The Quest for Happiness in Self-tracking Mobile Technology
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Transcript of The Quest for Happiness in Self-tracking Mobile Technology
MA in New Media and Digital Culture
Media Studies Department | Faculty of Humanities
The Quest for Happiness in Self-Tracking Mobile
Technology
Ana Crisostomo
Student number 10397124
+31(0) 629 169 166
Rustenburgerstraat 354-3
1072 HD Amsterdam
Thesis Supervisor: Bernhard Rieder
December 2013
Abstract:
The practice of self-tracking became more accessible to the general public in recent years through
the widespread use of connected portable devices (in particular smartphones), improved human
biometric sensors, platforms and services specifically designed for monitoring purposes, and
enhanced online data storage solutions. In this context, a movement labeled Quantified Self has been
gaining an increasing number of followers on a global scale, which has also propelled additional
media coverage towards this specific type of personal activity.
Besides contextualizing self-monitoring practices generally considered, this study focuses on the
ones in the affective domain in particular, commonly known as mood and happiness tracking. The
examination aims at understanding the possible causes and potential consequences of the
displacement of these experiments from an exclusively clinical and academic environment to a wide
public arena, and the expansion of its focus from mental patients (on a chronic or episodic basis) and
research subjects to a large population previously considered healthy and functional.
To achieve that goal, the research relies on a multi-disciplinary approach borrowing concepts and
theories from fields such as Media Studies, Psychology, Philosophy, and Economics, combined with
an empirical work focused both on the technological platforms and the individual practices. From the
conceptual and empirical analysis emerges a phenomenon occupying a particular space framed in
the intersection of technology, wellness and wellbeing, as well as science, threatening to redefine
personal identity and individual behavior by expanding the limits of self-awareness and the scope
for self-improvement.
Keywords: self-tracking, quantified self, affective monitoring, mood tracking, happiness
measurement
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Table of Contents
1. Introduction ............................................................................................................................................. 7
2. An Historical Overview of Self-Tracking Practices ............................................................................. 13
2.1 – Analog logging .............................................................................................................................. 13
2.2 – Digital monitoring ........................................................................................................................ 15
3. A Social Contextualization of Current Self-Tracking Practices .......................................................... 18
3.1 – The emergence of the Quantified Self (QS) group ..................................................................... 18
3.2 – The QS group within the self-tracking spectrum ....................................................................... 21
4. A Functional Analysis of Self-Tracking Practices ................................................................................ 25
4.1 – A definition of Personal Informatics (PI) and a taxonomy for self-tracking ........................... 25
4.2 – The stages of the self-tracking process ....................................................................................... 26
5. A Conceptual Analysis of Self-Tracking Practices ............................................................................... 31
5.1 – The intensified inward gaze, healthism and the pursuit of the perfect self ............................. 32
5.2 – The quantifying proposition and the normalized self ............................................................... 34
5.3 – Surveillance and the data double ................................................................................................ 36
5.4 – The cyborg, the exoself and the posthuman ............................................................................... 38
5.5 – Technology as a misleading, persuasive or nudging agent ....................................................... 40
6. A Psychological Analysis of Affective Assessment .............................................................................. 43
6.1 – A collective perspective ............................................................................................................... 43
6.2 – An individual perspective ............................................................................................................ 45
6.2.1 – Definition and assessment of mood and emotion ............................................................... 45
6.2.2 – Definition and assessment of happiness ............................................................................. 48
7. An Empirical Analysis of Self-Tracking Practices ............................................................................... 51
7.1 – An analysis of the QS group ......................................................................................................... 51
7.1.1 – Characterization of the QS group activities ......................................................................... 51
7.2 – An analysis of affective self-tracking tools ................................................................................. 56
7.2.1 – Focus and Usage domain ...................................................................................................... 57
7.2.2 – Tracking mode and Input and Output types ....................................................................... 62
7.2.3 – Data privacy, Social sharing and Data comparison ............................................................. 66
7.3 – An analysis of (QS) affective self-tracking experiments ............................................................ 70
7.3.1 – General information and Objectives .................................................................................... 72
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7.3.2 – Duration and Indicators ........................................................................................................ 73
7.3.3 – Tools, Methods and Results .................................................................................................. 74
8. Discussion .............................................................................................................................................. 76
8.1 – QS: in the intersection of technology, wellness, wellbeing, and science .................................. 76
8.1.1 – A recursive public empowered through technology .......................................................... 77
8.1.2 – The quest for an amplioself ................................................................................................... 78
8.1.3 – Introveillance as a new type personal type of surveillance ................................................ 79
8.1.4 – The expansion of a personal science ..................................................................................... 81
8.2 – The role of affective self-tracking................................................................................................ 83
8.2.1 – The optimal point of personal monitoring .......................................................................... 83
8.2.2 – The challenges of a “political economy of happiness” ........................................................ 84
9. Conclusion .............................................................................................................................................. 86
References .................................................................................................................................................. 89
Tools ......................................................................................................................................................... 109
Appendix .................................................................................................................................................. 113
Appendix 1 – Quantified Self website indicators .............................................................................. 113
Appendix 2 – Quantified Self Show&Tell events’ indicators ............................................................ 114
Appendix 3 – Web queries for “Quantified Self” ............................................................................... 116
Appendix 4 – General self-tracking applications .............................................................................. 117
Appendix 5 – Mood and happiness self-tracking applications ........................................................ 119
Appendix 6 – Prototypes and products which infer personal mood from physiological indicators
.............................................................................................................................................................. 126
Appendix 7 – Affective self-tracking experiments ............................................................................ 130
Appendix 8 – Eight Affect Concepts in the Circumplex Model ......................................................... 137
Appendix 9 – Profile of Mood States (POMS) .................................................................................... 138
Appendix 10 – Positive And Negative Affect Schedule (PANAS) Test ............................................. 141
Appendix 11 – Implicit Positive and Negative Affect Test (IPANAT) .............................................. 142
Appendix 12 – Subjective Happiness Scale (SHS) ............................................................................. 144
Appendix 13 – Satisfaction With Life Scale (SWLS).......................................................................... 145
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List of Figures
Figure 1 – Screenshot from Gary Wolf’s dashboard of personal analytics …………………………………… 9
Figure 2 – A photo of Buckminster Fuller’s Dymaxion Chronofile ……………………….…..………..………... 14
Figure 3 – A plot of a third of a million email messages sent by Stephen Wolfram since 1989 …... 16
Figure 4 – Typologies of Individual Tracking …………………………………………………………………………… 22
Figure 5 – Stage-Based Model of Personal Informatics ………………………………......………………………….27
Figure 6 – Computing devices as social actors ………………………………………………………..………………... 41
Figure 7 – Screenshot from Wellness Tracker ………………………………………………………………...……….. 59
Figure 8 – Screenshot from MebHelp Mood Tracker ………………………………………………………………… 60
Figure 9 – Screenshot from Track Your Happiness ……………………………………………...…………………… 63
Figure 10 – Screenshot from My Smark …………………………………………………………………...……………… 64
Figure 11 – Screenshot from Moodscope ………………………………………………………………………………… 65
Figure 12 – Screenshot from MoodPanda ……………………………….…………………………………...………….. 68
Figure 13 – Screenshot from MoodPanda (community) ………………………………………………………….. 62
List of Tables
Table 1 – General indicators about the QS website (November 2013) ………………………….………….. 113
Table 2 – Oldest QS Meetup groups (November 2013) ………………………………………………………….. 114
Table 3 – Top 10 QS Meetup groups by number of members (November 2013) …………………….. 114
Table 4 – Top 10 QS Meetup groups by number of previous meetings (November 2013) ………. 115
Table 5 – Top 10 QS Meetup groups by number of (member) reviews (November 2013) ……….. 115
Table 6 – Wikipedia articles for “Quantified Self” (December 2013) ………………………………………. 116
Table 7 – Google Scholar results for the query “Quantified Self” (December 2013) …………………. 116
Table 8 – List of general self-tracking applications ………………………………………………………………… 117
Table 9 – Examples of mood and happiness self-tracking applications ……………………………………. 119
Table 10 – Examples of prototypes and products which infer personal mood from physiological
indicators ……………………………………………………………...………………………………………………………………. 126
Table 11 – Examples of self-tracking experiments (from the QS Meetups) in chronological order
……………………………………………………………………………………………………………………………………………… 130
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List of Graphs
Graph 1 – Number of published articles in the QS website per year and per author (November 2013)
…………………………………………………………………………………………………………………………………...…………… 19
Graph 2 – QS Meetup members by region / country (November 2013) …………………………………….. 52
Graph 3 – QS Meetup groups by region / country (November 2013) ………………..…………………….… 53
Graph 4 – Top 50 keywords used to describe the QS local Meetup groups (November 2013) …… 54
Graph 5 – Top 10 hashtags related to #quantifiedself (November 2013) ………………………………….. 55
Graph 6 – Specific focus of affective self-tracking applications (November 2013) …………………...… 58
Graph 7 – Usage domains of affective self-tracking applications (November 2013) …………………... 58
Graph 8 – Tracking modes featured in affective self-tracking applications (November 2013) ……. 63
Graph 9 – Privacy settings of affective self-tracking applications (November 2013) ………………..… 67
Graph 10 – Social sharing featured in affective self-tracking applications (November 2013) ……... 68
Graph 11 – Data comparison types featured in affective self-tracking applications (November 2013)
………………………………………………………………………………………………………………………………………………... 70
Graph 12 – Goals of affective self-tracking experiments (November 2013) …………………..…………… 73
Graph 13 – Types of introveillance according to tracking mode and focus ………………………………. 80
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1. Introduction
Self-tracking is a concept which has recently gained traction, in particular in the last five years, as
evidenced by the increasing number of media and academic articles published about the topic
(Lupton, The Rise of the Quantified Self as a Cultural Phenomenon), and the hype surrounding
consumer products and services catering for that particular need on several fronts. Forbes
announced 2013 to be the year of digital health (Nosta) and several indicators seem to support that
claim. In the 2013 edition of the Consumer Electronics Show (CES), an annual innovation showcase
unavoidable for most of the industry professionals, one fourth of the exhibits were dedicated to
health and fitness (Carroll). Still in the technological area, competitions with significant awards are
being held to spur radical innovation in personal healthcare technology1 and many startups are
actively exploring the wellbeing and wellness market (Lebowit).
The activity of systematically logging data about oneself is not novel, is not limited to health, and does
not necessarily rely on digital technology. What changed recently was a set of conditions which made
self-tracking more accessible and appealing to the general public: the widespread use of connected
portable devices (in particular smartphones), improved human biometric sensors, platforms and
services specifically designed for monitoring purposes, and enhanced online data storage solutions.
Such technological developments, combined with a favorable reception, originated specific practices
under novel labels.
The term ‘self-tracking’ is often employed in association with other expressions, such as ‘personal
analytics’2 or ‘personal metrics’ (information based on personal data), ‘personal informatics’3 (the
technology used to collect, manage and visualize personal data), and ‘the quantified self’. The latter
is a designation coined by Wired magazine editors Gary Wolf and Kevin Kelly in 2007, to label the
belief that the answers to many fundamental questions in life reside within the individual, and that
improvement can only be achieved through measurement (Kelly, What is the Quantified Self?). Rather
than announcing a future trend, the terminology merely labeled a situation which was already a
1 See the Qualcomm Tricorder XPRIZE <http://www.qualcommtricorderxprize.org/> (a designation inspired by the tricorder device from the fictional science fiction TV series Star Trek), a $10 million competition in this field.
2 This term has been popularized by the experiments of the scientist Stephen Wolfram (see section 2.2) and his Wolfram Alpha Personal Analytics tool for Facebook <http://www.wolframalpha.com/facebook/>.
3 This is commonly attributed to the researcher Ian Li (see section 4.1) who has also created the website <http://personalinformatics.org/>, a central resource in this particular area.
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reality within their network of acquaintances (Wolf, Know Thyself: Tracking Every Facet of Life, from
Sleep to Mood to Pain, 24/7/365). Their website <http://quantifiedself.com/> developed into a
central platform for a movement which rapidly expanded, virtually and physically, beyond the Silicon
Valley area to become truly global4.
The embracing spirit of the movement generated an informal community5 which is open to any self-
tracker, or individual interested in the monitoring process, and encompasses all types of tracking
experiments. For the above reasons, it would be difficult to approach the topic of current self-tracking
practices without referring to this group, which by no means implies that self-tracking practices do
not occur outside the Quantified Self (QS) domain. In fact, one would have to operationalize the
concept of self-tracking in order to identify practices which fall outside the scope of the definition.
Self-tracking can be understood as the individual practice of systematically gathering data in the
personal life domain for a certain period of time with a specific goal. Within this definition, practices
can be distinguished according to the type of awareness involved (conscious or non-conscious), and
type of initiative (self-initiated or mandated by other). While personal data monitoring may be a
byproduct of many daily routines involving digital technology (i.e. web browsing), this study will only
focus on voluntary, conscious and self-initiated experiments such as the ones where individuals track
their mood or measure their happiness levels on a daily basis. While these activities can be carried
out by most individuals with access to basic technology (which ultimately can be the ‘pen and paper’
type), it is likely that the most active and involved self-trackers, as well as the most diverse and
innovative experiments, will be found among the QS group. Since there is, at the moment, no other
organization or established movement assembling the above characteristics, this collective is
considered as a prime source for the empirical investigation in this study.
In general terms, self-tracking activities are conducted in categories such as nutrition, fitness, sleep,
health, cognition and mood, either in an isolated or in an integrated fashion - see an example of
monitored personal indicators in Figure 1.
5 In a recent article, Sociology PhD student Whitney Erin Boesel argues that Quantified Self represents already something more stable than a movement and can be referred to as a community (Boesel, Data Occupations). However, considering how recent the phenomenon is, how diverse the practices it entails are, and the little research that has been done on the matter, I will employ in this study the term ‘group’ instead of ‘community’.
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Figure 1 – Screenshot from Gary Wolf’s dashboard of personal analytics
Source: <http://www.wired.com/medtech/health/magazine/17-07/lbnp_knowthyself>
These divisions do not exhaust all possibilities and individual observations can be classified under
other categories, such as relationships and lifestyle. Experiments which deal directly with physical
indicators appear to be more common than the ones which are concerned with cognitive and affective
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dimensions6. One possible reason may be that in the case of physical and behavioral tracking, it has
been more clearly established ‘what’ needs to be measured and ‘how’, as well as the reason ‘why’
might be considered more conventional and, therefore, more easily accepted. Monitoring cognitive
and affective states can involve a higher level of complexity and uncertainty, which may imply that
not all individuals are willing, or interested, in performing this type of self-tracking. In the case of
affective logging, the process can also become rather sensitive, as it deals with information associated
to emotions and moods which can be perceived as more intimate.
Since self-tracking encompasses such a wide variety of fields and practices, it becomes more valuable
to direct this research to one specific area, taking into account that studies on self-monitoring of
affective dimensions appear to be academically under-represented in comparison to ones on physical
health7. This focus also allows a more precise delineation of the field of study for the empirical stage,
eventually leading to more specific results.
While the current research will naturally examine some elements and properties of self-tracking
practices in general terms, as a required contextualization for the topic, I will try to direct its scope,
as much as possible, to affective monitoring – an area in which QS experiments on mood and
happiness can be found. It is relevant to highlight this ‘tentative’ nature, since many affective
experiments display a holistic character involving other indicators so, in some cases, it might not be
feasible to completely disentangle affective monitoring from other types of tracking.
The guiding research question for the present study is then following: how can current self-tracking
practices be defined and contextualized from a technological and social perspective?
That overarching interrogation will be then supported by the following three sets of sub-questions:
1. What types of self-tracking experiments are currently being undertaken and
by whom? What does the process of self-tracking entail? (sections 3, 4 and 7)
2. What are examples of affective self-tracking practices and technologies? To
which extent do practices of affective self-tracking aided by mobile technology
impact self-perception and individual behavior? (sections 6 and 7)
6 As a reference, in a 2012 U.S. survey conducted by the Pew Research Center, less than 1% of the health apps downloaded by smartphone owners was related to mood (Fox, Mobile Health 2012 14).
7 When analyzing literature on ‘digital health’ and ‘mobile health’ the vast majority of the examples provided refers to studies of the physical body.
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3. What are the identifying features of these particular practices and
technologies? What are their possible causes and their potential impact from
an ideological and social point of view? (sections 5 and 7)
Self-tracking practices aided by mobile technology, framed in the context of this recent movement,
are a multi-faceted phenomenon lacking a formal definition and delimitation and, for that reason, I
considered beneficial to present relevant sets of concepts within their disciplinary domain first.
These theories and models, initially described independently, inform then different empirical
approaches producing specific results. It is in the subsequent discussion stage that all elements
become truly integrated and that their connection produces additional insights.
This study is structured into six main topical sections: some presenting broader conceptual
perspectives and others focused on more specific and pragmatic approaches.
The first section will include a brief historical introduction to self-tracking experiments, and the
second one will provide a social contextualization of these practices by introducing and describing
the Quantified Self group.
The third one will present a functional approach to self-tracking describing the types and stages of
the self-tracking practice.
The fourth section will refer to conceptual approaches which grant different entry points to the self-
tracking theme, including ideas related to topics such as healthism, quantification, surveillance,
posthumanism, and technology as a social actor.
The fifth section will introduce the affective component by describing attempts to gauge well-being
at a collective level and, more importantly, by presenting several theories and models of examination
and assessment of affective states on an individual basis.
The empirical work, incorporated in the sixth section, will include three different types of
observation. The first one will be dedicated to the QS group (with a brief analysis of its website and
Show&Tell groups) with the goal of contextualizing the phenomenon from a social perspective; the
second one will be focused on the monitoring platforms (with the examination of a sample of 25
applications dedicated to mood and happiness tracking) aiming at providing a technological
contextualization; and the third and last one will be focused on the self-tracking practices (with a
comparative analysis of 20 QS presentations on self-tracking experiments in this area) with the
objective of situating these practices both from a social and technological perspective.
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The goal of the above framework is to facilitate the collection, interpretation and correlation of
meaningful material, which will then be translated into a valid contribution to the present thematic
field.
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2. An Historical Overview of Self-Tracking Practices
The practice of systematically self-tracking some type of personal data does not require technology
and can ultimately rely solely on human memory. However, if one considers only the written
evidence of such practice, then there are a few historical cases worth referring as examples of self-
experimentation and self-monitoring.
2.1 – Analog logging
In the sixteenth century, the Italian physician and professor Sanctorius Sanctorius was already
keeping a personal record of his weight, before and after every meal, as well as ingested food and
excrements for 30 years, in order to analyze the energy expenditure of a human being (Neuringer
79). Curiously, a similar personal experiment is being currently undertaken by Computer Science
researcher Larry Smarr8 in an attempt to gather a more accurate insight about his personal health,
but in this case using state of the art technology. The amount of information and level of detail
between the two is incomparable, as the Italian physician, unlike the north-American researcher,
could not have possibly conceived that, for instance, “human stool has a data capacity of 100,000
terabytes of information stored per gram” (Bowden).
Nevertheless, personal monitoring does not mandate a quantitative approach or a health interest. On
a more qualitative level, the first records of personal diaries used in a systematic manner for a
significant period of time, are also dated from the sixteenth century (Samuel Pepys is referred to as
being the earliest well-documented diarist). In the eighteenth century, Benjamin Franklin devised a
system to track his daily behavior according to thirteen human virtues he believed to lead to an ideal
life (Houston). In the subsequent centuries, several eminent figures such as Queen Victoria, Sigmund
Freud, Virginia Woolf, Anaïs Nin, among many others, reflected their daily routine and internal
impressions into written memories (Blythe). In some cases, it was precisely the personal account of
a particular type of existence that brought attention to an individual’s life, as it happened with the
8 Larry Smarr is often referred as an example to illustrate a highly detailed and scientific type of self-tracking in the health domain. He has been the subject of numerous interviews in the media and has also given a TEDMED talk on his experiments in 2013: <http://www.tedmed.com/talks/show?id=18018>.
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posthumously published Diary of Anne Frank, possibly one of the most read personal diaries
worldwide.
The American visionary architect, entrepreneur, inventor and author Buckminster Fuller is said to
have the most well-documented human life in history: starting in 1920 and for the subsequent 63
years, conceiving his own life as an experiment, he documented his daily existence resorting to
physical records ranging from notes to letters, from sketches to bills and receipts in a personal
project he labelled the Dymaxion Chronofile9 (Krausse and Lichtenstein 14) (see Figure 2).
Resembling this enterprise in format, was Andy Warhol’s experiment with Time Capsules: a collection
of 612 cardboard boxes containing all sorts of personal items which he systematically filed, sealed
and stored for over a decade until his death in 198710.
Figure 2 – A photo of Buckminster Fuller’s Dymaxion Chronofile
Source: <http://www.bavc.org/sam-green-talks-buckminster-fuller>
9 This collection occupies a linear extension of more than 350 meters and is currently available at the Stanford University Library <http://library.stanford.edu/collections/r-buckminster-fuller-collection>.
10 This collection takes approximately 2.500 square meters and currently resides at The Andy Warhol Museum, Pittsburgh: <http://www.warhol.org/collection/archives/>. It is also possible to explore the content of one of the boxes online through an interactive application in the Museum’s website.
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2.2 – Digital monitoring
The range and level of detail of such physical collections is soon seriously challenged by individuals
who adopt technology to support the collection of their personal information. Already in 1945, in an
article for The Atlantic Monthly, Vannevar Bush presented the Memex concept, a device where
personal information (such as books, records and communications) would be stored with the goal of
supporting individual human memory (Bush). In the 1980s and 1990s, the first experiments related
to lifelogging11 appeared, based on more widely available portable and wearable technology12,
followed then by initiatives on several fronts.
In the early 2000s, Microsoft announced the company’s investigation efforts towards a project
entitled MyLifeBits, directly inspired in the Memex for which the subject, their researcher Gordon
Bell, had already started collecting personal data13 (Scheeres). Initially in close relation to that
project, a series of workshops on the topic of Continuous Archival and Retrieval of Personal
Experiences (CARPE) were organized from 2004 to 2006, attracting academic and corporate
researchers working in the field.
In many instances, the inward gaze starts assuming a public dimension. Projects of lifecasting (video
broadcasting one’s life through digital media) arise as art experiments, such as Quiet: We Live in
Public in 1999 by Josh Harris14, and as television shows (the most notorious example being Big
Brother). In 2003, a military research proposal connected to individual surveillance is presented by
the U.S. based Defense Advanced Research Projects Agency (DARPA). The project, then under the
name of LifeLog, aimed at mapping all relationships, memories, events and experiences of an
individual, but it was suspended the following year, probably due to public privacy concerns
(Shachtman).
11 Lifelogging can be defined as “a comprehensive archive of an individual's quotidian existence created with the help of pervasive computing technologies” (Allen 48).
12 During these two decades, the Canadian professor and researcher Steve Mann designs, builds and wears several versions of computerized eyewear which allowed the recording of events as seen by his eyes (Mann, My “Augmediated” Life).
13 A book on the experiment and related considerations has been published by Gordon Bell and Jim Gemmell under the title Total Recall: How the E-Memory Revolution Will Change Everything.
14 A documentary on Josh Harris and this particular experience in which the 100 volunteers agreed to live together for 30 consecutive days in a closed and fully (video) surveyed environment was released in 2009 <http://weliveinpublic.blog.indiepixfilms.com/>.
