P E R S O N A LI N F O R M A T I C S& C O N T E X TUSING CONTEXT TO REVEAL FACTORSTHAT AFFECT BEHAVIORIAN LIANIND DEY   JO...
AliceJust entered collegeStarted gaining weightFamily history of heartdisease                          2
AliceManage her time better,so she can findopportunities to be active.                              3
Pedometer            4
5
6
7
Calendar           8
Calendar        Location                   9
Calendar        Location               Weight                        10
Calendar        Location               WeightFood Consumption  General Health     Mood                        11
Calendar                          Location                                 Weight                  Food Consumption       ...
Dashboard            13
Opportunity!34% of U.S. adults are obese(National Health and ExaminationSurvey, 2010)27% of adult internet usershave track...
ThesisA personal informatics systemthat allows users to associatecontext with behavioral informationcan betterreveal facto...
Model of Personal InformaticsCreated a model to guide the design ofpersonal informatics systems.                          ...
Model of Personal InformaticsField StudiesShowed evidence in field studies that contextcan reveal factors that affect behav...
Model of Personal InformaticsField StudiesVisualization SupportExplored what kinds of visualization supportpersonal inform...
Model of Personal InformaticsField StudiesVisualization SupportPersonal Informatics DashboardDeveloped a personal informat...
Model of Personal InformaticsField StudiesVisualization SupportPersonal Informatics Dashboard                             ...
GoalCreate a model as a guide in designingpersonal informatics systems.                                         21
Survey and InterviewsRecruited 68 people who use personalinformatics toolsAsked participants what tools they use andproble...
Sample Questions•  How difficult is it to collect this personal   information?•  How do you explore this collected personal...
AnalysisIdentified problems that people experienced.Affinity diagrams to identify themes.Derived a model composed of:•  5 st...
5 Stages  PREPARATION   COLLECTION   INTEGRATION   REFLECTION   ACTION                                                    ...
PREPARATION   COLLECTION   INTEGRATION   REFLECTION   ACTION                                                              ...
PREPARATION   COLLECTION   INTEGRATION   REFLECTION   ACTION                     Alice                     Wanted to becom...
PREPARATION   COLLECTION    INTEGRATION   REFLECTION   ACTION                           Pedometer                         ...
PREPARATION   COLLECTION   INTEGRATION   REFLECTION   ACTION                                                      Synchron...
PREPARATION   COLLECTION   INTEGRATION   REFLECTION   ACTION                               ActiveInactive                 ...
PREPARATION   COLLECTION   INTEGRATION   REFLECTION   ACTIONThe stage when peoplechoose what they are going todo with thei...
Properties of the Stages1.  Problems cascade.2.  Stages are iterative.3.  User- vs. System-driven4.  Uni- vs. Multi-facete...
Properties of the Stages1.  Problems cascade.2.  Uni- vs. Multi-faceted.3.  Stages are iterative.4.  User- vs. System-driv...
1. Problems cascade.Problems in the earlier stages can affect thelater stages.                                            ...
1. Problems cascade.                    Active    Inactive                  Inactive       M!   T! W! Th! F! Sa! Su! M!   ...
1. Problems cascade.                    Active    Inactive                  Inactive       M!   T! W! Th! F! Sa! Su! M!   ...
1. Problems cascade.Problems in the earlier stages can affect thelater stages.Consider all the stages when buildingpersona...
2. Uni- vs. Multi-facetedUsers expressed desire to see associationsbetween different facets of their lives.“If it were eas...
2. Uni- vs. Multi-facetedMost personal informatics are uni-faceted.Some personal informatics toolshave multi-faceted colle...
2. Uni- vs. Multi-faceted                    Active    Inactive                  Inactive       M!   T! W! Th! F! Sa! Su! ...
2. Uni- vs. Multi-faceted     Calendar           Location        Weight                        Active    Inactive         ...
2. Uni- vs. Multi-facetedMost personal informatics are uni-faceted.Explore support for collecting dataon multiple facets o...
Benefits of the ModelIdentified the problems with existing tools.Highlights the many challenges of buildingeffective persona...
PREPARATION   COLLECTION   INTEGRATION   REFLECTION   ACTION                                                              ...
Model of Personal InformaticsField StudiesVisualization SupportPersonal Informatics Dashboard                             ...
Field StudiesDiary StudyIMPACT 1.0IMPACT 2.0                46
Physical ActivityLack of physical activity is a common problemthat leads to obesity, diabetes, and highblood pressure.Lack...
Sedentary People & WalkingResearch suggests that they are less aware oftheir physical activity and how to becomeactive. (S...
application. This is shown in Fig. 2c and d.                                         network. The network inputs are the s...
