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      Real-world heart rate norms in the Health eHeart study

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          Abstract

          Emerging technology allows patients to measure and record their heart rate (HR) remotely by photoplethysmography (PPG) using smart devices like smartphones. However, the validity and expected distribution of such measurements are unclear, making it difficult for physicians to help patients interpret real-world, remote and on-demand HR measurements. Our goal was to validate HR-PPG, measured using a smartphone app, against HR-electrocardiogram (ECG) measurements and describe out-of-clinic, real-world, HR-PPG values according to age, demographics, body mass index, physical activity level, and disease. To validate the measurements, we obtained simultaneous HR-PPG and HR-ECG in 50 consecutive patients at our cardiology clinic. We then used data from participants enrolled in the Health eHeart cohort between 1 April 2014 and 30 April 2018 to derive real-world norms of HR-PPG according to demographics and medical conditions. HR-PPG and HR-ECG were highly correlated (Intraclass correlation = 0.90). A total of 66,788 Health eHeart Study participants contributed 3,144,332 HR-PPG measurements. The mean real-world HR was 79.1 bpm ± 14.5. The 95th percentile of real-world HR was ≤110 in individuals aged 18–45, ≤100 in those aged 45–60 and ≤95 bpm in individuals older than 60 years old. In multivariable linear regression, the number of medical conditions, female gender, increasing body mass index, and being Hispanic was associated with an increased HR, whereas increasing age was associated with a reduced HR. Our study provides the largest real-world norms for remotely obtained, real-world HR according to various strata and they may help physicians interpret and engage with patients presenting such data.

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          A direct comparison of wet, dry and insulating bioelectric recording electrodes.

          Alternatives to conventional wet electrode types are keenly sought for biomedical use and physiological research, especially when prolonged recording of biosignals is demanded. This paper describes a quantitative comparison of three types of bioelectrode (wet, dry and insulating) based on tests involving electrode impedance, static interference and motion artefact induced by various means. Data were collected simultaneously, and in the same physical environment for all electrode types. Results indicate that in many situations the performance of dry and insulating electrodes compares favourably with wet electrodes. The influence of non-stationary electric fields on shielded dry and insulating electrode types was compared to wet types. It was observed that interference experienced by dry and insulating electrode types was 40 dB and 34 dB less than that experienced by wet electrode types. Similarly, the effect of motion artefact on dry and insulating electrodes was compared to wet types. Artefact levels for dry and insulating electrodes were significantly higher than those for wet types at the beginning of trials conducted. By the end of the trial periods artefact levels for dry and insulating types were lower than wet electrodes by an average of 8.2 dB and 6.8 dB respectively. The reservations expressed in other studies regarding the viability of dry and insulating electrodes for reliable sensing of biosignals are not supported by the work described here.
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            Physical activity and the prevention of coronary heart disease.

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              Diagnostic Performance of a Smartphone‐Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting

              Background Diagnosing atrial fibrillation (AF) before ischemic stroke occurs is a priority for stroke prevention in AF. Smartphone camera–based photoplethysmographic (PPG) pulse waveform measurement discriminates between different heart rhythms, but its ability to diagnose AF in real‐world situations has not been adequately investigated. We sought to assess the diagnostic performance of a standalone smartphone PPG application, Cardiio Rhythm, for AF screening in primary care setting. Methods and Results Patients with hypertension, with diabetes mellitus, and/or aged ≥65 years were recruited. A single‐lead ECG was recorded by using the AliveCor heart monitor with tracings reviewed subsequently by 2 cardiologists to provide the reference standard. PPG measurements were performed by using the Cardiio Rhythm smartphone application. AF was diagnosed in 28 (2.76%) of 1013 participants. The diagnostic sensitivity of the Cardiio Rhythm for AF detection was 92.9% (95% CI] 77–99%) and was higher than that of the AliveCor automated algorithm (71.4% [95% CI 51–87%]). The specificities of Cardiio Rhythm and the AliveCor automated algorithm were comparable (97.7% [95% CI: 97–99%] versus 99.4% [95% CI 99–100%]). The positive predictive value of the Cardiio Rhythm was lower than that of the AliveCor automated algorithm (53.1% [95% CI 38–67%] versus 76.9% [95% CI 56–91%]); both had a very high negative predictive value (99.8% [95% CI 99–100%] versus 99.2% [95% CI 98–100%]). Conclusions The Cardiio Rhythm smartphone PPG application provides an accurate and reliable means to detect AF in patients at risk of developing AF and has the potential to enable population‐based screening for AF.
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                Author and article information

                Contributors
                Jeffrey.olgin@ucsf.edu
                Journal
                NPJ Digit Med
                NPJ Digit Med
                NPJ Digital Medicine
                Nature Publishing Group UK (London )
                2398-6352
                25 June 2019
                25 June 2019
                2019
                : 2
                : 58
                Affiliations
                [1 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Division of Cardiology, Department of Medicine, and the Cardiovascular Research Institute, University of California, San Francisco, Cardiology (San Francisco, CA, United States), ; 505 Parnassus Avenue, San Francisco, CA 94143 USA
                [2 ]Azumio, inc (Palo Alto, CA, United States), 145, 255 Shoreline Drive, Redwood City, CA 94065 USA
                [3 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Department of Epidemiology and Biostatistics, University of California San Francisco (San Francisco, CA, United States), ; 505 Parnassus Avenue, San Francisco, CA 94143 USA
                Author information
                http://orcid.org/0000-0002-8490-0270
                http://orcid.org/0000-0002-0310-3326
                http://orcid.org/0000-0002-1334-8861
                Article
                134
                10.1038/s41746-019-0134-9
                6592896
                31304351
                523d3e09-bc5a-4cc7-82d8-4e9864504fa2
                © 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
                : 18 February 2019
                : 30 May 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000070, U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB);
                Award ID: U2CEB021881
                Award ID: U2CEB021881
                Award ID: U2CEB021881
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100008240, Fonds de Recherche du Québec-Société et Culture (FRQSC);
                Award ID: 35261
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: K23.HL135274
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                epidemiology,predictive markers
                epidemiology, predictive markers

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