You would think that it would be obvious, but it is not: what is a biosensor? Biosensors have been defined by Intel anthropologist Dawn Nafus (2016a: xiii) as devices that ‘indicate something about the body or the physical environment’, and biosensing as a practice that ‘uses information technology to understand something about bodies or the environment in which they live, whether the technology is at the cutting edge or not’. Standing at the intersection of everyday experience, scientific, medical and technical knowledges, media-platform economies, transformations in the biopolitics of health care, and cultural-material imaginaries of digital health, it is hardly surprising that biosensors are lively assemblages. They increasingly form a stage – a platform – on which problems of childhood, puberty, sexualities, reproduction, wellness, fitness, disease, ageing, medical expertise, health-care provision, economic productivity and citizenship, among others, play out.

Questions teem around these broad, pragmatic definitions of biosensors and biosensing. Are biosensors really concerned with ‘the body’ or ‘the physical environment’, or are they an outgrowth of the fitness and health-lifestyle industries? (Given that things seem to get inside us, do bodies live in an environment anyway?) Do the ideas of ‘indicating’ and ‘understanding’ accommodate what people do with biosensors? What else do they do with them? Do biosensors and biosensing challenge or extend traditional medical authority? Does biosensing increase people’s control over their health, as is often claimed? What is the connection between more data and health? Who is biosensing who? What is the relation between biosensing and people’s anxiety about their health? How do people subvert or reinvent (p.2) biosensors to assure themselves of health? How does biosensing participate in making certain forms of selfhood and group viable? What socio-material networks do biosensing practices produce and rely on? Who is profiting from biosensing and who is not, and how? What new forms of work and care are produced in biosensing? How does the dream of continuous monitoring animate biosensing? How might collectives or communities thrive through biosensing? What, if any, policy framework might meet public concerns about health biosensors?

In this book, we offer some ways of responding to this panoply of questions and perhaps framing some new ones. We discuss a range of biosensors and biosensings, testing the boundaries of the technical definitions of biosensor in order to access emerging configurations and experiences that we consider relevant. Our approach to biosensing practices centres on contrasts between different biosensors and biosensing practices broadly concerned with health.

Table 0.1 suggests something of the gamut of biosensing practices and the range of understandings that might develop from them. The columns lay out a range of types of biosensing practice and correspond to contrasting approaches to health. Fitness, self-monitoring, clinical medicine, social care and environmental biosensing practices all relate to health. What is health? The absence of disease or illness? This definition will not do. It is a definition by negation, as in ‘woman is the absence of man’. A concept of health needs positive and diverse content. The World Health Organisation definition from 1946 (WHO, 2006) is often quoted: ‘Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity’. Another more puzzling definition, less anchored in absence, comes from the philosopher of medicine George Canguilhem (1989: 190), who sees health as ‘the regulatory flywheel of the possibilities of reaction’. Canguilhem, writing in the mid-20th century, conjures up a slightly steampunk image of a spinning wheel storing up energy, and evening out (p.3)

(p.4) the flow of energy in an engine. Put in less engineered images, health is the capacity to bounce back from, recover from or adapt to altered conditions, including those associated with illness and disability. Biosensing practices engage with health in different ways, depart from different experiences, situations and knowledges of bodies, and yield different institutional, material and discursive arrangements. Whether and how they spin the regulatory flywheel is moot. They do pluralise health. The health of a fitness programme differs from the health of an environmental campaign for clean air; the health of continuous blood sugar monitoring for diabetes management differs from the monitoring of ovulation to maximise chances of conception, and so on. They configure and sometimes define individual and group identities. Given many healths, or many constructions of health for bodies, biosensing practices multiply.

Although we do not devote a chapter to each column of the table, the central columns of the table are the focus of the chapters: self-health, biomedical and social care biosensing (we have not conducted research on environmental biosensing, but excellent existing work in this field includes Tironi and Rodríguez Giralt [2017] and Pritchard, Gabrys and Houston [2018]). The five main biosensing practices we discuss – fertility monitoring, stress biosensing, DNA genotyping and falls monitoring – span times and places, life stages, forms of sensor and data, practices of interpreting and understanding, modes of practical organisation, and knowledge and expertise. In our analysis of these significantly different cases, we attend to the irreducible slippages and misalignments that arise when biosensing operates amid cultures and communities differently affected by institutional, state, corporate and political agencies. We want to make sense of areas of health – fertility, stress, susceptibilities and ageing – that concern people’s sense of self, their sense of individual and collective agency, and their capacity to practically engage with the problems and potentials of their health.

(p.5) Each of the biosensing practices that we explore has power-laden regulatory, economic and epistemic facets, sometimes reaching deep into the institutions and experiences of gender, kinship, ageing, citizenship and ethnicity. Each has deep temporal structures within life-worlds. Each permeates embodied senses of selfhood and relationality. Falls detectors, ovulation monitors, heart-rate monitors, cortisol tests and DNA genotyping have very different ways of dealing with bodies in time and the temporal structuring of life. The devices produce different kinds of data: micro-second time-stamped acceleration measurements; a series of time-and date-stamped temperature readings; a chemical concentration measurement (possibly date-stamped); and a list of half-a-million or more DNA variants without any obvious time or date stamp, but indirectly referring to patterns of heredity sometimes spanning geological epochs. Much of this data offers little immediate purchase for biological knowledge, let alone clinical intervention in any strict sense. Much of the data cannot be understood apart from individual medical histories, family history, patterns of everyday life and a whole habitus that renders them potentially meaningful. Our focus on stress, fertility, ageing and genetic susceptibility highlights both the times and bodies on different scales, ranging from the molecular (or even sub-molecular in the case of DNA variations), through cells (oocytes), to bodies across generations.

