From Quantified Self to Quality of Life
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“Know Thyself” is a motto leading the Quantified Self (QS) movement, which at first originated as a “hobby project” driven by self-discovery, and is now being leveraged in wellness and healthcare. QS practitioners rely on the wealth of digital data originating from wearables, applications, and self-reports that enable them to assess diverse domains of their daily life. That includes their physical state (e.g., mobility, steps), psychological state (e.g., mood), social interactions (e.g., a number of Facebook “likes”) and environmental context they are in (e.g., pollution). The World Health Organization (WHO) recognizes these four QS domains as contributing to individual’s Quality of Life (QoL), with health spanning across all the four domains. The collected QS data enables an individual’s state and behavioral patterns to be assessed through these different QoL domains, based on which individualized feedback can be provided, in turn enabling to improve the individual’s state and QoL. The evidence of causality between QS and QoL is still being established, as only data from limited cases and domains exist so far. In this chapter, we discuss the state of this evidence via a semi-systematic review of the exemplary QS practices documented in 609 QS practitioners’ talks and a review of the 438 latest available personal wearable technologies enabling QS. We discuss the challenges and opportunities for the QS to become an integral part of the future of healthcare and QoL-driven solutions. Some of the opportunities include using QS technologies as different types of affordances supporting the goal-oriented actions by the individual, in turn improving their QoL.
KeywordsHuman-computer interaction Mobile health Tracking and self-management systems Ubiquitous computing and sensors Physiologic modeling and disease processes
Quantified Self (QS) is a relatively young trend, where individuals focus on tracking own state and behavioral patterns with the help of old-fashioned paper-and-pencil methods, or, on a growing scale – with a support of personalized devices (wearables and smartphones) for continuous, ideally unobtrusive tracking. The QS movement is lead by the motto “Know Thyself” and has been enabled by the high spread and adoption of the Internet and its services, as well as ubiquitous availability of personal devices like smartphones with embedded sensors, enabling implicit and explicit tracking services. For example, in the United States in 2015, 89% of the population used the Internet, and 72% owned a smartphone, and these numbers are increasing yearly (Poushtr 2016). Self-tracking is a real trend in the US. It is estimated that 60% of the US population in 2013 tracked some aspect of their life (e.g., weight, exercise, mood), 33% of adults tracked health indicators or symptoms (e.g., blood pressure, blood sugar, headaches, or sleep patterns), and 12% tracked a health indicator on behalf of someone they cared for (Fox 2013). Added together, seven out of ten US adults said they tracked at least one health indicator. It was shown that 50% of these trackers record their notes in some organized way, such as on paper (29%) or using technology (21%), i.e., 8% of trackers use a medical device (e.g., a glucose meter), 7% use an app or another tool on their mobile phone or device, 5% use a spreadsheet, 1% use a website or another online tool. It was also shown that 46% of self-trackers admitted that tracking changed their overall approach to maintaining their health or the health of someone for whom they provided care. For 40% of them tracking led them to get a first-hand medical consultation or motivated them to get a second opinion, and for 34% of them it affected a decision about how to treat an illness or condition. As Fox (2013) has shown, self-trackers are more likely be living with chronic conditions themselves or be caring for a loved one, who is living with such a condition; and overall, they are more likely to report that tracking had an impact on their health.
These self-trackers are essentially QS practitioners. They rely on the wealth of digital data originating from QS technologies embracing wearables, applications, and self-reports that enable them to track different aspects of their physical or psychological health, social interactions and environmental conditions they are in. These four aspects constitute the individuals’ Quality of Life (QoL), defined by World Health Organization (WHO 1995) as “individuals’ perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns”; health spans across all the four domains. The collected QS data enables an individual’s state and behavioral patterns assessment in the different QoL domains, based on which individualized feedback can be provided, in turn enabling to improve the individual’s state and hence enabling to improve their QoL. The evidence for correlations/causalities between QS and QoL is still being established, as only data for limited cases and domains exist so far. Along our research we have already mapped a selection of large collaborative research projects in different QoL domains (Wac et al. 2015). We have concluded that most of the projects are in the domains of a person’s physical health (majority), and some in social interactions, and environmental resources. The least number of projects are related to the psychological health aspects; although this domain is quickly catching up. In this chapter we further discuss the evidence for the QS/QoL correlations/causalities by mapping a selection of QS approaches in different QoL domains and analyzing this evidence. Specifically, we systematically assess 609 QS-community endorsed practices (answering question “what do people track”) and the 438 latest personal wearable technologies enabling QS (answering question “what can people track”). Given the QS practices and wearables database we by no means claim to have a complete set of data, as the selected data sources may be incomplete, as well as the field is evolving, and some recent advances may not be documented yet. Furthermore, in this chapter, we also discuss a broad range of challenges (e.g., lack of evidence, privacy and security aspects) and opportunities (unobtrusiveness, longitudinal data collection) for the QS approach to become an integral part of the future healthcare and QoL-driven solutions, including the opportunity for the QS technologies as different types of affordances supporting the goal-oriented actions by the individual, in turn improving his/her QoL.
