Personal Informatics and Context: Using Context to Reveal Factors that Affect Behavior

Personal Informatics+Context 
Using Context to Reveal Factors that Affect Behavior

Ian Li
Anind Dey, CMU, Co-chair
Jodi Forlizzi, CMU, Co-chair
Niki Kittur, CMU
John Stasko, Georgia Tech

Ian Li Personal Informatics+Context Thesis Proposal

Alice
•  20 years old
•  Family history of heart
disease
•  Wants to be more active,
but donʼt know how
because sheʼs busy

2

3

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1573
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Transcribe to Excel

M
T
W Th
F
Sa
Su
M
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Active

Inactive
Inactive

M
T
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F
Sa
Su
M
T

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Factors
•  Lack of time
•  Lack of motivation
•  Activities
Active
•  Location
•  People
Inactive
Inactive

M
T
W Th
F
Sa
Su
M
T

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Location
Activity
People
Ofﬁce
Shopping
Family

Active

Inactive
Inactive

M
T
W Th
F
Sa
Su
M
T

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Location
Activity
People

Problem
Pedometer only recorded one type of
information.

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Physical Activity

Finance

Electricity

Diabetes

Health

Mood

http://personalinformatics.org/tools

10

Thesis
A personal informatics system  
that allows users to associate  
context with behavioral information  
can better reveal factors that affect
behavior, compared to systems that only
show behavioral information.

11

Thesis Questions
Is a PI system with context better at
revealing factors that affect behavior?
•  YES, I will show this in 3 completed
studies and my proposed work.

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Location
Activity
People

Location: Park
Activity: Hiking
People: Friend
Step counts: 7531

13

Location
Activity
People

UbiComp
Sensors
Data mining

14

Thesis Questions
How do we build a PI system with context?
•  Alice had to do a lot to get data and reﬂect
on them.
•  Issues collecting data? Reﬂecting on
data?
•  It is not as easy as just automating the
system.
•  Whether the system is manual or
automated has an effect on the userʼs
awareness.
15

Introduction
Stage-Based Model of PI
Prototypes

Diary Study

IMPACT 1.0

IMPACT 2.0
Proposed Work
Conclusion
16

Introduction
Prototypes

Diary Study

IMPACT 1.0

IMPACT 2.0
Proposed Work
Conclusion
17

Thesis Questions

•  Create a framework as guide in designing
personal informatics systems.
•  Building a PI system involves many parts
each with their own HCI issues.

18

Survey and Interviews
68 people who use personal informatics

Advertised the survey in blogs about
personal informatics.

What tools they use and their problems

19

Sample Questions
•  How difﬁcult is it to collect this personal
information?
•  How do you explore this collected personal
information?
•  What patterns have you found?

Transcript of the survey is at:
http://personalinformatics.org/lab/survey
20

Analysis
Identiﬁed barriers that people experienced.

Afﬁnity diagrams to identify themes.

Derived a model composed of:
•  5 stages

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5 Stages
PREPARATION COLLECTION INTEGRATION REFLECTION ACTION

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5 Stages

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Alice
•  Wanted to become
active
•  Decided to track her
physical activity
•  Chose to track step
counts using a
pedometer

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Mon
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Tue
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Wed
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Thu
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Fri
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Sat
7528
Sun
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Tue
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Transcribe to Excel

M
T
W Th
F
Sa
Su
M
T

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Active

Inactive
Inactive

M
T
W Th
F
Sa
Su
M
T

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The stage when people
choose what they are going
to do with their new-found
understanding of themselves.
•  Alerts
•  Incentives
•  Suggestions
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Other research have explored these different
stages in isolation
•  Collection
•  MyLifeBits (Gemmell et al. 2006)
•  SenseCam (Hodges et al. 2006)
•  Reﬂection
•  Casual InfoVis (Pousman et al. 2007)

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1. Barriers cascade.
2. Stages are iterative.

