Clinical trial NCT04757233: Personalizing Self-management in Diabetes - Pilot Study
Official title: Dynamically Tailoring Interventions for Problem-Solving in Diabetes Self-Management Using Self-Monitoring Data
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Type 2 Diabetes Mellitus
Keywords: GlucoType
Informatics
Diabetes
Lead sponsor: Columbia University
Sponsor class: OTHER
Collaborators: name: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
First posted: 2021-02-17
Enrollment: 20
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: Pre-post pilot study
Primary purpose: OTHER

Brief 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.

Eligibility criteria: Inclusion Criteria:

* Age 18-65 years
* A diagnosis of Type 2 Diabetes.
* A participant of the Washington Heights/Inwood Informatics Infrastructure for Comparative Effectiveness Research (WICER), a patient of the AIM clinic, or a patient of a participating Federally Qualified Health Center (FQHC) health center for at least 6 months
* Has participated in at least one diabetes education session at the participating site in the last 6 months
* Proficient in either English or Spanish
* