Quantified Bodies in the Checking Loop: Analyzing the Choreographies of Biomonitoring and Generating Big Data

ISSN: 1795-6889 www.humantechnology.jyu.fi Volume 12(1), May 2016, 56–73 56 QUANTIFIED BODIES IN THE CHECKING LOOP: ANALYZING THE CHOREOGRAPHIES OF BIOMONITORING AND GENERATING BIG DATA Abstract: Biomonitoring digital devices have become popular in physical activities and are receiving intensive focus as motivational and support vehicles for health. The aim of this article is to develop a new theoretical framework to analyze biomonitoring from the two perspectives constituting the opposite ends of the big data spectrum: individual (micro) and institutional (macro). In applying phenomenology of the body, discussions of choreography, and Latour’s actor–network theory, I seek to evolve a choreographybased approach that can outline feedback systems between embodied practices and the macrolevel choreography of big data. Health informatics data as economic and political assets are illustrated based on netnography. Netnographic methodology pays close attention to online fieldwork and media texts. Emphasizing the lived body in the analysis of knowledge infrastructure, I aim to contribute to the theoretical discussion of human– data interaction. The findings suggest that highly intimate, personal technology can distance people from their lived bodies. Keywords: biomonitoring, wearable technologies, choreography, phenomenology, embodiment, big data, human–data interaction. © 2016 Jaana Parviainen and the Agora Center, University of Jyväskylä DOI: http://dx.doi.org/10.17011/ht/urn.201605192620 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Jaana Parviainen University of Tampere Finland Quantified Bodies in the Checking Loop 57 INTRODUCTION The interests of health policymakers and employers have increasingly become involved with selfcare interventions, such as healthy diets and physical exercise, to provide health benefits for people and reduce health-care costs. The use of self-monitoring digital equipment in physical exercise, likewise, is becoming increasingly popular and receiving more and more attention as a motivator and support vehicle for self-care and well-being. Self-monitoring, or biomonitoring, refers to tools such as wearable electronic sensors and mobile phones with apps that collect, store, process, and display data about bodily functions. Also known as self-tracking, self-quantification, or the quantified self, biomonitoring has flourished within the domain of health and physical exercise, where a strong demand from competitive sports and biomedical technology resulted in the commercialization of consumer health devices. Compared with a simple pedometer that counts each step and shows the overall figures to its user, the new generation of fitness tracker devices—like the Fitbit, Jawbone Up, and, more recently, Apple’s iWatch—are small computers that also can collect, generate, and share data about the physical body. With this information, individuals can manage multiple aspects of their personal health informatics. The designers of fitness trackers (see, e.g., Medynskiy & Mynatt, 2010) emphasize that biomonitoring is intended to motivate persons to achieve healthy lifestyles. Some fitness trackers are aimed towards full-day activity monitoring, while others focus on single workout monitoring. Users employ biomonitoring technology in different roles within their lives. The design of personal health informatics devices is grounded on the belief that such systems can, through the collection and presentation of personal information, promote individuals’ selfawareness and that improved self-awareness consequently leads to self-insight, self-control, and positive, healthy behavioral change (e.g., Khovanskaya, Adams, Baumer, Voida, & Gay, 2013). The quantified self (QS) movement is a network of people whose aim is to promote selfmonitoring activities by acquiring data on various aspects of users’ physical bodies to improve personal health or professional productivity (Quantified Self Labs, 2015). By examining biomonitoring through the lens of phenomenology, I propose to rethink self-tracking data production and its rationalities. The fundamental aim of phenomenological philosophy is to develop a greater understanding of individuals’ experiences through the consciousness of the experiencer (Giorgi, 2009). The phenomenological approach, as used here, is based on a phenomenological notion of the body, more specifically the distinction between the physical body, or Körper, and the lived body, or Leib (Husserl, 1954/1970; Leder, 1990, 1998). The primacy of Körper has been highlighted in many disciplines, including biomedical technology, and in diverse aspects of sport sciences, physical education, and human–computer interaction (HCI). In the everyday discourse of physical exercise, the body usually concerns Körper, a corporeal entity that consists of muscular fibers, complex brainwaves, neural pathways, circulation, and so on. In many popular fitness-training programs, people are expected to modify the Körper by building muscle, burning fat and calories, stretching muscles and tendons, and improving cardiovascular functioning. In trying to provide an alternative to mind–body dualism, phenomenologists (see, e.g., Gallagher & Zahavi, 2012; Parviainen, 2011) outline the third category between body and the mind, the Leib, as a conscious, active, reflexive, and embodied entity. Following Husserl’s notion of Leib, Merleau-Ponty (1945/1962, p. 