A FRAMEWORK FOR DOMAIN-DRIVEN DEVELOPMENT OF PERSONAL HEALTH INFORMATICS TECHNOLOGIES A Dissertation Presented to the Faculty of the Graduate School of Cornell University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Elizabeth Lindley Murnane January 2017 c 2017 Elizabeth Lindley Murnane ALL RIGHTS RESERVED A FRAMEWORK FOR DOMAIN-DRIVEN DEVELOPMENT OF PERSONAL HEALTH INFORMATICS TECHNOLOGIES Elizabeth Lindley Murnane, Ph.D. Cornell University 2017 This dissertation advances a vision of Personal Health Informatics (PHI), a class of tools that can leverage personal data to support health self- management. Today, a powerful combination of factors is coming together that can facilitate the creation of these technologies and amplify their bene- fits. Namely, the world is awash in data, software and sensors continue to cap- ture more, increasingly capable algorithms are helping humans make sense of it all, and ubiquitous devices (that people are keen to use to manage their well- ness) can deliver this information via individually-tailored, insight-enabling, personally-empowering, health-enhancing feedback. A central argument of this dissertation is that domain knowledge can help drive PHI development in order to fully capitalize on the potential of these tech- nologies. A central contribution of this dissertation is a framework for engaging in domain-driven development. In specifying this reusable development pat- tern, I provide guidance on moving through stages of domain inquiry, domain- driven health assessment, and domain-aware intervention design. To begin, I describe what domain knowledge encompasses, why it is valu- able, and how to synthesize insights from diverse sources in order to gain an ap- preciation of the role technology can play in a given context. I then explain how this understanding can inform research goals, strategies for assessing significant health determinants, and implications for designing effective interventions. To demonstrate this process in practice, I present my own research as a case study on developing domain-driven technology that supports healthy sleep, daily performance, and emotional wellbeing. Overall, I argue that a domain- driven approach that foregrounds a deep understanding of a targeted aspect of health, together with a compassion for the lived experiences of users, will produce technological solutions that better meet individual needs and promote more positive outcomes. BIOGRAPHICAL SKETCH Broadly speaking, Elizabeth’s research interests lie in human-computer interac- tion, personal informatics, recommender systems, social computing, and per- sonalization. She is particularly compelled by applications in the domains of personal information management, civic innovation, and health. Elizabeth received her Bachelor of Science in Mathematics with Computer Science in 2007 from the Massachusetts Institute of Technology (MIT), where her undergraduate research in the Computer Science and Artificial Intelligence Laboratory (CSAIL) focused on information visualization and conversational agents. Upon graduating, Elizabeth co-founded and spent four years as the lead engineer of Architexa, a CSAIL spinoff that built interactive visualization tools to help software developers make sense of and share important aspects of source code. Since 2011, Elizabeth has been a PhD student in Information Science at Cor- nell University. In her graduate studies, she has continued to explore her over- arching research goal: designing and deploying technologies that are aware of idiosyncratic user needs and that support people in managing various aspects of their daily lives. Much of Elizabeth’s current work (out of which this disser- tation grew) is on developing novel mediums for manually collecting personal data, lightweight algorithms for passive sensing and user modeling, and inter- faces that provide tailored feedback and experiences. In pursuing these directions, Elizabeth collaborates with interdisciplinary teams comprised of domain experts from critical theorists to legal scholars to clinicians. Through these partnerships she hopes to continue pushing the boundaries of how systems can enable more positive interactions with and through technology, on individual, group, and societal levels. iii ACKNOWLEDGEMENTS I would like to extend a huge thanks to everyone who supported me throughout the course of my PhD and helped to make this dissertation happen. I am especially appreciative of my fantastic committee who saw me through. Dan Cosley has embodied what it means to be a true mentor, Geri Gay has been an incredible advocate and often felt like a co-advisor, and Claire Cardie has been a valuable source of interdisciplinary perspective. Humbly, I realize that it would not be possible to acknowledge by name every other individual who has had a positive impact on me over the years; so though I give broad thanks, it is heartfelt. I am tremendously grateful for my family as well as friends, colleagues, and collaborators