Daniel A. Epstein
University of California, Irvine
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Featured researches published by Daniel A. Epstein.
ubiquitous computing | 2015
Daniel A. Epstein; An Ping; James Fogarty; Sean A. Munson
Current models of how people use personal informatics systems are largely based in behavior change goals. They do not adequately characterize the integration of self-tracking into everyday life by people with varying goals. We build upon prior work by embracing the perspective of lived informatics to propose a new model of personal informatics. We examine how lived informatics manifests in the habits of self-trackers across a variety of domains, first by surveying 105, 99, and 83 past and present trackers of physical activity, finances, and location and then by interviewing 22 trackers regarding their lived informatics experiences. We develop a model characterizing tracker processes of deciding to track and selecting a tool, elaborate on tool usage during collection, integration, and reflection as components of tracking and acting, and discuss the lapsing and potential resuming of tracking. We use our model to surface underexplored challenges in lived informatics, thus identifying future directions for personal informatics design and research.
human factors in computing systems | 2015
Felicia Cordeiro; Daniel A. Epstein; Edison Thomaz; Elizabeth Bales; Arvind Krishnaa Jagannathan; Gregory D. Abowd; James Fogarty
Although food journaling is understood to be both important and difficult, little work has empirically documented the specific challenges people experience with food journals. We identify key challenges in a qualitative study combining a survey of 141 current and lapsed food journalers with analysis of 5,526 posts in community forums for three mobile food journals. Analyzing themes in this data, we find and discuss barriers to reliable food entry, negative nudges caused by current techniques, and challenges with social features. Our results motivate research exploring a wider range of approaches to food journal design and technology.
designing interactive systems | 2014
Daniel A. Epstein; Felicia Cordeiro; Elizabeth Bales; James Fogarty; Sean A. Munson
As people continue to adopt technology based self tracking devices and applications, questions arise about how personal informatics tools can better support self tracker goals. This paper extends prior work on analyzing and summarizing self tracking data, with the goal of helping self trackers identify more meaningful and actionable findings. We begin by surveying physical activity self trackers to identify their goals and the factors they report influence their physical activity. We then define a cut as a subset of collected data with some shared feature, develop a set of cuts over location and physical activity data, and visualize those cuts using a variety of presentations. Finally, we conduct a month long field deployment with participants tracking their location and physical activity data and then using our methods to examine their data. We report on participant reactions to our methods and future design opportunities suggested by our work.
human factors in computing systems | 2016
Daniel A. Epstein; Monica Caraway; Chuck Johnston; An Ping; James Fogarty; Sean A. Munson
Recent research examines how and why people abandon self tracking tools. We extend this work with new insights drawn from people reflecting on their experiences after they stop tracking, examining how designs continue to influence people even after abandonment. We further contrast prior work considering abandonment of health and wellness tracking tools with an exploration of why people abandon financial and location tracking tools, and we connect our findings to models of personal informatics. Surveying 193 people and interviewing 12 people, we identify six reasons why people stop tracking and five perspectives on life after tracking. We discuss these results and opportunities for design to consider life after self tracking.
conference on computer supported cooperative work | 2015
Daniel A. Epstein; Bradley H. Jacobson; Elizabeth Bales; David W. McDonald; Sean A. Munson
Many research applications and popular commercial applications include features for sharing personally collected data with others in social awareness streams. Prior work has identified several barriers to use as well as discrepancies between designer goals and how these features are used in practice. We develop a framework for designing and evaluating these features based on an extensive review of prior literature. We demonstrate the value of this framework by analyzing physical activity sharing on Twitter, coding 4,771 tweets and their responses and gathering 444 reactions from 97 potential tweet recipients, learning that specific user-generated content leads to more responses and is better received by the post audience. We conclude by extending our findings to other sharing problems and discussing the value of our design framework.
ubiquitous computing | 2013
Daniel A. Epstein; Alan Borning; James Fogarty
Personal informatics applications in a variety of domains are increasingly enabled by low cost personal sensing. Although applications capture fine-grained activity for self reflection, sharing is generally limited to high level summaries. There are potential advantages to fine-grained sharing, but also potential harms. To help investigate this complex design space, we employ Value Sensitive Design to consider whether and how to share fine grained step activity. We identify key values and value tensions, and we develop scenarios to highlight these. We then design a set of data transformations that seek to maximize the benefits while minimizing the harms of detailed sharing. These include a novel approach to interactive modification of fine grained step data, allowing people to remove private data and using motif discovery to generate realistic replacement data. Finally, we conduct semi structured interviews with 12 participants examining these scenarios and transformations. We distill results into a set of design considerations for fine-grained physical activity sharing.