139) described the lived body, corps vécu, as a conscious subject that is never a mere physical thing but has its own intentionality and body Parviainen 58 awareness. Merleau-Ponty (1945/1962) and Sheets-Johnstone (1999) have stressed that bodily sensations, in particular tactile and kinesthetic sensations, may have clear meanings without overt symbolic (linguistic) value. The sense making of bodily movements does not reside in words but is evoked in the meeting of the spatial and material world and other living beings. Today’s biosensors can provide robust data about physical bodies, such as pulse, step count, blood pressure, and so on. By collecting and processing data based on software, some biosensors also provide verbal or textual signals to users, such as, “exercise harder” or “drink water.” However, people can experience a range of vague bodily feelings and sensations that they cannot monitor. For instance, individuals cannot expect to receive a diagnosis of the feeling of resonating with other lived bodies in everyday life, no matter how well the sensors work; making sense of their vague bodily feelings during exercise poses problems that go beyond reading signals from biosensors. The phenomenological approach of this study aims at making sense of how biomonitoring equipment collects physical data from physical bodies but cannot reach lived bodies, the bodily sensations, feelings, kinesthesia, affects, atmospheres, and desires in and between bodies. However, despite its clear assets as a method, phenomenology sometimes fails to address the social context of lived experiences (Langridge & Ahern, 2003). Furthermore, although phenomenology can help generate structures of lived experiences, it is not suitable for making generalizations about institutional structures (Mayoh & Onwuegbuzie, 2015). As Savat (2013) suggested, technological ensembles now take place on a greater scale than they ever did before. To meet the aims within the broader theoretical framework of HCI, actor–network theory (ANT; e.g., Latour, 1997) and theoretical discussions of choreography (e.g., Schiller & Rubidge, 2014) are brought together to highlight the role of movement in biomonitoring. One of the greatest strengths of phenomenological methodology is its flexibility and adaptability that allow for its incorporation within other disciplines (e.g., Dourish, 2001; Garza, 2007). Given the rise of social networking and mobile technologies—and the ever-increasing digitalization of leisure and daily actions, including fitness activity—the quantity of personal data being generated today has reached an unprecedented scale. Big data is considered here primarily as a knowledge infrastructure generated through an ensemble of techniques (Ajana, 2015). When a society is becoming data-driven (Pentland, 2013), it is important to make distinctions regarding how personal data are created. Observed data, such as online shopping behavior, are inferred and created from information about individuals collected by programs (Mortier, Haddadi, Henderson, McAuley & Crowcroft, 2015; World Economic Forum, 2011). Data can be intentionally created by individuals through online social network profiles, which is a process called volunteered data. Personal data created by wearables are observed data but people usually provide it through online social network profiles, so it becomes volunteered data. To describe this data collection and its political and economic implications, an ethnomethodological approach or, being more specific, a “netnographic” approach, was used in this study. As a modification of the term ethnography, netnography refers to online fieldwork that follows from the conception of ethnography as an adaptable method (Kozinets, 2010). The Internet has become an important site for research, so a number of researchers have utilized online communities—including newsgroups, weblogs, forums, and social networking websites—to examine various phenomena. Recent media texts, such as newspaper items, columns, and blogs, are used to illustrate how personal health informatics is collected through sensors and trackers to generate big data. Quantified Bodies in the Checking Loop 59 When individuals are bound through their digital devices to processes in which their personal data are collected, analyzed, and traded, it is necessary to reflect on the feedback mechanism of this system. HCI research has traditionally focused on the interactions between humans and computers-as-artifacts. However, Elmqvist (2011) and Haddadi, Mortier, McAuley and Crowcroft (2013) suggested that it is time to recognize the phenomenon of human–data interaction (HDI). HDI does not concern interaction between humans and computers generally but, rather, between humans and the analysis of large, rich personal datasets. As Haddadi and his fellows (2013, p. 5) stated, “HDI overlaps HCI but is not contained within it.” HDI concerns people interacting with an apparently mundane knowledge infrastructure that they do not necessarily recognize or understand or would rather ignore. Opening up such infrastructure and its dynamics is a challenge because the scale of these systems is much bigger than usually considered in interactional studies. To understand the dynamism of how information is generated, two perspectives are important: the individual (micro) and the infrastructural or institutional (macro). As Klauser and Albrechtstlund (2014) suggested, these constitute the opposite ends of the big data spectrum. These opposites are not