A Stage-Based Model of Personal Informatics Systems (CHI 2010 Talk)

A Stage-Based Model 
of Personal Informatics Systems
Ian Li
Anind Dey
Jodi Forlizzi

HCII, Carnegie Mellon University
Ian Li, Anind Dey, Jodi Forlizzi A Stage-Based Model of Personal Informatics CHI 2010

Gnothi seauton.

2

Know thyself.

3

Self-knowledge is valuable.

4

A way to get self-knowledge
Collect information about yourself,  
e.g., oneʼs behaviors, habits, and thoughts.

Reﬂect on the information about yourself.

5

Personal Informatics
A class of systems that help people  
collect and reﬂect on their behavior  
to gain self-knowledge

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Physical Activity

Finance

Electricity

Diabetes

Health

Mood

http://personalinformatics.org/tools

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

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1. Alice prepares.

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2. Alice collects data.
Mon
1573
Tue
4392
Wed
4537
Thu
5842
Fri
10258
Sat
7528
Sun
1368
Mon
1497
Tue
1837

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3. Alice transcribes data.
Transcribe to Excel

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4. Alice reﬂects on the data.

Active

Inactive
Inactive

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5. Alice takes action.
Walk in the park
instead of
watching TV

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Model of Personal Informatics
PREPARATION COLLECTION INTEGRATION REFLECTION ACTION

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1.  Barriers cascade.
2.  Stages are iterative.
Design 
3.  User- vs. System-driven
Guidelines
4.  Facets
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Introduction
Surveys and Interviews

The Stages

Properties of the Stages
Case Studies
Conclusion

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Introduction

The Stages

Case Studies
Conclusion

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Self-tracking
Personal analytics
Living by numbers

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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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Other projects have combined collection and
reﬂection on personal information
•  Physical Activity: FishʼnʼSteps (Lin ʼ06), Shakra
(Maitland ʼ06), UbiFit (Consolvo ʻ08)
•  Sustainability: StepGreen (Mankoff ʼ08), UbiGreen
(Froehlich ʼ09)
•  Many systems for ﬁnance, health, physical
activity, productivity, etc.

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Why a model  
of Personal Informatics?
A growing ﬁeld with many HCI challenges
•  Tools are used over a long period of time.
•  User is involved throughout the process.

No comprehensive list of problems

Developers need a guide for development  
and assessment of these tools
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Introduction

The Stages

Case Studies
Conclusion

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Survey
What personal informatics tools they use

What problems they encountered

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Survey Questions
•  How difﬁcult is it to collect this personal
information?
•  What was your initial motivation to reﬂect
on this collected personal information?
•  What patterns have you found?

Transcript of the survey is at:
http://personalinformatics.org/lab/survey
24

Participants
Advertised the survey in blogs about
personal informatics.

68 users of personal informatics tools

11 participated in follow-up interviews over
instant messenger

25

Types of Information
Automatically collected
•  Financial institutions (banks, credit cards)
•  Utility companies (electricity, heating)
•  Computers (email and browsing history)

26

Types of Information
Manually collected
•  Fewer participants, but greater variety

Calendar events, status updates, work
activities, blog posts, weight, exercise,
browser bookmarks, time at work, mood,
journal, sleeping habits, food consumption, productivity,
health, medication intake, symptoms, miles ran, sports
activities, blood pressure, blood sugar level, dream journal,
step counts, relationship status, books read, transportation

27

Reasons
Interested in personal data
•  “data nerd”
•  “a student of information visualization”
•  “this data is about ME (her emphasis).”

Trigger events (e.g., problems with physical
activity, nutrition, weight, etc.)

28

Analysis
Identified barriers that people experienced.

Affinity diagrams to identify themes

Derived a model composed of:
•  5 stages
•  4 properties
http://www.flickr.com/photos/ludens/3185982588/

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Introduction
Personal Informatics Systems

The Stages

Case Studies
Conclusion

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Preparation
The stage before people start collecting
information.
•  What information to record
•  How to record the information

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Preparation Barriers
•  Choosing the right information to collect
•  Finding the right tool to use

33


Collection
The stage when people collect information
about themselves (e.g., inner thoughts, behavior,
social interactions, and their immediate environment).

34


Collection Barriers
•  Using the tool
•  Remembering
•  Lack of time
•  Motivation
•  Finding data
•  Accuracy
35


Collection Barriers
•  Using the tool
One problem is: 
•  Remembering
“Keeping up the
•  Lack of time
motivation to do so;
•  Motivation
like ﬁnding payback
for the investment of
•  Finding data
time and effort.”
•  Accuracy
36


Integration
The stage when the information from the
Collection stage is prepared, combined, and
transformed for the user to reﬂect on.

