1 MET). Accelerometer data were collected second by second using the Actigraph model 7164. For the analysis, we used the distribution of counts (10th, 25th, 50th, 75th, and 90th percentiles of a minute's second-by-second counts) and temporal dynamics of counts (lag, one autocorrelation) as the accelerometer feature inputs to the ANN. To examine model performance, we used the leave-one-out cross-validation technique. The ANN prediction of METs root-mean-squared error was 1.22 METs (confidence interval: 1.14-1.30). For the prediction of activity type, the ANN correctly classified activity type 88.8% of the time (confidence interval: 86.4-91.2%). Activity types were low-level activities, locomotion, vigorous sports, and household activities/other activities. This novel approach of applying ANNs for processing Actigraph accelerometer data is promising and shows that we can successfully estimate activity METs and identify activity type using ANN analytic procedures.
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              The validation of Fibit Zip™ physical activity monitor as a measure of free-living physical activity

              Background The new generation of activity monitors allow users to upload their data to the internet and review progress. The aim of this study is to validate the Fitbit Zip as a measure of free-living physical activity. Findings Participants wore a Fitbit Zip, ActiGraph GT3X accelerometer and a Yamax CW700 pedometer for seven days. Participants were asked their opinion on the utility of the Fitbit Zip. Validity was assessed by comparing the output using Spearman’s rank correlation coefficients, Wilcoxon signed rank tests and Bland-Altman plots. 59.5% (25/47) of the cohort were female. There was a high correlation in steps/day between the Fitbit Zip and the two reference devices (r = 0.91, p < 0.001). No statistically significant difference between the Fitbit and Yamax steps/day was observed (Median (IQR) 7477 (3597) vs 6774 (3851); p = 0.11). The Fitbit measured significantly more steps/day than the Actigraph (7477 (3597) vs 6774 (3851); p < 0.001). Bland-Altman plots revealed no systematic differences between the devices. Conclusions Given the high level of correlation and no apparent systematic biases in the Bland Altman plots, the use of Fitbit Zip as a measure of physical activity. However the Fitbit Zip recorded a significantly higher number of steps per day than the Actigraph.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Writing – original draft
                Role: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                11 June 2020
                2020
                : 15
                : 6
                Affiliations
                [1 ] Research Unit for Dietary Studies, The Parker Institute, Bispebjerg and Frederiksberg Hospital, The Capital Region, København, Denmark
                [2 ] Department of Public Health, University of Southern Denmark, Esbjerg, Denmark
                [3 ] Biomathematics & Statistics Scotland (James Hutton Institute), Aberdeen, Scotland, United Kingdom
                [4 ] School of Psychology, Faculty of Medicine and Health, University of Leeds, Leeds, England, United Kingdom
                [5 ] Centro Interdisciplinar para o Estudo da Performance Humana, Faculdade de Motricidade Humana, Universidade de Lisboa, Lisbon, Portugal
                [6 ] The National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark
                [7 ] The Boden Institute of Obesity, Nutrition, Exercise & Eating Disorders, The University of Sydney, Sydney, Australia
                [8 ] Department of Public Health, Section for General Practice, University of Copenhagen, Copenhagen, Denmark
                Linneaus University, SWEDEN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Article
                PONE-D-19-24901
                10.1371/journal.pone.0234426
                7289355
                32525912
                © 2020 Mikkelsen 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.

                Page count
                Figures: 1, Tables: 2, Pages: 13
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100007601, Horizon 2020;
                Award ID: 643309
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100001275, Oak Foundation;
                Award ID: OCAY-18-774-OFIL
                The NoHoW study has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 643309. The Parker Institute is supported by a core grant from the Oak Foundation (grant agreement number: OCAY-18-774-OFIL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Body Limbs
                Arms
                Wrist
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Body Limbs
                Arms
                Wrist
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Pelvis
                Hip
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Pelvis
                Hip
                Medicine and Health Sciences
                Public and Occupational Health
                Physical Activity
                Engineering and Technology
                Electronics
                Accelerometers
                Biology and Life Sciences
                Biochemistry
                Bioenergetics
                Medicine and Health Sciences
                Public and Occupational Health
                Physical Activity
                Physical Fitness
                Exercise
                Medicine and Health Sciences
                Sports and Exercise Medicine
                Exercise
                Biology and Life Sciences
                Sports Science
                Sports and Exercise Medicine
                Exercise
                Medicine and Health Sciences
                Cardiology
                Heart Rate
                Research and Analysis Methods
                Research Facilities
                Research Laboratories
                Custom metadata
                There are legal restrictions on sharing data that contain potentially identifying or sensitive person information. The restrictions are imposed by The Danish Data Protection Agency ( https://www.datatilsynet.dk/english/). Data used in the current study will be made available upon request after application to the NoHoW data controller. The application procedure can be obtained from the Research Unit for Dietary Studies at The Parker Institute ( bfh-eek@ 123456regionh.dk ) or from the NoHoW data controller Graham Horgan ( Graham.Horgan@ 123456bioss.ac.uk ).

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