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A Context-Aware Smartphone Application to Mitigate Sedentary Lifestyle

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A Context-Aware Smartphone Application to Mitigate Sedentary Lifestyle A Context-Aware Smartphone Application to Mitigate Sedentary Lifestyle by Qian He A Thesis Submitted to the Faculty of the WORCESTER POLYTECHNIC INSTITUTE In partial fulfillment of the requirements for the Degree of Master of Science in Computer Science by September 2014 APPROVED: Professor Emmanuel O. Agu, Thesis Advisor Professor Candace L. Sidner, Thesis Reader Professor Craig E. Wills, Head of Department Abstract Sedentary lifestyles are ubiquitous in modern societies. Sitting, watching television and using the computer are examples of sedentary behaviors that are currently com- mon worldwide. Many research results show that the length of time that a person is sedentary is linked with an increased risk of obesity, diabetes, cardiovascular disease, and all-cause mortality. Determining how best to motivate people to become more active is not only necessary but also imperative. The electronic pedometer, as a proven device to increase physical activity, has been widely accepted by consumers for decades. As smartphones are functionally able to run accurate pedometer apps, we explore the potential of leveraging context-aware (e.g. location, identity, activity and time) smartphone application|more advanced pedometer|to help people mit- igate sedentary lifestyle. The smartphone application we developed, \On11", intel- ligently tracks people's physical activities and identifies sedentary behaviors. With the knowledge it learns from the users, On11 provides recommendations based on users' geographic patterns. Our study consists of four steps: (1) a pre-survey that helps us comprehend people's views on physical activity, how people use their smartphones, and how smartphone applications may help them to be more active, (2) a large scale Twitter study (over 3 months, analyzed 929,825 running-related tweets) that determines how difficult it is for people to keep performing the most popular exercise|running, (3) a 2-week trial of our smartphone app which promotes an easier exercise|walking, and (4) a post-survey for subjects who participated in the app trial to validate if the app works as expected. Acknowledgements I would like to express my deepest gratitude to my advisor, Prof. Emmanuel O. Agu, for his support and guidance throughout the research. His continued support led me to the right path. I would also like to extend my appreciation to my reader, Prof. Candace L. Sid- ner, who waited my thesis for one year and attended my thesis defense presentation through phone call from London. I hope she will give me the chance to pay her phone bill. :) I would also like to extend my deepest gratitude to my family. Without their support, I would not have the courage to drop out from the University of Chinese Academy of Sciences and come to Worcester Polytechnic Institute. Also, I thank my roommate and dear friend, Jia Wang, Ph.D. Candidate. When- ever I got frustrated and exhausted, I will feel much better after asking him \How is your Ph.D. thesis going?". Big comfort, buddy! i Contents 1 Introduction 1 1.1 Background . .4 1.1.1 Physical Activity . .4 1.1.2 Activity Tracker . .5 1.1.3 Sedentary Lifestyle . .6 1.1.4 Health Behavior Change . .6 1.1.5 Social Network Mining . .7 1.2 Problem Statement . .8 1.3 Thesis Structure . .8 2 Pre-survey on Physical Activity and Smartphone App 9 2.1 Demographic Information . 10 2.2 Usage of Smartphone . 14 2.2.1 Smartphone . 14 2.2.2 Function Usage . 15 2.2.3 Mobile Application . 17 2.3 Physical Activity . 