Author(s):
- Lena Mamykina Ph.D.
- Matthew E. Levine B.A.
- ,Patricia G. Davidson Ph.D.
- ,Arlene M. Smaldone Ph.D., C
- PNP, CDE,
- Noemie Elhadad Ph.D.
- David J. Albers Ph.D.
Abstract:
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.