7.2 Quantified Self (QS)
Quantified Self (QS), referred to as, amongst the others, “self-tracking”, “life-logging”, “personal analytics” or “personal informatics”, is a term encompassing a form of self-monitoring/self-tracking of individual’s daily life activities and analyzing patterns and trends in, e.g., physical activity, nutrition, weight, mood, productivity data, usually to enable the individual’s engagement with and reflection upon these patterns and trends, and potentially leading to behavior change strategies build upon this data (Choe 2014). The QS-enabled engagement is data-driven and very personal, as opposite to engagement with generic lifestyle recommendations like “move more” “eat more greens” without an indication of what exactly means “more”, and where does the individual stand on this goal. More specifically, the QS tracking focuses on quantification of the daily life of individuals and ways of improving different aspects of their activities, like getting more physical activity, losing weight, eating better or getting better quality sleep; as supported by the quantitative data captured by the individuals. The QS encompasses the objective (e.g., steps), as well as subjective (e.g., mood or pain assessments) data being collected by the individual (Swan 2013).
The QS concept has been coined in the US by Gary Wolf and Kevin Kelly (the WIRED magazine1 journalists) in 2008 building the Quantified Self community since then (Wolf and Kelly 2014). In 2012 the QS community involved 7000 self-trackers organized in 50 meeting groups around the world (so-called “meet-ups”), while today it involves over 125,000 self-trackers along 100+ meet-ups. In fact, the QS concept has been known for longer, Samuel Pepys was the first self-tracker in 1660–1669, he kept a daily written diary with personal, professional and public activities and events and own reflections (Pepys 1660). Self-tracking has become easier and more accessible with the advent of personal mobile technologies – either enabling easier capturing of data in electronic form (e.g., like notes on a smartphone, within an app) or capturing the events and data automatically and unobtrusively via an app or a wearable device. The mobile apps and wearables constitute the QS technologies that enable self-trackers to easier capture the data, aggregate and organize it, analyze it (e.g., statistically), interpret and display it in a meaningful ways. That in turns enables the self-tracker to define actions to take, in turn changing the resulting data being collected. The QS movement has grown significantly, also influencing the research in healthcare, by inspiring health and state tracking solutions delivered on mobile platforms, and being referred to as mhealth (K Wac 2012). Diverse scientific journals and magazines cover the QS movement theme from healthcare perspective, like Biotechnology by Nature (Elenko et al. 2015) and Translational Medicine by Science (Steinhubl et al. 2015). The QS practice is sometimes seen as “narcissistic” and “self-centered”, but the behavior is argued as being the result of the self-tracker being curious about own life and state and trying to improve own behaviors, in turn improving own QoL. The fact is, that many of the QS self-tracking projects fade with time, i.e., once the target behavior has been changed and is maintained by an individual (Eysenbach 2005).
According to (Lupton 2016), there are five different practices of QS: private, pushed, communal, imposed and exploited. The private QS practice implies an intrinsic motivation for data being collected for own use and self-improvement, optimization of life along the slogan: “self-knowledge through numbers”. An example of “private” QS results research includes our research on Heart Rate (HR) patterns over three months (Katarzyna Wac 2014) or 12 years of longitudinal study and self-experimentation with weight control by (Roberts 2012). The pushed QS practice implies that there is an incentive for engaging individuals in data collection, where the data serves other actors or agencies, e.g., healthcare practitioners, insurance companies, and so on. The communal QS practice implies that data collected by an individual is being shared with a community on a social media for, e.g., competition, social comparison or encouragement purposes. The imposed QS practice implies that individuals are obliged to collect some QS data and share with other actors or agencies, e.g., due to workplace compliance (e.g., truck drivers) or when participating in drug addiction program. The exploited QS practice implies that data collected by an individual is being exploited commercially for another purpose (e.g., advertising) or sold in bulk for a better understanding of the target population. In this chapter we focus mostly on the private QS practice, enabling the individual to self-track to improve their behaviors and resulting QoL.
7.3 Context and Methods
In this section, we discuss the state of the art in Quantified Self (QS) domain via a semi-systematic assessment of (1) the exemplary QS practices and (2) the latest available personal wearable technologies enabling the self-tracking in QS practice. By no means we claim this discussion to be exhaustive, as the selected data sources may be incomplete, as well as the field is evolving, and some recent advances may not be documented.
