Sleeping With Technology - Designing for Personal Health

8/13/2019 Sleeping With Technology - Designing for Personal Health

1/140

!"#$%&'( *' +,'-'#

./.0123

!"#$% '(#)%"*#+%#+ ,"-**%.

/01-.+%#+ 213(34#*15%6 731#0.% %( 83.#+#%9% :%+%(*150;;%( %(

73.)0< ,-*#(%** 71533.

7+-=< >"%0? @344-(#10+#3( 7+-=#%*7-;%")#*3"? 8"3AB C"B >( D013E*

F5%4%? 7.%%;#(G H#+5 F%15(3.3G< 80"+ II

C%*#G(#(G A3" 8%"*3(0. J%0.+5

F5%*#* *-E4#++%= +3 3E+0#( +5% G"0=% 3A

K0*+%" 3A 71#%(1% #( @344-(#10+#3( 7+-=#%*

L%H K%=#0 0(= 731#%+< #( 2-"3;%

>10=%4#1 M%0" NOPQRNOPS


2/140

0

I solemnly declare that I have written this masters thesis Sleeping

with technology Designing for Personal Health myself.

I am aware of the rules on plagiarism and have therefore ensured

that these have been applied in this masters thesis.

15-11-2013 Christel De Maeyer


3/140

4

"#$%&' ()*+(


4/140

3

Sleeping with Technology Part II

Designing for Personal Health

Is 24/7 self-monitoring creating enough awareness andpersuasion to get a balanced lifestyle? Will self-monitoring

affect general wellbeing among self-monitored people?


5/140

2


6/140

5

Short Abstract

We observe that more smart devices are becoming part of our

daily life, and people that quantify aspects of their lifestyle are

becoming more mainstream. In doing so, they leave a huge digitalfootprint behind in an active and passive way.

We notice that the Quantified Self is mainly focused on creating

awareness towards a healthier lifestyle. We learn that there are

opportunities for realizing healthcare that is more oriented and

organized around prevention. Not only on an individual level, but

also on a population level. Patterns might be discovered in user

data helping to support predictions in a more granular and

personalized way. At the same time, a lot of questions arise when

using Quantified Self. How do these device integrate in peoples

daily life? Are they as effective as we think? Do they create enough

awareness and persuasion to create a sustainable and healthier

lifestyle? Do they facilitate a structural behavior change with the

user? Do they continue the lifestyle they adopted during the

tracking period? Or are we seeing more a temporary phenomenon

in the usage and behavior changes?

Other questions that arise are privacy, integration in the European

Social Security system, new business models around these devices

and apps.


7/140

6

,-./012,0/130 +

12,0/130 4*567"./8, 49

4: ;/1?6"./5 .> @6/,=1,;A6 063-?89A: =?@A?;89 /G/7/747 +8H;:< =?@A?;89 /G4:(: "61/12B6 86A;36, E063-+ 8> I>;?CHA9*? 0./70707 *ACA =>


8/140

1

370707 ?8!;A: D


9/140

G

K:4: 3. 0-6 M=1


10/140

/.

Thanks To:

Supervisor Prof. Dr. An Jacobs, SMIT VUB, Belgium

Dr. BJ Fogg, Persuasive Technology Lab, Stanford University, US

Mark Nelson, Peace Innovation Lab, Stanford University, US

Quantified Self Community, my tribe

Friends for listening to my experiments

BodyMedia Inc. in delivering the armbands used in this research

Mike McGrath, proofreading this thesis

The participants who were dedicated for 7 months in joining thisresearch

Taking a break for doing this master and especially to focus on my

research was one of my better moves. Being able to take time to

read, write, joining conferences and having a peaceful, healthy

lifestyle, was just great, relaxing and liberating. Apart for the exam

stress !. My mission in doing this was to valorize my years of

experience in the digital field, and starting to specialize in the next

big thing, Personal Informatics. I believe this has a great future and

is the next logical step in the digital world and digital health.

Thinking about health as a skill instead of an illness.

I would also like to thank all the people around me to be such good

listeners and to gave their trust and be so open about their lives.

Not getting bored about my ongoing talks about Quantified Self,

sleep and other obsessions. The numerous people I talked to at

conferences. The opportunity I had to have a break-out session on

sleep tracking during the Quantified Self conference, gave more

insight from different people, I would otherwise never had.

BodyMedia Inc., Zeo Inc. and Lark, who give their trust in providing

me with the necessary equipment, their on going support, skype

talks and so forth.


11/140

//

Especially thanks to An Jacobs for the critical approach and thinking

towards my subject and writing. In feeding me to think in different

ways towards the subject.

As I see myself as a research subject, I kept track of my habits -study-write among other things, during this academic year to see

my time investment. Lift application tracked my habits and

summarized the frequency of my habits.

The writing includes writing for my thesis, writing papers, and

writing articles concerning my research. In total 86 check ins. The

habit says 30 minutes, but it is much more than that, usually


12/140

/0

around 4 hours writing a day. I started tracking in October. In total

344 hours, 43 days, 1,5 month.

The study moments are mainly reading for my master thesis; there

are 79 check ins starting October 2012. It is possible I forgot tocheck in, but Im pretty rigid about it. So lets say that I studied an

average of 4 hours per check in (there will be peaks here and there),

that would be 316 hours 39,5 days, 1,3 month.

In addition, there are the classes that are not included in the study

check ins. An academic year is more or less 6 months activity; in

the master year we had 4 hours a week (October-December, 13

weeks, 52 hours), 8 hours a week (February May, 9 weeks, 72

hours, I noted a few weeks less, because I was abroad for a few

weeks and not attending the lectures)

In total 784 hours, is 98 days, 3,2 months on full time basis 8 hours

a day. Spread over 9 months one academic year.

An overview on my Green Span Behavior.


13/140

/4

Abstract

We observe that more smart devices are becoming part of our

daily life, and people that quantify aspects of their lifestyle are

becoming more mainstream. In doing so, they leave a huge digitalfootprint behind in an active and passive way.

We notice that the Quantified Self is mainly focused on creating

awareness towards a healthier lifestyle. We learn that there are

opportunities for realizing healthcare that is more oriented and

organized around prevention. Not only on an individual level, but

also on a population level. Patterns might be discovered in user

data helping to support predictions in a more granular and

personalized way. At the same time, a lot of questions arise when

using Quantified Self. How do these device integrate in peoples

daily life? Are they as effective as we think? Do they create enough

awareness and persuasion to create a sustainable and healthier

lifestyle? Do they facilitate a structural behavior change with the

user? Do they continue the lifestyle they adopted during the

tracking period? Or are we seeing more a temporary phenomenon

in the usage and behavior changes?

Other questions that arise are privacy, integration in the European

Social Security system, new business models around these devices

and apps.

In this thesis, we would like to focus on the adoptation of the

devices and behavior changes that these devices might trigger.

We discuss these different aspects based on the explorative insights

we collected by doing empirical research creating a social quasi-

experimental set up for a mid long term period (project duration:

sept 2012-sept 2013). We started with 10 participants who were

not using any tracking devices at the start of the project. We gave

each participant a BodyMedia armband and access to the BodyMedia

Activity manager to monitor their progress and goals. The activeresearch testing period took place during two periods of two


14/140

/3

months (fall spring), giving us the opportunity to look into

seasonal differences. The gap during the months in-between we

evaluated whether the participants spontaneously tracked

themselves. Next, recruited from the start of the research a controlgroup of people with matching profiles (N=10) who did not have an

armband. From the control group, we collected lifestyle information

with the same tools (weekly survey and in depth interview) we used

with the 10 people wearing the armband (experimental group). This

allowed us to evaluate the effect of attention by the

researcher/coach in making these lifestyle changes. In order to get

a clearer view of the behavior types, we used Foggs Behavior Grid

to map the profiles of the participants and their progress. We use

Fogg Behavior Model to evaluate the Persuasive Technology side of

the BodyMedia device.

