Thesis Defense - Personal Informatics and Context: Using Context to Reveal Factors that Affect...

P E R S O N A L I N F O R M A T I C S & C O N T E X T USING CONTEXT TO REVEAL FACTORS THAT AFFECT BEHAVIOR

ANIND DEY Co-Chair

JODI FORLIZZI Co-Chair

NIKI KITTUR HCII

JOHN STASKO Georgia Tech

IAN LI

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Alice Just entered college

Started gaining weight

Family history of heart disease

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Alice Manage her time better, so she can find opportunities to be active.

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Pedometer

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Calendar

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CalendarLocation

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CalendarLocation

Weight

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CalendarLocation

Weight

Food Consumption General Health

Mood

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CalendarLocation

Weight

Food Consumption General Health

Mood

http://personalinformatics.org/tools

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Dashboard

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Opportunity!

34% of U.S. adults are obese (National Health and Examination Survey, 2010)

27% of adult internet users have tracked health data online (Pew Internet Report, The Social Life of Health Information, 2011)

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.

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Model of Personal Informatics

Created a model to guide the design of personal informatics systems.

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Field Studies

Showed evidence in field studies that context can reveal factors that affect behavior.

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Field Studies

Visualization Support

Explored what kinds of visualization support personal informatics systems should provide.

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Field Studies


Personal Informatics Dashboard

Developed a personal informatics dashboard that makes it easier for users to associate different kinds of data in a single interface.

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Field Studies



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Goal Create a model as a guide in designing personal informatics systems.

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Survey and Interviews Recruited 68 people who use personal informatics tools

Asked participants what tools they use and problems they’ve encountered.

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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

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Analysis Identified problems that people experienced.

Affinity diagrams to identify themes.

Derived a model composed of: •  5 stages

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COLLECTION REFLECTIONPREPARATION INTEGRATION ACTION

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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Pedometer

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Synchronize data to web site.

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Active

Inactive Inactive

M! T! W! Th! F! Sa! Su! M! T!

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

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Properties of the Stages 1.  Problems cascade. 2.  Stages are iterative. 3.  User- vs. System-driven 4.  Uni- vs. Multi-faceted

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Properties of the Stages 1.  Problems cascade. 2.  Uni- vs. Multi-faceted. 3.  Stages are iterative. 4.  User- vs. System-driven

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1. Problems cascade. Problems in the earlier stages can affect the later stages.

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1. Problems cascade.

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Active

Inactive Inactive


1. Problems cascade.

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Active

Inactive Inactive


1. Problems cascade. Problems in the earlier stages can affect the later stages.

Consider all the stages when building personal informatics tools.

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2. Uni- vs. Multi-faceted Users expressed desire to see associations between different facets of their lives.

“If it were easily collected, information on food intake, calories, fat, etc., would make an interesting starting point for analysis.” User who tracks medication intake

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2. Uni- vs. Multi-faceted

Most personal informatics are uni-faceted.

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

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Active

Inactive Inactive



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Active

Inactive Inactive


Calendar Location Weight

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

Explore support for collecting data on multiple facets of one’s life.

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Benefits of the Model Identified the problems with existing tools.

Highlights the many challenges of building effective personal informatics tools.

A common framework for describing, comparing, and evaluating personal informatics tools.

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Field Studies



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Field Studies

Diary Study

IMPACT 1.0

IMPACT 2.0

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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.

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

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

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Physical Activity Awareness

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Products

Research

UbiFit Consolvo et al. ’08

Shakra Maitland et al. ‘06

Fish ‘n Steps Lin et al. ‘06

want to wait for an update, he or she can manuallysynchronise via the Sync menu option.

Users specify in advance the peers they wish to shareresults with, but at any time they can change this list.Figure 2a shows the Compare Daily Activity screen thatusers can view to assess their performance in relation totheir peers. For a week’s overview of their own activity,users may use the Week’s Activity screen shown in Fig. 2b.In order to provide real time feedback to the user, ananimated representation of the user’s current mode ofactivity runs continuously on the main screen of theapplication. This is shown in Fig. 2c and d.