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In the second half of the 2000s, with the popularization of social media platforms, and alongside a
growing interest in the field of information visualization, many projects incorporating personal
metrics emerge15. Two names which are often referred to in the context of personal analytics are
Stephen Wolfram and Nicholas Felton. The first started consciously gathering information about his
email messages back in 1989 (aspects such as volume, date and time – see Figure 3), incorporating
afterwards indicators on keystrokes, calendar events, and phone calls, having compiled more than
one million data points which he then visually represented in chronological graphs where life
patterns became visible (Wolfram). The latter began publishing an Annual Report of his life in 2005
and has continued to do so on a yearly basis, consolidating statistics on the usage of time, books read,
photos taken, places visited, food ingested, among many other indicators (Felton). In this case, the
emphasis is put not only in the different life indicators which might be tracked every year, but also
on the visual representation of the information – features which lead to all of his yearly reports being
sold out.
Figure 3 – A plot of a third of a million email messages sent by Stephen Wolfram since 1989
Source: <http://blog.stephenwolfram.com/2012/03/the-personal-analytics-of-my-life/>
15 See as examples, the entries for the 2008 competition on Personal Information Visualization by FlowingData <http://flowingdata.com/2008/09/09/winner-of-the-personal-visualization-project-is/>.
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Although quite diverse in nature and format, these examples illustrate the possibilities that lie within
the personal logging domain. The goals attributed to these activities can range from self-discovery to
(posthumous) self-preservation, from self-improvement to self-creation.
Currently, the rapid expansion of the smartphone market and the development of new consumer
connected devices and wearables16, as well as growing media awareness of self-monitoring
experiences, has sparked curiosity among the general public17 about self-tracking possibilities, and
has encouraged individuals who had already engaged in such activities to share their experiences
more widely. The following section will then describe the social context of these present practices
through the examination of the QS group.
16 In 2011 the number of Internet connected devices (9 billion) surpassed already the world human population (approximately 7 billion), and two thirds of those devices fell under the mobile category with estimates pointing to 12 billion connected mobile devices in 2020 (Swan, Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0 218).
17 It has also created reactions within the artistic community. As an example, see The Monthly Sculptures Determined by the Daily Quantification Records by British artist Ellie Harrison. The referred sculptures derived from a project in which she tracked, on a daily basis, fourteen different aspects of her life for one year: <http://www.ellieharrison.com/index.php?pagecolor=3&pageId=project-monthlysculptures>.
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3. A Social Contextualization of Current Self-Tracking Practices
3.1 – The emergence of the Quantified Self (QS) group
Wired magazine editors Gary Wolf and Kevin Kelly introduced in 2007 the concept of ‘Quantified Self’
(QS)18 to designate the personal monitoring and measuring practices they observed in their direct
network of acquaintances (Wolf, Know Thyself: Tracking Every Facet of Life, from Sleep to Mood to
Pain, 24/7/365). Collaborating with one of the leading publications in the technology industry, which
some proclaim to advocate techno-libertarian values (Willis), both names are active figures in
identifying innovative trends emerging in the technological landscape. Kelly was actually one of the
founding members of the magazine in 1993 and is considered to be a reputable figure in the
technological sphere. He has also published numerous articles and books that span beyond the topic
of technology, and founded two non-profit organizations (Kelly, Biography). Wolf is similarly a
prolific writer19 and is currently working on a book under the title The Quantified Self. He is interested
in the topic of self-knowledge but on a larger scale, and in that domain he invokes the term
‘macroscope’ to refer to a “technological system that radically increases our ability to gather data in
nature, and to analyze it for meaning” (Wolf, QS & The Macroscope).
The idea of personal insights combined with measurements is also patent in the QS motto “Self
knowledge through numbers” visible on their website <http://quantifiedself.com> which serves as
an important platform in a collaborative movement attracting users from all over the world. In a
period of six years (from September 2007 to October 2013) more than 800 articles were published
in that website by 34 authors (see Graph 1). However, the nuclear publishing team consists of Gary
Wolf, Kevin Kelly, previous Director Alexandra Carmichael20, and current Program Director Ernesto
18 The ‘Quantified Self’ concept attracted more mainstream attention through a TED talk given by Gary Wolf in 2010. In nearly three years, the video <http://www.ted.com/talks/gary_wolf_the_quantified_self.html> has gathered approximately 400.000 visualizations.
19 In his personal website, he refers that one of his favorite articles is about the supermemo <http://www.wired.com/medtech/health/magazine/16-05/ff_wozniak>, a learning system that uses spaced repetition to seal knowledge in memory devised by the Polish researcher Piotr Wozniak. This system could be ultimately classified as a tool for cognitive self-improvement.
20 Alexandra Carmichael is co-founder of the collaborative health research site CureTogether, a Research Affiliate at the Institute for the Future, and a regular blogger on personal data topics (Wolf, Welcome Alexandra Carmichael!).
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Ramirez21. The web articles range from presentations of personal projects to suggested literature
related to the self-tracking topic, from summaries of previous QS Show&Tell events to interviews
with tool makers, including also any relevant updates on new devices, platforms, and upcoming
gatherings.
Graph 1 – Number of published articles in the QS website per year and per author (within the top 4
publishing authors) (November 2013)
Besides sharing information online, the QS group is also engaged in regular face-to-face interaction:
the list of events includes already five Global Conferences (the last one held in San Francisco listed
more than 400 participants), and more than 600 meetings organized by approximately 100 local
groups in cities in all continents22.
21 Ernesto Ramirez is a PhD candidate in Public Health at the University of California, San Diego (Carmichael, Welcome Ernesto Ramirez!).
22 These Show&Tell meetings can be initiated by active users in any country and differ from the Global Conferences which are directly organized by the social enterprise created by the founders Gary Wolf and Kevin Kelly to support the QS movement designated by QS Labs (also responsible for the website). However, this central group provides recommendations for local initiatives and is also willing to contribute with financial or logistic support. More detailed information can be found on this page <http://quantifiedself.com/how-to-start-your-own-qs-showtell/>.
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The movement is open to all types of self-tracking and encourages users to share their experiences
in the areas of health, nutrition, sleep, fitness, cognition, mood and happiness, focusing on the
methodology used, as well as the results achieved. At a first glance, the objective does not seem to
greatly differ from the one established by the aforementioned CARPE (Continuous Archival and
Retrieval of Personal Experiences) workshops held until 2006 (see section 2.2), but while those
accepted only professional researchers (either from the academic or corporate spheres) and had a
formal structure, the QS initiative is open to anyone who has engaged in some meaningful type of
self-monitoring project and is usually conducted with a certain degree of informality.
Users do not have to comply with the established rules of the scientific method, but the group
considers the results of this (researcher) citizen science (Cornell, Making citizen scientists) or personal
science (Roberts) to be valuable and relevant to the scientific community. The recognized benefits of
research centered on one single individual (the n=1 type of studies are also present in science23) can
include the existence of repeated, longitudinal data, and customized treatments, while the potential
risks comprise aspects such as mortality, history, maturation and treatment fidelity (Carmichael,
Daniel Gartenberg: The Role of QS in Scientific Discovery). The concern with strict scientific validity is
not a driving force in most experiments, since the goal does not relate to generalizing the results to a
population, but understanding their meaning for the individual and eventually to inspire others to
undertake an analogous type of examination. Similarly to scientific practice, these experiments
cannot deliver certainty, but only methodically explore a range of possibilities with the prospect of
meaningful results.
The participants create their own experiments and try to document them as well as possible. A
personal presentation, often video recorded and then posted online24, is structured according to the
three QS prime questions (Wolf, Our Three Prime Questions): 1) What did you do?, 2) How did you do
it?, and 3) What did you learn?.
The experiences do not have necessarily to rely on the latest technological devices – the users can
resort to simple spreadsheets, basic word processing software, or a combination of both basic and
complex techniques - and the results do not have to be purely expressed in numerical values, which
might constitute a surprise for those who are less familiarized with the group’s activities taking into
consideration its slogan (“Self knowledge through numbers”). In fact, the words “quantified” and
23 On this matter, see the 1981 article by Allen Neuringer “Self-experimentation: A Call for Change”.
24 In October 2013, the Quantified Self group in Vimeo <https://vimeo.com/groups/quantifiedself> included more than 500 videos from presentations at the Global Conference and the local Show&Tell meetings covering a time period of four years.
21
“numbers” should not be taken literally (see section 5.2 on the theme of quantification), since they
serve mainly to emphasize aspects related to experimentation, systematization, data and, to a certain
extent, technology. In that sense, a more accurate version of that sentence could be “Self knowledge
through data”. The focus of the movement is placed in the sharing and learning features, so
customized self-tracking methodologies trying to establish unusual correlations between different
datasets, or using DIY or ‘hacked’ devices, are welcome. The ‘Quantified Self’ moniker has also
inspired reactions from other communities, such as the artistic one with at least two art exhibitions25
under that title organized so far.
3.2 – The QS group within the self-tracking spectrum
The QS group is only the visible side of a larger group of self-trackers. The following illustration,
proposed by Sociology Ph.D. student Whitney Erin Boesel, categorizes the activity of individual
monitoring according to criteria such as personal intention and awareness, and helps position the QS
group within the wider tracking spectrum.
25 The first art exhibition was held in 2011 at the LAB Gallery in Dublin, Ireland (see <http://www.dublincity.ie/RecreationandCulture/ArtsOffice/TheLAB/PreviousExhibitions/Pages/QuantifiedSelf.aspx>) and the second one in 2012 at the Gallery Project in Detroit, Michigan, U.S, (see <http://www.annarbor.com/entertainment/gallery-project-quantified-self/>).
22
Figure 4 – Typologies of Individual Tracking (dimensions not to scale)
Source: <http://thesocietypages.org/cyborgology/2013/05/22/what-is-the-quantified-self-now/>
The wider circle refers to all forms of monitoring, including the ones which are performed at a macro
level, and therefore related to issues of societal surveillance which I briefly touched upon in section
5.3, but are not the focus of this study. Situated within that wider circle is the area of individual
tracking which includes conscious and non-conscious monitoring. The latter refers, for instance, to
the user’s digital trail or information captured without the individual being aware of it (i.e. logging
aspects of the user’s online behavior, such as visited websites for profiling purposes). Regarding what
is then considered to be voluntary self-tracking, it is possible that this activity is either performed
upon request from another individual or organization (i.e. the request from a physician for medical
reasons26) or self-initiated. The boundaries between these typologies are not always so clearly
26 Members of the medical community and the health industry are regular attendees of the QS Global Conferences and many are excited with the possibilities offered by this type of technology contemplated under the ‘digital health’ or ‘m-health’ (mobile health) scope (Lupton, M-health and Health Promotion: The Digital Cyborg and Surveillance Society) (Wiederhold) (Dolan).
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defined in reality, and the terminology used may not be the most accurate, but the benefit of such a
scheme is to facilitate an initial approach to these categories and understand the distinction between
groups. As previously stated, this study is located within the sphere of voluntary, conscious and self-
initiated experiments.
Following the above classification, some questions naturally impose themselves. The first one is:
beyond the QS group, how many people are performing an activity which might fall under the ‘self-
tracking’ category27? The definition of the term may vary depending on the source cited, but the one
used for this study has been operationalized in the Introduction section.
Early in 2013, the Pew Research Center divulged the results of a survey on the current status of health
self-tracking in the U.S. and, even though 70% of the respondents admitted to track some type of
health indicator, nearly 50% of those did not take note of the values and, from those who did, only
21% did it with the use of technology. Furthermore, it was found that the act of self-monitoring is
closely linked with chronic conditions, since only 19% of the self-trackers claimed not to have any
chronic disease (Fox, Tracking for Health). In the previous year, the same organization published a
report on mobile health where 19% of smartphone owners (45% of the U.S. population) had
downloaded at least one health app on their phone. More than 80% of these health apps pertained to
the exercise, diet and weight categories (Fox, Mobile Health 2012 11).
In January 2013, Forrester published the findings of their market research study on health tracking
devices where a mere 4% of the U.S. adult population is estimated to match the profile of a consumer
who would be interested in purchasing a fitness wearable (Colella). The perception of active self-
monitoring is here also associated with chronic conditions, a very specific health goal, or an obsessive
type of personality. Even if by 2012 figures, more than 500 companies in the health industry were
developing self-tracking tools (Swan, The Quantified Self: Fundamental Disruption in Big Data Science
and Biological Discovery 86), apps and wearable devices do not seem to be extensively popular within
the mainstream consumer market. At least, not yet.
In 2013, a report from IMS Research estimated that installation of sports and fitness apps on
smartphones would grow 63% from 2012 to 2017 (IHS Electronics and Media Press Release). It is
relevant to clarify, taking into consideration the apparently conflicting information, that the purchase
of a device or the installation of an app does not imply its regular use. As alluded by some observers,
27 The aforementioned Sociology Ph.D. student Whitney Boesel published in 2013 an article exploring in more detail the topic of exclusion from the QS community and definition of its membership status (Boesel, You, Me, Them: Who is the Quantified Self?).
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the predicament with these types of devices lies precisely in the lack of sustainable use (Swan, Sensor
Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0 240).
The above observations deserve two brief notes. The first is that self-tracking does not require a
smartphone or a wearable device, so many reports fail to account for such cases. The second is that
if one would want to be accurate in the definition of self-tracking, monitoring aspects related to
behavior and lifestyle would also have to be included. In such scenario, it would not be possible to
propose realistic figures regarding the number of people committed to practices of self-tracking.
Following the question of volume, comes one of characterization: are there particular features which
distinguish active self-trackers from the remainder of the population? In a QS website post dating
from 2010, the ex-NASA engineer Matthew Cornell proposes the potential attributes of the ‘data-
driven personality’ of a self-tracker which can be summed up as follows: insatiable curiosity,
willingness to take risks and continuously change, skepticism, problem solving mentality, and early
adoption of gadgets (Cornell, Is There a Data-Driven Personality?). It is naturally an insider’s
perspective which can be conflicting with the external image of individuals with a compulsive or
obsessive personality, as referred to in the results of the study previously mentioned, or with a
narcissist disposition – a matter tackled empirically by Gary Wolf in 2009. A survey based on the
questions from an approved narcissist psychological assessment test was distributed among the QS
group, and no correlation was found between conducting self-tracking activities and levels of
narcissism above average (Wolf, Are Self-Trackers Narcissists?). The results could eventually be
contested, since the sample considered was relatively small and not necessarily representative, but
the main objective was to highlight the fact that if there are particular traits that differentiate self-
trackers from non-self-trackers, then self-centeredness is not one of them.
The subsequent section moves beyond contextualization efforts to focus more specifically on the self-
monitoring practice per se and the particular process it entails.
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4. A Functional Analysis of Self-Tracking Practices
In order to further clarify the notion of self-tracking, it is fruitful to analyze these monitoring
practices from a functional perspective and examine the several elements and stages involved in the
process. The studies published in this domain can provide specific terminology and a structured
approach which may be of assistance in the empirical stage.
4.1 – A definition of Personal Informatics (PI) and a taxonomy for self-tracking
The term personal informatics28, defined as systems which “help people collect personally relevant
information for the purpose of self-reflection and gaining self-knowledge” (Li, Dey and Forlizzi, A
Stage-Based Model of Personal Informatics Systems 558), can comprise functions related to personal
information management29, social networking30, coordination31, and memory32 (Li, Dey and Forlizzi,
Understanding My Data, Myself: Supporting Self-Reflection With Ubicomp Technologies 408), besides
the ones related to health and wellbeing referred previously. While the first are relevant functions, it
is important to state that they are not directly examined in this research, since their nature is rather
distinct from the one under analysis.
28 The website <http://personalinformatics.org> created by Ian Li, who published a PhD thesis on “Personal Informatics & Context: Using Context to Reveal Factors That Affect Behavior” in 2011, seems to be a central platform for several resources in this field, ranging from lists of personal informatics tools to papers on the topic.
29 This category can include standard and popular tools such as calendars <http://www.google.com/calendar>, contact lists <http://www.plaxo.com/>, mind maps <http://www.mindmup.com>, notes <http://evernote.com/>, reminders <http://www.rememberthemilk.com/>, among many others.
30 In this case, taking note of one’s habits or preferences might be only the means to the goal of establishing or reinforcing social contact. Users can then dutifully record, for instance, their listening habits <http://www.last.fm/>, reading selection <http://www.goodreads.com/>, or places visited <https://apps.facebook.com/tripadvisor/> as a means to promote social networking.
31 This item can be closely intertwined with the personal information management function and it can also include tools which are commonly used in a professional environment.
32 On the subject of technological devices primarily conceived to aid individual memory, read the 2006 article “iRemember - A Personal Long Term Memory Prosthesis” reporting an experiment conducted by Sunil Vemuri, Chris Schmandt, Walter Bender from the MIT Media Lab.
26
The classification of a self-tracking project varies depending on the criteria employed. In the first MA
thesis on the QS group published in 2012, Anthropology student Adam Butterfly conducted an
ethnographical study within the QS collective and identified three axes according to which these
personal experiments could be categorized: 1) degree of technological involvement, 2) level of
complexity, and 3) goal type (ranging from driven or exploratory) (Butterfly).
Such taxonomy brings to life a three-dimensional spectrum of possibilities within self-tracking
experiments and, while the axes are independent, they can at times be closely intertwined. For
instance, the device choice may be associated with the goal established. The terms persuasive
technology and mindful or reflective technology (Munson) can be used to differentiate between tools
which try to steer the user’s behavior towards a certain direction and tools which focus on insights
based on individual behavior33. So the mere choice of one device over another can influence,
consciously or not, the development of an experiment and the opposite can also happen: the
formulation of a certain goal determining the choice of technology. Simultaneously, projects which
started as being mostly exploratory and considering many variables can become more concentrated
on particular goals with a reduced number of variables, or vice-versa. It is a fluid field where the
position of the experiment can continuously shift under the guidance of its author. In order to better
understand the possibilities offered within those three axes, the following section will examine the
steps of the self-tracking process.
4.2 – The stages of the self-tracking process
The Stage-Based Model of Personal Informatics proposed by Li, Dey and Forlizzi in 2010 provides a
supportive scheme on the self-tracking process. In the model the authors identified five consecutive
stages guiding a self-monitoring procedure: 1) preparation, 2) collection, 3) integration, 4) reflection,
and 5) action as illustrated in Figure 5.
33 These terms should not be mistaken for the dichotomy between fast technology and slow technology (Hallnäs and Redström 201) where the first is based on efficiency and performance, and the second one on contemplation and reflection. One recent example of contemplative technology is the Decelerator Helmet by the German designer and artist Lorenz Potthast <http://www.lorenzpotthast.de/deceleratorhelmet/>.
27
Figure 5 - Stage-Based Model of Personal Informatics
Source: Li, Dey and Forlizzi, A Stage-Based Model of Personal Informatics Systems 561
Once the motivation to conduct a self-tracking experiment is set, the individual enters the
preparation phase of the process translating the initial intention into a goal which can be rather
abstract (as in the case of an exploratory study), or quite specific34. Then follow decisions regarding
the type of data to collect, along with the methodology and regularity of the procedure. The data can
include physiological indicators (i.e. heart rate, body temperature, skin galvanic response), physical
activity (i.e. steps taken), affective conditions (i.e. mood), behavior (i.e. hours spent executing certain
activities), and these categories are not mutually exclusive. Some authors claim that some of the most
surprising and meaningful experiments derive from the combination of high valence (i.e. mood) and
low valence (i.e. heart rate) human values which create more actionable results (Swan, Sensor Mania!
The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0 239).
The data type definition will then impact the technology in the collection stage, which can range from
being user-driven (also labeled as active collection) to system-driven (or passive collection), with
several possibilities within that spectrum depending on the complexity and goals of the initiative.
Manual operations are commonly deemed as more demanding, as they depend on the individual’s
motivation and discipline. On the other hand, automated collection can also bring about
34 According to some theories, personal goals can be classified within a hierarchical scale ranging from very abstract to very specific including the following four levels respectively: system concept, principle level, program level, and sequence level (Li, Dey and Forlizzi, Understanding My Data, Myself: Supporting Self-Reflection with Ubicomp Technologies 409).
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disadvantages, especially when a high volume of data is being harvested in an exploratory study
where the correlations between the indicators has not been clearly established up-front35.
Another important choice relates to the collection frequency, which can either be continual (hourly,
daily, weekly), or episodic (only when a particular event happens). These decisions can precede the
choice of gadget or, if the device is actually the guiding element of the experiment, be a byproduct of
the technology selected. Currently, there is a wide variety of products and services in this area which
allow the choice between platforms catering for highly specific needs or supporting a generic
purpose36.
The third stage presented – integration – refers to the act of processing the data into a structured
visual output, and its duration is determined by the answers to the initial questions. If the data
collection is user-driven and manual, then the user is responsible for producing the information
visualization directly. When technology is driving the operational side of the experience, this step can
be relatively short since the visualization is usually automatically generated. Without delving too
deeply into the information visualization field37, it might be relevant to refer that several studies have
been conducted to examine how different elements of personal data visualization impact the user’s
subsequent behavior38. A number of tools offer the possibility of personal customization within a pre-
established range of options, even if some authors argue that personal data should be matched with
deeply customized visualizations for additional meaning (Aseniero, Carpendale and Tang).
It is possible that amidst the self-tracking experiment, the user decides to change the technological
platform or device used (the reasons can be connected to inconvenient data collection, complexity of
the technology involved, issues with data visualization, among others), which then raises questions
related to the interoperability of the data39. In cases where the data migration is not possible, or it
35 As claimed by some authors, the success of passive lifelogging depends on establishing relationships between captured items and focusing on the truly relevant ones (Gemmell et al. 54).
36 Some authors associate more comprehensive approaches with multi-faceted systems and targeted ones with uni-faceted systems (Li, Dey and Forlizzi, A Stage-Based Model of Personal Informatics Systems 564). See Table 8 for some examples in the generic category.
37 In the second half of the 2000s some authors categorized information visualization projects dealing with individual data for personal consumption under the label ‘casual information visualization’. For more information, see the 2007 article “Casual Information Visualization: Depictions of Data in Everyday Life”.
38 In the 2013 paper “Persuasive Performance Feedback: The Effect of Framing on Self-Efficacy”, the authors study the impact of three types of framing effects on individual behavior: valence of performance, presentation type, and data unit (Choe et al.).
39 The topic of data portability is also discussed within the QS group and self-trackers are advised to consider this aspect prior to running the experiments (Plattel). To tackle this problem, as well as facilitating the simultaneous use of data from different devices, several products and services dedicated to API integration
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implies a level of technical knowledge which the user does not possess, then the monitoring process
needs to be re-initiated40. This is one of the risks of what is designated by the barriers cascade
property of the model, where initial complications trickle down to ensuing phases.
In the reflection stage, the user approaches the gathered personal data critically. According to another
study conducted by Li, Dey and Forlizzi, the user can then ask questions fitting into one or more of
the following six categories: 1) status (focusing on the present), 2) history (analyzing the data
longitudinally), 3) goals (what still needs to be achieved or which targets should be set), 4)
discrepancies (examining the difference the current status and future goals), 5) context
(concentrating on secondary elements related to the main indicators collected), and 6) factors
(understanding correlation and establishing causality between elements) (Li, Dey and Forlizzi,
Understanding My Data, Myself: Supporting Self-Reflection With Ubicomp Technologies 408). The
boundaries between those categories are not always clearly distinguishable, as one type of
interrogation may naturally lead to another one, but some might be more common in an exploratory
experiment (which Li describes as the discovery phase), and others in a program with specific
objectives (maintenance phase).
When the data is derived from an automated, or semi-automated, system working in a continuous
mode, volume can become a challenging factor in the interpretation phase. Some authors refer in this
context the materialization of new data flows which demand a fine-tuned ability to identify
patterns41, anomalies, and establish correct correlations at a faster pace (Swan, Sensor Mania! The
Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0 235). The
obstacle does not usually rely on the harvesting of the data itself, but on the following sense making
stage42.
