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      Clinical Validation of Heart Rate Apps: Mixed-Methods Evaluation Study

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          Abstract

          Background

          Photoplethysmography (PPG) is a proven way to measure heart rate (HR). This technology is already available in smartphones, which allows measuring HR only by using the smartphone. Given the widespread availability of smartphones, this creates a scalable way to enable mobile HR monitoring. An essential precondition is that these technologies are as reliable and accurate as the current clinical (gold) standards. At this moment, there is no consensus on a gold standard method for the validation of HR apps. This results in different validation processes that do not always reflect the veracious outcome of comparison.

          Objective

          The aim of this paper was to investigate and describe the necessary elements in validating and comparing HR apps versus standard technology.

          Methods

          The FibriCheck (Qompium) app was used in two separate prospective nonrandomized studies. In the first study, the HR of the FibriCheck app was consecutively compared with 2 different Food and Drug Administration (FDA)-cleared HR devices: the Nonin oximeter and the AliveCor Mobile ECG. In the second study, a next step in validation was performed by comparing the beat-to-beat intervals of the FibriCheck app to a synchronized ECG recording.

          Results

          In the first study, the HR (BPM, beats per minute) of 88 random subjects consecutively measured with the 3 devices showed a correlation coefficient of .834 between FibriCheck and Nonin, .88 between FibriCheck and AliveCor, and .897 between Nonin and AliveCor. A single way analysis of variance (ANOVA; P=.61 was executed to test the hypothesis that there were no significant differences between the HRs as measured by the 3 devices. In the second study, 20,298 (ms) R-R intervals (RRI)–peak-to-peak intervals (PPI) from 229 subjects were analyzed. This resulted in a positive correlation (rs=.993, root mean square deviation [RMSE]=23.04 ms, and normalized root mean square error [NRMSE]=0.012) between the PPI from FibriCheck and the RRI from the wearable ECG. There was no significant difference ( P=.92) between these intervals.

          Conclusions

          Our findings suggest that the most suitable method for the validation of an HR app is a simultaneous measurement of the HR by the smartphone app and an ECG system, compared on the basis of beat-to-beat analysis. This approach could lead to more correct assessments of the accuracy of HR apps.

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          Most cited references19

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          The impact of the MIT-BIH Arrhythmia Database

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            On the Analysis of Fingertip Photoplethysmogram Signals

            Photoplethysmography (PPG) is used to estimate the skin blood flow using infrared light. Researchers from different domains of science have become increasingly interested in PPG because of its advantages as non-invasive, inexpensive, and convenient diagnostic tool. Traditionally, it measures the oxygen saturation, blood pressure, cardiac output, and for assessing autonomic functions. Moreover, PPG is a promising technique for early screening of various atherosclerotic pathologies and could be helpful for regular GP-assessment but a full understanding of the diagnostic value of the different features is still lacking. Recent studies emphasise the potential information embedded in the PPG waveform signal and it deserves further attention for its possible applications beyond pulse oximetry and heart-rate calculation. Therefore, this overview discusses different types of artifact added to PPG signal, characteristic features of PPG waveform, and existing indexes to evaluate for diagnoses.
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              Mobile health technology evaluation: the mHealth evidence workshop.

              Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research. Published by Elsevier Inc. on behalf of American Journal of Preventive Medicine.
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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                August 2017
                25 August 2017
                : 5
                : 8
                : e129
                Affiliations
                [1] 1 Mobile Health Unit Faculty of Medicine and Life Sciences Hasselt University Hasselt Belgium
                [2] 2 Department of Cardiology Ziekenhuis Oost-Limburg Genk Belgium
                [3] 3 Department of Public Health and Primary Care KU Leuven Leuven Belgium
                Author notes
                Corresponding Author: Thijs Vandenberk thijs.vandenberk@ 123456uhasselt.be
                Author information
                http://orcid.org/0000-0002-4956-0872
                http://orcid.org/0000-0002-4804-9466
                http://orcid.org/0000-0002-7903-1736
                http://orcid.org/0000-0002-0800-6840
                http://orcid.org/0000-0002-0294-3059
                http://orcid.org/0000-0001-7639-944X
                http://orcid.org/0000-0002-1746-7585
                http://orcid.org/0000-0003-2442-656X
                http://orcid.org/0000-0002-7681-3011
                http://orcid.org/0000-0001-9344-0248
                http://orcid.org/0000-0002-9713-8721
                http://orcid.org/0000-0001-5244-1930
                http://orcid.org/0000-0003-3118-7560
                Article
                v5i8e129
                10.2196/mhealth.7254
                5591405
                28842392
                46f5c180-1ab5-48ce-a06f-5361ae11fb97
                ©Thijs Vandenberk, Jelle Stans, Christophe Mortelmans, Ruth Van Haelst, Gertjan Van Schelvergem, Caroline Pelckmans, Christophe JP Smeets, Dorien Lanssens, Hélène De Cannière, Valerie Storms, Inge M Thijs, Bert Vaes, Pieter M Vandervoort. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 25.08.2017.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

                History
                : 3 January 2017
                : 4 February 2017
                : 20 March 2017
                : 21 July 2017
                Categories
                Original Paper
                Original Paper

                heart rate,software validation,remote sensing technology

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