Mobile, wearable and ingestible health technologies : towards a critical research agenda

In this article, we review critical research on mobile and wearable health technologies focused on the promotion of ‘healthy lifestyles’. We begin by discussing key governmental and policy interests which indicate a shift towards greater digital integration in health care. Subsequently, we review relevant research literature, which highlights concerns about inclusion, social justice, and ownership of mobile health data, which we argue, provoke a series of key sociological questions that are in need of additional investigation. We examine the expansion of what counts as health data, as a basis for advocating the need for greater research into this area. Finally, we consider how digital devices raise questions about the reconfiguration of relationships, behaviours, and concepts of individuality

Evaluation of cell-based and surrogate SARS-CoV-2 neutralization assays

Determinants of protective immunity against SARS-CoV-2 infection require the development of well-standardized, reproducible antibody assays. This need has led to the emergence of a variety of neutralization assays. Head-to-head evaluation of different SARS-CoV-2 neutralization platforms could facilitate comparisons across studies and laboratories. Five neutralization assays were compared using forty plasma samples from convalescent individuals with mild-to-moderate COVID-19: four cell-based systems using either live recombinant SARS-CoV-2 or pseudotyped viral particles created with lentivirus (LV) or vesicular stomatitis virus (VSV) packaging and one surrogate ELISA-based test that measures inhibition of the spike protein receptor binding domain (RBD) binding its receptor, human angiotensin converting enzyme 2 (hACE2). Vero, Vero E6, HEK293T expressing hACE2, and TZM-bl cells expressing hACE2 and transmembrane serine protease 2 were tested. All cell-based assays showed 50% neutralizing dilution (ND50) geometric mean titers (GMTs) that were highly correlated (Pearson r = 0.81–0.89) and ranged within 3.4-fold. The live-virus assay and LV-pseudovirus assays with HEK293T/hACE2 cells showed very similar mean titers: 141 and 178, respectively. ND50 titers positively correlated with plasma IgG targeting SARS-CoV-2 spike and RBD (r = 0.63–0.89), but moderately correlated with nucleoprotein IgG (r = 0.46–0.73). ND80 GMTs mirrored ND50 data and showed similar correlation between assays and with IgG concentrations. The VSV-pseudovirus assay and LV-pseudovirus assay with HEK293T/hACE2 cells in low and high-throughput versions were calibrated against the WHO SARS-CoV-2 IgG standard. High concordance between the outcomes of cell-based assays with live and pseudotyped virions enables valid cross-study comparison using these platforms. 24

Disruption and the political economy of biosensor data

Science and Technology Studies has long held that the frames and definitions designers give to new tools matter enormously for how those tools are initially received, and ultimately modified, by users (Fischer, 1994; Kline and Pinch 1996). Discourses are powerful forces in technology design, shaping, for instance, how gender and racial inequalities get designed into technologies (Suchman 2002). The startups working in biosensing and self-tracking present a case to examine the role that power plays in the discursive process of framing new technologies. One frame often used for defining new data tools and services include their abilities for “disruption,” or the perceived ability of technologies to upend the status quo of power within established industries or social institutions. In this chapter we present findings on our research in the start-up environment in the consumer wellness field which is relatively less regulated and the more closely regulated field of mobile medical applications. We use two cases of health data innovation to present possibilities for scholars and practitioners to think about both the processes and discourses of disruption, and how these discourses might affect how people design and use new technologies. Our goal here is not to make normative or evaluative judgements about the roles that disruption discourses play in society. We hope to show that disruption discourses limit, in part, the possibilities for people to imagine technologies bridging existing social contexts and categories. Disruption limits such vision by overlooking the distinct roles for and relationships around data across contexts. Data can have different expectations to different people within and across different social institutions (Fiore-Gartland and Neff, forthcoming). Social institutions produce the tools and methods for making data even sensible or intelligible. However, as a concept for thinking about technology disruption helps to reproduce existing discourses of institutional power, even as people using disruption to describe what they are doing purport to change, replace, or disrupt those same power arrangements

Communication, mediation, and the expectations of data: data valences across health and wellness communities

Communication technologies increasingly mediate data exchanges rather than human communication. We propose the term data valences to describe the differences in expectations that people have for data across different social settings. Building on two years of interviews, observations, and participation in the communities of technology designers, clinicians, advocates, and users for emerging mobile data in formal health care and consumer wellness, we observed the tensions among these groups in their varying expectations for data. This article identifies six data valences (self-evidence, actionability, connection, transparency, “truthiness,” and discovery) and demonstrates how they are mediated and how they are distinct across different social domains. Data valences give researchers a tool for examining the discourses around, practices with, and challenges for data as they are mediated across social settings