Research on FactorsPhysical activity is affected by lack of time,choice of activities, the environment, andsocial influence...
Research on FactorsDiabetes awareness of blood sugar level andfood consumption (Frost & Smith ’03)•  Images of food associ...
Research on FactorsAsthma patients videotaping daily routinesfound that they are in the presence of harmfulallergens more ...
Prototypes Step Counts               53
Prototypes Step Counts              }   Activity                  Contextual  Location                  Information   Peop...
Field StudiesDiary StudyIMPACT 1.0IMPACT 2.0                55
GoalBefore building a prototype,explore what people would do when theyhave access to both physical activity andcontextual ...
SenseWear   Pedometer   Booklet                        Date:                        Time    How active were you?   What?  ...
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 wit...
Takeaways“Housework and walking to the bus stop cancontribute, really. I mean, I take that forgranted in terms of energy e...
Matching SenseWear graphs with booklet                    FRI DEC 8, 2:03 ...                      Start Time             ...
SummaryParticipants made associations between theirphysical activity and contextual informationhelping them become aware o...
Field StudiesDiary StudyIMPACT 1.0IMPACT 2.0                62
Pedometer   Booklet                      63
64
Plus-Context                 eDay withcontext labels                 fTable and chartof steps andcontext                 g...
Pedometer   Booklet         Dashboard                             Steps   Baseline      1 week                            ...
Participants30 participants (B1-B30)•  Sedentary. Pre-screened using Stages of   Exercise Behavior Change (Marcus et al. 1...
Mentioned Context“It helped me realize which activities were more important. For example, I didn’t understand the importan...
Of the 30 participants…                            Mentioned Context                             (Activities, Location, Pe...
IMPACT supportsreflection on context“The [visualization] I used the most was theone asking who I was with; I hadn’t realize...
Possible Improvements“IMPACT gave a lot of cool information, buthaving to input all the various factors was ahassle.” B4  ...
Possible Improvements“IMPACT gave a lot of cool information, but having to input all the various factors was a hassle.” B4...
Possible Improvements“IMPACT gave a lot of cool information, but having to input all the various factors was a hassle.” B4...
Field StudiesDiary StudyIMPACT 1.0IMPACT 2.0                74
Automatic Collectionof Steps and Location                Bluetooth GPS                                75
Facilitated Collectionof Activities and People                           76
Automated Integration            Bluetooth Sync                              77
78
Mobile Phone                       Dashboard                      Collected   Baseline           Steps Only               ...
Baseline Phase   Intervention Phase                       Control    Baseline          Steps-Only                     IMPA...
Participants35 participants (C1-C35)•  Sedentary. Pre-screened using Stages of   Exercise Behavior Change (Marcus et al. 1...
ResultsNo complaints about inputting data.But people complained about carryingmultiple devices.•  “I would not like carryi...
Awareness of factors increased forall groups between the phases                                32,./2*"   4.)5(67,*8"   -9...
Mentioned Context                            Mentioned Context                             (Activities, Location, People) ...
Short-Term Benefits/Problems                          Short-term  IMPACT 1.0           Harder to collect, Manual Collection...
Long-term reflectionWhat is the value of contextual information inthe long-term?6-months later when they were more likely t...
Follow-Up InterviewsExpressed interest in comparing over longperiods of time.Curious about the peaks in physical activity....
Long-Term Benefits/Problems                          Short-term          Long Term  IMPACT 1.0            Harder to collect...
Overall SummaryProvided some evidence that a system thatshows context can reveal factors that affectbehavior.But the value...
PREPARATION   COLLECTION   INTEGRATION   REFLECTION   ACTION                                                              ...
PREPARATION   COLLECTION   INTEGRATION   REFLECTION   ACTION                                                              ...
Model of Personal InformaticsField StudiesVisualization SupportPersonal Informatics Dashboard                             ...
GoalDetermine what kinds of questions people askabout their data.Determine when contextual information isuseful.          ...
Participants15 participants (P1-15) to interview.                                        94
Procedure1-hour interviews•  I observed participants using their personal   informatics tool.                             ...
AnalysisIdentified the kinds of questions people askedabout their data.Affinity diagrams to identify themes.Derived 6 kinds ...
Six Kinds of Questions      Status What is my current status?     History       GoalsDiscrepancy     Details     Factors  ...
Six Kinds of Questions      Status     History What happened in the past?      GoalsDiscrepancy     Details     Factors   ...
Six Kinds of Questions      Status     History      Goals What goals should I pursue?Discrepancy     Details     Factors  ...
Six Kinds of Questions      Status     History      GoalsDiscrepancy How does my behavior compare     Details to my goals?...
