Using Personal Informatics Data in Collaboration among People with Different Expertise

Dissertation by Chia-Fang Chung: “Many people collect and analyze data about themselves to improve their health and wellbeing. With the prevalence of smartphones and wearable sensors, people are able to collect detailed and complex data about their everyday behaviors, such as diet, exercise, and sleep. This everyday behavioral data can support individual health goals, help manage health conditions, and complement traditional medical examinations conducted in clinical visits. However, people often need support to interpret this self-tracked data. For example, many people share their data with health experts, hoping to use this data to support more personalized diagnosis and recommendations as well as to receive emotional support. However, when attempting to use this data in collaborations, people and their health experts often struggle to make sense of the data. My dissertation examines how to support collaborations between individuals and health experts using personal informatics data.

My research builds an empirical understanding of individual and collaboration goals around using personal informatics data, current practices of using this data to support collaboration, and challenges and expectations for integrating the use of this data into clinical workflows. These understandings help designers and researchers advance the design of personal informatics systems as well as the theoretical understandings of patient-provider collaboration.

Based on my formative work, I propose design and theoretical considerations regarding interactions between individuals and health experts mediated by personal informatics data. System designers and personal informatics researchers need to consider collaborations occurred throughout the personal tracking process. Patient-provider collaboration might influence individual decisions to track and to review, and systems supporting this collaboration need to consider individual and collaborative goals as well as support communication around these goals. Designers and researchers should also attend to individual privacy needs when personal informatics data is shared and used across different healthcare contexts. With these design guidelines in mind, I design and develop Foodprint, a photo-based food diary and visualization system. I also conduct field evaluations to understand the use of lightweight data collection and integration to support collaboration around personal informatics data. Findings from these field deployments indicate that photo-based visualizations allow both participants and health experts to easily understand eating patterns relevant to individual health goals. Participants and health experts can then focus on individual health goals and questions, exchange knowledge to support individualized diagnoses and recommendations, and develop actionable and feasible plans to accommodate individual routines….(More)”.