It may or may not be possible to locate all biosensing practices in Table 0.1. Either way, the point is to suggest diagrammatically the variety of positions opened up by biosensing, both in relation to the settled landscapes of biomedical practices and in relation to the transverse relationalities that could emerge. Does a self-health platform where people exchange, correlate and interpret exercise-related data afford the interventions, techniques of self or subjectivity that change the relation of self to self? Does the proximity of social care to state or civil society organisations such as social services or charities alter when telecare monitoring platforms connect social groups differently (family, friends, call (p.6) centre workers and social workers)? Like many tables, the most important comparisons and combinations may lie slightly outside what is tabulated in the rows and columns shown. Diagonal contrasts across the table might be more important than any illusion of synoptic completeness, but we want Table 0.1 to illuminate some of the possible contrasts and connections.

Layers of biosensing practice

Across the plural and problematic realities of health, the rows of Table 0.1 locate the cultural, political, economic and institutional layers of biosensing practice. Although in each chapter we deal with specific biosensors as devices – ovulation monitors, falls monitors, cortisol tests and DNA microarrays – we begin here with a preliminary observation: it is impossible to find a biosensor in isolation. Like tools or bodies, they always belong severally to a collective or an assemblage.¹ Biosensors only work when configured within a heterogeneous array of other devices, systems, materials and organisation. A falls monitor, the device that some people (are asked to) wear to detect when they fall over, requires not only a communication infrastructure – a telephone or Internet connection – but usually a telecare monitoring centre that can initiate action in response to a fall should one be detected. Nowadays, there is likely to be an app and a web portal for family and friends, not to mention a call-centre software platform running in the background, logging, scheduling, monitoring and dashboarding its way to efficiency. A fertility monitor, which could be a worn basal body thermometer, might be linked to a mobile phone app, a team of remote experts accessing a database of readings and an analytics interface, and an online discussion (p.7) forum. None of these elements, not even the biosensing device itself, is straightforward.

It is not particularly useful, therefore, to regard biosensors as technologies in the sense of the engineering application of science to practical problems. Much of biosensing concerns what happens to the data, how they are understood and how they intersect with or escape other forms of knowledge and expertise. An engineering perspective struggles to accommodate how biosensing might become ‘technologies of self’ (Rettberg, 2014), techniques of working on self in order to attain certain states of body and mind. In any case, as we will soon argue, biosensors increasingly operate within platform environments in which problems of regulation, property, ethics, inclusion and control are writ large for self and other, for individual and collective, for citizen, customer, corporation and state.

Not only are biosensors woven into a fabric of practice, things and places, but the core biosensing operation of indicating something about the health state of a body cannot always be reliably accomplished. Biosensing faces difficulties rooted deeply in health. Doctors and scientists, among other people, measure physiological states from time to time. Physiological states – temperatures, blood hormone levels connected with stress and levels of DNA expression affecting drug metabolism – are routinely understood as measurable signs of health. However, no exactness, comprehensiveness or sheer volume of physiological data can exhaust the existential inexactness of health. Health can be parameterised in terms of norms – of body temperature, cholesterol levels, cortisol levels, heart rate, blood pressure and so on – that derive from many measurements of observations. However, experience of health or illness, even a life with chronic illness or disability, has no fixed constants, exact parameters or laws. One person’s experience of chronic incapacity may well correspond in terms of physiological observations to another’s experience of well-being. George Canguilhem’s (1989: 201, 199) working definition of health as ‘a feeling of assurance in life (p.8) to which no limit is fixed’ and illness or disease as ‘a reduction in the margin of tolerance for the environment’s inconstancies’ implies that experiences of health may diverge from physiological ‘constants’ or averages derived from accumulated measurements (usually of populations). To take an example that might not be straightforward, cortisol levels do not go up for all people under stress. As we detail in Chapter 2, for some people in distress, cortisol levels go down.

Added to the indeterminacy of health measurement, the lines between body and environment are blurred. Bodies are so plurally entangled in the world that it sometimes hardly makes sense to speak of them as separate from an environment. Concepts of body, skin, self, life, memory, matter, differences, knowledge and action (to list just a few) developed over decades of research in feminist theory, science and technology studies, and cultural anthropology (for example, Barad, 2007; Bennett, 2009) have become radically ecological. We might imagine (see Chapter 4) someone falling out of a chair as a paradigmatic case of a clear and distinct change in body state that could be sensed by a device. It remains, however, a challenge to build a wearable biosensor that can reliably distinguish between someone slowly sitting down and someone sliding off their chair onto the floor. As we write, it seems that the latest Apple Corporation watch, with a biosensor for falls, cannot detect someone jumping off a 17-foot climbing wall (Chen, 2018). The general challenge for biosensing derives from the indeterminacy of the line between body and not-body/environment. Does sliding from a chair to the floor constitute a fall or an intended change in position? It keeps getting worse for biosensing. The act of measuring stress or even a basic physiological variable such as blood pressure changes bodies. Furthermore, the biological integrity of the human body seems to be on the edge of disintegration. We might think of the human microbiome as a remarkably deep-acting internal environment, modulating immune system responses (p.9) and inflammation reactions, as well as making it possible for us to accomplish elementary feats such as eating.

The indeterminacy of body-environments cascades difficulties for the biosensors. Devices have to be coordinated with each other (hence the many biosensing practices that work by assembling measurements of many bodies). Their results/measurements/indications have to be analysed, interpreted and re-situated amid experiences of health and disease, as well as programmes of action and different forms of expertise and judgement. The devices become products not in their own right, but within arrangements that depend on groups and communities of practice, especially in the form of platforms such as social media. Platforms, institutions, social groups and modes of knowledge are just some of the different facets that unfold around the devices, even if the devices, as triumphs of miniaturisation or intelligence, often seem to be increasingly, sometimes disconcertingly, autonomous.

