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      Comparison of wrist-worn Fitbit Flex and waist-worn ActiGraph for measuring steps in free-living adults

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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

          Introduction

          Accelerometers are commonly used to assess physical activity. Consumer activity trackers have become increasingly popular today, such as the Fitbit. This study aimed to compare the average number of steps per day using the wrist-worn Fitbit Flex and waist-worn ActiGraph (wGT3X-BT) in free-living conditions.

          Methods

          104 adult participants (n = 35 males; n = 69 females) were asked to wear a Fitbit Flex and an ActiGraph concurrently for 7 days. Daily step counts were used to classify inactive (<10,000 steps) and active (≥10,000 steps) days, which is one of the commonly used physical activity guidelines to maintain health. Proportion of agreement between physical activity categorizations from ActiGraph and Fitbit Flex was assessed. Statistical analyses included Spearman’s rho, intraclass correlation (ICC), median absolute percentage error (MAPE), Kappa statistics, and Bland-Altman plots. Analyses were performed among all participants, by each step-defined daily physical activity category and gender.

          Results

          The median average steps/day recorded by Fitbit Flex and ActiGraph were 10193 and 8812, respectively. Strong positive correlations and agreement were found for all participants, both genders, as well as daily physical activity categories (Spearman's rho: 0.76–0.91; ICC: 0.73–0.87). The MAPE was: 15.5% (95% confidence interval [CI]: 5.8–28.1%) for overall steps, 16.9% (6.8–30.3%) vs. 15.1% (4.5–27.3%) in males and females, and 20.4% (8.7–35.9%) vs. 9.6% (1.0–18.4%) during inactive days and active days. Bland-Altman plot indicated a median overestimation of 1300 steps/day by the Fitbit Flex in all participants. Fitbit Flex and ActiGraph respectively classified 51.5% and 37.5% of the days as active (Kappa: 0.66).

          Conclusions

          There were high correlations and agreement in steps between Fitbit Flex and ActiGraph. However, findings suggested discrepancies in steps between devices. This imposed a challenge that needs to be considered when using Fibit Flex in research and health promotion programs.

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          Most cited references 28

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          How many steps/day are enough? Preliminary pedometer indices for public health.

          Pedometers are simple and inexpensive body-worn motion sensors that are readily being used by researchers and practitioners to assess and motivate physical activity behaviours. Pedometer-determined physical activity indices are needed to guide their efforts. Therefore, the purpose of this article is to review the rationale and evidence for general pedometer-based indices for research and practice purposes. Specifically, we evaluate popular recommendations for steps/day and attempt to translate existing physical activity guidelines into steps/day equivalents. Also, we appraise the fragmented evidence currently available from associations derived from cross-sectional studies and a limited number of interventions that have documented improvements (primarily in body composition and/or blood pressure) with increased steps/day.A value of 10000 steps/day is gaining popularity with the media and in practice and can be traced to Japanese walking clubs and a business slogan 30+ years ago. 10000 steps/day appears to be a reasonable estimate of daily activity for apparently healthy adults and studies are emerging documenting the health benefits of attaining similar levels. Preliminary evidence suggests that a goal of 10000 steps/day may not be sustainable for some groups, including older adults and those living with chronic diseases. Another concern about using 10000 steps/day as a universal step goal is that it is probably too low for children, an important target population in the war against obesity. Other approaches to pedometer-determined physical activity recommendations that are showing promise of health benefit and individual sustainability have been based on incremental improvements relative to baseline values. Based on currently available evidence, we propose the following preliminary indices be used to classify pedometer-determined physical activity in healthy adults: (i). or=10000 steps/day indicates the point that should be used to classify individuals as 'active'. Individuals who take >12500 steps/day are likely to be classified as 'highly active'.
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            Increasing Physical Activity With Mobile Devices: A Meta-Analysis

