Mobile Devices for Facilitating Group Fitness and Visualization of Fitness … · 2020-05-31 · Mobile Devices for Facilitating Group Fitness and Visualization of Fitness Data Shuai

Mobile Devices for Facilitating Group Fitness and Visualization ofFitness Data

Shuai Liu

Thesis submitted to the Faculty of the

Virginia Polytechnic Institute and State University

in partial fulfillment of the requirements for the degree of

Master of Science

in

Computer Science and Application

Andrey Esakia, Chair

D. Scott McCrickard

Sang Won Lee

May 04, 2019

Blacksburg, Virginia

Keywords: Fitness, Motivation, Interventions, Group Goals, Social Recognition,

Visualization


Shuai Liu

(ABSTRACT)

Lack of physical activity is a major problem contributing to diseases and poor health. Nowa-

days, mobile fitness apps serve in important roles in encouraging and facilitating people to

do more physical exercise. Many apps focus primarily on individual behavioral strategies,

such as displaying individual steps to encourage physical activity. Such strategies help evoke

one’s internal motivation such as peer recognition and competition achievement. However,

such apps usually de-emphasize or ignore interpersonal behavioral strategies, such as team

rank. And group-based strategies are very important in aspects such as peer recognition

and can facilitate more physical activity. This research explores the design strategies of

group-based dynamic approaches for encouraging physical activity in small-size groups. The

development effort takes into account the different roles of mobile devices and laptops and

the evaluation explored the effectiveness of the design.


Shuai Liu

(GENERAL AUDIENCE ABSTRACT)

Lack of physical activity is a major problem contributing to diseases and poor health. Nowa-

days, mobile fitness apps like Fitbit and Runkeeper help encourage people to exercise. Many

apps focus primarily on things like steps for each person. However, this research shifts the

focus to small team goals and motivations, such as team rank and team progress toward an

overall goal. This research explores ways to get people motivated through showing them in-

formation on their mobile phone or a web site. Several different visual displays were created

and evaluated.

Acknowledgments

This research would not have been possible without the previous research done by Dr. Andrey

Esakia, and many thanks to Dr. Scott McCrickard who provided much advice in the way I

finished this research and in reading my revisions. Thanks to all my team members Jixaing

Fan, Nicholas Gill, Aditya Anil Mungad, and Zhennan Yao, who have done a great job to

support this thesis. Thanks to my committee members who offered support. Also, thanks

to all the participants and experts involved in this study who provided great feedback.

iv

Contents

List of Figures ix

List of Tables xi

1 Introduction 1

2 Related Work 7

2.1 Importance of Physical Activity . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2 FitAware and Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3 Other Fitness Apps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.4 Visualization Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.5 Environment Influence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3 Beyond FitAware? 17

3.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.2 FitAware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.3 Beyond FitAware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4 Redesign and Development of FitAware Apps 22

4.1 Methodology details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

v

4.1.1 Phase I - Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.1.2 Phase II - Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.1.3 Phase III - Prototype . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.1.4 Phase IV - Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.2 Design Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

4.2.1 SeekBar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

4.2.2 Calendar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4.2.3 Pie Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

4.2.4 Lock Screen Notification . . . . . . . . . . . . . . . . . . . . . . . . . 44

4.2.5 Like Feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

4.2.6 Daily, 3 days, 5 days, and Weekly challenges . . . . . . . . . . . . . . 46

4.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

5 Visualization of FitAware Web 48

5.1 Design Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

5.1.1 Personal Dashboard . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

5.1.2 Team Dashboard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

5.1.3 Geographic Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

5.1.4 Carbon Dioxide Footprint . . . . . . . . . . . . . . . . . . . . . . . . 54

5.2 Evaluation of the Visualizations . . . . . . . . . . . . . . . . . . . . . . . . . 56

5.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

6 Discussion 60

7 Conclusions and Future Work 64

7.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Bibliography 67

Appendices 75

Appendix A FitAware App Screenshot 76

A.1 Home page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

A.2 Group Member Information . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

A.3 Teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

A.4 Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

A.5 Notification/Widget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Appendix B Survey evaluation 79

Appendix C FitAware Web Screenshot 87

Appendix D Personas 89

Appendix E Wireframes 91

Appendix F Sketches 93

Appendix G Expert Reviews 95

List of Figures

2.1 Google Fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1 FitEx Interfaces in FitAware . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.2 Smartwatch User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.1 UX lifecycle wheel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.2 Rearranging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.3 Paraphrase and Synthesize . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.4 WAAD Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.5 Extraction of User Requirements . . . . . . . . . . . . . . . . . . . . . . . . 27

4.6 Fitness focused user of the individual aspects . . . . . . . . . . . . . . . . . 31

4.7 Sketch of the “Dashboard/home page” . . . . . . . . . . . . . . . . . . . . . 32

4.8 Storyboard for the Mental Model . . . . . . . . . . . . . . . . . . . . . . . . 33

4.9 System Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.10 Survey data I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.11 Survey data II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

4.12 Linear SeekBar vs Circular SeekBar . . . . . . . . . . . . . . . . . . . . . . . 42

4.13 Calendar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

ix

4.14 Team Pie Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4.15 Lock Screen Notification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4.16 Like Feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

5.1 Personal Dashboard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

5.2 Team Dashboard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

5.3 Bar Chart Race Animation . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

5.4 Geographic Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

5.5 Carbon Footprint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

6.1 Survey data III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

C.1 Personal Dashboard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

C.2 Team Dashboard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

C.3 Bar Chart Race Animation . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

C.4 Geographic Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

C.5 CO2 Footprint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

List of Tables

G.1 Personal Page Q&A with expert #1 . . . . . . . . . . . . . . . . . . . . . . 96

G.2 Team Page Q&A with expert #1 . . . . . . . . . . . . . . . . . . . . . . . . 97

G.3 Bar chart racing Page Q&A with expert #1 . . . . . . . . . . . . . . . . . . 98

G.4 Geographic map Page Q&A with expert #1 . . . . . . . . . . . . . . . . . . 99

G.5 CO2 footprint Page Q&A with expert #1 . . . . . . . . . . . . . . . . . . . 100











xi

Chapter 1

Introduction

Individuals who have regular daily routines to do physical exercise are less likely to get dia-

betes, cancer, and cardiovascular disease, and physical activity helps to maintain functional

abilities and well-being, and can help with anti-aging[1]. Since the industrial revolution,

more and more advanced technologies appeared and helped to reduce the necessary phys-

ical activity of individuals, such as cars and trains. People can utilize these technologies

to improve their productivity by saving a lot of travel time but also diminish the chances

to do exercises such as walking. As time went on, the lack of active physical activity has

become a more severe problem in the modern world. About 31 percent of adults worldwide

are inactive, ranging from 17 percent in southeast Asia to 43 percent in the Americas and

the eastern Mediterranean[2].

In order to help people increase physical activity and stay healthy, some interventions can be

applied. This research expands on work on a group-based fitness application: FitAware[3].

FitAware is a smartwatch centered system that focuses on small group community-based

intervention. Small group focused intervention results in better participant engagement due

to the interpersonal factors that occur in small and cohesive groups[4]. These interventions

often combined individual (goal-setting, self-monitoring, and feedback) and interpersonal

(such as peer recognition and social comparison) behavioral strategies to improve physical

activity behaviors[5, 6, 7, 8, 9]. The basic goal of FitAware is to support the goals of the

FitEx program, which is a group dynamic based, statewide physical activity intervention

1

administered by Virginia Cooperative Extension public health practitioners. The success of

FitAware was in its ability to convey glanceable data about individual and group performance

within a proven FitEx exercise program. In his dissertation[3], Esakia deployed a 8-week

statewide physical activity community intervention that led to the development of guidelines

for the FitAware system. The research focused on the smartwatch components, noting that

less attention was given to ways to delve into the data on a smartphone or web site. As

explained in Chapter 3, this thesis seeks to address this need.

The goal of this research is to encourage in-depth reflection on physical activity data through

four design components: group goals, social recognition, physical activity data visualiza-

tions, and environmental factors. In past years, many research provided the basis for the

understanding about group cohesion and the implementation of group dynamics strategies

in physical activity[10], few studies attempted to directly measure information about group

goals, norms, communication, cooperation, or other group factors other than the assessment

of group cohesion. It is clear that more work is needed to develop and understand the theory

behind these interventions and to measure the group factors considered necessary to change

physical activity[10]. One study indicated that goal setting has proven to be an effective

strategy for changing physical behaviors[11]. To verify how different time period fitness

goals change users’ physical activity behaviors, a four weeks user study was conducted to

test the effectiveness of Daily, 3 days, 5 days, Weekly time period goals(Section 4.2.6). So-

cial Recognition in fitness apps enables people to recognize individual or group achievements

in physical activities, thus encouraging them to achieve more[12]. Therefore, a study was

performed to test how social recognition encourage or discourage users’ physical activity in

small group competitions, a four weeks user study was conducted to test the effectiveness

of ”Like feature”(Section 4.2.5). Modify users’ behavior such as increased physical activity

by promoting understanding of individual and group data that reflects daily habit patterns

2

and makes those data available for behavior change[13]. Although most studies in the field

believe promoting understanding of users’ data that reflects the behavior change, they have

overlooked the application of interactive visualizations. This research will examine how

interactive visualizations of users’ physical activity data promote users’ understanding of in-

dividual and group data, a four weeks user study, and expert reviews were conducted to test

the effectiveness of those visualizations(Chapter 4.2 & 5.1). Existing research shows that

environmental awareness is known to influence people to choose green travel behavior, in

their study, environmental awareness(27.334%) is the most influenced factor comparing with

other factors like habits, personal norms, perceived behavioral control, and environmental

behavioral intentions, according to participants’ cumulative contributions[14]. Despite the

intense focus on physical activity impacts on health, few researchers have examined the im-

pacts of physical activity on the environment. This research will examine how environmental

awareness(CO2 footprint in Section 5.1.4) impacts users’ physical activity behaviors, expert

reviews were conducted to test the effectiveness of this visualization.

To accomplish the goal, this research effort introduces a redesigned FitAware system that

includes two smartphone apps for Android and iOS (Chapter 4) and web visualization (Chap-

ter 5). The redesigned FitAware system tried to address the issues that the previous system

had such as the small screen limitation on smartwatches cause that limited information to be

displayed, and provide more group-based interventions to help people increase their physical

activity through the four hypotheses. The redesigned FitAware system provides a series

of well designed interactive visualizations to help people perceive much more data on their

phones like steps, distances, and heart points (a measurement encourages people staying

active, users are able to get heart points from activities that get their heart pumping). Nev-

ertheless, the redesigned system is not limited to smartphones. A large display like laptop

or desktop size web visualization is provided to display more historical data. FitAware web

3

provides more interactive visualizations to help people understand their overall physical ac-

tivity trend, make reflections on group fitness data, and apply their physical activity progress

to multiple angles such as virtual thru-hike and environmental contributions. Overall, the

essential goal of FitAware is to motivate people to increase their physical activity.

This thesis integrates the efforts of many different people, including my own undergraduate

research projects and class projects. The contributions of this thesis focus on two of those

efforts: a class project in Virginia Tech’s CS 5714 class, and an independent study effort as

part of McCrickard’s research group.

The mobile app design was important to provide users with multiple aspects of their personal

and team fitness data. During this mobile app redesign in CS 5714, a 4 person group

was tasked with a mission to redesign the mobile app. The group was composed with

Jixiang Fan and Nicholas Gill who are both Computer Science master students, Aditya Anil

Mungad who is from Industrial and Systems Engineering and myself. We worked through

a whole process of the User Experience (UX) lifecycle wheel: analysis, design, prototype,

and evaluation[15]. After the group completed the Hartson’s wheel model[15], an Android

app and a iOS app were developed. The group’s goal was to redesign the mobile interface

of the companion application for FitAware that is able to incite users in both individual

and group competition. We included five different attributes to measure the user’s physical

activity: Steps, Duration, Heart Points, Distance and Calories. The app is not only focused

on group-based interventions but also helps each team find the best activity cycles, there

are four options: Daily, Three days, Five days, and Weekly. The rationale for our design

decisions and the results of a usability study is explained in Chapter 4.

Web-based visualizations were necessary to support in-depth multifaceted explorations of

data for the FitAware program, particularly in situations when the single view of a mobile

device would not convey enough information. The research started with reviewing data

4

visualization related research papers and identifying the usability effectiveness of existing

research about promoting people to understand data. One of the examples given by Goetze,

the co-founder of Iodine, notes that when patients see the new design from digital tables into

color charts, they get a better sense of their health data[16]. The key to data visualization

is to better understand the data and extract information that other methods cannot[17].

Therefore, it is clear that more work is needed to develop a systematic and comprehensive

visualization for demonstrating the users’ physical activity data. As part of this thesis work

a web-based visual tool: FitAware web was developed to take advantage of larger screens to

demonstrate a combination of visualizations to help users perceive their individual and group

data from multiple angles and make reflections on group fitness data in order to increase

their physical activity. More information can be found in Chapter 5.

