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

Personal Informatics+Context  Using Context to Reveal Factors that Affect Behavior Ian Li Anind Dey, CMU, Co-chair Jodi Forlizzi, CMU, Co-chair Niki Kittur, CMU John Stasko, Georgia Tech Ian Li Personal Informatics+Context Thesis Proposal

Alice •  20 years old •  Family history of heart disease •  Wants to be more active, but donʼt know how because sheʼs busy Ian Li Personal Informatics+Context Thesis Proposal 2

Ian Li Personal Informatics+Context Thesis Proposal 3

Mon 1573 Tue 4392 Wed 4537 Thu 5842 Fri 10258 Sat 7528 Sun 1368 Mon 1497 Tue 1837 Ian Li Personal Informatics+Context Thesis Proposal 4

Transcribe to Excel M T W Th F Sa Su M T Ian Li Personal Informatics+Context Thesis Proposal 5

Active Inactive Inactive M T W Th F Sa Su M T Ian Li Personal Informatics+Context Thesis Proposal 6

Factors •  Lack of time •  Lack of motivation •  Activities Active •  Location •  People Inactive Inactive M T W Th F Sa Su M T Ian Li Personal Informatics+Context Thesis Proposal 7

Location Activity People Ofﬁce Shopping Family Active Inactive Inactive M T W Th F Sa Su M T Ian Li Personal Informatics+Context Thesis Proposal 8

Location Activity People Problem Pedometer only recorded one type of information. Ian Li Personal Informatics+Context Thesis Proposal 9

Physical Activity Finance Electricity Diabetes Health Mood http://personalinformatics.org/tools Ian Li Personal Informatics+Context Thesis Proposal 10

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. Ian Li Personal Informatics+Context Thesis Proposal 11

Thesis Questions Is a PI system with context better at revealing factors that affect behavior? •  YES, I will show this in 3 completed studies and my proposed work. Ian Li Personal Informatics+Context Thesis Proposal 12

Location Activity People Location: Park Activity: Hiking People: Friend Step counts: 7531 Ian Li Personal Informatics+Context Thesis Proposal 13

Location Activity People UbiComp Sensors Data mining Ian Li Personal Informatics+Context Thesis Proposal 14

Thesis Questions How do we build a PI system with context? •  Alice had to do a lot to get data and reﬂect on them. •  Issues collecting data? Reﬂecting on data? •  It is not as easy as just automating the system. •  Whether the system is manual or automated has an effect on the userʼs awareness. Ian Li Personal Informatics+Context Thesis Proposal 15

Introduction Stage-Based Model of PI Prototypes Diary Study IMPACT 1.0 IMPACT 2.0 Proposed Work Conclusion Ian Li Personal Informatics+Context Thesis Proposal 16

Thesis Questions Is a PI system with context better at revealing factors that affect behavior? How do we build a PI system with context? •  Create a framework as guide in designing personal informatics systems. •  Building a PI system involves many parts each with their own HCI issues. Ian Li Personal Informatics+Context Thesis Proposal 18

Survey and Interviews 68 people who use personal informatics Advertised the survey in blogs about personal informatics. What tools they use and their problems Ian Li Personal Informatics+Context Thesis Proposal 19

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 Ian Li Personal Informatics+Context Thesis Proposal 20

Analysis Identiﬁed barriers that people experienced. Afﬁnity diagrams to identify themes. Derived a model composed of: •  5 stages Ian Li Personal Informatics+Context Thesis Proposal 21

5 Stages PREPARATION COLLECTION INTEGRATION REFLECTION ACTION Ian Li Personal Informatics+Context Thesis Proposal 22

5 Stages PREPARATION COLLECTION INTEGRATION REFLECTION ACTION Ian Li Personal Informatics+Context Thesis Proposal 23

PREPARATION COLLECTION INTEGRATION REFLECTION ACTION Alice •  Wanted to become active •  Decided to track her physical activity •  Chose to track step counts using a pedometer Ian Li Personal Informatics+Context Thesis Proposal 24

