People strive to obtain self-knowledge. A class of systems called personal informatics is appearing that help people collect and reflect on personal information. However, there is no comprehensive list of problems that users experience using these systems, and no guidance for making these systems more effective. To address this, we conducted surveys and interviews with people who collect and reflect on personal information. We derived a stage-based model of personal informatics systems composed of five stages (preparation, collection, integration, reflection, and action) and identified barriers in each of the stages. These stages have four essential properties: barriers cascade to later stages; they are iterative; they are user-driven and/or system-driven; and they are uni-faceted or multi-faceted. From these properties, we recommend that personal informatics systems should 1) be designed in a holistic manner across the stages; 2) allow iteration between stages; 3) apply an appropriate balance of automated technology and user control within each stage to facilitate the user experience; and 4) explore support for associating multiple facets of people’s lives to enrich the value of systems.
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A Stage-Based Model of Personal Informatics Systems (CHI 2010 Talk)
1. 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
2. Gnothi seauton.
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3. Know thyself.
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5. A way to get self-knowledge
Collect information about yourself,
e.g., oneʼs behaviors, habits, and thoughts.
Reflect on the information about yourself.
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6. Personal Informatics
A class of systems that help people
collect and reflect on their behavior
to gain self-knowledge
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7. Physical Activity
Finance
Electricity
Diabetes
Health
Mood
http://personalinformatics.org/tools
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8. 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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9. 1. Alice prepares.
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10. 2. Alice collects data.
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11. 3. Alice transcribes data.
Transcribe to Excel
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12. 4. Alice reflects on the data.
Active
Inactive
Inactive
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13. 5. Alice takes action.
Walk in the park
instead of
watching TV
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14. Model of Personal Informatics
PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
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15. Model of Personal Informatics
PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
1. Barriers cascade.
2. Stages are iterative.
Design
3. User- vs. System-driven
Guidelines
4. Facets
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16. Introduction
Personal Informatics
Surveys and Interviews
The Stages
Properties of the Stages
Case Studies
Conclusion
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17. Introduction
Personal Informatics
Surveys and Interviews
The Stages
Properties of the Stages
Case Studies
Conclusion
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18. Personal Informatics
Self-tracking
Personal analytics
Living by numbers
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19. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
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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20. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
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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21. 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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22. Introduction
Personal Informatics
Surveys and Interviews
The Stages
Properties of the Stages
Case Studies
Conclusion
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23. Survey
What personal informatics tools they use
What problems they encountered
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24. 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
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25. Participants
Advertised the survey in blogs about
personal informatics.
68 users of personal informatics tools
11 participated in follow-up interviews over
instant messenger
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26. Types of Information
Automatically collected
• Financial institutions (banks, credit cards)
• Utility companies (electricity, heating)
• Computers (email and browsing history)
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27. 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
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28. 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.)
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29. 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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30. Introduction
Personal Informatics Systems
Surveys and Interviews
The Stages
Properties of the Stages
Case Studies
Conclusion
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31. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
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32. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
Preparation
The stage before people start collecting
information.
• What information to record
• How to record the information
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33. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
Preparation Barriers
• Choosing the right information to collect
• Finding the right tool to use
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34. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
Collection
The stage when people collect information
about themselves (e.g., inner thoughts, behavior,
social interactions, and their immediate environment).
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35. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
Collection Barriers
• Using the tool
• Remembering
• Lack of time
• Motivation
• Finding data
• Accuracy
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36. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
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
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37. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
Integration
The stage when the information from the
Collection stage is prepared, combined, and
transformed for the user to reflect on.
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38. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
Integration Barriers
• Organization
• Scattered
visualizations
• Transcribing data
• Multiple inputs
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39. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
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.”
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40. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
Reflection
The stage when people reflect on their
personal information.
• Users may reflect immediately (short-term)
• Or after several days or weeks (long-term)
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41. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
Reflection Barriers
• Lack of time
• Self-criticism
• Visualization
• Interpretation
• Sparse data
• No context
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42. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
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
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43. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
Action
The stage when people choose what they are
going to do with their new-found
understanding of themselves.
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44. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
Action Barriers
• Not knowing what to do with the
information
• Alerts
• Incentives
• Suggestions
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45. Introduction
Personal Informatics
Surveys and Interviews
The Stages
Properties of the Stages
Case Studies
Conclusion
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46. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
1. Barriers cascade
2. Stages are iterative
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47. 1. Barriers Cascade
Problems in the earlier stages can affect the
later stages.
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48. 1. Barriers Cascade.
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49. 1. Barriers Cascade.
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50. 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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51. 1. Barriers Cascade
Design Guideline
Consider all the stages when designing PI
systems.
PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
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52. 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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53. 2. Stages are Iterative
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54. 2. Stages are Iterative
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55. 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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56. 2. Stages are Iterative.
Design Guideline
Flexibility is important.
• Support easy importing and exporting of
data.
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57. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION
1. Barriers cascade.
2. Stages are iterative.
3. User- or system-driven
4. Facets
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58. 3. User- vs. System-driven
User-driven
System-driven
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59. 3. User- vs. System-driven
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User-driven
System-driven
Collection
Integration
Reflection
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60. 3. User- vs. System-driven
User-driven
System-driven
Collection
Integration
Reflection
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61. 3. User- vs. System-driven
Design Guideline
Consider the tradeoffs between user-driven
and system-driven stages.
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62. 4. Facets
Peopleʼs lives are composed of many facets.
• Home life vs. work life
• Daily interactions with other people
• Health
• Finance
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63. 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
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64. 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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65. 4. Facets
Location
Activity
People
Office
Shopping
Family
Active
Inactive
Inactive
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66. 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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67. Model of Personal Informatics
5 Stages
4 Properties
• Design guidelines
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68. Introduction
Personal Informatics
Surveys and Interviews
The Stages
Properties of the Stages
Case Studies
Conclusion
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69. Case Studies
1. Twitter-based systems
2. Mint (http://mint.com)
3. IMPACT
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70. 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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71. Two prototypes – Two studies
IMPACT 1.0
IMPACT 2.0
Manual collection
Semi-automated
collection
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72. 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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73. 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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74. Introduction
Personal Informatics
Surveys and Interviews
The Stages
Properties of the Stages
Case Studies
Conclusion
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75. 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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76. 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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77. 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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