There are three major components: Android app, IOS app, and Web visualization. These

seek to motivate the users to increase physical activity based on their individual and group

data, in order to help them have a better understanding of themselves and the group’s phys-

ical activity data we applied a series of interactive visualizations. FitAware not only tracks

the users’ physical activity data but also provides clear visualizations of their individual and

group data. Furthermore, the apps can provide a social feature by sending notifications to

users’ teammates for encouraging them to increase physical activity. In order to attract as

many users as possible, both Andriod and IOS apps were developed due to the large shares

of both platforms (60.1% for IOS and 39.7% for Android in the US[18]), either one couldn’t

be ignored.

The visualizations on the FitAware apps are more focused on instant data such as the current

steps, heart points and short term data such as daily, weekly data. However, based on the

interviews with participants who experienced our redesigned FitAware apps, participants

reported that the limited information displayed on the home page, and it’s not easy to

5

use multiple page jumps when they tried to locate certain data or visualizations. A few

limitations of smartphone apps have been identified such as long term physical activity data

is hard to understand details, users highly rely on the information displayed on the home

page, and multiple page jumps increase users’ learning time of the apps. FitAware Web is

very important for the overall system that helps people understand their physical activity

data even more. Users can get a sense of their physical activity trends in the long term

such as weeks or months, and make reflections on group fitness data. It utilized multiple

angles such as group-based interventions, thru-hike challenges, environmental influence to

incite users to get more and more physical activity. So, instead of showing instant data,

FitAware Web focuses on the historical data which includes both short and long term data

and demonstrates those data by systemic and comprehensive interactive visualizations.

6

Chapter 2

Related Work

This chapter details the related work that is most relevant to this thesis. Section 2.1 describes

the importance of physical activity and the current fitness level of people, culminating in a

description of the FitEx program. Section 2.2 describes the need for better fitness, the role

of fitness programs like FitEx, and the role that fitness technologies can play in encouraging

participation in fitness programs. Section 2.3 talks about some popular fitness apps on the

market. Section 2.4 describes the importance of visualizations on understanding the data

and some approaches for designing the fitness-related visualizations.

2.1 Importance of Physical Activity

Physical activity can improve your health and reduce your risk of diabetes, cancer, and car-

diovascular disease. Physical activity has both immediate and long-term health benefits[19].

According to the Physical Activity Guidelines for Americans, adults need at least 150 min-

utes a week of moderate-intensity aerobic exercise, or 75 minutes of high-intensity Physical

Activity, or both[20]. But only about 23 percent of adults ages 18 to 64 meet both criteria,

according to a new report from the national center for health statistics (NCHS). Another 32

percent met one of the criteria but not both, and nearly 45 percent did not meet either[21].

These needs resulted in the development of the FitEx program at Virginia Tech and else-

where, a multi-university program that demonstrated the effectiveness of group-dynamics

7

based programs in advancing physical fitness[22, 23, 24]. This work examines ways that

small, tight-knit groups of people can encourage each other to pursue exercise goals in a

community fitness program. By leveraging small groups for which the people know each

other, there is improved accountability within the team. The team worked together, both

cooperatively and competitively, often with greater success than for other fitness programs.

FitEx is to compare the physical activity program developed using integrated research prac-

tice partnerships with the typical efficacy effectiveness dissemination pipeline model pro-

gram, such as Active Living Every Day, ALED[23]. ALED is a behavior change program

that helps people overcome their barriers to physical activity by providing a non-traditional

exercise program[25]. One key need for the FitEx program was a collection of tech-related

tools for automating the collection, dissemination, and understanding of fitness data, as

this was largely done by program workers in FitEx. This led to the creation of FitAware,

described in the next section.

2.2 FitAware and Related Work

Community-based interventions are based on the fact that individual behaviour is influenced

by interactions with a variety of social environments[26]. Based on the previous work done

by Esakia[3], in order to help people gain more physical activity, some community-based

interventions can be applied. Because the effectiveness of combining individual and group-

based behavioral strategies to encourage physical activity behavior and influence social norms

has been demonstrated[3]. Many interventions have been successful in using interpersonal

influence levels to improve physical activity behavior by utilizing social factors present in

small groups[10]. Some social apps are using this strategy to motivate people to do more

physical activity. People may not focus their daily steps but they do care much more on

8

their rank in their communities. One study of fitness apps suggests that the existence of

social facilitation features will have a positive impact on the success of fitness apps[12]. The

study collected the data of fitness apps from Mobilewalla[27], an independent app rating

agency collects, analyzes, and presents mobile app related data from App Store and Google

Play native app stores, and measure the success of the fitness apps by the number of active

users[28], the worthiness of the app or the referral value[29], and user’s favorable feeling

towards the app[30]. For example, Nike + Training and RunKeeper allow users to share

their performance and accomplishments in social networking and compare their performance

with their friends. Social recognition features allow people to satisfy their inner psychological

needs, can enhance individuals’ physical activity behavior and help individuals achieve fitness

goals[31]. In this thesis, it will introduce an integrated social recognition feature: Like feature

that encourages individuals to achieve more in small group competitions.

Unlike other smartphone apps, Esakia applied a multi component smartwatch centered sys-

tem to facilitate group based strategies for promoting physical activity within small socially

connected teams(less than 5 people). Esakia wanted to leverage the advantages of smart-

watches that users can perceive their information by a glance, and it’s not required to open

any app. It’s a very good approach to obtain instant information, users are able to see their

real-time data by raising their arm.

Esakia leveraged an eight-week statewide physical activity community intervention that re-

sulted in guidelines for system development and testing in an educational setting[3]. The

interviews, which Esakia conducted, showed that users started to learn about how many

steps they can complete in their daily activities (u1-2: “I couldn’t get out of the house in the

morning and into my office without 3000 steps, so I knew that that is how much it would

show up and start the day with”, u2-1: “To put a load of clothes in the washer I’m taking 35

steps going from upstairs to downstairs into the washer”)[3]. It’s very interesting, since most

9

people don’t really know and even notice those information. The interviews also indicated

that group-based interventions do motivate people to stay competitive. For example, one of

participant said that “Yeah so if I had like 10,000 steps and I’m still ranked third then it

made me want to get more and because I had already done a lot and it is still as third” and

another participant tried to stay higher in the rankings by walking more “I would always

try to be number one as much as I could so I would like to go on longer walks and you

know also the weather was changing it was kind of timed nicely to spring so you could do

more and more activity”[3]. Those interviews indicated that the tech-centered community

interventions result in positive reflections on people’s physical activity and provided good

references to accomplish this study.

In the past 30 years, Albert Carron has been recognized as the father of group dynamics

in physical activity. His research provides the basis for our understanding about group

cohesion and the implementation of group dynamics strategies in physical activity[10]. His

conceptual models are a good framework for designing strategies for different group factors,

but they serve only for enhancing group cohesion. No study attempted to directly measure

information about group goals, norms, communication, cooperation, or other group factors

other than the assessment of group cohesion. It is clear that more work is needed to develop

and understand the theory behind these interventions and to measure the group factors

considered necessary to change physical activity[10]. In one research about goal-setting,

they ran a 3 month field study with 28 individuals who had a weekly goal, the goal was

calculated by walking duration. Employing goals intervention has been shown to be an

effective strategy for changing physical behavior[11], based on the interview results that

they conducted at the end of the 3-month field study. As part of their study to develop

persuasive techniques to encourage people to stay active, they explored people’s behavior

changes to different goal-setting considerations, specifically the goal source(i.e., who set the

10

goal?) and the goal time frames(i.e., what time period goals should an individual have to

pick)[11]. In this thesis, it will study the four different time period fitness goals(Daily, 3

days, 5 days, and Weekly) modify users’ physical activity behaviors.

2.3 Other Fitness Apps

This section reviews some of the popular fitness apps and describes research studies that

have explored their effectiveness. For example,

Figure 2.1: Google Fit

Google Fit is a fitness tracking platform developed by Google for the Android operating

system Wear OS and Apple’s IOS. It is a single collection of APIs that merges data from

multiple applications and devices[32].

Fitbit, Inc. is an American company headquartered in San Francisco, California. The

company’s products include activity trackers and wirelessly enabled wearable technology

devices that measure the number of steps walked, heart rate, sleep quality, climbing steps

and other personal indicators related to fitness[33].

11

Nike training club is the product of Nike company. It can help you reach your fitness goals.

Keep fit with free workouts of strength, endurance, yoga and flexibility anytime, anywhere.

From weight training to full-equipment workouts, find personalized exercise advice just for

yourself[34].

We found that most apps have similar features, allowing users to track physical activity, set

fitness goals, and share progress on social media to promote changes in healthy behavior.

However, a study called “the dirty secret of wearables” notes, “these devices fail to drive

long-term sustained engagement for a majority of users.” Endeavour Partners Research found

“more than half of U.S. consumers who have owned a modern activity tracker no longer use

it. A third of U.S. consumers who have owned one stopped using the device within six

months of receiving it”[35]. All of these apps only focus on individual fitness and lack of

competition. To motivate the users using the apps, the developer only designs and allows

users to share their daily steps or records to their social media such as Facebook and Twitter.

One of the key factors for fitness and fitness apps is competition[36]. Based on the research

done by Centola and lead author Jingwen Zhang, They divided the participants into four

groups to test how different types of social networks affected their exercise levels. The four

groups were: individual competition, team support, team competition and control group.

They found that “competition motivated participants to exercise the most, with attendance

rates 90 percent higher in the competitive groups than in the control group. Team compe-

tition highly drove the students to work out than individual competition, with participants

in the former taking a mean of 38.5 classes a week and those in the latter taking 35.7” [37].

Fitbit is often regarded as the best fitness app on the market right now, as it not only

offers great user experience but also supports some degree of community-based interventions

and short term competition challenges (which coincide with the points mentioned in this

12

research). However, Fitbit and other fitness apps overlook the advantages of interactive

visualizations on helping people understand more about their physical activity data. Right

now, the tendency of app design is using minimal design elements and making it functional

in order to reduce the user learning time of apps. Nevertheless, there are still opportunities

to do more work on balancing minimal design and more visualizations. In this thesis, it will

introduce a series of interactive visualizations that help people understand more about their

physical activity data and without many complicated design elements.

2.4 Visualization Design

More and more people are using smart devices such as smartphones, smartwatches, wrist-

bands, and etc. to track their physical activity. There were 29.57 million active users of

Fitbit products by 2019, and Fitbit takes up around 30 percent of the fitness device mar-

ket share[38]. These fitness devices can collect data describing various aspects of human

behavior. However, understanding the daily increase in the amount of self-tracking data re-

trieved across multiple domains requires translating data points and trends into interactive

visualizations[39]. In research about data visualization to facilitate reflection in personal

informatics, they suggest that the reflection stage is an essential part of modeling and using

personal informatics systems[40, 41, 42] to facilitate the understanding of self-tracking data

that reflect daily habit patterns and make this data available for behavioral change[13]. Using

charts[43, 44, 45], notifications[46], narrative[47], and abstract art[48] have been suggested to

facilitate self-reflection. The study gave a list of examples of existing fitness apps to support

the effectiveness of making observations and insights obtained from interactive visualizations

of self-tracking data, they applied heuristic principles to evaluate the data interpretable at

a glance, the discovery of trends in multiple data streams. This thesis will testify a series of

13

interactive visualizations of users’ physical activity data on promoting users’ understanding

of individual and group data such as, enable users to see goal progress for multiple factors

and exact information about the most important goal at a glance(section 4.2.1), and the

dashboard of data visualization(Trends, Contributions, Comparisons)(section 5.1.1 & 5.1.2).

In the research about health-related data visualization in applications[17], they talked about

many different kinds of data visualization, and unitized the existing health apps on the

market as examples. Yingxin pointed out that although a lot of health apps offer the

features to monitor and visualize users’ health data, people don’t really know how to use

those features and they don’t understand the information on the app. Also, these apps often

contain too much obscure visualization which keeps users from understanding their data[49].

In the paper[17], they introduced two kinds of visualizations: quantitative and qualitative

visualization and made a deep comparison. The purpose of quantitative data visualization

is to let the user intuitively feel the value of the number, rather than to let the user mine the

information from the original data. For example: if the data is easy for the user to understand

and requires some degree of accuracy, such as real-time blood pressure measurements, it is

best to display it in numerical form. For more complex cases, such as time series data, it

is necessary to apply some charts or tables[17]. Qualitative data visualization refers to data

that cannot be measured by numbers, such as category information. It can be text or an

image. It can describe things or emotions in more detail than quantitative expressions[17].

These two kinds of visualization: quantitative and qualitative are very helpful for designing

the FitAware Web. FitAware Web is not only limited to demonstrate the form of numbers,

charts but also help users establish a deeper understanding of their physical activity such

as what 10,000 steps mean when hiking on the Appalachian trail, how much carbon dioxide

you can reduce by walking 10,000 steps, and etc.

Many visualizations have been proposed to guide the creation and analysis of visual systems,

14

but these models are not closely related to the question of how to evaluate these systems[50].

