Patient-Generated Data
Data generated by patients outside clinical settings carries immense potential in informing decisions. However, the current ecosystem of tools does not help realize the potential of this data. I study the use of this data and design data interfaces to support analysis, interpretation, and decision-making in data-driven care of chronic health conditions. I draw from theories of sensemaking and techniques from information visualization to characterize practices of patients and clinicians and translate those practices to interface features. I am further interested in studying different types of tools that leverage patient data, such as clinical decision-support tools.

Context and Context-Awareness in Health
To support patients with chronic health conditions, it is important to understand the contexts in which self-care activities are performed and overall, the role of contextual factors in affecting health-related behaviors. I study the lived experiences of people to understand how context can be better utilized in interventions for assisting users in health-related activities. Specifically, I have studied the influence of context on the management of type 1 diabetes and on planning for physical activity. I am also interested in studying practices of designers who create context-based applications using health data.

Self-Reporting and EMA
Many researchers rely on self-reported inputs from their participants to get data. However, self-reporting remains a burdensome activity despite improvements in technology. I have collaborated with several colleagues to explore the use of situated self-reporting devices as compared to mobile phones for self-reporting. We have observed that mobile phones complement the use of stand-alone devices for self-reporting. I am interested in further assessing multi-device set-ups to improve self-reporting technology and experience.