7.3.1 Quantified Self Talks
At each of the QS community meet-up, there are a series of self-tracking projects being presented voluntary by the community members. Each QS talk is structured along three questions, imitating a scientific approach to a topic, i.e., “What did you do”, “How did you do that” and “What have you learned”. Answering the first question enables to elaborate on the self-tracker’s “research” context and question(s), as well as assumptions taken (if any). Answering the second question leads to elaboration on research methods employed, while answering the third – on the results and findings, especially brought back to the self-tracker personal experience and context. Usually, based on the self-experimentation, a self-tracker point outs some causality between tracked variables, or at least a correlation, that enables them to make more informed decisions in their daily life and improve the tracked aspects. The meet-up talks of the exemplary QS practices’ from around the world are being selected by the QS community managers (Wolf & Kelly) and are being posted on the official QS Vimeo channel.2
Previously, Choe et al. (2014) analyzed 53 talks posted along 2008–2013 from the QS website and then 30 talks posted from 2013–2014. Choe et al. (2015) focused on analyzing the talks by their visualization content and insights, i.e., how people track what they track and how they visualize the data. They found that the top variables that self-trackers experimented with were: physical activity, food consumption, weight, mood, work productivity and cognitive performance. Additionally, 56% of the self-trackers monitored the designated data with a wearable, 40% with an excel spreadsheet and 21% with custom software app or “pen and paper” method (multiple overlapping answers were possible per a self-tracker). In this chapter, we focus on further analysis of the QS talks for what do people track.
At the time of this research there were in total 1006 talks available online for 2008–2016. However, some talks were (a) duplicated (i.e., same speak, same talk, different venue), (b) “meet-up” introductory talks, or other (c) event-based talks (“Quantified Self Public Health Symposium 2015”), (d) panel discussions, (e) philosophical talks, or discussed (f) a specific broader aspects of self-tracking (e.g., ethics, privacy, scientific approaches), or (g) a self-tracking concept at large (i.e., without an individual’s self-tracking project behind), or (h) a framework for data fusion and/or data analytics or a product/service enabling self-tracking. These talks were omitted from our analysis; we have included only talks presenting a personal self-tracker story. For each of the talks, the self-tracker has improved some of the aspects of the analyzed behaviors, or learned something new, as presented in the talk (Answering the question “what did you learn”?); however the analysis of these results is beyond the scope of this book chapter. Overall, we have identified 609 talks being then analyzed for the purpose of our research to answer the question on “what do people track”?
7.3.2 Quantified Self Technologies: Wearables (and Apps)
Self-tracking is on a growing scale enabled by the ubiquitous availability of personal computing and communication devices and services—including personal wearable devices and mobile applications and services. We have analyzed the state of these QS technologies by analyzing a database of wearables available from Vandrico Inc.3 (being lead by Deloitte), which is free and claims to be an up-to-date source of information about the latest technologies. At the time of this research there were a total 438 wearables available online for 2001–2016 and beyond (i.e., some wearables were marked as “to be released soon”). Each wearable has already a meta-data identifying its sensors, e.g., accelerometer, its goal, i.e., what phenomena are to be tracked (e.g., physical activity, sleep) and where is the wearable to be placed (e.g., wrist). Many of the wearables are also paired with their web-based services for advanced analytics and visualization. In our analysis we do not discuss the wearable or the web-based components separately; we are just focusing on “what can people track” with a specific device.
7.3.3 Methods: Data Acquisition and Tagging
To analyze the information about the self-tracking projects presented along with the 609 QS talks and the self-tracking possibilities of 438 wearables, each talk, and each wearable has been assigned a tag or set of tags representing the behavioral topic/aspect being tracked.
The tags to code the talks were either (a) derived from the talk/wearable description itself (e.g., ‘nutrition’, physical ‘activity’) or (b) assigned following the similarity of the topic with the domain represented by a tag, e.g., ‘gluten-free diet’ tracking has been coded as ‘nutrition’, ‘steps’ or ‘running’ coded as ‘activity’. This way, for example, the ‘activity’ tag corresponds to talks/wearables tracking different types of daily life (physical) activities, of different duration, location, intensity, and include the calories burned, movements tracking of different parts of the body, and motion tracking. Moreover, the ‘weight’ tag embraces topics related to weight loss and fat loss and muscle management. ‘Brain activity’ corresponds to any EEG-based brain activity tracking or influencing it via neuro-feedback or influencing own focus, attention, intelligence or alertness with nutrition, caffeine, alcohol, intake of oils or medicaments. The ‘communication’ tag embraces wearables that are hands-free, remote, and go beyond the SIM-enabled phone. The ‘interaction’ tag corresponds to any new interaction techniques, either gesture-based or based on novel interfaces including 3D sound and vision, haptic, microphone, and screens. A ‘relationship’ tag is used for the topics related to social interaction and communication – to distinguish them from the above-ones specifically relating to novel communication and interaction modalities.