Six Kinds of Questions      Status     History      GoalsDiscrepancy     Details What other things happened     Factors du...
Six Kinds of Questions      Status     History      GoalsDiscrepancy     Details    Factors What influences my behavior    ...
Importance of the QuestionsNot all questions are important all the time.Some questions are more important thanothers as pe...
Importance of the QuestionsNot all questions are important all the time.Some questions are more important thanothers as pe...
Maintenance PhaseParticipants already know how differentfactors affect their behavior, so they just wantto know what their...
Maintenance PhaseCurrent StatusP13 just tracks the minutes that he spends onFacebook, Twitter, and other social mediasites...
Maintenance PhaseDiscrepancyP1 uses Mint to keep track of herexpenditures to see whether she is meetingthe budget that she...
Maintenance PhaseThese kinds of questions were the mostimportant:•  Status•  Discrepancy                                  ...
Discovery PhaseParticipants collect several types ofinformation to find out what factors affecttheir behavior.Participants ...
Discovery PhaseFinding factors that affect their behavior.P3 has diabetes and she tracked her bloodglucose levels and her ...
Discovery PhaseFiguring out goalsP8 tracks the quality of her sleep so that she isbetter rested. She explores her sleep da...
Discovery PhaseThese kinds of questions were the mostimportant:•  History•  Goals•  Details•  Factors                     ...
Discovery PhaseThese kinds of questions were the mostimportant:•  History•  Goals•  Details•  Factors}   Contextual inform...
Discovery PhaseThese kinds of questions were the mostimportant:         }•  History     Next Question•  Goals       What v...
Timeline SketchesHistory         Goals                         GoalDetails        Factors                                115
ResultsHistory: Looking back in time.Participants generally agreed that the timelinesketches were the most appropriate for...
Results                          GoalGoals:   Seeing goals information.“I like having the goal line...I always like being ...
ResultsDetails: Seeing details to reason what         happened.“When looking at exercise there are a couple of times where...
ResultsFactors: Comparison of different         kinds of data.“The most interesting thing here is the ability to compare t...
SummaryContextual information and multiple types ofdata is important during the Discovery phase.Described visualization fe...
SummaryContextual information and multiple types ofdata is important during the Discovery phase.Described visualization fe...
Model of Personal InformaticsField StudiesVisualization SupportPersonal Informatics Dashboard                             ...
GoalBuild a personal informatics dashboard thatallows users to see multiple kinds of datatogether.Develop an approach that...
Visualization FeaturesHistory          Goals                          GoalDetails         Factors                         ...
Data IntegrationData Sources                   Dashboard                               125
Data IntegrationData Sources                   Dashboard                               126
Problems with Data IntegrationDashboard has to:Access DataParse DataVisualize Data                                 127
Problems with Data IntegrationDashboard has to:Access Data         Managing many data                    sources w/ differ...
Problems with Data IntegrationDashboard has to:Access Data         No standard format for                    the different...
Problems with Data IntegrationDashboard has to:Access Data         Dashboard has to create                    visualizatio...
Visualization Integration                            131
Visualization Integration   Data Sources                        Dashboard                                    132
Visualization Integration   Data Sources Widgets                          Dashboard                                      133
Visualization Integration   Data Sources Widgets                          Dashboard                                      134
Benefits of Viz IntegrationDashboard has to:Accessing Data      Provide an API that                    data sources can use...
Benefits of Viz IntegrationFor the perspective of data sources:Maintain control of the data.They can choose how the data is...
INNERTUBE            137
ImplementationProgrammed in Javascript.1.  Innertube API2.  Innertube Widgets3.  Innertube Dashboard                      ...
Innertube APIData sources create visualization widgetsusing static images, Javascript, and/or Flash.Data sources use the A...
Innertube APIGet the date and range of visualizations todisplay.Get the currently highlighted data point.Change the appear...
Innertube WidgetsFitbit StepsGPS Location                    141
Innertube WidgetsWeatherSleepBusynessEnergy LevelMoodNotes                    142
Innertube Dashboard                      143
Demo ofInnertube Dashboard                      144
Field Study15 participants recruited via Craigslist.Were not tracking their physical activity.                            ...
Data Collection for 1 weekAutomatically Collected Manually CollectedStep Counts using Fitbit MoodGPS Location             ...
Returned to the LabUsed Innertube while thinking-aloud.•  What they were looking for•  What they were finding•  What proble...
Results13 of the 15 participants agreed thatInnertube was useful.                                        148
Results“It allowed me to factor in location, times, and activity in order for me to assess where I may be able to increase...