from Cornell University and beyond — I look forward to our continued connection. I am also indebted to all of the open-minded participants willing to be involved in my research and all the known or anonymous reviewers that helped shape its presentation. Finally, I feel very fortunate to have received a Graduate Research Fellow- ship from the National Science Foundation, whose funding afforded me the flexibility to undertake a number of diverse projects, broaden my horizons, and transform ideas into reality. iv TABLE OF CONTENTS Biographical Sketch . iii Acknowledgements . iv Table of Contents . .v List of Tables . vii List of Figures . viii 1 Introduction 1 1.1 Motivation . .2 1.2 Dissertation Overview & Contributions . .7 2 Background 10 2.1 The Roles of Technology in Supporting Health . 10 2.1.1 The Roots of Modern Medicine . 11 2.1.2 The Age of Behavior Change . 14 2.1.3 The Individual as the Nexus of Health Management . 19 2.2 Personal Health Informatics (PHI) . 21 2.2.1 Capturing Personal Data . 25 2.2.2 Analyzing Data to Assess Health . 32 2.2.3 Delivering User-Facing Feedback . 34 2.3 Domain-Driven Personal Health Informatics . 48 2.3.1 Defining Domain Knowledge . 48 2.3.2 The Value of Domain-Driven PHI . 49 2.3.3 Examples of Domain-Driven PHI . 51 2.3.4 A Domain–Practice Gap in HCI . 53 2.4 A Framework for Domain-Driven PHI Development . 57 3 Domain Inquiry 62 3.1 An Overview of the Inquiry Process . 63 3.2 Selecting an Application Area . 70 3.2.1 Sleep, Cognitive Performance, and Emotional Wellness . 70 3.2.2 Opportunities for Technology . 74 3.2.3 Related Academic and Commercial Work . 75 3.2.4 Connecting with Chronobiology . 81 3.3 Gathering Domain Knowledge . 84 3.3.1 Chronobiology & Circadian Rhythms . 85 3.3.2 Chronotype . 87 3.3.3 Circadian Disruption . 88 3.3.4 Traditional Assessment Methods from Chronobiology . 91 3.4 Informing PHI Development . 94 3.4.1 Defining Scope . 94 3.4.2 Soft Sensing for Health Assessment . 97 v 4 Domain-Driven Health Assessment 100 4.1 Method . 100 4.1.1 Participants & Procedures . 101 4.1.2 Data . 102 4.2 Experiment 1 Findings . 111 4.2.1 Daily Rhythms in Sleep and Social Technology Use . 111 4.2.2 Leveraging Social Data for Sleep Sensing . 114 4.2.3 Sleep’s Links with Neurobehavioral Functioning . 122 4.3 Experiment 2 Findings . 129 4.3.1 Smartphone Application Usage Patterns . 129 4.3.2 Circadian Alertness Rhythms Reflected in App Use . 132 4.3.3 Connecting App Use and Alertness with Sleep . 138 4.4 Discussion . 140 4.4.1 Limitations and Future Work . 144 4.4.2 Implications for Domain-Driven Health Assessment . 147 5 Domain-Aware Intervention Design 151 5.1 Chronobiology-Aware PHI . 153 5.2 Chronobiology-Informed Sleep Support . 154 5.2.1 Personalizing Sleep Hygiene Recommendations . 155 5.2.2 Stabilizing Circadian Disruptions . 158 5.3 Biologically-Friendly Productivity Technology . 165 5.3.1 Self- and Social-Awareness . 166 5.3.2 Scheduling and Activity Management . 171 5.3.3 Performance-Predictive Systems . 175 5.4 Mental Health Management . 178 5.4.1 Participatory Design of MoodRhythm . 179 5.4.2 Iteratively Refining Design Guidelines . 182 5.4.3 Implications for Preemptive Interventions . 187 6 General Discussion and Conclusion 190 6.1 Summary of Chapters and Contributions . 191 6.2 Discussion . 194 6.2.1 Technology as a Double-Edged Sword . 195 6.2.2 Manual and Passive Modes . 201 6.2.3 Avoiding Over-Personalization of Tailored Experiences . 204 6.2.4 A Dual (Not Duel) Domain & Data Driven Approach . 210 6.3 Opportunities for Future Work . 215 6.3.1 Moving From Personal to Collective Informatics . 215 6.3.2 Applying the Framework in Areas Beyond Health . 218 6.3.3 Open Challenges of Data, Analysis, and Design . 224 6.4 Concluding Remarks . 229 Bibliography 230 vi LIST OF TABLES 4.1 Categories of applications used by participants in Experiment 2, with examples and amounts of applications and usage events. 109 4.2 Average sleep duration for each participant according to social- sensor data, screen on/off data, and ground truth sleep diary data. (* denotes inferences that fall within 95% confidence inter- val based on diary self-reports, p < :01). 114 4.3 Median values of CMC-based activity levels following nights of Adequate vs. Inadequate sleep. Significant differences in medi- ans marked on variable name (**p < :001; ***p < :0001). 124 4.4 Median values of cognitive functioning variables following nights of Adequate vs. Inadequate sleep. Significant differences in medians marked on variable name (*p < :05; **p < :01). 126 4.5 Median values of sentiment expressed in Facebook posts follow- ing nights of Adequate vs. Inadequate sleep. Significant differ- ences in medians marked on variable name (***p < :0001). 127 4.6 Median values of usage features during low vs. high alert- ness. Significant differences in medians marked on variable name (∗p < :05).............................. 137 5.1 Guidelines for designing self-monitoring technologies to sup- port mental health management.