3.1 Sensing activity

The current activity of the user is inferred using patterns offluctuation in GSM signal strength and changes to the IDsof detected cells. This method has been demonstrated as areliable and unobtrusive way of sensing current activity [2],and has the advantage over the more traditional approach ofusing an accelerometer in that it does not require additionalsensor hardware as in Sensay [17] and the multimodalsensor board of [11]. Similarly, while the processing ofphysiological and biometric data could complement ourapproach, the benefits of encapsulating the system within amobile phone would be lost. An alternative approach wouldbe to utilise the positioning information available fromsome mobile phone networks, however this approachfrequently involves prohibitive cost, as well as dependingupon much of the same technology as our client basedmonitoring.

Rather like a traditional accelerometer, the levels ofsignal strength fluctuation change when a mobile phone ismoved. For example, Fig. 3 shows the total signal strengthfluctuation across all monitored cells during successive 30-stime periods whilst walking, remaining still and travelling

in a motor car. The figure illustrates that it is relatively easyto distinguish between moving and remaining stationary,but at times, the pattern of fluctuation whilst walking willmatch that of driving and vice versa. This is due to thestop–start nature of both walking and travelling in a motorcar in urban areas. When driving, a greater geographicaldistance will typically be covered over a given time periodwhen compared to that of running or walking. As such it ispossible to use the rate of change of neighbouring cells toinfer travel by car.

To classify these patterns we use an artificial neuralnetwork. The network inputs are the sum of signal strengthfluctuation across all monitored cells, and the number ofdistinct cells monitored over a given time interval. Thenetwork consists of a single layer of eight hidden neurons;weights are learnt using back propagation. The networkoutputs the currently sensed activity for the given inputvalues. The network is trained by repeatedly presenting datacollected during each method of movement.

The current activity of the user is conditionally depen-dent upon their previous activity. In order to provide instantfeedback to the user interface, the neural network deliber-ately does not model this behaviour. Instead, when deter-mining if any additional minutes have been earned, weapply task knowledge based upon the output from theneural network over the previous two and a half minutes.This enables noise to be filtered out and a more accuraterepresentation of the users’ activities achieved. For exam-ple, periods of low signal strength fluctuation such asstopping at traffic lights whilst driving can be ignored whenplaced between periods of high fluctuation where manydistinct neighbouring cells were monitored. It could beargued that activity would be more accurately inferred if alonger rolling filter had been applied to the GSM data.Introducing longer filters would have increased the likeli-hood of active minutes ‘disappearing’ from the users’

Figure 2 The phone interface. Images a and b show screens for examining relative and individual activity levels: compare Daily Activity andThis Week’s Activity Images. c and d show two of the screens showing the estimated current activity level: Stationary and Walking

Mobile Netw Appl (2007) 12:185–199 189

Fish’n’Steps: Encouraging Physical Activity with an Interactive Computer Game 263

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Fig. 1. One participant’s display after approximately two weeks into the trial in the Fish'n'Steps team-condition, also the public kiosk and pedometer platform, which rotated through each of the team fish-tanks. The components of the personal display include: 1) Fish Tank - The fish tank contains the virtual pets belong to the participant and his/her team members, 2) Virtual Pet – The participant’s own fish in a frontal view on the right side next to the fish tank, 3) Calcula-tions and feedback - improvement, burned calories, progress bar, personal and team ranking, etc., 4) Chat window for communicating with team members.

To evaluate the effect of Fish’n’Steps, we recruited 19 participants from the staff of Siemens Corporate Research to participate in a 14-week study. Two experimental conditions were designed to separately assess the impact of the virtual pet and the social influences. Application of the TTM to assess behavior that changed during the study demonstrated that Fish’n’Steps was a catalyst of a positive change for 14 out of 19 participants. This effect was evident in either an increase in their daily step count (for 4 participants), a change in their attitudes towards physical activity (for 3 partici-pants) or a combination of the two (for 7 participants). The greatest change in daily number of steps was by the participants who were at the TTM’s intermediate levels of the behavior change. For these participants the game provided just enough motivation to translate mental readiness into action.