Results“It gave me concrete contexts, in space and time, by which I could measure and evaluate my own physical activity. I...
Results“I thought certain widgets [factors] were less useful before I used the PI dashboard, and then I changed my mind af...
Future WorkImprove the usability of the InnertubeDashboard.Make the Innertube API available todevelopers. Coming soon!Crea...
SummaryDescribed visualization integration, an easierapproach to building personal informaticsdashboards.Implemented Inner...
Conclusion             154
ContributionsCreated a model to guide the design ofpersonal informatics systems.Showed evidence that contextual informatio...
ContributionsExplored what kinds of visualization supportpersonal informatics systems should provide.Developed an easier w...
Future WorkDeploy longer field studies.Conduct studies in other behavior domains.Explore how to convert awareness of factor...
Thank you!To my committee, Anind Dey, Jodi Forlizzi, Niki Kittur, and John Stasko.To the many who have helped along the wa...
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Thesis Defense - Personal Informatics and Context: Using Context to Reveal Factors that Affect Behavior

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Personal informatics systems help people collect and reflect on behavioral information to better understand their own behavior. Because most systems only show one type of behavioral information, finding factors that affect one’s behavior is difficult. Supporting exploration of multiple types of contextual and behavioral information in a single interface may help.

To explore this, I developed prototypes of IMPACT, which supports reflection on physical activity and multiple types of contextual information. I conducted field studies of the prototypes, which showed that such a system can increase people’s awareness of opportunities for physical activity. However, several limitations affected the usage and value of these prototypes. To improve support for such systems, I conducted a series of interviews and field studies. First, I interviewed people about their experiences using personal informatics systems resulting in the Stage-Based Model of Personal Informatics Systems, which describes the different stages that systems need to support, and a list of problems that people experience in each of the stages. Second, I identified the kinds of questions people ask about their personal data and found that the importance of these questions differed between two phases: Discovery and Maintenance. Third, I evaluated different visualization features to improve support for reflection on multiple kinds of data. Finally, based on this evaluation, I developed a system called Innertube to help people reflect on multiple kinds of data in a single interface using a visualization integration approach that makes it easier to build such tools compared to the more common data integration approach.

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

  1. 1. P E R S O N A LI N F O R M A T I C S& C O N T E X TUSING CONTEXT TO REVEAL FACTORSTHAT AFFECT BEHAVIORIAN LIANIND DEY JODI FORLIZZI NIKI KITTUR JOHN STASKOCo-Chair Co-Chair HCII Georgia Tech
  2. 2. AliceJust entered collegeStarted gaining weightFamily history of heartdisease 2
  3. 3. AliceManage her time better,so she can findopportunities to be active. 3
  4. 4. Pedometer 4
  5. 5. 5
  6. 6. 6
  7. 7. 7
  8. 8. Calendar 8
  9. 9. Calendar Location 9
  10. 10. Calendar Location Weight 10
  11. 11. Calendar Location WeightFood Consumption General Health Mood 11
  12. 12. Calendar Location Weight Food Consumption General Health Moodhttp://personalinformatics.org/tools 12
  13. 13. Dashboard 13
  14. 14. Opportunity!34% of U.S. adults are obese(National Health and ExaminationSurvey, 2010)27% of adult internet usershave tracked health dataonline (Pew Internet Report, TheSocial Life of Health Information, 2011) 14
  15. 15. ThesisA personal informatics systemthat allows users to associatecontext with behavioral informationcan betterreveal factors that affect behaviorcompared to systems that only showbehavioral information. 15
  16. 16. Model of Personal InformaticsCreated a model to guide the design ofpersonal informatics systems. 16
  17. 17. Model of Personal InformaticsField StudiesShowed evidence in field studies that contextcan reveal factors that affect behavior. 17
  18. 18. Model of Personal InformaticsField StudiesVisualization SupportExplored what kinds of visualization supportpersonal informatics systems should provide. 18
  19. 19. Model of Personal InformaticsField StudiesVisualization SupportPersonal Informatics DashboardDeveloped a personal informatics dashboardthat makes it easier for users to associatedifferent kinds of data in a single interface. 19
  20. 20. Model of Personal InformaticsField StudiesVisualization SupportPersonal Informatics Dashboard 20
  21. 21. GoalCreate a model as a guide in designingpersonal informatics systems. 21