21 2.3.1 Current Health Condition . 21 2.3.2 Frequency . 23 ii 2.3.3 What . 24 2.3.4 Exercise Locations . 24 2.3.5 Exercise Times . 24 2.4 Attitude toward Physical Activity and Mobile Apps . 26 2.4.1 Physical Activity . 26 2.4.2 Mobile Apps Used . 30 2.5 Other Questions . 34 2.6 Summary . 34 3 Establishing the Difficulty of Running 37 3.1 Introduction . 37 3.2 Background . 38 3.2.1 Physical Activity Tracking . 38 3.2.2 Data Mining on Public SNS . 40 3.3 Methodology . 41 3.3.1 Data Source . 41 3.3.2 Tool Design . 42 3.3.3 Tool Implementation . 43 3.4 Data Analysis . 44 3.4.1 Running Statistics . 45 3.4.2 Running Patterns . 49 3.5 Summary . 57 4 A Context-aware Smartphone Application: On11 58 4.1 Background . 59 4.2 Context Awareness . 59 4.2.1 Time . 59 iii 4.2.2 Location . 60 4.2.3 Weather . 60 4.2.4 Personal Information . 60 4.3 App Design . 62 4.3.1 Function Design . 62 4.3.2 User Interface Design . 64 4.4 Implementation . 68 4.4.1 Activity Logging . 69 4.4.2 Inactiveness Detector . 74 4.4.3 Recommendation System . 74 4.4.4 Profile & Goal Setting . 77 4.4.5 Weather & Background Image Updater . 80 4.4.6 Battery Manager . 81 4.5 Preliminary Evaluation of App Functionality . 82 4.5.1 Calorie Estimation . 82 4.5.2 Battery Consumption . 85 5 User Study of On11 89 5.1 Study Design . 89 5.1.1 Risks to Study Participants . 90 5.1.2 Benefits to Participants and Others . 90 5.1.3 Confidentiality . 90 5.1.4 Compensation . 90 5.2 Study Protocol for User Guide Tutorial . 91 5.3 Problems Faced in App Trial . 91 5.4 Post-survey & the Summary of User Study . 92 5.4.1 Consensuses . 92 iv 5.4.2 Other Comments . 94 5.4.3 Summary . 95 6 Conclusion 96 Appendices 98 A Establishing the Difficulty of Running 99 B User Study Protocol 103 B.1 Introduction . 103 B.2 Purpose . 103 B.3 Ground Rules . 104 B.4 Consent Form . 104 B.5 Demonstration . 104 B.6 Android Phone Setup . 105 B.7 Closing Statements . 105 C Sample of the Post-survey 106 v List of Figures 1.1 Age-Adjusted Estimates of the Percentage of Adults Who Are Phys- ically Inactive, 2008 . .3 2.1 Age . 10 2.2 Weight . 10 2.3 Height . 11 2.4 Body Mass Index (BMI) . 11 2.5 BMI Classification . 11 2.6 Waist Size . 12 2.7 Gender . 12 2.8 Ethnicity . 13 2.9 Year of School . 13 2.10 \How many smartphones do you own?" . 15 2.11 \How many tablets do you own?" . 16 2.12 \What type of mobile phones and tablets do you have?" . 16 2.13 \How many SMS or messages through data network (like BlackBerry Messenger, iOS iMessage, WhatsApp, Kik, etc) do you send receive perday?" ................................. 17 2.14 \How much time do you spend making answering phone calls per day?" . 17 vi 2.15 \How much time do you spend browsing web on mobile devices (smart- phone, tablet, etc) per day?" . 18 2.16 \How much time do you spend watching movies on mobile devices (smartphone, tablet, etc) per day?" . 18 2.17 \How much time do you spend listening to music on mobile devices (smartphone, tablet, etc) per day?" . 18 2.18 \How much time do you spend on playing games on mobile devices (smartphone, tablet, etc) per day?" . 19 2.19 \How many apps have you installed on your mobile devices (smart- phone, tablet, etc) on average?" . 19 2.20 \What are your favorite apps on your mobile device?" . 20 2.21 \Have you ever used any health, fitness, or wellness apps on mobile device?" . 21 2.22 \Please list the health, fitness, and wellness apps you have used." . 22 2.23 \On a scale of 1{10, how healthy do you think you are?" . 22 2.24 \How many times per week do you exercise, currently?" . 23 2.25 \How many times per week do you wish you can exercise?" . 23 2.26 \What exercises do you currently do?" . 25.
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