PREPARATION COLLECTION INTEGRATION REFLECTION ACTION Mon 1573 Tue 4392 Wed 4537 Thu 5842 Fri 10258 Sat 7528 Sun 1368 Mon 1497 Tue 1837 Ian Li Personal Informatics+Context Thesis Proposal 25

PREPARATION COLLECTION INTEGRATION REFLECTION ACTION Transcribe to Excel M T W Th F Sa Su M T Ian Li Personal Informatics+Context Thesis Proposal 26

PREPARATION COLLECTION INTEGRATION REFLECTION ACTION Active Inactive Inactive M T W Th F Sa Su M T Ian Li Personal Informatics+Context Thesis Proposal 27

PREPARATION COLLECTION INTEGRATION REFLECTION ACTION The stage when people choose what they are going to do with their new-found understanding of themselves. •  Alerts •  Incentives •  Suggestions Ian Li Personal Informatics+Context Thesis Proposal 28

PREPARATION COLLECTION INTEGRATION REFLECTION ACTION Other research have explored these different stages in isolation •  Collection •  MyLifeBits (Gemmell et al. 2006) •  SenseCam (Hodges et al. 2006) •  Reﬂection •  Casual InfoVis (Pousman et al. 2007) Ian Li Personal Informatics+Context Thesis Proposal 29

PREPARATION COLLECTION INTEGRATION REFLECTION ACTION 1. Barriers cascade. 2. Stages are iterative. Ian Li Personal Informatics+Context Thesis Proposal 30

1. Barriers Cascade. Problems in the earlier stages can affect the later stages. Ian Li Personal Informatics+Context Thesis Proposal 31

1. Barriers Cascade. Location Activity People Ofﬁce Shopping Family Active Inactive Inactive M T W Th F Sa Su M T Ian Li Personal Informatics+Context Thesis Proposal 32

1. Barriers Cascade. Problems in the earlier stages can affect the later stages. → Consider all the stages when designing PI systems. Ian Li Personal Informatics+Context Thesis Proposal 33

2. Stages are Iterative. Users may need to incorporate new types of data, tools, and processes as they progressed through the stages. Ian Li Personal Informatics+Context Thesis Proposal 34

2. Stages are Iterative. Location Activity People Location: Park Activity: Hiking People: Friend Step counts: 7531 Ian Li Personal Informatics+Context Thesis Proposal 35

2. Stages are Iterative. Users may need to incorporate new types of data, tools, and processes as they progress through the stages. → Flexibility is important, but consider user needs early to minimize missed data. Ian Li Personal Informatics+Context Thesis Proposal 36

PREPARATION COLLECTION INTEGRATION REFLECTION ACTION 1. Barriers cascade. 2. Stages are iterative. 3. User- or system-driven 4. Uni- or multi-faceted Ian Li Personal Informatics+Context Thesis Proposal 37

3. User- vs. System-driven The stages can be user-driven, system- driven, or a combination of both. Ian Li Personal Informatics+Context Thesis Proposal 38

3. User- vs. System-driven Mon 1573 Tue 4392 Collection Wed Thu 4537 5842 Fri 10258 Combination Sat Sun Mon 7528 1368 1497 Tue 1837 Integration Transcribe to Excel User-driven Reﬂection Excel graphs System-driven Ian Li Personal Informatics+Context Thesis Proposal 39

3. User- vs. System-driven Collection System-driven Integration System-driven Reﬂection System-driven Ian Li Personal Informatics+Context Thesis Proposal 40

3. User- vs. System-driven The stages can be user-driven, system- driven, or a combination of both. → Explore the tradeoffs between user-driven and system-driven stages. Ian Li Personal Informatics+Context Thesis Proposal 41

Uni- vs. Multi-faceted Most personal informatics are uni-faceted. Some personal informatics systems have multi-faceted collection, but only support uni-faceted reﬂection. Ian Li Personal Informatics+Context Thesis Proposal 42

