From Personal Informatics to Personal Analytics: Investigating How Clinicians and Patients Reason About Personal Data Generated with Self-Monitoring in Diabetes

Diabetes self-management continues to present a significant challenge to millions of individuals around the world, as it often requires significant modifications to one’s lifestyle. The highly individual nature of the disease presents a need for each affected person to discover which daily activities have the most positive impact on one’s health and which are detrimental to it. Data collected with self-monitoring can help to reveal these relationships, however interpreting such data may be non-trivial. In this research we investigate how individuals with type 2 diabetes and their healthcare providers reason about data collected with self-monitoring and what computational methods can facilitate this process.