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      Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research

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          Abstract

          Researchers have increasingly begun to use consumer wearables or wrist-worn smartwatches and fitness monitors for measurement of cardiovascular psychophysiological processes related to mental and physical health outcomes. These devices have strong appeal because they allow for continuous, scalable, unobtrusive, and ecologically valid data collection of cardiac activity in “big data” studies. However, replicability and reproducibility may be hampered moving forward due to the lack of standardization of data collection and processing procedures, and inconsistent reporting of technological factors (e.g., device type, firmware versions, and sampling rate), biobehavioral variables (e.g., body mass index, wrist dominance and circumference), and participant demographic characteristics, such as skin tone, that may influence heart rate measurement. These limitations introduce unnecessary noise into measurement, which can cloud interpretation and generalizability of findings. This paper provides a brief overview of research using commercial wearable devices to measure heart rate, reviews literature on device accuracy, and outlines the challenges that non-standardized reporting pose for the field. We also discuss study design, technological, biobehavioral, and demographic factors that can impact the accuracy of the passive sensing of heart rate measurements, and provide guidelines and corresponding checklist handouts for future study data collection and design, data cleaning and processing, analysis, and reporting that may help ameliorate some of these barriers and inconsistencies in the literature.

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            This review presents a practical summary of the missing data literature, including a sketch of missing data theory and descriptions of normal-model multiple imputation (MI) and maximum likelihood methods. Practical missing data analysis issues are discussed, most notably the inclusion of auxiliary variables for improving power and reducing bias. Solutions are given for missing data challenges such as handling longitudinal, categorical, and clustered data with normal-model MI; including interactions in the missing data model; and handling large numbers of variables. The discussion of attrition and nonignorable missingness emphasizes the need for longitudinal diagnostics and for reducing the uncertainty about the missing data mechanism under attrition. Strategies suggested for reducing attrition bias include using auxiliary variables, collecting follow-up data on a sample of those initially missing, and collecting data on intent to drop out. Suggestions are given for moving forward with research on missing data and attrition.
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                Author and article information

                Contributors
                bwn@uoregon.edu
                Journal
                NPJ Digit Med
                NPJ Digit Med
                NPJ Digital Medicine
                Nature Publishing Group UK (London )
                2398-6352
                26 June 2020
                26 June 2020
                2020
                : 3
                : 90
                Affiliations
                [1 ]ISNI 0000 0004 1936 8008, GRID grid.170202.6, Department of Psychology, , University of Oregon, ; Eugene, OR USA
                [2 ]ISNI 0000000122986657, GRID grid.34477.33, Department of Psychiatry and Behavioral Sciences and Department of Rehabilitation Medicine, , University of Washington, ; Seattle, WA USA
                [3 ]ISNI 0000 0004 1936 9000, GRID grid.21925.3d, Department of Medicine, , University of Pittsburgh, ; Pittsburgh, PA USA
                [4 ]ISNI 0000 0001 2179 2404, GRID grid.254880.3, Geisel School of Medicine, Dartmouth College, ; Hanover, NH USA
                [5 ]ISNI 0000 0001 2179 2404, GRID grid.254880.3, Center for Technology and Behavioral Health, Dartmouth College, ; Hanover, NH USA
                [6 ]ISNI 0000000122986657, GRID grid.34477.33, Department of Psychiatry, , University of Washington, ; Seattle, WA USA
                [7 ]Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA USA
                Author information
                http://orcid.org/0000-0002-5474-7674
                http://orcid.org/0000-0002-3318-7495
                Article
                297
                10.1038/s41746-020-0297-4
                7320189
                32613085
                109b3a6b-7732-40a1-95e3-9a2bdb15fa42
                © The Author(s) 2020

                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
                : 30 January 2020
                : 4 June 2020
                Categories
                Review Article
                Custom metadata
                © The Author(s) 2020

                psychology,cardiovascular diseases,risk factors,biomarkers

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