Uni- vs. Multi-faceted 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 Ian Li Personal Informatics+Context Thesis Proposal 43

Uni- vs. Multi-faceted Location Activity People Ofﬁce Shopping Family Active Inactive Inactive M T Th F Sa Su M T Ian Li Personal Informatics+Context Thesis Proposal 44

Uni- vs. Multi-faceted Most personal informatics are uni-faceted. → Explore support for multiple facets throughout the stages. •  I explore using contextual information. Ian Li Personal Informatics+Context Thesis Proposal 45

Thesis Questions Is a PI system with context better at revealing factors that affect behavior? How do we build a PI system with context? •  Created a framework to analyze PI systems. •  When designing, consider all the stages. Ian Li Personal Informatics+Context Thesis Proposal 46

Thesis Questions Is a PI system with context better at revealing factors that affect behavior? → Deploy prototypes in ﬁeld studies. How do we build a PI system with context? → Build prototypes that explore different ways of supporting context. Ian Li Personal Informatics+Context Thesis Proposal 48

Domain: Physical Activity Ian Li Personal Informatics+Context Thesis Proposal 49

Why 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. Ian Li Personal Informatics+Context Thesis Proposal 50

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 data Physical 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 2 Research 3 4 !! Fig. 1. One participant’s display after approximately two weeks into the trial in the Fish'n 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 ʻn 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. ʼ08 Maitland et al. ʻ06 Lin et al. ʻ06 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 Ian Li Personal Informatics+Context Thesis Proposal (for 4 participants), a change in their attitudes towards physical activity (for 3 pa 51 pants) or a combination of the two (for 7 participants). The greatest change in

Research on Factors 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. Ian Li Personal Informatics+Context Thesis Proposal 52

Activity Location People Physical Activity Level Ian Li Personal Informatics+Context Thesis Proposal 53

Activities Location People Physical Activity Level Ian Li Personal Informatics+Context Thesis Proposal 54

Research on Factors Diabetes awareness of blood sugar level and food consumption (Frost & Smith ʼ03) •  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. Ian Li Personal Informatics+Context Thesis Proposal 55

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. ʻ00) •  Users videotaped daily routines, but a trained observer looked at the video for assessment. •  Matt Leeʼs embedded assessment work Ian Li Personal Informatics+Context Thesis Proposal 56

Sedentary People and Walking Research suggests that they are less aware of their physical activity and how to become active (Sallis & Hovell 1990) Focused on walking because it is easier to integrate into daily life. (Norman & Mills 2004) Ian Li Personal Informatics+Context Thesis Proposal 57

Thesis Questions Is a PI system with context better at revealing factors that affect behavior? •  Run ﬁeld studies with prototypes. How do we build a PI system with context? •  Build several prototypes to try different ways of supporting context. Ian Li Personal Informatics+Context Thesis Proposal 59

Thesis Questions Is a PI system with context better at revealing factors that affect behavior? Diary Study How would people ﬁnd factors using context? IMPACT 1.0 Would context reveal factors that affect behavior? IMPACT 2.0 What is the value of context in the long term? Ian Li Personal Informatics+Context Thesis Proposal 61

Thesis Questions How do we build a PI system with context? Collection Integration Reflection Diary Prototype user-driven user-driven user-driven IMPACT 1.0 user-driven user-driven system-driven IMPACT 2.0 combination system-driven system-driven Ian Li Personal Informatics+Context Thesis Proposal 62

Thesis Questions Time-Stamped Aggregated End-of-Day Real-time Ian Li Personal Informatics+Context Thesis Proposal 63

Date: Time How active were you? What? Where? With whom? Time How activ 6a: 1p: : : : : : : 7a: 2p: : : : : : : 8a: 3p: : : : : : : 9a: 4p: : : : : : : 10a: 5p: : : : : : : 11a: 6p: : : : : : : SenseWear Booklet 12p: 7p: : : : : : : Ian Li Personal Informatics+Context Thesis Proposal 64

