CAREER: Advancing Personal Informatics through Semi-Automated and Collaborative Tracking

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This research examines a novel self-tracking approach called semi-automated tracking to help people easily engage with a rich set of personal data, such as weight, activities, sleep pattern, and medication use. In principle, being aware of self-tracking data can help people reflect on their health condition and understand how their behavior affects their progress toward goals, potentially improving health and well-being. In practice, however, self-tracking is hard. Manual tracking approaches such as diaries require much effort, while automated tracking approaches such as wearable sensing tools significantly reduce the tracker's awareness, accountability, and involvement compared to manual tracking. To address these problems, this research proposes to design and develop a semi-automated tracking platform, combining both manual and automated data collection methods. The platform will enable people to design and customize their own tracking tools and capture many kinds of personal data depending on individuals' diverse tracking needs. As the customization itself can be hard, the platform will also support collaborative tracking by incorporating templates created based on experts' input. The proposal will test these ideas while working with two user groups who can benefit from practicing self-tracking: (1) older adults living in retirement communities, and (2) clinicians and surgical patients in prehabilitation programs (that is, dietary and exercise plans for enhancing patients' health prior to surgery). The differences in individuals, their motivations, and the demands of tracking between the older adult and prehabilitation groups will provide insight on how to design semi-automated and collaborative tracking tools. The principal investigator (PI) will use both the resulting case studies and research platform in her courses on human-computer interaction and personal informatics, and make the curricula openly available to other educators and researchers. The PI will also work closely with programs at her institution to involve people from under-represented groups in the research, including undergraduates, women, and minorities.The first phase of the research will focus on learning people's concerns, needs, and challenges regarding the use of tracking technologies via formative studies. The research team will conduct a technology probing study using a platform that supports customizable manual tracking, along with interviews and observations of older adults, clinicians, and surgical patients. During this phase, the research team will continue developing the research platform, which will be used as the technical basis for the proposed studies and interventions. These formative studies will generate insights into how these populations currently approach self-tracking: what do they do and what would they like to do, and what makes it hard and what are they afraid of. These insights will provide general design guidelines for the research platform, which will include the semi-automated tracking elements designed to balance people's information needs and data capture burden while enhancing their engagement. In the second phase, the research team will test the feasibility of the semi-automated tracking approach with a short-term deployment study followed by design iterations. Then the revised platform will be deployed longitudinally to test its efficacy with both older adults and surgical patients. In the third phase, the research team will focus on the collaborative tracking approach, aiming to help patients configure self-tracking settings and collect high quality data that are useful for clinicians. To support these goals, the research team will conduct design workshops with clinicians to generate templates for common prehabilitation regimens, which will later be incorporated in the research platform. The collaborative tracking elements of the platform will be evaluated in a longitudinal study in hospitals. This research will contribute to the growing bodies of knowledge in personal informatics and health informatics. It will inform us of elders' and surgical patients' self-tracking practices and ways in which self-tracking tools should be designed to support the needs of various stakeholders, including partners, caretakers, and clinicians. As a way to expand the impact of the work, the research team will disseminate the semi-automated tracking platform to academic communities (e.g., behavioral scientists, personal informatics researchers), medical communities (e.g., clinicians and patients), Quantified Self communities (i.e., dedicated self-trackers), and individuals who wish to engage in self-tracking practices.