ubiquitous computing | 2016
Daniel A. Epstein; Jennifer H. Kang; Laura R. Pina; James Fogarty; Sean A. Munson
People stop using personal tracking tools over time, referred to as the lapsing stage of their tool use. We explore how designs can support people when they lapse in tracking, considering how to design data representations for a person who lapses in Fitbit use. Through a survey of 141 people who had lapsed in using Fitbit, we identified three use patterns and four perspectives on tracking. Participants then viewed seven visual representations of their Fitbit data and seven approaches to framing this data. Participant Fitbit use and perspective on tracking influenced their preference, which we surface in a series of contrasts. Specifically, our findings guide selecting appropriate aggregations from Fitbit use (e.g., aggregate more when someone has less data), choosing an appropriate framing technique from tracking perspective (e.g., ensure framing aligns with how the person feels about tracking), and creating appropriate social comparisons (e.g., portray the person positively compared to peers). We conclude by discussing how these contrasts suggest new designs and opportunities in other tracking domains.
human factors in computing systems | 2017
Daniel A. Epstein; Nicole B. Lee; Jennifer H. Kang; Elena Agapie; Jessica Schroeder; Laura R. Pina; James Fogarty; Julie A. Kientz; Sean A. Munson
We consider why and how women track their menstrual cycles, examining their experiences to uncover design opportunities and extend the fields understanding of personal informatics tools. To understand menstrual cycle tracking practices, we collected and analyzed data from three sources: 2,000 reviews of popular menstrual tracking apps, a survey of 687 people, and follow-up interviews with 12 survey respondents. We find that women track their menstrual cycle for varied reasons that include remembering and predicting their period as well as informing conversations with healthcare providers. Participants described six methods of tracking their menstrual cycles, including use of technology, awareness of their premenstrual physiological states, and simply remembering. Although women find apps and calendars helpful, these methods are ineffective when predictions of future menstrual cycles are inaccurate. Designs can create feelings of exclusion for gender and sexual minorities. Existing apps also generally fail to consider life stages that women experience, including young adulthood, pregnancy, and menopause. Our findings encourage expanding the fields conceptions of personal informatics.
human factors in computing systems | 2017
Ravi Karkar; Jessica Schroeder; Daniel A. Epstein; Laura R. Pina; Jeffrey Scofield; James Fogarty; Julie A. Kientz; Sean A. Munson; Roger Vilardaga; Jasmine Zia
Diagnostic self-tracking, the recording of personal information to diagnose or manage a health condition, is a common practice, especially for people with chronic conditions. Unfortunately, many who attempt diagnostic self tracking have trouble accomplishing their goals. People often lack knowledge and skills needed to design and conduct scientifically rigorous experiments, and current tools provide little support. To address these shortcomings and explore opportunities for diagnostic self tracking, we designed, developed, and evaluated a mobile app that applies a self experimentation framework to support patients suffering from irritable bowel syndrome (IBS) in identifying their personal food triggers. TummyTrials aids a person in designing, executing, and analyzing self experiments to evaluate whether a specific food triggers their symptoms. We examined the feasibility of this approach in a field study with 15 IBS patients, finding that participants could use the tool to reliably undergo a self-experiment. However, we also discovered an underlying tension between scientific validity and the lived experience of self experimentation. We discuss challenges of applying clinical research methods in everyday life, motivating a need for the design of self experimentation systems to balance rigor with the uncertainties of everyday life.
human factors in computing systems | 2016
Daniel A. Epstein; Felicia Cordeiro; James Fogarty; Gary Hsieh; Sean A. Munson
Many people struggle with efforts to make healthy behavior changes, such as healthy eating. Several existing approaches promote healthy eating, but present high barriers and yield limited engagement. As a lightweight alternative approach to promoting mindful eating, we introduce and examine crumbs: daily food challenges completed by consuming one food that meets the challenge. We examine crumbs through developing and deploying the iPhone application Food4Thought. In a 3 week field study with 61 participants, crumbs supported engagement and mindfulness while offering opportunities to learn about food. Our 2x2 study compared nutrition versus non-nutrition crumbs coupled with social versus non-social features. Nutrition crumbs often felt more purposeful to participants, but non-nutrition crumbs increased mindfulness more than nutrition crumbs. Social features helped sustain engagement and were important for engagement with non-nutrition crumbs. Social features also enabled learning about the variety of foods other people use to meet a challenge.