We have no simple way of conceptualising the diversity of biosensing practices because the lives that they pertain to, actually and potentially, are inherently diverse and indeterminate. This is not to say that biosensors and biosensing practices thwart normalisation. Medical and health institutions and forms of expertise are usually criticised, at least in the humanities and social sciences, for their normalising gaze – for their propensity to observe, measure and classify people (‘cases’) according to measures of population-level variations. Biosensing often acts normatively; that is, acts to intensify existing norms defined by biopolitical and neoliberal governmentality, with its population measures and controls. One could present a Foucauldian analysis of this tendency running through much contemporary biosensing practice, from fitness to environmental monitoring, in which normalising statements would be webbed into the production of well bodies, active in maintaining their capacity to work and to consume. The vertical axis of Table 0.1 (p.10) is effectively a gradient of organisational configurations (ranging from localised devices and measurements to institutions and knowledges, from technical operations to statements) that could contribute to the normalisation of vital properties of attributes of citizens, patients, workers and consumers. Fitness, self-health, social care and clinical medicine are not in any relation of degree to each other, although we might say that they range between health (fitness) and disease (social care and clinical medicine), and that biosensing forms part of a discursive formation enunciating subject positions, constituting objects of knowledge and action, and defining gradients of agency and control. Announcements by some life insurers in the US that they would only issue insurance to customers wearing continuously monitored biosensors for heart rate and health are clear statements of intentions to normalise (Baryln, 2018).

Even as they normalise bodies and experiences, biosensing practices that count or record some state of activity such as steps or heart rate can diverge greatly from coordinated programmes of action executed more or less coherently by institutions or large organisations such as health-care providers or hospitals. A platform designed and constructed to gather, aggregate and circulate biosensing data, such as the online ovulation monitoring systems or the social media-enabled DNA genotyping services we discuss in later chapters, differ greatly from the clinical settings in which such biosensing might otherwise take place, creating niches in the broader formations of biomedicalisation, normal and pathological, and disease and health. We will discuss some of the emergences of biosensing in detail in the chapters that follow, but for now, Table 0.1 serves to highlight the very different ecologies of biosensing, and to highlight the need for broad lines of questioning and a re-conceptualisation of biosensors in which frequently used terms such as ‘health’, ‘device’, ‘bodies’ and ‘data’ are not treated as single or static concepts, but figured as plural and contingent.

Biosensors multiply

Our approach to biosensing – somewhat differently to other work that emphasises practices of consumption (Lupton, 2014; Davies, 2015; Harris et al, 2016), surveillance associated with devices and services (Kenner, 2008; Rettberg, 2014), the regulation of medical technologies (Faulkner, 2009), or the politics of health expectations and hopes (Petersen, 2015) – attends to the significance of both the ‘bio’ and ‘sensing’ in biosensing practice. ‘Bio’ has several different resonances in this (p.12) book. A ‘bio’ in the sense of a biography, a detailed account of the beginnings, course and ends of a perhaps incomplete life, is definitely part of many of the practices we describe. Every biosensor we discuss – ovulation monitors, falls detectors, cortisol tests, heart-rate monitors and DNA genotyping – inscribes lives within biographies, within stories of major events and intimate experiences, albeit not necessarily in the standard forms of the biographical genre. If we understand health as a set of securities in the present and assurances for the future, then we can expect biographical narratives to be of considerable importance in relation to both experiences of continuity and disruption (Bury, 1982). The life course itself might end up flowing differently, being widened or constricted by tracking and testing. We can imagine a life course being nudged, jolted and marked by its biosensings.

Every biosensor also has a direct relation to ‘bio’ in the sense of bios, the organised form of life on which biomedicine and biopolitical governmentality concentrate in the management of populations of individuals (Foucault, 1990; Rose, 2007). It may be worth considering, we suggest, how biological processes continually exceed or overflow the biological knowledges that are meant to model and structure them as knowable. When we begin to understand biosensors and biosensing as experimental arrangements (especially in their sometimes novel collective formations), we also envisage the possibility that biological concepts change in biosensing practice. Karen Barad’s work on the entwining of concept and experiment in quantum physics provides useful theoretical guidance here. As she writes: ‘in the absence of appropriate experimental arrangements, concepts do not have determinate meanings’ (Barad, 2007: 296). The point here, and one that has often been made in the social studies of science, is that biological concepts, like scientific concepts more generally, depend on experimental apparatus for their grounding. Devoid of experimental or clinical apparatus, concepts lose material relevance. Biosensings may have the capacity to put (p.13) existing physiological concepts back into an experimental setting via collective arrangements that, as yet, scarcely appear scientific. This may explain the differences between the many scientists actively engaging with biosensing practices and the medical clinicians who approach them warily (West et al, 2017).

Biosensing, we found in some of our cases, prepares the ground for an extension of clinical medicine into everyday life. In projects that seek to aggregate DNA genotyping data and to connect them to other biosensor data (for example, the fitness tracking and genotyping project Infinome [Infinome, 2018]), novel hybridisations of different senses of ‘bio’ might be in play. Whether in the form of a susceptibility to disease detected by a genetic test (Rose, 2007), or in the promise of a transformed relation to self (as in the quantified self-related sleep, diet and exercise tracking practices described in Neff and Nafus [2016]), biosensors inevitably hybridise different senses of ‘bio’.