  22. 22. Survey and InterviewsRecruited 68 people who use personalinformatics toolsAsked participants what tools they use andproblems they’ve encountered. 22
  23. 23. 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 23
  24. 24. AnalysisIdentified problems that people experienced.Affinity diagrams to identify themes.Derived a model composed of:•  5 stages 24
  25. 25. 5 Stages PREPARATION COLLECTION INTEGRATION REFLECTION ACTION 25
  26. 26. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION 26
  27. 27. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION Alice Wanted to become active Decided to track her physical activity Chose to track step counts using a pedometer 27
  28. 28. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION Pedometer 28
  29. 29. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION Synchronize data to web site. 29
  30. 30. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION ActiveInactive Inactive M! T! W! Th! F! Sa! Su! M! T! 30
  31. 31. PREPARATION COLLECTION INTEGRATION REFLECTION ACTIONThe stage when peoplechoose what they are going todo with their new-foundunderstanding of themselves. 31
  32. 32. Properties of the Stages1.  Problems cascade.2.  Stages are iterative.3.  User- vs. System-driven4.  Uni- vs. Multi-faceted 32
  33. 33. Properties of the Stages1.  Problems cascade.2.  Uni- vs. Multi-faceted.3.  Stages are iterative.4.  User- vs. System-driven 33
  34. 34. 1. Problems cascade.Problems in the earlier stages can affect thelater stages. 34
  35. 35. 1. Problems cascade. Active Inactive Inactive M! T! W! Th! F! Sa! Su! M! T! 35
  36. 36. 1. Problems cascade. Active Inactive Inactive M! T! W! Th! F! Sa! Su! M! T! 36
  37. 37. 1. Problems cascade.Problems in the earlier stages can affect thelater stages.Consider all the stages when buildingpersonal informatics tools. 37
  38. 38. 2. Uni- vs. Multi-facetedUsers expressed desire to see associationsbetween 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 38
  39. 39. 2. Uni- vs. Multi-facetedMost personal informatics are uni-faceted.Some personal informatics toolshave multi-faceted collection,but only support uni-faceted reflection. 39
  40. 40. 2. Uni- vs. Multi-faceted Active Inactive Inactive M! T! W! Th! F! Sa! Su! M! T! 40
  41. 41. 2. Uni- vs. Multi-faceted Calendar Location Weight Active Inactive Inactive M! T! W! Th! F! Sa! Su! M! T! 41
  42. 42. 2. Uni- vs. Multi-facetedMost personal informatics are uni-faceted.Explore support for collecting dataon multiple facets of one’s life. 42
  43. 43. Benefits of the ModelIdentified the problems with existing tools.Highlights the many challenges of buildingeffective personal informatics tools.A common framework for describing,comparing, and evaluating personalinformatics tools. 43
  44. 44. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION 44
  45. 45. Model of Personal InformaticsField StudiesVisualization SupportPersonal Informatics Dashboard 45
  46. 46. Field StudiesDiary StudyIMPACT 1.0IMPACT 2.0 46
  47. 47. Physical ActivityLack of physical activity is a common problemthat leads to obesity, diabetes, and highblood pressure.Lack of awareness of physical activity is onereason why people are not active. 47
  48. 48. Sedentary People & WalkingResearch suggests that they are less aware oftheir physical activity and how to becomeactive. (Sallis & Hovell 1990)Encourage walking because it is easier tointegrate into daily life. (Norman & Mills 2004) 48
  49. 49. 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 dataPhysical 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 2Research 3 4 !! Fig. 1. One participant’s display after approximately two weeks into the trial in the Fishn 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 ‘n 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. ’08 Maitland et al. ‘06 Lin et al. ‘06 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 49 (for 4 participants), a change in their attitudes towards physical activity (for 3 pa pants) or a combination of the two (for 7 participants). The greatest change in
  50. 50. Research on FactorsPhysical activity is affected by lack of time,choice of activities, the environment, andsocial influence. (Sallis & Hovell 1990)CDC suggests understanding of factors tocircumvent barriers to physical activity. 50
  51. 51. Research on FactorsDiabetes awareness of blood sugar level andfood 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. 51
  52. 52. Research on FactorsAsthma patients videotaping daily routinesfound that they are in the presence of harmfulallergens more often than they realized(Rich et al. ‘00)•  Users videotaped daily routines, but a trained observer looked at the video for assessment. 52
  53. 53. Prototypes Step Counts 53
  54. 54. Prototypes Step Counts } Activity Contextual Location Information People 54
  55. 55. Field StudiesDiary StudyIMPACT 1.0IMPACT 2.0 55