In one research about evaluating the visualizations, evaluation can be divided into three

levels: formative method, summative method, and exploratory method. The formative

approach is designed to guide designers on how to improve the system and answer the

question, “can I make it better?” The summative approach is designed to measure the

performance of a system and answer the question “is it correct?” The exploratory approach

is designed to answer the question “can I understand more?”[50]. For this research, formative

methods have been applied such as heuristic evaluations, cognitive walkthroughs, and expert

reviews which are “can I make it better?”. The evaluation of information visualization is

complex because to fully understand a tool, one must evaluate not only the visualization

itself but also the complex processes that the tool supports[51]. In one study about the

evaluation of information visualization[51], it described some very detailed approaches to

conduct an evaluation in information visualization and listed benefits and limitations of

each approach with the scenarios. Some of the evaluation questionnaires conducted in their

study [51] suggested a helpful reference for the expert reviews conducted in this research.

2.5 Environment Influence

In the past years, global warming has become a well-known issue on the planet, it’s the

slow rise in the average temperature of the earth’s atmosphere caused by large amounts of

greenhouse gas emissions[52]. Greenhouse gases include carbon dioxide, methane, nitrous

oxide and other gases[53]. Although carbon dioxide is not the most powerful greenhouse gas,

it contributes the most to global warming because it can be produced anywhere: a large

portion comes from the fossil fuels(coal, oil and natural gas) burning[53]. An important

measure to reduce carbon emissions from transport is to substitute more energy-efficient

15

options such as public transport, walking, and cycling for fossil fuel vehicles to meet the

same level of travel demand[54]. A recent study[14] shows that physical activity due to

increased biking and walking would have a profound impact on health while contributing to

State greenhouse gas reduction[55]. Another study about the factors that impact on people’s

travel behaviors indicated that environmental awareness now has become one of the most

affected factors. In the study, they conducted an empirical study that identify people’s travel

behavior by dividing people into three colors of travel behavior each with two dimensions of

motivation and behavior, Green travel behavior (both environmental motivation and travel

mode choice are green), Red travel behavior (neither environmental motivation nor travel

mode choice are green), Gray travel behavior (either environmental motivation or travel

mode choice is green)[14]. The result concluded that Green travel behavior was the largest

group(36.2%) based on 1236 samples of travel behaviors. In this thesis, it will narrow down

from the big picture of identifying people’s travel behavior to an interactive visualization

that focuses on motivating people to walk more through environmental awareness: a CO2

footprint visualization.

My work builds on the results of Carron, Estabrooks, Harden, and Esakia[3, 10, 23] by

leveraging the small group focused intervention model through the creation of interactive

mobile displays and web-based visualizations that make use of the connections within groups

and small size of the groups in conveying information. I identified, crafted, and implemented

displays and visualizations that leveraged these aspects of the user population to help people

become more engaged with their personal fitness goals.

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Chapter 3

Beyond FitAware?

3.1 Motivation

Physical activity is one of the most important components of individual and community

success in promoting health and preventing disease. The benefits of physical activity go

beyond maintaining a healthy weight and reduce the risk of many diseases that affect physical

and mental health, including coronary heart disease, stroke, high blood pressure, type 2

diabetes, metabolic syndrome, colon, breast and depression[56].

The prior research analyzed data from the national health and nutrition examination survey

(NHANES)[57], which collected health information from a sample of adults over the age of

18. The results showed that about 25 percent said they spent more than eight hours a day

sitting, and 44 percent said they did not engage in moderate to vigorous physical activity

each week. 11 percent said they spent more than eight hours a day sitting, with little physical

activity in their spare time. Only 3 percent said they sat for less than four hours a day and

were very active. The data NHANES collected, indicated that there are a large number of

US adults who are physically inactive and have high sitting time.

The neglect of physical activity in my community made me come up with an idea of develop-

ing a platform that can facilitate people for physical activity based on their individual and

social group behavioral strategies, creating competition and cooperation with fixed physical

17

activity cycles, in order to encourage physical activity and influence on social norms.

3.2 FitAware

The base FitAware goals are to support the objectives of the FitEx program, a group

dynamics-based, statewide physical activity intervention administered by Virginia Coop-

erative Extension public health practitioners (also referred to as “agents”)[23]. FitAware is

a smartwatch-centered system developed by Esakia that uses sensors to automatically track

physical activity and leverages the advantages of the watch form factor to facilitate both

group and individual level behavioral strategies via non-interruptive, glanceable, and fre-

quent feedback updates[3]. The initial version of FitAware designed by Esakia was mainly

focused on the smartwatch interface and had minimal designs on smartphones and the web

since the smartphone component was meant to exchange the data between the smartwatch

and the web server(see Figure 3.1).

Figure 3.1: FitEx Interfaces in FitAware

In Esakia’s research[3], an 8 weeks statewide physical activity community-based intervention

was conducted, culminating in guidelines for system development that have been tested

in educational settings on the previous FitAware system. He collected a number of great

feedback from the participants, which offered some great materials for me to keep working

on this study.

18

3.3 Beyond FitAware

Figure 3.2 is the initial design of FitAware on the smartwatch. The purpose of this smart-

watch application is to design a smartwatch user interface for quick glancing at the personal

and other teammates’ daily steps information, UI design was mainly focused on interpersonal

activities. There are four different color curves; the white curve stands for the user’s own

steps, the other three colors represent the rest of the team members’ steps, and the short

white curve is the overall team progress including the all four users steps. Time is always

displayed in the middle of the screen since time is the primary use for a watch. Those curves

are used for the visualization of the percentage of the daily steps (complete a full circle to

finish the goal), users are able to see how far they are from the daily goal using relative

comparison by concentric arcs, instead of showing the number of steps remaining.

Figure 3.2: Smartwatch User Interface

Several important research problems remained to be addressed in the previous version of

19

FitAware system. The previous system was focused on the smartwatch interface design,

however, some field studies about smartwatch usage show that most of the interactions

are quick glances or peek at the smartwatch, which were lower than 5 seconds. These

quick glances limit the number of information users can take in[58]. Meanwhile, the small

size screen on smartwatches limits the number of information that can be displayed on

it. Smartwatches are best for quick glances on information, like the current progress of

users’ steps, and rank. However, other than quick glanceable information, there is still a lot

of detailed information included in FitAware system, for example, not just showing users’

personal physical activity data but also showing the comparable visuals to their teammates’

data since this is a group-based intervention app, not a personal training app. Even though

the previous system includes a smartphone component, the design was outdated and more

important the original purpose of the app was to serve as a data exchange component

for the smartwatch interface. Because back to that time, smartwatches heavily relied on

smartphones. Therefore, we had to start by analyzing the users’ demands and run through

the whole UX lifecycle wheel. However, we were able to obtain the users’ feedback from

experiencing the previous FitAware system from Esakia’s study which helped us save a lot of

time and more time can be focused on analyzing the user demands. The redesigned FitAware

apps mainly focus on providing a series of well designed interactive visualizations to help

people perceive much more data, and also perceive their teammates’ data in order to drag

them into this group-based intervention. Those interactive visualizations are not limited to

show steps, rank but also things like showing each members’ step contributions of their team

goal in one activity cycle (shown by a pie chart in chapter 4.2.3), a social encouraging feature

by sending encouraging notifications to team members (4.2.5). Furthermore, we found that

the smartphone apps are still not enough to show all the information users wanted after we

ran through a one month period of user study. For example, one of the participants from our

study said that “It’s easy to compare yourself to your teammates on daily physical activity

20

data but not so much in 5 days or weekly periods. Even though I was able to see mine and my

teammates’ overall percentage to complete the goal in a 5 days activity period, I cannot see

the comparable visuals with others during that time”. There is another participant reported

that “I don’t like tapping 3 times to open a weekly graph, that seems too many”. After

we received those feedback, we tried to address those issues by improving the design, but

the limitation of the screen size on smartphones cannot be ignored. To avoid making the

home screen too crowded and cut down the unnecessary information, we had to make a few

multiple page jumps in our design. In order to show more information based on the users’

physical activity data, and move beyond the smartphone apps, a web-based visualization

tool is needed to solve the small screen limitations. Through the laptop or desktop size

screen, a combination of visualizations can be shown to promote users’ understanding of their

individual and group data from multiple angles in both short and long term. For example, on

the team dashboard page, (5.1.2) users are able to find out each members’ step contributions

of their team goal by a pie chart, meanwhile, they can perceive the breakdown of progress

across the group members on each day in an activity cycle; on CO2 footprint page(5.14),

instead of showing the number of steps they walked, a CO2 footprint visualization, which

shows how much CO2 emission users can reduce by walking substitute driving, was designed

to motivate users to increase their physical activity since environmental awareness has been

one of the most important factors of people choosing travel behavior[14]. Overall, FitAware

is a multi-platform fitness application on smartwatches, smartphones, and web-based. It

facilitates users to gain more physical activity by in-group competition and cooperation, and

incite users to stay competitive by demonstrating systematic and comprehensive interactive

visualizations with individual and group physical activity data in a certain activity cycle.

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Chapter 4

Redesign and Development of

FitAware Apps

This chapter describes the process of crafting two interactive smartphone FitAware apps.

The goal of creating those interactive apps is to support users in exploring their individual

and group physical activity data. Our approach is to use the Wheel usability engineering

method introduced by Hartson and Pyla [15]. This work was done as part of a class project.

In order to have a full and comprehensive redesign progress, a 4 person group was formed

with Jixiang Fan and Nicholas Gill who are both Computer Science master students, Aditya

Anil Mungad who is from Industrial and Systems Engineering, and myself. Our goal was to

redesign the mobile interface of the companion application for FitAware.

The Wheel process resulted in several novel changes. First, we crafted a main dashboard

that includes the fitness progress of both individuals in the team and the overall team. Each

team is able to pick a different time period as its goal, to match the frequency with which

they exercise. There are four options: Daily, Three days, Five days, and Weekly. The reason

we chose these four different periods is that we found that most of the apps in the market

focus on long-term fitness challenges, however, our survey shows that half of the users will

forgo a long-term challenge in a couple of days or a week, those four short period challenges

help people of different fitness level complete the challenge in case they lose heart. More

details will be explained in section 4.2.6.

22

Second, we also designed a teammates’ list that allows users to check their teammates’

progress. Users can join different teams and have competitions between the team members

and also the competition between each team. Based on the different period goal their team

picked�the notification system would notify all the team members about their current rank

in the group and team rank and how many steps needed to walk for completing the preset

goals at the end of the period. For example, if a team picked Three Days as their goal, all

the team members would receive the notification reports every three days. The purpose of

the designs is trying to motivate people to walk more steps, get more involved as a member

of a fitness team, and helping people find their best physical activity cycle such as Daily,

Three Days, Five Days, or Weekly.

In order to design a high-quality app, we employed the UX lifecycle wheel[15] and began

with analysis. We created a work affinity activity diagram (WAAD) by contextual inquiry

(CI) notes, the notes we used are the feedback from the previous FitAware study done by

Esakia, details can be found in section 4.1.1. In the design phase, we crafted user personas,

storyboards, and wireframes to address the draft designs, details refer to section 4.1.2. Then,

I developed the first prototype in Android Studio based on our wireframes. and after the

Android app was finished, my teammate, Jixaing developed the iOS version based on the

android version, more information elaborated in section 4.1.3. While we had two apps ready,

we started to conduct evaluations. Our evaluations include a project showcase with around 20

HCI or usability engineering background students by heuristic evaluation and think aloud

and a survey evaluation with 8 participants who used the app within one month, details

provided in section 4.1.4.

23

4.1 Methodology details

User experience design (UXD or UED) is the process of improving user satisfaction with a

product by improving the availability, accessibility, and fun that interaction with the product

provides[15]. We employed the process presented by Rex Hartson and Pardha Pyla in The

UX Book. A UX lifecycle wheel of the four elemental activities - Analyze, Design, Implement

and Evaluate - is being used.

Figure 4.1: UX lifecycle wheel

4.1.1 Phase I - Analysis

For the Fitaware project, we endeavor to contribute to an area of the project involving the

companion app. Smartwatch interface is small and can only be used to display 3-4 concise

information points about the user activity. On the other hand, a user requires his activity

data to be recorded in great detail. Only with the use of detailed charts and correlations,

an user can make the right decisions regarding his health goals. Hence, the development of

an useful companion app is essential to the success of the FitAware project. The application

needs to be compatible with the user’s choice of smartphone OS (iOS, Android, Web-Based

24

Platform) and hardware employed.

Taking into account these factors, a method of Contextual Inquiry was employed to investi-

gate and understand user’s activities in the context of their current work practice, using an

existing product.

Our endeavor to take on the redesign of the companion app comes in the second phase of

the FitAware Project. The first phase involved 27 participants across the state of Virginia,

USA testing out the individual and group-based dynamics of the designed experiment. They

used a Pebble Smartwatch to track their progress and a rudimentary companion app to

understand the data collected.

Primary sources of user work activity data are in-situ observations and interviews. Since we

did not have access to real users of the project, due to time and geographical restrictions, we

had to rely on secondary sources of work activity data. We sourced the user feedback from

Esakia’s dissertation that resulted from the first phase of the project[3].

Method of Contextual Analysis, second activity in the Analysis Phase, was employed to

convert raw contextual feedback into work activity notes. These work activity notes have

been transformed into a Work Activity Affinity Diagram (WAAD).

• Step I: Rearranging the Contextual Inquiry (CI) notes into rough groups

to paraphrase and synthesize work activity notes.

25

Figure 4.2: Rearranging

• Step II: Paraphrase and synthesize CI notes to form working activity notes

with concise points.

