Efficacy Study of Interactive Web Application for Problem Solving in Diabetes Management
Condition: Diabetes Mellitus
MeSH terms: Diabetes Mellitus
Study type: Interventional

Summary: 
      The main hypothesis of this research is that use of an informatics intervention for
      problem-solving in diabetes management, Mobile Diabetes Detective (MoDD), by individuals with
      type 2 diabetes will lead to positive improvements on a number of primary and secondary
      outcomes related to their health and their management of diabetes. The primary outcomes are a
      reduction in individuals' glycolated hemoglobin (HbA1c), improvement in their problem-solving
      abilities, and self-care behaviors. Secondary outcomes include a reduction in individuals'
      fasting blood glucose (BG); improvement in individuals' self-efficacy, and in emotional
      aspect of living with diabetes. We hypothesize that primary and secondary outcome effects
      will be sustained at three months and twelve months. Exploratory outcomes include a decrease
      in individuals' Cardiovascular Risk (Body Mass Index, Blood Pressure, Total, low-density
      lipoprotein (LDL) and high-density lipoprotein (HDL) Cholesterol levels, and Framingham
      Cardiovascular Risk Score). We also hypothesize that improvements in clinical outcomes
      (HbA1c, fasting BG and Cardiovascular Risk) will be mediated by the improvements in
      problem-solving abilities and self-efficacy.
    

Detailed description: 
      Well-developed problem-solving is essential to successful diabetes management results in
      better diabetes self-care behaviors, and leads to improvements in clinical outcomes.
      Problem-solving is central to many self-management and behavior change programs; the American
      Diabetes Association (ADA) includes problem-solving as a critical self-care behavior. Given
      the importance of problem solving skills, innovative diabetes education programs, such as
      Discovering Diabetes, have been developed and shown to be effective in fostering independent
      problem-solving.

      At the same time, many care management programs and diabetes education centers struggle with
      staffing shortages, limited funding, and competitive time demands. As a result, 50 to 80% of
      individuals with diabetes experience significant knowledge and skill deficits. Health
      Information Technology (HIT) can make successful interventions available to more diverse
      populations. At present, however, many HIT interventions target improved patient-clinician
      communication and logging and monitoring, rather than focusing more specifically on fostering
      problem-solving skills. Moreover, few HIT interventions have been rigorously evaluated in
      controlled trials. The main contribution of this research is a theoretically-grounded HIT
      intervention, Mobile Diabetes Detective (MoDD), that incorporates best practices and current
      guidelines for supporting and fostering individuals' problem-solving skills in context of
      diabetes self-management. In our prior work we developed and evaluated a mobile application
      for reflection and discovery in diabetes management, MAHI (Mobile Access to Health
      Information). MAHI helped individuals with diabetes capture diabetes-related experiences and
      reflect on them under a supervision of a diabetes educator. The proposed intervention, MoDD
      will further extend this prior work, specifically focusing on guided problem-solving through
      experimentation. The intervention will utilize an open source platform for disease
      self-management developed by the research team.

      If the results are achieved, the project will have significant impact both locally and
      globally. Locally, diabetes continues to be a major problem in NYC, particularly among
      disadvantaged populations, many of whom are served by the Health Resources and Services
      Administration (HRSA) funded Community Health Centers (CHCs) participating in this study. In
      the past 10 years, the number of people with diabetes in NYC has more than doubled. An
      estimated 530,000 adult New Yorkers have been diagnosed with diabetes, with another 265,000
      having diabetes but are unaware. In the HRSA funded CHCs in New York State, 8% of the adult
      patients have a diagnosis of diabetes. At the same time, our prior studies showed that
      despite such barriers as low health literacy or lower socio-economic status, disadvantaged
      populations in NYC can greatly benefit from informatics interventions that target health and
      wellness. The proposed research will use HIT to partially assuage the ongoing challenge of
      control and management of diabetes. The expected improvement in problem-solving skills has
      been shown to lead to improved self-care behaviors, such as a more careful diet and
      appropriate level of exercise, and significant reduction in HbA1c7, which in turn has been
      linked to reduction in diabetes-related complications. Thinking more broadly, this research
      can provide new insights into facilitating problem-solving in diabetes management with HIT,
      as an alternative to more traditional staff-intensive interventions.
    

Intervention name(s): Mobile Diabetes Detective (MoDD)
Intervention type(s): Behavioral
Intervention description(s): MoDD is a web-based application that is designed to help individuals with diabetes identify specific problems related to glycemic control, and engage in problem-solving process. MoDD includes a number of messages that explain its users the nature of various problems related to glycemic control, aspects of individuals' behaviors that might have contributed to these problems, and alternative behaviors that could help to improve glycemic control. In addition to these messages displayed on the MoDD website, study participants may receive SMS messages with reminders to test blood glucose, or to follow the selected new behavior.
Allocation: Randomized
Intervention model: Parallel Assignment
Masking: None (Open Label)
Primary purpose: Supportive Care
Keywords: Diabetes; Chronic disease; problem solving; self management