The interpretation of the individual data can then lead to behavioral change, even though the
experiment does not necessarily have to achieve the action stage, and can remain solely as a personal
management have emerged (some examples: Fluxstream <https://fluxtream.org/>, Healthgraph <http://developer.runkeeper.com/healthgraph>, Sense <http://open.sen.se/>, Singly <http://singly.com/>, Sympho <http://sympho.me/>).
40 Even when the data migration is a feasible possibility, some authors point to the (de)contextualization of the data captured by a certain piece of technology as one of the challenges faced by personal informatics tools (Brubaker, Hirano and Hayes).
41 In a QS post from 2010, Matthew Cornell provides some basic strategies to pursue meaningful patterns in personal data (Cornell, Patterns.)
42 Curiously, Vannevar Bush alerted for a similar issue already in his 1945 article “As We May Think”: “The difficulty seems to be, not so much that we publish unduly (…), but rather that publication has been extended far beyond our present ability to make real use of the record” (Bush).
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exploration. There is a set of cognitive and behavioral theories which are usually presented in
research related to personal change. One of them is the Trans-Theoretical Model of Behavior
Change43, which proposes change as a sequential operation incorporating five stages
(precontemplation, contemplation, preparation, action, and maintenance), and ten different types
processes (under the experiential and behavioral categories) (Velicer et al.). Other studies refer the
Social Cognitive Theory (Bandura 1) which also emphasizes the external social context of the
individual. Other theoretical frameworks presented to examine the topic of intentional behavioral
change, include the Cognitive Dissonance Theory (Festinger), focusing on the establishment of
internal consonance, and the Presentation of Self Theory (Goffman) building a metaphor between
regular human interaction and a theatrical performance 44. While it would be interesting to explore
behavioral approach, this study will not focus directly on the action stage due to its specific scope.
From the above description, it is important to retain that the collection and reflection stages are
particularly important as being the ones which can be user-driven or system-driven – a useful
distinction to bear in mind when empirically analyzing self-tracking experiments. Additionally, it will
be useful to verify if some of the issues reported above, such as data interoperability and information
overload, are commonly faced by self-trackers.
However, prior to the empirical part, it is relevant to examine the self-tracking practices also from a
conceptual point of view in order to characterize the social, cultural and technological context in
which they occur.
43 Some health focused studies present a critical perspective towards this model, claiming that it focuses more on attitude than behavior, and that it has its limitations when addressing long-term goals (Maitland et al. 2).
44 One study, building upon the premises of most of the above theories, and complementing it with empirical research, proposed the following eight properties when designing a self-tracking app or device leading to successful behavioral change: it should be 1) abstract and reflective, 2) unobtrusive, 3) public, 4) aesthetic, 5) positive, 6) controllable, 7) trending / historical, and 8) comprehensive (Consolvo, McDonald and Landay 408). Other studies underline aspects such as usability, goal consonance, and understanding of the underlying technology as important elements (Andrew, Borriello and Fogarty).
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5. A Conceptual Analysis of Self-Tracking Practices
The goal of self-knowledge and the desire for self-improvement have informed numerous theories
and movements throughout human history. In Ancient Greece, the philosopher Socrates constantly
provoked his fellow citizens, encouraging them to have a critical stance regarding what they
considered to be their self-knowledge. In this regard, one of the famous sentences attributed to him
- “the unexamined life is not worth living for a human being” (Plato) - illustrates his belief in the
individual practice of systematic inquisition45.
For several centuries, at least in the western world, the pursuit of knowledge was situated in the
realm of the transcendental, and genuine insight, whether about oneself or the universe, would only
be obtained through religion. In the seventeenth century the focus started shifting from the
contemplation of the divine towards the analysis of the terrestrial and humane, with rationalist
thinkers such as Descartes (“I think, therefore I am”), and towards the observational with empiricist
authors such as Locke (with the concept of the human being as a blank slate). With the advent of the
Enlightenment in the eighteenth century, science and secular education emerged as fundamental
sources of knowledge, a situation which for some was still compatible with religious faith, while for
others it implied an abrupt rupture with tradition, leading to impactful events as the French
Revolution. Another development worth referring is related to the notion of quantification applied
to the social and individual spheres, which is materialized in the utilitarian theories advocated by
Bentham and Stuart Mill, focusing on the maximization of happiness and the calculus of pleasure.
In the modern period, thinkers from a diversity of fields held views which may be of interest to briefly
invoke in the light of self-tracking emotional states before zooming into contemporary theories
which already integrate technology as a central element. Self-knowledge as a constitutional
individual concern is emphasized by Kierkegaard (“one must first learn to know oneself before
knowing anything else” (Kierkegaard 10), who was heavily influenced by Socrates. Nonetheless, the
path to attain such knowledge was by no means consensual. Some thinkers, such as Nietzsche and
Emerson, argued that focusing on the past would be detrimental for the individual, and that the
ability to forget was essential for personal happiness. Others, namely Freud, claimed that only
through understanding the past was one able to gather meaningful insights and reduce personal
suffering (a distinct proposition from the one aiming at maximizing happiness).
45 For an in-depth analysis of the hermeneutics of the self in the Greco-Roman philosophy and its comparative examination with Christian spirituality, read Foucault’s text “Technologies of the Self”.
32
Additional discussions can revolve around the fact that self-discovery is secondary to the capacity of
personal development, as stated by Foucault: “Modern man, is not the man who goes off to discover
himself, his secrets and his hidden truth; he is the man who tries to invent himself” (Foucault, The
Foucault Reader 42). On a more structural level, one could argue whether this capacity for invention
would ultimately lead to self-improvement and satisfaction, or even if happiness itself, which most
human beings claim to tirelessly pursue, is altogether a hypocritical category, as polemically argued
by Žižek, since it drives the individual to dream about things he does not really want (Žižek 60).
This section will then refer to specific contemporary theories supporting the social, cultural and
political contextualization of self-tracking practices with the purpose of understanding the causes
and the potential consequences of this phenomenon.
5.1 – The intensified inward gaze, healthism and the pursuit of the perfect self
The contemporary uncertainty “predisposes the postmodern self to take uneasy refuge in the most
basic shelter of all: his or her own body” (Chrysanthou 470). The outward gaze in the quest for
knowledge and purpose (i.e. in religion, in social community), shifts towards the self and gradually
zooms into every aspect of the individual existence, amplifying its weaknesses and revealing the
unfulfilled potential. This is the privileged ground for many of the self-monitoring activities under
study, whether they are concerned with fitness, particular health aspects or mood and happiness.
Self-monitoring activities are usually conducted in the spirit of gathering self-knowledge, which will
ultimately lead to self-improvement. This procedure stresses the notion of the human being as an
inherently flawed figure, but aspiring to a model of perfection which is believed to be achieved
through an iterative and conscious process. Currently, the attribute of excellence resides, first and
foremost, within the individual, an idea clearly illustrated by Chrysanthou’s statement: “perfectibility
is displaced from the political sphere to the personal” (Chrysanthou 471). This goal can be
accomplished in several fronts, but particular emphasis is placed on the physical, intellectual and
emotional wellbeing.
According to the same author, health has become a new ideology, and within this healthism
movement, intensified through the means of connected mobile technology, the onus is also
transferred from the public and collective dimension, to the private and personal one (Crawford
33
365). The individual is fully accountable for the ‘self’ which develops into an identity defining
resource. To some extent, this resource can be managed as any other economic item. As described by
Martin: “The person comes to be made up of a flexible collection of assets; a person is proprietor of
his or her self as a portfolio” (Martin 582). In this sense, illness, identified mostly as a preventable
undesirable condition, acquires an additional connotation and can be seen, in some situations, as a
moral transgression (Hogle 702) (Rich and Miah 164) resulting from the individual’s negligent
behavior.
This perspective implies the redefinition of the ‘healthy’ and the ‘sick’ conditions, and many authors
argue that this current trend has dissipated the boundaries between these two binary states, creating
a large spectrum of intermediate situations or, as put by Chrysanthou, the “kingdom of the in-
between” (471). Being in a good condition no longer entails the mere absence of disease, but also the
active concern on illness prevention and continuous efforts of self-improvement, which extend the
temporal radius of health monitoring (Lupton, M-health and health promotion: The digital cyborg and
surveillance society 234). Another possible perspective broadens the concept of sickness, augmenting
its intervention scope to the area external to the body, and creating space for a ‘surveillance medicine’
which transcends the purely medical discourse (Armstrong 393) (Rich and Miah 164).
If Freud had already identified guilt as essentially a modern problem, then the situation is only
aggravated in nowadays’ information society where people literally become what they know
(Chrysanthou 473) or, in other words, they become their information (O’Hara, Tuffield and Shadbolt
166). An overload of information, based on data which in many cases may contain inconsistencies
and contradictions, raises uncertainty and can lock the supposedly empowered individual in a state
of paralysis and consequent alienation.
These overwhelming feelings can be unconsciously fueled by the individual directly through the
participation in social media networks, where personal information becomes the currency which
activates and maintains the connection. Some authors stress the inherent contradiction that the
participation in such platforms encloses: “Caught in reflexive networks (…) we lose the capacity for
reflection. Our networks are reflexive so that we don’t have to be” (Dean, Blog Theory 78). The inward
gaze, initially aiming at personal reflection, is then lost amidst an ocean of decontextualized and
amalgamated data which does not provide the individual with any additional personal insight.
It will then be relevant in the empirical phase to try to understand if self-trackers subscribe to their
personal experiments in the hope of attaining ‘a perfect self’ and how they cope with a potential
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information overload in this context (while performing an experiment and in the period following its
conclusion).
5.2 – The quantifying proposition and the normalized self
The term personal analytics is occasionally employed to classify self-tracking experiments, a
designation which targets the quantitative character of these activities. The title of the central
phenomenon in this field – the Quantified Self – confirms the relevance of this facet, as the movement
could have been possibly established under other applicable titles such as “Monitored Self”,
“Reflective Self”, “Experimental Self”, or even “Datified Self”.
The tendency to process several aspects of a personal existence through a measuring lens is not
necessarily surprising, considering that numerical values allow efficient sense-making of a large
volume of data and encourage comparison. As expressed by Espeland and Stevens: “one virtue of
commensuration is that it offers standardized ways of constructing proxies for uncertain and elusive
qualities” (Espeland and Stevens 316).
The danger, some promptly argue, is the misleading perception that numbers derived from self-
tracking activities are unbiased figures since, as asserted by Lupton, in these politics of measurement,
the numbers are not neutral (Lupton, Quantifying the Body: Monitoring and Measuring Health in the
Age of mHealth Technologies 399). Numerical indicators prescribe, implicitly or explicitly, a certain
order and, in this case, subjugate the individual to the statistical notions of normal distribution curve
and average deriving from the collective, dictating what should be considered ‘normal’ and what falls
outside the acceptable standards (Hogle 698). The abstract state of normalcy is then imposed by
statistical measures.
The initial seduction exerted by the structuring effect of numbers, could easily be converted into
oppression, when the self-tracker unsuccessfully struggles to create values which fit within the
established intervals, and compares himself/herself to other users who manage to do so. In general
terms, this new reality of homogenization has been criticized by several authors, namely Foucault
who saw it as a form of social control. The individual differences are acknowledged, but only to be
suppressed under the authoritarian power of normalization (Foucault, Discipline & Punish: The Birth
of the Prison 199). Previously, Adorno and Horkheimer had also engaged in the same type of
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quantification critique, claiming that “(…) society is ruled by equivalence. It makes dissimilar things
comparable by reducing them to abstract quantities” (Horkheimer and Adorno 5)46.
Other perspectives acknowledge this function of any quantifying act, but bypass the (negative)
judgmental stance, asserting that this a fundamental operation guiding, consciously or not, many
aspects of social life and therefore it is more beneficial to focus on the examination of its premises
instead. Some authors define the calculation of value as a sequential three-step procedure including
detaching the entities to be measured, associating them through transformation and, finally,
producing a new entity resulting from the previous manipulation (Callon and Muniesa 1231). The
main idea to be retained is the one of conversion of an entity into another in order to facilitate their
relationship.
The notion of calculation seems relatively unproblematic when it deals with entities easily rendered
as countable, but becomes complex once it touches upon intangible features. Several theories,
especially within the Economics literature, are presented to examine the issue of measuring
apparently intangible indicators. According to some authors, the value of anything (including
intangibles) relies on human choice (which can be manifested explicitly when directly stated or
implicitly when expressed through actions) (Hubbard 183), so value is inherently contextual and
constantly depends on the comparison terms. Commensuration is then a fundamentally relative,
highly interpretative and deeply political operation (Espeland and Stevens 315), regardless of the
types of entities at stake. An interesting concept in this context is the one of qualculation, initially
coined by Cochoy, broadening the notion of calculation to include judgment (Callon and Law 718).
Commensuration distances itself from a mechanical or technical process and it develops into a
complex operation depending on technology, level of visibility and agents involved (Espeland and
Stevens 318).
In self-monitoring, this discussion becomes more pertinent when the measurements are related to
affective dimensions. One possible question which could be derived from the above examination is
whether the numerical assessment of an indicator, such as mood or happiness, forcefully implies the
reduction, and eventual distortion, of a particular reality, or if it is merely an alternative perspective
towards a specific situation. Referring to something as ‘incommensurable’ often reflects the
individual’s concern that the calculative act may pose a threat to the examined entity (Espeland and
46 One recent project which targeted this quantification trend dominating also social media network, was the Facebook Demetricator by Benjamin Grosser: a web browser extension which removes all numerical values provided in the system’s automated messages regarding network activities (such as how many friends ‘liked’ a certain post) <http://bengrosser.com/projects/facebook-demetricator/>.
36
Stevens 332). However, if one was to consider that quantification itself does not constitute a threat
to the intangible feature, then the attention might have to be directed at the calculation methods and
strategies instead, in order to unveil their underlying proposition and political postulate.
In the observational stage, it will then be important to examine the commensuration process of the
self-tracking technological platforms and study their similarities and differences, while trying to
identify predominant techniques and afforded types of comparison (intra and/or inter-individual).
5.3 – Surveillance and the data double
Another conceptual viewpoint on self-tracking activities is through the surveillance angle. The notion
of surveillance is usually associated with the use of unperceived modern technology (such as CCTV
cameras), but the purpose of contemporary surveillance has surpassed the functions related to order,
control, and discipline to incorporate also dimensions connected to profit and entertainment
(Haggerty and Ericson 616), giving rise to new categories of ‘surveillance knowledge’ (Lyon, 450). In
parallel, the widespread consumer access to mobile technology with some type of recording (text,
video, audio), storing, and retrieval functionalities has allowed monitoring to move from the public
to the domestic realm (Lupton, Quantifying the Body: Monitoring and Measuring Health in the Age of
mHealth Technologies 401), therefore effectively democratizing surveillance (O’Hara, Tuffield and
Shadbolt 168).
Bossewitch and Sinnreich dissect the classic notion of surveillance in order to update it by focusing
on the variation of the three possible information flows (positive, negative and neutral) between the
individual and the surrounding environment. Their model gives rise to eight different situations
(panopticon, sousveillance, transparency, off the grid, black hole, promiscuous broadcaster,
voracious collector, disinformation) in which the ‘quantified self’ type of monitoring is categorized
under ‘voracious collector’ (Bossewitch and Sinnreich 235).
In such cases, where the monitoring activity is initiated by the individual and targeted at the self, the
concept of ‘data double’ emerges as a relevant thought. Haggerty and Ericson allude to it in the
context of ‘surveillance assemblages’ where the human body is abstracted and de-assembled,
converted afterwards into data which is the constitutive material of a new being: the ‘data double’
(Haggerty and Ericson 606). This abstract figure, made of pure information, can then be processed
and, for instance, compared with other similar entities through commensuration, as discussed in the
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previous section. While neither real nor unreal, such entity could be defined by Baudrillard as
hyperreal: a simulacra more real than the original, eventually leading to its destruction (Baudrillard
81).
In an antipodal perspective, this ‘datified persona’ can serve as the anchor for a physical identity
“simply by [the] weight of evidence, complexity and comprehensiveness” of the data gathered
(O’Hara, Tuffield and Shadbolt 157). This enduring digital existence can simultaneously fulfill the
human desire that rejects mortality and physical ephemerality (Allen 52).
However, such ‘datification’ process may result in the redefinition of personal identity. As stated by
Bossewitch: “the fact that digital databases can now tell volumes more about us than we know about
ourselves suggests that the very process of identity-construction is in distress” (Bossewitch and
Sinnreich 227). This line of thought extends to another related aspect: memory. If the ability to forget
is a natural and necessary human feature47, what can the practice of systematic logging imply? The
possibility of an omnipresent flawless memory raises psychological, ethical and legal concerns (Allen
55). The ability to recall the past is not always a desirable procedure, both from an individual and a
social perspective. Ultimately, when not limited in any manner, this new type of surveillance could
threaten to “rip apart the fabric of constructive deception that currently weaves together individuals,
social groups and nations” (Bossewitch and Sinnreich 238). Nevertheless, it is crucial to retain that
self-tracking is not synonym to lifelogging, and that personal monitoring projects can greatly vary in
duration, volume, and type of data gathered.
Other authors frame the topic from another, perhaps less bleak and more pragmatic, angle preferring
to focus on crowdsourcing alternatives where trackers would voluntary donate their individual data
streams to a centrally organized ‘biobank’, following the Wikipedia model (Swan, Sensor Mania! The
Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0 228). Taking
ownership of his/her personal data, the empowered individual would participate in a collective
scheme designed to establish a valuable resource publicly available.
From this section, it is significant to retain two aspects to be analyzed in the empirical part: one is
related to data privacy concerns and to which extent these are reflected in self-monitoring
47 While the transience of human memory appears to be an inevitable property, there were continuous attempts throughout history, from Ancient Greece to the Renaissance (Giordano Bruno being one name to be generally recognized in this domain), to improve it by extending our mental capacity to retain and retrieve information (Yates). More recent experiments rely on technology to identify the precise moment of forgetting in order to trigger once more the information to be preserved, in this way maximizing the memory process (Wolf, Want to Remember Everything You'll Ever Learn? Surrender to This Algorithm).
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technologies and self-tracking experiments; and the other one is connected to the possible
consequences of continuous and systematic self-tracking at a personal level.
5.4 – The cyborg, the exoself and the posthuman
From a certain perspective, self-trackers deal with the domain of the self as a black-box. Within
cybernetic theory, this concept represents a unit processing certain inputs into specific outputs
without revealing the internal mechanisms which allow the process to happen in such given manner
(Von Hilgers 43). The self is effectively behold as opaque and the only method to render it more
transparent is through a feedback loop (another foundational element of cybernetics) based on
systematic data gathering. This viewpoint requires a certain individual detachment producing
consequently two distinct entities, the observing subject and the observed object (or, as described in
the previous section, the data double), whose relationship is mediated through technology.
Technology does not play a secondary or passive role in this scenario – it is the critical tool which
guides the individual towards the idealized perfect self. For some authors, these tracking devices
provide the individual with augmented abilities through a multiplicity of novel exosenses from which
an extended exoself would emerge (Swan, The Quantified Self: Fundamental Disruption in Big Data
Science and Biological Discovery 95). The individual would no longer be confined within the realm of
his innate biological capacities, which are seen as limited and limiting, as illustrated through the
example of a company slogan provided by Lupton: “Your body is the ultimate interface problem.
Sometimes, it just doesn’t give you the feedback you need… We create the tight feedback loops your
body is missing to keep you healthy” (Lupton, Quantifying the Body: Monitoring and Measuring Health
in the Age of mHealth Technologies 397)48.
The topic of blurring boundaries between human and machine leads naturally to cyborg related
theories where the work of Haraway occupies a central position. Framed within a feminist discourse,
cyborgs are presented as hybrid entities to expose the frail distinction between ‘natural’ and
‘artificial’, and to highlight the inherently connected character of individuals (Haraway 173). While
the idea of technogenesis, the co-evolution of human beings and technology (Hayles, How We Think:
Digital Media and Contemporary Technogenesis 10), is mostly unproblematic, the thought of fusion
48 Cases such as Neil Harbisson’s and his eyeborg, commonly referred to as the first recognized cyborg in the world, are worth mentioning in this domain, since they go beyond the restoration of innate human faculties to introduce novel abilities and skills <https://vimeo.com/51920182>.
39
between the two entities seems somehow unsettling for some, triggering techno-dystopian visions
of robotized beings devoid of spontaneity and genuine emotion. Such conflicting approaches
illustrate the classical technological divide between the “integrated” and the “apocalyptic”, referring
to the terms coined by Eco (Caeser 29).
An argument which, to a certain extent, undercuts those dystopian concerns is the one that
individuals have always been posthuman, and that conscious agency is not threatened by technology,
since the assumption of complete human autonomy is essentially an illusion (Hayles, How We Became
Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics 288). For Hayles, it is then not a
matter of exclusion or replacement of the human, but instead of the progressive integration of
technology, creating a symbiotic relationship between both entities. The same posthuman
terminology is utilized by the transhumanist movement, but holding a more radical connotation
which clearly implies the abandonment of the human condition as currently known. Transhumanists
are defined by their foundational belief in the timeline of technical progress marked by a point of
singularity in which “the speed of technical progress is faster than human comprehension of that
progress” (Kelty 87). According to Kelty, this countermodern viewpoint regards technological
progress as inevitable and even independent of human life.
A more moderate approach (distancing itself from an antihuman position) proposes a technological
enhanced environment supporting human betterment, but not independent from human control. The
vision of polymaths is characterized as including “a detailed sense of the present, and the project of
the present, in order to imagine how the future might be different” (Kelty 79). Such approach
champions intervention practices where technology occupies a merely instrumental function.
Nevertheless, these apparently antagonistic perspectives do share a common feature: according to
the same author, both transhumanists and polymaths are recursive publics since they are “concerned
with the ability to build, control, modify, and maintain the infrastructure that allows them to come
into being in the first place” (Kelty 7). A pragmatic interest and a pro-active involvement are then key
shared characteristics between these two groups.
Some thinkers consider the above distinctions to be artificial and, ultimately, unnecessary when
concerning progress. Latour, for instance, maintains that “society and technology are not two
ontological distinct entities but more like phases of the same essential action” (Latour 129). Change
and innovation would be more accurately described in terms of a succession of association and
substitution of different actants, so the emphasis is directed at the functional component of the
process instead of the nature of the elements involved.
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It is then meaningful to inquiry in the observational stage of this study how the technological element
is approached by self-trackers and how relevant it becomes in the self-monitoring process when
holistically considered.
5.5 – Technology as a misleading, persuasive or nudging agent
In current self-tracking activities, technology plays an important role. If in some of the
aforementioned perspectives, the self was examined as a black-box, then technology itself can be seen
as the impenetrable entity by some authors. Chun, for instance, describes the machine as the
concealing and misleading element, declaring that users maintain a deceitful relationship with
technology. According to her, interfaces only provide an indirect experience of the operations at stake
and, therefore, the individual should be aware that the real power lies in what is left unseen (Chun
316).
Similarly to commensuration activities (see section 5.2), technological devices are not neutral since
they act as an active agent implicated within a complex network of power relations (Lupton, M-Health
and Health Promotion: The Digital Cyborg and Surveillance Society 233). The mindset of the individual
as fully managing his own usage of a particular technological device should then be critically
examined. As stated by Lupton: “technologies are not simply configured by their users, but in turn
shape their users in various ways by creating new ways of thinking, feeling and being” (Lupton,
Quantifying the Body: Monitoring and Measuring Health in the Age of mHealth Technologies 400). This
configuration does not have to occur in an explicit, restrictive and forceful manner. According to the
nudge theory, as proposed by Thaler and Sunstein (54), behavioral change can successfully happen
when the choice architect (in this case, the one responsible for the technology) creates an
environment which implicitly invites the user to opt for certain choices instead of others.
