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      Digital health and the biopolitics of the Quantified Self

      research-article
      Digital health
      SAGE Publications
      Big Data, biopolitics, digital health, healthcare, neoliberalism, Quantified Self, Quantified Us, privacy, self-tracking, solidarity

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          Abstract

          Recent years have witnessed an intensive growth of systems of measurement and an increasing integration of data processes into various spheres of everyday life. From smartphone apps that measure our activity and sleep, to digital devices that monitor our health and performance at the workplace, the culture of measurement is currently on the rise. Encouraged by movements such as the Quantified Self, whose motto is ‘self knowledge through numbers’, a growing number of people across the globe are embracing practices of self-quantification and tracking in the spirit of improving their wellbeing and productivity or charting their fitness progress. In this article, I examine the biopolitical aspects of the Quantified Self practices, exploring some of the ideologies and rationalities underlying self-tracking culture. I argue that such practices represent an instantiation of a ‘biopolitics of the self’ whereby the body is made amenable to management and monitoring techniques that often echo the ethos of neoliberalism. Rather than being restricted to an individualized form, self-tracking practices are also becoming part of a biosocial and communal phenomenon in which individuals are incited to share with others information about their physical activities and biodata. In exploring some examples of this data sharing culture, I critically address the extent to which the sharing of personal physical data can be seen as a ‘solidaristic’ act that can contribute to a larger Big Data ecosystem and inform the wider medical community and healthcare research and policy. I link this discussion to debates on ‘data philanthropy’, highlighting the emerging tension between philanthropic discourses of data sharing and issues of privacy. From here, I go on to discuss further ethical and political concerns, particularly in relation to data security and the marked shifts in healthcare responsibilities.

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          Most cited references16

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          Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0

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            Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified Self, and the Participatory Biocitizen

            The concepts of health and health care are moving towards the notion of personalized preventive health maintenance and away from an exclusive focus on the cure of disease. This is against the backdrop of contemporary public health challenges that include increasing costs, worsening outcomes, ‘diabesity’ epidemics, and anticipated physician shortages. Personalized preventive medicine could be critical to solving public health challenges at their causal root. This paper sets forth a vision and plan for the realization of preventive medicine by 2050 and examines efforts already underway such as participatory health initiatives, the era of big health data, and qualitative shifts in mindset.
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              Crowdsourced Health Research Studies: An Important Emerging Complement to Clinical Trials in the Public Health Research Ecosystem

              Background Crowdsourced health research studies are the nexus of three contemporary trends: 1) citizen science (non-professionally trained individuals conducting science-related activities); 2) crowdsourcing (use of web-based technologies to recruit project participants); and 3) medicine 2.0 / health 2.0 (active participation of individuals in their health care particularly using web 2.0 technologies). Crowdsourced health research studies have arisen as a natural extension of the activities of health social networks (online health interest communities), and can be researcher-organized or participant-organized. In the last few years, professional researchers have been crowdsourcing cohorts from health social networks for the conduct of traditional studies. Participants have also begun to organize their own research studies through health social networks and health collaboration communities created especially for the purpose of self-experimentation and the investigation of health-related concerns. Objective The objective of this analysis is to undertake a comprehensive narrative review of crowdsourced health research studies. This review will assess the status, impact, and prospects of crowdsourced health research studies. Methods Crowdsourced health research studies were identified through a search of literature published from 2000 to 2011 and informal interviews conducted 2008-2011. Keyword terms related to crowdsourcing were sought in Medline/PubMed. Papers that presented results from human health studies that included crowdsourced populations were selected for inclusion. Crowdsourced health research studies not published in the scientific literature were identified by attending industry conferences and events, interviewing attendees, and reviewing related websites. Results Participatory health is a growing area with individuals using health social networks, crowdsourced studies, smartphone health applications, and personal health records to achieve positive outcomes for a variety of health conditions. PatientsLikeMe and 23andMe are the leading operators of researcher-organized, crowdsourced health research studies. These operators have published findings in the areas of disease research, drug response, user experience in crowdsourced studies, and genetic association. Quantified Self, Genomera, and DIYgenomics are communities of participant-organized health research studies where individuals conduct self-experimentation and group studies. Crowdsourced health research studies have a diversity of intended outcomes and levels of scientific rigor. Conclusions Participatory health initiatives are becoming part of the public health ecosystem and their rapid growth is facilitated by Internet and social networking influences. Large-scale parameter-stratified cohorts have potential to facilitate a next-generation understanding of disease and drug response. Not only is the large size of crowdsourced cohorts an asset to medical discovery, too is the near-immediate speed at which medical findings might be tested and applied. Participatory health initiatives are expanding the scope of medicine from a traditional focus on disease cure to a personalized preventive approach. Crowdsourced health research studies are a promising complement and extension to traditional clinical trials as a model for the conduct of health research.
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                Author and article information

                Journal
                Digit Health
                Digit Health
                DHJ
                spdhj
                Digital health
                SAGE Publications (Sage UK: London, England )
                2055-2076
                1 February 2017
                Jan-Dec 2017
                : 3
                : 2055207616689509
                Affiliations
                [1-2055207616689509]King's College London, UK; Aarhus Institute of Advanced Studies, Denmark
                Author notes
                [*]Btihaj Ajana, King's College London, 26-29 Drury Lane, London WC2B 5RL, United Kingdom; Aarhus Institute of Advanced Studies, Høegh-Guldbergs Gade 6B, Building 1630, 8000 Aarhus C, Denmark. Email: bajana@ 123456aias.au.dk ; btihaj.ajana@ 123456kcl.ac.uk ; Twitter: @MetricLife
                Article
                10.1177_2055207616689509
                10.1177/2055207616689509
                6001256
                29942580
                6cc70845-7c33-4e44-aad0-2ed355ba094c
                © The Author(s) 2017

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License ( http://www.creativecommons.org/licenses/by-nc-nd/3.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 17 August 2016
                : 16 December 2016
                Categories
                Original Research
                Custom metadata
                January-December 2017

                big data,biopolitics,digital health,healthcare,neoliberalism,quantified self,quantified us,privacy,self-tracking,solidarity

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