While the overall findings were encouraging, there are a number of possibilities for future investigations. For example, selecting participants from our own research or-ganization limited representativeness of the sample to highly educated individuals relatively open to adopting new technologies. In addition, the game highlighted the importance of careful selection of incentives: unachievable or not challenging goals can fail to inspire the desired change.

2 Interventions for Behavior Change

There are a variety of techniques developed over the years to motivate behavior change. Traditional techniques are usually delivered by a trained specialist in either individual or group settings. Examples of these techniques include goal-setting, self-assessment, or monitoring of achieved progress [17] . Two particular approaches that influenced the current project include motivating behavior change by cultivating a

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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.

Research on Factors

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Research on Factors Diabetes awareness of blood sugar level and food consumption (Frost & Smith ’03) •  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. ‘00)

•  Users videotaped daily routines, but a trained observer looked at the video for assessment.

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Prototypes Step Counts

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Prototypes Step Counts

Activity

Location

People

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} Contextual Information

Field Studies

Diary Study

IMPACT 1.0

IMPACT 2.0

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Goal Before building a prototype, explore what people would do when they have access to both physical activity and contextual information.

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Continue to the next page.!

SenseWear Booklet Pedometer

Takeaways “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.” A1

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Activity Location Person

Takeaways “Housework and walking to the bus stop can contribute, really. I mean, I take that for granted in terms of energy expenditure.” A4

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Activity Location

Matching SenseWear graphs with booklet entries.

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8 hrs 49 m...

Duration of Vi...

Not detect...

Sleep

Not detect...

Lying Do...

11346

Step Count

1 hr 43 m...

Physical Activity (2.5 ME...

438 cal

Active ...includes off-body

estimate of 4 cal1041 calori...

Total EE

- Fri Dec 8, 2006 02:03 PMSession end

End Time

- Fri Dec 8, 2006 05:14 AM

Start Time

Start5:14 AM

End2:03 PM

FRI DEC 8, 2:03 ... THU DEC 7, 2:16 ...

cindy

8 hrs 49 m...

Duration of Vi...

Not detect...

Sleep

Not detect...

Lying Do...

11346

Step Count

1 hr 43 m...

Physical Activity (2.5 ME...

438 cal

Active ...includes off-body

estimate of 4 cal1041 calori...

Total EE

- Fri Dec 8, 2006 02:03 PMSession end

End Time

- Fri Dec 8, 2006 05:14 AM

Start Time

Start5:14 AM

End2:03 PM

FRI DEC 8, 2:03 ... THU DEC 7, 2:16 ...

cindy

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

Can a prototype support this in a field study with more people?

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Field Studies

Diary Study

IMPACT 1.0

IMPACT 2.0

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Booklet Pedometer

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had an interface to label time segments with contextual information, including a participant’s activity, location, and

people the participant was with. In addition to the per-day

and per-week visualizations of step counts, they also used

the online service to see visualizations showing the

association between their daily activities and their number

of steps (Figure 3e, 3f, & 3g). Users carried diary booklets

with additional fields for contextual information.

FIELD STUDY

We used the IMPACT system to explore if associating

physical activity with context can produce a greater self-

awareness about one’s own behaviors and can then

motivate an improvement in physical activity. We had two

hypotheses:

H1: Users who recorded the context of their physical

activity would find more opportunities to be physically

active.

H2: Users who find more opportunities to be physically

active would increase the number of steps they take.

Participants and Method

We recruited 43 participants, 14 males and 29 females,

using a recruiting web site, newsgroups, and flyers. The

ages of the participants ranged between 19 and 55. All

participants owned a computer and had high-speed online

access at home or at work. Participants received $100 for

their participation.

Participants wore a pedometer (Omron HJ-112 Walking Style) (Figure 2a) all day throughout the duration of the

study. Participants also carried a pocket-sized diary booklet

to record the time and their step counts. When in the Plus-

Context phase, participants received a diary booklet with

additional fields for contextual information (Figure 2b).

Participants entered their booklet entries into the IMPACT

web site at their convenience.