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1. Barriers Cascade.
Problems in the earlier stages can affect the
later stages.

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Location
Activity
People
Ofﬁce
Shopping
Family

Active

Inactive
Inactive

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T
W Th
F
Sa
Su
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T

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Problems in the earlier stages can affect the
later stages.

→ Consider all the stages when designing PI
systems.

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2. Stages are Iterative.
Users may need to incorporate new types of
data, tools, and processes as they
progressed through the stages.

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Location
Activity
People

Location: Park
Activity: Hiking
People: Friend
Step counts: 7531

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Users may need to incorporate new types of
data, tools, and processes as they progress
through the stages.

→ Flexibility is important, but consider user
needs early to minimize missed data.

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1. Barriers cascade.
2. Stages are iterative.
3. User- or system-driven
4. Uni- or multi-faceted

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3. User- vs. System-driven
The stages can be user-driven, system-
driven, or a combination of both.

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Mon
1573
Tue
4392
Collection
Wed
Thu

4537

5842
Fri
10258

Combination
Sat
Sun
Mon

7528

1368

1497
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1837

Integration
Transcribe to Excel
User-driven

Reﬂection
Excel graphs
System-driven
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Collection
System-driven

Integration
System-driven

Reﬂection
System-driven
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The stages can be user-driven, system-
driven, or a combination of both.

→ Explore the tradeoffs between user-driven
and system-driven stages.

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Uni- vs. Multi-faceted
Most personal informatics are uni-faceted.

Some personal informatics systems have
multi-faceted collection, but only support
uni-faceted reﬂection.

42

Users expressed desire to see associations
between different facets of their lives.
•  “To understand trends in symptoms,
behaviors, and circumstances.” P26
•  “If it were easily collected, information on
food intake, calories, fat, etc., would make
an interesting starting point for analysis.”
P49 who tracks medication intake

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Location
Activity
People
Ofﬁce
Shopping
Family

Active

Inactive
Inactive

M
T
Th
F
Sa
Su
M
T

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Most personal informatics are uni-faceted.

→ Explore support for multiple facets
throughout the stages.
•  I explore using contextual
information.

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Thesis Questions

•  Created a framework to analyze PI
systems.
•  When designing, consider all the stages.

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Introduction
Prototypes

Diary Study

IMPACT 1.0

IMPACT 2.0
Proposed Work
Conclusion
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Thesis Questions
→ Deploy prototypes in ﬁeld studies.

→ Build prototypes that explore different
ways of supporting context.

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Domain: Physical Activity

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Why Physical Activity?
Lack of physical activity is a common
problem that leads to obesity, diabetes, and
high blood pressure.

Lack of awareness of physical activity is one
reason why people are not active.

50

application. This is shown in Fig. 2c and d. network. The network inputs are the sum of signal strength
fluctuation across all monitored cells, and the number of
3.1 Sensing activity distinct cells monitored over a given time interval. The
network consists of a single layer of eight hidden neurons;
The current activity of the user is inferred using patterns of weights are learnt using back propagation. The network
fluctuation in GSM signal strength and changes to the IDs outputs the currently sensed activity for the given input
of detected cells. This method has been demonstrated as a values. The network is trained by repeatedly presenting data

Physical Activity Awareness
reliable and unobtrusive way of sensing current activity [2],
and has the advantage over the more traditional approach of
using an accelerometer in that it does not require additional
sensor hardware as in Sensay [17] and the multimodal
collected during each method of movement.
The current activity of the user is conditionally depen-
dent upon their previous activity. In order to provide instant
feedback to the user interface, the neural network deliber-
sensor board of [11]. Similarly, while the processing of ately does not model this behaviour. Instead, when deter-
physiological and biometric data could complement our mining if any additional minutes have been earned, we
approach, the benefits of encapsulating the system within a apply task knowledge based upon the output from the
mobile phone would be lost. An alternative approach would neural network over the previous two and a half minutes.
be to utilise the positioning information available from This enables noise to be filtered out and a more accurate
some mobile phone networks, however this approach representation of the users’ activities achieved. For exam-

Products
frequently involves prohibitive cost, as well as depending
upon much of the same technology as our client based
monitoring.
ple, periods of low signal strength fluctuation such as
stopping at traffic lights whilst driving can be ignored when
placed between periods of high fluctuation where many
Rather like a traditional accelerometer, the levels of distinct neighbouring cells were monitored. It could be
signal strength fluctuation change when a mobile phone is argued that activity would be more accurately inferred if a
moved. For example, Fig. 3 shows the total signal strength longer rolling filter had been applied to the GSM data.
fluctuation across all monitored cells during successive 30-s Introducing longer filters would have increased the likeli-
time periods whilst walking, remaining still and travelling hood of active minutes ‘disappearing’ from the users’

Fish’n’Steps: Encouraging Physical Activity with an Interactive Computer Game

1 2

Research
3

4
!!
Fig. 1. One participant’s display after approximately two weeks into the trial in the Fish'n
team-condition, also the public kiosk and pedometer platform, which rotated through e
the team fish-tanks. The components of the personal display include: 1) Fish Tank - Th
tank contains the virtual pets belong to the participant and his/her team members, 2) Virtu
Figure 2 The phone interface. Images a and b show screens for examining relative – The participant’s own fish in alevels:view on the right side next to the fish tank, 3) Ca
and individual activity frontal compare Daily Activity and
This Week’s Activity Images. c and d show two of the screens showing the estimated current activity level: Stationary and progress bar, personal and team ra
tions and feedback - improvement, burned calories, Walking

UbiFit  Shakra
Fish ʻn Steps
etc., 4) Chat window for communicating with team members.

To evaluate the effect of Fish’n’Steps, we recruited 19 participants from the
Consolvo et al. ʼ08
Maitland et al. ʻ06
Lin et al. ʻ06
of Siemens Corporate Research to participate in a 14-week study. Two experim
conditions were designed to separately assess the impact of the virtual pet an
social influences. Application of the TTM to assess behavior that changed durin
study demonstrated that Fish’n’Steps was a catalyst of a positive change for 14 o
19 participants. This effect was evident in either an increase in their daily step
(for 4 participants), a change in their attitudes towards physical activity (for 3 pa
51
pants) or a combination of the two (for 7 participants). The greatest change in

Research on Factors
Physical activity is affected by lack of time,
choice of activities, the environment, and
social inﬂuence. (Sallis & Hovell 1990)

CDC suggests understanding of factors to
circumvent barriers to physical activity.

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Activity

Location

People

Physical Activity Level

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Activities

Location

People

Physical Activity Level

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Research on Factors
Diabetes awareness of blood sugar level and
food consumption (Frost & Smith ʼ03)
•  Images of food associated with blood
sugar level.
•  Used in a class where people discussed
their images and blood sugar level.
•  Made a prototype, but only tested with one
person.
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Research on Factors
Asthma patients videotaping daily routines
found that they are in the presence of harmful
allergens more often than they realized
(Rich et al. ʻ00)
•  Users videotaped daily routines, but a
trained observer looked at the video for
assessment.
•  Matt Leeʼs embedded assessment work

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Sedentary People and Walking
Research suggests that they are less aware
of their physical activity and how to become
active (Sallis & Hovell 1990)

Focused on walking because it is easier to
integrate into daily life. (Norman & Mills 2004)

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Introduction
Prototypes

Diary Study

IMPACT 1.0

IMPACT 2.0
Proposed Work
Conclusion
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Thesis Questions
•  Run ﬁeld studies with prototypes.

•  Build several prototypes to try different
ways of supporting context.