Requirements and restrictions can be substituted by incentives and nudges by, for instance,
presenting particular default options in some functionalities which will, most likely, lead to a
particular action from the user.
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One strategy to gather knowledge on the type of technological practices which configure human
behavior, is to perceive these devices as social actors49. In one of the sections of the book “Persuasive
Technology: Using Computers to Change What We Think and Do”, Fogg pursues that logic by
extending the functions of technology beyond the tool and medium dimensions, as illustrated by
Figure 6.
Figure 6 – Computing devices as social actors
Source: Fogg, Persuasive Technology: Using Computers to Change What We Think and Do 32
According to his theory, technology as a social agent can motivate and persuade individuals through
the following five types of social cues: 1) physical (i.e. being attractive), 2) psychological (i.e.
displaying similarity and affiliation50), 3) linguistic (i.e. using a friendly tone or praise), 4) social
dynamics (i.e. resorting to peer pressure and the rule of reciprocity), and 5) social roles (i.e. assuming
a role of authority).
The argument is that while individuals, at least the most significant percentage, perceive
technological devices as non-human entities, they interact with them in a social manner. Studies
demonstrate that, for instance, individuals maintain a personal relationship with their mobile phones
(Matthews et al. 116). Being aware of this phenomenon, technology providers strive to increasingly
humanize the platforms they develop in order to promote their adoption and continued use.
49 The same claim had already been investigated previously by other academics. See the 1994 study entitled “Computers are Social Actors” by Nass, Steuer, and Tauber.
50 On this matter, see the 1995 article “Can Computer Personalities Be Human Personalities” by Nass et al.
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From this section, it will be relevant to analyze in the empirical part of the study the relationship
between technology and self-trackers, how platforms encourage systematic monitoring, and what
type of tools are preferred by self-trackers, as well as the reasons behind such preferences.
Prior to the empirical section, it is still valuable to examine some of the affective commensuration
methodologies applied at a collective and individual level in order to identify similar and divergent
aspects between these and the techniques employed in self-tracking practices.
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6. A Psychological Analysis of Affective Assessment
As referred previously, the concept of self-tracking is usually associated with experiments in the
fitness, nutrition and health areas. In those cases, many of the indicators at stake (i.e. miles ran,
calories ingested, blood sugar levels) are quantified using standardized measures, even if the
techniques or methodologies used to gauge some of them are at times not consensual, leading to
differences in values and discussions around precision51.
The term wellness is often referred in this health context, while the notion of wellbeing is usually
associated with a more holistic perspective, which also includes the psychological and social
dimensions of the individual. The specific focus of this study lies within the wellbeing domain and is
directed at the self-tracking of affective dimensions, which are more commonly known in the QS
group as personal mood monitoring and happiness experiments.
In the current section, several types of affective assessment methodologies are presented in order to
examine, at a later stage, to which extent the premises of self-tracking experiments differ, for
instance, from the ones employed in psychological monitoring in a clinical or in a research
environment. In other words, the main purpose is to investigate the relationship between the
methodologies of these personal practices and the ones employed in scientific research.
6.1 – A collective perspective
Although a systematic quantitative analysis might seem, at first, incompatible with the concepts of
emotion and happiness, this is an idea which surpasses the realm of the individual, as exemplified by
current governmental and institutional initiatives. In 2011 the UK government requested its Office
of National Statistics to measure the nation’s wellbeing (Rogers, So, how do you measure wellbeing
and happiness?), with the first results of the survey being published in 2012 (Rogers, Happiness index:
the UK in happiness, anxiety and job satisfaction). In other countries, such as Canada, the wellbeing
indicator, due to its perceived importance, was established through a collaborative research initiative
51 As an example, read a 2013 article on the performance comparison of several fitness trackers <http://news.cnet.com/8301-33620_3-57602925-278/how-my-body-rejected-activity-trackers-and-the-quantified-self/>.
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instead of a governmental one52. International organizations have also tried to capture these subtler
dimensions in ways which allowed a customized country comparison: the Better Life Index53 from
the OECD (Organization for Economic Cooperation and Development) is an often referred project,
not only for its concept, but also for its information visualization component. The values from such
indicators can be derived from the individual’s direct assessment of his/her own happiness, as in the
cases of the Kingdom of Bhutan54 and of the north-American city of Sommerville (Tierney), or
inferred from an aggregation of existing indicators (i.e. Canada). No unique solution has been
accepted as a universal response to that challenge and new projects emerge relying on
crowdsourcing efforts55.
Some argue that reports based on self-assessment do not provide an accurate overview since “what
is being assessed, and how, seems too context dependent to provide reliable information about a
population’s well-being” (Schwarz and Strack 80). These measures, while being relevant, could
become less subjective and more utilitarian if pursued differently. In a 2006 paper, Kahneman and
Krueger propose the U-Index, an indicator designed to measure the proportion of time people spend
in an unpleasant emotional state, with the premise that “many policymakers are more comfortable
with the idea of minimizing a specific concept of misery than maximizing a nebulous concept of
happiness” (Kahneman and Krueger 22).
52 For more information, visit <https://uwaterloo.ca/canadian-index-wellbeing/about-canadian-index-wellbeing/history>.
53 See <http://www.oecdbetterlifeindex.org/>.
54 For more information on the Gross National Happiness indicator from the Kingdom of Bhutan, see <http://www.grossnationalhappiness.com/articles/>.
55 See the collaborative initiative H(app)thon Project < http://happathon.com/about/>.
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Beyond the political sphere, several experimental and artistic initiatives have been created to inquiry
and portray emotions and happiness at a macro level56: these can be focused around a geographical
area or a particular spatial context57, an event58, a defined time period59, or a specific type of source60.
Affect is also being accepted as an important aspect in business and corporate contexts with the
recognition that emotions play a significant role in decision making (Seo and Barrett 923) and,
therefore, an increased level of self-awareness can contribute to a more productive environment
where processes are managed more efficiently61. This consideration is not dissimilar from the
previous suggestion that the modern pursuit of productivity in Western culture demanded a new
type of body from the individual (Hogle 697). Perhaps the current business focus on innovation
(instead of mere productivity) requires from the individual also an increased level of wellbeing.
6.2 – An individual perspective
6.2.1 – Definition and assessment of mood and emotion
Beyond political, social and even economic concerns, the measures of affect play an equally relevant
role on a personal level. Possibly the most common and evident function for this sort of monitoring
is related to mood disorders, including depression and bipolarity. Several psychological tests can be
used to clinically assess such disorders, but self-monitoring affect is not forcefully related to a
56 Some authors designate the aggregation of individual data at a macro level as high-frequency data (Swan, Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0 239).
57 See the projects Bio Mapping <http://biomapping.net/> (Christian Nold), Emotional Cities <http://www.emotionalcities.com/blog/?page_id=2> (Erik Krikortz), Public Faces <http://richardwilhelmer.com/projects/fuhl-o-meter> (Richard Wilhelmer), MoodMap <http://themoodmap.co.uk/> (Priyesh Patel and Daniel Saul).
58 See the 2012 project Emoto <http://moritz.stefaner.eu/projects/emoto/> by Moritz Stefaner, Drew Hemment, and Studio NAND.
59 See the project Pulse of the Nation <http://www.ccs.neu.edu/home/amislove/twittermood/> by several researchers from Northeastern University and Harvard University.
60 See the 2006 project We Feel Fine < http://www.wefeelfine.org/mission.html/> by Jonathan Harris and Sep Kamvar.
61 Many companies, including large Silicon Valley corporations, have been adopting mindfulness programs in order to raise employees’ self-awareness (Essig).
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diagnosed (chronic or episodic) pathology. The QS group is a fertile source of examples in this area
with mood being recognized as a tracking category on its own62.
In order to contextualize self-tracking experiments in this area and establish some reference points,
one should examine the indicators at stake. While there seems to be a broad agreement that affect
comprises both emotions and moods (Schnall 59), in many studies the terms ‘mood’, ‘emotion’, and
‘affect’ are used interchangeably – a problematic situation especially when one wishes to proceed
with a commensuration activity. Based on a critical review of the literature available on the matter,
Ekkekakis offers a distinction between the three terms (Ekkekakis 322). He refers to Russell and
Feldman Barrett to define core affect as a "neurophysiological state consciously accessible as a simple
primitive non-reflective feeling most evident in mood and emotion but always available to
consciousness" (322), and emotion as a "complex set of interrelated sub-events concerned with a
specific object" (322). Finally, mood is defined in direct comparison to emotions as being more
diffuse, more global, and lasting longer. The assessment methodologies should then be considered
depending on the specific entity to be measured.
In the same article, the author provides a taxonomy to classify some of the theoretical psychological
models of affective assessment according to the measured element and the methodology employed.
Dimensional measures of affect are divided between single-item (i.e. Self-Assessment Manikin, Affect
Grid, Feeling Scale, Felt Arousal Scale) and multi-item (i.e. Positive and Negative Affect Schedule,
Activation Deactivation Adjective Check list); measures of mood are classified as multi-item
dimensional (i.e. Multiple Affect Adjective Checklist, Profile of Mood States) and multi-item specific
(i.e. Depression Inventory, Hamilton Rating Scale for Depression); and measures of emotion are
characterized as multi-item specific (i.e. State-Trait Anxiety Inventory).
This taxonomy is by no means simple or consensual and the boundaries between core affect, emotion
and mood assessment are often unacknowledged in reality. For instance, some models which are
presented as targeting mood, end up only focusing on momentary emotions. However, to some
authors the boundaries between mood and emotion might be difficult to trace, since the scope of the
latter is considered to be wider: emotions can be conceptualized as discrete or (multi-)dimensional,
be event-related or diffuse, be connected to states or to traits (Larsen and Fredrickson 41).
62 The QS website forum has a category dedicated to mood <https://forum.quantifiedself.com/forum-mood> and by October 2013, there were 40 website posts tagged with the keyword ‘mood’ and 10 with the keyword ‘happiness’.
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What is essential to retain is that there is no unanimity regarding what is precisely measured, and
how, in the affective domain: this is a complex territory filled with problematic and subtle
distinctions. In any case, despite the lack of consensus, it is valuable to indicate some of the most
commonly referred theories and models in order to provide some reference points for the posterior
analysis of self-tracking technologies and experiments.
Some of the most widespread psychological models used are based on two-dimensional structures.
The 1980 Circumplex Model of Affect (Russell A Circumplex Model of Affect) classified mood according
to valence degree (positive or negative) and level of arousal (intense or weak) (see Appendix 8).
Others established a list of internal states which the user would need to rank within a given scale.
The 1971 Profile of Mood States (POMS) (McNair, Lorr and Droppleman Manual for Profile Mood
States) was based on a list of 65 adjectives to be assessed on a five-point scale (see Appendix 9), and
the results would be examined according to six dimensions: 1) tension and anxiety, 2) anger and
hostility, 3) fatigue and inertia, 4) depression and dejection, 5) vigor and activity, and 6) confusion
and bewilderment. Similarly, the 1988 Positive and Negative Affect Schedule (PANAS) (Watson, Clark
and Tellegen 1063) proposed a test including 20 distinct emotional states (translated into adjectives
such as excited, hostile, attentive, afraid) which the individual would evaluate using a scale from one
(very slightly or not at all) to five (extremely) to rate his/her present or past situation (see Appendix
10). The 2009 Implicit Positive and Negative Affect Test (IPANAT) (Quirin, Kazen, and Kuhl) was
based on the assumption that emotions are revealed implicitly and used a list of artificial words
which the user would have to rank on a four-point scale according to six different states (happy,
helpless, energetic, tense, cheerful, inhibited) (see Appendix 11). A quantitative type of assessment
appears to be privileged in most cases. Regardless of the particular method employed, it is crucial to
contextualize the deriving results, and understand that “emotion is not equivalent, nor can it be
reduced to, any single measure” (Larsen and Fredrickson 43). Additionally, it is important to consider
the possibility of the measurement reactivity effect, that is, the fact that the study itself provokes
changes in the subjects being measured (French).
Besides methodologies of self-report, other techniques can be utilized to evaluate affective states, if
even less commonly applied. These would include assessment by an external observer or through
physiological indicators (facial expressions, electrodermal, respiratory, cardiovascular, and brain
electrical activity, vocal patterns), and behavioral indicators (cognitive appraisals, action tendencies,
performance measures) (Larsen and Fredrickson 50). A few years ago, some of these methods,
especially those in the physiological domain, posed issues related to the fact that they were costly,
time consuming, and rather intrusive for the user. While recent developments have minimized such
48
problems, these techniques still underperform in distinguishing affective subtleties since they are,
for example, able to identify emotional intensity, but not type of character (i.e. inability to distinguish
between two negative emotions). For this reason, these techniques are frequently used in
combination with self-report methods.
In the empirical stage, it will be pertinent to verify if the technological platforms and self-trackers
make use any of the psychological assessment models referred above and, if not, how their
techniques differ.
6.2.2 – Definition and assessment of happiness
Other models and experiments are specifically created to measure a positive condition such as
happiness which is more specific as a goal, but perhaps even more problematic in terms of
operationalization and commensuration. While a considerable amount of literature in Psychology is
dedicated to pathologies, the debate around topics which do not focus solely on negative
psychological elements has increased. In 1999 Kahneman, Diener and Schwarz published a
compilation of 28 papers under the title Well-Being: The Foundations of Hedonic Psychology
announcing a new field of study which would be concerned with “what makes experiences and life
pleasant or unpleasant” (Kahneman, Diener, and Schwarz, Well-being: The Foundations of Hedonic
Psychology IX). Another specialized branch which has been gaining supporters is designated Positive
Psychology and is defined as the “science of positive subjective experience, positive individual traits,
and positive institutions” which “promises to improve quality of life and prevent the pathologies that
arise when life is barren and meaningless” (Seligman and Csikszentmihalyi 5). In this scenario, one
would expect upcoming research to provide further academic models of happiness assessment,
identifying its multiple dimensions, as well as (external and internal) causes.
Popular wisdom argues that “happiness is relative”, but studies have shown that it might be less
relative than one is led to believe, based on the fact that there are several objective factors which
directly contribute to an increasing or decreasing level of happiness, and that a state of happiness
should not be mistaken with one of contentment (Veenhoven 26). According to Veenhoven, the latter
is only the cognitive component of happiness, but this personal condition also relies on an affective
component, labeled hedonic element, related to the “gratification of innate bio-psychological needs
which do not adjust to circumstances” (Veenhoven 32).
49
There are several examples of models used for happiness measurement on an individual level, but I
will only refer two of them as many others differ only slightly from the logic employed in these. The
1999 Subjective Happiness Scale (SHS) (Lyubomirsky and Lepper, A Measure of Subjective Happiness:
Preliminary Reliability and Construct Validation) consisted of four questions about the level of per-
sonal happiness and happiness in general, where the answers were selected from a seven-point scale
(see Appendix 12). In the same direction, but departing from a solely positive premise, was the pre-
vious 1985 Satisfaction With Life Scale (SWLS) (Diener et al., The Satisfaction With Life Scale), which
used five general statements about life satisfaction and requested the individual to express agree-
ment or disagreement on a seven-point scale (one corresponding to strongly agree and seven to
strongly disagree) (see Appendix 13).
When assessing happiness, Kahneman considers crucial the focus on actual experiences rather than
on past reflections, which are skewed towards the highest or the lowest points of the experienced
events (Kahneman, Objective Happiness 22). Larsen and Fredrickson similarly highlight the impact of
elements, such as timing and context, when performing affective evaluations (Larsen and
Fredrickson 42). On the other hand, Seligman believes that positing too much weight on the
evaluation of present experiences can obscure a holistic perspective of the self, highlighting only
ephemeral emotions (Wallis). To illustrate the comprehensive nature of happiness, this psychologist
proposes a distinction between ‘pleasant life’ (pursuing positive emotions), ‘good life’ (pursuing
gratification through the use of personal strengths), and the ultimate ‘meaningful life’ (pursuing
something larger than the self through the use of personal strengths and virtues) (Seligman 262-3).
Another feature which is worth mentioning, is the fact that some theories postulate that happiness,
or unhappiness, are merely temporary reactions to particular events, and that individuals return to
a state of neutrality shortly after. The concept of a personal neutral default state, or set point, is a
central idea in the 1971 treadmill theory proposed by Brickman and Campbell. However, recent
research has proven that such set points are not neutral (instead, they are mostly positive), that they
differ between individuals, and that they can change throughout one’s life (Diener, Lucas, and Scollon,
Beyond the Hedonic Treadmill: Revising the Adaptation Theory of Well-Being). From this perspective
emanates a more flexible notion of happiness, loosening its ties from the idea of habituation and
validating the personal quest to maximize happiness on an individual level.
Following this brief review, it becomes clear that, even if one would aim at examining the affective
self-tracking applications and experiments according to the academic validity of their monitoring
premises and terminology employed, that would be a challenging task. As previously noted, there is
50
no consensus regarding assessment in this field. However, taking into consideration that self-
tracking experiments do not intend to reach evidence which can be extrapolated to a wider
population, but only to find results which are meaningful at a personal level, that type of investigation
might not even be especially meaningful in this particular case. So, in the observational stage it will
be relevant to consider, besides the affective assessment models already referred in section 6.2.1, if
self-tracking applications tend to focus more on mood and emotions in general or in happiness in
particular. It will be equally significant to examine which other personal elements are monitored
alongside mood and happiness.
51
7. An Empirical Analysis of Self-Tracking Practices
7.1 – An analysis of the QS group
The goal of the first part of the empirical section is to contextualize self-tracking practices from a
social perspective, that is, to describe who is performing this type of personal tracking and how self-
trackers perceive these monitoring activities. For such purpose, the QS group will be under analysis.
The term Quantified Self was, as described previously, initially coined by Kevin Kelly and Gary Wolf
who also created in 2007 a website under the same moniker to publish content related to any aspect
of the self-tracking practices. The reported content and associated activities attracted increasingly
more attention from technology and tracking enthusiasts and the media in general63. However, this
movement does not have a formal structure and it is arguable whether it is merely another passing
trend or if it is producing a genuine community (Boesel, Data Occupations). In order to clarify the
scope of the observations in this particular section, it is important to note that I will refer to the QS
group and its activities merely as the collective directly affiliated and / or acknowledged by the
central QS Labs team <http://www.quantifiedself.com/qs-labs> through their website
<http://www.quantifiedself.com>.
7.1.1 – Characterization of the QS group activities
The QS group has been growing since the end of 2007 and, while it is not possible to provide a specific
account regarding the number of active users (which would also require the definition of what
constitutes an active user), the values from their social media platforms may help draft a more
precise image.
In October 2013 the closed QS LinkedIn group created in 2009 by Gary Wolf
<http://www.linkedin.com/groups?gid=1785228> had gathered approximately 1.400 members,
63 The interest also seems to be growing in the academia field. When querying the expression “quantified self” on Google Scholar, it is possible to see the number of results duplicating on a yearly basis from 2008 to 2013 (see Table 7).
52
their 2010 Facebook group <http://www.facebook.com/groups/quantifiedself/> had more than
3.500 members, their 2012 Facebook page <http://www.facebook.com/QuantifiedSelf> more than
1.200 ‘Likes’, and their 2010 Twitter account <http://twitter.com/quantifiedself> listed around
8.500 followers64. In the same date, their QS website forum <http://forum.quantifiedself.com/> had
2.400 registered users, and if one were to add the members of all local Meetup groups (the Show&Tell
events are organized using the Meetup platform <http://www.meetup.com/>), then the number
would be close to 20.000, even though it is important to note that one individual can be a member of
several groups (especially those within a reasonable geographical proximity), so this value does not
reflect the number of unique individuals.
Geographically, it might be important to note that, despite being present in approximately 30
countries, this movement still appears to be predominantly centered in the North-American
territory: half of the groups are based in the U.S. and Canada, and these groups host two thirds of the
total QS Meetup users. Within the North-American territory, the west coast is particularly active. The
movement’s presence in African and Latin American territories is minimal and Asia also lags behind
North America and Europe (see Graphs 2 and 3, and Tables 2, 3, 4 and 5).
Graph 2 – QS Meetup members by region / country (November 2013)
64 The established general hashtags are #qs (even though this one also stands for content related to the Quacquarelli Symonds group <http://www.qs.com> and therefore it is not preferred) and #quantifiedself, but specific ones are used for local Show&Tell events (i.e. Amsterdam #qsams) and global conferences (i.e. Quantified Self Europe Conference 2011: #qs2011, #qseurope).
U.S. (West)37%
U.S. (East)26%
Canada4%
Europe25%
Asia5%
Oceania2%
LATAM1%
53
Graph 3 – QS Meetup groups by region / country (November 2013)
Another indicator which may reinforce these findings is the number of Wikipedia articles (see Table
6). The first article on “Quantified Self” was created in English in 2010 and by December 2013 there
were only four other language versions: German (2012), French (2012), Chinese (2012), and Dutch
(2013).
In order to gather a better understanding of the self-perceived activities and goals of the QS group at
a global level, I decided to query all the local Meetup groups, extract the tags used to categorize them,
and compile the information in a word cloud identifying the most common labels (see Graph 4).
Besides the obvious descriptors ‘quantified’ and ‘self’, there is a clear focus on technology related
aspects, health and self-improvement, but also science and education. These diverse interests are
reflected in the type of members which the movement attracts: from UX designers to academics, from
clinicians to computer programmers, from fitness enthusiasts to patients with chronic diseases.
While the particular preferences between members may differ, they share an interest in learning and
sharing their knowledge on one aspect (or more) of the self-tracking process.
U.S. (West)17%
U.S. (East)27%
Canada5%
Europe31%
Asia12%
Oceania3%
LATAM4%
Africa1%
54
Graph 4 – Top 50 keywords used to describe the QS local Meetup groups
(Word cloud produced via TagCrowd with data extracted from 102 Meetup groups in November
2013)
The results from this internal analysis are also mostly confirmed through a brief query of the Twitter
hashtags commonly associated with the general #quantifiedself (see Graph 5). Examining only the
top ten hashtags, 60% are related to health in general and digital or mobile health in particular, and
20% are related to technology. Considering that the tool utilized (hashtagify.me) only provides access
to the top ten hashtags, it is not possible to verify if science related hashtags would also be present
in the wider content network.
55
Graph 5 – Top 10 hashtags related to #quantifiedself
(Hashtag network produced via Hashtagify.me for the query #quantifiedself in November 2013)
56
7.2 – An analysis of affective self-tracking tools
The objective of the second part of the empirical section is to contextualize self-tracking practices
from a technological perspective by examining several monitoring platforms and identifying their
main premises and features.
In order to grasp the options currently available for the general public in the affective self-tracking
area, I made use of two main sources to obtain a list of curated references. The first one was the
Personal Analytics website: in this platform the ‘mood’ category (‘happiness’ was not present in the
classification) <http://personalinformatics.org/tools/tagged/mood> listed twelve tools in October
2013. However, some of those application were no longer available, so only six were still operational.
The second one was the QS website and its guide page <http://quantifiedself.com/guide/tag/mood>.
In this case, 59 tools were tagged with the keyword ‘mood’ in October 2013, but many of those were
just general applications (which could be used to monitor any indicator – see Table 8), some were
related to stress, and others could be labeled as ‘gratitude’ applications. In the end, less than half
could be considered to belong to the affective self-tracking category exclusively.
The compiled list (see Table 9) does not attempt to be an exhaustive inventory of the applications
available in this domain65, but only to provide a robust sample of the most common types of tools.
After researching all of the 25 listed tools (and registering in the ones which were available online),
I conceived a taxonomy which would allow a basic description of the platforms and their comparative
examination. Additionally, these categories were also aimed at providing information which would
allow answering the questions from previous sections related to aspects such as:
the commensuration process, the techniques used and the types of individual comparison
afforded (section 5.2);
data privacy concerns (section 5.3);
how platforms encourage systematic tracking (section 5.5);
what is the main affective focus of the tools (section 6.2.2);
whether there are direct references to psychological assessment models (section 6.2.1).