            Background Regular physical activity has established physical and mental health benefits; however, merely one quarter of the U.S. adult population meets national physical activity recommendations. In an effort to engage individuals who do not meet these guidelines, researchers have utilized popular emerging technologies, including mobile devices (ie, personal digital assistants [PDAs], mobile phones). This study is the first to synthesize current research focused on the use of mobile devices for increasing physical activity. Objective To conduct a meta-analysis of research utilizing mobile devices to influence physical activity behavior. The aims of this review were to: (1) examine the efficacy of mobile devices in the physical activity setting, (2) explore and discuss implementation of device features across studies, and (3) make recommendations for future intervention development. Methods We searched electronic databases (PubMed, PsychINFO, SCOPUS) and identified publications through reference lists and requests to experts in the field of mobile health. Studies were included that provided original data and aimed to influence physical activity through dissemination or collection of intervention materials with a mobile device. Data were extracted to calculate effect sizes for individual studies, as were study descriptives. A random effects meta-analysis was conducted using the Comprehensive Meta-Analysis software suite. Study quality was assessed using the quality of execution portion of the Guide to Community Preventative Services data extraction form. Results Four studies were of “good” quality and seven of “fair” quality. In total, 1351 individuals participated in 11 unique studies from which 18 effects were extracted and synthesized, yielding an overall weight mean effect size of g = 0.54 (95% CI = 0.17 to 0.91, P = .01). Conclusions Research utilizing mobile devices is gaining in popularity, and this study suggests that this platform is an effective means for influencing physical activity behavior. Our focus must be on the best possible use of these tools to measure and understand behavior. Therefore, theoretically grounded behavior change interventions that recognize and act on the potential of smartphone technology could provide investigators with an effective tool for increasing physical activity.
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              ActiGraph and Actical physical activity monitors: a peek under the hood.

              Since the 1980s, accelerometer-based activity monitors have been used by researchers to quantify physical activity. The technology of these monitors has continuously evolved. For example, changes have been made to monitor hardware (type of sensor (e.g., piezoelectric, piezoresistive, capacitive)) and output format (counts vs raw signal). Commonly used activity monitors belong to the ActiGraph and the Actical families. This article presents information on several electromechanical aspects of these commonly used activity monitors. The majority of the article focuses on the evolution of the ActiGraph activity monitor by describing the differences among the 7164, the GT1M, and the GT3X models. This is followed by brief descriptions of the influences of device firmware and monitor calibration status. We also describe the Actical, but the discussion is short because this device has not undergone any major changes since it was first introduced. This article may help researchers gain a better understanding of the functioning of activity monitors. For example, a common misconception among physical activity researchers is that the ActiGraph GT1M and GT3X are piezoelectric sensor-based monitors. Thus, this information may also help researchers to describe these monitors more accurately in scientific publications.
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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
                24 February 2017
                2017
                : 12
                : 2
                Affiliations
                [1 ]Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
                [2 ]Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore
                [3 ]PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, Brunei Darussalam
                [4 ]Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, Ontario, Canada
                [5 ]Institute of Social Medicine, Epidemiology and Health Economics, Charité University Medical Centre Berlin, Berlin, Germany
                Vanderbilt University, UNITED STATES
                Author notes

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

                • Conceptualization: AC FMR.

                • Data curation: AC FMR AG.

                • Formal analysis: AC FMR SN AG.

                • Funding acquisition: FMR.

                • Investigation: AC FMR.

                • Methodology: AC FMR SN.

                • Project administration: AC FMR.

                • Resources: AC FMR MSB MP AG.

                • Software: AC FMR SN AG MSB.

                • Supervision: FMR DK.

                • Validation: AC FMR.

                • Visualization: AC.

                • Writing – original draft: AC FMR SN DK.

                • Writing – review & editing: AC FMR SN DK.

                Article
                PONE-D-16-33057
                10.1371/journal.pone.0172535
                5325470
                28234953
                © 2017 Chu 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: 3, Tables: 3, Pages: 13
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001352, National University of Singapore;
                Award ID: WBS: R-608-000-117-646
                Award Recipient :
                This research was supported by a grant WBS: R-608-000-117-646 from the National University of Singapore.
                Categories
                Research Article
                Medicine and Health Sciences
                Public and Occupational Health
                Physical Activity
                Engineering and Technology
                Electronics
                Accelerometers
                Biology and Life Sciences
                Biomechanics
                Biological Locomotion
                Walking
                Biology and Life Sciences
                Physiology
                Biological Locomotion
                Walking
                Medicine and Health Sciences
                Physiology
                Biological Locomotion
                Walking
                Medicine and Health Sciences
                Public and Occupational Health
                Health Promotion
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Limbs (Anatomy)
                Arms
                Wrist
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Limbs (Anatomy)
                Arms
                Wrist
                Engineering and Technology
                Equipment
                Measurement Equipment
                Biology and Life Sciences
                Behavior
                Recreation
                Sports
                Biology and Life Sciences
                Sports Science
                Sports
                Medicine and Health Sciences
                Public and Occupational Health
                Global Health
                Custom metadata
                Due to ethical restrictions set by the National University of Singapore Institutional Review Board, study data cannot be made publicly available. Requests for data may be sent to Anne Chu (email: anne.chu@ 123456u.nus.edu ), Falk Müller-Riemenschneider (email: ephmf@ 123456nus.edu.sg ) or the National University of Singapore Institutional Review Board (email: irb@ 123456nus.edu.sg ).

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