Tracking Booklet Reﬂection 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: No Feedback : : No Feedback : : : : 9a: 4p: : : : : : : 10a: 5p: : : : : : : 11a: 6p: : : : : : : 12p: 7p: : : : : : : Continue to the next page.! Date: Time How active were you? What? Where? With whom? Time How active were you? What? Where? With whom? 6a: 1p: : : : : : : 7a: 2p: : : : : Aggregated : : 8a: 3p: : : : : : : 9a: 4p: : : : : : : 10a: 5p: : : Real-time : : : : 11a: 6p: : : : : : : 12p: 7p: : : : : : : Continue to the next page.! Date: Time How active were you? What? Where? With whom? Time How active were you? What? Where? With whom? 6a: 1p: : : : : : : 7a: 2p: : : : : Time-Stamped : : 8a: 3p: : : : : : : 9a: 4p: : : : : : : 10a: 5p: End-of-Day : : : : : : 11a: 6p: : : : : : : 12p: 7p: : : : : : : Continue to the next page.! Ian Li Personal Informatics+Context Thesis Proposal 65

Setup 4 female participants (A1-A4) •  Ages 25-50 •  Sedentary. Pre-screened using Stages of Exercise Behavior Change (Marcus et al. 1998) Audio-taped interviews every week Ian Li Personal Informatics+Context Thesis Proposal 66

SenseWear Tracking Booklet No Pedometer SenseWear Physical Activity Graph Printouts Information 1 2 3 Ian Li Personal Informatics+Context Thesis Proposal 67

Results Excellent compliance over 3 weeks •  At least one activity recorded per hour Ian Li Personal Informatics+Context Thesis Proposal 68

In all phases, participants found factors that affected their physical activity. Week 1, A3: “Writing down had an effect. I would think ʻOh good I have something active to write down.ʼ Like when I would carry my laundry to the Laundromat on foot.” Ian Li Personal Informatics+Context Thesis Proposal 69

In all phases, participants found factors that affected their physical activity. Week 2, A1: “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.” Ian Li Personal Informatics+Context Thesis Proposal 70

FRI DEC 8, 2:03 ... Start Time Matching SenseWear graph printouts End Time - Fri Dec 8, 2006 05:14 AM Session end - Fri Dec 8, 2006 02:03 PM with booklet entries. End 2:03 PM 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... 438 cal 1 hr 43 m... 11346 Not detect... Not detect... 8 hrs 49 m... Ian Li Personal Informatics+Context Thesis Proposal 71

Matching SenseWear graph printouts with booklet entries. Week 3, A2: “The paper feedback [SenseWear] told me when the intensity was greater than other times, so I was able to gauge my activities like if I just walk upstairs…I was calling them in the diary ʻaerobic mini burstʼ.” Ian Li Personal Informatics+Context Thesis Proposal 72

Matching SenseWear graph printouts with booklet entries. Week 3, A1: “I had a lot of data, probably too much to decipher, but it was good. I didnʼt really compare everything to my booklet, only the peaks on the charts to see what I was doing.” Ian Li Personal Informatics+Context Thesis Proposal 73

Detailed reﬂection Immediate awareness Time-Stamped Aggregated End-of-Day Real-time Ian Li Personal Informatics+Context Thesis Proposal 74

Thesis Questions Is a PI system with context better at revealing factors that affect behavior? •  Participants made associations between their physical activity and contextual information helping them become aware of factors that affected their physical activity. → Can we do this in a ﬁeld study of a prototype with more people? Ian Li Personal Informatics+Context Thesis Proposal 75

Thesis Questions How do we build a PI system with context? Collection Integration Reflection Diary Prototype user-driven user-driven user-driven IMPACT 1.0 → Help users make directuser-driven user-driven associations. system-driven → Value2.0 aggregated real-time info and IMPACT in end-of-day combination time-stamped info. system-driven system-driven Ian Li Personal Informatics+Context Thesis Proposal 76