Figure 4.3: Paraphrase and Synthesize

• Step III: WAAD development from work activity notes.

26

The goal of the WAAD is to cluster work activity notes into groups which imbibe

a common theme and can be used to extract key user requirements - design ideas,

questions and data “holes” - for design and development.

Figure 4.4: WAAD Development

• Step IV: Extraction of User Requirements.

After the WAAD is formed, a walkthrough was done to extract key user requirements.

Issues pertaining to development of the back-end code were sidelined (with a “X”) as

they are not in the purview of our goal. Issues pertaining to features and elements

required by the users in the application were highlighted (with a “tick mark”).

Figure 4.5: Extraction of User Require-ments

27

Requirements extracted from WAAD:

• For an individual user: No. of steps and percentage of progress

• Ranking Within a Team

– Steps updated every 5 seconds

– Rank updated every 1 hour

• Tracking Teammates

– General view of all teammates’ progress

– Current steps compare to the goal

– Graph (Number of Steps vs time of the day)

• Notification:

– Updates in person rank within a team every 3 hours

– Updates in team rank

– Achievements, Records, Goal Achieved

– Reminder to walk is counter less than 50% of goal of the day

• Progress Reminders by Captains (default messages)

– Goal is not being met

– Encouragement message

• Personal History

– Daily/Weekly/Monthly tracking

– Graph/Counter

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• Personal Information

– Fitness data at initialization

– Demographic

– Group selection/Change

• Widget on lock screen

– Personal Steps

– Personal Rank

– Team Steps

– Team Rank

29

4.1.2 Phase II - Design

Supported with the groundwork from Phase I, we move on to the next phase, Design.

Starting with User Personas, which are fictional roles created based on contextual data, the

stakeholders in the project are used to define the target population in order to represent

different types of users and provide individual participation.

Creating roles can help us step out of ourselves. We can recognize that different people have

different needs and expectations, which can also help us identify our target users. Roles

make the design task at hand less complex, guide our thinking process, and help us achieve

our goal of creating a good user experience for the target group of users[59].

We focused on the individual and group-based aspects of the project, dividing it up between

casual and serious users. We practiced persona creation from Grudin Pruitt research on the

subject[59]. Personas are presented in Appendix D.

The scenarios created based on the personas are:

• Motivated individual

Mike just finished his classes and on his way to his lab. There are two paths from the

classroom to his lab. Path A is just half miles further than path B. Mike opened the

FitAware app and checked his weekly steps. He found that the current steps are far

from his weekly goal. So he decided to walk more to catch up on the pace and choose

path A.

30

Figure 4.6: Fitness focused user of the individual aspects

• Competitions between group members

Steve and Bruce are good friends. They both like doing sports in the gym, especially

the treadmill. One day, when Steve finished his regular fitness schedule and relaxed by

playing with his mobile phone. He was curious about how many steps Bruce exercised

this week. So, he opened FitAware. Bruce and he were in the same group. So he

quickly checked Bruce’s weekly step records and found Bruce was ranked first in a

group who has just 500 more steps than him. Steve thought 500 steps is not a heavy

work for him so he backed to the treadmill for an extra half exercise and then replaced

Bruce as the 1st ranked user in their group.

• Competitions between groups

Suppose we have two groups, the group members belong to two different fitness clubs.

Both of the clubs want to prove that they are better than the other clubs. FitAware

provides them a platform to compete with each other. The group leader checked the

steps every day and sent the notifications to the group members encouraging them to

hold the exercise or push ahead.

After creating user personas, ideation and brainstorming sessions were conducted by the

31

team. Coming from a creative paradigm, we drew few initial sketches of what the app could

look like without detailing too many elements. These rough sketches were quick to create

but helped us visualise what areas of a mobile screen should be allocated which information

points.

The previous app designed by Esakia, was rather bland and just gave four quadrants on the

home screen. It also used plain text, with no formatting. Using just numbers seemed to not

show how close or far away was the user from his/her goal. Hence, using circular seekbars to

present relative progress of the user and the group was an important result from sketching.

Figure 4.7: Sketch of the “Dashboard/home page”

From the scientific paradigm, we wanted to see how an user would use our product throughout

the day. We got together and Storyboard-ed a Mental Model starting with following the life

of one of the user persona and his interaction with another user persona.

32

“Mike, a student at Virginia Tech, attends classes in the morning. He walks to classrooms

and does an intense treadmill run in the evening. He meets up with his fitness instructor,

Rock. Rock has been monitoring Mike’s progress throughout the day (using the Group features

of the app). Rock reminds him of the goal set for the day and tells him to try to catch up.

Mike is still running at 11pm in the night trying to catch up (individual feature). Will he be

able to do it?...”

Figure 4.8: Storyboard for the Mental Model

From the engineering paradigm, Wireframes (Appendix E) were used to represent what user

interface elements can be incorporated in the prototype. Circular seekbars, number counters,

high contrast colors, ranking tables, emojis, motifs and other icons are located at critical

interaction points.

33

4.1.3 Phase III - Prototype

Based on our design phase, we believed that we already had a good design of the app. Also,

I had a rich experience with android development, so we decided to directly implement the

Android app in Android Studio rather than do some Marvel mockup. Then, we could get our

app in testing and some evaluation results sooner. In order to have a fully functional app,

we need to implement both front-end and back-end. First, building a database on Google

Firebase for storing all the user accounts and physical activity data we would collect later.

Then, developing an Android app that includes all the design elements we came up with in

the design phase and the Google fit API was implemented to calculate out the raw data of

the user’s physical activity. Next, all the raw data that Google fit API calculated would be

uploaded to my Firebase database and several functions on Firebase to generate the users’

steps, duration, heart points, calorie consumption and etc from the raw data. At last, the

app would run with a redesigned user interface and fetch the users’ physical activity data

from my Firebase database.

34

Figure 4.9: System Workflow

There were two challenges during the development. One was the front-end and back-end

sync problem. The app encountered large UI delays and sometimes the app crashed because

of the high memory usage on my first prototype. Since we wanted the app to be able to track

the users’ steps every 5 seconds in order to do “real-time” steps tracking, the high frequency

of data exchange crashed the app. So we had to lower the data updating frequency to every

15 seconds.

Another challenge is the android background activity. We aimed to track the users’ physical

activity data all the time, so we need to make sure the app is running in the background all

the time no matter the app is running, closed or killed. There was an API: Android Services

could do this in Android Studio, but Google made some changes and they ditched the Service

API to some degree which doesn’t work the way like before. So, some research and work

need to be done on a new API: the Work Manager. It turned out the Work Manager is a

much more efficient API after figured out how it works. However, the Work Manager can

only run in the background every 15 minutes, which means we could not even do every 15

seconds of step tracking when the app is closed. After a discussion we had, we decided to

remove the “real-time” steps tracking feature. We carefully thought few people would stare

at their number of steps on the phone while they were walking, so showing detailed and

systematic periodical reports could be better.

After the Android app was developed, Jixaing developed IOS app in Xcode. The Android

version fetching the step data from Google Fit and IOS version fetching data from Apple

Health. User data for both versions are stored and retrieved based on the architecture

supported by the Firebase back-end platform. In order to update the step data to the

Firebase, the Android version uses the Work Manager and IOS version to use the Core

35

Location and Core Motion to keep tracking the steps in the background. The working

principles are quite different. The Work Manager allows the system to wake up the app

and run the data upload function every 15 minutes. Core Location and Core Motion would

automatically upload the step data when the system detected users’ movement.

4.1.4 Phase IV - Evaluation

The first part evaluation methods we used are heuristic evaluation and think aloud during

our project showcase.

Heuristic evaluation is a way to find usability problems in user interface design by having

a group of evaluators examine the interface and determine whether it conforms to accepted

usability principles[60]. The evaluation was conducted with around 20 students who have

either HCI or usability engineering background, the evaluation was focused on identifying

the usability effectiveness of FitAware apps. We first let the participants explore both the

IOS version and the Android version each for 5 minutes. Discussing the usability designs.

For example, would the pie chart design motivate you to walk more steps? In our calendar

design, there are green colors and red colors, green means the user finished more than

50% steps, red means they did not reach 50% steps, would this design be meaningful for

participants? After, we would provide one or two other fitness apps such as Nike Training

Club, Google Fit and Apple health. Ask the participants to compare the FitAware with

these fitness apps. Tell us which features or designs they think are good/bad. If the function

was implemented in both apps, for example, displaying the steps, which design would be

preferred. Based on what theories or concepts they think it’s better. Finally, we would show

them the features/functions they have not found/explored, discussing with participants how

they miss these features and how to improve. We want to know which designs are good,

36

which are not. Moreover, how to improve these drawbacks. So, we could provide a better

user experience.

Thinking aloud method require users to explicitly state and say all of their inner thoughts

while performing a task. This enables the observer to understand the user’s mental model

of the task or problem. In addition, it shows the problem-solving techniques used by the

user[61].

Both of these can lead to design choices that will better take into consideration the user’s

needs. In our project, the main goal we use the think-aloud method is to explore and analyze

one important question “Do IOS and Android versions be consistent with each other”. If the

users switch their phones from iPhone to Samsung Galaxy and find the FitAware interfaces

are different, which causes them hard to find the data they want to view, then this is not a

good user experience.

Both of these can lead to design choices that will better take into consideration the user’s

needs. During the showcase, we firstly designed 10 tasks to let the tester finish such as

“Create a new team”. We observed the processing and recorded all the wrong steps that

testers have taken. And then we discussed with testers why they failed or what factors make

them confused.

Here are the feedback we collected:

• The calendar is hidden when you first log in, it’s not clear that the user would know

this function.

• Altering the views for daily and weekly steps.

• Teams instead team, which would make you confuse the page is about your own team

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details.

• Team steps need a clear name.

• Need a starting tutorial to let users familiar with the systems.

• A pie chart or limited steps for each user in the team goal steps. Otherwise, one can

finish all steps but others may walk zero steps.

• The profile should be a pop-up window instead of a new page.

• Low UI delay. Not keep refreshing every time you switch the page.

• Colors are not too significant.

• Worry about too much red circle in the calendar would cause the user to feel frustrated.

Survey evaluation:

For the second part of the evaluation, we formed two study groups with four users each (one

has a daily activity cycle, another one has a 5 days activity cycle), and asked them to use

the app for one month. After that, all participants will fill out a survey. The main goal of

this survey is to evaluate the user experience and have a better comprehension of our user

needs. To distinguish from other fitness apps and improve FitAware, we need to know what

features attract our users most and why they prefer it. In addition, we want to test how

often people check their physical activity data based on different activity cycles.

The survey results are attached in Appendix B. Based on the results, we found several

interesting results. The most important one is that most of the users open the app for

“Step” data(Figure 4.10). Based on this, what we could do is to highlight the step data.

We can do more research, for example, reading more articles from health organizations and

find the recommended daily steps. Moreover, When the user opens the FitAware app, we

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can have a pop-up screen and display like “Hi XXX, you have XXX steps. You are XXX

steps left to keep healthy”. Within this feature, it will definitely motivate the user to walk

more and ensure the user opens the app at least one time per day. The reason we want

the users to open the app at least one time per day is that we want all users to participate

in group competitions. One case could be a user always finishes more than 70% of the

team goals. However, all his teammates are inactive players. They do not engage in group

competitions. So every time, his group only ranked second. The user would feel disappointed

if this situation keeps happening for a long time and he would give up eventually. This is the

worst case and we should try to avoid it. This is why the survey evaluation is so important,

it helps us understand what the user demands. And we could highlight these features to

motivate the users.

Figure 4.10: Survey data I

Furthermore, we identified an unsatisfactory design from our survey. For the notification

drawer, nearly half of the users have never read the notification drawer(Figure 4.11). The

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reasons we thought of are first: maybe some users do not know the app has this function. We

believe that the user needs time to learn the app, but in the meantime, we could make the

design much better. We can create a starting guide for the FitAware new register users. They

could quickly explore all the features we provided. Secondly, we think the notification drawer

on mobile devices does not have as significant influences as the smartwatch system. Many

users prefer to open the app instead of reading the notification drawer. But our group still

thinks this is a very good feature for users. This is a very clear table for users to have a quick

look for their steps. What we could do is regularly send notifications like “Hey XXX, guess

how many steps you have walked today”. When the user scrolls down the screen, they would

definitely see the notification drawer. Our current confusion is which format for steps should

we display on the notification drawer. Should we provide a number of steps or percentages

or steps/goals? Would steps/goals would motivate users more and more meaningful in the

notification drawer? And how frequently should we send the notification? 5 minutes per the

notification is definitely annoying. Would 5 hours per notification be too long for users? We

cannot assert right now. We need to do more interviews and tests to find the best solution.