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Introduction
Prototypes

Diary Study

IMPACT 1.0

IMPACT 2.0
Proposed Work
Conclusion
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Thesis Questions

Diary Study
How would people ﬁnd factors using context?
IMPACT 1.0
Would context reveal factors that affect behavior?
IMPACT 2.0
What is the value of context in the long term?
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Thesis Questions
Collection Integration Reflection

Diary
Prototype user-driven user-driven user-driven

IMPACT 1.0
user-driven user-driven system-driven

IMPACT 2.0
combination system-driven system-driven

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Thesis Questions

Time-Stamped
Aggregated
End-of-Day
Real-time

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Date:
Time How active were you? What? Where? With whom? Time How activ
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SenseWear
Booklet
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Tracking
Booklet
Reﬂection
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Continue to the next page.!

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Real-time
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Setup
4 female participants (A1-A4)
•  Ages 25-50
•  Sedentary. Pre-screened using Stages of
Exercise Behavior Change (Marcus et al. 1998)

Audio-taped interviews every week

66

SenseWear Tracking

Booklet

No Pedometer SenseWear
Physical Activity Graph Printouts
Information

1 2 3
67

Results
Excellent compliance over 3 weeks
•  At least one activity recorded per hour

68

In all phases, participants found factors
that affected their physical activity.

Week 1, A3:
“Writing down had an effect. I would think ʻOh
good I have something active to write down.ʼ
Like when I would carry my laundry to the
Laundromat on foot.”

69

In all phases, participants found factors
that affected their physical activity.

Week 2, A1:
“It was nice to see that I walked more than I
did. There was one day when I was
babysitting. I walked so much with the
baby. I walked all over campus.”

70

FRI DEC 8, 2:03 ... Start Time

Matching SenseWear graph printouts End Time
- Fri Dec 8, 2006 05:14 AM

Session end - Fri Dec 8, 2006 02:03 PM
with booklet entries.
End
2:03 PM

FRI DEC 8, 2:03 ... Start Time
- Fri Dec 8, 2006 05:14 AM
End Time
Session end - Fri Dec 8, 2006 02:03 PM

Start End
5:14 AM 2:03 PM

Active ... Physical Activity (2.5 ME... Step Count Lying Do... Sleep Duration of Vi...
438 cal 1 hr 43 m... 11346 Not detect... Not detect... 8 hrs 49 m...

71

Matching SenseWear graph printouts
with booklet entries.

Week 3, A2:
“The paper feedback [SenseWear] told me
when the intensity was greater than other
times, so I was able to gauge my activities
like if I just walk upstairs…I was calling them
in the diary ʻaerobic mini burstʼ.”

72

Matching SenseWear graph
printouts with booklet entries.

Week 3, A1:
“I had a lot of data, probably too much to
decipher, but it was good. I didnʼt really
compare everything to my booklet, only the
peaks on the charts to see what I was
doing.”

73

Detailed reﬂection
Immediate awareness

Time-Stamped
Aggregated
End-of-Day
Real-time

74

Thesis Questions

•  Participants made associations between
their physical activity and contextual
information helping them become aware of
factors that affected their physical activity.

→ Can we do this in a ﬁeld study of a
prototype with more people?
75

Thesis Questions

Diary

IMPACT 1.0
→ Help users make directuser-driven
user-driven
associations.
system-driven

→ Value2.0 aggregated real-time info and
IMPACT in
end-of-day combination
time-stamped info.
system-driven
system-driven

76

IMPACT 1.0

77

Thesis Questions

Diary Study
IMPACT 1.0
IMPACT 2.0
78

Thesis Questions

Diary

IMPACT 1.0

IMPACT 2.0

79

Pedometer
Booklet

80

81

Plus-Context
e
Day with "
context labels

f
Table and
charts of steps
and context

g
Steps by hour
and by period
of day

Figure 3. a) Interface for recording steps. Steps-Only additions.
b) One day of steps. c) Week of steps by day. d) Week of steps for82

Pedometer
Booklet
Web Site

IMPACT 1.0

Steps-Only
Steps-Only
Steps-Only

Baseline
Steps-Only
No Vis

83

Setup
30 participants (B1-B30)