Participants used the Baseline version of IMPACT during

the first week of the study. After the first week, participants

were randomly assigned to use the Steps-Only version or

the Plus-Context version for 3 weeks. After this period of 3 weeks, participants used the other version for 3 more

weeks.

During the Steps-Only phase, participants recorded their

step counts every time they changed activities or about once

an hour. During the Plus-Context phase, participants

recorded their step counts and the context of their activity

(the kind of activity, location, and the people they were

with). Participants were instructed to record every time they

changed their context, such as when they changed

activities, moved locations, or met with a different person.

The researchers met with the participants 4 times throughout the study: at the Start of the study, after

Baseline, after Steps-Only, and after Plus-Context. During

these meetings, participants received materials and

instructions for the phase they were about to begin.

Participants also answered questionnaires. Four participants

Baseline

a

Steps-Only

b

c

d

Plus-Context

e

f

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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 for each day. Plus-Context additions. e) Context labeler. f) Table and

chart showing association between steps and context. g) Steps by hour and period of day.

Day with context labels

Table and chart of steps and context

Steps by hour and by period of day

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Pedometer Booklet Dashboard

IMPACT 1.0 3 weeks

Visualization of Steps

Steps-Only 3 weeks

Baseline 1 week

Steps

Steps Activity Location People

Steps

Visualization of Steps & Context

Participants 30 participants (B1-B30) •  Sedentary. Pre-screened using Stages of

Exercise Behavior Change (Marcus et al. 1998)

Questionnaires at the end of each phase

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Mentioned Context “It helped me realize which activities were more important. For example, I didn’t understand the importance of walking home versus taking the bus.” B8

“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

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Of the 30 participants…

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Mentioned Context (Activities, Location, People)

IMPACT 1.0 Visualization

of Steps and Context

13 participants

Steps-Only Visualization of Steps

7 participants

Baseline No Visualizations

6 participants

IMPACT supports reflection on context “The [visualization] I used the most was the one asking who I was with; I hadn’t realized that I was so sedentary most of the time I spent with my friends.” B1

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

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

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

Next: IMPACT 2.0

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Field Studies

Diary Study

IMPACT 1.0

IMPACT 2.0

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Automatic Collection of Steps and Location

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Bluetooth GPS

Facilitated Collection of Activities and People

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Automated Integration

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Bluetooth Sync

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Mobile Phone Dashboard

IMPACT 2.0

Visualization of Steps

Steps-Only

Baseline

Visualization of Steps & Context

Collected Steps Only

Collected Steps Only

Collected Steps, Activity, Location, and People

1 2 3 4 5 6 7 8

Control

Baseline Steps-Only

IMPACT 2.0

Baseline Phase Intervention Phase

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Participants 35 participants (C1-C35) •  Sedentary. Pre-screened using Stages of

Exercise Behavior Change (Marcus et al. 1998)

Questionnaires at the end of each phase

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Results

No complaints about inputting data.

But people complained about carrying multiple devices. •  “I would not like carrying two devices (GPS

and phone), that was too much.” C30

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Awareness of factors increased for all groups between the phases

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Mentioned Context

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Mentioned Context (Activities, Location, People)

IMPACT 2.0 Visualization

of Steps and Context

6 of 11 participants

Steps-Only Visualization of Steps


Control No Visualizations


Short-Term Benefits/Problems

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Short-term

IMPACT 1.0 Manual Collection

Harder to collect, but more engaged

IMPACT 2.0 Automated Collection

Easier to collect, but less engaged

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

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

Curious about the peaks in physical activity.

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

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Long-Term Benefits/Problems

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Short-term Long Term

IMPACT 1.0 Manual Collection

Harder to collect, but more engaged

No reflection opportunity

IMPACT 2.0 Automated Collection

Easier to collect, but less engaged

Has reflection opportunity

Overall Summary Provided some evidence that a system that shows context can reveal factors that affect behavior.

But the value of the data is highly dependent on the type of support.

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Field Studies



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Goal Determine what kinds of questions people ask about their data.

Determine when contextual information is useful.

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Participants 15 participants (P1-15) to interview.