The results show a wide range of aspects that come in play while

using the BodyMedia device. In choosing for a variety in the

participants, the mid-long term setup, working with a control group,

we gained a lot of insights in this research. Rich in findings and

future research possibilities. In parallel with Foggs Behavior Model,

we see positive and negative behavior based on the three-core

motivators sensation anticipation social cohesion. Additionally,

the simplicity factors that influence the ability to achieve a behavior

change, were elements that rose within our research, as well. These

factors are time in terms of planning and seasons, brain cycles in

terms of emotions that come with the data analysis, physical effort,

none-routines, building new habits and maintaining them. Last but

not least the out of the ordinary effect that takes place while

wearing these devices, which we also see in Swans arguments.

Through the whole research process, we see an interesting

evolution in the control and experimental group. Somehow the

experimental group creates a certain dependency on the technologywhile the control group goes through a more cognitive process. The


15/140

/2

little changes they made during the research were somehow more

embedded in their brain. During the research, we were able to

validate Foggs theory in using the Behavior Model and the Behavior

Grid. Almost every aspect of this theory became prevalent in theresearch.

Keywords

personal health, lifelogging, quantified self, self management,

personal informatics, behavior design

1.IntroductionIn recent years, we see increasing problems in peoples lifestyle:

lack of physical activity, the wrong eating habits and not sleeping

enough hours. This results in more health risks and chronic diseases.

According to the World Health Organisation 60% of the global

population has not succeeded in having a minimum, moderate 30-

minute of daily physical activity. This inactivity contributes to large

medical costs (WHO - 2003).

Technological solutions that create new insights through monitoring

and quantifying ourselves could help us get on the way to a more

active lifestyle with increased wellbeing. Some details about

Quantified Self in the US market. 60% of US adults currently

tracking their weight, diet or exercise routine. 33 % US adults

tracking other aspects such as blood sugar, blood pressure,

headaches or sleep patterns 1. 27 % US Internet users tracking

health data online2, 9 % have signed up for text message health

alerts. There are 40.000 smartphone health applications available 3.

/"&&P^__XXX7ST%[7'QS_N'X%_0./0_/._/0G/4_%'([U,TW$NOUR,-U\'TMR'UW'-U'('R'N&UP'#%MN,($`'QUR'Q$T$N'0"&&P^__P'X$N&'#N'&7M#O_a_R'Q$,_[$('%_#'PM#&%_0.//_P$Pb%MT$,(b($['bM[b"',(&"b$N[M7PQ[

4"&&P^__P'X$N&'#N'&7M#O_a_R'Q$,_K$('%_>'PM#&%_0./0_=;=b+M\$('D',(&"0./07PQ[


16/140

/5

This thesis is not intended as a summation of all the available

devices and apps on the market, or as indicating any preferences.

As we see in the studies above, most of the devices are designed

and developed in the US. Perhaps thats because preventive healthcare is a more urgent topic in the US than in Europe, due mainly to

the American healthcare system and the American lifestyle.In

recent years weve seen a lot of mobile apps and sensor wearables

coming on the market which have a focus on health or wellbeing,

measuring physical activity, calorie burning, sleep and steps. They

come in different shapes and sizes and price ranges. These devices

are accessible but at this stage more popular with the early adopter

population that has a great interest in self-monitoring and data

visualisation of themselves. They might become mainstream, but

there are some barriers at the moment. (Swan, 2013) put the self-

tracking barriers in two categories, the first is from a practical

perspective, and the second is the mindset. The practical one is

about making the self-tracking tools more easy to use, less

expensive or inexpensive, comfortable and above all automated,

which Swan refers to as passive tracking or passive data collection

(Swan, 2013, p.BD93). We are not completely agreeing with the

complete automated process, passive tracking. This might lower the

awareness, as the user is not consciously involved anymore in the

data gathering. We do agree that the data gathering progress

should be very seamlessly, but the user had to have some kind of

involvement in a certain way. Swan is also talking about different

ways to give incentives like social support, arise community

awareness and financial incentives to change peoples behavior. The

example Swan uses for financial incentives is the one from Safeway

Health Measures4, where employees are getting incentives to stay

at the same weight or lower weight based on an average weight per

3 "&&P^__T%#%$&'7%,['X,-7TMR_P'MP('_'RP(M-''%_"',(&"UX'((N'%%_cP#'&&-="M&M


17/140

/6

year. Their contribution in their health insurance would be

influenced based on their weight specifications and healthy living.

This is the start of social sorting which we will explain later on.

Privacy might be at risk and is differently handled in Europe then inthe US. The second barrier is the mindset; this is more about

cultural, social and philosophical aspects. For example, individuals

might find that the self-tracking is an alien activity. This aspect is

in parallel with the social cohesion and social acceptance within

Foggs simplicity factors. We will elaborate on this later in the

theory and results of our research. Obviously people will not change

behavior just like that it is a difficult process; there are many things

to consider when developing and designing these self-tracking tools,

whether they are devices or apps.

Well-designed products and well-designed technology could be tools

to create awareness and facilitate behavior change. This thesis will

focus on Persuasive Technology coined by Dr. BJ Fogg (Fogg, 2003)

in his research on Behavior Design. Its about applying behavior

design to different behavior types with the intent of arriving at a

certain behavioral goal for a specific behavior type. It is an area

that starts from different behavior types and how we can apply

design with intent having a certain behavior type in mind, with the

goal of changing someones behavior. Hence the title of this thesis

Designing for Personal Health. In developing the different aspects

of this thesis, we worked with a use case to evaluate the whole

technology adoptation process of our participants and integration in

their everyday lives. We also use the theory of Persuasive

Technology (Fogg, 2003) to analyse the device and its design for

persuasion. The device we chose to use is the BodyMedia on-body

armband from BodyMedia Inc. This FDA approved device is

positioned as a consumer product in the US. In Belgium, it is widely

used in research facilities and hospitals.


18/140

/1

The thesis will cover 4 main chapters to frame the theory of

Designing for Personal Health. First Persuasive Technology

Framework where we discuss the different Persuasive Technology

models in different media, second Personal Informatics, the toolswhich have the aim to measure and change behavior to a certain

extent, third Behavior change and Self-monitoring, how behavior

change can happen within a self-monitoring environment, looking at

different behavior models, fourth from Quantified Self to Preventive

Healthcare.

We will close with our research model, results and conclusion.

1.1. Framework of Persuasive technologyBJ Fogg coined persuasive Technology in early 2000 (Fogg, 2003).

Persuasive technology is technology designed to influence peoples

behavior through social pressure or persuasion. Web applications,

mobile apps and mobile devices are becoming more focused on

motivating and influencing users. Besides the usability of websites,

mobile apps and devices, integration of motivation principles will be

increasingly important to help people to achieve their goals. We see

this happen in different areas, like productivity products,

collaboration platforms, social networks, e-commerce and more

recently also in a variety of mobile apps and mobile devices.

In past years, weve seen an evolution in Persuasive Technology, on

different platforms in different areas and domains. We intend to set

out a framework of Persuasive Technology to get a clear view on

what is happening today in this area and in which medium.

1.1.1. Online Persuasive TechnologyBJ Fogg frames Persuasive Technology (Fogg, 2003, p 1) or

Behavior Design as any device, application, platform that allows

interactivity and is designed to change peoples attitudes or

behavior. We first saw this happening on the Internet where


19/140

/G

persuasive technology is used for stimulating sales, for example,

based on profiles of members or accounts.The user receives related

offers based on his or her buying behavior or profile on that website

(e.g. Amazon), resulting, for example, in purchasing more books orother goods (Fogg, 2003).

1.1.2. Mass Interpersonal PersuasionThe development of social networks and the opening of platforms to

third parties to develop applications and games is one of the biggest

steps for Mass Interpersonal Persuasion (MIP). MIP is working with

the social influence we all create when posting to news feeds or

sharing games and apps. This is also known as the social influence

dynamic. (BJ Fogg, 2007)

1.1.3. Mobile PersuasionMobile phones make persuasion very powerful. The persuasive

experience can be personal and can tackle more delicate behavior

changes. In addition, it is also focused and targeted (Fogg, 2007).

