An interventional study of GlucoType in Type 2 Diabetes Mellitus, sponsored by Columbia University. Completed at 2 sites in United States. Open to participants aged 18 Years to 65 Years. Per ClinicalTrials.gov, last updated 2024-12-12.
Sponsored by Columbia University · Not applicable, Interventional, and Other
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.
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.
9,589 studies on the registry are indexed under Diabetes Mellitus, Type 2; 1,332 are open to participants now.
This study's enrollment of 20 is below the median of 81 across 7,713 interventional studies indexed under Diabetes Mellitus, Type 2.
Browse Diabetes Mellitus, Type 2 studies →Columbia University is the lead sponsor of 1,135 studies on the registry; 195 are open to participants now.
Of its 179 completed or terminated interventional studies of FDA-regulated products, 146 (82%) have results posted.
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Exclusion Criteria:
Intervention: GlucoType Single arm study; all participants assigned to use the intervention
Behavioral: GlucoType
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.
Change in score on Summary of Diabetes Self-Care Activities Questionnaire (SDSCA)
Change in score on Summary of Diabetes Self-Care Activities Questionnaire (SDSCA) - 12-item with 5 sub-scales (diet, exercise, home blood glucose testing, foot care, smoking status). The respondent is asked how many days in the past week he/she performed the behavior (ranges from 0 to 7); higher scores indicates higher performance.
Time frame: From Baseline to 4 weeks
This study is completed, as verified in Dec 2024. You cannot join it, but the record below documents what was studied.
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