65 As an example, when querying the keyword ‘mood’ in the iTunes store in the same time period, 500 iPhone apps and 300 iPad apps were retrieved, with approximately the same values for the keyword ‘happiness’. When performing the same type of search for Android apps, 240 results are retrieved for both ’mood’ and ‘happiness’. While this is a relatively high number, it necessary to note two aspects: 1) not all the apps retrieved aimed at self-tracking (as a primary or even secondary purpose); 2) among the ones which specify a monitoring objective, the services offered vary between a quite limited set of functionalities.
57
The categories considered were then the following:
1) ID (for convenient reference);
2) Tool name and Type (web, mobile application, wearable);
3) Focus (mood, emotions, happiness);
4) Usage Domain (consumer, business, medical, research);
5) Tracking Mode (if the data collection is active and/or passive);
6) Input Type (the nature and format of the data collected: numerical selection, free text, heart
rate, etc.66);
7) Output Type (the format in which the information is presented: graph, text, colors);
8) Data Privacy (if data privacy is highlighted or mentioned as an application feature);
9) Social Sharing (if the application highlights or refers to social sharing functionalities);
10) Data Comparison (if intra-individual and/or inter-individual comparison was presented);
11) Tool Description (brief explanation on how the platform works).
7.2.1 – Focus and Usage domain
Mood was the explicit focus category for the majority of platforms, followed then by emotions,
feelings, and happiness (see Graph 6). However, the distinction between those concepts was not
always clear, and tools which targeted mood tried to assess it by asking the user how he/she was
feeling at one particular moment in the day. The questions encountered ranged from “How do you
feel right now?” (i.e. Track Your Happiness) to “Rate your mood” and “Rate your day” (i.e. I Rate My
Day). The most common goals referred included improved self-awareness and self-management, and
in some cases the social sharing feature was equally highlighted as an objective on its own (i.e.
Expereal).
66 A 2012 QS article (Carmichael, How Is Mood Measured?) provides an overview of the possibilities available in the market.
58
Graph 6 – Specific focus of affective self-tracking applications
One main top-level element which originated some substantial differences was the usage domain –
see overall distribution on Graph 7.
Graph 7 – Usage domains of affective self-tracking applications
Mood48%
Emotions12%
Feelings12%
Happiness12%
Wellbeing8%
Life4%
Mental Health4%
0
5
10
15
20
25
Consumer Medical Business Research
59
Applications in the medical domain, explicitly targeting individuals with mood pathologies, relied on
a much more comprehensive and detailed series of indicators, including medication and specific
external aspects (see Figure 8), than the ones not specifically aimed at that target audience. The
terminology used could also be different in some cases, with applications primarily destined for
clinical usage requesting the user to classify his/her mood in a scale from depressed to manic (see
Figure 7), while the remaining ones tended not to use those terms and relied on more common
qualifying descriptors (see Figure 9). The platforms destined primarily at the management of
pathologies also tended to underline features related to data privacy and confidentiality, with many
of them being available as downloadable applications instead of online systems, so the data would be
stored locally and not ‘in the cloud’.
Figure 7 – Screenshot from Wellness Tracker <http://tracker.facingus.org/moods>
60
Figure 8 – Screenshot from MebHelp Mood Tracker
<http://www.medhelp.org/user_trackers/gallery/mood>
61
Beyond the clinical domain, a few tools were framed within a business perspective: one (Affdex),
directed at marketers, promised to deliver emotional insights regarding brands and products
through the interpretation of consumers’ facial expressions, and two others (CompanyMood and
GROW) provided wellbeing assessment within a corporate environment.
62
7.2.2 – Tracking mode and Input and Output types
Concerning the tracking mode, passive monitoring (or system driven) based on physiological
indicators was not a prevailing alternative, even if there are products based on that premise available
for the general consumer (EmWave2, Qsensor), and it is presented as an area with unexplored
potential67 (Carmichael, Matt Dobson on Quantifying Emotions) (Carmichael, Exploring the Future of
Mood Tracking). A possible explanation can be related to the fact that devices equipped with sensors
may be still perceived (physically and/or psychologically) as more intrusive, and that physiological
measurements may fail at inferring affective states with similar intensity but different character (i.e.
positive versus negative). Many self-trackers face challenges when trying to correlate physiological
indicators with emotional states (see row 3 of Table 11) and for some of them, even after several
years of self-tracking, no correlation is found (see row 14 of Table 11). Another factor is cost related,
as these devices are still considerably more expensive than self-assessment technologies (which in
many cases are made available for free as web or mobile applications). Finally, a higher level of
knowledge and commitment might be required when using physiological indicators, since the
inference of affective states from corporal data demands personal interpretation. In any case, it might
be interesting to check some of the recently conceived prototypes and products using physiological
measures to infer individual mood, even if they do not aim at self-tracking as an explicit and primary
purpose (see a sample list sorted by chronological order on Table 10), to create a more solid
impression on the status of this type of technology.
The majority of the self-tracking tools examined requested direct self-assessment (user driven
systems) (see Graph 8). This was done via one or more general questions which either prompted the
user to select a point within a numerical / textual / visual scale (see Figure 9), or offered the user the
possibility to select one or more emotional states from a pre-defined list (see Figure 10). Excluding
the tools targeting more specifically mood disorders, only two of the general consumer platforms
examined made a direct reference to psychological models (Moodscope and My Smark). However,
even in the cases where there was no explicit reference to scientific theories (such as the ones
referred in section 6), the quantitative assessment logic employed did not differ greatly.
67 For more information on this research area, see the long list of experiments lead by MIT’s Affective Computing: http://affect.media.mit.edu/projects.php.
63
Graph 8 – Tracking modes featured in affective self-tracking applications
Alongside this type of evaluation, many applications enabled the user to add information about
activities and events in free text format, so meaningful comparisons and correlations could be
established between mood and situational elements (which later on could be identified either as a
cause or as a consequence of a particular affective state).
Figure 9 – Screenshot from Track Your Happiness <http://www.trackyourhappiness.org>
0
5
10
15
20
25
Active Passive
64
Figure 10 – Screenshot from My Smark <https://www.mysmark.com>
The user was generally advised to record this type of data on a daily basis, even if this was not a
mandatory action. Most platforms implemented a reminder system working via email or sms
messages, which were either sent at the same time (i.e. MoodChart), or randomly throughout the day
(i.e. Track Your Happiness). In one case (Moodscope), the reminders sent were not mere automated
instructions, but included personal messages written by other users who struggled to some extent to
maintain a balanced mood.
Considering that most of the applications available relied on quantitative approaches, the preferred
output assumed the shape of a chart, offering an historical perspective over the data captured (see
Figure 11), but also allowing zooming into particular data entries. This type of visualization granted
an easy access to trends, patterns and statistical values such as an average (be it intra-individually or
inter-individually). The amount of information displayed was usually a corollary of the amount of
65
questions asked and the additional information input by the user. It could be interesting to delve
more deeply into the information visualization area and compare applications in relation to which
data is presented to the user and in which format. Unfortunately, taking into account the relatively
broad perspective of this study, this specific type of examination falls outside of the considered scope.
Figure 11 – Screenshot from Moodscope <http://www.moodscope.com/>
Two of the examined self-assessment tools established premises which did not seem to emphasize
the quantitative comparison between affective data and offered different methods for affective
monitoring. One of them was an online daily journal (750words) and the other one a mood color
selection application (MoodJam). It is interesting to note that, while there are several other online
and mobile applications which include these same functionalities, those are not usually classified as
serving an affective assessment purpose.
In the journal case, the user was requested to write 750 words per day as a healthy routine which
was concerned with mental and emotional spontaneity (the data was not meant to be shared and this
is the reason why it was not considered blogging). In the mood color platform, the user was prompted
to select one color from a palette, select its valence (from a -100 to 100 scale), and associate it with
an adjective to describe the current mood. Both provided the user with a rather high degree of
flexibility in comparison to other tools which confined the possible answers to numerical scales or
lists of adjectives. The two tools also shared another common aspect which was related to user
engagement through gamification components (which was only available in one other device
EmWave2). In the first case, the platform attributed points according to the number of words written
66
and how regularly the user would write, with the highest and lowest score being displayed in a ‘wall
of fame’ and a ‘wall of shame’ respectively. In the second case, the user was allowed to employ an
extended number of colors as his/her number of recorded moods increased.
While supporting a more open-ended usage, these tools also catered for systematization and
comparison to a certain degree. The journal entries were submitted to textual analysis (through a
Regressive Imagery Dictionary system) which inferred emotions and mental concerns from the
entered words. In the mood color selection, entries were gathered in monthly color palettes
organized by type of valence (positive, neutral, negative), where colors and adjectives could be
compared.
7.2.3 – Data privacy, Social sharing and Data comparison
There was a variety of possibilities regarding data privacy and social sharing. Some applications
positioned themselves as highly personal and intimate tools, while others emphasized the social
sharing component68, and others tried to cater for intermediate solutions (see Graph 9).
In general, platforms which mentioned mood pathologies and gathered more detailed information
on the user’s life, tended to highlight aspects related to privacy and confidentiality. As previously
referred, some of them would even be available as downloadable applications so the data would be
stored locally, providing the user the feeling of complete control over his/her personal information
(i.e. bStable, ChronoRecord, OptimismOnline).
In most online applications, data privacy was also mentioned as an essential service feature (beyond
the mandatory Privacy Policy reference), and in many instances the user could customize the privacy
settings and decide which data would be available to whom in which format. In some cases, the user
could disclose personal information to his/her clinician, a few selected friends or family members69
and in one example (Moodscope) this feature had been converted into a notification system which
68 Some online platforms have been created with the purpose of connecting users who are tracking similar personal indicators in order to find a solution or an improvement for their current situation. Some of the most popular ones are PatientsLikeMe <http://www.patientslikeme.com/> and CureTogether <http://curetogether.com/>.
69 On this matter, see the 2010 research conducted by Moodscope with results regarding their ‘buddy’ system: <https://www.moodscope.com/bundles/moodscopeweb/files/Moodscope_Research.pdf>.
67
would trigger alerts to this selected network once the monitored mood data were below a certain
value. This was announced as being one effective method to increase the user’s mood.
Graph 9 – Privacy settings of affective self-tracking applications
The data could also be made available to a wider personal network, mostly using social media
networks such as Twitter or Facebook (see Figure 12) and two of the listed tools actually worked
with a Facebook login (HappyFactor and Expereal). However, social sharing does not appear to be
the most relevant feature in these platforms (see Graph 10). Another possibility was when the
platforms themselves created a support community where users could feel safe sharing their data
(anonymously or not) in the knowledge that all the remaining users were there for similar reasons
(see Figure 13).
Not mentioned as a feature
52%
Highlighted feature
24%
Customisable privacy settings
16%
Mentioned feature (but not highlighted)
8%
68
Figure 12 – Screenshot from MoodPanda <http://moodpanda.com>
Graph 10 – Social sharing featured in affective self-tracking applications
Not mentioned as a feature
44%
Optional feature36%
Highlighted feature
20%
69
Figure 13 – Screenshot from MoodPanda (community) <http://moodpanda.com>
The privileged type of data comparison was intra-individual (see Graph 11), that is, the user referred
to his/her own previous values as a monitoring reference. In many cases (especially concerning tools
used to track mood pathologies), there was no possibility of inter-individual comparison and the user
was the single reference unit. When social sharing of personal data was allowed, then two main
situations emerged: the user was able to compare his/her values to global aggregated values or the
user could examine the values, and associated personal information, of other users on an individual
basis (anonymously or not).
70
Graph 11 – Data comparison types featured in affective self-tracking applications
In summary, while the tool sample considered was relatively small in absolute terms, it included
examples from a variety of domains, methodologies and techniques, to illustrate the differences and
similarities among affective self-tracking platforms and devices and to gather information to answer
questions derived from previous sections. In the following section, the focus will then shift from
technology to individual practices.
7.3 – An analysis of (QS) affective self-tracking experiments
The goal of the third and final part of the empirical section is to contextualize self-tracking practices
from both a social and a technological perspective by analyzing individual experiments. Particular
attention is dedicated to the initial goals, the applied methodology and the obtained results, as well
as the personal interpretation of the same.
The QS website hosts numerous reports and video presentations from Show&Tell events describing
self-tracking experiments. In order to create an overview of the experiments in the affective area, I
browsed the QS website in the temporal range from September 2007 to October 2013 (see Table 1)
0
5
10
15
20
25
Intra-individual Inter-individual (aggregatedvalues)
Inter-individual (singularvalues)
71
for posts related to ‘mood’ and ‘happiness’ having found 20 video presentations within this domain70.
These presentations were structured according to the three QS prime questions: 1) What did you do?,
2) How did you do it?, and 3) What did you learn?.
Similarly to the previous empirical exercise, I designed a taxonomy which would facilitate the
analysis of the different experiments and help answering questions from previous sections related
to aspects such as:
the goals of self-tracking activities (section 5.1);
possible issues with data privacy and potential consequences of systematic self-monitoring
(section 5.3), including information overload (section 5.1);
how technology is perceived as an element in the tracking process (section 5.4);
which tools might be preferred by self-trackers and why (section 5.5);
whether psychological assessment models and theories are used as a reference in the process
(section 6.2.1);
whether there is a particular focus on a specific affective dimension (section 6.2.2).
This classification included then the following categories (see Table 11):
1) ID (for convenient reference);
2) Name, Date and Location (identifying the presenter and the presentation);
3) Experiment Objective (exploratory, specific);
4) Duration and Frequency (of the experiment and the data collection);
5) Tools Used (ready-made or self-designed);
6) Indicators (type of data collected);
7) Description of the Method (used in the Collection stage as described in section 4.2);
8) Description of the Result(s) (used in the Reflection stage as described in section 4.2).
70 It is conceivable that the QS Vimeo channel contains other video presentations related to this topic, but taking into consideration that many videos are not tagged and include no description (especially the ones uploaded earlier on), it would only be possible to identify their content by visualizing each one of them individually. Unfortunately, this is not a feasible option, considering the fact that there are over 500 videos at this point and the duration of some of them can be as long as 30 minutes.
72
7.3.1 – General information and Objectives
Following the order of the established categories, I observed that three quarters of the self-
experimenters in the considered sample were men71, and that more than half of the presentations
took place in the north-American territory72. Most of the users had an occupation related to
technology and/or design, or were involved in academic research. In some cases, the individual
experiments led the users to build web or mobile applications which were later on made available to
the general public (i.e. HappyFactor, Moodscope, 750Words). Some of the presenters were avid self-
trackers who had engaged in a diversity of experiments throughout the years (i.e. Buster Benson
<http://busterbenson.com/>; Konstantin Augemberg <http://measuredme.com/>).
Concerning the objective of the experiments, this was probably less unanimous than one would
expect initially (at least in the manner they were explicitly phrased by the users). The goals included
assessing mood (see rows 2, 6, 7, 11, 16, 17, 19 of Table 11), predicting depressive states (see rows
5, 13), correlating physiological indicators with moods and emotions (see rows 3, 9, 12, 14), assessing
and improving the happiness level (see rows 1, 4, 8, 15, 18), monitoring wellbeing (see row 20), and
building holistic self-tracking tools (see row 10) (see Graph 12). It is then challenging to classify them
as strictly exploratory in opposition to being specific or vice-versa, since most of them included
aspects of both domains. The overall goals cited were mainly related to self-awareness and self-
improvement, while not making reference to an absolute external purpose such as a search for ‘a
perfect self’ (a more suitable description would be ‘a better self’).
71 The gender disparity is also mentioned in one article by PhD student Whitney Erin Boesel (Boesel, You, Me, Them: Who is the Quantified Self?). A recent phenomenon, perhaps a reaction to this situation, is the emergence of Meetup groups for female self-trackers (QSXX) in San Francisco, New York and Boston.
72 This finding does not imply that affective self-experiments are not being executed by other self-trackers in other locations, but either these presentations are not being video recorder or they are not given the same type of visibility in the QS website.
73
Graph 12 – Goals of affective self-tracking experiments
7.3.2 – Duration and Indicators
In some instances, the monitoring period could be extended to years (this was usually the case for
users with mood pathologies - see row 2), but in the majority of cases it would only occur
systematically for a few weeks or months as an episodic intervention. In one occasion, the user was
only able to unveil the full potential of the gathered data retroactively, when realizing that his tracked
music listening patterns could be matched with his mood swings, and therefore could be used to
predict depressive states (see row 5). In terms of frequency, daily data collection was the most
common procedure: some users collected data once per day, others multiple times (see rows 7, 15);
some tracked it at a specific time every day (see row 14), and others at random moments (see row
18).
Most trackers focused on indicators based on emotional self-assessment in conjunction with daily
activities (type and duration) and events. Some tried to establish a connection between physiological
indicators (i.e. heart rate) and affective ones in more complex experiments which would often lead
to inconclusive results (see rows 3, 9, 14). A popular method for affective self-assessment was based
0
1
2
3
4
5
6
7
assessing mood assessing andimproving the
happiness level
correlatingphysiological
indicators withmoods andemotions
predictingdepressive states
monitoringwellbeing
building holisticself-tracking tools
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on numerical scales, but there was not a consensual affect element selected. For instance, some users
would just evaluate how they felt towards a certain day retroactively (see row 14), others would rate
daily events (see row 8), and others would try to assess their mood in particular moments of the day
(see row 7). The different modes of inquiry would then inform more or less detailed versions of
affective assessment.
7.3.3 – Tools, Methods and Results
In terms of the self-tracking process, the most striking element was related to customization. All the
examined experiments were self-designed, either in terms of methodology or in terms of technology
(in fact, as referred previously, some users created consumer apps subsequently).
The methodology employed was in some cases relatively basic (i.e. a numerical mood ranking scale)
and using a format which the user felt was more intuitively applicable to his/her case (see rows 1, 7,
8). A few self-trackers preferred to search inspiration for their techniques in psychological models
(see rows 2, 15). In similar fashion, the level of technological complexity was quite diverse. On one
side, some of the experiments relied on rather basic techniques using mobile reminders and
spreadsheets (see rows 7, 8, 14, 16, 18). On the other, some experiments could be quite complex,
especially when collecting different types of data and including physiological and behavioral
indicators (see rows 3, 6, 10, 14). In such cases, the experiment itself would also demand a longer
period of preparation, since the necessary tools would have to be either entirely or partially built or
modified. It might be important to add that that the level of technological complexity is not correlated
with the level of methodological complexity or degree of (academic) validity. Some experiments were
relatively simple from a technical point of view (i.e. using just one app to record data), but then their
procedure was multi-variable and made use of several psychological models (see row 15). In
opposition, some experiments were quite innovative and somehow demanding in technical terms
(i.e. embedding sensors in pills), while their methodological premise was rather straightforward (see
row 6). Nevertheless, in every situation there was an explicit concern about consistency and
systematization.
The results from the experiments were somehow diverse. For some self-trackers, the experiment(s)
did not lead to any specific conclusion in the affective area: this could either be due to data overload
(see row 3), or just lack of correlation between the indicators collected (see row 14). For others, the
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findings were not surprising and merely confirmed their initial suspicions (see rows 1, 5), or served
mainly as an exploratory procedure which could be followed-up in the future (see rows 4, 10). The
majority of the self-trackers did report the experience as positive since it increased self-awareness
(see rows 6, 20), led to identifying factors which triggered positive and negative moods (see rows 7,
17) and impacted the level of personal happiness (see rows 8, 15, 18). In one case, the self-tracker
did not gather any specific personal insights while performing the monitoring, but reported that the
regularity of the procedure, as well as the fact that he could share his tracking data with two close
friends, as the main elements which contributed to a more balanced and overall more positive mood73
(see row 2). One user intended to apply the knowledge unveiled through her personal conclusions
into building a predictive system which would activate triggers to a selected group of friends
whenever her mood would be below a certain value (see row 17). Other studies seemed to move in
the same predictive direction: a Psychology PhD student was using a platform (Ginger.io) which
would allow forecasting specific individual moods through the analysis of (passive) mobile data, such
as number and duration of calls and text messages, and Bluetooth and GPS information (see row 13).
In a few instances, the experiments also allowed some findings on a methodological level. When
gathering both physiological data via passive collection (i.e. heart rate) and affective data through
self-assessment (i.e. mood level), the modes of recording can become incompatible, as the first
function on a continuous mode and the second on an intermittent mode (see row 9). In the same
experiment, the user concluded that a bottom-up approach might be more advisable in these
procedures, that is, to build the context after collecting the data. Another self-tracker, after years of
self-monitoring, also defended that objective and subjective indicators should not be correlated (see
row 14). The same user warned that precision can be counter-productive (the values are not
necessarily what is relevant, instead what should be retained is their contextual significance), and
suggested ‘agnostic’ tools and Boolean tracking options as the best alternatives to monitoring
personal information. Another interesting aspect worthy of mention is the fact that this self-tracker
accepted having conflicting views towards self-monitoring, despite doing it for several years and
having designed a number of tools (one of them is the online journal 750words examined in section
7.2). Besides referring some occasional issues related with privacy and sharing personal information
online, the main reason to his partial skepticism might be associated with the fact that he did not
manage to gather the personal insights he would expect after so many different attempts (especially
to what concerned the correlation between what he labeled as ‘objective’ and ‘subjective’ data).
73 This type of measurement reactivity is denominated Hawthorne Effect (Moodscope: How It Works). In such cases, the observed subjects improve their behavior as a result of being observed.
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8. Discussion
The concept of systematic affective self-monitoring in a non-clinical environment is not novel – the
field of Psychology has been applying experience sampling methods74 and daily reconstruction
methods75 for several years, in many cases with the use of digital and mobile technology. Recent
studies also confirm that “mobile phones are likely to become an increasingly important adjunct at
the disposal of clinical psychologists and researchers”76 (Clough and Casey 290).
However, a fundamental change occurred at a technological level: more hardware and software
solutions are made available to the end consumer at a lower price. Such modification displaces these
practices from an exclusively clinical and academic environment to a wide public arena, and expands
its focus from mental patients (on a chronic or episodic basis) and research subjects to a large
population previously considered healthy and functional. This modification surpasses the
technological field and the psychological domain and should be evaluated from a social perspective
also.
8.1 – QS: in the intersection of technology, wellness, wellbeing, and science
In very recent years, the emergence of the ‘Quantified Self’ concept generated an active movement
which is translated into a global association of individuals who might appear as rather heterogeneous
at a first glance, but are united in their interest in one or more aspects of the self-tracking practice,
be it for a personal or a professional purpose. Within the larger group there are several different
areas of concern which can at times intersect completely or partially. Considering the data gathered
from the QS website and the local Show&Tell events (including the users’ professional occupation), I
identified three main areas of interest within this movement: technology, wellness and wellbeing, as
74 This method is defined as a “research procedure that consists of asking individuals to provide systematic self-reports at random occasions during the waking hours of a normal week” (Larson and Csikszentmihalyi).
75 This method involves a retrospective perspective where the user is requested to “fill out a diary corresponding to event of the previous day” (Kahneman et al., Toward National Well-Being Accounts 431).
76 In this domain, the authors refer advantages to psychological interventions via mobile phone such as flexibility, objectivity, increased self-disclosure, and social support. They also alert for the need to create standardized procedures, ethical guidelines and provide adequate training to therapists.
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well as science. In the following sub-sections, I will elaborate on the elements of such intersection
and on the specific characteristics related to each one of these domains in particular.