With the release of the smartphones, we see an explosion of mobile

apps that are targeted to change peoples behavior especially in the

area of wellbeing and health. We can look at the smartphone as a

wearable device, but I see a distinction in this device. The

smartphone is usually in our pocket or bag and not necessarily

attached to our body. At the same time, we see devices coming in

the market in the form of wrist or armbands, smart clothing, to

name some examples, which have sensors to measure whatever we

do and which we wear. We can call these wearable devices or

wearable objects. These forms of mobile persuasion are the new

trend that makes people aware of a certain behavior and are

designed to change those behaviors at the same time. For example

wearable devices measure calorie burning, physical activity frommoderate to vigorous, how many miles run, etc. Early devices sync


20/140

0.

to website application where theres a dashboard to follow your

everyday progress. Recent devices synchronize in real-time and

give immediate feedback. (Swamy & Slek, 2012)

1.2. Wearable devices TechnologiesAlthough it is not our intention to give a list of all the devices and

apps that are on the market these days, it makes sense to highlight

some of them and go a bit deeper into the technology they use.

Basically, you have 2 categories in this area. Devices that have a

synchronous or asynchronous data transfer process.

1. Wearable devices that synchronize in real-time with blue toothor wireless technology, synchronous data syncing process.

2. Wearable device that come with a USB connection to uploadthe data online. These are older versions of the system but

still very up to date, a-synchronous syncing. In this category,

you usually have an app available that shows the data on

your smartphone Android or IOS phones.

The future lies in optimizing both systems, synchronized to each

other web and mobile. The optimal solutions lies in realtime data

synchronization and sending realtime information that can stimulate

the motivation of the user while in a certain mode. When the users

opt in for such a feature, we call this putting Hot Triggers in

motivated peoples path (BJ Fogg, 2007). In our research, we focus

on the arm and wristbands that are in todays market. As

mentioned before in the introduction, we chose BodyMedia armband

to work with, but it is interesting to look at the different models that

are there today.

1.2.1. The arm or wristbandsToday there are four popular armbands/wristbands on the market

The arm or wristbands presented here are designed to change

lifestyles to varying degrees. Their primary focus is on preventive


21/140

0/

healthcare, and all are measuring physical activity, calorie burning

and sleep. The one exception Nike Fuel, it does not track sleep.

Since we use these measure indicators in our research, it makes

sense to take a look at the different models.

1. Jawbone Up measures sleep, physical activity in time (steps),

calorie burning, and food intake.

/#GB P D0HE3(% '; +%15(#10. *;%1#A#10+#3(*

2. Nike Fuel measures steps, calorie burning and physical activity

/#GB N L#9% /-%. +%15(#10. *;%1#A#10+#3(*


22/140

00

3. Lark Life just launched during the writing of this thesis, measures

sleep, physical activity (steps) and food intake

/#GB Q T0"9 T#A% +%15(#10. *;%1#A#10+#3(*

4. BodyMedia measures sleep, calorie burning, physical activity in

time, steps, food intake

/#GB S ,3=


23/140

04

BodyMedia is one of the markets first, with about 11 years of

product research. With sensors placed on the arm, it is the only one,

which can truly be called an armband. Lark Life, for example, is

using an intelligent accelerometer, while Jawbone Up is detectingmicro movements for physical activity and during sleep by using

MotionX algorithms to check to see which sleep phase you are in.

The algorithm is based on biomechanics and the mechanics of

machine motion.5

1.2.2. Data Presentation in Dashboards and Apps ThatCome with These Devices

Wearable devices have a generic architecture, where sensors collect

data and the collected data is transferred to a mobile application

or/and web application. Then one can analyze and learn from the

presented data. The user can act on the data, share the data or

choose not to.

/#GB U >"15#+%1+-"% 8%"*-0*#)% *


24/140

03

to determine food input. Some of the apps integrate photo material

while others allow the user to input food data. None of these

solutions is really accurate, and all are time consuming.

The longer one uses the device and gather data, the more feedbackone will get from the dashboard system. One can also detect

patterns in ones habits and have the ability to act on them. All

these applications, whether mobile or web have more or less the

same follow up system, which we will call a taxonomy of self-

tracking devices. This is an adapted taxonomy from the one of

(Ananthanarayan, Siek, 2012). Some of the follow up systems will

be more successful than others. We will further discuss why some of

them are more successful than others.

1.2.3. Taxonomy of Self Tracking Process

/#GB V >=0;+%= F0W3(34< *%.A 43(#+3"#(G ;"31%**

1.2.3.1. Goal SettingGoal setting is an important element in self-monitoring. Goal

orientation helps to achieve the goals and also identifies the issues

?'([RMN$&M#$NOP#MT'%%

EM,( %'&&$NO

*,&,$N&'#P#'&,&$MN

!M,T"$NO_[''Q\,TW (MMP

='#%MN,(

TM,T"$NO Q$,#-

[SNT&$MN


25/140


26/140

05

realtime synchronization process. Lark Life, for example, sends

immediate feedback when inputting new information or upon the

synching process.

1.2.3.4. Personal CoachingIn the taxonomy of self-tracking it is interesting to consider a fourth

element, personal coaching (Fogg, 2007). It could be a service that

is build around these devices. Professional coachers in a specific

domain can deliver services; caretakers specialized in diets and

nutrition, sports or movement physical activity coaches that we

already know from fitness centers for examples.

So far weve been focused on arm and wristbands in the self-

tracking area, but as mentioned before there are other approaches

in this field, like mobile applications, smart clothing, heart rate belts,

body scales, all these tools are categorized as Personal Informatics.

2.Personal Informatics!Personal Informatics are a class of tools that help people

collect personally relevant data, information for the purpose

to self-reflection and self-monitoring. These tools help

people gain self-knowledge about ones behaviors, habits

and thoughts. It goes by other names such as Living by

numbers, personal analytics, quantified self, and self-

tracking. (Ian Li, 2011, p1)

The devices we illustrated in chapter 1.2. come with their own

dashboards and systems to interpret the data. The dashboard is the

data layer presentation of a database that lies behind that layer. In

reality, there is much more information available that the user does

not see the dashboard presents a summary of all the data. There

are numerous apps that are available as well that use the same


27/140

06

systematic to track oneself in independent apps on iOS or Android

phones. Many of these apps have a focus on health and a better

quality of life. Some of them use crowdsourcing to validate eating

habits, like The Eatery app. We will illustrate some of thesedashboards as examples.

/#GB X ,3=


28/140

01

/#GB [ T0"9 T#A% =0*5E30"= 3( +5% #853(%

/#GB PO F5% 20+%"< 0;; -*%* 1"3H=*3-"1#(G A3" +"019#(G


29/140


30/140

4.

In order to achieve a goal, there is a set of behavior changes, plans

and objectives that go with the desired end-point. For example in

order to achieve weight loss, one needs to learn about healthy food,

portion control and of what a structured healthy diet consists. Thisknowledge helps to maintain the newly created habit, and in this

manner the new lifestyle that goes with it to achieve the goal.

In our chapter on behavior change, we go deeper into this subject

because setting goals is not enough the goals need to be desirable

in order to stimulate motivation.

Within this construction, it is important that the user thinks about

realistic goal setting, think in baby steps (Fogg 6) For example, one

might want to walk at least 30 minutes a day. Instead of planning

this in one walk, one could do different walks of 5 or 10 minutes

during the day, which take less planning and the goal will be easier

to achieve.

In maintaining goals, it is interesting to reflect on the past, as well.

We can do this through these tools of Personal Informatics. Besides

active tracking, there is also passive tracking. This means

individuals leave a huge digital footprint behind by just using the

Internet, this gives the ability to create a more holistic view on

ones lifestyle. This digital footprint could be used to gather and

evaluate phases in ones life that reflect on ones life in general,

ones digital history.

2.2. Digital History - Mirrors of Life ExperienceA personalized digital history is constructed in different ways. In this

construction of digital history, we see positive and negative effects.

We will start with the positive effects and then go into the negative

effects, as well.