There were in total 160 unique tags identified for the QS talks, and 58 tags for the wearables, and as some of these were overlapping, 192 unique tags have been leveraged in the further data analysis.
After each QS talk/wearable has been coded with the tag(s), clouds of tags were created. A cloud of tags is a visualization of a frequency of a given tag in a given set of words as a weighted list. The absolute frequency of a tag corresponds to a font size—the more frequently the tag has appeared, the larger the font size. In the figures, a color of the tags does not have any meaning. Tag clouds were created with the Wordle4 web application. The results are as follows.
7.4 Quantified Self Talks
As one can conclude from the figure, physical activity (97 talks) and nutrition (72) are the most likely to be tracked by the individuals, followed by weight (47), sleep (47), productivity (31) and emotions (28). Concerning the emotions, self-trackers focus on both positive (e.g., happiness, content, gratitude, 14 talks) and negative ones (stress, anger, grief, 14 talks). The least tracked, i.e., only by one individual, are, for example: flossing teeth, exposure to light, odd events, lying or flying.
From Fig. 7.2 we conclude that ‘activity’ was always a prevalent topic to be tracked, along all the years with ‘nutrition’, ‘sleep’ and ‘weight’ gaining importance in time. ‘Brain activity’ (22 projects) was popular in 2012 with the advent of wearable, portable and affordable EEG-based brain trackers (e.g., Emotiv). Genetics (8), genomics (8), blood (12) and microbiome (5) analysis become popular along the years, since commercial companies started to provide affordable and easy to use tests to consumers at large.
7.5 Quantified Self Technologies
The QS self-tracking is on a growing scale enabled by ubiquitous availability of specific technologies embraced within the personal computing and communication devices and services. These devices and services collect multiple types of high-resolution data (e.g., location, physical activity) longitudinally and unobtrusively, provide some type of service visualizing this data to its user and are minimally obtrusive and wearable (even fashionable in some cases).
7.5.1 Raw Sensor Data Acquisition
The raw sensor embedded in a wearable is mostly an accelerometer (209), gyroscope (83), some type of button-based interface (116), touch interface (73), kinesthetic interface (vibrator, 71) or LCD-based display (70), digital clock (100), heart rate monitor (82), GPS (78), including microphone (64) and audio speaker (78).
7.5.2 Behaviors Tracked/Enabled
Wearables can track physical activity (207), sleep (47), geo-localization (20), phone notifications (57) and phone controls (44), as well as enable behaviors or novel form of interactions (e.g., gesture) with connected objects (29) and/or communications (17). There are other wearables that can track other behaviors or phenomena including eating, foot pressure, urinary infections and dreaming.
7.5.3 Positioning on the Body
7.5.4 Raw Data Sensor and Behaviors and Positioning on the Body
From Fig. 7.6 we observe that early development (i.e., around 2010–2102) implies that QS “sensors” are just buttons and (simple) displays, while accelerometers appeared in 2013 and became an integral part of a wearable. Following that, (physical) activity was always an integral tracked behavioral variable, with phone notifications and controls appearing along the way, especially powered by advancements in short range communication like Bluetooth, enabling data exchange between a wearable and a phone. The most common positioning is the wrist. However, some recent advancements in miniaturization have enabled them to be placed on the head, torso, or become “anywhere”-based wearables.
7.5.5 Behaviors and Positioning on the Body
The physical activity type of behavior is a prevalent behavior being tracked from toe to head; anywhere on the body. Human hands become an interface for phone controls and phone notifications. Novel wearables in the area of interaction and communication are interfaced through hands or some part of the head. Especially the head has become a natural positioning for wearables enabling augmented/virtual reality (AR/VR). These developments are propelled by the emerging developments in personal electronic devices, having ever-increasing capacity of batteries and computing and communications capabilities, while being miniaturized to become unobtrusive part of everyday objects.
7.6 Quality of Life (QoL)
Since the WHO proposal, there have been many specialized QoL scales developed to evaluate a person’s QoL. For example, there are scales for a given physical and psychological health condition (e.g., cancer), a given population (e.g., elderly), ethnicity (e.g., a Hispanic) or professional role (e.g., a nurse). There are even separate scales being developed for the QoL of animals. Additionally, current QoL research focuses on disabilities and older populations, specifically enabling them to have larger mobility (Schulz 2012; Kanade 2012).