Disruption and the political economy of biosensor data

Science and Technology Studies has long held that the frames and definitions designers give to new tools matter enormously for how those tools are initially received, and ultimately modified, by users (Fischer, 1994; Kline and Pinch 1996). Discourses are powerful forces in technology design, shaping, for instance, how gender and racial inequalities get designed into technologies (Suchman 2002). The startups working in biosensing and self-tracking present a case to examine the role that power plays in the discursive process of framing new technologies. One frame often used for defining new data tools and services include their abilities for “disruption,” or the perceived ability of technologies to upend the status quo of power within established industries or social institutions. In this chapter we present findings on our research in the start-up environment in the consumer wellness field which is relatively less regulated and the more closely regulated field of mobile medical applications. We use two cases of health data innovation to present possibilities for scholars and practitioners to think about both the processes and discourses of disruption, and how these discourses might affect how people design and use new technologies. Our goal here is not to make normative or evaluative judgements about the roles that disruption discourses play in society. We hope to show that disruption discourses limit, in part, the possibilities for people to imagine technologies bridging existing social contexts and categories. Disruption limits such vision by overlooking the distinct roles for and relationships around data across contexts. Data can have different expectations to different people within and across different social institutions (Fiore-Gartland and Neff, forthcoming). Social institutions produce the tools and methods for making data even sensible or intelligible. However, as a concept for thinking about technology disruption helps to reproduce existing discourses of institutional power, even as people using disruption to describe what they are doing purport to change, replace, or disrupt those same power arrangements

Critique and contribute: A practice-based framework for improving critical data studies and data science

What would data science look like if its key critics were engaged to help improve it, and how might critiques of data science improve with an approach that considers the day-to-day practices of data science? This article argues for scholars to bridge the conversations that seek to critique data science and those that seek to advance data science practice in order to identify and create the social and organizational arrangements necessary for a more ethical data science. We summarize four critiques that are commonly made in critical data studies: data are inherently interpretive, data are inextricable from context, data are mediated through the socio-material arrangements that produce them, and data serve as a medium for the negotiation and communication of values. We present qualitative research with academic data scientists, “data for good” projects, and specialized cross-disciplinary engineering teams to show evidence of these critiques in the day-to-day experience of data scientists as they acknowledge and grapple with the complexities of their work. Using ethnographic vignettes from two large multi-researcher field sites, we develop the following set of concepts for analyzing and advancing the practice of data science and improving critical data studies: 1) communication is central to the data science endeavor; 2) making sense of data is a collective process; 3) data are starting, not end, points, and 4) data are sets of stories. We conclude with two calls to action for researchers and practitioners in data science and critical data studies alike. First, creating opportunities for bringing social scientific and humanistic expertise into data science practice simultaneously will advance both data science and critical data studies. Second, practitioners should leverage the insights from critical data studies to build new kinds of organizational arrangements, which we argue will help advance a more ethical data science. Engaging the insights of critical data studies will improve data science. Careful attention to the practices of data science will improve scholarly critiques. Genuine collaborative conversations between these different communities will help push for more ethical—and better—ways of knowing in increasingly data-saturated societies

Critique and contribute: A practice-based framework for improving critical data studies and data science

What would data science look like if its key critics were engaged to help improve it, and how might critiques of data science improve with an approach that considers the day-to-day practices of data science? This article argues for scholars to bridge the conversations that seek to critique data science and those that seek to advance data science practice in order to identify and create the social and organizational arrangements necessary for a more ethical data science. We summarize four critiques that are commonly made in critical data studies: data are inherently interpretive, data are inextricable from context, data are mediated through the socio-material arrangements that produce them, and data serve as a medium for the negotiation and communication of values. We present qualitative research with academic data scientists, “data for good” projects, and specialized cross-disciplinary engineering teams to show evidence of these critiques in the day-to-day experience of data scientists as they acknowledge and grapple with the complexities of their work. Using ethnographic vignettes from two large multi-researcher field sites, we develop the following set of concepts for analyzing and advancing the practice of data science and improving critical data studies: 1) communication is central to the data science endeavor; 2) making sense of data is a collective process; 3) data are starting, not end, points, and 4) data are sets of stories. We conclude with two calls to action for researchers and practitioners in data science and critical data studies alike. First, creating opportunities for bringing social scientific and humanistic expertise into data science practice simultaneously will advance both data science and critical data studies. Second, practitioners should leverage the insights from critical data studies to build new kinds of organizational arrangements, which we argue will help advance a more ethical data science. Engaging the insights of critical data studies will improve data science. Careful attention to the practices of data science will improve scholarly critiques. Genuine collaborative conversations between these different communities will help push for more ethical—and better—ways of knowing in increasingly data-saturated societies