5"&&P^__&$N-",\$&%7TMR_


31/140

4/

Personal Informatics is one of the tools that allow tracking one or

more aspects of ones life. Each tool has its own technical

environment to keep track and evaluate ones progress. Individuals

who track themselves are leaving a real trail of data behind in thedigital space and building a digital history of their digital life. These

trails are also built through social networks, mobile apps, check-ins

and online via the web. This digital history could be used to give a

positive empowerment to the users and stimulate behavior changes

in different areas. Looking back and reflect on the past in order to

do better in the future and maybe re-framing ones life (Ramirez,

Hekler, 2012).

Basically, we see two forms of tracking. One is passive tracking,

and usually done by using cookies. They are used within websites to

keep track of the users activity online. For example, Google uses

this in their search engine for Adsense and also to provide

personalized search results. The second form is active tracking.

Here the user posts info about themselves, for example, in the news

feeds on Facebook, check-ins in Foursquare, Flickr photos and so

forth. All this data is saved in databases and available for evaluation

of oneself. The logical step would be software that aggregates an

overview of all this data in one place, your personal content, your

digital narrative story or diary to look back on certain periods or

phases in ones life. Examples of this are fluxstream.com, an open

source personalized data visualization framework, or Microsoft

Health Vault.


32/140


33/140

44

identity.

Remembering

Intentions

Remembering prospective events in ones

life (prospective memory), as opposed to

things that happened in the past. Our

everyday activities require that we

constantly defer actions, and plan future

activities, for example, run errands, take

medication or show up for appointments!

F0E.% P /#)% "]*6 _7%..%(6 :5#++09%"6 NOPO6 ;XQRXS`

Based on the different data information that is gathered by the user,

and the goal setting that a user can do, one could get more advice

or recommendations for living a healthier life. In addition, one can

detect success stories and periods that were less successful, or

periods where one is less dynamic or more dynamic. Happy and less

happy times. The user can detect contextual factors in his or her life.

Like seasonal effects that influence outdoors sports or exercise.

Peak periods at work could be roadblocks to eating healthy, and so

forth. These insights could help to optimize lives for the better.

Basically optimizing lives in a way it fits best for an individual.

Lift is a nice example of an application that lets you keep track of

ones habits. We will use my own Lift tracking example.


34/140

43

/#GB PQ Z)%")#%H 3A H"#+#(G QO 4#(-+%* Z1+3E%" NOPN a D-.< NOPQ _#+ #* 01+-0..< 43"% +5%( QO 4#(-+%*6 E-+ +3#..-*+"0+%`B

December has a peak period. I was writing papers for AAAI

Conference and Chi 2013. January exams, February a short holiday,

March in San Francisco on a conference, April finishing my ideas on

the theoretical framework of this thesis, June exams, July started

again to fine tune and processing research results. The contextual

factors in this case are mainly time and availability to write and

priority.

Another example is Exercise

/#GB PS 2W%"1#*% 50E#+ 3)%")#%H Z1+3E%" NOPN a D-.< NOPQ


35/140

42

As my exercise - running and Tai Chi- is mainly an outdoor activity,

usually in the morning, it is sensitive to the weather, time and my

health conditions. In addition, I use Instagram to take a picture

after my Tai Chi session and Runkeepr to keep track of my activity.I use the BodyMedia armband to provide a detailed overview on

steps. Moderate or vigorous activity and calorie burning.

/#GB PU b-(9%%;%" 3)%")#%H D0(-0"< a D-.< NOPQB

/#GB PV I(*+0G"04 134;#.% A"34 F0# @5# 7%**#3(*B

These three tracking methods illustrate how these apps can be used.

They give an overview on my activity status, weather conditions,

and mental states, available time or health conditions. To optimize

my winter activity, I should think about indoor activities, only I

dont like to do that, so I most probably will not act on it, not

motivated! Everybody will use these in different ways, as to how

they fit for each person nd of course it is a bit of work to keep track

and put in the check-ins.


36/140

45

In this example, there are some contextual factors at play in doing

my activity, weather, time and availability. We go more into depth

on this when we talk about the research results, as they are

important factors to consider. There are more contextual factors atplay in peoples lives, some we can try to control, but with other

factors it is not always possible. We discuss other side effects of

these devices and apps in the next chapter.

2.3. Different Effects in Personal InformaticsSo far we have illustrated a positive view and possible positive

impact on individuals. But technology is never neutral and can havedifferent side effects, as well. For now, we will look at two different

levels and their impact. First we look at the macro level of the Big

Data that becomes available online, second, the micro level data

driven life data mining and behavior mining of users profile

building the digital divide.

2.3.1. The Big Data IdeaFirst we want to clarify what Big Data is. A collection of data sets

so large and complex that it becomes difficult to process using on-

hand database management tools or traditional data processing

applications, (Snijders, Matzat, Reips, 2012,p1). The challenges

include capture, curation, storage, search, sharing, analysis, and

visualization.

Our focus is on sharing and analysis of Big Data in Personal

Informatics.

As mainly companies today develop Personal Informatics tools,

usually startups, the gathered data is stored at their servers, and

they own the data. We see examples in collaborative platforms like

PatientsLikeMe, Cure Together. Data gathered by sensor devices

like myZeo, BodyMedia, Lark and many others. Social platforms like

Facebook, professional platforms like LinkedIn, and so forth. In the


37/140

46

act of using the former mentioned apps, platforms and devices, the

data is gathered by the user and stored on the servers by the

providing companies, we find a dichotomy in this act. In one way,

sharing your health or wellbeing data on former mentionedplatforms is an opportunity to learn and stimulate each other, even

do competitions in specialised groups, for example, the best sleeper,

the best blood pressure, burn most calories, and so forth. This can

be seen in online communities that improve health or wellbeing

(Topol, 2012). On the flip side, these platforms are in the hands of

entrepreneurs and the companies who deliver these services. The

data might be exploited for other reasons that the user is not really

aware of.

An example is PatientsLikeMe, where people can share their medical

conditions, and how they cope. The business model is to exploit the

database, instead of using advertising (Goetz, 2010). Other

example: myZEO platform gives the ability to compare your own

data with all the others who are posting their sleep data,

categorized by gender and age. While writing this thesis, myZeo

went bankrupt, and the website is completely down. Users cannot

access their data anymore no warnings were sent out. What

happens with users data in cases like this?

Analyzing the gathered data could lead to a form of social sorting

(Lyon, 2003) of a population, when this happens in a non-

anonymous way like we saw in the Safeway example in the

introduction. The employees get a financial incentive when they

keep their weight in control. In this way, the privacy of individuals is

seriously threatened. It might lead to a dictatorial regulation in

healthcare, insurance and so forth. This threatens our privacy in

general and may be used in our advantage or disadvantage. If the

data of a given person shows that he or she has a sedentary

lifestyle and there is no positive progress in the data and the datawould also show that the person is not eating very healthily, he or


38/140

41

she could be notified of not taking care of her or his healthy

lifestyle. And since the process is not showing any changes, there

might be a conclusion that there is no willingness to changing bad

habits, resulting in a higher health insurance rate or not getting anyhealth insurance at all.

2.3.2. A Data-driven Life or Living byNumbers

Logging oneself is not really new, people do this all the time. They

keep a diary of their life they keep their expenses, track their

weight and so forth. The difference now is that we have sensors

that can do this for us. They are more mechanically accurate, not

biased, not emotionally involved - machines dont have empathy

and dont have memory problems. Human memory is poor (Wolf,

2011). At the same time, the unemotional character of these

machines/sensors can be very confronting and blunt, and may not

give the feedback that users expect. In using these devices or apps,

we also notice a human biased perception by the user. For example

within sleep tracking, the data shows good results, while the user

thinks he, or she didnt sleep well, in the mind of the user is a

different perception.

The dashboards, which summarize ones tracked activities, could

give negative results to the user. Creating a tunnel effect with the

user. Users often go not into detail of the dashboard information,

looking only at one aspect of it and are attracted by the visual

information, having a tunnel effect view (Sosik, Cosley, 2012). And

as mentioned before the feedback can create a kind of a nagging

effect, which results in an irritated user. It is important in the

design of the dashboard to consider positive and constructive

feedback to the user, so there is stimulation towards motivation

instead of negative experiences that lower the users self-esteemand by consequence lowers their motivation.