IMPACT 1.0 Ian Li Personal Informatics+Context Thesis Proposal 77

Pedometer Booklet Ian Li Personal Informatics+Context Thesis Proposal 80

Plus-Context e Day with " context labels f Table and charts of steps and context g Steps by hour and by period of day Figure 3. a) Interface for recording steps. Steps-Only additions. Ian Li Personal Informatics+Context Thesis Proposal b) One day of steps. c) Week of steps by day. d) Week of steps for82

Pedometer Booklet Web Site IMPACT 1.0 Steps-Only Steps-Only Steps-Only Baseline Steps-Only No Vis Ian Li Personal Informatics+Context Thesis Proposal 83

Setup 30 participants (B1-B30) •  Sedentary. Pre-screened using Stages of Exercise Behavior Change (Marcus et al. 1998) Questionnaires at the end of each phase Ian Li Personal Informatics+Context Thesis Proposal 84

Steps-Only IMPACT 1.0 Baseline IMPACT 1.0 Steps-Only 1 2 3 4 5 6 7 Ian Li Personal Informatics+Context Thesis Proposal 85

Results Same level of physical activity awareness between Steps-Only and IMPACT Ian Li Personal Informatics+Context Thesis Proposal 86

Greater awareness of factors After using IMPACT, participants   self-reported greater awareness of factors that affect physical activity (5-point Likert scale) 3.93 (IMPACT) vs. 3.57 (Steps-Only) F[1,58] = 5.32, p < .05 Ian Li Personal Informatics+Context Thesis Proposal 87

Greater awareness of factors Mentioned context •  “It has been interesting to see what times of the day I'm most active.” B17 •  “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 Ian Li Personal Informatics+Context Thesis Proposal 88

Greater awareness of factors Mentioned Context Mentioned Context Excluding Time IMPACT 1.0 18 out of 30 13 Steps-Only 13 out of 30 7 Control 11 out of 30 6 Ian Li Personal Informatics+Context Thesis Proposal 89

IMPACT was rated most useful “The [context] I used the most was the one asking who I was with during my most active periods…I hadnʼt realized that I was so sedentary most of the time I spent with my friends.” B1 Ian Li Personal Informatics+Context Thesis Proposal 90

But IMPACT was harder to use 17 of 30 participants preferred Steps-Only. “IMPACT gave a lot of cool information, but having to input all the various factors was a hassle.” B4 Ian Li Personal Informatics+Context Thesis Proposal 91

Problem can be ﬁxed Those who preferred Steps-Only said: •  “There were times I wanted to explain my context.” B22 •  “IMPACT should be provided as an option.” B30 90% reported they would continue using IMPACT if collection of context was automated. Ian Li Personal Informatics+Context Thesis Proposal 92

Thesis Questions Is a PI system with context better at revealing factors that affect behavior? •  Context can increase awareness of factors that affect physical activity. Ian Li Personal Informatics+Context Thesis Proposal 93

Collection Thesis Questions Integration Reflection Diary How do we build a PI system with context? Prototype Collection user-driven Integration user-driven Reflection user-driven IMPACT 1.0 Diary user-driven user-driven system-driven Prototype user-driven user-driven user-driven Visualizations IMPACT 2.0 IMPACT 1.0 combination system-driven system-driven → Users need help collecting and integrating user-driven user-driven system-driven IMPACT over a long period of time. data 2.0 combination system-driven system-driven Ian Li Personal Informatics+Context Thesis Proposal 94

IMPACT 2.0 Bluetooth GPS Ian Li Personal Informatics+Context Thesis Proposal 99

IMPACT 2.0 Context Input Ian Li Personal Informatics+Context Thesis Proposal 100

IMPACT 2.0 Bluetooth Sync Ian Li Personal Informatics+Context Thesis Proposal 101

Mobile Phone Web Site IMPACT 2.0 GPS Context Input Steps-Only Steps-Only Steps-Only Control Steps-Only No Vis Ian Li Personal Informatics+Context Thesis Proposal 103

Setup 35 participants (C1-C35) •  Sedentary. Pre-screened using Stages of Exercise Behavior Change (Marcus et al. 1998) Questionnaires at the end of each phase Ian Li Personal Informatics+Context Thesis Proposal 104