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Procedure 1-hour interviews •  I observed participants using their personal

informatics tool.

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Analysis Identified the kinds of questions people asked about their data.

Affinity diagrams to identify themes.

Derived 6 kinds of questions.

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Six Kinds of Questions Status History

Goals Discrepancy

Details Factors

What is my current status?

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Six Kinds of Questions Status

History Goals

Discrepancy Details Factors

What happened in the past?

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History Goals


What goals should I pursue?

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History Goals


How does my behavior compare to my goals?

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History Goals


What other things happened during a particular point in time?

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History Goals

Discrepancy Details

Factors What influences my behavior over a long period of time?

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Importance of the Questions Not all questions are important all the time.

Some questions are more important than others as people’s information needs change.

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Importance of the Questions Not all questions are important all the time.

Some questions are more important than others as people’s information needs change.

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Maintenance Phase

Discovery Phase &

Maintenance Phase Participants already know how different factors affect their behavior, so they just want to know what their current status is.

Participants have already identified their goals. They are only concerned with whether they are meeting their goal.

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Maintenance Phase Current Status

P13 just tracks the minutes that he spends on Facebook, Twitter, and other social media sites, because he already know how these affects his productivity.

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Maintenance Phase Discrepancy

P1 uses Mint to keep track of her expenditures to see whether she is meeting the budget that she had set for herself.

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Maintenance Phase These kinds of questions were the most important: •  Status •  Discrepancy

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Discovery Phase Participants collect several types of information to find out what factors affect their behavior.

Participants are trying to figure out what their goals are.

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Discovery Phase Finding factors that affect their behavior.

P3 has diabetes and she tracked her blood glucose levels and her food consumption to find out their interaction.

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Discovery Phase Figuring out goals

P8 tracks the quality of her sleep so that she is better rested. She explores her sleep data to “spot trends for which I can take corrective action.”

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Discovery Phase These kinds of questions were the most important: •  History •  Goals •  Details •  Factors

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} Contextual information & Multiple types of data


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Next Question What visualization features would help users find answers to these kinds of questions? }

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Goal

History Goals

Details Factors

Timeline Sketches

Results

History: Looking back in time.

Participants generally agreed that the timeline sketches were the most appropriate for the Discovery phase.

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Results

Goals: Seeing goals information.

“I like having the goal line...I always like being able to see what my baseline should be and if I am above or below.” P5

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Goal

Results

Details: Seeing details to reason what happened.

“When looking at exercise there are a couple of times where I really didn’t meet my goal, so it will be really nice to be able to say ‘why didn’t I meet my goal then?’” P4

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Results

Factors: Comparison of different kinds of data.

“The most interesting thing here is the ability to compare two different time frames because I’m really interested in the relationship between data.” P6

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Summary Contextual information and multiple types of data is important during the Discovery phase.

Described visualization features to help people answer questions during the Discovery phase.

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Summary Contextual information and multiple types of data is important during the Discovery phase.

Described visualization features to help people answer questions during the Discovery phase.

Next: Built a personal informatics dashboard with the visualization features.

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Field Studies



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Goal Build a personal informatics dashboard that allows users to see multiple kinds of data together.

Develop an approach that makes it easier to build.

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Goal

History Goals

Details Factors

Visualization Features

Data Integration

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Data SourcesDashboard


Data Integration

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Problems with Data Integration Dashboard has to:

Access Data

Parse Data

Visualize Data

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Access Data

Parse Data

Visualize Data

Managing many data sources w/ different APIs.

The data source loses control of the data.

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Access Data

Parse Data

Visualize Data

No standard format for the different types of data that users collect.

Dashboard has to create parsers for each format.

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Access Data

Parse Data

Visualize Data

Dashboard has to create visualizations for each type of data.

Duplicates creation of the visualizations.

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Visualization Integration

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Widgets


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Widgets

Benefits of Viz Integration Dashboard has to:

Accessing Data

Parsing Data

Visualizing Data

Provide an API that data sources can use.

Manage the communication between widgets.

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Benefits of Viz Integration For the perspective of data sources:

Maintain control of the data.

