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

ana.crisostomo@gmail.com

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.

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“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/>).

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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.

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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/>.

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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”.

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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

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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

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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

+ Facebook

(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 +

Twitter

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)