Baseline Phase Intervention Phase Control Control IMPACT 2.0 Steps-Only 1 2 3 4 5 6 7 8 Ian Li Personal Informatics+Context Thesis Proposal 105

Results Complaints were not about the tedium of writing things down, but about having to carry multiple devices. •  “I would not like carrying two devices (GPS and phone), that was too much.” C30 •  “I would use [the prototype] if I could use the software on my own cell phone.” C17 Ian Li Personal Informatics+Context Thesis Proposal 106

Awareness of factors increased for all groups between the phases 32,./2*" 4.)5(67,*8" -9:;3<"=#>" $" !"#$%&%''()*(+#,-)$'( %#$" %" !#$" !" &'()*+,)" -,.)/0),12," F[2,32] = 3.98, p = .0547 Ian Li Personal Informatics+Context Thesis Proposal 107

Similar awareness of factors Mentioned Context Mentioned Context Excluding Time IMPACT 2.0 8 out of 11 6 Steps-Only 8 out of 12 3 Control 6 out of 12 5 Ian Li Personal Informatics+Context Thesis Proposal 108

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 Ian Li Personal Informatics+Context Thesis Proposal 109

Follow-Up Interviews Participants: •  Control (5) •  Steps-Only (6) •  IMPACT 2.0 (3) Ian Li Personal Informatics+Context Thesis Proposal 110

Follow-Up Interviews Expressed interest in comparing over long periods of time. Curious about the peaks in physical activity. But only those who collected contextual information had reminders of what happened during those peaks. Ian Li Personal Informatics+Context Thesis Proposal 111

Collection vs. Reflection Short-term Long-term Reflection Reflection Manual GOOD NOT GOOD Collection Automated NOT GOOD GOOD Collection Ian Li Personal Informatics+Context Thesis Proposal 112

Thesis Questions Is a PI system with context better at revealing factors that affect behavior? Automation has an effect: •  In the short term, reduced interaction with context data so not difference in awareness of factors. •  In the long term, users had data to effectively reﬂect on factors. Ian Li Personal Informatics+Context Thesis Proposal 113

Diary Thesis Questions Prototype user-driven user-driven user-driven How do we build a PI system with context? IMPACT 1.0 Collection user-driven Integration user-driven Reflection system-driven Diary IMPACT 2.0 Prototype combination user-driven system-driven user-driven system-driven user-driven IMPACT 1.0 •  Automation is valuable in the long term, user-driven user-driven system-driven IMPACT 2.0as useful in the short term. but not combination system-driven system-driven Ian Li Personal Informatics+Context Thesis Proposal 114

Thesis Questions Is a PI system with context better at revealing factors that affect behavior? •  Diary Study and IMPACT 1.0: YES  Context can increase awareness of factors that affect physical activity. •  IMPACT 2.0: DEPENDS  Automation reduces immediate awareness, but helps with long-term reﬂection. Ian Li Personal Informatics+Context Thesis Proposal 115

Thesis Questions How do we build a PI system with context? Collection Integration Reflection Diary Need visualization Prototype Support user-driven user-driven user-driven Need to reduce burden IMPACT 1.0 of Collection and user-driven user-driven system-driven Integration stages. Consider the effect of IMPACT 2.0 automation on combination system-driven system-driven immediate awareness. Ian Li Personal Informatics+Context Thesis Proposal 116

Introduction Stage-Based Model of PI Completed Work Diary Study IMPACT 1.0 IMPACT 2.0 Proposed Work Conclusion Ian Li Personal Informatics+Context Thesis Proposal 117

IMPACT 3.0? Ian Li Personal Informatics+Context Thesis Proposal 118

Thesis Questions Is a PI system with context better at revealing factors that affect behavior? •  How do people use context in the visualizations to ﬁnd factors that affect behavior? •  How do we support comparison between different types of context? •  Infer cause-and-effect? Ian Li Personal Informatics+Context Thesis Proposal 119