They can choose how the data is visualized.

Create a widget and it can be used with widgets that others have made.

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I N N E R T U B E

Implementation Programmed in Javascript.

1.  Innertube API

2.  Innertube Widgets

3.  Innertube Dashboard

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Innertube API Data sources create visualization widgets using static images, Javascript, and/or Flash.

Data sources use the API to communicate with the dashboard and vice versa.

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Innertube API Get the date and range of visualizations to display.

Get the currently highlighted data point.

Change the appearance of the widget. •  Set height of the widget. •  Reload the widget.

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Innertube Widgets Fitbit Steps

GPS Location

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Innertube Widgets Weather

Sleep Busyness Energy Level Mood Notes

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Innertube Dashboard

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Demo of Innertube Dashboard

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Field Study 15 participants recruited via Craigslist.

Were not tracking their physical activity.

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Data Collection for 1 week Automatically Collected

Step Counts using Fitbit GPS Location Weather Information

Manually Collected

Mood Amount of Sleep Busyness Energy Level People Notes

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Returned to the Lab Used Innertube while thinking-aloud. •  What they were looking for •  What they were finding •  What problems they encountered

Answered questionnaires about Innertube.

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Results 13 of the 15 participants agreed that Innertube was useful.

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Results “It allowed me to factor in location, times, and activity in order for me to assess where I may be able to increase physical activity.” P12

“Seeing the temperatures of the times I went on my runs and knowing how well I did on them would allow me to determine the best condition for me to run in.” P14

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Results “It gave me concrete contexts, in space and time, by which I could measure and evaluate my own physical activity. Interacting with that data gave me the opportunity to hypothesize about what factors influenced my own physical activity, and what specifically motivated me or discouraged me from exercising.” P9

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Results “I thought certain widgets [factors] were less useful before I used the PI dashboard, and then I changed my mind after using it, because their usefulness became apparent to me.” P9

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Future Work Improve the usability of the Innertube Dashboard.

Make the Innertube API available to developers. Coming soon!

Create a directory of Innertube Widgets, so people can find widgets easily.

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Summary Described visualization integration, an easier approach to building personal informatics dashboards.

Implemented Innertube, an example of visualization integration.

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Conclusion

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Contributions Created a model to guide the design of personal informatics systems.

Showed evidence that contextual information can reveal factors that affect behavior.

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Contributions Explored what kinds of visualization support personal informatics systems should provide.

Developed an easier way to build personal informatics dashboards to help users associate different kinds of data in a single interface.

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Future Work Deploy longer field studies.

Conduct studies in other behavior domains.

Explore how to convert awareness of factors to changes in behavior (Action stage).

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

To the many who have helped along the way: Gary Hsieh, Erin Walker, Karen Tang, Scott Davidoff, Amy Ogan, Ruth Wylie, Moira Burke, Queenie Kravitz, Gabi Marcu, Rebecca Gulotta, Matt Lee, Turadg Aleahmad, Aruna Balakrishnan, Min Kyung Lee, Tawanna Dillahunt, Sunyoung Kim, Chloe Fan, Jenn Marlow, Jason Wiese, Stephen Oney, Chris Harrison, Julia Schwarz, Eliane Stampfer, Samantha Finkelstein, Aubrey Shick, Matt Easterday, Bilge Mutlu, Andy Ko, Johnny Lee, Ido Roll, Jeff Nichols, Jeff Wong, Jennie Park, Sara Kiesler, Laura Dabbish, Scott Hudson, Tessa Lau, Fernanda Viegas, Jaime Teevan, Alexandra Carmichael, Gary Wolf.

To HCII, QoLT, the Ubicomp Lab, and the Quantified Self.

To my family, Papa, Mama, Robin, and Cassandra.

This work is based on research supported by the National Science Foundation under Grant No. IIS-0325351 and EEEC-0540865.

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Thesis Defense - Personal Informatics and Context: Using Context to Reveal Factors that Affect...

Health & Medicine

Transcript of Thesis Defense - Personal Informatics and Context: Using Context to Reveal Factors that Affect...