‘Sensing’ too has multiple resonances. The term ‘sensor’ conveys an image of a discrete device, measuring or detecting changes in bodies and environments: steps, falls, heart beats, blood sugar, cortisol concentrations or DNA variants. But the image of a discrete device measuring or observing falls, genetic risks or physiological changes is inadequate to understand sensing practices. Living bodies are full of sensings. What might be called biosensing practices go hand in hand with life in general. Hormones such as cortisol, as we will see in Chapter 2, are themselves biosensors that detect and signal changes in bodily state. To have (or be) a body, to experience in the widest sense of that term, is perhaps a matter of sensing practice, of plural ‘heterogeneities doing things’ (Bennett, 2009: 122). If bodies sense, then biosensing devices are a provisionally stabilised version of what variously takes place in bodies all the time. The question of whether a biosensor is in us or outside, whether biosensing is something a body does or something done to a body, remains open to reconfiguration. As the philosopher William James (1976: 139) puts it: ‘“outer” and “inner” are (p.14) names for two groups into which we sort experiences according to the way in which they act upon their neighbours’. His argument implies that biosensing is already part of what it means to be alive. Biosensing devices are ‘outer’ sortings of experiences that could just as well be ‘inner’, or the object of attention of some subject. What happens when biosensing becomes an ‘outer’ experience? A new observer is arranged. A device to observe bodies, even the ostensibly simple movements of a body taking steps or falling over, presents an experience of observing for someone or something – the observer – for whom the body steps or falls. Biosensors as devices in the strict sense always sense for an observer, even if that observer happens to be the one who bears the biosensor. A basic question for any biosensor follows: for whom does the biosensor sense? As we will see in the chapters that follow, some quite radically different organisations and institutions are shaped around different answers to that question.

If biosensing practices combine the multiple senses of ‘bio’ and sensing introduced here, we might expect biosensors themselves to be relatively plural and actually difficult to strictly define or delimit. (We refer readers again to Table 0.1 where these different facets of biosensing are laid out.) We might expect biosensing to be complicated by different expectations, to be animated by speculative investments (especially as an outgrowth of ‘biocapital’, the financial investment in biomedical technologies and techniques such as stem cell therapies and so-called ‘biological’ drugs from the 1990s onwards [Sunder Rajan, 2006]), to be riven by controversies in the wake of competing claims for significance and veracity (we see this particularly around DNA genotyping biosensing practices), and to be materially diverse in their configurations and implementations. It might be that the forms of knowing they afford and their relation to existing forms of regulation (for instance, of confidentiality or anonymity) will be contentious. In drawing together some diverse instances of biosensing practice in this book, we aim to map the frictions in the (p.15) ‘cultural logic’ of biosensing (Nafus, 2013). By exploring how falls, saliva or the temperature of a body wend their way across wearable motion sensors, DNA spotted on a glass plate, a camera lens, the thermocouple of a thermometer or the antibodies on an immunoassay strip (cortisol testing), and then into online platforms with their databases, informational retrieval mechanisms and programmable user interfaces, proliferating across apps and forums and connecting to monitoring centres and sometimes clinics, where distributed forms of action and knowledge come into play, we intend to map this pluralising experience.

These overflows and multiplications are not always immediately visible, and they sometimes develop gradually. For instance, Fitbit users track steps, heart rate and sleep time over extended periods. Some people maintain an active interest in these measurements because they have a weight-loss programme, compete with other people or participate in organised exercise. However, other people lose interest in their steps or their heart rate. Some Fitbit devices continue to sync data with other devices such as tablets and phones, and with the Fitbit platform (see: www.fitbit.com), regardless of wearers’ awareness, interest or participation. Fitbit biosensing data pools and aggregates at the platform level, allowing new biosensing observations to take shape. Yahoo Finance recently published an analysis of many billion hours of Fitbit data (Pogue, 2018). The analysis presents patterns of heart-rate variability in different parts of the world, and at different times of the year. Like many conventional studies of heart disease, this data showed variation in heart rates across ethnic groups. Less conventionally, however, it also demonstrated that significant cultural events – Christmas, Easter, Thanksgiving, Divali, Eid al-Fitr, Passover and so on – raise the heart rates of whole populations for several weeks at a time. Whether or not these findings are relevant or important, novel regimes of observation are taking shape through proliferating and distributed biosensing. The cultural-material-lived logic (p.16) of biosensing cuts across relatively separate domains of telecare, genotyping, cortisol tests and ovulation monitoring in different ways. As the rows of Table 0.1 indicate, the pluralising logics of biosensing practice mean that analytical work will need to consider different layers of practices, as well as the horizontal spectrum of different domains. The 150 billion hours of heart-rate data accrued on the Fitbit platform is only one instance among very many of the data economies surrounding biosensors. The case of the 23andMe genotyping service, which we discuss in Chapter 3, is another, as is the data collected through menstrual-tracking apps such as Clue, discussed in Chapter 1.

The biosensing health platform economies of ‘my’ and ‘our’ data

We started from a definition of a biosensor as a device that ‘indicates’ body or environmental state and biosensing practice as the use of information technology to understand something about indications of body-environment state. Biosensor devices and information technology, particularly in their contemporary instances, are far more extensively enmeshed than these rather flat definitions suggest. The enmeshing often takes the form of a platform, an infrastructurally layered zone where relations between different groups are figured and configured. From a platform perspective, biosensors are just one component in a multilayered assemblage that pulls together web user interfaces, databases, apps, servers, developers, designers, algorithms, engineers, scientists and computer infrastructures, among others. Biosensors generate data, but the act of using, wearing, configuring, observing or monitoring a biosensor is caught in a dense network of relations, many of which might not be immediately visible in the device or adjacent interfaces that have recently tended towards graphically simple, swipey, bright, touch-controlled, animated aesthetics.

(p.17) Information technology, in the form of mobile devices and their platform support, is used to ‘understand’ the data, but understanding encompasses a broad range of modalities, many of which unfold across platforms. Sometimes, understanding is mundane and individual. A simple display of heart rate might be all that it entails. In other cases, understanding is highly leveraged through predictive inferences constructed on the basis of models, for instance, as in: the results reported for DNA genotyping, which rely on the statistical models underpinning genome-wide association studies; the falls detectors that now rely on machine-learning algorithms (Pannurat et al, 2014); the ‘individualized predictions’ of the Clue menstrual-tracking app (Clue, 2018); or the boson-detection models repurposed in the ‘Natural Cycles’ fertility-tracking app (Scherwitzl et al, 2015). Very often, what felt like personal data is indirectly aggregated and subsumed into other data sets, as Kate Crawford, Jessa Lingel and Tero Karppi (2015) describe in their account of wearable self-tracking devices. Fitabase illustrates one version of a platform mode of understanding: ‘Your Participants, Your Data, Your Platform’ (Fitabase, 2018). Fitabase aggregates data from many wearable devices and allows researchers to ‘stay on top of participant compliance’. The ‘self-knowledge’ on offer on many of the self-tracking devices is only one side of the platform story. It is very difficult to even approach the topic of biosensors without reference to platform-centred transformations in data practices in science, commerce, industry, government and civil society, as well as without reference to what might be termed the biosensing platform health economy (to conflate just a few different concerns!). Almost every aspect of data, every attribute of it as a conventional, material, epistemic, transactional, administrative or governmental form, undergoes transformation under platform conditions. Some authors call this transformation ‘platformisation’ (Helmond, 2015; Plantin et al, 2016). Others go so far as to speak, with some justification, of ‘platform capitalism’ (Srnicek, 2016). In (p.18) following chapters, we discuss some of the transformations in the context of biosensing.

Given the platform realities of contemporary information technology, the term ‘our data’ highlights a keystone issue for biosensing. How data becomes – or doesn’t become – ‘my’, ‘our’, ‘their’ or ‘your data’ is a litmus test for a range of localised difficulties, ambivalences, exploits and experiments associated with biosensors and biosensing. Data sharing slips into data owning. The questions of what it means for data to be ‘ours’ individually or collectively, and what we possess or own of the data, are currently matters of broad social and economic struggle, involving many actors ranging from nation states to data activists, from corporations to patients and citizens. The stakes in these struggles are bound up with platform politics more generally. Structural-level problems concerning regimes of transparency, veracity, property, work and discrimination abound here, as do possibly new practices of care and constitution of self. We need only think of the strange contrasts in sensitivities to health records and social media data – people seem sensitive to any attempts to aggregate and marketise health data, but relatively indifferent to the mundane data-mining of their everyday lives by social media platforms – to recognise that platform data economies have complicated variations and affordances. Although we do not wish to bracket out or sidestep problems of ownership, surveillance, control, neoliberal governmentality or commercial exploitation, we will suggest that biosensing data has some specificities in terms of social groups, institutions, interventions and knowledges that are worth attending to.

In a citizens’ panel we conducted in Lancaster, UK, in 2013, discussions took place concerning fertility monitoring and personal genetic tests (Mort et al, 2016; see also Chapters 1 and 3). The most animated of the discussions concerned the problem of ‘our’ data. The plural possessive ‘our’ rather than the singular possessive ‘my’ was notably common. Around the time of the panel, the National Health Service (NHS) in England (p.19) had launched the ‘care.data’ campaign (NHS, 2013), a project to aggregate or ‘share’ all general practice patient records to create a data set capable of supporting data-intensive biomedical research. The campaign went badly wrong when many of the 22 million households in England did not receive the explanatory leaflet and many other people objected to the very idea of ‘care.data’. The project was paused in early 2014 and finally abandoned in 2016 after many GPs opted out alongside many patients. In the citizens’ panel, ‘our’ often referred to broad collective groups such as national populations or the group of all patients. In some other settings, as we will see, ‘our’ might have a more restricted sense relating to groups of people who share a medical condition or a disease, or a group of people related by descent or kinship. In all these settings, ‘our’ carries a raft of senses of belonging and inclusion.

The term ‘our’ bears implicit reference to the possibility of dispossession, or data that is ‘not ours’ because it has somehow become ‘theirs’. In biosensing practices, boundaries between ‘ours’ and ‘theirs’ are often inscribed by platformising practices that are difficult to negotiate collectively, let alone individually. (Current debates in Europe, North America and Asia concerning the state regulation of social media platforms, as well as the 2018 European Union General Data Protection Regulations, are salient evidence of the difficulty of these fraught negotiations [EU, 2016].) As we will suggest, the constituent subject forms of individuality are being renegotiated in biosensing. The biosensors installed to monitor movements and detect falls in the houses of older people generate data about how those people inhabit their homes, and this sensing informs, for better or worse, plans and actions directly affecting what it means to age, and, indeed, how ageing takes place. Other biosensors and biosensing practices, rather than intensifying the compliance of patients under surveillance to norms, afford deviations and variations from normal treatments or diagnoses (Nafus, 2016b). There are certainly emergent communities of practice (p.20) around self-tracking, in reanalysis of DNA genotyping data and environmental biosensing practice which suggest that we need not be entirely pessimistic about the outcomes of negotiations around the meaning of data (Sharon, 2018). The ever-present shadowing of ‘ours’ by its opposite, ‘not ours’, implies that redefinitions or re-individuations of ‘our’ will be a particularly important reference point.