37


Integration Barriers
•  Organization
•  Scattered
visualizations
•  Transcribing data
•  Multiple inputs

38


Integration Barriers
“Itʼd be neat if I could
•  Organization
graph [the data]
•  Scattered straight from the web
site instead of
visualizations
manually typing in the
•  Transcribing data
data to a
•  Multiple inputs
spreadsheet.”

39


Reflection
The stage when people reflect on their
personal information.
•  Users may reflect immediately (short-term)
•  Or after several days or weeks (long-term)

40


Reﬂection Barriers
•  Lack of time
•  Self-criticism
•  Visualization
•  Interpretation
•  Sparse data
•  No context
41


Reﬂection Barriers
“Itʼs hard to get a
•  Lack of time
holistic view of the
•  Self-criticism
data since the time
ﬁlters are at most one
•  Visualization
month and Iʼd like to
•  Interpretation
look at several
•  Sparse data
months at once.”
•  No context
42


Action
The stage when people choose what they are
going to do with their new-found
understanding of themselves.

43


Action Barriers
•  Not knowing what to do with the
information
•  Alerts
•  Incentives
•  Suggestions

44

Introduction

The Stages

Case Studies
Conclusion

45


1. Barriers cascade
2. Stages are iterative

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

47

1. Barriers Cascade.

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1. Barriers Cascade.

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1. Barriers Cascade
P44 lacked time and motivation during
Collection stage.

About Reﬂection stage, he said:
“I wish I could report successes on this front,
but my lack of regular collection has made
this difﬁcult.”

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1. Barriers Cascade
Design Guideline
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 progress through the stages.

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2. Stages are iterative.
P48 switched between Google spreadsheets,
Daytum, and your.ﬂowingdata to collect
restaurants visited.

But the tools did not allow importing data.

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2. Stages are Iterative.
Design Guideline
Flexibility is important.

•  Support easy importing and exporting of
data.

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1. Barriers cascade.
2. Stages are iterative.
3. User- or system-driven
4. Facets

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3. User- vs. System-driven

User-driven
System-driven

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Mon
1573
Tue
4392
Wed
4537
Thu
5842
Fri
10258
Sat
7528
Sun
1368
Mon
1497
Tue
1837

User-driven
System-driven

Collection

Integration

Reﬂection

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User-driven
System-driven

Collection

Integration

Reﬂection

60

Design Guideline
Consider the tradeoffs between user-driven
and system-driven stages.

61

4. Facets
Peopleʼs lives are composed of many facets.
•  Home life vs. work life
•  Daily interactions with other people
•  Health
•  Finance

62

4. Facets
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

63

4. Facets
Most personal informatics are uni-faceted.

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

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4. Facets
Location
Activity
People
Ofﬁce
Shopping
Family

Active

Inactive
Inactive

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4. Facets
Design Guideline
Supporting multiple facets may help users
ﬁnd associations between facets of their
lives.

→ Explore support for multiple facets.

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5 Stages

4 Properties
•  Design guidelines

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Introduction

The Stages

Case Studies
Conclusion

68

Case Studies
1.  Twitter-based systems
2.  Mint (http://mint.com)
3.  IMPACT

69

IMPACT
Different from most personal informatics
systems for physical activity:

•  Collects physical activity information  
and context (e.g., type of activity, location, people)

•  Visualizations to help users become aware 
of factors in their lives that affect their
physical activity.
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Two prototypes – Two studies
IMPACT 1.0
IMPACT 2.0
Manual collection
Semi-automated
collection

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

IMPACT 1.0
Manual GOOD
NOT GOOD
Collection

IMPACT 2.0
Automated NOT GOOD
GOOD
Collection

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The model and IMPACT
The model helped analyze the different
aspects of IMPACT.

IMPACT highlights the necessity to consider
the interactions between the different stages 
(e.g., Collection vs. Reﬂection)

IMPACT shows value of multi-faceted support
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Introduction

The Stages

Case Studies
Conclusion

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Contribution: Barriers and Model
Identiﬁed a list of problems
•  Highlights the many HCI challenges of
building effective personal informatics tools

Deﬁned a model of personal informatics
•  Common framework for describing,
comparing, and evaluating such systems

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Contribution: Design Guidelines
Described 4 properties with implications for
design of personal informatics systems
1.  Consider the design of all the stages.
2.  Flexibility between tools is important.
3.  Balance automation and user control.
4.  Explore support for ﬁnding relationships
between facets of oneʼs life.

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Thank you!
http://personalinformatics.org/
http://personalinformatics.org/lab/model

Ian Li
ianli@cmu.edu
Anind Dey
anind@cs.cmu.edu
Jodi Forlizzi
forlizzi@cs.cmu.edu

Funded by

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A Stage-Based Model of Personal Informatics Systems (CHI 2010 Talk)

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