combined in an arbitrary manner but include complex feedback systems. In the endeavor to study the intimacy inherent in everyday use of wearable computers combined with big data development, the notion of choreography, Latour’s notion of ANT, and the phenomenology of the body help to develop a coherent theoretical framework. The notion of choreography and its related concepts, such as kinesthesia and kinesphere, assist in capturing an intimate integration of everyday use of wearable sensors and lived bodies. In this paper, my aim is to develop a new choreography-based theoretical framework to analyze biomonitoring on a larger scale, instead of as a mere personal activity. The notion of HDI (Haddadi et al., 2013) assists discussions of human–technology choreographies to consider what kind of feedback systems link the microlevel choreographies of personal biomonitoring to the macrolevel choreography of collecting big data. This paper begins, firstly, with how the processes of collecting personal data as health informatics are illustrated based on a netnographic methodology. Secondly, choreography as a theoretical framework is introduced to show how it assists in analyzing biomonitoring in everyday life. Next, particular attention has been focused on the phenomenological view of embodiment to show why individuals cannot reach the lived body through fitness trackers. After, my aim is to outline how a “checking loop,” such as a microlevel choreography, has become normalized as an embodied practice in the context of fitness and well-being. The paper concludes by discussing feedback systems that turn the macrolevel choreography of generating big data back toward citizens and consumers to establish new types of embodied disciplines and health care policies. BIOMONITORING AND PERSONAL HEALTH INFORMATICS Personal health informatics data, which have emerged only in recent years, represent an entirely new class of data. They refer to self-collected intimate data about one’s own health and health-related activities, often obtained autonomously from smartphones, activity trackers, wearable devices, and other sensors. Self-tracking applications can collect all kinds of everyday activities, thoughts, and statuses into discrete data that can be stored, analyzed, and used to guide to positive outcomes. They are quantifiable, analytical data about health, Parviainen 60 habits, and routines, from the temperature in a particular sleep environment to the exact amount of time people have been still, sitting in a chair (e.g., Li, Dey, & Forlizzi, 2011; Thomaz, 2013). An increasing number of people are carrying smartphones and devices with them all day, every day, and these devices can be used to collect data. Most modern smartphones have a plethora of sensors built into them and many of these have self-tracking applications. Smartphones are primarily telephony devices, but the inclusion of multiple sensors along with a suitable app execution environment can turn them into general purpose, self-tracking devices. The sensors of smartphones may include an accelerometer, gyroscope, barometer, heart rate sensor, thermometer, proximity meter, and navigation systems (Barcena, Wueest, & Lau, 2014). Apps also assist users in collecting data from the physical body that sensors currently cannot capture, such as data on moods, food and drink consumption, aches and pains, and so on. In addition to smart phones, fitness trackers like Fitbit, Jawbone UP, and Lullaby sense many types of human activities and dissect these activities into quantified measures, such as the number of hours in REM sleep. In monitoring physiological measurements, such as pulse, respiration, and blood pressure in terms of fitness activities, fitness trackers do not just give feedback to the mover, but they also can allow data to be collected and processed based on proprietary software and algorithms. Wearable devices typically have a small and light form factor, letting users wear them on the wrist like a wristband or as a watch. Alternatively, they can be attached to sports equipment such as running shoes, clothes, bikes, and more. These devices usually contain accelerometers and gyroscopic sensors that are responsible for generating the data. Barcena and his fellows (2014, p. 12) stated that, “By reading the stream of data from these sensors and then applying data processing algorithms, the devices can recognize patterns to identify the wearer’s current activity.” To indicate how many calories the user has burned, for example, a tracker needs to access data on the user’s age, gender, height, and weight and add them to data about the user’s heart rate, estimation of perspiration, and how many steps have been taken, and then ultimately process all this data to generate the single measure based on its algorithm. To translate the raw data into actual figures and statistics on their screens, fitness trackers use slightly different algorithms. As Thomaz (2013, p. 3) suggested, before fitness trackers and wearables emerged, “We were not conscious of how many flights of stairs we climbed on a given day or the exact amount of time spent brushing our teeth.” Therefore, from the perspective of phenomenology, it is clear that these systems are actually tracking tacit bodily activities and make these trivial routines more visible in daily life. In fact, biomonitoring quantifies a dimension of physical bodies of which people were not aware and considered as irrelevant information. This stream of data is of interest now because