Figure 4.11: Survey data II

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4.2 Design Decisions

This section describes a series of design decisions on FitAware Apps. Section 4.2.1 describes

a glanceable visualization: circular SeekBars, integrated with users’ current physical activity

data and the percentage of their goal. Section 4.2.2 talks about a custom calendar view that

helps users perceive their everyday performance by showing green and red circles. A team pie

chart was described in section 4.2.3, it helps each team member learn their contributions to

their team and make self-reflections on it. Section 4.2.4 talks about a lock screen notification,

in case some users don’t prefer to open the app every time, then they are still able to check

some basic data such as steps and rank without opening the app. Section 4.2.5 describes the

Like feature, in which users are able to send encouraging notifications to their teammates,

and each notification users received will increment the number next to the little heart icon

showing on the rank board. Then, the last section 4.2.6 describes the reasons we designed

Daily, 3 days, 5 days, and Weekly challenges for the FitAware system.

4.2.1 SeekBar

When we first designed the FitAware App, we spent a lot of time (on CI notes, interviews)

to discuss what we want to show the users on the home screen, and what users can see when

they only spent 30 seconds to take a quick glance at the app. We wanted to design a UI

that is able to show as much information as possible, but not over-complicated and doesn’t

take a user too much time to check it. We also looked at the former version of FitAware

designed by Esakia. In his app, he used a traditional linear SeekBar to visualize the relations

between steps and goals. Linear SeekBars are good for indicating the one progress, but for

our project purpose, we want to include multiple attributes[39, 62]. Since we need to not

only include the steps but also the other attributes like duration (how long the user walked),

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heart points (how long the user takes it up a notch and get their hearts pumping harder),

distance, and calorie, we decided to use circular SeekBars.

Figure 4.12: Linear SeekBar vs CircularSeekBar

The multiple circular SeekBars with different colors will give a better experience while the

users give a quick glance[39, 62]. Meanwhile, from the previous interviews and study[62], the

feedback shows that users think the circular SeekBars have better look and giving him the

impulse to complete the circle to 100 percent. Although we do realize that this design might

confuse users a little bit and will take time for users to get used to, we think the overall

experience with circular SeekBars is better than linear SeekBar.

4.2.2 Calendar

We also decided to include a calendar on the home page. As you can see below, there are

green and red circles on each date, green means the user’s steps have reached more than 50

percent of their goal, the red means they didn’t reach their daily goal. The dates without

either circle mean there is no data on that day, the reason could be they never open the app

on that day. In order to minimize the home-screen, we added a drop-down button, allowing

the users to either display the calendar or hide it. Also, this decision is possible to confuse

certain users, but we still decided to go this way after thinking critically about the drawbacks

and advantages and comparing the similar strategies on other apps available on the market.

42

Figure 4.13: Calendar

4.2.3 Pie Chart

One of the feedback we got from the showcase is how we can show the individual contributions

to the team. If one of the team members always finished the team goal for the whole team,

there was no team cooperation but just a single person carrying the whole team. Thus, in

order to motivate every member to make a contribution to the team goal equally, we added a

pie chart that visualizes every team member’s percentage of their steps when the user clicks

the arrow button on the default home page. Before we made the decision to use a pie chart,

we compared it with other popular graph techniques such as histograms. But we believed

that the pie chart was more trivial and easy to read.

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Figure 4.14: Team Pie Chart

4.2.4 Lock Screen Notification

When we first tested the app inside the group in the spring semester of 2019, we found

that it’s hard for us to keep opening the app and check the steps every day. Therefore, we

decided to add a notification that always shows up on the notification drawer. Instead of

opening the app, users are able to see those data by just swipe down the notification drawer.

We believe that this notification can show what the majority of users care about the most.

For wilder lovers such as thru-hikers, it provides better convenience to them to take a quick

glance rather than open the app, see the data, and close the app. But this does not mean

these four columns can substitute the app because users can derive more precise information

when they get back home from the wild and nature.

Figure 4.15: Lock Screen Notification

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4.2.5 Like Feature

In order to improve the interaction between the team members, we included a like button

in the team members’ rank page. Anyone who is in the team is able to view the team

member’s daily steps’ rank, if they really like some teammates’ progress or the contributions

they have made to their team, they can simply click the little heart button (Figure 3.13) to

acknowledge their works. And the teammates would receive a notification of who just liked

their progress. We think it can facilitate peer recognition and motivate the team members

who received like notifications to do more physical activities.

Figure 4.16: Like Feature

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4.2.6 Daily, 3 days, 5 days, and Weekly challenges

During the process we reviewed some fitness apps such as Fitbit and Nike Training Clubs,

we also found that the fitness section on the app store is filled with all kinds of 30 days

challenges of physical activity apps. From our perspective, the long-term challenge benefits

our body more, in other words, it forces the users to keep using the app until they finish

the challenge. However, does every user need a long-term challenge, even for a beginner?

Can everyone finish the long-term challenge? Based on our survey, the results are polarizing.

Half of the users would give up in the first several days/first week. Others can persist until

they close to or finish the challenge. Considering FitAware is a step-tracking fitness app, we

designed to provide 4 different time periods challenge-daily, 3 days, 5 days, and weekly.

• Daily challenge: The reason we chose daily is that when people get back home from

the office or school each day (this is what most people do every day), they might just

want to look at their daily reports and know how many steps they have walked for a

daily basis. And we believe this is what most people are willing to challenge themselves.

Also, we tried many other popular fitness apps such as Wechat Sport and Fitbit, they

all used the daily report and show the daily ranking.

• 3 days challenge: The 3 days challenge is considered for any short term outdoor

activities such as climbing, long trail hiking, that could take one to three days. Instead

of receiving a report every day and sum the daily steps for the duration of the trip,

now the user is able to check the overall report during the short term activity until it

ends which is an advantage of 3 days challenge.

• 5 days challenge: The 5 days challenge is designed for a group of people who basically

don’t have many physical activities during the weekend or they don’t want to do their

physical activities during the weekend as competitions. So, their competitions would

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be focused on the five workdays.

• Weekly challenge: The weekly challenge is a natural way to check physical activity

data. For some beginners who don’t necessarily know which challenge is suited for

them, Weekly challenge is a great option to start with. And some users who rarely

check their progress reports, but still care how many steps they have walked in a weekly

period.

4.3 Contributions

Redesigning FitAware system and addressing the limitations of the previous system. Crafting

two interactive FitAware apps both Android and IOS versions by using the Wheel usability

engineering method introduced by Hartson and Pyla [15]. Conducting interviews to identify

the usability problems on FitAware apps with around 20 HCI or usability engineering back-

ground students by heuristic evaluation and thinking aloud. Conducting a one month period

user study with 8 participants on FitAware apps and collecting survey feedback from them.

Illustrating that the goal setting and social recognition interventions had positive impacts

on changing people’s physical behaviors.

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Chapter 5

Visualization of FitAware Web

This chapter describes the creation of interactive web visualizations that support the in-

depth exploration of FitEx and FitAware users’ individual and group data. FitAware Web

helps users get more sense of their physical activity trend in the long term such as weeks

or months, and makes reflections on group fitness data. It utilized multiple angles such as

group-based interventions, thru-hike challenges, environmental influence to incite users to

get more and more physical activity.

The redesigned FitAware apps are able to address most of the limitations that the previous

smartwatch-centered system existed, for example, more information can be displayed on

smartphone screens, more interactive visualizations can be provided, etc. However, based on

our interviews with the participants we conducted for the redesigned FitAware apps, users

still want to perceive more information from their individual and group physical activity

data. After receiving the feedback from FitAware apps and reviewing a few studies about

data visualizations, a dashboard with comprehensive physical activity data visualizations

has been started to develop. One study[16] demonstrated the impact of data visualization

as a complete and accurate representation of health data. Goetze who is the co-founder of

Iodine and author of “The Decision Tree: Taking Control of Your Health in the New Era

of Personalized Medicine” ran a project to redesign lab test results from digital tables into

color charts. He notes that when patients see the new design, they get a better sense of their

health[16].

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The key to data visualization is to better understand the data and extract information that

other methods cannot[17]. Before starting to design the FitAware Web, research about dif-

ferent visualization tools such as the D3 library, the Apexcharts library, and other JavaScript

libraries have been conducted in order to find some helpful dynamic visualizations that can

promote understanding of individual and group data and makes those data available for be-

havior change. In addition, a variety of traditional data charts like bar charts, linear charts,

pie charts, and etc. have been studied to understand their advantages and disadvantages for

later implementations. The visualization of information determines how data is presented

to people, which may be their first impression of a complex data set. This impression has a

profound effect on people’s understanding of data[63]. The purpose of those visualizations

is to help users quickly understand their current performance, historical physical activity

trends, and stay engaged in team competition or cooperation. Due to the limited interactive

visualizations that can be displayed on smartphones, a comprehensive web-based dashboard

with all the data we collected from the mobile devices will be implemented. The purpose of

this web visualization tool is to take advantage of large screens to demonstrate a combination

of visualizations to help users perceive their individual and group data from multiple angles

and make reflections on group fitness data in order to increase their physical activity.

In section 5.1, it describes all the design elements have been applied in FitAware web and how

those design decisions were made. After the implement ion of the FitAware web, a couple

of expert reviews have been conducted to identify the usability problems and strengths of

the FitAware visualizations, the approaches are described in section 5.2, detailed feedback

collected from experts can be found in Appendix G.

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5.1 Design Decisions

There are four primary web pages on the FitAware Web, including a personal dashboard, a

team dashboard, a geographic map, and a carbon footprint. We discuss the design of each

in this section.

5.1.1 Personal Dashboard

The personal dashboard page contained four traditional charts. It aims to examine how those

charts help users understand more about their physical activity data and make reflections.

Are they able to learn their physical activity trend? Can they understand their heart rate

heatmap and make improvements for the next activity cycle? Or they just ignore everything

they saw on this page? The first chart is a triple circular bar chart that inherits from the

mobile app design element(Figure 4.12) with three physical activity attributes: duration,

steps, and heart points, they can show the percentage of the current progress of the goal

that users pre-set. it’s easier for the users who had experience with the FitAware mobile

application to understand this consistent design element crossed multiple platforms, it could

help them have a more smooth transition to FitAware Web. However, the circular bar

chart can only show the percentage, there are three other charts for showing more detailed

information on each attribute.

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Figure 5.1: Personal Dashboard

A line chart was applied to demonstrate users’ walking duration in their activity cycle(daily,

3 days, 5 days or weekly. the Figure 5. shows an example of a weekly activity cycle), a line

chart is a good way to show trends in chronological order, clearly show the relationship to

continuous periodic data, and visualize data trends at a glance[64].

The bar chart was used to show the users’ walking steps in their activity cycle. Bar chart is

a very effective visual effect in data reports because it’s easier for users to identify patterns

or trends rather than to look at digital tables[64].

In order to demonstrate a more comprehensive overview of how users are really staying

active based on their heart points, a heat map was applied. Since heat maps are more visual

than standard reports, which is easier to understand the data at a glance. This makes them

easier to access, especially for those who are not used to analyzing large amounts of data.

Furthermore, heatmaps can give a more comprehensive overview of how users are actually

behaving[65].

5.1.2 Team Dashboard

On the team dashboard page, there are five different visualizations. They sever for different

purposes but focus on two principles: competition and cooperation. It tries to figure out

which attributes users care the most such as rank, contributions, how many steps, a goal

completed/incompleted, and how to emphasize those key points by simple and intuitive

visuals. The first one is a pie chart that also inherits from the FitAware mobile app, that

gives a quick view of the percentage shares of each team member and how they performed

in their activity cycle.

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Figure 5.2: Team Dashboard

The second visualization is a radar chart that shows the difference between each team mem-

ber by the axis on every single day in their activity cycle. It’s a visual method that can

help users acknowledge whether they or their teammates are morning or afternoon walkers

when they are in Daily activity cycle, or whether they or their teammates are Monday or

Wednesday walkers when they are in Weekly activity cycle (Figure 5. shows the Weekly

example). The story behind this visualization is that one night around one am before I went

to sleep, I checked my progress on the FitAware app, I noticed that one of my teammates

had already reached three thousand steps. I was surprised and I even thought maybe the

app didn’t zero correctly. However, I found that everyone else on my team showed 0 or less

than one hundred steps. On the second day, I asked my teammate, he said he was walking

the dog at midnight. Also, radar charts are especially good for visualizing comparisons of

quality data, multiple attributes can be compared along their axis, and over differences are

apparent by the size and shape of the polygons[66].

Next, a horizontal stacked bar chart was designed to show the changes in each team member’s

steps contribution over time. Users can perceive who made the maximal or minimal contri-

butions in their team over time and the overall team growth over time. Then, a bar chart

was applied with all the team members’ steps data. It’s able to clearly compare the daily

performance between different members at a glance, and the trends of steps over time[64].

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Last but not least, bar chart race animation was developed, which demonstrates an inner

team competition, by D3 APIs, it included all team members’ steps data within one activity

cycle. There are two components, timeline and dynamic bar chart. When the timeline

moving from right to left means from the first day to the last in an activity cycle, the bar

chart would update the users’ steps data simultaneously. Users are able to perceive the

dynamic changes of each team member’s steps and the rank shifting over time by viewing

this visualization. If they want to look at the performance of a specific day, they can also

adjust the “thumb” on the timeline manually.

Figure 5.3: Bar Chart Race Animation

Not only including the quantitative visualizations, such as numerical data and its graphical

representation in the form of numbers or charts but also including some qualitative visu-

alizations which cannot be measured by numbers to motivate users to keep active in other

angles such as hiking and environment concerns.