The movement of data across platforms suggests that the meaning of ‘our data’ and its implicit forms of possession, ownership and property have not, and perhaps cannot, stabilise. The regimes of property relations concerned with ‘our data’ are manifold, complex and power-laden. In England, public opposition to care.data largely concerned risks to the confidentiality of patient–doctor relations, but it also voiced worries about how the integrated data would be used in commercial or industry research and the possible implications for various forms of insurance. Property relations and the forms of ownership associated with data are fraught. As we will see in our exploration of the poorly named ‘direct-to-consumer’ genetic tests, questions of who owns the data and even whose data is analysed are still very much matters of contestation. As our citizens’ panel vocally suggested, being citizens (and data citizens in particular) was just as important as being data consumers or subjects of consumer data. Versions of the problems of platform possession surface not only in our citizens’ panel, but also in various ethnographic and media settings. Business models premised on data aggregation vie with cooperative and sometimes socio-political efforts to relocate or migrate data out of the platforms on which it first came into being.

The platform alignments of biosensing

It is not always easy to situate biosensors in relation to bodies and time or, indeed, to life as it is lived individually, in groups or in communities. Data, in the pluralities of its creation, (p.21) production, accumulation and transformation, is the tissue that potentially connects body states to experiential or experimental understanding, but it connects them ambiguously, loosely and partially. Only exceptionally does the data from a biosensor afford immediate unambiguous observation, understanding or actions in relation to bodies and self. Usually, data has to be collected, accumulated, transformed, rendered in graphical form, subjected to analytical processes of modelling, assimilated to other data sources or interpreted in the light of scientific and clinical knowledges that introduce a welter of assumptions, presuppositions, representations and norms with them. Biosensor data hardly ever aligns directly with experience in any of its modalities. It has to be worked on, and this work is porous, distributed and expansive.

A major concern of this book is to depolarise some of the debates over passive versus active agency in understanding biosensor data. How do we hold in focus the agencies that range from the mildly active consumption of wearable gadgets and apps to highly activated collectives or communities of biosensing? We address this problem by attending to the platform configurations of biosensing practice. Platforms are the main place where agency problems play out, and where the expansive, distributed and connective work of understanding can take place. Collectives sometimes emerge on the edges of or between platforms. However, we lack good understandings of platform biosensing.

In better understanding the platform realities of biosensing, the concept of biomedical platform proposed by Paul Keating and Alberto Cambrosio (2003), in Biomedical platforms: Realigning the normal and the pathological in late-twentieth-century medicine, offers an important lead. Scientific and clinical knowledges, practices and value regimes do not and cannot coincide precisely because health – in any of the guises typified in the columns of Table 0.1 – is a ‘feeling of assurance’ (Canguilhem, 1989: 201), not a norm prescribed by biological processes. (For biology, a disease is (p.22) biologically just as normal as the absence of illness.) Keating and Cambrosio, departing from Canguilhem’s (1989) philosophical account of the non-alignment between biology and medicine and between the normal and the pathological, define biomedical platforms as ‘material and discursive arrangements that act as the bench upon which conventions concerning the biological or normal are connected with conventions concerning the medical or the pathological’ (Keating and Cambrosio, 2003: 332).

Keating and Cambrosio suggest that biomedical platforms, especially the quasi-automated imaging and testing instruments now common in hospital pathology labs and clinics, take shape at the intersection of different knowledge practices coming out of biology and medicine. Biomedical platforms for assaying or imaging tissue samples are not scientific or clinical instruments like a thermometers or scales. Although they certainly incorporate measurements and observations, biomedical platforms do more than measure or observe. (Through an extended case study of how cell flow cytometry machines moved to the centre of HIV/AIDs diagnosis and monitoring, Keating and Cambrosio illustrate differences between scientific instruments and biomedical platforms.) They extend beyond measurement or observations ‘insofar as they embody regulations and conventions of equivalence, exchange and circulation’ (Keating and Cambrosio, 2003: 324). Biomedical platforms encompass regulations, standards or conventions concerning what counts as a clinically valid measurement, or apply categories and clusterings derived from clinical experience in their technical operations (algorithms, classifications or implementations of rules in code). The ‘material and discursive arrangements’ or ‘configurations’ (Suchman, 2012) of platforms align the different realities of biological and medical knowledge practices. They locally stabilise the zone of slippage that runs between biological accounts of living things and medical accounts of disease or illness as pathology. They establish rapprochements between experimental configurations concerned with forms of life and (p.23) regulated clinical expertise concretised around norms of disease and illness.

Biomedical platforms that image, test, diagnose or monitor illness rely on prior negotiations and alignments of different knowledge practices. (Current efforts to establish precision medicine based largely on genomic science and its sequencing platforms are another example.) Although biology and medicine thereby connect in many respects (as highlighted in the accounts of biomedicalisation presented by Adele Clarke et al [2010]), we will propose that the irreducibility of illness, disease and health experience to biology is also vital to biosensing.² The negotiation of different approaches to living (and living data) coming from biology and medicine underpins wide-ranging cultural logics of biosensing.

Biosensors and biosensing have a complex relation to preexisting biomedical platforms such as medical scanners. The non-coincidence between biology and medicine affects biosensing in manifold ways. Although biosensing is not regulated by the conventions and standards applicable to biomedical platforms, its platforms also cross over between biology and health. In order to respect the specificities of both the life sciences and medicine, and to register the importance of various practices and technologies that bridge between them, we pay close attention in this book to platform alignments, forms of regulation and convention associated with biosensors, and configurations that assemble or address different biological and clinical knowledges. In our different case studies – stress, fertility monitoring, telecare and personal genetic testing – we explore the ramifications of the (p.24) biology– medicine fault line as it runs through the handling and management of data, the different social groupings, expertises and networks taking shape, and the struggles over what counts as knowledge and for whom.