8.1.1 – A recursive public empowered through technology
Within the technological domain, some self-trackers may regard their activities as falling under the
transhumanist or the polymath category, but I would advance that the QS group should be
considered, first and foremost, as a recursive public. As defined by Kelty, this is “a public that is vitally
concerned with the material and practical maintenance and modification of the technical, legal,
practical, and conceptual means of its own existence as a public” (Kelty 5). While the QS group does
not represent the totality of self-trackers, it does distinguish itself from the larger group by having a
more pro-active, interventionist and utilitarian approach: specific individual problems are tackled
through self-designed solutions, which can be independent from accepted knowledge in other
domains (i.e. clinical practice).
In many instances, the individuals initiate the process of self-tracking because they have not been
provided solutions for their particular issues within the conventional medical institutions. This
perceived disconnection between the private and institutional spheres can be illustrated by Martin’s
statement: “if the social contract was originally seen as a means of keeping unruly individuals in
order, we have now arrived at an odd and chilling reversal: order and rationality now reside in the
mind or brain of individuals, and disorder reigns (and is celebrated) in social institutions” (583).
Some of the self-trackers are disillusioned with the system and others are also critical towards
common practices and the limited knowledge of the respective practitioners: as stated by self-tracker
Larry Smarr, in many occasions, the idea that you can feel what is going on with you, as frequently
questioned by doctors, is epistemologically incorrect (Ramirez, Larry Smarr: Where There Is Data
There Is Hope).
Self-reliance and pro-activity are determining aspects within the QS group and technology is then the
means which delivers a sense of self-empowerment to the individual, who feels capable of
independently conducting a sound experiment leading to genuine personal insight. In this context, as
stated by Hansen, “technology allows for a closer relationship to ourselves, for a more intimate
experience of the very vitality that forms the core of our being” (Hansen 589). Even though
technology plays an important part, it is vital to clarify that the self-trackers studied were the ultimate
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designers of their experiments, leading the operations in every stage of the process. This scenario is
potentially distinct from the self-tracking activities external to the QS group where users adopt
specific apps or devices and allow the collection and reflection stages of the monitoring process to be
mostly led by technology instead.
8.1.2 – The quest for an amplioself
Regarding the wellness and wellbeing aspect, the healthism trend materializes the concern for an
improved self, which is firstly more visible and more widely accepted at a physical level (i.e. nutrition,
fitness), but increasingly incorporates other dimensions, such as intellectual and emotional,
integrated in a holistic approach. This movement threatens to blur the boundaries of the standard
concept of ‘sickness’ by continuously expanding the field for individual enhancement. The abstract
notion of ‘perfect self’ is translated by some as ‘normalized self’, derived from the quantification
component of the self-tracking procedure, and by others as ‘exoself’, derived from the augmented
abilities of new ‘exosenses’ facilitated by technology. The main criticism contained in the first term
relates to the fact that the values from an individual would be judged in comparison to the values of
the collective and submitted to the ‘tyranny of the global average’. Considering that the QS
experiments are ‘n=1’ procedures which are intended to gather additional insights on the self-tracker
only, intra-individual references become more significant than inter-personal ones (this was also
observable in the empirical analysis of the affective self-monitoring platforms) and, for this reason,
this criticism loses some of its weight. Another criticism is derived from the exclusive numerical
nature of the experiments. As stated by Morozov “the hidden hope behind self-tracking is that
numbers might eventually reveal some deeper inner truth about who we really are, what we really
want, and where we really want to be” (232). While most self-experiments aim at enhancing self-
awareness, the reports analyzed are hardly focused on numbers, as the users tend to emphasize the
qualitative learnings from their experiences.
The logic proposed by the second term (‘exoself’) seems more applicable to the QS reality, but I would
advance the notion of ‘amplioself’ instead, since these new senses, while extending the default
biological capacity of the human being, would not be external (‘exo’) to the individual. Kevin Kelly
describes this idea in an interesting manner: he states that quantification is only an intermediate
state in QS and that technology will support the creation of these new individual senses, but that the
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former will be dispensable once the latter are fully operational (Carmichael, Kevin Kelly on The
History and Future of QS). He illustrates this with the example of an experiment where a user wore a
digital compass attached to a belt which would tingle in a certain way to indicate the north. Within a
short period of time, the user was immediately able to indicate that direction without the aid of the
device, since that capacity had been incorporated by his body through habituation. Nevertheless, this
particular path to self-awareness can have the opposite effect and raise questions. As framed by
Singularity University ambassador in The Netherlands Yuri van Geest, “if you outsource your
awareness to technology, do you risk losing your intuition?” (Boesel, The Woman vs. The Stick:
Mindfulness at Quantified Self 2012). An answer he provides, curiously within the same field, points
out that GPS devices are presumed to have weakened people’s sense of direction. This apparent
contradiction can incite one to ponder: are individuals using technology to (re)learn what they used
to know prior to its use? Is the trend following a circular movement or is it merely a redefinition and
redistribution of tasks worthy of conscious human attention? Or, from a different angle, is this an
attempt to partially deconstruct, using Baudrillard’s terminology, the simulacrum?
Another significant aspect still in the wellbeing domain is the one which questions the source of
concern for an improved self: should it be placed on an individual sphere only or should it be analyzed
also from a political and economic perspective? It might be relevant to consider that many
governments struggle with healthcare costs and therefore channel their efforts into policies of
prevention and promotion of personal accountability in wellness and wellbeing matters. Business
priorities are shifting from productivity to innovation and, if the first goal needed primarily a healthy
body, the second one also demands an emotionally balanced and happy individual. As observed in
the empirical section, some self-tracking applications are already targeted at affective assessment
within the work environment. Nations become increasingly concerned with measures of collective
wellbeing and companies invest in personal awareness activities, such as mindfulness courses. In this
context, improving oneself intellectually and emotionally is no longer an option but a requirement.
8.1.3 – Introveillance as a new type personal type of surveillance
The classical notion of surveillance proves to be outdated to accurately describe the panoply of
tracking possibilities currently available, and several authors have proposed different new terms
considering the source, type, and calculus of vigilance. Some classify self-trackers under the category
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of ‘voracious collectors’, but I believe this expression fails to account for the essential nature of self-
tracking activities from an individual perspective. I would instead propose the term ‘introveillance’
to describe the monitoring activity initiated by the individual and targeted at the self, and I would
introduce two classifying top-level categories: one referring to the monitoring mode (‘continuous’ or
‘episodic’) and another one referring to the tracking focus (‘holistic’ or ‘targeted’). From such
taxonomy four main typologies would arise: continuous holistic introveillance, continuous targeted
introveillance, episodic holistic introveillance, and episodic targeted introveillance (see Graph 13).
Graph 13 – Types of introveillance according to tracking mode and focus
Episodic experiments (the ones with a pre-defined duration) appear to be more common than
continuous ones among self-trackers, except in cases of a chronic condition, which then leads the
duration of the procedure. Targeted experiments (the ones aimed at monitoring one indicator or
explaining one specific dimension) also appear to be more frequent than holistic ones, possibly due
to the fact that individuals are compelled to embark in such experiments to solve one particular
problem. While the focus of one experiment might be rather limited, this does not imply that the
procedure will not have an exploratory character and consider multiple variables. This is often the
case in affective studies where the individual may not know precisely which aspects trigger certain
moods or increase the personal happiness level, and therefore collects data beyond this area to find
Tracking mode
Tracking focus
Continuous Holistic
Introveillance
Episodic Holistic
Introveillance
Episodic Targeted
Introveillance
Continuous Targeted
Introveillance
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possible correlations. In this domain, it is relevant to distinguish self-tracking experiments also
according to their objective. Considering once more the information gathered in the empirical stage,
I would propose that self-tracking activities can incorporate three main types of purposes: 1) to
understand, 2) to improve, and 3) to predict. For example, a self-tracker may want to discover the
particular sources of a negative mood and set up an experiment to identify those causes; in this case
the goal would be to understand. Once the causes had been successfully identified, the individual may
want to minimize the presence of those adverse elements in his/her daily life to achieve a more
balanced or an overall more positive mood; the objective is then steered to improvement. When the
individual is able to recognize regular patterns over time, then this information can be used to
anticipate periods where negative moods may prevail; the goal is, in this case, to predict. One
experiment may contain one or all of the above referred purposes, depending on the information the
user already possesses, and how long the experiment is set to last.
One aspect which is often referred alongside surveillance is privacy. This is a matter occasionally
discussed within the QS collective, but acknowledging that the notions of privacy and personal
information are being currently redefined at a broader scale, most self-trackers do not consider the
topic to be more pressing in the self-tracking area than in any other domain. Moreover, especially
within the QS group, most users seem willing to share their monitored personal data, contributing to
an open learning environment. It is important to add that the type of monitoring examined in this
study is self-initiated and self-aware. Unlike the participation in social media platforms, the
individual engages with a particular technological platform or device with the primary goal of
tracking personal information, and not to communicate or share information with others. From my
observations, and within the recursive public spirit, the self-trackers considered are more concerned
with having full access to their complete datasets in a raw format (preferring in many occasions the
use of ‘agnostic’ tools), than assuring that their personal data is not shared with other individuals or
institutions.
8.1.4 – The expansion of a personal science
The association of technology with self-experimentation in these monitoring practices raises
questions targeting the foundations of science. Self-experimentation (or n=1 studies) is not a novel
practice in the scientific domain, but affordable technology adds a level of precision, detail and
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systematization which was not possible before. Kevin Kelly states that QS experiments are changing
the scientific method by questioning paradigms and engineering new methods (i.e. conducting
experiments with multiple variables instead of just one) (Carmichael, Kevin Kelly on The History and
Future of QS). This is announced as the emergence of the citizen or personal science which will mark
the breaking point between, for instance, the medicine of the past and the medicine of the future.
Nevertheless, the self-tracking experiments considered in this investigation did not completely
disregard traditional science – in some cases, psychological models were used as an information
source to establish hypotheses and set up empirical methodologies. In most situations there was not
a direct reference to specific scientific theories, but the methodology employed, especially on a
commensuration level, did not differ greatly. Self-trackers exhibited an explicit concern regarding
consistency within their experiments, while maintaining a critical perspective towards the
methodology employed and the results obtained. The QS Show&Tell events proved then to be
privileged spaces to learn how other individuals tackled similar issues and to submit one’s
experiment to the feedback of the collective.
It is essential to retain that the goal of self-monitoring is to reach a conclusion which is valid at an
individual level (not at a population level), a change which could potentially imply the revision of
some of the scientific validity and reliability premises. Similarly, and as mentioned previously,
notions such as a group average may hold diminished relevance in a context where the individual is
the sole examination unit, and intra-individual comparisons might be favored in relation to inter-
individual ones (in the affective domain in particular).
Would such personal science be available to all? Besides the growing number of wearable devices,
many of the wellness and wellbeing self-tracking applications are available as inexpensive or even
free mobile apps. Still, the adoption rates are relatively low outside the group of fitness enthusiasts,
patients with chronic diseases, and technology fore-runners. The data collection and integration is
facilitated by these tools, but the user still has to commit to a systematic monitoring procedure for a
certain period of time. The lack of sustainable use is indicated as one of the challenges faced by this
type of monitoring technology. In order to increase the level of motivation, a few of these platforms
and devices are already incorporating gamification strategies (as observed in the empirical stage),
which include competition and reward components, and personalization elements, which allow
capturing additional data or visualizing it in a particular manner. Another challenge may reside in
the data reflection stage, since the data interpretation at a personal level cannot be outsourced to
technology. The information visualization component assumes a fundamental function in this
domain and, even though some platforms allow a certain degree of customization regarding the
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manner in which the data is displayed, it will still be confined to the provider’s default logic (which
might not be evident to the user) within the pre-defined possibilities. Many self-trackers advise the
use of agnostic tools (i.e. spreadsheets) to record the collected data in its ‘rawest’ form, but this
decision implies a greater investment, in terms of time and effort, when integrating and presenting
the data (even if it can minimize other issues related to data interoperability and barriers cascade).
Managing data also requires a new set of skills which can be framed under the data literacy category.
While the technology may be widely available, in association with under-developed data analysis
skills and lack of critical interpretation, it may lead to a defective personal science unable to deliver
genuine insights to the individual.
8.2 – The role of affective self-tracking
8.2.1 – The optimal point of personal monitoring
The main possibilities and challenges of self-tracking practices have been described above in a
generic mode. But are there particular aspects which should be additionally taken into account when
considering, more specifically, affective self-monitoring practices? In the empirical research
conducted, I observed that none of the self-trackers studied reported negative consequences in
relation to the presented experiments, despite the discrepancy in the volume and type of personal
insights gathered. However, this does not imply that adverse reactions cannot occur. In an emotional
2010 post (Carmichael, Why I Stopped Tracking), the QS Director Alexandra Carmichael announced
that she would cease her self-tracking activities (which included mood monitoring), since she had
noticed that these had fueled self-torturing mechanisms which would regularly bring about a sense
of failure and guilt. In one other case, the individual, having tracked numerous affective indicators
using different methodologies for more than one decade without reaching any valuable personal
insight, held conflicting views regarding the monitoring process.
Academic research in this area may help interpret these facts. In one Psychology study, self-
examination is concluded to lead to increased self-knowledge as long as: the activity is temporally
limited and the focus is directed at observed personal facts (‘How am I?’), rather than questioning
personal features (‘Why am I like this?’) (Hixon and Swann 42). In fact, many of the self-trackers
examined in the present study were conducting experiments limited in time and with the purpose of
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better assessing mood and specific causes for certain affective states. The few users who were
performing affective self-tracking experiments in a continuous fashion, were usually compelled to do
so due to some type of affective disorder (i.e. depression, bipolarity).
Other scientific studies claim that self-reflection might actually be a deficient source of self-
knowledge considering the limited access to individual consciousness (Wilson and Dunn 513). In that
sense, the development of technology based on physiological indicators (i.e. heart rate, galvanic skin
response) to infer internal affective states, might help tapping into the implicit internal processes
while combining them to the explicit ones captured via direct self-assessment techniques. That same
research goes further by suggesting that a considerable amount of accurate self-knowledge is gained
instead through the accounts of others, and by observing personal behavior. While self-tracking
technology may support the latter, it does not necessarily encourage (at least directly) the first one.
The social sharing possibilities contemplated in some of the tools examined were aimed at emotional
support and occasional intervention (i.e. one close friend or family member would be notified once
the self-tracker would register a mood state below a certain value), but not factual observation of
situations or individual behavior. Can certain individuals become then trapped within the personal
limitations of self-reflection and self-knowledge, pursuing an elusive insight which is not available
through the monitoring methods they are using?
Sloterdijk states that “people are in search of everything, except existence itself. One has to, before
one really starts living, first do something else, fulfil one more requirement, fulfil one desire that is
more important at the moment…” (De Cock 1). Can then self-tracking practices be a camouflaged
form of individual escapism from a particular external reality or, on the contrary, are they an attempt
to fulfil the complete potential of a personal existence?
8.2.2 – The challenges of a “political economy of happiness”
While “happiness gives the appearance of a universal, apolitical category that compels acceptance
because of its self-evident goodness and desirability” (Duncan 105), one should strive to examine it
from a more critical perspective. A “political economy of happiness” has emerged in the last three
decades with an increasing number of research papers and theories being published on the topic in
the fields of Psychology, Sociology and Economics, and also fueled by an increased political and
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corporate focus on wellbeing (Duncan 85). Several authors criticize what could be described as a new
‘ideology of happiness’, accusing it of oversimplifying the concept of happiness and promoting
illusive life perspectives. As described by Hedges, within this perspective, “those who fail to exhibit
positive attitudes, no matter the external reality, are in some ways ill” (Hedges 119). The approach
taken has a utilitarian character and it is essentially aimed at the optimization of human experience.
However, as pointed out by Schneider, “perhaps genuine happiness is not something you aim at, but
is (…) a byproduct of a life well lived” and “a life well lived does not settle on the programmed or
neatly calibrated” (Schneider 35). Situated on the other side of the spectrum are psychoanalytical
views based instead on the impossibility of human happiness, outlining a more complex and somber
picture, where the unconscious dimensions and the unfulfilled desires play a crucial role in the
individual behavior. As analyzed in the Psychological section, different notions of happiness inform
distinct commensuration models and complicate the discussion concerning the validity of the
methodologies employed.
Still, the most substantial challenge that affective self-tracking activities face might surpass the
methodology concern and reside in the existential reflection on human nature and the ultimate
purpose of one’s life. If ‘the unexamined life is not worth living’, to which extent does the self-tracked
life lead to self-knowledge, self-improvement, and happiness? As defended by Giddens, “self-identity,
as a coherent phenomenon, presumes a narrative” (Giddens 76), so these practices may require an
adequate contextualization in order to be fruitful. In that sense, it is fundamental for self-trackers to
distinguish the monitoring process from its goals and to clearly delineate the scope of the tracking
procedure. If initially self-tracking is designed to reflect specific elements of a personal existence, this
relationship can be easily inverted to position the monitoring practice as the leading element of
individual daily routines.
The impact of this potential reversal deserves particular attention and if self-tracking practices in
general, and affective self-monitoring in particular, become more prominent in the near future, then
further research should be steered towards this specific domain to inquiry in which manner
conflicting perspectives and interests are being reconciled.
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9. Conclusion
In 1981, Neuringer conjectured a future where a science of the self would be celebrated and “instead
of the often depressing "How are you?" people would greet one another with "What experiments are
you doing?"” (Neuringer 93). Thirty years later, self-experimentation has been made more accessible
to the general public in a variety of personal domains through the use of inexpensive mobile
technology, and self-tracking - the individual practice of systematically gathering data in the personal
life domain for a certain period of time with a specific goal - has become more visible through the
activities classified under the recent ‘Quantified Self’ label.
The structure of the present study was designed to provide a contextual approach to the
phenomenon (through an historical, social, conceptual, and functional perspective), and also to
facilitate the understanding of the affective practice in particular (through the psychological and
empirical perspective). While not being an exhaustive examination, the added value of the current
investigation resides in the selection and association of interdisciplinary theories and models, in the
empirical analysis of the technological platforms and individual practices (besides a brief
examination of the QS group activities), as well as the introduction of new terminology.
As stated in the initial research question, this study aimed at defining current self-tracking practices
and describing their social and technological context and, for that purpose, it focused more
specifically on the QS group, its experiments and surrounding monitoring platforms. The self-
propelled QS group, initially located in the San Francisco Bay Area, quickly expanded to a global scale
movement open to all self-trackers who were willing to learn and/or share their interest in this type
of personal monitoring, either digitally (via the QS website and social media platforms) or via the
frequent local Show&Tell events. The primary activities of the group can be defined as emerging from
the intersection between technology, wellness and wellbeing, as well as science, and therefore attract
participants who are, personally and/or professionally, involved in those areas. Being a recent
phenomenon (the movement and the self-tracking practice considered in this particular
technological setting), it has not yet gathered a substantial amount of academic research. Within the
studies already published, the majority tackles the topic from a physical health perspective, referring
aspects related to nutrition, fitness, and particular bodily pathologies. For this reason, and
acknowledging the unfeasibility of examining all categories, the current investigation was directed
to the affective domain examining more specifically mood and happiness experiments.
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On a generic level, self-monitoring practices challenge conventional medicine, its premises and
practitioners through technologies which offer self-empowerment. Self-trackers do not expect
answers and solutions to be readily provided by external institutions and, therefore, prefer to pro-
actively lead the process of formulation of their own diagnosis and prognosis. This challenge extends
to academic research and to the scientific method itself which, while not being fully invalidated, might
need to be actualized through a new type of personal science providing relevant knowledge on an
individual level, rather than on a population one.
On a more specific angle, the affective self-tracking phenomenon can be studied from a macro and
micro perspective. In the macro approach, there are ideological and economic aspects to consider,
with governments and corporations increasingly concerned with the promotion and assessment of
individual well-being. Happiness becomes a utilitarian measure with consequences at a micro level.
The individual is then responsible for managing his/her affective life as a set of assets with the
purpose of maintaining a balanced mood and achieving a high level of happiness, besides assuring
the satisfactory maintenance of his/her physical health.
The vast majority of self-trackers examined in this study did not report adverse consequences during
or after the monitoring experiments. In fact, most individuals claimed to be satisfied with their
experiments, even if the process had proven not to be equally insightful for all. However, several
examples and theories emphasize the relevance of some specific elements, such as the duration and
the particular focus of the procedure, in order to avoid after effects which would be predominantly
negative. As long as executed with a specific goal in mind and designed as an episodic intervention,
affective self-practices can enhance self-knowledge and eventually contribute to self-improvement.
It becomes then necessary to further understand the possible purposes, methodologies, benefits and
limitations of such experiments, in order not to embark in these practices purely as a byproduct of
the emergence of new technology available at a low cost.
Self-tracking practices as described in the current investigation, especially in the affective domain,
are still mostly confined to a particular population group which I have classified as a recursive public,
characterized by its pragmatic interest, critical spirit and pro-active involvement. The expansion of
these practices to the general public coupled with an uncritical acceptance of this type of technology,
could give rise to a sort of ‘digital hypochondria’ fueled by governmental institutions targeting at cost
cuts on the healthcare system and corporations exploring the potentialities of a new anxiety market.
This potential shift requires particular attention. If, in the near future, “our similarities will not be
based on shared participation in social life, but on a shared search for individual betterment” (Martin
88
583), then it becomes crucial to remain vigilant and cautious in this quest, both individually and
collectively, in order to distinguish the “betterment process” from its (ultimate) goals, and to assure
that the search for personal improvement is adequately framed from a societal perspective.
89
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109
Tools
750words <http://750words.com/>.
Affdex Facial Coding <http://www.affdex.com/technology/affdex-facial-coding/>.
Ask Me Every <http://www.askmeevery.com/>.
bStable <http://www.mcgrawsystems.com/>.
ChartMyself <https://www.chartmyself.com/>.
ChronoRecord <http://www.chronorecord.org/patients.htm>.
CompanyMood <https://www.company-mood.com/>.
Daily Diary <http://www.dailydiary.com/>.
Daytum <http://daytum.com/>.
Disciplanner <http://www.disciplanner.com/>.
EmWave2 <http://www.heartmathstore.com/item/6310/emwave2>.
Evernote <http://evernote.com/>.
Expereal <http://expereal.com/>.
Fluxstream <https://fluxtream.org/>.
Ginger.io <http://ginger.io/>.
GoodReads <http://www.goodreads.com/>.
110
Google Calendar <http://www.google.com/calendar>.
GottaFeeling <http://gottafeeling.com/>.
Graphitter <http://www.grafitter.com/>.
Graphomatic <http://graphomatic.net/>.
Grow <http://growhq.com/>.
Happiness <http://goodtohear.co.uk/happiness>.
HappyFactor <http://howhappy.dreamhosters.com/>.
Hashtagify.me <http://hashtagify.me/>.
Healthgraph <http://developer.runkeeper.com/healthgraph>.
iLogger <https://itunes.apple.com/fi/app/ilogger/id319110300>.
I Rate My Day <http://www.iratemyday.com/>.
Last.Fm <http://www.last.fm/>.
Life Game <https://tree.mindbloom.com/>.
LifeMetric <http://lifemetric.com/>.
LifeTick <http://lifetick.com/>.
Limits <http://www.juicycocktail.com/software/limits/>.
111
LumenTrails <http://www.lumentrails.com/>.
Meetup <http://www.meetup.com/>.
MindMup <http://www.mindmup.com>.
Mood Tracker <http://www.medhelp.org/user_trackers/gallery/mood>.
Mood247 <https://www.mood247.com/>.
MoodChart <https://moodchart.org/Default.aspx>.
MoodJam <http://moodjam.com/>.
MoodPanda <http://moodpanda.com/>.
Moodscope <https://www.moodscope.com/>.
Moodtracker <https://www.moodtracker.com/>.
Moody Me <http://www.medhelp.org/land/mood-diary-app>.
MySmark <https://www.mysmark.com/>.