5.1.3 Geographic Map

In order to tell what a thousand or ten thousand steps really mean in a qualitative visual-

ization, a geographic map was designed to show the equivalent walking distance in a certain

hiking trail by MapBox API. For example, there was one team from our previous study who

are some trail hiker enthusiasts. They really want to hike through the Appalachian Trail or

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Pacific Crest Trail one day, but those trails are very long, could take five months to hike

through. However, this geographic map can transfer users’ daily, weekly, or monthly walk-

ing distance to the equivalent walking distance on Appalachian Trail, Pacific Crest Trail, or

others. For people who plan to hike through those long trails in the future, this geographic

map can help them get a basic idea of how many steps they need at least to finish the trail

virtually, and things will get even hard in the real world. The purpose of this geographic

map visualization on the FitAware web is to guide the users to understand what those steps

really mean in one factor: hiking. For hikers, this could be a virtual method to measure

themselves, for other users, it could be an enjoyment when completing a national trail in

virtual.

Figure 5.4: Geographic Map

5.1.4 Carbon Dioxide Footprint

With environmental issues getting more and more attention, global warming is a hot topic.

One of the main causes of global warming is that we produce too many greenhouse gases,

which contribute to the greenhouse effect. Greenhouse gases include carbon dioxide, methane,

nitrous oxide and other gases[53]. Carbon dioxide emitted mainly from fossil fuels: coal, oil

and natural gas. Although carbon dioxide is not the most powerful greenhouse gas, it con-

tributes the most to global warming because it can be produced anywhere[53]. One of the

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large carbon dioxide emissions is cars. The FitAware app aimed to motivate people to walk

more which also means they can reduce the use of their cars in order to reduce carbon dioxide

emissions. So, a visualization was designed to indicate how much carbon dioxide people can

reduce by walking. It tries to test out whether or not people would walk more steps and

reduce the use of their cars in order to reduce CO2 emission. This a very ideal approach

to ask people to gain more steps. Nevertheless, it is an entirely different approach to view

physical activity and tells users you are saving the environment.

Figure 5.5: Carbon Footprint

However, it would not make sense to most users if the app only provides the number of

pounds of carbon dioxide they have reduced by walking. A study[67] shows that trees also

help remove carbon dioxide from the atmosphere during photosynthesis, and return oxygen

to the atmosphere as a by-product[67]. In order to show more qualitative information to

users, the number of pictures of trees was applied to present carbon dioxide reduction by

calculating how many trees are needed to absorb the carbon dioxide in a certain time that

is equivalent to the users reduced by walking(See Figure 5.5).

According to Motor Trend, average fuel economy in the United States will rise to 24.7 miles

per gallon by 2018[68], and a gallon of gasoline burns, it produces about 20 pounds of carbon

dioxide[69], which means people can reduce 20 pounds carbon dioxide by walking 24.7 miles,

or 50000 steps (over 2,000 steps to walk one mile[70]). One tree at about 10 years can absorb

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4 pounds of carbon dioxide per month[67]. Based on the data and calculation, reducing 1

pound carbon dioxide needs to walk 2500 steps. If people wish to save 4 pounds of carbon

dioxide, which is the amount of carbon dioxide a tree can absorb in one month, they need to

walk 10,000 steps. 10,000 steps are also the recommended steps per day for our users. Which

means if users are able to complete the 10,000 steps in a day, a tree icon will be added to their

dashboard, indicating that they have reduced 4 pounds of carbon dioxide, that amount is

equivalent to a tree needs to absorb in a month, the tree icons will be accumulated monthly.

5.2 Evaluation of the Visualizations

Due to COVID-19, people are quarantined at home. Instead of performing an empirical

evaluation or conducting interviews face to face, a remote analytic evaluation was conducted

with a small group of expert reviewers[71] through a series of questions about the FitAware

Web. The questions focused on identifying the usability problems and strengths of the

FitAware web visualizations.

The analytic evaluation presented an image or video for each key component of the visualiza-

tion, with a series of questions for each expert to answer. There are two kinds of questions:

multiple-choice questions and open-ended questions. The multiple-choice questions aimed

to help experts go through each page and understand the focus of the visualization on each

page. The open-ended questions collect detailed feedback and recommendations from each

expert. The experts not only need an in-depth understanding of usability best practices,

but also need extensive past experience with usability studies, and are not involved in cre-

ating the design to be reviewed[71]. Therefore, three HCI experts with strong experience

and knowledge of usability principles and some knowledge about fitness and exercise were

reached to test the visualizations, One was a former collegiate athlete who knows about

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competition and training; he has an HCI-related Master degree and is working toward an

HCI-related Ph.D. Another has worked in usability and has built outdoor apps. The last

expert is a collegiate assistant professor who has a degree in Human Nutrition, Foods, and

Exercise (along with one in CS with an HCI focus) and familiarity with FitEx and FitAware.

The goal of the evaluation was to ensure a reasonable level of usability and to probe whether

the visualizations would be compelling and interesting to users. A list of strengths and

usability problems of each visualization was identified by the expert reviews. For each

usability problem, a clear explanation was asked to provide in order to address all the

potential problems and prevent the same mistakes from happening elsewhere as well. The

techniques applied here was introduced by Nielsen’s UX expert reviews[71].

The feedback from the experts is very appreciated, as they have provided detailed explana-

tions on the usability problems they pointed out, some with great recommendations. Two

experts got all questions right, one expert had some misunderstanding of the map visual-

ization. There is a long gray line showing the whole Pacific Crest Trail and a short blue

line over the gray line showing the progress the user has made on the map visualization.

However, he misunderstood it, thinking the gray one is the progress the user made. The

reason behind it is that there isn’t any direction signs included on the map to help users

understand the visualization. This design flaw was addressed by adding a direction sign,

and user status labels[72].

All three experts had some positives on the team dashboard. They like the combination of

multiple charts applied on this page. One of the experts thinks that “a combination of ‘team

horizontal stacked bar chart’ and ‘team bar chart’ does a great job conveying the breakdown

of progress across the group members.” Also, I was glad to see that one expert pointed out

that everyone did poorly on Tuesday by reading the radar chart. He wonders if the weather

was bad on that day or whether the entire team is a group of coworkers that happened to

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be working on a group project on that day. That’s a very impressive point he learned from

the radar chart.

The three experts all think the CO2 footprint visualization is very interesting. One expert

said that he was surprised that a large number of trees are freed up to absorb CO2 when

people walk 250,000 steps. Another expert thinks seeing more trees gives an easy way to

know who is doing more without seeing the numbers. This is what aimed at this visualiza-

tion, showing a qualitative visualization to users on their physical activity data rather than

numbers. However, the last expert mentioned that it didn’t meet his expectations because

there is a design flaw that nowhere indicated the number of trees on the visualization (as

all three experts mentioned) and the increment of each row contained 13 trees (he expected

ten or some other common increment).

Based on the feedback, many usability problems were addressed on the FitAware Web. The

expert reviews identified a few minor issues that I never noticed. However, only conducting

expert reviews are not enough for the system, since some issues that experts may not think

about, for example, the real audience has very specific knowledge or needs[71]. A large group

of user testing is needed for more deep evaluation.

5.3 Contributions

Implementing a working interactive web system that supports the in-depth exploration of

FitAware users’ individual and group physical activity data. It helps users get more sense

of their physical activity trend in the long term, making reflections on group fitness data.

Testing the usability and effectiveness of the visualizations by multiple angles such as group-

based interventions, thru-hike challenges, environmental influence to incite users to get more

and more physical activity. Identifying the usability problems and strengths of FitAware web

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visualizations by a small group of expert reviews with three HCI background experts. Sug-

gesting that interactive visualizations successfully promoted people’s understanding of their

individual and group physical activity data and the potential effectiveness of environmental

factors on motivating people to do more physical activity.

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Chapter 6

Discussion

Lack of physical activity is a major problem of diseases and poor health[73], Esakia in-

troduced a community-based intervention to help people gain more physical activity by a

smartwatch centered system in his research[3]. Based on the interviews conducted by Esakia,

the system showed some very positive influence on motivating the users to stay competitive

in groups and gain more physical activity[3]. However, the small screen on smartwatches

limited the amount of information that can be shown[58], and the lack of interactive visualiza-

tion on the previous system left spaces for further research. Therefore, our group redesigned

the mobile apps of FitAware with a series of interactive visualizations in order to promote

users’ understanding of individual and group data and make those visualizations available for

behavior change. A web-based dashboard has also been developed for demonstrating users’

individual and group physical activity data by a systematic and comprehensive visualization

in a certain activity cycle.

Goal setting has proven to be an effective strategy for changing physical behaviors[11]. A

study[11] illustrates that the two important factors on how goal setting changes people’s

physical behaviors are the goal source(i.e., who set the goal?) and the goal time frames(i.e.,

what time period goals should an individual have to pick)[11]. In this thesis, we compared

two study groups one with a daily goal and another one with 5 days goal, each group has 5

people. Based on the result, the daily goal group always made more steps(around 3000 to

5000 steps) than the 5 days group in a 5 days time frame within a total of 6 cycles. It shows

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that goal time frames have a noticeable impact on changing people’s physical behaviors.

Social recognition in fitness apps enables people to recognize individual or group achieve-

ments in physical activities, thus encouraging them to achieve more[12]. Based on the survey

result(Figure 6.1) from 4 weeks of user study, five out of eight participants voted Strongly

Agree on ”Like feature” motivating them to do more physical activity. Users who received

the notifications that liked their steps progress from their teammates would check their steps

rank board more often than usual. One of the participants said, “Before I received a message

from my teammate I know I am in a competition, whatever I do, I am in a competition and

that’s all I know. However, when I received that message my teammate liked my progress

made to the team, I realized that they care what I did to the team, I better not to be the

slacker in the team.” Those feedback from participants indicates that the intervention to

help people gain more physical activity by FitAware has been well performed.

Figure 6.1: Survey data III

The study [39] has examined a couple factors on promoting users’ understanding in order

to modify users’ behavior by heuristics principles[60]. They illustrated that single activ-

ity dashboard dials or circular seekbars have simplified visualization of complex patterns

reflecting goal-oriented accomplishments, and evaluated the data trends in multiple data

streams which was inspired by financial analysis charts. In this research, there are many

61

interactive visualizations that have been applied to modify users’ behavior by promoting

understanding of individual and group data that reflects daily habit patterns. We have con-

ducted the analytic evaluations[60, 61, 71] with HCI and usability engineering background

students, and HCI experts to identify the usability issues on our system. The newly designed

system has enabled users to glance goal progress for multiple factors and exact information

about the most important goal by circular seekbar[39, 62](section 4.2.1), enabled group views

of data visualization(dashboards) with perceiving physical activity trends, team members’

contributions and comparisons[10, 43, 44, 45](section 5.1.2).

There is a study tested five factors on choosing green travel behavior, environmental aware-

ness is the most affected factor according to participants’ cumulative contributions[14]. This

research also tried to facilitate people to choose green travel behavior like walking by vi-

sualizing the number of trees that could absorb the same amount of CO2 they reduced by

walking in a group-based view. Due to the circumstance affected by the COVID-19, we could

not run an outdoor study. Instead, expert reviews[71] have been conducted to evaluate the

usability effectiveness of the CO2 footprint visualization. Based on the expert reviews, they

were all able to understand the purpose of this visualization. However, there were a couple

of usability issues such as uncommon increment on each row, and a numeric indicator for

the freed-up trees. More importantly, they suggested that seeing more trees does give an

easy way to know who is doing more physical activity without seeing the numbers.

Additionally, a few great feedback was provided by three HCI experts after they evaluated

the FitAware Web. They have helped me uncover many usability issues and provided a

couple of useful recommendations based on their knowledge. One of the experts suggested

using headings, labels provided visibility of system status[72]. These solved a lot of problems

when users misunderstood some of the visualizations.

After we walked through the whole process of crafting the new version Fitaware system,

62

we are able to tell that the group-based intervention and interactive visualization can help

people increase their physical activity. FitAware system also promotes users’ understanding

of their individual and group physical activity data that reflects habit patterns and makes

those data available for behavior change.

Overall, FitAware apps and FitAware web supplement each other. The vast majority of

Americans (81%) now own a smartphone[74], it has become people’s daily driver and for

some are the most often used device every day. Meanwhile, since people carry around their

smartphones all the time, FitAware apps are able to the advantages of pushing notifications

with users’ fitness reports, showing them instant data, offering interactive engagement such

as the like feature mentioned in section 4.2.5. On the other hand, FitAware web can provide

some features that are limited on smartphones. For example, a series of weekly or monthly

interactive visualizations can be displayed on a large screen, it offers a much more compre-

hensive visual. It helps users easily identify their long term physical activity trend from the

big picture, and also dig into some daily details.

Although there is some positive feedback about the new FitAware system on motivating users

to do more physical activity and promoting their understanding of individual and group data

based on our 4 weeks user study and the evaluations, the results are still limited due to the

small study group with only eight participants and three HCI experts. Those data can not

explain the average users’ behaviors. However, with all the tools provided by the FitAware

system, a larger study can be run now.

63

Chapter 7

Conclusions and Future Work

7.1 Conclusions

We have retraced our steps, numerous times, in the cyclic nature of UX design in order to

complete a fully functional product grounded in computer science and HCI research methods.