Norms are a sticky issue for biosensing platforms. Illness is often clinically evaluated through norms or statistical measures of central tendencies in populations. These norms are embedded in biomedical platforms as part and parcel of their diagnostic operation. Such norms have a much more tenuous presence on biosensing platforms. The obvious example in our group of case studies would be the genetic tests. They often rely on non-clinical genomic research (especially genome-wide association studies, as discussed in Chapter 4), whose clinical significance (and statistical significance) cannot be easily mapped onto individual experiences of illness or health. The fact that the significance of biosensor data, even in the vast quantities produced by contemporary DNA genotyping devices, cannot be immediately evaluated in terms of health and disease triggers more complex attempts to probe around, to find signals or patterns within the data that could, even if only in the future, be clinically significant. Personalised medicine is one such organisational attempt to render biology more clinically relevant (Tutton, 2016). In extreme cases, and for some of the biosensing we discuss, avalanches of data flow untapped, un-enacted, un-understood in big data un-analytics.

What does tracking the internal fault line running through biosensing platforms mean in practice? Sometimes, biosensing attempts to test connections between seemingly unrelated life or health events. From a platform perspective, it is particularly important to attend to the regulatory practices and conventions developing in and around biosensing as it connects different biosensors and aggregates data sets. As we will suggest, these regulatory practices often entail reconfigured relations between individuals and groups, altered arrangements for the circulation of data (data being uploaded, shared and variously (p.25) distributed as part of its analysis and interpretation), and substantively novel material arrangements for the organisation of practices of monitoring and intervention. Data ownership and property rights are an increasingly important zone of regulatory attention. However, it is not always certain in what regulatory zone biosensing occurs. Sometimes, biosensing practices entrain individuals in a pre-clinical departure zone, queued for imminent proximity with clinical expertise (for instance, ovulation monitoring prepares the ground in various ways for assisted reproductive technologies such as in vitro fertilisation [IVF], and the ‘kitemarking’ of health-tracking apps by the UK NHS sets up a pre-clinical path [Pym, 2014]). At other times, a medically approved biosensor finds unexpected biosensing uses (an ovulation device approved for contraception might find uses in conception). In any case, the regulatory conventions for many biosensors are not set or fixed in the same way as they might be for accepted clinical medicine and biomedical platforms. For instance, DNA genotyping, despite the intensive involvement of biomedical researchers, still produces highly variable test results, with alarming consequences for participants: one testing service might predict early onset Alzheimer’s disease and another might not (Hercher, 2018). In regulatory terms, they function as quasi-biomedical platforms. More dramatically, a biosensing platform might reshape the regulatory regime of existing biomedical platforms: DNA ancestry testing might effectively deregulate existing forms of biomedical governance; and it is possible to use DNA genotyping platforms to sidestep, for instance, the UK regulations pertaining to anonymity protecting egg and sperm donation for use in IVF (Weaver, 2018).

Cultural logics of biosensing

The fact that clinicians often regard biosensor-derived data with suspicion, and biosensing itself as a possible symptom of (p.26) mental illness or ‘begging for diagnosis’ (West et al, 2017: 8), is significant. It suggests that biosensing is not at home in the theatres of biomedical practice. The regulatory dynamics at the heart of the platforms animate what Dawn Nafus (2013: xviii) (echoing the work of cultural critic Frederic Jameson [1991] on the ‘cultural logic’ of late capitalism) calls a ‘cultural logic of biosensing’. This logic, Nafus argues, describes how the invention and innovation of biosensors overflows the marketing and product development focused on devices. The fact that most people lose interest in wearable devices after a few days/weeks/months does not dampen ongoing excitement about their potential to revolutionise individual and public health. From a commercial perspective, the rapid loss of interest is a problem. Viewed from the perspective of a cultural logic of biosensing, gadgets are only part of broader transverse relations that bring disparate people and groups into contact, and platforms might be able to arrange connections that go well beyond the limits of devices as products with defined features.

The cultural logic of biosensing, configured on platforms, operates in the zone of slippage between biological forms in all their rich sensing entanglements and life lived according to norms of intrinsic variability. Biosensing as a platform practice, we somewhat tentatively suggest, culturally realigns biology and medicine around health. We recognise that biomedical research strives to overcome the existential indeterminacies of health and disease experiences by bringing, among other things, biology to bear on disease. We know that medical imaging, clinical trials, stratified medicine, personalised medicine, precision medicine and, in some respects, evidence-based medicine all respond to the inexactness or variability of health – and medical expertise – by treating patients as members of populations. We know too that health is not a measurable object, but a regulatory feeling of the possibilities of reaction to the inconsistencies of environments. It follows that no biosensor, regardless of its precision or accuracy, (p.27) and regardless of its biological or technical sophistication (including the ‘smarts’ of machine learning), can by itself make up for the inevitable misalignments between biological laws expressed as statistics (means, ranges, deviations, other measures of population variability and so on) and experiences of health and disease in which any variation can be normative. Biosensing work as understanding will require the comparisons and recalibrations that make sense of the variations in terms of norms. One might think of fitness training or the many tracking practices associated with the Quantified Self movement as occurring in the space of health understood as a feeling of assurance in life. We should add too that it is hard to imagine how biosensing could even up disparities such as the different life expectancies between rich and poor areas.