39/140


40/140

3.

The identification with machines and technology goes further than

just holding information about individuals. They also are a way of

creating a status symbol of oneself. Todays devices create a sort of

affection status with the users it can become something they love,and trust, and thus users have big expectations of their devices.

To work further on the idea of designing Personal Informatics tools

or devices, one needs to think about the idea that these tools and

devices start caring about their user. The user is central in

designing these apps and devices. As with the affectionate robots

used in nursing homes (Taggart, Turkle, Kidd, 2005), they will

become inevitable in certain individuals everyday life.

2.5. The Digital DivideYou might find the statement technology will become inevitable in

individuals everyday life kind of a bold statement, and it is.

Discussing these topics with peers, we tend to forget about the

digital divide. In order to use Personal Informatics tools, we depend

on Internet and broadband access. Relying on smart devices, smart

clothing and users who are digital literate. According to Digimeter

2011 (Flemish region), 7,1 % of the Flemish population has no

computer at home. Of households that own a computer 91,4% has

an Internet connection 89,8%. Smartphone users 40,4 %, not

detailed if they are owners or not. Of the smartphone owners,

23,8 % has a mobile data subscription. These statistics shows that

not everybody has a smartphone or Internet connections.

Personal Informatics is very high on the Maslow pyramid (Maslow,

1943) and at this time not really integrated in everybodys life. On

the contrary, one might get very different reactions and approaches

towards this topic; we will talk more about this in our research

results. The Maslow hierarchy of needs shows that people seek to

satisfy gradually higher human needs with physical needs like food

and shelter, going higher up towards security and safety, friendship


41/140

3/

and family, self-esteem, confidence and achievement to morality,

creativity, problem solving, acceptance of facts. Personal

informatics is about self-improvement in a self-centered

environment for self-actualization.

/#GB PX K0*.3H 8


42/140

30

the Personal Informatics environment. We examine how we gain

self-knowledge, and what are we doing with it. This chapter ends

with behavior change and motivation and the different theoretical

behavior models we can use to facilitate behavior change within aPersonal Informatics environment.

3.1. Self-monitoring and Self-observationSelf-monitoring and self-observation in this thesis refers to gather

information for oneself and to be able to observe the gathered

information and our behavior while wearing wearable devices. Inour case, we use the BodyMedia armband. The self-monitoring and

observation is happening in a natural setting and not in a clinical

setting. One wears the devices when they want and where they

want. The aim for our research is just to see how these devices can

integrate with an individuals everyday life.

- Self-observation, observing ones behavior, and analysingones data gathered in the self-monitoring. (Stephen M.

Johnson, Geoffry White, 1971) In the study of Self-

observation as an agent of behavioral change, the belief is

that self-monitoring leads to prediction and self-observation

could have an impact on behavior change. The study also

suggests that self-observation procedures may often be

reactive and that this reactivity might be considered as an

agent of behavioral change. (Stephen M. Johnson, Geoffry

White, 1971)

People can observe different aspects of their lives. The Principles of

Psychology, (James, 18907) talks about The Constituents of The Self.

The material self, the social self and the spiritual self.

6"&&P^__P%-T"T(,%%$T%7-M#WS7T,_d,R'%_=#$NT$P('%_P#$N/.7"&R


43/140

34

- The material self, my or mine, our body, seems to be theinnermost part of the self. This includes our children, our

family, our clothes, tangible objects that we call mine or my.

- The social self, our environment, and the recognition we getfrom our friends, how other people see us, we like to get

noticed in some way.

- The spiritual self, our most inner or subjective being.According to Ian Li (2011, p10), in Personal Informatics

The material self: tools that let you track where you are

(Foursquare), keep track of your books (Shelfari), photos

(Flickr), BodyMedia armband, tracks your body, etc

The social self, tools that track how you relate to others

(Klout), how your professional network is (LinkedIn Maps),

representation of yourself (Facebook, Foursquare)

The spiritual self, the online diaries, blogs, journals that are

online, mood apps and happiness apps, where people talk

about their emotions and feelings, publicly or privately.

Other aspects of the self are its behavior, how we act, what our

attitudes are, in relation to our environments, and other people

that surround us (Skinner, 1938).

- Self-monitoring, a procedure in a (mostly) natural settingwhere someone monitors or records a certain behavior and its

occurrences, with the goal to know more about ones self

(Korotitsch & Nelson, 1999, p 415).

Within the self-monitoring, Korotitsch & Nelson see two

components. First, one must discriminate or notice an

occurrence of the target behavior. This may be an action,

thought or feeling. Second - the client must produce a record

of the occurrences as well as any additional information (e.g.

intensity ratings, or antecedent stimuli),(Korotitsch & Nelson,

1999, p 415).


44/140

33

In Personal Informatics, self-monitoring is done by either wearable

sensor devices or mobile apps. These devices are still in early stage

and are not 100 % accurate. As we have seen in previous chapters,

we note mechanically accurateness and human perceptionaccurateness, where the latter might be biased.

Issues with self-monitoring, when one makes notes about a certain

behavior and its occurrences, in a clinical environment or natural

setting, there is always a problem in accuracy, especially in a

private setting. Besides the monitoring, there are always situational

factors that might influence the data or make it vary.

With the self-monitoring procedure, accuracy is estimated with

three types of criteria:

- First, comparison to data obtained by independent directobservers

- Second, comparison of self-monitored data to mechanicaldevices

- Third, comparison to behavioral by products, self-monitoredcalorie intake might be evaluated relative to weight changes

(Mahoney, Moura, & Wade, 1973)

One needs to wear these devices on a regular basis to get an

accurate view and discover patterns. Then, one needs to learn how

to interpret the data. In order to stay motivated and to learn these

different aspects, personal coaches or therapists might be an

additional help to trigger the behavior change and maintain the

changes.

In the idea of self-monitoring and self-observation comes self-

knowledge. The whole idea of self-monitoring is to create self-

knowledge and self-insight.

3.2. Self-knowledge and Self - insightSelf-knowledge refers to knowledge of ones particular mental

states, including ones beliefs, desires and sensations. It also


45/140

32

sometimes used to refer to knowledge about persisting self-identity

conditions, or character traits (Brie, Gertler, 2011)

Gaining self-knowledge is one of the goals of Personal Informatics,

but it is not so easy and often a failure. There are several reasonswhy that can be noted why this is happening. One of them is

personal motives and the ways are mind is working. One of the

main reasons might be because people tend to be motivated to

keep negative experiences outside their consciousness, because

they generate unpleasant feelings or provoke anxiety. (Wilson &

Dunn, 2004) Self-knowledge is said to be quite limited,

though repression is usually so successful that people do not

know that it is limited. (Wilson & Dunn, 2004, p17.3).People

repress negative experiences because it is so easy to do that they

are not aware of the repression unless removing the repressive

forces provokes it.

Other research shows that memories are positively biased. People

tend to remember positive events and negative events tend to fade

away. This bias has two causes one lies in peoples perception, and

the second one is the effect of fading away in the case of negative

events. (Walker, Skowronski, 2003). Fading away effect has two

causes, mobilization and minimization (Taylor, 1991). When a

person has a negative experience, these two mechanisms are

activated. The first mechanism is the mobilization of resources.

When a negative event happens, people mobilize their biological,

psychological, and social resources to cope with the immediate

consequences of the event. The second mechanism is minimization.

People activate opponent processes with the goal of

minimizing the impact of the event. It occurs biologically,

cognitively, and socially (Walker, Skowronski, 2003, p 206).

Ways of increasing self-knowledge include introspection, learning

about one selfs mental states or recent past events. We saw in TheDigital History that gathering all the digital content that is out there


46/140

35

about one self could help in reliving certain episodes of our lives and

help to reflect on it. Another way to gain self-knowledge is looking

at one self through the eyes of others and observing our own

behavior. In a way, we look through the devices to ourselves andsee how we behave in certain areas.