Thesis Questions How do we build a PI system with context? •  Reduce the cost of using personal informatics system while increasing awareness of factors that affect physical activity. Ian Li Personal Informatics+Context Thesis Proposal 120

UNI-FACETED vs. MULTI-FACETED uni-faceted multi-faceted Ian Li Personal Informatics+Context Thesis Proposal 121

Collection Integration Reflection Diary Need visualization Prototype Support user-driven user-driven user-driven Need to reduce burden IMPACT 1.0 of Collection and user-driven user-driven system-driven Integration stages. Consider the effect of IMPACT 2.0 automation on combination system-driven system-driven immediate awareness. Design semi- IMPACT 3.0 automated collection combination system-driven system-driven without losing immediate awareness Ian Li Personal Informatics+Context Thesis Proposal 122

PREPARATION COLLECTION INTEGRATION ITERATIVE Forced users to collect three types of contextual information. •  Some of them may not have been useful, i.e. they are incurring a cost in Collection, but not providing a beneﬁt during Reﬂection. Ian Li Personal Informatics+Context Thesis Proposal 123

PREPARATION COLLECTION INTEGRATION ITERATIVE The prototypes did not support context other than activity, location, and people. •  Weather •  Mood •  Nutrition •  Other data sources: calendar, email Ian Li Personal Informatics+Context Thesis Proposal 124

PREPARATION COLLECTION INTEGRATION ITERATIVE The prototypes did not count other types of physical activity. •  Swimming •  Biking •  Sports Ian Li Personal Informatics+Context Thesis Proposal 125

PREPARATION COLLECTION INTEGRATION ITERATIVE •  Allow users to select factors that is important to them. •  Support factors other than activity, location, and people. •  Count other types of physical activity. Ian Li Personal Informatics+Context Thesis Proposal 126

BARRIERS CASCADE The prototypes experienced several barriers that affected their usage. Subsequent prototypes addressed some of these barriers. Keep these in mind! Ian Li Personal Informatics+Context Thesis Proposal 127

IMPACT 3.0 Requirements 1.  Maintain immediate awareness in semi- automated collection. 2.  Give choices for other types of context and physical activity. 3.  Explore visualizations of context and physical activity. Ian Li Personal Informatics+Context Thesis Proposal 128

1. Maintain Immediate Awareness Low-cost manual collection where visualizations are shown after input.   (ES+feedback, Hsieh et al. 2008) •  Once during the day, the system asks the user to input data manually. After, the user shows visualizations to the user of other automatically collected information. Ian Li Personal Informatics+Context Thesis Proposal 129

1. Maintain Immediate Awareness Encourage daily use of the visualizations. •  Alert the user of an interesting fact. •  “You were active while walking in the park.” •  Make it easy for users to ﬁnd what they are looking for. •  Direct users to the graph that would answer their question.  Ian Li Personal Informatics+Context Thesis Proposal 130

2. Other types of context and PA Integrate other sources of context •  Online weather information •  Event information from calendars •  Status updates •  Other personal informatics tools (e.g., MoodJam, your.ﬂowingdata) Ian Li Personal Informatics+Context Thesis Proposal 131

2. Other types of context and PA Allow logging of other physical activity. Equate amount of physical activity with amount of walking •  1 hour of softball = 30 minutes of walking Ian Li Personal Informatics+Context Thesis Proposal 132

Personal Informatics Browser Ian Li Personal Informatics+Context Thesis Proposal 133

3. Explore Visualizations in Detail Problems occurred in stages before Reﬂection, now I can explore this stage in detail. •  Visualizations for comparing between instances of physical activity •  Visualizations for making cause-and-effect associations between context and physical activity Ian Li Personal Informatics+Context Thesis Proposal 134

3. Explore Visualizations in Detail Housework Shopping Same Physical Activity Level  Different Context Ian Li Personal Informatics+Context Thesis Proposal 135

