In this research we propose to develop and evaluate a new breed of decision-support tools that will facilitate problem-solving in diabetes using a combination of an interactive website and SMS messaging server. Past research has provided convincing evidence that well-developed problem-solving abilities are essential to successful diabetes management, result in better diabetes self-care behaviors, and help individuals with diabetes improve their health. Health Information Technology (HIT) can assist individuals in developing the necessary problem-solving abilities. At present, however, many HIT interventions target improved patient- clinician communication and improved logging and monitoring, rather than focusing more specifically on fostering problem-solving skills. The long term goal of this research is to develop theoretically-grounded, practice-based HIT interventions for facilitating effecting diabetes self-management through problem-solving, experimentation, and discovery. In our prior work we identified a set of strategies used by diabetes educators to foster diabetes problem-solving. In this project we will use these strategies to develop a decision-support tool-Mobile Diabetes Detective (MoDD)-that will help individuals with diabetes to identify specific, personally significant challenges in diabetes management (such as undesirably high fasting Blood Glucose (BG) values), generate alternative strategies for addressing these challenges (such as introducing a bedtime snack), evaluate these strategies through simple experiments (measuring BG before and after an activity of interest), and monitor their outcomes (through interactive visualization or simple textual summary). A wizard-like interface on an interactive website will help individuals develop personal learning plans. SMS messages to individuals'mobile phones will remind them of the planned experiments and allow reporting the results of the experiments. MoDD will be built using an open source platform for chronic disease self- management (Salud!) developed by the research team;we plan to release the resulting tool to the research community. We will evaluate the effectiveness of MoDD in a Randomized Controlled Trial (RCT) and assess its potential public health impact using RE-AIM framework.

Public Health Relevance

The proposed HIT solution will be designed to help individuals with diabetes improve their problem-solving abilities, develop better self-care behaviors and improve their health. As such, this project has a high potential public health impact.

National Institute of Health (NIH)
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Research Project (R01)
Project #
Application #
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Hunter, Christine
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Columbia University (N.Y.)
Internal Medicine/Medicine
Schools of Medicine
New York
United States
Zip Code
Heitkemper, Elizabeth M; Mamykina, Lena; Travers, Jasmine et al. (2017) Do health information technology self-management interventions improve glycemic control in medically underserved adults with diabetes? A systematic review and meta-analysis. J Am Med Inform Assoc 24:1024-1035
Mamykina, Lena; Heitkemper, Elizabeth M; Smaldone, Arlene M et al. (2017) Personal discovery in diabetes self-management: Discovering cause and effect using self-monitoring data. J Biomed Inform 76:1-8
Albers, David J; Levine, Matthew; Gluckman, Bruce et al. (2017) Personalized glucose forecasting for type 2 diabetes using data assimilation. PLoS Comput Biol 13:e1005232
Heitkemper, Elizabeth M; Mamykina, Lena; Tobin, Jonathan N et al. (2017) Baseline Characteristics and Technology Training of Underserved Adults With Type 2 Diabetes in the Mobile Diabetes Detective (MoDD) Randomized Controlled Trial. Diabetes Educ 43:576-588
Mamykina, Lena; Heitkemper, Elizabeth M; Smaldone, Arlene M et al. (2016) Structured scaffolding for reflection and problem solving in diabetes self-management: qualitative study of mobile diabetes detective. J Am Med Inform Assoc 23:129-36
Mamykina, Lena; Levine, Matthew E; Davidson, Patricia G et al. (2016) Data-driven health management: reasoning about personally generated data in diabetes with information technologies. J Am Med Inform Assoc 23:526-31
Cole-Lewis, Heather J; Smaldone, Arlene M; Davidson, Patricia R et al. (2016) Participatory approach to the development of a knowledge base for problem-solving in diabetes self-management. Int J Med Inform 85:96-103
Mamykina, Lena; Smaldone, Arlene M; Bakken, Suzanne R (2015) Adopting the sensemaking perspective for chronic disease self-management. J Biomed Inform 56:406-17