Optimism Online <http://www.findingoptimism.com/>.
Plaxo <http://www.plaxo.com/>.
QSensor <http://www.qsensortech.com/overview/>.
Remember The Milk <http://www.rememberthemilk.com/>.
rTracker <http://www.realidata.com/cgi-bin/rTracker/iPhone/rTracker-main.pl>.
112
Sense <http://open.sen.se/>.
Singly <http://singly.com/>.
Sympho <http://sympho.me/>.
Symptom Journal <http://www.symptomjournal.com/>.
TagCrowd <http://tagcrowd.com/>.
TallyZoo <http://www.tallyzoo.com/>.
The Carrot <http://thecarrot.com/>.
Track and Share <http://www.trackandshareapps.com/>.
Track Your Happiness <http://www.trackyourhappiness.org/>.
TripAdvisor Facebook App <https://apps.facebook.com/tripadvisor/>.
Wellness Tracker <https://www.facingus.org/>.
113
Appendix
Appendix 1 – Quantified Self website indicators
Table 1 – General indicators about the QS website (November 2013)
Date first published article 28/09/2007
Date last article considered for the study 31/10/2013
Number of publishing authors 34
Number of 2007 articles 27
Number of 2008 articles 47
Number of 2009 articles 83
Number of 2010 articles 165
Number of 2011 articles 184
Number of 2012 articles 200
Number of 2013 articles (up to 31/10/2013) 101
Total published articles in 6 years 807
(Back to section 7.3)
114
Appendix 2 – Quantified Self Show&Tell events’ indicators
Table 2 – Oldest QS Meetup groups (November 2013)
Group Date
Founded # Members
# Past Meetups
# Reviews
Bay Area 31/07/2008 3508 91 41
New York 18/04/2009 1994 40 25
Boston 12/01/2010 1252 26 24
Sydney 26/02/2010 211 5 19
Seattle 05/05/2010 549 14 15
London 09/07/2010 1375 35 22
Amsterdam 29/07/2010 912 39 13
Chicago 21/08/2010 386 11 16
Toronto 07/09/2010 479 19 23
San Diego 20/09/2010 396 16 13
(Back to section 7.1.1)
Table 3 – Top 10 QS Meetup groups by number of members (November 2013)
Group Date
Founded # Members
# Past Meetups
# Reviews
Bay Area 31/07/2008 3508 41 91
New York 18/04/02009 1994 25 40
London 9/7/2010 1375 22 35
Boston 12/1/2010 1252 24 26
Silicon Valley 31/12/2010 1129 11 21
Amsterdam 29/07/2010 912 13 39
San Francisco 25/07/2012 571 6 3
Seattle 5/5/2010 549 15 14
Toronto 7/9/2010 479 23 19
Berlin 17/10/2012 471 7 6
(Back to section 7.1.1)
115
Table 4 – Top 10 QS Meetup groups by number of previous meetings (November 2013)
Group Date
Founded # Members
# Past Meetups
# Reviews
Bay Area 31/07/2008 3508 41 91
Portland 15/06/2011 332 29 17
New York 18/04/02009 1994 25 40
Boston 12/1/2010 1252 24 26
Toronto 7/9/2010 479 23 19
London 9/7/2010 1375 22 35
Denton 17/01/2012 22 21 0
Sydney 26/02/2010 211 19 5
Chicago 21/08/2010 386 16 11
Washington DC 9/10/2010 319 16 12
(Back to section 7.1.1)
Table 5 – Top 10 QS Meetup groups by number of (member) reviews (November 2013)
Group Date
Founded # Members
# Past Meetups
# Reviews
Bay Area 31/07/2008 3508 41 91
New York 18/04/2009 1994 25 40
Amsterdam 29/07/2010 912 13 39
London 09/07/2010 1375 22 35
Boston 12/01/2010 1252 24 26
Silicon Valley 31/12/2010 1129 11 21
Toronto 07/09/2010 479 23 19
Portland 15/06/2011 332 29 17
San Diego 20/09/2010 396 13 16
Seattle 05/05/2010 549 15 14
(Back to section 7.1.1)
116
Appendix 3 – Web queries for “Quantified Self”
Table 6 – Wikipedia articles for “Quantified Self” (in chronological order – December 2013)
Language Article URL Creation
Date # Versions
English http://en.wikipedia.org/wiki/Quantified_Self 2010 151
German http://de.wikipedia.org/wiki/Quantified_Self 2012 45
French http://fr.wikipedia.org/wiki/Quantified_Self 2012 41
Chinese http://zh.wikipe-
dia.org/wiki/%E9%87%8F%E5%8C%96%E7%94%9F%E6%B4%BB 2012 4
Dutch http://nl.wikipedia.org/wiki/Quantified_Self 2013 28
(Back to section 7.1.1)
Table 7 – Google Scholar results for the query “Quantified Self” (December 2013)
Year # Articles
2007 8
2008 8
2009 15
2010 47
2011 96
2012 214
2013 402
(Back to section 7.1.1)
117
Appendix 4 – General self-tracking applications
Table 8 – List of general self-tracking applications
ID Tool Name Tool Type Tool Description
1 Ask Me Every Web app <http://www.askmeevery.com/> A general tracking application based on the
users predefined questions.
2 ChartMyself Web app <https://www.chartmyself.com/> A platform for an integrated health approach
where several wellness and wellbeing aspects can be monitored simultaneously.
3 Daily Diary Web app <http://www.dailydiary.com/> A general tracking application allowing full flex-
ibility on type of data tracked.
4 Daytum Web app <http://daytum.com/> A general tracking application created by designer Nich-
olas Felton.
5 Disciplanner Web app <http://www.disciplanner.com/> A general tracking application focused on
long-term goals.
6 Ginger.io Android and iPh-
one app
<http://ginger.io/> A general tracking application which infers personal infor-
mation directly from mobile usage data.
7 Graphitter Web app <http://www.grafitter.com/> A general tracking tool which facilitates data
sharing on social networks.
8 Graphomatic Web app <http://graphomatic.net/> A general tracking application allowing full flexibil-
ity on type of data tracked.
9 iLogger iPhone app <https://itunes.apple.com/fi/app/ilogger/id319110300> A general tracking
application allowing full flexibility on type of data logged.
10 Life Game Web app <https://tree.mindbloom.com/> A platform for an integrated health approach
where several wellness and wellbeing aspects can be tracked and monitored.
11 LifeMetric Web app <http://lifemetric.com/> A general tracking application which allows sharing
data with other users.
12 LifeTick Web app <http://lifetick.com/> A general tracking application focused on pre-defined
goals.
13 Limits iPhone, iPod
touch app
<http://www.juicycocktail.com/software/limits/> A general tracking applica-
tion allowing full flexibility on type of data tracked.
14 LumenTrails iPhone, iPod
touch, iPad app
<http://www.lumentrails.com/> A general tracking application allowing full
flexibility on type of data monitored.
15 rTracker iPhone app <http://www.realidata.com/cgi-bin/rTracker/iPhone/rTracker-main.pl> A
general application which allows full flexibility on type of data monitored.
16 Symptom
Journal Web app
<http://www.symptomjournal.com/> An application dedicated to tracking sev-
eral types of health symptoms.
17 TallyZoo iPhone app <http://www.tallyzoo.com/> A general tracking application allowing full flexi-
bility on type of data monitored.
118
ID Tool Name Tool Type Tool Description
18 The Carrot Web, iPhone app
(+ devices)
<http://thecarrot.com/> An application which allows tracking several individ-
ual health aspects, including goals.
19 Track and
Share iPhone app
<http://www.trackandshareapps.com/> A general tracking application allow-
ing full flexibility on type of data tracked.
(List built from data gathered in October 2013)
(Back to section 7.2)
119
Appendix 5 – Mood and happiness self-tracking applications
Table 9 - Examples of mood and happiness self-tracking applications
ID
Tool
Name /
Type
Focus /
Goal
Usage
Domain
Tracking
Mode
Input
Type
Output
Type
Data
Privacy
Social
Sharing
Data
Compari-
son
Tool Description
1
750words
Web app
(Free)
Source: QS
Mood /
Improve
self-under-
standing
Consumer Passive
and Active
Free Text Text, Chart High-
lighted fea-
ture
Not men-
tioned as a
feature
Intra-indi-
vidual
(specifics)
+ Inter-in-
dividual
(points)
<http://750words.com/> The user is requested to
write 750 words per day and the tool performs a
sentiment analysis of the text based on the Regres-
sive Imagery Dictionary to calculate the emotional
character of the content along with other passive
metrics (i.e. typing speed). Includes gamification
(points system) and personalization elements (cus-
tomizable metadata).
2
Affdex Fa-
cial Coding
Webcam
(Price not
an-
nounced)
Source: QS
Emotion /
Gain emo-
tional in-
sights
Business Passive Facial ex-
pression
Table,
Chart
Not men-
tioned as a
feature
Not men-
tioned as a
feature (it
is meant
for market-
ing usage)
Inter-indi-
vidual (ag-
gregated
values)
<http://www.affdex.com/technology/affdex-fa-
cial-coding/> The tool is targeted at marketers
looking for consumer insights. Employing ad-
vanced computer vision and machine learning
techniques, it reads emotional states from tacit fa-
cial expressions.
3
bStable
Software
(from $99)
Source: QS
Mood /
Improve
mental
health
Medical Active Numerical
and textual
value selec-
tion, (also
free text)
Chart Mentioned
feature
Not con-
templated
(only with
clinician)
Intra-indi-
vidual
<http://www.mcgrawsystems.com/> The tool is
aimed primarily to support patients with a diag-
nosed pathology and physicians. It includes a very
long list of items to be filled in by the patient on a
regular basis.
120
ID
Tool
Name /
Type
Focus /
Goal
Usage
Domain
Tracking
Mode
Input
Type
Output
Type
Data
Privacy
Social
Sharing
Compari-
son Tool Description
4
Chron-
oRecord
Software
(Free)
Source: PI
and QS
Mood /
Track
mood dis-
orders
Medical,
Research,
Consumer
Active Mostly nu-
merical
and textual
value selec-
tion, (also
free text)
Chart High-
lighted fea-
ture
Not men-
tioned
(only with
clinician)
Intra-indi-
vidual
<http://www.chronorecord.org/patients.htm>
The software is catering for both patients and pro-
viders. The users are requested to daily log their
mood, sleep, medications and life events.
5
Company-
Mood
Web app
(Free)
Source: QS
Mood /
Analyze
employees’
moods
Business Active Numerical
selection
Chart Allows
anony-
mous or
transpar-
ent mode
Not men-
tioned
(data avail-
able to the
employer)
Intra and
Inter-indi-
vidual
<http://www.company-mood.com/> The tool is
targeted at companies to measure their employees’
mood. The users (employees) rank their mood
from 1 to 100 and the company gets an overview of
the overall mood (which can also be organized by
teams/departments).
6
EmWave2
Wearable +
Software
($169)
Source: QS
Emotion /
Control
emotional
reactions
Consumer Passive Heart rate Chart Not men-
tioned as a
feature
(but the
data is in
the soft-
ware)
Optional
feature
Intra-indi-
vidual
<http://www.heartmath-
store.com/item/6310/emwave2> The tool collects
data through a pulse sensor and translates the
heart rhythm information into graphics with the
objective of making the correlation between physi-
ological indicators and emotional states more visi-
ble to the user. Include gamification components.
7
Expereal
iPhone app
(Free)
Source: QS
Life /
Rate, ana-
lyze, share,
compare
Consumer Active Numerical
selection +
Image, Lo-
cation, Free
text
Chart Allows an-
onymity -
relies of
the user’s
choices
High-
lighted fea-
ture (login
is via Face-
book)
Intra and
Inter-indi-
vidual
<http://expereal.com/> The user ranks his/her
mood on a scale from 0 to 10 and gets to see the
results, which can also be shared and compared
with other Facebook users, through several graphs.
There is emphasis on the social aspect.
121
ID
Tool
Name /
Type
Focus /
Goal
Usage
Domain
Tracking
Mode
Input
Type
Output
Type
Data
Privacy
Social
Sharing
Compari-
son Tool Description
8
GottaFeel-
ing
iPhone app
($2.99)
Source: PI
and QS
Feelings /
Increase
self-aware-
ness, man-
age, share
feelings
Consumer Active Textual se-
lection +
Free text
Chart Not men-
tioned as a
feature
Optional
feature
Intra-indi-
vidual (ag-
gregated
values
available
online)
<http://gottafeeling.com/> The user is asked to se-
lect his/her current feeling from a list, add some
personal notes and then eventually share the re-
sults.
9
Grow
Web app
(Free for
individu-
als)
Source: QS
Wellbeing
/ Assess
wellbeing
Consumer,
Business,
Medical
Active Numerical
and textual
selection
Chart High-
lighted fea-
ture
Not men-
tioned as a
feature
Intra-indi-
vidual (for
individu-
als)
<http://growhq.com/> A tool for individual, pro-
fessional, research and organizational use. The ini-
tial online assessment contains a long list of ques-
tions about feelings and life satisfaction indicators.
10
Happiness
iPhone app
($4.49)
Source: QS
Happiness
/ Track
happiness,
improve
self-aware-
ness
Consumer Active Numerical
and textual
selection +
Free text
Chart Not men-
tioned as a
feature
Not men-
tioned as a
feature
Intra-indi-
vidual
<http://goodtohear.co.uk/happiness> The tool re-
quests the user to set reminders on the app to track
personal happiness with a certain regularity allow-
ing also adding personal notes along with the meas-
urements.
11
HappyFac-
tor
Web app +
Mobile
(Free)
Source: PI
& QS
Happiness
/ Track and
improve
happiness
Consumer Active Numerical
selection +
Free text
Chart Not men-
tioned as a
feature
High-
lighted fea-
ture (The
login is
done
through
Facebook)
Intra-indi-
vidual (ag-
gregate
values
available
online - op-
tional)
<http://howhappy.dreamhosters.com/> The app
sends a regular text message to the user asking
"How happy are you right now?" for which the an-
swer can answer with a value from a ten-point
scale.
122
ID
Tool
Name /
Type
Focus /
Goal
Usage
Domain
Tracking
Mode
Input
Type
Output
Type
Data
Privacy
Social
Sharing
Compari-
son Tool Description
12
I Rate My
Day
Web app
(Free)
Source: QS
Feelings /
Track ,
share feel-
ings
Consumer Active Numerical
selection +
Free text
Chart Not men-
tioned as a
feature
Optional
feature
Intra-indi-
vidual (ag-
gregated
values
available
online - op-
tional)
<http://www.iratemyday.com/> A social commu-
nity website where the user gets to rate his/her day
on a scale from 1 ("Worst") to 5 ("Great") on a daily
basis and share the ratings with other community
users.
13
Mood
Tracker
Web app
(Free)
Source: QS
Mood /
Track
mood
Consumer,
Medical
Active Numerical
and textual
selection +
Free text
Chart Customiza-
ble privacy
settings
available
Optional
feature
Intra and
inter-indi-
vidual
<http://www.medhelp.org/user_trackers/gal-
lery/mood> The tool allows the user to record
his/her mood on a five-point scale alongside condi-
tions, symptoms and treatments.
14
Mood247
Web app +
Mobile
(Free)
Source: QS
Mood /
Monitor
mood
Consumer,
Medical
Active Numerical
selection +
Free text
Chart High-
lighted fea-
ture
Optional
feature
Intra-indi-
vidual
<http://www.mood247.com/> The tool sends a
daily text message to the user requesting him/her
to rank his/her mood on a ten-point scale.
15
MoodChart
Web app
(Free)
Source: QS
Mood /
Track
mood
Consumer,
Medical
Active Numerical
and textual
selection
Chart Not men-
tioned as a
feature
Not men-
tioned as a
feature
Intra-indi-
vidual
<http://moodchart.org/Default.aspx> The applica-
tion requests the user to situate his/her mood in a
seven-point scale, allowing also to add medication,
hospitalization periods and important events.
123
ID
Tool
Name /
Type
Focus /
Goal
Usage
Domain
Tracking
Mode
Input
Type
Output
Type
Data
Privacy
Social
Sharing
Compari-
son Tool Description
16
MoodJam
Web app
(Free)
Source: PI
& QS
Mood /
Track,
share
mood
Consumer Active Colors,
Text
Colors,
Text
Not men-
tioned as a
feature
High-
lighted fea-
ture
Intra and
inter-indi-
vidual
<http://moodjam.com/> The tool is described as
an online diary that allows users to express their
moods and feelings through patterns of color (and
words describing a certain mood). There is empha-
sis on the social aspect. Includes gamification ele-
ments (added number of colors).
17
Mood-
Panda
Web app,
iPhone, An-
droid app
(Free)
Source: QS
Mood /
Track,
share
mood
Consumer Active Numerical
selection +
Free text
Chart Customiza-
ble privacy
settings
available
High-
lighted fea-
ture
Intra and
inter-indi-
vidual
<http://moodpanda.com/> The tool allows the
user to rank his/her point through the selection of
a value on a ten-point scale. There is emphasis on
the community aspect.
18
Moodscope
Web app
(Free)
Source: QS
Mood /
Track
mood
Consumer Active Numerical
selection +
Free text
Chart High-
lighted fea-
ture
Optional
feature
(within
limited cir-
cle)
Intra-indi-
vidual
<http://www.moodscope.com/> The tool relies
requests the user to select one value in a four-point
scale regarding different feelings based on the
PANAS model. It emphasizes an optional social as-
pect through the 'buddy' system.
19
Mood-
tracker
Web app +
Mobile
(Free)
Source: PI
& QS
Mood /
Track
mood
Consumer,
Medical
Active Numerical
and textual
selection +
Free text
Chart High-
lighted fea-
ture
Optional
feature
Intra-indi-
vidual
<http://www.moodtracker.com/> The application
requests the user to go through a list of questions
about mood, symptoms, medication and sleep on a
regular basis with the answers then converted into
a daily/monthly report.
124
ID
Tool
Name /
Type
Focus /
Goal
Usage
Domain
Tracking
Mode
Input
Type
Output
Type
Data
Privacy
Social
Sharing
Compari-
son Tool Description
20
Moody Me
iPhone app
(Free)
Source: QS
Mood /
Track
mood
Consumer Active Numerical
and textual
selection +
Free text
Chart Not men-
tioned as a
feature
Not men-
tioned as a
feature
Intra-indi-
vidual
<http://www.medhelp.org/land/mood-diary-
app> The application requests the user to go
through a list of questions about mood, symptoms,
lifestyle, medication and health and the answers
are then converted into a daily report. It also incor-
porates ‘mood lifting’ strategies with photos.
21
MySmark
Web app
(Free)
Source: QS
Feelings /
Track,
share feel-
ings
Consumer Active Textual
and color
selection +
Free text
Chart Not men-
tioned as a
feature
High-
lighted fea-
ture
Intra-indi-
vidual
<http://www.mysmark.com/> The tool requests
the user to select his/her mood from the 'rose of
emotions' (based on Plutchik's wheel of emotions)
to provide a historical view on personal moods.
22
Optimism
Online
Web, iPh-
one/iPad,
software
(Free)
Source: PI
& QS
Mental
health /
Track men-
tal health
Consumer,
Medical
Active Numerical
and textual
selection +
Free text
Table,
Chart
Not men-
tioned as a
feature
Optional
feature
(within
limited cir-
cle)
Intra-indi-
vidual
<http://www.findingoptimism.com/> The appli-
cation requests the user to input information about
mood (using a scale), symptoms, triggers and ‘stay
well’ strategies providing charts and reports as a
result.
23
QSensor
Wearable +
Software
Source: QS
Emotions /
Measure
emotional
arousal
Consumer,
Medical,
Research,
Business
Passive +
Active
Electroder-
mal activity
+ Free text
Chart Not men-
tioned as a
feature
Optional
feature
Intra and
inter-indi-
vidual
<http://www.qsensortech.com/overview/> The
tool is composed by a wearable, wireless biosensor
that measures emotional arousal via skin conduct-
ance and software where the data is visualized. It
also allows adding personal annotations to the re-
sults. (discontinued while this study was being ex-
ecuted)
125
ID
Tool
Name /
Type
Focus /
Goal
Usage
Domain
Tracking
Mode
Input
Type
Output
Type
Data
Privacy
Social
Sharing
Compari-
son Tool Description
24
Track Your
Happiness
Web app
(Free)
Source: PI
& QS
Happiness
/ Find
causes and
correlates
of happi-
ness
Consumer Active Numerical
and textual
selection
Chart Not men-
tioned as a
feature
Not men-
tioned as a
feature
Intra-indi-
vidual
<http://www.trackyourhappiness.org/> A set of
different questions (about current feelings, actions
and environment) is sent to the user up to five
times per day until the goal of 50 measurements is
achieved providing then a ‘Happiness report’.
25
Wellness
Tracker
Web app
(Free)
Source: QS
Wellness /
Track well-
ness
Medical,
Consumer
Active Numerical
and textual
selection +
Free text
Chart Mentioned
feature
Not men-
tioned as a
feature
Intra-indi-
vidual
<http://www.facingus.org/> The tool requests the
user to go through a list of questions about mood
(on a nine-point scale), symptoms, lifestyle, medi-
cation and health and the answers are then con-
verted into charts and daily reports.
Source PI - Personal Informatics website <http://personalinformatics.org/>
Source QS - Quantified Self website <http://quantifiedself.com/>
(List built from data gathered in October 2013)
(Back to section 7.2)
126
Appendix 6 – Prototypes and products which infer personal mood from physiological indicators
Table 10 - Examples of prototypes and products which infer personal mood from physiological indicators (while not primarily aiming at mood tracking) in chronological order
ID Designa-
tion
Product
Type
Input Type Output
Type
Description Additional Information
1 Smart Sec-
ond Skin
Dress
Clothing Several physi-
ological indi-
cators
Scent Sensors embedded in the dress
will detect the user's mood chang-
ing and select the appropriate fra-
grance for each situation.
<http://www.smartsecondskin.com/main/smartsec-
ondskindress.htm> A project from Jenny Tillotson, a
Senior Research Fellow in the sensory, aroma and med-
ical field in Fashion & Textiles Design.
2 Mood Phone Mobile
Phone
Voice Light color Recognizing patterns of speech,
the phone would activate certain
light colors helping users to inter-
pret the mood of the person on the
other side of the receiver.
<http://www.pratt.duke.edu/news/mood-phone-con-
cept-wins-motorola-competition> A concept by John Fi-
nan which won the Motorola competition (MOTOFWRD)
in 2006.
3 P702iD
FOMA
Mobile
Phone
Voice Light color Analyzing the tone of voice and
speech patterns of the user, the
device would display a light color
with a certain intensity according
to his/her mood.
<http://news.cnet.com/8301-17938_105-9663597-
1.html> A 2006 Panasonic product produced in collabo-
ration with NTT DoCoMo.
4 Skin Probe Clothing Several physi-
ological indi-
cators (heart
rate, respira-
tion, etc.)
Light color,
intensity,
shape
Using biometric sensors, the dress
would change outer light color, in-
tensity and shape according to the
wearer’s emotional state.
<http://www.de-
sign.philips.com/philips/sites/philipsdesign/about/de-
sign/designportfolio/design_futures/dresses.page> A
2006 concept by Philips.
127
ID Designa-
tion
Product
Type
Input Type Output
Type
Description Additional Information
5 Dr. Whippy Vending Ma-
chine
Voice Ice-cream By asking questions to the user,
the machine detects his/her mood
through voice analysis and pro-
vides the amount of ice-cream ac-
cordingly (the lower the mood, the
more ice cream).
<http://gizmodo.com/298892/dr-whippy-ice-cream-
machine-measures-sadness-delivers-diabetes> Concept
by Demitrios Kargotis presented at 2007 Ars Technica
festival in Linz Austria.