  56. 56. GoalBefore building a prototype,explore what people would do when theyhave access to both physical activity andcontextual information. 56
  57. 57. SenseWear Pedometer Booklet Date: Time How active were you? What? Where? With whom? Time How active were you? What? Where? With whom? 6a: 1p: : : : : : : 7a: 2p: : : : : : : 8a: 3p: : : : : : : 9a: 4p: : : : : : : 10a: 5p: : : : : : : 11a: 6p: : : : : : : 12p: 7p: : : : : : : Continue to the next page. 57
  58. 58. 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 Activity Location Person 58
  59. 59. Takeaways“Housework and walking to the bus stop cancontribute, really. I mean, I take that forgranted in terms of energy expenditure.” A4 Activity Location 59
  60. 60. Matching SenseWear graphs with booklet FRI DEC 8, 2:03 ... Start Time - Fri Dec 8, 2006 05:14 AM End Time entries. End Session end - Fri Dec 8, 2006 02:03 PM 2:03 PM, 2:16 ... 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...off-bodyof 4 cal 438 cal 1 hr 43 m... 11346 Not detect... Not detect... 8 hrs 49 m... 60
  61. 61. SummaryParticipants made associations between theirphysical activity and contextual informationhelping them become aware of factors thataffected their physical activity.Can a prototype support thisin a field study with more people? 61
  62. 62. Field StudiesDiary StudyIMPACT 1.0IMPACT 2.0 62
  63. 63. Pedometer Booklet 63
  64. 64. 64
  65. 65. Plus-Context eDay withcontext labels fTable and chartof steps andcontext gSteps by hourand by periodof day Figure 3. a) Interface for recording steps. Steps-Only additions. 65 b) One day of steps. c) Week of steps by day. d) Week of steps for
  66. 66. Pedometer Booklet Dashboard Steps Baseline 1 week Visualization of Steps StepsSteps-Only 3 weeks Visualization of Steps & Context StepsIMPACT 1.0 Activity Location 3 weeks People 66
  67. 67. Participants30 participants (B1-B30)•  Sedentary. Pre-screened using Stages of Exercise Behavior Change (Marcus et al. 1998)Questionnaires at the end of each phase 67
  68. 68. 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 cant say I wasnt expecting, but I also had no idea that walking around Squirrel Hill for just an hour or two made such a difference.” B24 68
  69. 69. Of the 30 participants… Mentioned Context (Activities, Location, People) IMPACT 1.0 13 participants Visualization of Steps and Context Steps-Only 7 participants Visualization of Steps Baseline 6 participants No Visualizations 69
  70. 70. IMPACT supportsreflection on context“The [visualization] I used the most was theone asking who I was with; I hadn’t realizedthat I was so sedentary most of the time Ispent with my friends.” B1 70
  71. 71. Possible Improvements“IMPACT gave a lot of cool information, buthaving to input all the various factors was ahassle.” B4 71
  72. 72. Possible Improvements“IMPACT gave a lot of cool information, but having to input all the various factors was a hassle.” B490% reported they would continue usingIMPACT if collection of context wasautomated. 72
  73. 73. Possible Improvements“IMPACT gave a lot of cool information, but having to input all the various factors was a hassle.” B490% reported they would continue usingIMPACT if collection of context wasautomated.Next: IMPACT 2.0 73
  74. 74. Field StudiesDiary StudyIMPACT 1.0IMPACT 2.0 74
  75. 75. Automatic Collectionof Steps and Location Bluetooth GPS 75
  76. 76. Facilitated Collectionof Activities and People 76
  77. 77. Automated Integration Bluetooth Sync 77
  78. 78. 78
  79. 79. Mobile Phone Dashboard Collected Baseline Steps Only Visualization of Steps CollectedSteps-Only Steps Only Collected Steps, Activity, Visualization Location, and People of Steps & ContextIMPACT 2.0 79
  80. 80. Baseline Phase Intervention Phase Control Baseline Steps-Only IMPACT 2.01 2 3 4 5 6 7 8 80
  81. 81. Participants35 participants (C1-C35)•  Sedentary. Pre-screened using Stages of Exercise Behavior Change (Marcus et al. 1998)Questionnaires at the end of each phase 81
  82. 82. ResultsNo complaints about inputting data.But people complained about carryingmultiple devices.•  “I would not like carrying two devices (GPS and phone), that was too much.” C30 82
  83. 83. Awareness of factors increased forall groups between the phases 32,./2*" 4.)5(67,*8" -9:;3<"=#>" $"!"#$%&%()*(+#,-)$( %#$" %" !#$" !" &()*+,)" -,.)/0),12," F[2,32] = 3.98, p = .0547 83
  84. 84. Mentioned Context Mentioned Context (Activities, Location, People) IMPACT 2.0 6 of 11 participants Visualization of Steps and Context Steps-Only 3 of 12 participants Visualization of Steps Control 5 of 12 participants No Visualizations 84
  85. 85. Short-Term Benefits/Problems Short-term IMPACT 1.0 Harder to collect, Manual Collection but more engaged IMPACT 2.0 Easier to collect,Automated Collection but less engaged 85
  86. 86. Long-term reflectionWhat is the value of contextual information inthe long-term?6-months later when they were more likely tohave forgotten the data 86