Questionnaires at the end of each phase

84

Steps-Only IMPACT 1.0
Baseline

IMPACT 1.0 Steps-Only

1 2 3 4 5 6 7
85

Results
Same level of physical activity awareness
between Steps-Only and IMPACT

86

Greater awareness of factors
After using IMPACT, participants  
self-reported greater awareness of factors
that affect physical activity (5-point Likert
scale)

3.93 (IMPACT) vs. 3.57 (Steps-Only)
F[1,58] = 5.32, p < .05

87

Mentioned context
•  “It has been interesting to see what times
of the day I'm most active.” B17
•  “It turns out I get the most walking done to
and from work, which I can't say I wasn't
expecting, but I also had no idea that
walking around Squirrel Hill for just an
hour or two made such a difference.” B24

88

Mentioned Context Mentioned Context
Excluding Time

IMPACT 1.0 18 out of 30
13
Steps-Only 13 out of 30
7
Control 11 out of 30
6
89

IMPACT was rated most useful
“The [context] I used the most was the one
asking who I was with during my most active
periods…I hadnʼt realized that I was so
sedentary most of the time I spent with my
friends.” B1

90

But IMPACT was harder to use
17 of 30 participants preferred Steps-Only.

“IMPACT gave a lot of cool information, but
having to input all the various factors was a
hassle.” B4

91

Problem can be ﬁxed
Those who preferred Steps-Only said:
•  “There were times I wanted to explain my
context.” B22
•  “IMPACT should be provided as an option.” B30

90% reported they would continue using
IMPACT if collection of context was
automated.

92

Thesis Questions

•  Context can increase awareness of factors
that affect physical activity.

93

Collection
Thesis Questions
Integration Reflection

Diary
Prototype
Collection
user-driven Integration
user-driven Reflection
user-driven

IMPACT 1.0
Diary
Visualizations
IMPACT 2.0
IMPACT 1.0 combination system-driven system-driven

→ Users need help collecting and integrating

IMPACT over a long period of time.
data 2.0

94

IMPACT 2.0

95

Thesis Questions

Diary Study
IMPACT 1.0
IMPACT 2.0
96

Thesis Questions

Diary

IMPACT 1.0

IMPACT 2.0

97

IMPACT 2.0

98

IMPACT 2.0

Bluetooth GPS

99

IMPACT 2.0

Context Input

100

IMPACT 2.0

Bluetooth Sync

101

102

Mobile Phone
Web Site

IMPACT 2.0
GPS
Context Input

Steps-Only
Steps-Only
Steps-Only

Control
Steps-Only
No Vis

103

Setup
35 participants (C1-C35)


104

Baseline Phase Intervention Phase

Control

Control IMPACT 2.0

Steps-Only

1 2 3 4 5 6 7 8
105

Results
Complaints were not about the tedium of
writing things down, but about having to carry
multiple devices.
•  “I would not like carrying two devices (GPS
and phone), that was too much.” C30
•  “I would use [the prototype] if I could use
the software on my own cell phone.” C17

106

Awareness of factors increased for
all groups between the phases
32,./2*" 4.)5(67,*8" -9:;3<"=#>"
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F[2,32] = 3.98, p = .0547

107

Similar awareness of factors
Mentioned Context Mentioned Context
Excluding Time

IMPACT 2.0 8 out of 11
6
Steps-Only 8 out of 12
3
Control 6 out of 12
5
108

Long-term reﬂection
What is the value of contextual information in
the long-term?

6-months later when they were more likely to
have forgotten the data

109

Follow-Up Interviews
Participants:
•  Control (5)
•  Steps-Only (6)
•  IMPACT 2.0 (3)

110

Follow-Up Interviews
Expressed interest in comparing over long
periods of time.

Curious about the peaks in physical activity.