Personal Informatics aims to present an accurate view towards the

user. The sensors gather raw data, and there are no emotions

involved as we saw in the side effects of Personal Informatics. This

data can trigger either positive or negative confrontations. Either

way this has an impact on the usage of these devices or apps that

aim to stimulate behavior change. We will discuss this in our

research results. It is worthwhile to think about the self-perception

and reality; it might not always be in our advantage, but can create

awareness and self-consciousness that results in a behavior change.

3.3. Behavior Change and MotivationAs seen in the previous chapters on self-knowledge and self-

observation, people might be triggered towards behavior change.

They might get motivated to do something on their current situation

if it seems necessary. People might use Personal Informatics for

different reasons. Since this is an early stage phenomenon, they

might do it because it is cool and hip to do, meanwhile learning

about facts and figures of themselves on how they behave. There

might also be a category who has medical conditions, who want to

track themselves and keep log files when certain episodes occur.

For example, by using a blood pressure measuring device that

keeps track of your blood pressure. Can detect the peaks of a rise

of blood pressure and what might have caused it. It might also be

on the demand of specialist to keep track of your weight in case of

thyroid gland problems for example. The users goal is to know

about themselves and specifically about certain behaviors. By

getting the self-knowledge, self-insights (Hixon & Swann, 1993) and


47/140


48/140

31

/#GB PYB I(+"#(*#1 0(= %W+"#(*#1 43+#)0+#3( _b


49/140

3G

If intrinsic motivation plus these three elements in extrinsic

motivation can be triggered by the wearable devices weve talked

about in previous chapters, one can almost be certain that a change

of behavior might occur, because of a self identification with thedevices. We will look at this assumption closer in our research

results, where we can map this theory together with Foggs

Behavior Model.

3.5. Foggs Behavior Model1. There needs to be Motivation2. There needs to be an Ability to act3. There needs to be a Trigger

/#GB P[ ,D /3GG ,%50)#3" K3=%.6 5++;?ddHHHBE%50)#3"43=%.B3"GdNOOXB

In addition to these three elements that need to come together at

the same time, there are subcomponents that make a behavior

change more accessible than others.

1. AbilitySimplicity depending on the audience and context

there might be trade offs.


50/140

2.

i. Moneyii. Timeiii. Physical effortiv. Brain cycles (require lots of thinking)v. Social deviance (out of comfort, not the usual)vi. None routine

2. Motivation, 3 core motivatorsa. Sensation Pleasure and painb. Social cohesion Social acceptance and social rejectionc. Anticipation Hope and fear

In addition to BJ Fogg behavior model, we also need to look at

different methods on how we can map certain behavior types,

match target behaviors and look for solutions to achieve in behavior

change. Mapping behavior types and behavior targets help us look

at patterns in behavior change and come to solutions. We will look

at behavior change through different models, but in our research we

chose for the Behavior Grid Model, which maps 15 different

behavior types.

3.6. Foggs Behavior GridThe Behavior Grid is based on the work of Fogg9and is an outcome-

based method to for classifying research and design in Persuasive

Technology. The Behavior Grid is based on different types of

behavior and consists of 15 certain behavior types. In the grid, the

rows refer to a different behavior duration: dot behavior is behavior

change that happens only once, for example, stop smoking; second

a span behavior, occurs for a certain time span, for example,

Ramadan in Islamic community; third, path behavior is where the

behavior change becomes a routine and is basically a behavior

change for life, for example, becoming a vegetarian. The columns in

G"&&P^__\'",V$M#O#$Q7M#O


51/140

2/

the grid refer to behavior familiarity or change. The first two

columns (Green) deal with new behavior, familiar behavior is a

(Blue) behavior, the last 3 columns are about familiar behavior, but

deal with change. (Purple) Behavior is dealing with increasing abehavior, (Gray) is dealing with decreasing a behavior, while

(Black) is dealing with stopping a certain behavior. We will look

further on in this classification of behavior types and explain each

one of them since it is the basis of our research, but first it is

needed to look at previous theories of classifying behavior changes.

There have been several methods in classifying behaviors, but we

can distinguish two main traditions, Banduras Efficacy Theory

(previously Social Cognitive Theory).

Banduras Self-efficacy, peoples beliefs about their

capabilities to produce effects (Bandura,1994, p 2).

Self-efficacy, is the belief of people to have influence on events or

episodes in their lives by performing certain actions. When people

have high self-confidence, they will experience difficult episodes as

challenges rather than problems to avoid. People with a high self-

efficacy have the feeling they are more in control of things.

The second tradition is the one of Transtheoretical Model (TTM, also

called the Stages of Change Model), Prochaka and DiClemente.

/#GB NO F"0(*+5%3"%+#10. K3=%.6 8"3150*90 0(= C#1.%4%(+%6 P[[Xe +5% .0*+ *%f-%(1%* 0"% ;0"0..%. H#+5 /3GG]*

,%50)#3" K3=%.B


52/140

20

3.6.1. The Transtheoretical ModelThe Transtheoretical Model sees change as a process over several

stages involving progress, through a series of 6 stages.First Precontemplation, in this stage the individual is not yet

thinking about change or does not have the intent to change.

People in this stage are not aware a certain behavior, and they are

not motivated. Second Contemplating, people in this stage have the

intention to change a certain behavior, and most probably will do so

within 6 months. They are more aware of a certain behavior, but

also see pro and contra, which might lead to a long time of taking

no action. Third Preparation, here people are thinking about taking

action to change a certain behavior, they have a plan, and most

probably been thinking about it a long time. FourthAction, in this

stage, individuals have action, and there might be the first results

to measure change of behavior. Fifth Maintenance, this stage is

about maintaining the behavior change and avoid relapse. In other

words creating a routine. Sixth Termination, in this stage people,

are or will not have a relapse and have 0 temptation and have

100% self-efficasy (Prochaska, Velicier, 1997).

This model was also criticized (Adams and White, 2004), and we will

look at one aspect of it, which we can apply to our own research.

For example, someone wants to have more physical activity.

Physical activity is not a single behavior change, but a complex of

different specific actions such as a sport activity, leisure activity,

riding bike from home to work and back. All these things are

physical activity so behavior change will fall into different categories

and different stages as well (Adams and White, 2004).

The difference between Foggs Model and the Transtheorectical

Model is mainly that, within Foggs model, the user is already

motivated to start a behavior change. There are no progress steps.

Foggs behavior model is based on three elements that need to


53/140

24

come together at the same time! Motivation, ability and trigger (call

to action). The higher the ability, the higher the motivation will be.

As explained in the Behavior Model 3.5.

Now that we covered the different theories and have a deeperunderstanding of classifying behavior change, we can go into detail

with the Behavior Grid.

3.6.2. The Behavior Grid in Detail

/#GB NP ,%50)#3" g"#=6 /3GG NOPOB

In Personal Informatics, it is necessary when we design for people,

to think clearly what we want to achieve with the app or device we

are offering to the user. The Behavior Grid is a tool that makes you

think about these different aspects.

Let us take a closer look at each behavior type and implement them

in a hypothetic use case (the reference of this material is not online

nor in papers, but the documentation and beta versions of these are

available on demand. Or via mail ([email protected]).

3.6.2.1.A Green BehaviorA Green Dot Behavior:

We want someone to create a new behavior one time. Within health,

this could be for the women, breast cancer examination. With the

men at a certain age, it could be a prostate examination.


54/140

23

In order to achieve or to work towards this new behavior we need

to think about the Behavior Model, as well.

- We need to increase the number of triggers leading to thenew behavior (think about the breast cancer campaigns thatare organized on a regular basis. The personalized mailings

women get at the age of 50.

- Usually the ability is low with a new behavior. We need toenhance the ability, make it easier to do. In the case of breast

cancer, combine this with the yearly visit to the gynecologist.

- Bring the motivation to a higher point by decreasing the fear.Remember when we wrote about the core motivators in the

Behavior Model, fear is one of them. The fear to have breast

cancer might lead to ignorance, even though one knows

better. Having a yearly breast cancer check up after a certain

age is a smart thing to do.

According to Fogg within a Green Dot behavior we have two primary

problems: low ability and fear. The opposite motivator in fear is

hope. If a new behavior creates excitement and hope in the

foreseeing future the new behavior might be easier to achieve.