6 Skintilte Jewelry Several physi-
ological indi-
cators
Light A new type of jewelry based on
stretchable, flexible electronic
substrates that integrate energy
supply, sensors, actuators, and
display.
<http://www.de-
sign.philips.com/philips/sites/philipsdesign/about/de-
sign/designportfolio/design_futures/electronic_sens-
ing_jewelry.page> A 2007 concept by Philips in collabo-
ration with STELLA.
7 Hypnos Automobile Facial expres-
sion
Light, Scent A ceiling-mounted camera films
the driver's face and regularly
measures anthropometric data to
gauge emotions in order to auto-
matically adjust the cabin lighting
and fragrance accordingly.
<http://www.citroen.com.au/showroom/concept-
cars/citroen-hypnos> A concept car vehicle by Citroën
presented at the 2008 Paris International Motor Show.
8 Mood Pen Pen Heartbeat,
Skin tempera-
ture, Pressure
Ink colors,
stroke width,
styles and
flow continu-
ity
The pen incorporates sensors
which detect heartbeat, skin tem-
perature and pressure reflecting
them into the ink color, stroke and
style.
<http://www.newscientist.com/arti-
cle/dn13180#.UpIJGcRwphZ> A 2008 concept by
Philips.
128
ID Designa-
tion
Product
Type
Input Type Output
Type
Description Additional Information
9 FuChat Phone Voice and Body
temperature
Display, Text,
Sound, Lights,
and Color
The device detects the user’s tone of
voice and body temperature, and it
changes the display, text, sound,
lights, and color on the phone ac-
cordingly.
<http://www.tuvie.com/the-fuchat-an-environ-
mentally-friendly-phone-concept-that-detects-
your-emotions/> A concept which won the bronze
prize in the 'Concepts and Prototypes of Communi-
cation Tools' category at the 2008 International De-
sign Excellence Awards.
10 Rationalizer Wearable +
External de-
vice
Galvanic re-
sponse
Display, Text,
Sound, Lights,
and Color
Working with two components
(EmoBracelet and EmoBowl), the
arousal level of the user is measured
and reflected through different
lights, colors and patterns so the in-
tensity of feelings become clear.
<http://www.mirrorofemotions.com/> A 2009
concept by Philips and ABN AMRO on an emotion
awareness app for online investment decisions.
11 Share Happy Vending Ma-
chine
Facial expres-
sion
Ice-cream A vending machine incorporating fa-
cial recognition and programmed to
provide an ice-cream to the user
once upon his/her smile.
<http://www.sapient.com/en-us/sapient-
nitro/work.html#!project/157/unile-
ver_share_happy> A 2010 product by SapientNitro
for Unilever. Won the Bronze Cannes Cyber Lion -
Other Interactive Digital Solution.
12 Wearable Ab-
sence
Clothing +
Handheld de-
vice
Heartbeat,
temperature,
respiration
rate, galvanic
response
Text, sound The clothing contains wireless bio-
sensors that measure physiological
indicators and other electronics that
wirelessly connect to a smartphone.
Data from the sensors is converted
into one of 16 emotional states,
which cues a previously setup data-
base to send the wearer some inspi-
rational message.
<http://www.wearableabsence.com/> A 2010 pro-
totype deriving from a collaborative project be-
tween Studio subTela and Goldsmiths Digital Stu-
dios
129
ID Designa-
tion
Product
Type
Input Type Output
Type
Description Additional Information
13 Empathy Mobile Phone
+ Ring
Blood pres-
sure, Body
temperature,
Heart rate
Light color,
Social sharing
A sensor equipped ring connected to
the mobile phone shares the user’s
mood (inferred via biometric data)
with social networks and changes
the color of the phone accordingly.
<http://www.intomobile.com/2010/11/29/black-
berry-empathy-concept/> A 2010 concept by Dan-
iel Yoon for Blackberry.
14 Cold Feet Bouquet +
Ring
Galvanic re-
sponse
Light color A ring that the bride wears measur-
ing her galvanic skin response trans-
mits this data to the flower bouquet
including also light optics which
change color reflecting her mood.
<http://geekphysical.com/coldfeet_read-
more.php> A 2010 project by GeekPhysical.
(List built from data gathered in October 2013)
(Back to section 7.2.2)
130
Appendix 7 – Affective self-tracking experiments
Table 11 – Examples of self-tracking experiments (from the QS Meetups) in chronological order
ID Name, Date,
Location
Experiment
Objective
Period,
Frequency Tools Used Indicators
Description of the Method
(Data Collection)
Description of the Result(s)
(Data Reflection)
1
Atish Mehta
(computer pro-
grammer, author
of happyfac-
tor.com)
27/01/2009,
Bay Area
Assess and im-
prove happi-
ness
3 months
Daily
Self-built app
+ Text mes-
sages
Happiness +
events + activ-
ities
<http://quantified-
self.com/2009/02/measuring-mood-cur-
rent-resea/> Happiness was measured on
10-point scale triggered by random daily
text messages. The user could also add
word descriptors alongside the happiness
rating.
The happiness results were not surpris-
ing for the user. However, the experi-
ment did have a side effect: when rating
present situations, he would do evaluate
them from a wider perspective.
2
Jon Cousins
(advertising en-
trepreneur, au-
thor of
moodscope.com)
23/09/2010,
London
Assess mood Months
Daily
Deck cards +
Self-built app
Mood <http://quantified-
self.com/2010/11/jon-cousins-on-
moodscope/> After years of struggle with
periods of depression, the user designed
his own mood scoring system based on
the psychological test PANAS: a deck of
cards with 20 affective adjectives which
need to be ranked in a 4-point scale.
The experiment led the user to conclude
that if mood is regularly tracked and the
data is shared with (reliable) friends,
then his positive mood increases quite
significantly.
3
Julio Terra
(Telecommuni-
cations grad stu-
dent)
09/12/2010, NY
Correlate
physiological
responses to
mood and
emotions
4 months
Daily
Self-built
wearable de-
vices + Calen-
dar + Camera
Heart rate +
GSR + mood +
events + activ-
ities
<http://quantified-
self.com/2011/04/julio-terra-on-
moodyjulio/> The user wore a self-built
device capturing his physiological data
and he was occasionally prompted to log
information about his current situation
and emotional state.
The experiment did not lead to any par-
ticular conclusions at the time of the
presentation due to data overload and
the need to define a type of data visuali-
zation. The user has however noticed
that logging emotions does affect emo-
tions themselves.
131
ID Name, Date,
Location
Experiment
Objective
Period,
Frequency Tools Used Indicators
Description of the Method
(Data Collection)
Description of the Result(s)
(Data Reflection)
4
George Lawton
(journalist)
11/01/2011
Bay Area
Cultivate hap-
piness
2 x
2 weeks
Daily
Text messages
+ Mirror+
Heartmap app
Facial expres-
sion + Heart
rate
<http://quantified-
self.com/2011/02/george-lawton-on-
cultivating-happiness/> Based on previ-
ous studies analyzing the correlation be-
tween physiological indicators and happi-
ness, in particular facial expressions (i.e.
Paul Ekman), the user was interested in
tracking such aspects and using them as
indicators / predictors of emotional
states.
The experiment was mainly exploratory,
proposing these kind of measurements
as indicators to improve personal emo-
tional states.
5
Remko Siemerin
(UX designer)
16/05/2011,
Amsterdam
Predict de-
pressive states
7 years
Daily
Last.fm Music tracks
listened
<http://quantified-
self.com/2011/09/remko-siemerink-on-
mood-and-music/> The experiment was
conducted accidently. The user discov-
ered that the music listening habits he had
been tracking were correlated with his bi-
polar phases and therefore could be used
to predict them in the future.
The result was just a confirmation of the
user’s suspicion, but it served as a more
accurate barometer of the bipolar
phases.
6
Nancy
Dougherty
(engineer for
Protheus Digital
Health)
19/07/2011,
Bay Area
Study the pla-
cebo effect
Manage and
track mood
1 week
Daily
Pills + sensors
+ text mes-
sages
Biometric +
Behavior
<http://quantified-
self.com/2011/08/nancy-dougherty-on-
mindfulness-pills/> 4 different types of
placebo pills were conceived: to boost fo-
cus, energy, happiness, and reduce stress.
These pills had a sensor embedded which
tracked ‘mood’ and other biometrics indi-
cators (i.e. heart rate) and was connected
to the user’s mobile phone.
Taking the placebo pills (for energy and
focus) caused the desired effect, a fact
which highlighted the impact of a state of
mindfulness. This is described as a po-
tentially more effective and enjoyable
method to track and manage mood.
132
ID Name, Date,
Location
Experiment
Objective
Period,
Frequency Tools Used Indicators
Description of the Method
(Data Collection)
Description of the Result(s)
(Data Reflection)
7
Marie Dupuch
(Art and Design
student)
24/08/2011, NY
Be able to as-
sess personal
mood
Several weeks
3 x Day
Mobile phone
+ paper+
Mood + Activi-
ties + Events
<http://quantified-
self.com/2011/10/marie-dupuch-on-
mood-tracking/> Mood was tracked using
a colored 5-point scale along daily activi-
ties and events.
The user was able to identify clearly the
aspects which impacted her mood, both
positively and negatively and make
changes accordingly. A byproduct of her
experiment was an iPhone application
she designed.
8
Erik Kennedy
(UX designer)
26/10/2011, Se-
attle
Improve hap-
piness level
130 days
Daily
Google docs Happiness +
Activities
<http://quantified-
self.com/2011/12/erik-kennedy-on-
tracking-happiness/> Happiness was
measured on a 7-point scale along with
positive and negative events.
The user was able to pinpoint more
clearly aspects which were directly re-
lated to personal happiness and unhap-
piness.
9
Ute Kreplin (Psy-
chology PhD stu-
dent)
26/11/2011,
Amsterdam
Combine body
blogging with
mood tracking
Several days
Daily / Contin-
uous
Moodscope +
Sensors +
Mood + Heart
rate
<https://vimeo.com/groups/quantified-
self/videos/35917562> Mood was
tracked once a day using Moodscope, as
well as the heart rate (this data was
shared through Twitter).
The experiment led the user to some con-
clusions regarding the incompatibility of
mode of recording and the data contex-
tualization: a bottom/up might be a bet-
ter approach (building the context after
gathering the data).
10
Gareth MacLeod
(software engi-
neer and entre-
preneur)
30/11/2011, To-
ronto
Build tools
which enable
holistic track-
ing
1 week
Daily
Text messages Activities +
Emotions +
Sleep + Events
<http://quantified-
self.com/2012/04/gareth-macleod/>
Through mobile text reminders, the user
tracked a long list of indicators of different
nature to be able to find meaningful corre-
lations.
The user was able to find some meaning-
ful correlations between indicators. This
is presented as an exploratory study to
enable the improvement of the tracking
process and the data visualization stage.
133
ID Name, Date,
Location
Experiment
Objective
Period,
Frequency Tools used Indicators
Description of the Method
(Data Collection)
Description of the Result(s)
(Data Reflection)
11
Ian Li (founder of
moodjam.com)
25/01/2012,
Pittsburg
Track and dis-
play mood
- Moodjam Mood <https://vimeo.com/groups/quantified-
self/videos/36498860> The presentation
was basically a presentation of the appli-
cation the user built and the planned fu-
ture improvements.
-
12
Matt Dobson
(founder of
eitechnolo-
gies.co.uk/)
25/10/2012,
London
Quantify emo-
tions via phys-
iological indi-
cators
- - Physiological
indicators
<http://quantified-
self.com/2012/11/matt-dobson-on-
quantifying-emotions/> The presentation
offered an overview of the technologies
currently available which infer emotions
from physiological indicators such as gal-
vanic response, heart rate, facial recogni-
tion, speech tone, MRI, and breath.
-
13
Ryan Hagen
(Psychology PhD
student)
30/10/2012,
Boston
Track mental
health with
mobile tech-
nology
- - GPS location +
Mobile com-
munication +
Mood
<https://vimeo.com/groups/quantified-
self/videos/53471924> The presentation
was about a study the user planned to
conduct trying to correlate mobile usage
(via http://ginger.io/) with personal
mood (using the short form of PANAS).
A 2010 study was referred which suc-
cessfully correlated mobile usage (type
and communication patterns for calls
and text messages and GPS location)
with mood.
134
ID Name, Date,
Location
Experiment
Objective
Period,
Frequency Tools Used Indicators
Description of the Method
(Data Collection)
Description of the Result(s)
(Data Reflection)
14
Buster Benson
(author of sev-
eral tools buster-
benson.com)
12/11/2012,
San Francisco
Find the
meaning (of
data?)
Find a formula
correlating ob-
jective and
subjective in-
dicators
12 years ‘Agnostic’
tools (i.e.
spreadsheets)
Activities +
Emotions +
Emails (num-
ber and con-
tent) + Loca-
tion + …
<http://quantified-
self.com/2012/12/buster-benson-why-i-
track/> The presentation was basically an
overview of all the self-tracking projects
the user conducted in the last 12 years. He
admitted having conflicting opinions
about self-tracking and mentioned also is-
sues related to sharing personal infor-
mation.
The several experiments led to a few
learnings: precision can be counter-pro-
ductive, ‘agnostic’ tools can be the best
option to track, in many cases Boolean
categories are a good alternative, and ob-
jective and subjective indicators showed
no correlation in his case.
15
Konstantin Au-
gemberg (statis-
tician, founder of
meas-
uredme.com)
20/02/2013, NY
Find a per-
sonal happi-
ness formula
34 days
3 x Day
rTracker app Mood + Events <http://quantified-
self.com/2013/04/konstantin-augem-
berg-on-tracking-happiness/> Mood was
tracked on a 10-point scale as well as
other indicators (daily activities, dura-
tion) derived from different psychological
and behavioral models: the Ryff Scales of
Psychological Well-Being, the Schwartz’s
Value Theory, and the Lifestyle Theory
(inspired by Westherford’s ‘Slow Dance’).
The experiment enabled the user to pin-
point important aspects that were asso-
ciated with his happiness. This was posi-
tively correlated with a sense of mastery,
purpose, independence, and growth, as
well as time spent with loved ones and
activities such as cooking.
16
Jon Cousins,
Robin Barooah
(software de-
signer)
11/05/2013,
Amsterdam
Assess and im-
prove mood
Years
Daily
Moodscope /
Google calen-
dar + spread-
sheets
Mood + Events <https://vimeo.com/groups/quantified-
self/videos/66928697> The method for
Jon Cousins’ experiment was described
previously (see row 2). Robin tracked his
mood along with events and meditation
practice sharing the data with another
user.
The results from Jon Cousins’ experi-
ments were reported previously (see
row 2).
Robin was able to find correlations be-
tween some of the monitored indicators
and experienced some changes after a
treatment he underwent.
135
ID Name, Date,
Location
Experiment
Objective
Period,
Frequency Tools Used Indicators
Description of the Method
(Data Collection)
Description of the Result(s)
(Data Reflection)
17
Lana Oynova (UX
designer)
30/05/2013, NY
Assess mood 6 months
Daily
WhatAboutMe
(Intel) + So-
cialMe +
TripSQ +
Statigr.am
Social media
posts
<https://vimeo.com/69430625> The
user gathered information from the social
media platforms in which she participated
in order to assess her mood.
The experiment made more clear the
events and situations which were related
to certain moods. In the future the exper-
iment might have a predictive element,
prompting alerts to her social network
when she is feeling unhappy.
18
Ashish Mukharji
(writer runbare-
footrun-
healthy.com)
27/06/2013,
Bay Area
Find the
source of hap-
piness/ un-
happiness
3 years
Daily
Spreadsheet
Happiness +
Events
<http://quantified-
self.com/2013/07/ashish-mukharji-on-
three-years-of-tracking-happiness/>
Happiness was tracked on a 10-point scale
along with daily events.
The experiment made clear the aspects
which triggered unhappiness for the
user: lack of sleep, lack of social interac-
tion, spending too much time with cer-
tain people. On the other side, aspects
which caused happiness were: doing
hard physical exercise, having goals, be-
ing surrounded by friends.
19
Liz Miller (Neu-
rosurgeon, au-
thor)
30/07/2013,
London
Understand-
ing mood
- - Mood <https://vimeo.com/groups/quantified-
self/videos/71776733> The presentation
was basically about theories on mood
mapping including ideas such as: mood
has a physiological cause, mood predicts
behavior, and the difference between
emotions (external expression) and
moods (internal expression).
Mood can be monitored via 2 axis meas-
uring level of energy and level of wellbe-
ing creating 4 different states (stress, ac-
tion, depression, and calm).
Aspects which influence mood can be
grouped into: 1) health, 2) autonomy, 3)
environment, 4) social, 5) knowledge,
skills and experience.
136
ID Name, Date,
Location
Experiment
Objective
Period,
Frequency Tools Used Indicators
Description of the Method
(Data Collection)
Description of the Result(s)
(Data Reflection)
20
Roland White
(founder of hap-
pyhealthyapp.co
m)
26/09/2013,
London
Assess wellbe-
ing
27 days
Daily
Mobile phone Happiness +
Activities +
Nutrition +
Sleep + Exer-
cise
<https://vimeo.com/groups/quantified-
self/videos/77986002> Several indica-
tors were tracked on a 10-point scale:
wellbeing, sleep, nutrition, exercise, and
lifestyle.
The results showed higher and more
consistent scores by the end of the exper-
iment, highlighting the importance of
self-awareness.
137
Appendix 8 – Eight Affect Concepts in the Circumplex Model
Source: Russell, A Circumplex Model of Affect
(Back to section 6.2.1)
Arousal
Pleasure Misery
Sleepiness
Excitement Distress
Depression Contentment
138
Appendix 9 – Profile of Mood States (POMS)
Not at all A little Moderate Quite a bit Extremely
01. Friendly 1 2 3 4 5
02. Tense 1 2 3 4 5
03. Angry 1 2 3 4 5
04. Worn Out 1 2 3 4 5
05. Unhappy 1 2 3 4 5
06. Clear-headed 1 2 3 4 5
07. Lively 1 2 3 4 5
08. Confused 1 2 3 4 5
09. Sorry for things done 1 2 3 4 5
10. Shaky 1 2 3 4 5
11. Listless 1 2 3 4 5
12. Peeved 1 2 3 4 5
13. Considerate 1 2 3 4 5
14. Sad 1 2 3 4 5
15. Active 1 2 3 4 5
16. On edge 1 2 3 4 5
17. Grouchy 1 2 3 4 5
18. Blue 1 2 3 4 5
19. Energetic 1 2 3 4 5
20. Panicky 1 2 3 4 5
21. Hopeless 1 2 3 4 5
22. Relaxed 1 2 3 4 5
23. Unworthy 1 2 3 4 5
24. Spiteful 1 2 3 4 5
25. Sympathetic 1 2 3 4 5
26. Uneasy 1 2 3 4 5
27. Restless 1 2 3 4 5
28. Unable to cope 1 2 3 4 5
29. Fatigued 1 2 3 4 5
30. Helpful 1 2 3 4 5
139
31. Annoyed 1 2 3 4 5
32. Discouraged 1 2 3 4 5
33. Resentful 1 2 3 4 5
34. Nervous 1 2 3 4 5
35. Lonely 1 2 3 4 5
36. Miserable 1 2 3 4 5
37. Muddled 1 2 3 4 5
38. Cheerful 1 2 3 4 5
39. Bitter 1 2 3 4 5
40. Exhausted 1 2 3 4 5
41. Anxious 1 2 3 4 5
42. Ready to fight 1 2 3 4 5
43. Good-natured 1 2 3 4 5
44. Gloomy 1 2 3 4 5
45. Desperate 1 2 3 4 5
46. Sluggish 1 2 3 4 5
47. Rebellious 1 2 3 4 5
48. Helpless 1 2 3 4 5
49. Weary 1 2 3 4 5
50. Bewildered 1 2 3 4 5
51. Alert 1 2 3 4 5
52. Deceived 1 2 3 4 5
53. Furious 1 2 3 4 5
54. Effacious 1 2 3 4 5
55. Trusting 1 2 3 4 5
56. Full of pep 1 2 3 4 5
57. Bad-tempered 1 2 3 4 5
58. Worthless 1 2 3 4 5
59. Forgetful 1 2 3 4 5
60. Carefree 1 2 3 4 5
61. Terrified 1 2 3 4 5
62. Guilty 1 2 3 4 5
63. Vigorous 1 2 3 4 5
140
64. Uncertain about
things
1 2 3 4 5
65. Bushed 1 2 3 4 5
Source: McNair, Lorr and Droppleman, Manual for Profile Mood States
(Back to section 6.2.1)
141
Appendix 10 – Positive And Negative Affect Schedule (PANAS) Test
1 2 3 4 5
Very
slightly or
not at all
A little Moder-
ately
Quite a
bit
Extremely
_____ Interested _____ Irritable
_____ Distressed _____ Alert
_____ Excited _____ Ashamed
_____ Upset _____ Inspired
_____ Strong _____ Nervous
_____ Guilty _____ Determined
_____ Scared _____ Attentive
_____ Hostile _____ Jittery
_____ Enthusiastic _____ Active
_____ Proud _____ Afraid
Source: Watson, Clark, and Tellegen, Development and Validation of Brief Measures of Positive and
Negative Affect
(Back to section 6.2.1)
142
Appendix 11 – Implicit Positive and Negative Affect Test (IPANAT)
Doesn’t fit
at all
Fits some-
what
Fits quite
well
Fits very
well
SAFME
happy
helpless
energetic
tense
cheerful
inhibited
VIKES
happy
helpless
energetic
tense
cheerful
inhibited
TUNBA
happy
helpless
energetic
tense
cheerful
inhibited
TALEP
happy
helpless
energetic
tense
cheerful
inhibited
143
Doesn’t fit
at all
Fits some-
what
Fits quite
well
Fits very
well
BELNI
happy
helpless
energetic
tense
cheerful
inhibited
SUKOV
happy
helpless
energetic
tense
cheerful
inhibited
Source: Quirin, Kazen and Kuhl,
When Nonsense Sounds Happy Or Helpless: The Implicit Positive and Negative Affect
(Back to section 6.2.1)
144
Appendix 12 – Subjective Happiness Scale (SHS)
Instructions to participants: For each of the following statements and/or questions, please circle the
point on the scale that you feel is most appropriate in describing you.
1. In general, I consider myself:
1 2 3 4 5 6 7
not a very
happy per-
son
a very happy
person
2. Compared to most of my peers, I consider myself:
1 2 3 4 5 6 7
less happy more happy
3. Some people are generally very happy. They enjoy life regardless of what is going on, getting
the most out of everything. To what extent does this characterization describe you?
1 2 3 4 5 6 7
not at all a great deal
4. Some people are generally not very happy. Although they are not depressed, they never seem
as happy as they might be. To what extent does this characterization describe you?
1 2 3 4 5 6 7
not at all a great deal
Source: Lyubomirsky and Lepper, A Measure of Subjective Happiness: Preliminary Reliability and
Construct Validation
(Back to section 6.2.1)
145
Appendix 13 – Satisfaction With Life Scale (SWLS)
Instructions for administering the scale are:
Below are five statements with which you may agree or disagree. Using the 1-7 scale below, indicate
your agreement with each item by placing the appropriate number on the line preceding that item.
Please be open and honest in your responding.
The 7-point scale is:
1 = strongly disagree
2 = disagree
3 = slightly disagree
4 = neither agree nor disagree
5 = slightly agree
6 = agree
7 = strongly agree
1. In most ways my life is close to my ideal.
2. The conditions of my life are excellent.
3. I am satisfied with my life.
4. So far I have gotten the important things I want in life.
5. If I could live my life over, I would change almost nothing.
Source: Diener et al., The Satisfaction With Life Scale
(Back to section 6.2.1)