Understanding 'passivity' in digital health through imaginaries and experiences of coronavirus disease 2019 contact tracing apps.

IF 6.5 1区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY
Big Data & Society Pub Date : 2022-01-01 Epub Date: 2022-04-20 DOI:10.1177/20539517221091138
Alessia Costa, Richard Milne
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引用次数: 0

Abstract

Growing interest is being directed to the health applications of so-called 'passive data' collected through wearables and sensors without active input by users. High promises are attached to passive data and their potential to unlock new insights into health and illness, but as researchers and commentators have noted, this mode of data gathering also raises fundamental questions regarding the subject's agency, autonomy and privacy. To explore how these tensions are negotiated in practice, we present and discuss findings from an interview study with 30 members of the public in the UK and Italy, which examined their views and experiences of the coronavirus disease 2019 contact tracing apps as a large-scale, high-impact example of digital health technology using passive data. We argue that, contrary to what the phrasing 'passive data' suggests, passivity is not a quality of specific modes of data collection but is contingent on the very practices that the technology is supposed to unobtrusively capture.

Abstract Image

Abstract Image

通过对冠状病毒疾病的想象和体验,了解数字医疗中的 "被动性 "2019 年接触追踪应用程序。
人们越来越关注通过可穿戴设备和传感器收集的所谓 "被动数据 "在健康方面的应用,而无需用户主动输入。人们对被动数据寄予厚望,认为它们有可能为健康和疾病提供新的见解,但正如研究人员和评论家所指出的那样,这种数据收集模式也引发了有关主体代理权、自主权和隐私权的基本问题。为了探讨这些紧张关系在实践中是如何协商的,我们介绍并讨论了对英国和意大利 30 名公众的访谈研究结果,该研究考察了他们对 2019 年冠状病毒疾病接触追踪应用程序的看法和体验,该应用程序是使用被动数据的数字健康技术的一个大规模、影响大的实例。我们认为,与 "被动数据 "这一措辞所暗示的相反,被动性并不是特定数据收集模式的特质,而是取决于该技术本应不露痕迹地捕捉的实践本身。
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来源期刊
Big Data & Society
Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
10.90
自引率
10.60%
发文量
59
审稿时长
11 weeks
期刊介绍: Big Data & Society (BD&S) is an open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities, and computing and their intersections with the arts and natural sciences. The journal focuses on the implications of Big Data for societies and aims to connect debates about Big Data practices and their effects on various sectors such as academia, social life, industry, business, and government. BD&S considers Big Data as an emerging field of practices, not solely defined by but generative of unique data qualities such as high volume, granularity, data linking, and mining. The journal pays attention to digital content generated both online and offline, encompassing social media, search engines, closed networks (e.g., commercial or government transactions), and open networks like digital archives, open government, and crowdsourced data. Rather than providing a fixed definition of Big Data, BD&S encourages interdisciplinary inquiries, debates, and studies on various topics and themes related to Big Data practices. BD&S seeks contributions that analyze Big Data practices, involve empirical engagements and experiments with innovative methods, and reflect on the consequences of these practices for the representation, realization, and governance of societies. As a digital-only journal, BD&S's platform can accommodate multimedia formats such as complex images, dynamic visualizations, videos, and audio content. The contents of the journal encompass peer-reviewed research articles, colloquia, bookcasts, think pieces, state-of-the-art methods, and work by early career researchers.
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