  87. 87. Follow-Up InterviewsExpressed interest in comparing over longperiods of time.Curious about the peaks in physical activity.But only those who had visualizations ofcontextual information had reminders of whathappened during those peaks. 87
  88. 88. Long-Term Benefits/Problems Short-term Long Term IMPACT 1.0 Harder to collect, No reflection Manual Collection but more engaged opportunity IMPACT 2.0 Easier to collect, Has reflectionAutomated Collection but less engaged opportunity 88
  89. 89. Overall SummaryProvided some evidence that a system thatshows context can reveal factors that affectbehavior.But the value of the data is highlydependent on the type of support. 89
  90. 90. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION 90
  91. 91. PREPARATION COLLECTION INTEGRATION REFLECTION ACTION 91
  92. 92. Model of Personal InformaticsField StudiesVisualization SupportPersonal Informatics Dashboard 92
  93. 93. GoalDetermine what kinds of questions people askabout their data.Determine when contextual information isuseful. 93
  94. 94. Participants15 participants (P1-15) to interview. 94
  95. 95. Procedure1-hour interviews•  I observed participants using their personal informatics tool. 95
  96. 96. AnalysisIdentified the kinds of questions people askedabout their data.Affinity diagrams to identify themes.Derived 6 kinds of questions. 96
  97. 97. Six Kinds of Questions Status What is my current status? History GoalsDiscrepancy Details Factors 97
  98. 98. Six Kinds of Questions Status History What happened in the past? GoalsDiscrepancy Details Factors 98
  99. 99. Six Kinds of Questions Status History Goals What goals should I pursue?Discrepancy Details Factors 99
  100. 100. Six Kinds of Questions Status History GoalsDiscrepancy How does my behavior compare Details to my goals? Factors 100
  101. 101. Six Kinds of Questions Status History GoalsDiscrepancy Details What other things happened Factors during a particular point in time? 101
  102. 102. Six Kinds of Questions Status History GoalsDiscrepancy Details Factors What influences my behavior over a long period of time? 102
  103. 103. Importance of the QuestionsNot all questions are important all the time.Some questions are more important thanothers as people’s information needs change. 103
  104. 104. Importance of the QuestionsNot all questions are important all the time.Some questions are more important thanothers as people’s information needs change. Maintenance Discovery & Phase Phase 104
  105. 105. Maintenance PhaseParticipants already know how differentfactors affect their behavior, so they just wantto know what their current status is.Participants have already identified theirgoals. They are only concerned with whetherthey are meeting their goal. 105
  106. 106. Maintenance PhaseCurrent StatusP13 just tracks the minutes that he spends onFacebook, Twitter, and other social mediasites, because he already know how theseaffects his productivity. 106
  107. 107. Maintenance PhaseDiscrepancyP1 uses Mint to keep track of herexpenditures to see whether she is meetingthe budget that she had set for herself. 107
  108. 108. Maintenance PhaseThese kinds of questions were the mostimportant:•  Status•  Discrepancy 108
  109. 109. Discovery PhaseParticipants collect several types ofinformation to find out what factors affecttheir behavior.Participants are trying to figure outwhat their goals are. 109
  110. 110. Discovery PhaseFinding factors that affect their behavior.P3 has diabetes and she tracked her bloodglucose levels and her food consumption tofind out their interaction. 110
  111. 111. Discovery PhaseFiguring out goalsP8 tracks the quality of her sleep so that she isbetter rested. She explores her sleep data to“spot trends for which I can take correctiveaction.” 111
  112. 112. Discovery PhaseThese kinds of questions were the mostimportant:•  History•  Goals•  Details•  Factors 112
  113. 113. Discovery PhaseThese kinds of questions were the mostimportant:•  History•  Goals•  Details•  Factors} Contextual information & Multiple types of data 113
  114. 114. Discovery PhaseThese kinds of questions were the mostimportant: }•  History Next Question•  Goals What visualization features would help users find answers•  Details to these kinds of questions?•  Factors 114
  115. 115. Timeline SketchesHistory Goals GoalDetails Factors 115
  116. 116. ResultsHistory: Looking back in time.Participants generally agreed that the timelinesketches were the most appropriate for theDiscovery phase. 116
  117. 117. Results GoalGoals: 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 117
  118. 118. ResultsDetails: 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 118
  119. 119. ResultsFactors: 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 119
  120. 120. SummaryContextual information and multiple types ofdata is important during the Discovery phase.Described visualization features to helppeople answer questions during the Discoveryphase. 120