people have collectively understood that these previously ignored quantifiable dimensions implicate their state of health and well-being to the extent that they should attend to them. In effect, data collecting by biosensors constitutes the foundation of many health-related activities and behaviors. Recently launched fitness tracker devices, such as Apple’s iWatch, are already preparing to play a bigger role in collecting health informatics from their users. Smart fabrics and materials, smaller sensors and processors, and improved battery life within the ubiquitous communications infrastructure have all opened up a new world of possibilities for devices that can be worn and carried around all day. Microsoft and Apple are in competition to develop a smart watch that includes glucose-monitoring technology. Olson (2014, p. 1) pointed out that, glucose monitoring Quantified Bodies in the Checking Loop 61 is the holy grail because of the insights that could give into what someone has eaten. That would be a crucial data point for insurers because diet has a far greater impact on health than activity. Typically, persons who are actively tracking their bodies are sports enthusiasts. Using sensors, keen runners can collect data about their running activity to help them set performance goals and evaluate their progress. Being constantly logged in to their performance data, they can witness their dynamic physical condition and choose a proper technique to improve their results. Moreover, as Barcena and his fellows (2014, p. 6) stated, “There are also self-tracking geeks who are interested in documenting all facets of their daily lives in as much detail as possible in public and have turned the whole idea into an art form.” Aside from enthusiast users, many people may be just curious or wish to achieve a goal, such as losing weight, getting more sleep, or living a generally healthier lifestyle. While the health benefits of use of selftracking devices and apps cannot be scientifically proven, many people clearly believe they are beneficial for their motivation. Biomonitoring and personal informatics are perceived largely as a positive development because they offer people the opportunity to gain a more refined understanding of their physical body’s condition. Nevertheless, it is still important to remain critical and examine the extent to which the practice might affect their lives. Self-tracking is already a big business and is expected to grow rapidly. According to PricewaterhouseCooper’s (2014) report, 1 in 5 Americans owns some type of wearable technology. This figure does not include smartphones that can run self-tracking apps that would, if accounted for, amount to billions of units worldwide. According to a study by Fox and Duggan (2013), 69% of Americans regularly track their weight, diet, or exercise activity. One key technology driver behind big data is the potential of the wholesale of personal data (Barcena, et al., 2014). For instance, insurance companies are interested in gaining access to data generated by fitness trackers (Accenture, 2015) because big data would be extremely valuable for risk estimations. Never before have such huge amounts of health informatics been collected, transmitted, and stored about users. So the question of anonymity of data has become more relevant. The anonymity of data collection can be at risk when data are being sent from one device or location to another. One concern is that users are tracked without knowing other devices nearby are collecting information on them. Although some of the data could be considered highly sensitive, much of the information listed is not. For example, hospitals are required to carefully handle medical data, but data about the amount of water a person drinks daily are not seen as delicate information. Gaining access to personal health data is a potential goldmine for employers because these data can allow them to gain deep insight into their employees. Olson (2014, p. 1) suggested that “more employers are opting to monitor data being generated by fitness trackers—to the extent they can see it on a dashboard—and are holding their insured staff to account with rewards as part of a growing number of so-called corporate-wellness programs.” Employers are exploring ways to monitor their staff's wearable devices to help them reduce health-care costs. As part of corporate wellness programs, employers may offer their employees fitness trackers and, as a service for monitoring this data, they might reward employees for fitness activity. To explore further why personal data are not a mere personal issue, I consider in what kind of theoretical framework biomonitoring can be analyzed on a larger scale without losing touch with its intimate act. Parviainen 62 CHOREOGRAPHY AS THEORY Choreography can be divided into practice and theory. Plenty of guidebooks for dance students and other practitioners present how to make choreographies in practice (e.g., Blom & Chaplin, 1989; Smith-Autard, 1996; Tufnell & Crickmay, 1993). Then there is research literature that develops theoretical and philosophical approaches to choreography, mainly in the contexts of dance cultures and performance (e.g., Kozel, 2007; Lepecki, 2006; Manning, 2009). The line between practice and theory blurs, however, because movement theories usually emerge from practical knowledge and experience, involving a hermeneutic circle between theorizing and practicing. Rudolf Laban was a good example of a choreographer who also developed a