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

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

A Stage-Based Modelof Personal Informatics Systems

Ian Li Anind Dey Jodi Forlizzi

HCII, Carnegie Mellon University


Gnothi seauton.

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Know thyself.

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Self-knowledge is valuable.

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

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Ian Li, Anind Dey, Jodi Forlizzi A Stage-Based Model of Personal Informatics CHI 2010 7

Physical Activity

Finance

Health

Mood

Electricity

Diabetes

http://personalinformatics.org/tools


Alice •  20 years old •  Family history of heart

disease •  Wants to be more active,

but doesnʼt know how because sheʼs busy


1. Alice prepares.

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2. Alice collects data.

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3. Alice transcribes data.

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M T W Th F Sa Su M T

Transcribe to Excel


4. Alice reflects on the data.

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Active

Inactive Inactive



5. Alice takes action.

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Walk in the park instead of

watching TV


COLLECTION REFLECTIONPREPARATION INTEGRATION ACTION

Model of Personal Informatics

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Model of Personal Informatics

1.  Barriers cascade. 2.  Stages are iterative. 3.  User- vs. System-driven 4.  Facets

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DesignGuidelines


Introduction

Personal Informatics

Surveys and Interviews

The Stages

Properties of the Stages

Case Studies

Conclusion


Introduction



The Stages


Case Studies

Conclusion


Personal Informatics 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


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

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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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Types of Information Automatically collected •  Financial institutions (banks, credit cards)

•  Utility companies (electricity, heating)

•  Computers (email and browsing history)

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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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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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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

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

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

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

One problem is:“Keeping up the motivation to do so; like finding payback for the investment of time and effort.”

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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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Integration Barriers •  Organization •  Scattered

visualizations •  Transcribing data •  Multiple inputs

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Integration Barriers •  Organization •  Scattered

visualizations •  Transcribing data •  Multiple inputs

“Itʼd be neat if I could graph [the data] straight from the web site instead of manually typing in the data to a spreadsheet.”

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

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

“Itʼs hard to get a holistic view of the data since the time filters are at most one month and Iʼd like to look at several months at once.”

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

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Action Barriers •  Not knowing what to do with the

information •  Alerts •  Incentives •  Suggestions

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Introduction



The Stages


Case Studies

Conclusion


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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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

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2. Stages are Iterative

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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

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User-driven System-driven



Collection

Integration

Reflection

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Collection

Integration

Reflection

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3. User- vs. System-driven Design Guideline Consider the tradeoffs between user-driven and system-driven stages.

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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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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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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

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Active

Inactive Inactive

Location Office

Activity Shopping

People Family

M T Th F Sa Su M T W


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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Model of Personal Informatics 5 Stages

4 Properties •  Design guidelines

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Introduction



The Stages


Case Studies

Conclusion


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

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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 awareof factors in their lives that affect their physical activity.

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Two prototypes – Two studies IMPACT 1.0 Manual collection

IMPACT 2.0 Semi-automated collection

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Collection vs. Reflection

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Short-term Reflection

Long-term Reflection

IMPACT 1.0 Manual Collection

GOOD NOT GOOD

IMPACT 2.0 Automated Collection

NOT GOOD GOOD


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


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 [email protected] Anind Dey [email protected] Jodi Forlizzi [email protected]

Funded by

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

Technology

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