Keep this in mind because we will encounter this phenomenon again

later in the research results.

A Green Span Behavior:

A Green Span Behavior in health. Pieter wants to start a diet. Doing

a diet is a complex change. But it is usually done for a certain time

until one achieves his or her weight loss or weight gain. The idea of

a diet is also to make a structural change in ones eating habits. It

is therefor very close to a path behavior, as well. In a sense that

the knowledge one gains during the diet one tries to maintain this

new behavior to not have a relapse and gain x kilos or lose x kilos

in the near future.The Behavior Design Process would split in two phases:


55/140

22

- Phase 1, designing for commitment, the conversion- Phase 2, designing for maintaining, the continuation

Lets take the wearable devices as an example. We want people to

wear these devices (new behavior = Green), to discover theirbehavior patterns and to act on it when necessary.

Phase 1: designing for commitment (conversion)

- Increasing triggers to wear the devices. This can be done byawareness campaigns, advertising. Triggers will do a call to

buy the devices or to use apps. Some are free and will create

a faster commitment (simplicity factor of Money).

- Increasing the ability to wear the devices, the new behavior.Making them simple to use, invisible, hidden and discrete, not

stigmatizing.

- Increasing the motivation for wearing the devices. Bydecreasing fear, although they also trigger hope and pleasure.

In the case of the device also social acceptance plays a role.

Phase 2: designing for maintenance (continued practice)

In phase 2, the triggers are the most important element. To think

about triggers, we will introduce two new behavior types: Cycles

and Cues.

Think about cycles in terms of behavior that returns on a regular

basis. For example, brushing teeth, eating breakfast, lunch, dinner

and so on. Cycle behavior can also happen once a week or once

month and once a year, for example, practicing Tai Chi three times

a week.

Cues will happen in response to something. Something is broken

we will fix it. The doorbell rings, we open the door and so forth.

These cues happen and are usually unpredictable.

Cycles and Cues can become associated over time. For example

while practicing Tai Chi one listens to Asian meditation music, so

these two will become associated. If one is hearing meditationmusic, the Tai Chi will automatically come in ones mind too and


56/140

25

might create a Zen state of being, this is an implicit cue. An

explicit cue could be an text alert to tell you havent been moving

for more then an hour, in case of our wearable devices. The user

might act on this cue.Green Span Maintenance:

In order to design triggers in Green Span maintenance, we need to

know whether the behavior is a cycle or cue behavior, regular or

irregular behavior. For example, with the wearable devices, the

more you wear them, the better the device gets to know you, they

learn about the behavior what are cycle behaviors or irregular

behaviors. Depending on your behavior you will get cues

associated with your cyclical behavior, that is recorded while

gathering the data. Some apps will send a text message in the form

of Hey, it is 9.00 oclock, you usually have run at 9.00 AM, how

about start doing a run now?

Tracking your food is cycle cue behavior that is associated over

time. You eat on a regular basis. You start logging your food,

because you want to know your calorie intake. By using the app, it

will get to know your eating times because you have been logging

your food on a regular basis. When you forget to log your food, the

app will notice this and will send a text message saying: you

usually are eating at this time, dont forget to log your food. These

triggers we call hot triggers, they happen during or send out

during the activity and in the right window. This is optimal

Persuasive Technology.

Irregular cues and maintenance:

Irregular cues are unpredictable cues, and causes action when they

happen, as we seen in previous examples. Within the sefl-tracking

environment, we are always confronted with new behavior, using

the apps and devices are in itself a new behavior. To maintain theusage of these apps is challenging. We can put triggers or alerts for


57/140


58/140

21

word, although the complexity of the movements might result in

dropping out. The ability factor here is in the direction of physical

effort and brain cycle. Here is why. The Tai Chi is a series of

movement that will bring one in a kind of flow. There are a lot ofadvantages in learning Tai Chi, and one needs to keep this in mind

while learning this. One needs to memorize (brain cycle)the

movements and one need to learn balance, coordination of arms,

legs and hands (physical effort).

Other example, wearing a tracking device one just bought.

Depending on ones situation, this will create fearin confirming

ones behavior or not. Hopeto measure something and to change

something for the better. Pleasure, doing something cool with new

technology. It might be exciting to see how much one moves during

the day and when this all happens. Unexpected events might occur.

We will discuss the latter extensively in our research results.

Path behaviors have the aim to create routines, rituals even. A

behavior we do almost automatically is included in path behavior.

Examples, you always buy your music at the Itunes store or your

books at the Amazon store.

In the design process, we need to think the same as in the Green

Span Behavior.

The conversion is a Dot Green Behavior, it is new and done once.

The dot behavior will go into a span or path behavior it will go to a

lifestyle change.

3.6.2.2.A Blue BehaviorA Blue Dot Behavior is about performing a familiar behavior once.

In health eat an apple after lunch.

A Blue Behavior is something we need to think about carefully. One

behavior can be familiar to someone but at the same time, not for

someone else. Someone might be used to buy products online


59/140

2G

someone else might not have that familiar behavior. In a Blue

Behavior, there can be a mix of behavior types.

A Blue Behavior usually targets group or person who are familiar,

with a certain behavior.The ability in Blue Behavior will usually be higher than with a Green

Behavior.

Some Blue Dot behaviors are having a big impact or commitment,

others will have this less.

Buy a car is familiar behavior but costs a lot of money. While other

things might be free or low cost.

In a Blue behavior, the triggers are important. One already knows

the behavior, one just need that extra push to do it. Example, Piet,

wants to go for a run in the afternoon during the weekend. Piet

thought about it, but he never comes to it. Just one message could

give that extra push to go for a run. Via SMS or an app or putting it

in his calendar that sends an alert when the time is there.

If the triggers dont work, one might look at the ability factors, or

simplicity factors.

In our example of running, we might look at the time factor, does it

take a lot of time, do we need to prepare for a run. Piet might think

about putting the running gear ready in the morning, so he just

needs to put on running shoes and go.

Running is free; moneywill not be a factor. Is running socially

accepted? In some cultures not, in others it is more accepted.

Running could be a physical effort if one does it in the evening, one

might be tired. None- routine, one needs to plan a good moment

during the day, if it is disrupting another routineit is unlikely to

happen. Is it difficult to find a good running path? Find a good

environment to do the run might be a brain cycle.

In the Blue Span and Path Behavior, we need to think about the

previously explained examples and theory in the Green Behavior.Looking at the triggers, ability factors and motivation.


60/140


61/140

5/

We covered the routines of Span and Path behavior; in the Green

Behavior, they also apply to the Purple Span and Path Behavior.

Creating Commitment and continuation of the familiar behavior.

3.6.2.4.A Gray BehaviorThe Gray Behavior is about reducing a certain behavior. Here we

will reduce triggers and reduce the ability to do a certain behavior.

The opposite of what we do with the Purple Behavior. Some

examples of Gray Behavior: drinking less coffee, eat less junk food,

spent less time on Facebook and so forth.

The Gray Dot behavior is reducing a certain behavior one time.

They often induce a Span Behavior or Path Behavior over time. For

example, we want to stop smoking. It might be easy to not smoke

for one day. Then after one day of stop smoking, one try to stop for

a week and so forth. To eventually quit smoking completely.

Smoking is a very good example to use in this Gray Path Behavior.

Governments agreed on a campaign to make smoking forbidden

within public areas, bars and restaurants. This reduced the smoking

in these places, and were actions for some to quit smoking, creating

the awareness that smoking is not good for you. In addition, there

are the photos on the cigarettes that were really shocking.

Cigarettes became much more expensive and so forth. The

simplicity factors we spoke of before are applied in this case too.

Money, increasing the price of the cigarettes. Social acceptance,

making smoking not allowed in public spaces makes use of peer

pressure. If you lit a cigarette in bar now, people will look at you,

and not always with friendly eyes.

Within a Gray Behavior we want to

- Reduce the triggers- Reduce the ability to do something-

Reduce the motivation


62/140

50

We want the three elements that need to come together within The

Fogg Behavior Model not to happen at the same time, so most likely

they wont happen. There are fewer triggers, less ability and less

motivation.With a Gray behavior, we can come in a Purple behavior, as well.

