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      Connected Health User Willingness to Share Personal Health Data: Questionnaire Study

      research-article
      , MSc 1 , , , PhD 2 , , PhD 3
      (Reviewer)
      Journal of Medical Internet Research
      JMIR Publications
      connected health, personal health data, data sharing, questionnaire

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          Abstract

          Background

          Connected health has created opportunities for leveraging health data to deliver preventive and personalized health care services. The increasing number of personal devices and advances in measurement technologies contribute to an exponential growth in digital health data. The practices for sharing data across the health ecosystem are evolving as there are more opportunities for using such data to deliver responsive health services.

          Objective

          The objective of this study was to explore user attitudes toward sharing personal health data (PHD). The study was executed within the first year after the implementation of the new General Data Protection Regulation (GDPR) legal framework.

          Methods

          The authors analyzed the results of an online questionnaire survey to explore the willingness of 8004 people using connected health services across four European countries to share their PHD and the conditions under which they would be willing to do so.

          Results

          Our findings indicate that the majority of users are willing to share their personal PHD for scientific research (1811/8004, 22.63%). Age, education level, and occupation of the participants, in addition to the level of digitalization in their country were found to be associated with data sharing attitudes.

          Conclusions

          Positive attitudes toward data sharing for scientific research can be perceived as an indication of trust established between users and academia. Nevertheless, the interpretation of data sharing attitudes is a complex process, related to and influenced by various factors.

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          Most cited references61

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          Big data analytics in healthcare: promise and potential

          Objective To describe the promise and potential of big data analytics in healthcare. Methods The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. Results The paper provides a broad overview of big data analytics for healthcare researchers and practitioners. Conclusions Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome.
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            Identifying personal genomes by surname inference.

            Sharing sequencing data sets without identifiers has become a common practice in genomics. Here, we report that surnames can be recovered from personal genomes by profiling short tandem repeats on the Y chromosome (Y-STRs) and querying recreational genetic genealogy databases. We show that a combination of a surname with other types of metadata, such as age and state, can be used to triangulate the identity of the target. A key feature of this technique is that it entirely relies on free, publicly accessible Internet resources. We quantitatively analyze the probability of identification for U.S. males. We further demonstrate the feasibility of this technique by tracing back with high probability the identities of multiple participants in public sequencing projects.
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              An Extended Privacy Calculus Model for E-Commerce Transactions

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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                November 2019
                27 November 2019
                : 21
                : 11
                : e14537
                Affiliations
                [1 ] IT University of Copenhagen Copenhagen S Denmark
                [2 ] United Arab Emirates University Al Ain United Arab Emirates
                [3 ] University of Oulu Oulu Finland
                Author notes
                Corresponding Author: Maria Karampela makar@ 123456itu.dk
                Author information
                https://orcid.org/0000-0002-1352-8790
                https://orcid.org/0000-0001-7614-9731
                https://orcid.org/0000-0002-0161-1837
                Article
                v21i11e14537
                10.2196/14537
                6906622
                31774410
                4a2c72d7-edd0-4841-b4c2-cab9da022e13
                ©Maria Karampela, Sofia Ouhbi, Minna Isomursu. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 27.11.2019.

                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 the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 30 April 2019
                : 10 July 2019
                : 19 July 2019
                : 9 October 2019
                Categories
                Original Paper
                Original Paper

                Medicine
                connected health,personal health data,data sharing,questionnaire
                Medicine
                connected health, personal health data, data sharing, questionnaire

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