  121. 121. SummaryContextual information and multiple types ofdata is important during the Discovery phase.Described visualization features to helppeople answer questions during the Discoveryphase.Next: Built a personal informaticsdashboard with the visualization features. 121
  122. 122. Model of Personal InformaticsField StudiesVisualization SupportPersonal Informatics Dashboard 122
  123. 123. GoalBuild a personal informatics dashboard thatallows users to see multiple kinds of datatogether.Develop an approach that makes it easier tobuild. 123
  124. 124. Visualization FeaturesHistory Goals GoalDetails Factors 124
  125. 125. Data IntegrationData Sources Dashboard 125
  126. 126. Data IntegrationData Sources Dashboard 126
  127. 127. Problems with Data IntegrationDashboard has to:Access DataParse DataVisualize Data 127
  128. 128. Problems with Data IntegrationDashboard has to:Access Data Managing many data sources w/ different APIs.Parse Data The data source losesVisualize Data control of the data. 128
  129. 129. Problems with Data IntegrationDashboard has to:Access Data No standard format for the different types of dataParse Data that users collect.Visualize Data Dashboard has to create parsers for each format. 129
  130. 130. Problems with Data IntegrationDashboard has to:Access Data Dashboard has to create visualizations for eachParse Data type of data.Visualize Data Duplicates creation of the visualizations. 130
  131. 131. Visualization Integration 131
  132. 132. Visualization Integration Data Sources Dashboard 132
  133. 133. Visualization Integration Data Sources Widgets Dashboard 133
  134. 134. Visualization Integration Data Sources Widgets Dashboard 134
  135. 135. Benefits of Viz IntegrationDashboard has to:Accessing Data Provide an API that data sources can use.Parsing Data Manage theVisualizing Data communication between widgets. 135
  136. 136. Benefits of Viz IntegrationFor 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 withwidgets that others have made. 136
  137. 137. INNERTUBE 137
  138. 138. ImplementationProgrammed in Javascript.1.  Innertube API2.  Innertube Widgets3.  Innertube Dashboard 138
  139. 139. Innertube APIData sources create visualization widgetsusing static images, Javascript, and/or Flash.Data sources use the API to communicatewith the dashboard and vice versa. 139
  140. 140. Innertube APIGet the date and range of visualizations todisplay.Get the currently highlighted data point.Change the appearance of the widget.•  Set height of the widget.•  Reload the widget. 140
  141. 141. Innertube WidgetsFitbit StepsGPS Location 141
  142. 142. Innertube WidgetsWeatherSleepBusynessEnergy LevelMoodNotes 142
  143. 143. Innertube Dashboard 143
  144. 144. Demo ofInnertube Dashboard 144
  145. 145. Field Study15 participants recruited via Craigslist.Were not tracking their physical activity. 145
  146. 146. Data Collection for 1 weekAutomatically Collected Manually CollectedStep Counts using Fitbit MoodGPS Location Amount of SleepWeather Information Busyness Energy Level People Notes 146
  147. 147. Returned to the LabUsed Innertube while thinking-aloud.•  What they were looking for•  What they were finding•  What problems they encounteredAnswered questionnaires about Innertube. 147
  148. 148. Results13 of the 15 participants agreed thatInnertube was useful. 148
  149. 149. 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 149
  150. 150. 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 150
  151. 151. 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 151
  152. 152. Future WorkImprove the usability of the InnertubeDashboard.Make the Innertube API available todevelopers. Coming soon!Create a directory of Innertube Widgets, sopeople can find widgets easily. 152
  153. 153. SummaryDescribed visualization integration, an easierapproach to building personal informaticsdashboards.Implemented Innertube, an example ofvisualization integration. 153
  154. 154. Conclusion 154
  155. 155. ContributionsCreated a model to guide the design ofpersonal informatics systems.Showed evidence that contextual informationcan reveal factors that affect behavior. 155
  156. 156. ContributionsExplored what kinds of visualization supportpersonal informatics systems should provide.Developed an easier way to buildpersonal informatics dashboards to helpusers associate different kinds of data in asingle interface. 156
  157. 157. Future WorkDeploy longer field studies.Conduct studies in other behavior domains.Explore how to convert awareness of factorsto changes in behavior (Action stage). 157
  158. 158. 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, QueenieKravitz, Gabi Marcu, Rebecca Gulotta, Matt Lee, Turadg Aleahmad, ArunaBalakrishnan, 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, BilgeMutlu, 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. 158

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