For example, one wants to reduce cola drinking and instead will

drink more water. The Gray Dot behavior, lesscola drinking. The

Purple Dot Behavior drink morewater.

3.6.2.5.A Black BehaviorA Black Behavior is about stopping a certain behavior. A Gray

Behavior will often induce a Black Behavior. As seen in our example

when one smokes less or does not have the ability to smoke in a lot

of places will reduce the behavior and one might feel better about

his or her health that eventually one stops smoking. They same

theory applies here for stopping a certain behavior. We decrease

the trigger or stop the triggering. We decrease the ability or create

no ability those two elements will motivate less to do a certain

behavior.

3.6.2.6. How Behavior Types Relate to Each OtherBehavior types relate to each other in the Fogg Behavior Grid.

Purple Behavior is related to the Blue Behavior, it is already a

familiar behavior. The Blue Behavior is the heart of the Purple

Behavior. For example, if one has a Blue Behavior like swimming

once a week, and one will do it twice a week, at some point on, this

would be Purple Behavior, increasingones swimming activity.

Purple fades to Blue, if one does an increasing behavior, like the

Purple Behavior mentioned above, going for a swim twice a week

instead of once a week, it will become the norm and will become a

Blue Path behavior.The increasing part stops, and the swimmingtwice a week will be the norm.


63/140

54

The Gray Behavior will relate to a Purple Behavior, and also to Black

Behavior. The Gray Behavior might induce a Purple Behavior as

shown in the cola example. The Gray Behavior can also induce a

Black Behavior, as shown in our smoking example.

We covered the complete theory of Foggs Behavior Grid which

helps us to think more clearly about the design process of behavior

change. We would like to close this with a scenario of a personae

example who wants to change certain aspects in his lifestyle.

Think about the following scenario: Paul is living and working in

Ghent, he has a car and a bike, and since his work is always in the

office he decides that he wants to be outside more and meanwhile

increasehis physical activity. When he reaches one goal he can

adjust his goal again, or add another goal. Paul chose to take walks

during his lunch hour. He is going to increasehis physical activity

and hopes it will become routineand maintainhis newhabit.

Increasing a certain familiar behavior for a certain period is a

Purple Span behavior, but it also a new habit Green Dot Behavior,

and since Paul would like it to be routine and maintain his new habit

he will follow this path to become a Blue Path Behavior, it will

become a norm in his lifestyle. In other words, progressing from a

Purple Span to Blue Path behavior.

If we would like to design the new Fitibit, it might be interesting to

map our target audience to this grid. As the Fitibit is aiming at

increasing physical activity, and maybe creating new habits as well

in achieving the goal, we might look at a green span and a purple

span behavior with an ideal outcome of a blue path and a purple

path behavior. This means green, a new habit creation, for

examples Pauls walks between lunchtime. This increases his

physical activity going into a purple span behavior, as well. And Paul


64/140


65/140

52

People can get very knowledgeable about their life patterns,

attitudes and behavior towards their physical condition and lifestyle

in general. (Swan, 2012)

The health care sector so far has not really embraced all thepotential of the digital era. It is late in coming since health care is a

conservative industry. According to Eric Topol (2012) Medicine is

about to go through its biggest shakeup in history.

This chapter has the aim of looking at the state of preventive

healthcare today and possible future visions. We examine different

examples of digital health solutions, shifts that are happening in the

industry, and the related privacy and liability issues in this new area.

4.1. Preventive health care preventive medicinePreventive health care and preventive medicine are fairly new

concepts and not yet fully articulated in public dialogue. The

biggest shift in Preventive Medicine Health care is in the

concept that the patient is not just a patients treatment in a

personalized manner, but the patient, really is a participant,

or simply a person, becomes the nexus of action-taking and

empowerment (Swan, 2012, p95).

Through quantified self tools, that we discussed before in this thesis,

individuals are now able to get insight on life patterns, baselines

measures, and changes or variances at certain moments and how to

handle or go about this. Lets look into an expanded concept of

health and health care by Swan.


66/140


67/140

56

1. In the first tier, the numerous smart sensor devices that arewidely used in the US are represented. Being always-on

gives constant information about health and physical status.

The apps and devices also give baseline and variancesinformation to work with and improve on where necessary. In

the US it becomes a mainstream behavior to track oneself.

2. In the second tier we see peer collaborators and healthadvisors. This is the extension of the first tier in the middle

graph. Movements like quantified self, or interest groups arise

around these apps and devices where individuals talk about

their experiences and actions they take. New services will

arise in the form of personal advisors; coaching, preventive

care practitioners and so forth.

3. Finally, public health professionals are the third tier in thegraph. This extends when resources of prevention, wellbeing

have been exhausted, and deeper expertise is needed.

In prevention, the most essential element is the self-action taking,

and the always-on monitoring apps and devices that give insights

on peoples lives. The most successful initiatives for engaging

individuals in the health context so far have been the ones

who give personalized recommendations and secondary

social interactions, gamification, attractive data visualization

of contributed information and other modern techniques to

make using technologies fun and simultaneously achieving

behavior goals (Swan, 2012, p 98).

4.2. Different Ideas in Realizing Preventive Health CareWithin the preventive health care domain, we can distinguish

different ideas that already start playing a role in the digital health

environment. We will look at three of them. The Quantified Self,

Social Health Networks and The Big Health Data. We close with

some thoughts on privacy and ownership of the data.


68/140

51

4.2.1. The Quantified SelfThe Quantified Self tools that are on the market today give people

the ability to measure themselves in different ways. Weve

discussed these tools extensively in previous pages. These toolsgive rise to user groups or meetups, where individuals discuss these

tools and how they go about using all the data that is gathered.

Within these groups, we can define different audiences, early

adopters, technical people and individuals who are interested in

their lifestyle and wellbeing. This movement will become bigger,

professional and mature, and might grow towards a recognized

preventive healthcare domain.

4.2.2. Social Health NetworksSocial Networks have been popular for a while now, in addition we

see the rise of focused social networks. In health, we see social

networks like Patients Like Me, Cure Together. These two social

networks have the aim to gather information on different diseases.

MyDietation, and Asthmapolis now Propeller Health, have the aim

to focus on specific chronic disease. MyDietation is focused on

creating a professional relation between the user and professional

dietitians. Offering several services online. The user tracks his or

her diet, with a mobile app. The user synchronizes the data and is

followed by a professional dietitian, who can give recommendations

or feedback on the reported diet.

Propeller Health is one of the leading Mobile platforms for

respiratory health management. Propeller Health uses a small blue

tooth device that connects the inhaler to a mobile app, and gives

detailed information about the location where an asthma patient is

using the inhaler and how often. It creates not only a personal diary

for the user, but at the same time maps locations that are not ideal

for the asthma patient. The doctor gets a detailed overview on thepatients behavior and where the asthma symptoms are apparent or


69/140

5G

not under control. These social health networks give the ability to

people to share their information, experiences and struggles they

have with peers. Swan sees four layers in social health network

systems where different information can be found.

/#GB NS 7%")#1%* ;"3)#=%= E< *31#0. 5%0.+5 (%+H3"9* _7H0(6 NOO[6 ;S[U`B

One of the most popular networks and weve referred to it

numerous times, is Patients Like Me, started in 2004. In December

2008, had 26,059 patients. Since 2008 membership has grown by

10% per month, with the goal of having one million patients,

covering 200 diseases by 2012.

4.2.3. Big Health DataThe quantified self and social health networks gather a lot of data.

This so called big data provides the ability to learn about patterns

of an individuals lifestyle. Big data offers the possibility of

predicting certain aspects of a persons lifestyle.

In conclusion, we can look at the model proposed by The Institute

of the Future to come to a model that is relevant for different

aspects of the health care infrastructure. This is where software

services, sensors, wearable devices; medical equipment and

communications come together in one centered infrastructure.

8/13/2019 Sleeping With Te

Documents

Sleeping With Technology - Designing for Personal Health