In our research, we employ the WHOQOL as the most generic and applicable model across health states, populations, ethnicities, and professional roles of an individual. Additionally, in this chapter, we focus on an analysis of a potential role of QuantifiedSelf in improving QoL of any healthy, able-body individual.
7.7 From Quantified Self (QS) to Quality of Life (QoL): Where Is the Evidence?
Having presented our research material on Quantified Self (talks and technologies), as well as the approach to Quality of Life (QoL), in this section we qualitatively analyze the link between the two. The overarching question we attempt to answer is “if” and “how” the QS practice of self-tracking contributes to the QoL of the QS practitioners, i.e., self-trackers. The QS practice implies a behavior tracking (behavior assessment)—which may pave the way for behavior change, i.e., individual engages in self-experimentation (e.g., change of diet habits) that itself may be tracked by the QS technologies (e.g., nutrition logger, glucose level measurements), and which resulting behavioral effects may also be tracked by the QS technologies (e.g., better sleep). The QS technologies can serve as a behavior assessment tools in any point of the behavior change; supporting the behavior change with quantitative data. We explicitly do not employ any specific behavior change model drawn from psychology to fit our research into; as our research is rather exploratory and aims at understanding the current state of the art in the QS domain and its potential for the QoL improvement, rather than testing specific theories.
From the theoretical standpoint, (Petit and Cambon 2016) hypothesize that the practice of QS may have positive influence on his/her health by (1) transforming the individual’s relation to own body and health and (2) by empowering the individual to leverage their self-tracking efforts to better control their health and health-related decisions. For both hypotheses, a common denominator is that individuals practicing QS make better health decisions, which in turn leads them to have a better health state and quality of life. There are some example studies supporting at least partially these hypotheses for specific populations focusing on self-management of movement/physical activity and gait-related disorders (Shull et al. 2014) or self-management of type 2 diabetes (Goyal et al. 2016). What is important to notice in the context of our research is that, the QS community is self-selected and attracts highly motivated individuals to improve their own health state and quality of life. The above-posed hypotheses are likely to be confirmed for the highly motivated individuals (although there is no clear evidence documented within the literature yet).
What is not yet evident is the efficacy of using QS-inspired, self-tracking and mobile health technologies to transform the relationships with their own health, including health decision-making and behavior change in patient populations. So far the technology with the most evidence for behavior change is the SMS-based interventions in smoking cessation (Free et al. 2013a; Free et al. 2013b). The use of QS tools and methods as a support for disease prevention or management via behavior changes, both regarding their role (transformation of relationship vs. empowerment), as well as their efficacy, must be researched further, especially in clinical populations. We cannot put forward yet a hypothesis that the QoL of patients’ practicing QS would improve with time. Therefore, in the following paragraphs, we focus on general discussion considering the individuals practicing QS voluntarily, and we discuss their potential improvement of QoL, as enabled by the QS tools and methods.
As we can conclude from the previous section, the QS tracking focuses on the daily life of individuals and ways of improving different aspects of their activities, like getting more physical activity through a day, losing weight, eating better or having a better quality sleep. Ideally, QS enables the individuals to create and keep healthier habits, and in case they are suffering from a disease requiring medical opinion and treatment—track pain or other symptoms, which then could be discussed with a healthcare practitioner at their next visit. Overall, the QS methods and tools presented in the previous section can potentially contribute to an improvement of different aspects of individual’s physical and psychological health, social relationships or environmental conditions; and as these are the main dimensions of the individuals’ QoL. This way the QS practice can potentially contribute to the individual’s QoL.
This section maps the state of the art in the QS domain (i.e., the exemplary 609 QS practices and the 438 latest available personal wearable technologies), on the space of QoL domains, as defined by the WHO (employing the codes from the ‘code’ column in Fig. 7.8). We answer questions like “What do people track, which may contribute to their QoL”? (based on the QS talks), and “What can people track, which may contribute to their QoL”? (based on the QS wearables space).
The top WHO QoL sub-domains, in which QS practitioners self-track, include activities of daily life (physical activity, nutrition, etc., 315 talks), medications (65), sleep (52) and work (33) in the physical health domain, thinking (116), body image embracing weight (54), positive feelings (including mood, happiness, content, gratitude, 47 talks) and negative feelings (stress, anger, grief, 14) within the psychological health domain. Within the social relationships domain, QS practitioners self-track relationships (14), and within the environmental domain—status of their finances (20).