movement theory and movement analysis method. Laban’s effort–shape (1980) is a widely used method to analyze everyday bodily movements in different contexts, including HCI (e.g., Hummels, Overbeeke, & Klooster, 2007; Loke, Larssen, Robertson, & Edwards, 2007). However, as a movement theory, effort–shape has some limitations in the context of this study. Laban restrained the analysis of movements and gestures in a limited space and time scope so, as a theory of choreography, it is not necessarily the best approach to analyze movements whose scale can reach across the world. In addition, he did not give much weight to nonhuman agents and environments in making movements. My interest in this paper is to consider movement as a relational net or a reciprocal and dynamic matrix. The choreography is manifested and materialized by bodies, action, and environment rather than simply bodies making movements to create choreography. There is a reasonable amount of theoretical discussion of choreography (e.g., Butterworth & Wildschut, 2009; Foster, 1995; Hunter 2015; Klien, 2007; Parviainen, 2010; Robertson, Lycouris, & Johson, 2007; Schiller & Rubidge, 2014) that can assist in developing a coherent choreographic approach to understanding movement in the context of this study. I wish to (a) consider constellations of movements in which various actors/agents are involved and (b) disrupt notions of inside versus outside the body. In this context, choreography is not seen as belonging to the domain of dance. Rather, choreography refers to movements and activities in which movements appear to form meaningful interactions and relations in a lived space between various animate or inanimate agents. Thus, no choreographer alone could lead the dynamics of this constellation; rather, human and nonhuman agents have connections with each other that establish ongoing choreography. Treating human and nonhuman agents symmetrically here, choreography is seen to arise from their relations that become perceptible through movements, motion, patterns, and rhythms. This notion of choreography resonates with theories of the actor network (e.g., Latour, 1997; Law, 1992). Latour (1997) and his colleagues (e.g., Law, 1992) suggested that objects can become agents or actants with humans, influencing action as well as resulting from it. An assemblage of people, objects, and technologies are composed of heterogeneous elements that enter into relations with one another. When digital technologies, action, and materiality intertwine, human bodies do not remain independent entities from technologies. In this assemblage, embodied connections with digital technologies modify physical and lived bodies, forming new kinds of embodied practices. Deborah Lupton (2013, p. 400), alluding to Peter Freund, used the term “technological habitus” to describe how bodies develop new habits and routines to blend in with the function of technologies. She suggested that bodies do not intermesh smoothly or seamlessly within technology but rather there are disjunctions between bodies and objects. Embodied practices do not refer to mere habituation or the domestication of Quantified Bodies in the Checking Loop 63 technology but to how new bodily activities are developed to cope with or utilize new technologies. For instance, running as a form of physical activity is not dependent on any hightech gadgets. But, running with sensors and wearables depends on an assemblage of fitness trackers, apps, and perhaps link stations in the running environment. Although running with wearables mobilizes a new type of assemblage, it also changes the style of running when runners need to check certain numbers and figures on their wristbands to adjust their running speed. In this relational materialism, actants are managed to make new relationships and form new running routines as embodied practices. Latour’s (1997) approach to ANT emphasized agency and an assemblage of actors in which he did not give much concern to experiential aspects of these actants; in other words, how feelings, affects, and sensations influence relations among actants. The choreographic and phenomenological approach is to focus on the quality of action: How does the special quality of actants’ movement keep action alive and make things happen? To answer to this question, it is important to rethink the meaning of movement and kinesthesia (e.g., Husserl, 1973/1997; SheetsJohnstone, 1999) in an assemblage of people, objects, and technologies. It is necessary to consider what kind of role movement and lived bodies have in making affective relationships when pulling and pushing things towards a constellation. This notion of choreography challenges the traditional view of technology as residing in individual cognitive or psychological processes and instead shifts the focus of HCI toward embodied social activities and transitions in a network of heterogeneous elements. This choreographic approach recognizes the importance of the interaction of hardware and software technologies, bodies, and environments brought together in an active relationship and in a particular spatial configuration that can be both planned and improvised. Kinesthesia plays a central role here in understanding interaction among lived bodies and their interactions within material environments. As a sense of motion, kinesthesia is something that helps researchers recognizes differences and similarities within a person’s own movement qualities, haptic sensations, and the moving objects around