But only those who collected contextual
information had reminders of what happened
during those peaks.
111

Collection vs. Reflection
Short-term Long-term
Reflection
Reflection

Manual GOOD
NOT GOOD
Collection

Automated NOT GOOD
GOOD
Collection

112

Thesis Questions

Automation has an effect:
•  In the short term, reduced interaction with
context data so not difference in
awareness of factors.
•  In the long term, users had data to
effectively reﬂect on factors.

113

Diary Thesis Questions

IMPACT 1.0
Collection
user-driven Integration
user-driven Reflection
system-driven

Diary
IMPACT 2.0
Prototype combination
user-driven
system-driven
user-driven
system-driven
user-driven

IMPACT 1.0
•  Automation is valuable in the long term,

IMPACT 2.0as useful in the short term.
but not

114

Thesis Questions

•  Diary Study and IMPACT 1.0: YES 
Context can increase awareness of factors
that affect physical activity.
•  IMPACT 2.0: DEPENDS 
Automation reduces immediate
awareness, but helps with long-term
reﬂection.
115

Thesis Questions

Diary Need visualization
Prototype Support
user-driven user-driven user-driven

Need to reduce burden
IMPACT 1.0 of Collection and
user-driven user-driven system-driven Integration stages.

Consider the effect of
IMPACT 2.0 automation on
combination system-driven system-driven immediate awareness.

116

Introduction
Completed Work

Diary Study

IMPACT 1.0

IMPACT 2.0
Proposed Work
Conclusion
117

IMPACT 3.0?

118

Thesis Questions
•  How do people use context in the
visualizations to ﬁnd factors that affect
behavior?
•  How do we support comparison
between different types of context?
•  Infer cause-and-effect?

119

Thesis Questions

•  Reduce the cost of using personal
informatics system while increasing
awareness of factors that affect physical
activity.

120

UNI-FACETED vs. MULTI-FACETED

uni-faceted multi-faceted

121


Diary Need visualization
Prototype Support
user-driven user-driven user-driven

Need to reduce burden
IMPACT 1.0 of Collection and
user-driven user-driven system-driven Integration stages.

Consider the effect of
IMPACT 2.0 automation on
combination system-driven system-driven immediate awareness.

Design semi-
IMPACT 3.0 automated collection
combination system-driven system-driven without losing
immediate awareness
122

PREPARATION COLLECTION INTEGRATION

ITERATIVE

Forced users to collect three types of
contextual information.
•  Some of them may not have been useful,
i.e. they are incurring a cost in Collection,
but not providing a beneﬁt during
Reﬂection.

123


ITERATIVE

The prototypes did not support context other
than activity, location, and people.
•  Weather
•  Mood
•  Nutrition
•  Other data sources: calendar, email

124


ITERATIVE

The prototypes did not count other types of
physical activity.
•  Swimming
•  Biking
•  Sports

125


ITERATIVE

•  Allow users to select factors that is
important to them.
•  Support factors other than activity,
location, and people.
•  Count other types of physical activity.

126

BARRIERS CASCADE

The prototypes experienced several barriers
that affected their usage.

Subsequent prototypes addressed some of
these barriers.

Keep these in mind!

127

IMPACT 3.0 Requirements
1.  Maintain immediate awareness in semi-
automated collection.

2.  Give choices for other types of context
and physical activity.

3.  Explore visualizations of context and
physical activity.
128

1. Maintain Immediate Awareness
Low-cost manual collection where
visualizations are shown after input.  
(ES+feedback, Hsieh et al. 2008)
•  Once during the day, the system asks the
user to input data manually. After, the user
shows visualizations to the user of other
automatically collected information.

129

1. Maintain Immediate Awareness
Encourage daily use of the visualizations.
•  Alert the user of an interesting fact.
•  “You were active while walking in the
park.”

•  Make it easy for users to ﬁnd what they
are looking for.
•  Direct users to the graph that would
answer their question. 