The top WHO QoL sub-domains, enabled to be self-tracked by the currently available technologies include activities of daily life (mainly physical activity, 341 wearables), sleep (48), mobility (30) and medications (5) in the physical health domain, and thinking (13) within the psychological health domain. Within the social relationships domain, QS technologies enable new forms of human communications usually via a novel interface, e.g., kinesthetic, EEG (18), and within the environmental domain—status of their home environment (including home control, 45), get some leisure activities (32), practice their freedom via security and authentication/authorization enabling wearables (10) or check the status of their environment (pollution, noise, temperature, etc., 9 technologies).
As it can be concluded from Fig. 7.11, concerning the physical aspects of what QS self-trackers would like to track, and which technologies are not yet available, we can see that nutrition and productivity tracking can still be improved. For the psychological tracking, weight (although impossible to be measured via a wearable), as well as negative emotions and in the future—complex states of beliefs and self-esteem would be important for assessment, although neither being tracked now, nor being able to be tracked. For the social relationships, the relationship status, as well as sex-related aspects would be of interest to be tracked automatically via technologies. Social support is very important for mental health (Rueger et al. 2016; Wedgeworth et al. 2016), although neither being tracked now, nor being able to be tracked. For the environmental aspects, technologies could evolve to track individuals’ finances, leisure activities and transportation means. Access to healthcare, information, and knowledge is neither being tracked now, nor being able to be tracked, yet it may become a need in the future.
7.7.3 Behavioral Routines and QS Technologies as “Affordances”
Feldman and Pentland (2003) analyzed different types of routines, especially in the organizational context, distinguishing the ostensive and performative aspects of routines. The ostensive aspect of a routine enables people to guide, account for, and refer to specific performances of a routine (i.e., is the theory of the routine), and the performative aspect creates, maintains, and modifies the ostensive aspect of the routine (i.e., is the practice of the routine, e.g., context or other activities informing the theory). (Boillat et al. 2015) extended the theories of (Feldman and Pentland 2003) by researching how mobile applications act as affordances enabling specific goal-oriented actions in individual’s routines. An affordance of an object or an action or an environment relates to a design space of possibilities (e.g., actions) that it enables. (Boillat et al. 2015) has considered mobile applications as affordances contributing to the ostensive and performative aspects of an individual’s routine. In this chapter, we employ and further extend the work of (Boillat et al. 2015) assuming QS technologies (wearables and their corresponding mobile apps) as affordances and we discuss their support for the ostensive and performative aspects of a routine.
Roles of QS technologies as affordances in daily routine behaviors
QS Technology affordance role in a routine
(Feldman and Pentland 2003)
Affordance categories (based on Boillat et al. 2015) of QS technologies supporting individual routine behaviors
‘Representing’ (ostensive aspect)
Knowledge codification affordance
Codify the patterns of behaviors through the mobile application’s storyboard and navigation elements.
– Dashboard with defined goals for behavior
–Different behavioral goals categories (physical activity, sleep)
Codify the behaviors via forms and checklists
– A predefined selection of behavioral goals, e.g., 10,000 steps or 10 floors in a day
– By means of graphical interface, users can define their own behavioral goals, e.g., wake up at 7 am.
– Behavioral goals are possible to be defined for weekends/weekdays, work time (9 am-5 pm), morning/ afternoon/ evening etc.
Codify the behaviors via interactive visual graphics
– Visualization of behavioral goals via interactive visual graphics, e.g., “Happy Hill” goal representing a goal of 10 floors to be achieved each day
Document and trace affordance
Document the outcome of a routine behavior during execution
– Up-to-date real-time behavioral data visible (e.g., 5378 steps just now) with indication on how it relates to a predefined behavioral goal(s) (e.g., 10,000 steps)
Trace the observable behavior of individuals through logs
– Historical behavioral data (with indication on how it relates to a predefined behavioral goals) data available
‘Influencing’ (ostensive aspects)
Enrich routines through seamless access to information
– Up-to-date real-time behavioral data, as well as behavior goal(s) data visible anytime
Enrich the representation of routines
– Visualization of routines via interactive visual graphics e.g., Castle along one day, while “Happy Hill” (10 floors) the next day
‘Influencing’ (performative aspects)
Guide individuals by constraining the way of behaving and standardizing instances of routines
– Users get visual / acoustic / vibration notification to help them reach their goals.
– Reminders enabling to achieve a behavioral goal, e.g., “do not be a sitter” (goal being to stand up every hour)
Guide executants by validating the behavior performed
– Notifications, if behavioral goals are partially achieved, e.g., 50% of a goal
Guide individuals in individual ways of behaving by generating context-dependent routine instances
– Detecting when users are outside and encourage them to extend their walk “You need only 500 more steps to reach your next activity level”
As it can be seen from the table, the role of QS technologies in relation to routine behaviors can be ‘representing’ or ‘influencing’, both applied to ostensive and performative aspects, as follows.