them. When one lifts an object, this reveals something about the object’s weight. Rubbing ones fingers across an object reveals details about the texture and shape of the object. Squeezing an object says something about its compressibility. Thus, bodily movements are fundamentally intentional and mindful in themselves in a way that they have a special kind of reflective thinking (Sheets-Johnstone, 1999). However, in the present study, the dynamics of interaction among bodies reach beyond immanent bodily experiences and kinesthetic limits. In my previous studies with colleagues (Parviainen, Tuuri, & Pirhonen, 2013), we developed a notion of choreography that can help to evolve a coherent theoretical framework in which the emphasis is on the intimate physical contact of sensors combined with big data generated. We distinguished three scopes of choreography by using the terms micro-, local-, and macro-levels. In a microlevel analysis, the focus is on, for instance, the movement of the index finger swiping across the screen. These are the movements that take place within one’s kinesphere or internally. Local-level choreographies refer to bodies in their social interaction, for instance, when passengers, sitting or standing on the metro, hide behind their phones in trying to avoid eye contact with other people. The macrolevel choreography refers to largescale movements that go beyond human embodied efforts, for instance, to link transporting infrastructures or the Internet to create extensive trajectories. Thus, those passengers on the metro, hiding behind their mobiles, might read text messages from their friends on other continents or watch a video on YouTube that has been seen by over 100 million other people. Parviainen 64 Instead of focusing on material objects and singular gestures in using them, this choreographic approach offers a new theory to consider movements as trajectories, transitions, and relations in interaction design. This approach does not restrain the analysis of movements and gestures in a limited space/time scope but takes into account the scale of the three levels of choreography that are usually involved when people use digital devices in everyday life. Before I consider further the idea of choreography in the context of biomonitoring and fitness trackers, the notion of innesphere is introduced to illuminate the difference between the lived body and the physical body. INNESPHERE AND THE LIVED BODY Turning back to Laban (1966, p. 10), he created the concept of kinesphere that was defined as the “space which can be reached by easily extended limbs.” By kinesphere, Laban referred to bodily movements and activities in an “individual bubble.” Drawing on the sociological discussions of personal space (Moore & Yamamoto, 1988) and the phenomenological notion of subjective space, the concept of kinesphere can be also understood to include affective and social connotations forming lived space. The spatiality that individuals live in and through their bodies and that surrounds them is called lived space, and it plays a central role in local-level choreographies. Phenomenologists (e.g., Husserl, 1973/1997; Merleau-Ponty, 1945/1962) considered that the lived body cannot be treated as a mere material object that locates in the three-dimensional geometric space. In this sense, the notion of lived space differs from the schema of Euclidean three-dimensional space or a mere individual bubble. Objective space can be scrutinized and measured with fixed metrics, and it has become the foundation of the physical sciences and digital technology. To understand human movement in all its complexity, it is not enough to focus on physical bodily motion within the kinesphere but also to the inner movements of the body. If the kinesphere is the lived space that can be reached easily by extended limbs, innesphere concerns the internal space that can be reached under the skin. The skin should not be understood as a boundary between the kinesphere and the innesphere but rather as an interface that binds together inner bodily feelings and outer perception. The term “body image” aims to capture the internal and intimate feelings of one’s own body, but it emphasizes too much the visual and psychological aspects of embodiment. The notions of “body awareness” (Mehling et al., 2011) or “body topology” (Foster, 1997) are more helpful in understanding what is meant here by the dynamics of innesphere as a lived space. Body topography emphasizes the spatial character of the body as a lived space that describes internally felt body parts and spots and relations as a moving structure. Thus, movements within the body topography and innesphere do not just simply refer to bodily functions such as heartbeats or breathing, but how we feel them. Individuals usually locate the feeling of pain somewhere in their body topography, identifying its sensuous dynamism with terms like sharp, sore, itchy, stabbing, burning, stiff, stinging, tender, thumping, or tight. Feeling bodily sensations, as examples of movements within the innesphere, can also be imaginary and socially loaded. If meaning movements within the body topography can be imaginary or partly culturally constructed, then wearable technologies can hardly track them. Therefore, wearable technologies cannot track sensations, feelings, and movements within the body topography in a manner that makes immediate sense and intuitive understanding. Quantified Bodies in the Checking Loop 71 Fox, S., & Duggan, M. (2013). Tracking for