Personalizing Self-management in Diabetes - Pilot Study
Condition: Type 2 Diabetes Mellitus
MeSH terms: Diabetes Mellitus; Diabetes Mellitus, Type 2
Study type: Interventional

Summary: 
      The goal of this study is to conduct a pilot feasibility study a novel informatics
      intervention, GlucoType (also called Platano for Latino users) that incorporates
      computational analysis of self-monitoring data to help individuals with type 2 diabetes
      personalize diabetes self-management strategies. This study will include 20 individuals with
      type 2 diabetes mellitus (T2DM) recruited from economically disadvantaged and medically
      underserved communities to test Platano for 4 weeks to assess its acceptability and
      feasibility. The main outcome measures include problem-solving abilities in diabetes
      (Diabetes Problem-Solving Inventory (DPSA)) and self-reported diabetes self-care (Summary of
      Diabetes Self-Care Activities Questionnaire (SDSCA)). In addition, this study will include a
      controlled laboratory experiment to assess whether participants can understand and follow
      personalized nutritional goals generated by Platano.
    

Detailed description: 
      Growing evidence highlights significant differences in individuals' physiology and glycemic
      function and their cultural, social, and economical circumstances that impact diabetes
      self-management. These discoveries paved the way for precision medicine-an approach to
      personalizing medical treatment to an individual's genetic makeup, clinical history, and
      lifestyle. Computational learning methods have been successfully used for identifying
      clinical phenotypes-observable manifestations of diseases. Studies showed the benefits of
      tailoring not only medical treatment, but also behavioral interventions; however, tailoring
      typically relies on expert identification of tailoring variables and decision rules, and on
      standard surveys. Data collected with self-monitoring can more accurately reflect an
      individual's behaviors and glycemic patterns, thus highlighting their "behavioral
      phenotypes", yet such data are rarely utilized in tailoring.

      The ongoing focus of this research is on facilitating problem-solving in diabetes
      self-management. Well-developed problem-solving skills are essential to diabetes management
      result in better diabetes self-care behaviors lead to improvements in clinical outcomes and
      can be fostered with face-to-face interventions. Previous research suggested problem
      identification and generation of alternatives as critical steps in problem-solving in
      diabetes. In previous work, the investigators developed an informatics intervention that
      relied on expert-generated knowledge for assisting individuals on these steps of
      problem-solving. In this pilot feasibility study, the investigators study an alternative
      solution that relies on computational pattern analysis of data collected with self-monitoring
      technologies to tailor the problem-solving assistance to individuals' unique behavioral
      phenotypes. The intervention, GlucoType uses computational learning methods to identify
      systematic patterns in individuals' diet, physical activity, and sleep, captured with
      custom-built and commercial self-monitoring technologies, and correlates these patterns with
      fluctuations in individuals' blood glucose levels. GlucoType then uses this information to 1)
      identify behavioral patterns associated with high glycemic excursion, 2) formulate
      personalized goals to modify these behaviors, 3) provide in-the-moment decision support to
      help individuals be more consistent in meeting their goals.
    

Intervention name(s): GlucoType
Intervention type(s): Behavioral
Intervention description(s): GlucoType is an mobile Health intervention for facilitating self-management in T2DM built for iPhone and Android smartphones. GlucoType includes a custom-built interface for low-burden capture of diet and blood glucose (BG) levels and relies on a commercial activity tracker, FitBit, for capture of sleep and physical activity. It then applies computational phenotyping techniques to identify patterns of associations between daily activities and changes in BG levels. GlucoType uses an expert system developed by our research team to translate identified phenotypes into automatically-generated personalized behavioral goals for improving glycemic control formulated in natural language.
Intervention model: Single Group Assignment
Intervention model description: Pre-post pilot study
Masking: None (Open Label)
Primary purpose: Other
Keywords: GlucoType; Informatics; Diabetes