Digital phenotyping and the (data) shadow of Alzheimer's disease.

IF 6.5 1区社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY

Big Data & Society Pub Date : 2022-01-01 Epub Date: 2022-01-11 DOI:10.1177/20539517211070748

Richard Milne, Alessia Costa, Natassia Brenman

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引用次数: 0

Abstract

In this paper, we examine the practice and promises of digital phenotyping. We build on work on the 'data self' to focus on a medical domain in which the value and nature of knowledge and relations with data have been played out with particular persistence, that of Alzheimer's disease research. Drawing on research with researchers and developers, we consider the intersection of hopes and concerns related to both digital tools and Alzheimer's disease using the metaphor of the 'data shadow'. We suggest that as a tool for engaging with the nature of the data self, the shadow is usefully able to capture both the dynamic and distorted nature of data representations, and the unease and concern associated with encounters between individuals or groups and data about them. We then consider what the data shadow 'is' in relation to ageing data subjects, and the nature of the representation of the individual's cognitive state and dementia risk that is produced by digital tools. Second, we consider what the data shadow 'does', through researchers and practitioners' discussions of digital phenotyping practices in the dementia field as alternately empowering, enabling and threatening.

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数字表型和阿尔茨海默病的（数据）阴影。

在本文中，我们将探讨数字表型的实践和前景。在 "数据自我 "研究的基础上，我们将目光聚焦于一个医学领域，在这个领域中，知识的价值和性质以及与数据的关系一直在发挥着特别持久的作用，这就是阿尔茨海默病研究。通过对研究人员和开发人员的研究，我们以 "数据阴影 "为隐喻，探讨了与数字工具和阿尔茨海默病相关的希望与担忧的交汇点。我们认为，作为研究数据自我本质的一种工具，"阴影 "能够有效地捕捉数据表示的动态和扭曲性质，以及与个人或群体与其相关数据相遇时产生的不安和担忧。然后，我们会考虑与老龄化数据主体相关的数据阴影 "是什么"，以及数字工具所产生的个人认知状态和痴呆风险表征的性质。其次，我们将通过研究人员和从业人员对痴呆症领域数字表型实践的讨论，探讨数据阴影的 "作用"，即它既能增强能力，又能促进发展，还能造成威胁。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-

CiteScore

10.90

自引率

10.60%

发文量

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.