health. Retrieved August 21, 2015, from the Pew Research Center: http://www.pewinternet.org/files/old-media//Files/Reports/2013/PIP_TrackingforHealth%20with%20appendix.pdf Gallagher, S., & Zahavi, D. (2012). The phenomenological mind. London, UK: Routledge. Garza, G. (2007). Varieties of phenomenological research at the University of Dallas. Qualitative Research in Psychology, 4, 313–342. doi: 10.1080/14780880701551170 Giorgi, A. (2009). The descriptive phenomenological method in psychology. Pittsburgh PA, USA: Duquesne University Press. Haddadi, H., Mortier, R., McAuley, D., & Crowcroft, J. (2013). Human-data interaction (Technical Report No. 837). Retrieved October 19, 2015 from the University of Cambridge Computer Laboratory website: http://www.cl.cam.ac.uk/techreports/ Harwood, K. (2014). Algorithms: The next wearable tech frontier. Retrieved October 19, 2015, from http://www.wired.com/insights/2014/10/algorithms-wearable-tech-frontier/ Hummels, C., Overbeeke, K. C. J., & Klooster, S. (2007). Move to get moved: A search for methods, tools and knowledge to design for expressive and rich movement-based interaction. Personal and Ubiquitous Computing, 11, 677–690. Hunter, V. (2015). Moving sites: Investigating site-specific dance performance. London, UK: Routledge. Husserl, E. (1970). The crisis of European sciences and transcendental phenomenology (D. Carr, Trans.). Evanston, IL, USA: Northwestern University Press. (Original work published in 1954) Husserl, E. (1997). Thing and space. Lectures 1907 (R. Rojcewicz, Trans.). Dordrecht, the Netherlands: Kluwer Academic Publishers. (Original work published in 1973) Kaptelinin, V., & Nardi, B. A. (2006). Acting with technology: Activity theory and interaction design. Cambridge, MA, USA: The MIT Press. Kerr, I., & Earle, J. (2013). Prediction, preemption, presumption: How big data threatens big picture privacy. Stanford Law Review [online], 66, 65–72. Retrieved August 21, 2015, from www.stanfordlawreview.org/online/privacy-and-big-data/prediction-preemption-presumption Khovanskaya, V., Adams, P., Baumer, E. P.S., Voida, S., & Gay, G. (2013, April). The value of a critical approach to personal health informatics. Paper presented at the CHI 2013 workshop Personal Informatics in the Wild: Hacking Habits for Health & Happiness, Paris, France. Retrieved March 10, 2015, from http://www.personalinformatics.org/docs/chi2013/khovanskaya.pdf Klauser, F. R., & Albrechtslund, A. (2014). From self-tracking to smart urban infrastructures: Towards an interdisciplinary research agenda on big data. Surveillance & Society 12(2), 273–286. Klien, M. (2007). Choreography: A pattern language. Kybernetes, 36(7/8), 1081–1088. Kozel, S. (2007). Closer: Performance, technologies, phenomenology. Cambridge MA, USA: The MIT Press. Kozinets, R. V. (2010). Netnography: Doing ethnographic research online. Thousand Oaks CA, USA: Sage Publications. Laban, R. (1966). Choreutics. London, UK: MacDonald and Evans. Laban, R. (1980). The mastery of movement. London, UK: MacDonald and Evans. Langridge, M. E., & Ahern, K. (2003). A case report on using mixed methods in qualitative research. Collegian, 10(4), 32–36. doi: 10.1016/S1322-7696(08)60074-8 Latour, B. (1997). On actor–network theory: A few clarifications. Retrieved August 21, 2015, from the Centre for Social Theory and Technology, Keele University, UK: http://keele.ac.uk/depts/stt/stt/ant/latour.htm. Law, J. (1992). Notes on the theory of the actor network: Ordering, strategy and heterogeneity. Systems Practice, 5(4), 379–393. Leder, D. (1990). The absent body. Chicago IL, USA: University of Chicago Press. Leder, D. (1998). A tale of two bodies: The Cartesian corpse and the lived body. In D. Welton (Ed.), Body and flesh: A philosophical reader (pp. 117–130). Malden, MA, USA: Blackwell Publishers. Lepecki, A. (2006). Exhausting dance: Performance and the politics of movement. London, UK: Routledge. Parviainen 72 Li, I., Dey, A. K., & Forlizzi, J. (2011). Understanding my data, myself: Supporting self-reflection with ubicomp technologies. In J. Landay, Y. Shi, D. J. Patterson, Y. Rogers, & X. Xie (Eds.), Proceedings of the 13th International Conference on Ubiquitous Computing (UbiComp '11; pp. 405-414). New York, NY, USA: ACM. doi: 10.1145/2030112.2030166 Loke, L., Larssen, A. T., Robertson, T., & Edwards, J. (2007). Understanding movement for interaction design: Frameworks and approaches. Personal and Ubiquitous Computing, 11, 691–701. Lupton, D. (2013). Quantifying the body: Monitoring and measuring health in the age of mHealth technologies. Critical Public Health, 23(4), 393–403. doi: 10.1080/09581596.2013.794931. Manning, E. (2009). Relationscapes: Movement, art, philosophy. Cambridge, MA, USA: The MIT Press. Manovich, L. (2012). Trending: The promises and the challenges of big social data. In M. K. Gold (Ed.), Debates in the digital humanities (pp. 460–75). Minneapolis, MN, USA: The University of Minnesota Press. