Integrating behavior and context with glucose data to advance behavioral science and clinical care in diabetes

Recent advances in technology facilitate new ways of examining dynamic relationships between experiences, behavior, and glycemic variability that occur over time among individuals with diabetes. These technologies can also be used to guide “just-in-time” adaptive interventions. This chapter provides an overview of the importance of modeling individual trajectories in experience and behavior as related to glycemic variability and discusses how recent developments have simplified assessment of these relationships in the day-to-day life of people with diabetes. We propose that ecological momentary assessment is at the center of tracking patient-reported outcomes (PROs), behavior, and context on a daily basis. This approach allows for new ways to studycomplex associations between peoples’ experiences, mood, behavior, and glycemic control, and examine the role of contextual factors on these associations. However, big data generated by using new assessment technologies to track glucose and PROs pose new challenges for researchers, clinicians, and individuals with diabetes that need to be addressed in order to make these data useful for clinical practice. The insights generated with this approach contribute to the development and advancement of individualized and patient-centered assessment and treatments to improve diabetes outcomes.