Based on the evaluation results, we believe FitAware now provides a multifaceted system

to motivate users to do more physical activities and understand their progress toward their

goals.

In this thesis, we indicated the problems that most health apps are facing currently and my

solutions for solving those problems. In this project, my team and I redesigned the FitAware

app and applied the design to both the Android and IOS platforms. Our project follows

the step from the user experience design lifecycle process introduced by Rex Hartson and

Pardha Pyla in The UX Book[15]. In each phase, we applied multiple HCI methods such as

Persona, Scenarios, Heuristic Evaluation, and Problem Solving to implement the application.

In addition, a FitAware Web visualization was designed, in order to help people have a better

understanding of their physical activity data and make reflections on group fitness data.

64

7.2 Future Work

Conducting larger group user studies

In FitAware, it provides users 4 different time periods challenges, which is daily, 3 days, 5

days, and the weekly challenge. we still need to conduct at least eight weeks of study with

a large testing group. Assigned users into different time period groups. After one month

of testing, then switch each group into a different time period challenge, over and over.

Compared different time periods challenge results. Based on the percentages of the process

for each challenge cycle, analyze how different time period challenges would motivate users.

Right now, there is not too much research focus on this field. we want to explore more on

the relationship between time period challenges and physical activities.

Exploring more social features

A study suggests that one of the design considerations for outdoor technology should support

social interaction[75]. Support social interaction can be through co-located experiences or

capturing and sharing that information for others to see and interact with. The chat system

would allow group members to communicate with each other. Motivate each other and push

everyone to finish the group challenge. Like function motivates for individual users but the

chat system can be more suitable for a group-based project.

Deploying the full FitAware system

Due to COVID-19, we only conducted the user study on FitAware apps. However, FitAware

system is a multi-platform fitness system that offers smartwatch, smartphone, and web-based

user experience. Future research on group-based interventions for fitness could explore the

capabilities of the whole FitAware system to future improve user comprehension and reaction

on their physical activity data.

Exploring Other Interactive Visualizations

65

The implementations of interactive visualizations on the FitAware system are still in the very

early stage, there are some limitations identified by the several usability experts. Iterating

a couple of the UX lifecycle wheel could improve the usability effectiveness and explore the

new capabilities of interactive visualizations to further improve user comprehension.

66

Bibliography

[1] R. J. Shephard. Aging, physical activity, and health. Human Kinetics Publishers,

19971805726, 1997.

[2] Pedro C Hallal, Lars Bo Andersen, Fiona C Bull, Regina Guthold, William Haskell, Ulf

Ekelund, and et al. Global physical activity levels: surveillance progress, pitfalls, and

prospects. The Lancet, 2012.

[3] Andrey Esakia, D. Scott McCrickard, Samantha M. Harden, and Michael A. Horning.

Fitaware: Promoting group fitness awareness through glanceable smartwatches. 2018.

[4] M. L. Smith, N. K. Durrett, M. Bowie, A. Berg, B. A. Mccullick, A. C. Lopilato, and

D. Murray. Individual and group-based engagement in an online physical activity moni-

toring program in Georgia. Preventing Chronic Disease, 15. doi:10.5888/pcd15.170223,

2018.

[5] J Graham Thomas and Dale S Bond. Review of innovations in digital health technology

to promote weight control. Current diabetes reports, 14(5):485, 2014.

[6] Lora E Burke, Jing Wang, and Mary Ann Sevick. Self-monitoring in weight loss:

a systematic review of the literature. Journal of the American Dietetic Association,

111(1):92–102, 2011.

[7] Susan Michie, Charles Abraham, Craig Whittington, John McAteer, and Sunjai Gupta.

Effective techniques in healthy eating and physical activity interventions: a metaregres-

sion. Health Psychology, 28(6):690, 2009, 2009.

67

[8] Anna Khaylis, Themis Yiaslas, Jessica Bergstrom, and Cheryl Gore-Felton. A re-

view of efficacious technology-based weight-loss interventions: five key components.

Telemedicine and e-Health, 16(9):931–938, 2010.

[9] Deborah F Tate, Rena R Wing, and Richard A Winett. Using internet technology to

deliver a behavioral weight loss program. Jama, 285(9):1172–1177, 2001.

[10] Paul A. Estabrooks, Samantha M. Harden, and Shauna M. Burke. Group dynamics in

physical activity promotion: what works? Social and Personality Psychology Compass,

6(1):18–40, 2012., 2012.

[11] Consolvo S., Klasnja P., Mcdonald D. W., and Landay J. A. Goal-setting con-

siderations for persuasive technologies that encourage physical activity. Proceedings

of the 4th International Conference on Persuasive Technology - Persuasive 09. doi:

10.1145/1541948.1541960, 2009.

[12] Y. Duwaraka and K. Sangaralingam. Persuasive Technology for Smartphone Fitness

Apps. The Pacific Asia Conference on Information Systems (PACIS) 2013 proceedings,

2013.

[13] B.J. Fogg. Persuasive technology: Using computers to change what we think and do.

doi:10.1145/764008.763957, 2002.

[14] J. Geng, R. Long, H. Chen, and W. Li. Exploring the motivation-behavior gap in urban

residents’ green travel behavior: A theoretical and empirical study. Conservation and

Recycling, 125, 282–292. doi: 10.1016/j.resconrec.2017.06.025, 2017.

[15] R. Hartson and Pardha S. Pyla. The UX Book: Process and Guidelines for Ensuring a

Quality User Experience. Elsevier Inc, 2002.

[16] Thomas Goetz. It’s time to redesign the medical data. Ted, 2018.

68

[17] Yingxin Yu. Data Visualization in Health Related Application. 2018.

[18] Mobile Operating System Market Share United States Of America(Mar 2019 - Mar

2020). Statcounter, 2020.

[19] Phillip B Sparling, Neville Owen, Estelle V Lambert, and William L Haskell. Promoting

physical activity: the new imperative for public health. Health Education Research,

15(3):367–376, 2000, 2000.

[20] Richard D. Olson, Katrina L. Piercy, Richard P. Troiano, Rachel M. Ballard, Janet E.

Fulton, Deborah A. Galuska, and Shellie Y. Pfohl. Physical Activity Guidelines for

Americans 2nd edition. 2018.

[21] Debra L. Blackwell and Tainya C. Clarke. State Variation in Meeting the 2008 Federal

Guidelines for Both Aerobic and Muscle-strengthening Activities Through Leisure-time

Physical Activity Among Adults Aged 18–64: United States, 2010–2015. The Centers

for Disease Control and Prevention.

[22] Sallie Beth Johnson, Samantha M Harden, and Paul A Estabrooks. Uptake of evidence-

based physical activity programs: comparing perceptions of adopters and nonadopters.

Translational Behavioral Medicine, Volume 6, Issue 4, December 2016, Pages 629–637,

2016.

[23] Samantha M. Harden, Sallie Beth Johnson, Fabio A. Almeida, and Paul A. Estabrooks.

Improving physical activity program adoption using integrated research-practice partner-

ships: an effectiveness-implementation trial. Translational Behavioral Medicine, Volume

7, Issue 1, March 2017, Pages 28–38, 2016.

[24] Harden SM, Ramalingam NS, Breig SA, and Estabrooks PA. Walk This Way: Our Per-

69

spective on Challenges and Opportunities for Extension Statewide Walking Promotion

Programs. J Nutr Educ Behav. 2019 May;51(5):636-643, 2019.

[25] Active Living Every Day (ALED). The Centers for Disease Control and Prevention,

2019.

[26] Cheryl Merzel and Joanna D’Afflitti. Reconsidering community-based health promotion:

promise, performance, and potential. American journal of public health, 2003.

[27] Datta A., Dutta K., Kajanan S., and Pervin N. Mobilewalla: A Mobile Application

Search Engine. Mobile Computing, Applications, and Services, 172-187., 2012.

[28] C. Dellarocas, G. Gao, and R. Narayan. Are Consumers More Likely to Contribute On-

line Reviews for Hit or Niche Products? Journal of Management Information Systems,

27, 127-158, 2010.

[29] X. Li and L.M. Hitt. Price effects in online product reviews: an analytical model and

empirical analysis. MIS quarterly, 34, 809-832., 2010.

[30] S. Asur and B.A. Huberman. Predicting the future with social media. arXiv preprint

arXiv:1003.5699., 2010.

[31] John Houghton. The” what” and” why” of goal pursuits: Human needs and the self-

determination of behavior. Psychological inquiry, 11, 227-268., 2009.

[32] Google Fit. google.com/fit, 2020.

[33] Fitbit. fitbit.com/us/home, 2020.

[34] Nike Training Club. nike.com/ntc-app, 2020.

[35] Nir Eyal. Why Most Fitness Apps Don’t Work. medium, 2017.

70

[36] Andrew Cole. Fitness Apps Are Hard — And Other Reasons Why Our App Died.

Medium, 2018.

[37] Julie Sloane-Penn. Competition, Not support, Gets you to the gym. futurity.org, 2016.

[38] Number of active users of Fitbit from 2012 to 2019. Statista.

[39] A. Cuttone, M. K. Petersen, and J. E. Larsen. Four Data Visualization Heuristics to

Facilitate Reflection in Personal Informatics. Universal Access in Human-Computer In-

teraction. Design for All and Accessibility Practice Lecture Notes in Computer Science,

541–552. doi: 10.1007/978-3-319-07509-951, 2014.

[40] I. Li, A. Dey, and J. Forlizzi. A stage-based model of personal informatics systems. Proc. of

the 28th Int. Conf. on Human Factors in Computing Systems, pp. 557–566, 2010.

[41] R. Fleck and G. Fitzpatrick. Reflecting on reflection: framing a design landscape. Proc. of

the 22nd Conf. of the Computer-Human Interaction SIG of Australia on Computer-Human

Interaction, pp. 216–223, 2010.

[42] A. Konrad, S. Whittaker, and E. Isaacs. Short and long-term benefits of reflective technolo-

gies. Proc. of the CHI 2013 Workshop on Personal Informatics, 2013.

[43] I. Li, A. Dey, and J. Forlizzi. A stage-based model of personal informatics systems. Proc. of

the 28th Int. Conf. on Human Factors in Computing Systems, pp.557–566, 2010.

[44] I. Li, Y. Medynskiy, J. Froehlich, and J.E. Larsen. Personal informatics in practice: im-

proving quality of life through data. CHI 2012 Extended Abstracts on Human Factors in

Computing Systems, CHI EA 2012, pp. 2799–2802, 2012.

[45] I. Li, J. Froehlich, J.E. Larsen, C. Grevet, and E. Ramirez. Personal informatics in the wild:

hacking habits for health happiness. CHI 2013 Extended Abstracts on Human Factors in

Computing Systems, pp. 3179–3182, 2013.

71

[46] F. Bentley and K. Tollmar. The power of mobile notifications to increase well being logging

behavior. Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, pp. 1095–

1098, 2013.

[47] D. Pavel, D. Trossen, M. Holweg, and V. Callaghan. Lifestyle stories: correlating user infor-

mation through a story-inspired paradigm. Pervasive Computing Technologies for Healthcare,

pp. 412–415, 2013.

[48] C. Fan, J. Forlizzi, and A.A. Dey. A spark of activity: Exploring informative art as visual-

ization for physical activity. Proc. of the 2012 ACM Conf. on Ubiquitous Computing, pp.

81–84, 2012.

[49] Amon Rapp and Federica Cena. Personal informatics for everyday life: How users without

prior self-tracking experience engage with personal data. International Journal of Human-

Computer Studies 94, 2016.

[50] Tamara Munzner. A Nested Model for Visualization Design and Validation. IEEE Transac-

tions on Visualization and Computer Graphics, vol. 15, no. 6, pp. 921-928, 2009.

[51] Heidi Lam, Enrico Bertini, Petra Isenberg, Catherine Plaisant, and Sheelagh Carpendale.

Empirical Studies in Information Visualization: Seven Scenarios. IEEE Transactions on

Visualization and Computer Graphics, vol. 18, no. 9, pp. 1520-1536, 2012.

[52] E.L. Deci and R.M. Ryan. Global warming: the complete briefing. Cambridge University

Press., 2000.

[53] Carbon Dioxide, Methane, Nitrous Oxide, and the Greenhouse Effect. climatechange.lta.org,

2020.

[54] D. Poponi, T. Bryant, K. Burnard, P. Cazzola, J. Dulac, A. Fernandez Pales, and K. West.

72

Energy Technology Perspectives 2016: Towards Sustainable Urban Energy Systems. INTER-

NATIONAL ENERGY AGENCY., 2016.

[55] Neil Maizlish. Increase walking, biking, and public transportation: improve the health of

californians, save money, and reduce greenhouse gas emissions. Office of Health Equity

California Department of Public Health Sacramento, CA, 2016.

[56] Steven K. Galson. The Importance of Being Active Your Way. National Center for Biotech-

nology Information, 2009.

[57] Emily N. Ussery, Janet E. Fulton, and Deborah A. Galuska. Joint Prevalence of Sitting

Time and Leisure-Time Physical Activity Among US Adults, 2015-2016. Centers for Disease

Control and Prevention, 2018.