In the case of the QS technologies ‘representing’ ostensive aspects of routine behaviors, these technologies—and specifically their user interface with forms, checklists and visual elements—enable to codify the patterns of behaviors given the specific behavior types (e.g., physical activity) and goals (e.g., 10′000 steps a day). The specified behavior types and goals may or may be not driven by the latest state of the evidence in the health and QoL field (Higgins 2016).
In the case of the QS technologies ‘representing’ performative aspects of routine behaviors, these technologies—and specifically their sensing elements (e.g., accelerometer, Heart Rate)—enables them to document and trace the behavior outcomes—either during execution or through time-based logs (to be viewed after the behavior occurs).
In the case of the QS technologies ‘influencing’ ostensive aspects of routine behaviors, these technologies—and specifically their user interface with visual elements—enable them to enrich (and thus also influence) the representation of the routines by enabling the access to real-time and historical behavioral data and behavioral routines.
In the case of the QS technologies ‘influencing’ performative aspects of routine behaviors, these technologies- and specifically their interactive elements (e.g., screen, tactile or auditory feedback)—enables them to guide the individual by constraining or encouraging them to behave a certain way (e.g., feedback upon specific Heart Rate levels when performing physical activity) and validating behaviors just conducted against the pre-defined goals.
The table above represents a general view of the field of QS technologies and their role in relation to the goal-oriented action in performing daily routine behaviors in diverse QoL domains. For each QS technology instantiation analyzed earlier in this chapter, this table could be adapted to its specific behaviors enabled/tracked and its specific sensing and interaction capabilities. Considering a specific QS technological instantiation, e.g., a wearable as an affordance within the context of routine behaviors may open new avenues for design choices for this instantiation—depending on its role concerning to the targeted routine behavior and its ostensive or performative aspect for the routine itself.
In this section, we discuss the challenges and opportunities for QS to become an integral part healthcare and QoL-driven solutions. Additionally, in the scope of the opportunities, we analyze the QS approaches as different types of affordances supporting the behavioral routines and goal-oriented actions by the individual, in turn enabling them to improve their QoL.
7.8.1 QS Technologies for QoL Improvements: The Challenges and Opportunities
The Quantified Self field paves the way for self-monitoring and self-knowledge, and, as we show in this chapter—there are a variety of aspects individuals already track (leveraging on a growing scale QS technologies) and can track automatically (via QS technologies), enabled by advances in miniaturized, personalized devices, including smartphone and diverse mobile apps.
The challenges to be tackled before the QS technologies and developments can enable the QoL improvements and provide clear evidence for these improvements are as follows. First of all, what can be concluded from the results, is that what individuals “do track” differs from what QS technologies “enable them to track”—there is especially a shortage of technologies enabling behavior and state tracking in psychological health and social relationships domains. That can stem from the fact that phenomena in these domains are highly subjective, and cannot be easily quantified based on data solely monitored on, e.g., a wrist. The research in affective computing domain addresses this issue leveraging psychophysiological computing (Ciman and Wac 2016; Wac and Tsiourti 2014) and we can expect major developments in years to come.
Within the QS technologies themselves, we shall consider the accuracy and reliability of the devices themselves, for example, how “a step” is defined (Case et al. 2015; Piwek et al. 2016). The accuracy of the devices will become increasingly important when introducing the behavioral interventions for QoL improvements; inaccurate assessment data may lead to inaccurate interventions and in turn even negatively influence one’s QoL. There is already research to improve the accuracy of QS technologies and specifically personal wearable devices in a uniform way (Case et al. 2015), and recently the US-based FDA recommendations have been put forward to positively influence the accuracy of these wearables (Cortez et al. 2014). However, to enable the community (including the scientist) to understand and potentially improve the accuracy of the QS technologies, the manufacturers and service providers shall, ideally, publish the results of their accuracy evaluations in a peer-reviewed manner as well as enable an open, standardized, interoperable access to their data streams.
Additionally to the openness of the data, the QS technologies users must know how their data is secured and where it is stored and with whom it is shared (Lobelo et al. 2016). The data security and privacy may be an important aspect of the adoption of these technologies, especially in Europe (Leibenger et al. 2016), where the new the European Union’s new General Data Protection Regulation (2016/679 GDPR) will come into effect as a law across the EU after 25th May 2018. Some scientists have already discovered that even the companies, which seemed to be trustworthy by the QS community, are turning their user’s data for profit.5
Overall, the QS technologies and development do not provide clear evidence for QoL improvements, i.e., no strong evidence is available to date besides small indicative studies in fields of physical health. An obvious problem is, that the self-quantification experiments lack the rigorous controls and double blind of pharmaceutical trials. These results could also be effects of (a) inaccurate devices (as discussed above), (b) placebo effects (Shapiro 1968) (i.e., results acquired solely by a psychological effect of QS activity) or (c) the Hawthorne effect (Adair, and G., J. 1984) (i.e., results acquired due to an “observer” effect of QS activity).
