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      Managing Patient-Generated Health Data Through Mobile Personal Health Records: Analysis of Usage Data

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          Abstract

          Background

          Personal health records (PHRs) and mHealth apps are considered essential tools for patient engagement. Mobile PHRs (mPHRs) can be a platform to integrate patient-generated health data (PGHD) and patients’ medical information. However, in previous studies, actual usage data and PGHD from mPHRs have not been able to adequately represent patient engagement.

          Objective

          By analyzing 5 years’ PGHD from an mPHR system developed by a tertiary hospital in South Korea, we aimed to evaluate how PGHD were managed and identify issues in PGHD management based on actual usage data. Additionally, we analyzed how to improve patient engagement with mPHRs by analyzing the actively used services and long-term usage patterns.

          Methods

          We gathered 5 years (December 2010 to December 2015) of log data from both hospital patients and general users of the app. We gathered data from users who entered PGHD on body weight, blood pressure (BP), blood glucose levels, 10-year cardiovascular disease (CVD) risk, metabolic syndrome risk, medication schedule, insulin, and allergy. We classified users according to whether they were patients or general users based on factors related to continuous use (≥28 days for weight, BP, and blood glucose, and ≥180 days for CVD and metabolic syndrome), and analyzed the patients’ characteristics. We compared PGHD entry counts and the proportion of continuous users for each PGHD by user type.

          Results

          The total number of mPHR users was 18,265 (patients: n=16,729, 91.59%) with 3620 users having entered weight, followed by BP (n=1625), blood glucose (n=1374), CVD (n=764), metabolic syndrome (n=685), medication (n=252), insulin (n=72), and allergy (n=61). Of those 18,256 users, 3812 users had at least one PGHD measurement, of whom 175 used the PGHD functions continuously (patients: n=142, 81.14%); less than 1% of the users had used it for more than 4 years. Except for weight, BP, blood glucose, CVD, and metabolic syndrome, the number of PGHD records declined. General users’ continuous use of PGHD was significantly higher than that of patients in the blood glucose ( P<.001) and BP ( P=.03) functions. Continuous use of PGHD in health management (BP, blood glucose, and weight) was significantly greater among older users ( P<.001) and men ( P<.001). In health management (BP, weight, and blood glucose), overall chronic disease and continuous use of PGHD were not statistically related ( P=.08), but diabetes ( P<.001) and cerebrovascular diseases ( P=.03) were significant.

          Conclusions

          Although a small portion of users managed PGHD continuously, PGHD has the potential to be useful in monitoring patient health. To realize the potential, specific groups of continuous users must be identified, and the PGHD service must target them. Further evaluations for the clinical application of PGHD, feedback regarding user interfaces, and connections with wearable devices are needed.

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          Most cited references21

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          The Rise of Consumer Health Wearables: Promises and Barriers

          Lukasz Piwek and colleagues consider whether wearable technology can become a valuable asset for health care.
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            The Impact of Information Technology on Patient Engagement and Health Behavior Change: A Systematic Review of the Literature

            Background Advancements in information technology (IT) and its increasingly ubiquitous nature expand the ability to engage patients in the health care process and motivate health behavior change. Objective Our aim was to systematically review the (1) impact of IT platforms used to promote patients’ engagement and to effect change in health behaviors and health outcomes, (2) behavior theories or models applied as bases for developing these interventions and their impact on health outcomes, (3) different ways of measuring health outcomes, (4) usability, feasibility, and acceptability of these technologies among patients, and (5) challenges and research directions for implementing IT platforms to meaningfully impact patient engagement and health outcomes. Methods PubMed, Web of Science, PsycINFO, and Google Scholar were searched for studies published from 2000 to December 2014. Two reviewers assessed the quality of the included papers, and potentially relevant studies were retrieved and assessed for eligibility based on predetermined inclusion criteria. Results A total of 170 articles met the inclusion criteria and were reviewed in detail. Overall, 88.8% (151/170) of studies showed positive impact on patient behavior and 82.9% (141/170) reported high levels of improvement in patient engagement. Only 47.1% (80/170) referenced specific behavior theories and only 33.5% (57/170) assessed the usability of IT platforms. The majority of studies used indirect ways to measure health outcomes (65.9%, 112/170). Conclusions In general, the review has shown that IT platforms can enhance patient engagement and improve health outcomes. Few studies addressed usability of these interventions, and the reason for not using specific behavior theories remains unclear. Further research is needed to clarify these important questions. In addition, an assessment of these types of interventions should be conducted based on a common framework using a large variety of measurements; these measurements should include those related to motivation for health behavior change, long-standing adherence, expenditure, satisfaction, and health outcomes.
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              High-Definition Medicine

              The foundation for a new era of data-driven medicine has been set by recent technological advances that enable the assessment and management of human health at an unprecedented level of resolution – what we refer to as high definition medicine. Our ability to assess human health in high definition is enabled, in part, by advances in DNA sequencing, physiological and environmental monitoring, advanced imaging and behavioral tracking. Our ability to understand and act upon these observations at equally high precision is driven by advances in genome editing, cellular reprogramming, tissue engineering, and information technologies, especially artificial intelligence. In this review, we will examine the core disciplines that enable high definition medicine, and project how these technologies will alter the future of 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
                April 2018
                09 April 2018
                : 6
                : 4
                : e89
                Affiliations
                [1] 1 Department of Biomedical Informatics Asan Medical Center Seoul Republic Of Korea
                [2] 2 Clinical Research Center Asan Medical Center Seoul Republic Of Korea
                [3] 3 Department of Convergence Medicine Asan Medical Center University of Ulsan College of Medicine Seoul Republic Of Korea
                [4] 4 Medical Information Office Asan Medical Center Seoul Republic Of Korea
                [5] 5 Department of Cardiology Asan Medical Center University of Ulsan College of Medicine Seoul Republic Of Korea
                [6] 6 Department of Pulmonary and Critical Care Medicine Asan Medical Center University of Ulsan College of Medicine Seoul Republic Of Korea
                [7] 7 Department of Emergency Medicine Asan Medical Center University of Ulsan College of Medicine Seoul Republic Of Korea
                Author notes
                Corresponding Author: Jae-Ho Lee rufiji@ 123456gmail.com
                Author information
                http://orcid.org/0000-0002-4210-2094
                http://orcid.org/0000-0003-2048-3727
                http://orcid.org/0000-0002-0548-6738
                http://orcid.org/0000-0002-2338-9827
                http://orcid.org/0000-0002-2079-0498
                http://orcid.org/0000-0002-3610-486X
                http://orcid.org/0000-0002-4396-4403
                http://orcid.org/0000-0003-2619-1231
                Article
                v6i4e89
                10.2196/mhealth.9620
                5913571
                29631989
                42559dd6-bdd4-4210-b4be-da1dfb72155c
                ©Yu Rang Park, Yura Lee, Ji Young Kim, Jeonghoon Kim, Hae Reong Kim, Young-Hak Kim, Woo Sung Kim, Jae-Ho Lee. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 09.04.2018.

                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
                : 11 December 2017
                : 4 January 2018
                : 2 February 2018
                : 19 March 2018
                Categories
                Original Paper
                Original Paper

                personal health record,mobile health,patient engagement,patient-generated health data,health records, personal,telemedicine,patient participation

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