3. Explore Visualizations in Detail Ofﬁce Ofﬁce Different Physical Activity Level  Same Context Ian Li Personal Informatics+Context Thesis Proposal 136

Setup 40 participants •  Sedentary and active Questionnaires at the end of each phase Ian Li Personal Informatics+Context Thesis Proposal 137

Baseline Phase Intervention Phase IMPACT 3.0 Control Steps-Only 1 2 3 4 5 6 7 8 Ian Li Personal Informatics+Context Thesis Proposal 138

Awareness Effects Higher awareness of factors that affect physical activity. Maintain immediate awareness even when collection is semi-automated. Increased interest in the visualizations Ian Li Personal Informatics+Context Thesis Proposal 139

Psychological Effects Higher locus of control Higher self-efﬁcacy Self-reports of change in physical activity Ian Li Personal Informatics+Context Thesis Proposal 140

Thesis Questions Is a PI system with context better at revealing factors that affect behavior? •  Deployment of a personal informatics system that has minimal cost in collection without loss in benefit in reflection. •  Exploration of how people use context in visualizations to find factors that affect behavior. Ian Li Personal Informatics+Context Thesis Proposal 141

Thesis Questions How do we build a PI system with context? •  Designs that reduce the cost of using personal informatics system while increasing awareness of factors that affect physical activity. Ian Li Personal Informatics+Context Thesis Proposal 142

Limitation Focus on physical activity. •  PI systems share the same stages. •  Revealing factors that affect behavior beneﬁts the user. •  Diabetes (Frost & Smith 2003) •  Early work on smart meters (Betz 2010) Ian Li Personal Informatics+Context Thesis Proposal 143

Future Work Sharing data. •  Many PI systems allow sharing. •  Explore the quality of discussions when data is shared with context. Ian Li Personal Informatics+Context Thesis Proposal 144

Thesis Questions Is a PI system with context better at revealing factors that affect behavior? •  Showed that context can increase awareness of factors that affect behavior. •  Automation reduces immediate awareness, but helps with long-term reﬂection. •  Deployed a system that doesnʼt sacriﬁce long-term for immediate, vice versa. Ian Li Personal Informatics+Context Thesis Proposal 145

Thesis Questions How do we build a PI system with context? •  Created the stage-based model of PI as a framework to analyze PI systems. •  Created prototypes that support context in ﬁeld studies. •  Identiﬁed issues in supporting context and addressed them in subsequent prototypes. Ian Li Personal Informatics+Context Thesis Proposal 146

Schedule 2010 p m co I bi H U C Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Prototype Analysis IMPACT 3.0 Development Deployment Analysis Write Dissertation Ian Li Personal Informatics+Context Thesis Proposal 147

Thank you! To my advisors, Anind Dey and Jodi Forlizzi, and the rest of my committee, Niki Kittur and John Stasko. To people who have helped discussions, pilot studies:  Gary Hsieh, Scott Davidoff, Erin Walker, Karen Tang, Matt Easterday, Amy Ogan, Amy Hurst, Ruth Wylie, Moira Burke, Matt Lee, Gabi Marcu, Queenie Kravitz, Min Kyung Lee, Turadg Aleahmad, Tawanna Dillahunt, Brian Lim, Chloe Fan, Jenn Marlow, Jason Wiese, Sunyoung Kim, Aubrey Shick, Chris Harrison, Julia Schwarz, Bilge Mutlu, Andy Ko, Johnny Lee, Ido Roll, Jeff Nichols, Jeff Wong, Sara Kiesler, Laura Dabbish, Scott Hudson, Tessa Lau, Jaime Teevan, Fernanda Viegas, Jon Froehlich, UISTʼ09 and UbiCompʼ09 Symposia attendants, Alexandra Carmichael, Gary Wolf, Nathan Yau, Nicholas Felton, Ellie Harrison, Edison Thomaz Ian Li Personal Informatics+Context Thesis Proposal 148

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

by Ian Li on Apr 02, 2010

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Personal Informatics and Context: Using Context to Reveal Factors that Affect Behavior — Presentation Transcript