Nevertheless, there are opportunities for the QS technologies and developments to provide clear evidence for QoL improvements, as follows. First of all, the QS technologies field is expanding, as technologies get on a growing scale more and more miniaturized and hence minimally obstructive, more personalized, with more computing and communication power and longer battery lifetime (following Moore’s law (Schaller 1997)(Minerva and Crespi 2017)), enabling to conduct longitudinal QS data collection and analytics with masses.
Looking from the perspective of the QoL assessment and improvement, this field it is a very complex field and much research must be done in understanding the causality and correlations between the different QoL domains (physical, psychological, social interactions, environmental) and their contributions to the individual’s QoL. That must be done for both: healthy and pathologic populations. Some attempts are already documented in the literature (Bergland et al. 2016; Da Silva and Pereira 2017; McKee et al. 2015). The new research methods enabling to model these correlations and causalities are needed. Towards this end, the QS technologies may pave the way for experimentation within the four QoL domains, especially in N = 1 conditions, where the correlations and causalities could be disclosed and modeled for an individual. Such approach has been already introduced in the literature, especially for QS technologies-enabled behavior change and management and treatment options in chronic illness (Schork 2015; Swan 2013; Patel et al. 2015).
Many patients are self-trackers that have found QS-enabled solutions in areas that the traditional health system would never have studied or applied to their specific case. Given that these patients organize themselves on dedicated online social platforms, e.g. PatientsLikeMe.com, many of the individual patient’s self-experiments could be aggregated to form hypotheses with respect to, e.g., most effective management and treatment options for given patient and health state (age, gender, socioeconomics, broader context of life, health history), enabling the individuals’ QoL improvements. The hypotheses could be then further tested in new populations for their effectiveness. Such an approach would pave the way for highly personalized behavior change interventions leading to QoL improvements.
7.8.2 Limitation of the Work
The limitation of this survey is that it is not exhaustive and may miss important developments regarding QS technologies embedded in wearables, not yet documented due to their novelty. The same applies for the phenomena being tracked—potentially interesting aspects of QS individuals’ life may not be documented yet, although widely tracked and contributing the QoL improvements.
The opportunities for future work include more detailed analysis of the Quantified Self community as a whole, potentially getting into meta data of each single QS talk in each single city given worldwide, and understanding what is currently tracked and with which level of depth and if there is a success outcome (e.g., increased awareness or behavior change). As for the QS technologies, recent approaches and innovative ideas may be interesting to track on e.g., kickstarter.com platform—dedicated for upcoming design-based ideas for future products and services. Overall, the completeness of our approach may be challenged and fulfilled with further research in this domain.
7.9 Conclusive Remarks
This chapter has surveyed exemplary QS practices and latest available personal wearable technologies enabling Quantified Self approach and understanding “what do people track”, “what can people track” and how the tracked data can contribute to their Quality of Life improvements in physical, psychological, social interactions and environmental health domains. The least developments are within the mental health and social interactions domains. Overall, the evidence for the QS technologies contributing to individual’s QoL mostly lacks so far. We discussed challenges to be overcome and the opportunities for the QS to become an integral part of the future healthcare and QoL-driven solutions, including an opportunity for the QS technologies as different types of affordances supporting the goal-oriented actions by the individual, in turn improving their QoL.
Based on the progress witnessed in the domain, as well as the current state of the art, as documented in here and in related articles (Wac et al. 2015), we envision that the QS approach embracing the QS technologies and improving the individual’s QoL will be available for general public, and it will be embedded in the fabric of our daily life. It will be automated, accurate, easy to use, affordable, longitudinal and comfortable. Therefore little effort is required for self-tracking and self-improvement of own QoL. We envision that more and more individuals will be willing to and open to the possibility of higher self-awareness, understanding potentials behavioral choices, willing to change themselves for better QoL of themselves and those around them. We will be able to become scientists with ourselves being own subject to research – enabled by QS technologies to extend the mind and the body of oneself – and becoming an “exoself” (Swan 2013). The choice is ours if and how we wish to “Know Thyself.
This research is supported by the Swiss NSF MIQmodel (157003), AAL ANIMATE (6-071) and CoME (7-127) projects, and COST actions (1303, 1304). I appreciate the help of the QoL team members and collaborators with getting the data required for this chapter (especially Alexandre De Masi) and for overall feedback (especially Thomas Boillat).
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