The implication of the platform realignment argument is not that biosensors are destined to fail as innovations. Gadgets and devices may fall out of fashion, and there may even be large-scale abandonments of certain biosensing trajectories. Rather, the point here is different: biosensing practices, insofar as they concern health broadly construed as the ‘regulatory flywheel of possibilities of reaction’ (Canguilhem, 1989) or the capacity to bounce back, will be both normative and unstable. They inhabit the uneven terrain where biology and medicine, laboratory and clinic, encounter each other along the meandering fault line of biological processes and medical normalisation, with its classificatory operations. Even as biology strives and sometimes succeeds in measuring states of particular bodies in their subtly varying relations to experimentally accessible environments, medicine grapples with the problem of how to classify varying cases. If clinics, hospitals, pharmaceuticals and medical devices are some of the historically constituted architectures and artefacts of normalising efforts, the wearable devices, apps, research projects and publications crowding around platforms are the emerging architectures and configurations of the biosensing formation.

(p.28) Summary of the argument: sensing, biology–medicine and platforms

This introduction has climbed through steep terrain. Biosensors and biosensing are problematic on epistemic, economic, institutional and cultural grounds. In this book, we concentrate on bodies experienced in health and disease, ongoing tensions between biological, medical and other body knowledges, and the emerging platforms that reconfigure what it means to ‘have’ the data. The three themes shaping our account of biosensing can be summarised in terms of:

• • the indeterminacies of situated, living, environed bodies sensing and being sensed;

• • the slippages of biology and medicine, of science laboratories and medical clinics, as they regulate the incommensurabilities of life as biological form and life as lived experience; and

• • the gathering of data by devices and on platforms that align and configure experiences of health.

Overview of chapters

The case studies we explore in the chapters could be seen to follow the lifespan, from ovulation monitoring, to childhood and adult stress, through practices of prediction viewing genetic risk, to systems of remote or automated care for frail older people. We could have chosen many other case studies, such as home foetal heart monitoring or home dopplers, infectious disease biosensors, diabetes management, or military personnel monitoring. We chose case studies spanning significant experiences, events and durations throughout life. Some punctuate and others mark epochs of a life. Ultimately, they concern the lineaments of a life course.

Chapter 1 explores contemporary uses of fertility and sex hormone biosensing, asking how their use is rearticulating (p.29) sex/gender, sexuality and reproduction. Working with findings from Joann Wilkinson’s ethnography of ovulation monitoring and our citizens’ panel, and with textual materials about these kinds of biosensing, we critically analyse emerging practices around reproduction and sex/gender, asking how the collection and analysis of personal data is entangled with corporate biodigital platforms, and with feminist and other forms of collective politics.

Stress biosensing is the focus of Chapter 2. Beset by the notable complexities of titrating or measuring stress (either via so-called ‘stress hormones’, by heart rate or by galvanic skin response), these practices remain rather undeveloped compared to fertility biosensing. However, due to pre-existing narratives about stress and mental health and function (for example, found in literatures on childhood trauma and workplace stress), information about an individual’s stress and the collation of such information about particular groups is potentially of interest to many groups, including employers, foster carers and social services, and the military. We argue here that information about biological changes thought to be related to stress is only valuable if triangulated with other information about the person’s situation, including psychosocial histories and historical and geographical location.

Analysing data from our citizens’ panel work in Chapter 3, we explore how citizens make sense of such information, and whether, when and how knowing about future health risk might be desirable. How do citizens and users make sense of the complexities of genomic science that such devices bring them into contact with? How does direct-to-consumer genetic testing alter our perceptions of health, reproduction, families and futurity? DNA genotyping is a form of biosensing concerned with genetic risks, susceptibilities and relatedness. This chapter argues that genotyping services such as 23andMe prompt us to take seriously the platform realities of biosensing. DNA genotyping platforms attempt to aggregate data on (p.30) an unprecedented scale, to anchor the significance of DNA variations for health and kinship, and to connect DNA variations to health practices and health futures. The biosensors commonly used in DNA genotyping are microarrays. The amount of data that they produce poses challenges for biosensing. The Internet platforms and products associated with direct-to-consumer DNA genotyping embody a personalising approach to health and medicine, even though the connections between underpinning scientific findings concerning genomic variation and clinical interventions or treatments are mostly tenuous. At the same time, because the significance of the million or so variations reported in a typical DNA genotype is mostly undefined, the data has led to many individual and collective attempts to create new connections, to experiment with novel collective forms of analysis and treatment, and to build new relations between biology, medicine and everyday life.

How does biosensing reach into the lives of older people living at home? In Chapter 4, we examine care monitoring systems for older people, or ‘telecare’, as this has become known. We focus on the wearable falls detector – an alarm device that triggers, it is claimed, when a person trips or falls. We explore findings from ethnographies of home telecare and from citizens’ panel debates on how individuals and families live with such systems, and how falls detectors are constructed as workable. Following individuals’ interactions with telecare, we question the notion of self-tracking in this context, preferring the term ‘dys-tracking’ as better reflecting their relationship with automated devices. Falls detectors are technically highly complex, collecting data that is difficult to interpret. Ageing bodies are invariably assessed as low functioning and intrinsically at risk. Views from our citizens’ panels, however, show a more active and imaginative constituency, where practices of self-care exist alongside or in parallel with remote-care systems.

The Conclusion focuses on the gap between two prominent public narratives of health biosensing: the promissory-horizon (p.31) narratives that biosensing will soon solve many important and intractable problems; and the disappointment, overhype narrative, which suggests that biosensing often fails to deliver on its promise, and that users give up on their devices or use them only for narcissistic ends. We argue that research, policy and practice (everything from technology design to clinical medicine and health-related activism) would do well to pay attention to the ‘bio’ in all its senses and to platforms, recognising all the ways that we are living data and that data is lively. (p.32)