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung Rivers, A. (2011). Big data: The next frontier for innovation, competition, and productivity. Retrieved June 4, 2015, from the McKinsey Global Institute website: www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation Mayoh, J., & Onwuegbuzie, A. J. (2015). Toward a conceptualization of mixed methods phenomenological research. Journal of Mixed Methods Research, 9(1), 91–107. doi: 10.1177/1558689813505358 Medynskiy, Y., & Mynatt, E. D. (2010, April). From personal health informatics to health self-management. In CHI 2010 workshop Know Thyself: Monitoring and Reflecting on Facets of One's Life, Atlanta, GA, USA. Retrieved August 25, 2015, from http://www.personalinformatics.org/docs/chi2010/medynskiy_personal_health_informatics.pdf Mehling, W, E., Wrubel, J., Daubenmier, J., Price, C., Kerr, C. E., Silow, T., Gopisetty, V., & Stewart, A. (2011). Body awareness: A phenomenological inquiry into the common ground of mind–body therapies. Philosophy, Ethics and Humanities in Medicine, 6(6), 1–12. Merleau-Ponty, M. (1962). Phenomenology of perception (C. Smith, Trans.). New York, NY, USA: Routledge. (Original work published in 1945) Moore, C. L., & Yamamoto, K. (1988). Beyond words. New York, NY, USA: Gordon and Breach. Mortier, R., Haddadi, H., Henderson, T., McAuley, D., & Crowcroft, J. (2015). Human–data interaction: The human face of the data-driven society. Retrieved October 19, 2015, from the Cornell University Library website: http://arxiv.org/abs/1412.6159v2 Mowbray, S. (2013, June 27). I’m obsessed with walking 10,000 steps a day [Web log post]. Retrieved March 18, 2016, from http://simmerandboil.cookinglight.com/2013/06/27/walking-10000-steps-a-day/ Muller, B. (2004). (Dis)qualified bodies: Securitization, citizenship and “identity management.” Citizenship Studies, 8(3), 279–294. doi: 10.1080/1362102042000257005 Nagamura, T. (2015). The action of looking at a mobile phone display as nonverbal behavior/communication: A theoretical perspective. Computers in Human Behavior, 43, 68–75. doi: 10.1016/j.chb.2014.10.042 Nissenbaum, H. (2009). Privacy in context: Technology, policy, and the integrity of social life. Stanford, CA, USA: Stanford Law Books. Olson, P. (2014, June 14). Wearable tech is plugging into health insurance. Forbes [online; unpaginated]. Retrieved June 4, 2015, from http://www.forbes.com/sites/parmyolson/2014/06/19/wearable-tech-health-insurance Parviainen, J. (2010). Choreographing resistances: Kinaesthetic intelligence and bodily knowledge as political tools in activist work. Mobilities, 5(3), 311–330. Parviainen, J. (2011). The standardization process of movement in the fitness industry: The experience design of Les Mills choreographies. European Journal of Cultural Studies, 14(5), 526–541. Parviainen, J., Tuuri, K., & Pirhonen, A. (2013). Drifting down the technologization of life: Could choreography-based interaction design support us in engaging with the world and our embodied living? Challenges, 4(1), 103–115. doi: 10.3390/challe4010103 Quantified Bodies in the Checking Loop 73 Pentland, A. S. (2013). The data-driven society. Scientific American, 309(4), 78–83. doi: 10.1038/scientificamerican1013-78. PricewaterhouseCoopers. (2014). The wearable future [Consumer Intelligence Series]. Retrieved March 19, 2016, from http://www.pwc.com/us/en/industry/entertainment-media/publications/consumer-intelligenceseries/assets/pwc-cis-wearable-future.pdf Quantified Self Labs. (2015). Retrieved August 21, 2015, from http://quantifiedself.com/ Roberts, J. A., & Yaya, P., Honore, L., & Manolis, C. (2014). The invisible addiction: Cell-phone activities and addiction among male and female college students. Journal of Behavioral Addiction, 3(4), 254–265. Robertson, A., Lycouris, S., & Johson, J. (2007). An approach to the design of interactive environments, with reference to choreography, architecture, the science and the complex systems and 4D design. International Journal of Performance Arts and Digital Media, 3(2-3), 281–294. Sanches, P., Kosmack Vaara, E., Sjölinder, M., Weymann, C. & Höök, K. (2010, April). Affective health: Designing for empowerment rather than stress diagnosis. Paper presented at the CHI 2010 workshop Know Thyself: Monitoring and Reflecting on Facets of One’s Life, Atlanta, GA, USA. Retrieved March 10, 2014, from http://www.personalinformatics.org/docs/chi2010/sanches_affective_health.pdf Savat, D. (2013). Undoing the digital: Technology, subjectivity and action in the control society. London, UK: Palgrave. Schiller, G., & Rubidge, S. (2014). Introduction. In G. Schiller & S. Rubidge (Eds.), Choreographic dwellings: Practising place (pp. 1–10). New York, NY, USA: Palgrave Macmillan. Sheets-Johnstone, M. (1999). The primacy of movement. Amsterdam, the Netherlands: John Benjamins. Smith-Autard, J. M. (1996). Dance composition. London, UK: Lepus Book. de Souza e Silva, A., & Frith, J. (2012). Mobile interfaces in public spaces: Locational privacy, control, and urban sociability. London, UK: Routledge. Thomaz, E. (2013, April). A human-centered conceptual model for personal health informatics data. In CHI 2013 workshop Personal Informatics in the Wild: Hacking Habits for Health & Happiness, Paris, France. Retrieved August 25, 2015, from http://www.personalinformatics.org/docs/chi2013/thomaz.pdf Tufnell, M., & Crickmay, C. (1993). Body space image: Notes toward improvisation and performance. London, UK: Dance Books. World Economic Forum (in collaboration with Bain & Company). (2011). Personal data: The emergence of a new asset class. Retrieved October 19, 2015, from http://www3.weforum.org/docs/WEF_ITTC_PersonalDataNewAsset_Report_2011.pdf Author’s Note This research received funding from the Ministry of Education and Culture in Finland. All correspondence should be addressed to Jaana Parviainen University of Tampere School of Social Sciences and Humanities Kalevantie 4 Tampere, 33014, Finland Jaana.P[email protected] Human Technology: An Interdisciplinary Journal on Humans in ICT Environments ISSN 1795-6889 www.humantechnology.jyu.fi