130

2. Other types of context and PA
Integrate other sources of context
•  Online weather information
•  Event information from calendars
•  Status updates
•  Other personal informatics tools (e.g.,
MoodJam, your.ﬂowingdata)

131

2. Other types of context and PA
Allow logging of other physical activity.

Equate amount of physical activity with
amount of walking
•  1 hour of softball = 30 minutes of walking

132

Personal Informatics Browser

133

3. Explore Visualizations in Detail
Problems occurred in stages before
Reﬂection, now I can explore this stage in
detail.
•  Visualizations for comparing between
instances of physical activity
•  Visualizations for making cause-and-effect
associations between context and physical
activity

134

Housework
Shopping

Same Physical Activity Level 
Different Context
135


Ofﬁce

Ofﬁce

Different Physical Activity Level 
Same Context
136

Setup
40 participants
•  Sedentary and active


137

Baseline Phase Intervention Phase

IMPACT 3.0

Control

Steps-Only

1 2 3 4 5 6 7 8
138

Awareness Effects
Higher awareness of factors that affect
physical activity.

Maintain immediate awareness even when
collection is semi-automated.

Increased interest in the visualizations

139

Psychological Effects
Higher locus of control

Higher self-efﬁcacy

Self-reports of change in physical activity

140

Thesis Questions
•  Deployment of a personal informatics
system that has minimal cost in collection
without loss in benefit in reflection.
•  Exploration of how people use context in
visualizations to find factors that affect
behavior.

141

Thesis Questions

•  Designs that reduce the cost of using
personal informatics system while
increasing awareness of factors that affect
physical activity.

142

Limitation
Focus on physical activity.
•  PI systems share the same stages.
•  Revealing factors that affect behavior
beneﬁts the user.
•  Diabetes (Frost & Smith 2003)
•  Early work on smart meters (Betz 2010)

143

Future Work
Sharing data.
•  Many PI systems allow sharing.
•  Explore the quality of discussions when
data is shared with context.

144

Thesis Questions
•  Showed that context can increase
awareness of factors that affect behavior.
•  Automation reduces immediate
awareness, but helps with long-term
reﬂection.
•  Deployed a system that doesnʼt sacriﬁce
long-term for immediate, vice versa.

145

Thesis Questions
•  Created the stage-based model of PI as a
framework to analyze PI systems.
•  Created prototypes that support context in
ﬁeld studies.
•  Identiﬁed issues in supporting context and
addressed them in subsequent prototypes.

146

Schedule
2010
p
m
co

I
bi

H
U

C
Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Prototype Analysis

IMPACT 3.0 Development

Deployment

Analysis

Write Dissertation

147

Thank you!
To my advisors, Anind Dey and Jodi Forlizzi, and the rest of my
committee, Niki Kittur and John Stasko.

To people who have helped discussions, pilot studies: 
Gary Hsieh, Scott Davidoff, Erin Walker, Karen Tang, Matt Easterday,
Amy Ogan, Amy Hurst, Ruth Wylie, Moira Burke, Matt Lee, Gabi Marcu,
Queenie Kravitz, Min Kyung Lee, Turadg Aleahmad, Tawanna Dillahunt,
Brian Lim, Chloe Fan, Jenn Marlow, Jason Wiese, Sunyoung Kim,
Aubrey Shick, Chris Harrison, Julia Schwarz, Bilge Mutlu, Andy Ko,
Johnny Lee, Ido Roll, Jeff Nichols, Jeff Wong, Sara Kiesler, Laura
Dabbish, Scott Hudson, Tessa Lau, Jaime Teevan, Fernanda Viegas,
Jon Froehlich, UISTʼ09 and UbiCompʼ09 Symposia attendants,
Alexandra Carmichael, Gary Wolf, Nathan Yau, Nicholas Felton, Ellie
Harrison, Edison Thomaz
148

Personal Informatics and Context: Using Context to Reveal Factors that Affect Behavior

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