[58] Tanja Blascheck, Lonni Besançon, Anastasia Bezerianos, and Petra Isenberg Bongshin Lee.

Glanceable Visualization: Studies of Data Comparison Performance on Smartwatches. IEEE

Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 630-640, 2019.

[59] John Pruitt and Jonathan Grudin. Personas: practice and theory. DUX ’03: Proceedings

of the 2003 conference on Designing for user experiences, 2003.

[60] Jakob Nielsen and Rolf Molich. Heuristic evaluation of user interfaces. CHI ’90: Proceedings

of the SIGCHI Conference on Human Factors in Computing Systems, 1990.

[61] Jakob Nielsen. Thinking Aloud. Nielsen Norman Group, 2012.

[62] Michael Burch and Daniel Weiskopf. On the Benefits and Drawbacks of Radial Diagrams.

Handbook of Human Centric Visualization. Springer, New York, NY, 2014.

[63] Alessandro Marcengo and Amon Rapp. Visualization of Human Behavior data: the Quan-

tified Self. IGI GLOBAL, 2014.

73

[64] David J. Slutsky. The Effective Use of Graphs. Jnl Wrist Surg, 2014, 2014.

[65] Agnieszka Bojko. Informative or Misleading? Heatmaps Deconstructed. International Con-

ference on Human-Computer Interaction, 2009.

[66] Jarry H T Claessen and Jarke J. van Wijk. Flexible Linked Axes for Multivariate Data

Visualization. IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 12,

pp. 2310-2316, 2011.

[67] David J. Nowak. The effects of urban trees on air quality. USDA Forest Service, 2002.

[68] David Gregorio and Lisa Shumaker. U.S. vehicle fuel economy rises to record 24.7 mpg:

EPA. Reuters, 2018.

[69] Alternative Fuels Data Center – Fuel Properties Comparison. afdc.energy.gov, 2014.

[70] Catrine Tudor-Locke, Cora L Craig, Yukitoshi Aoyagi, Rhonda C Bell, and et al. How many

steps/day are enough? for adults. Int J Behav Nutr Phys Act 8, 79, 2011.

[71] Aurora Harley. UX Expert Reviews. Nielsen Norman Group, 2018.

[72] Aurora Harley. Visibility of System Status (Usability Heuristic). Nielsen Norman Group,

2018.

[73] Lisa Miles. Physical activity and health. Nutrition bulletin, 32(4):314–363, 2007, 2007.

[74] Mobile Fact Sheet. pew research center, 2019.

[75] Jerry Alan Fails and Mike Jones. Melding Adult and Child Perspectives on to Guide the

Design of Outdoor Technologies for Children. 2019.

74

Appendices

75

Appendix A

FitAware App Screenshot

A.1 Home page

76

A.2 Group Member Information

A.3 Teams

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A.4 Setting

A.5 Notification/Widget

78

Appendix B

Survey evaluation

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80

81

82

83

84

85

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Appendix C

FitAware Web Screenshot

Figure C.1: Personal Dashboard

Figure C.2: Team Dashboard

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Figure C.3: Bar Chart Race Animation

Figure C.4: Geographic Map

Figure C.5: CO2 Footprint

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Appendix D

Personas

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Appendix E

Wireframes

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Appendix F

Sketches

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Appendix G

Expert Reviews

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Screenshots Questions Answers

1. What best describesthe day-to-day trend forminutes walked for thisuser during this week?

a. Significant daily in-creases

b. Usually increaseswith occasional smalldecreases

c. Significant daily de-creases

2. On what day did the userexperience heavy phys-ical activity based onheatpoints heatmap?

a. Tuesdayb. Wednesdayc. Fridayd. Saturday

3. What other notableand interesting thingsdid you learn from thisvisualization?

4. Please provide commentsabout the visualizationusability and effective-ness.

1. a) to me it looks signif-icant. Especially from10 min to 41 min. Iam gonna ignore the dipfrom 41 min to 35 minbecause for the rest ofthe week the trend con-tinued.

2. d) Saturday at 18:00,the next most intensemoment was Sunday at16:00.

3. Looks like on Tuesdaythe user didn’t walk atall. Perhaps they didn’tcarry the phone? Sur-prised the difference insteps between Mondayand Tuesday is a lot big-ger than the differencebetween duration in min-utes. Also, how comethe user on Sunday withonly 10 minutes of activetime was able to do 7600steps?

4. The visualisation is slickand easy to read. Iwould like to see thegoal values superimposedon the duration linearchart. The heart pointmap should make theuser guilty about havingso many zeroes! Havingso many zeroes could beshocking and motivating.

Table G.1: Personal Page Q&A with expert #196


1. Who accumulated themost steps for this team?

a. Scottb. Andrewc. Jixiang

2. Which day did Jixiangwalk the most steps?

a. Mondayb. Wednesdayc. Fridayd. Saturday

3. Who won the Wednesdaycompetition?

a. Scottb. Shuaic. Jixiangd. Zhennan



1. a) I however over the piechart to find out thatScott did 81k steps

2. c) I looked at the colorcoded ‘team overallsteps’ and hovered overthe biggest red bar tofind out that Jixiang did11.4k steps.

3. d) Wednesday was closebetween Scott and Zhen-nan. The winner is (ac-cording to team weeklysteps) Zhennan.

4. Everyone did poorly onTuesday. I wonder ifthe weather was bad onthat day or whether theentire team is a groupof coworkers that hap-pened to be working ona group project on thatday. Zhennan likes to sitat home on Friday nightsprobably whereas Scottand Shuai move a lot.

5. I am confused about theanimated chart. Whatis it showing? Howcome some numbers werereduced over time? Ifound it difficult to lookat the radar chart. Ithink a combination of‘team overall steps’ and‘team weekly steps’ doesa great job conveying thebreakdown of progressacross the group mem-bers.

Table G.2: Team Page Q&A with expert #1

97


1. When was Scott leadingthe ranking?

a. Dec 15-17b. Dec 16-19c. Dec 17-19d. Dec 18-20

2. Who was leading therank on Dec 20th, 2019?


3. Who has the fewest stepsduring this activity cy-cle?




1. c) I used the seek but-ton to answer this ques-tion (Scott Dec 17-19).

2. Andrey

3. Zhennan

4. It seems that only Shuaiwas walking on Dec 16thwhich is very strange.Scott started with 11k+steps on Dec 17. Howis this possible? Did hebuffer his steps and synclater?

5. The animation was toofast and hard to keep upwith. Also, how can usersteps go down over time?Makes no sense to me.

Table G.3: Bar chart racing Page Q&A with expert #1

98


1. In what state is thisuser?

a. Washingtonb. Oregonc. California


a. Only Washingtonb. Washington and Ore-

gonc. The user has visited

all states on the map



1. a) Wash

2. Washington-> Oregon -> California

3. There was a gap in Cali-fornia and there is a bluehighlight in the begin-ning of the walk.The bluepart means the part al-ready covered and thegrey curved line is to in-dicate the remainder ofthe trip. I figured thisout by hovering over thehuman icon.

4. I noticed that at differentzoom levels the trip pathbreaks in two. Looks likea bug with the API.

Table G.4: Geographic map Page Q&A with expert #1

99


1. How much CO2 Scott re-duced in this activity cy-cle?

2. Who saved most CO2in this competition?How many trees is thatamount equivalent to?



1. 100lbs

2. Scott and Andreyequally at 25 trees each.

3. Surprisingly large num-ber of trees are freed upto absorb CO2 when peo-ple walk 250000 steps.

4. I would like to know ifthe steps walked by theusers were in addition totheir regular commute oras a substitute. Thisis important because ifthey walk in addition todriving then it is tech-nically incorrect to showthe freed-up trees. I wishthere was a numeric in-dicator for the freed-uptrees. I had to countthem manually. Also itis unclear over what timeperiod the users wereable to accumulate thismany steps. I would liketo see an indication ofthat too.

Table G.5: CO2 footprint Page Q&A with expert #1

100










1. b)

2. d)

3. I found the heart pointsviz a little hard to‘easily’ digest infor-mation (informationdense?). I’m not sureof the most appropriateimprovement to it, butthe coloring for thenumbers helps your eyesfind where your heartpoints have been high.The circular chart is alittle confusing for meas well – I understandit’s a progression forsteps and duration andthe heart points showyour highest for theday (I think). Thesteps chart and durationlinear chart was easy forme to understand, butshows only one piece ofinformation.

4. Visually, I like circu-lar chart because it eas-ily gives many piecesof information quickly.I found hovering themouse over on the linearchart and bar chart notuseful. The informationprovided is clearly visibleeasily.











1. a)

2. d)

3. d)

4. Lots of visualizationshere. I liked the teamover all steps pir chart,and team weekly stepsbar chart because itwas easy to see theinformation point blank.The radio chart waskinda hard to read dueto overlap. The visual-ization with the sliderwas interesting becauseI learned

5. The visualization withthe slider coule showmore information on thetimestamps. For in-stance, the slider seemsto hit a couple pointson Thursday in termsof tracking steps duringthat day.


102










1. c)

2. Andrey

3. Zhennan

4. I saw the progressionover time quite easily.

5. Like I mentioned be-fore: the visualizationwith the slider couleshow more informationon the timestamps. Forinstance, the slider seemsto hit a couple pointson Thursday in termsof tracking steps duringthat day.


103










1. a)

2. a)

3. I can see the progressionof the hike quite easily.

4. I think using a map isgreat as a backgroundto use for a visualiza-tion. Maps have beenused since early in manypeoples lives and providesome sense of directionon paper. Then hav-ing an overlay of map, inthis case tracking a hiker,shows progression and lo-cation at the forefront.


104






1. 100lbs

2. Scott

3. I can see how much CO2has been reduced basedon everyone’s activity.

4. I don’t feel one wayor the other with thisvisualization. I foundit weird that I couldchange the numbers us-ing a slider. I figured itwould automatically up-date as the numbers up-date. I do think seeingmore tree gives an easyway to know who is doingmore much more quicklywithout seeing the num-bers.


105










1. b)

2. d)

3. A tendency for higherheatpoints/heart pointsfrom 15:00 or higher,similar heart points at10:00, not sure what thex-axis means so cannotinterpret.

4. Titles were clearly pre-sented for each vis com-ponent, terms used werestraightforward and fa-miliar, though when re-visiting I was not sureif I was seeing only mydata or the team’s data.Generally usable thoughfurther specifics of eachaxis would help inter-pret the data, while daysof the week is easy toidentify what some otheraxes represent are un-clear. For example the yaxis on the duration lin-ear chart should be la-belled, though the titlehelps and hovering overthe data points help itshould be visible at aglance without the userneeding to extrapolateor interact with the vis.X-axis of heart pointheatmap is also unclear.If labelled appropriatelythe vis should be fairlyeffective.


106










1. a)

2. d)

3. d)

4. Scott walks much morethan others, his dataseems like an outlier.The visualization (specif-ically the weekly radarchart and team overallsteps share, and to alesser extent the teamoverall steps) makes thatclear.

5. Overall the visualizationwas fairly usable, inter-acting with the variousitems and data pointspresented me with the in-formation I sought, giv-ing me flexibility and ef-ficiency of use, other-wise I would have had tolook at the legend to de-termine who some datapoint represented or thevalue being representedon the chart (for exam-ple the number of steps).The headings, labels pro-vided visibility of systemstatus (Nielsen’s Heuris-tics)


107










1. c)

2. Andrey

3. Zhennan

4. Shuai seemed to be theonly one walking on 2019December 16 Mon, thisseemed to conflict withwhat was depicted inother vis.

5. Overall the visualizationwas fairly usable, I couldinteract with the time-line for the animation(giving me control andfreedom), the visualiza-tions were generally con-sistent, though the col-ors for each participantwithin the animated visdo not match the colorsassigned to the partici-pants in the other visu-alizations. The day ofthe week label for theanimated vis seems dis-tractingly far away frommy line of sight. Iliked being able to trackprogress by days but Iwould need to divert myattention away from theanimation to identify theday, the walking man didnot add much value, Iwould rather the day ofthe week be centered andmore visible











1. a)

2. b)

3. The user walked 74.7miles, which seems to bethe distance between cal-ifornia and washington(based on the map)

4. Not sure what this mapdepicts, is this a map in-tended to depict a virtualwalk or using GPS to re-flect an actual walk. Ifelt like when I hoveredon the path the user tookthat I should be pre-sented with some infor-mation, I wanted to learnmore and could not.


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1. 100lbs

2. Scott, 25 trees

3. The interactive timelinesfor each team memberwas kinda cool, with thetrees updating accord-ingly, though confusingafter you play with thatsince you cannot resetto the original / de-fault state, could seemthat every team mem-ber achieved the goal of30000.

4. Seems effective thoughthe responsive nature ofthe vis was confusing,on my horizontal res-olution a full row oftrees was 13 trees. IfI didnt count I wouldntknow, I expected ten orsome other common in-crement, then resizing itadjusted, which createdmore rows, usable butdidnt meet my expecta-tions.


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Mobile Devices for Facilitating Group Fitness and Visualization of Fitness … · 2020-05-31 · Mobile Devices for Facilitating Group Fitness and Visualization of Fitness Data Shuai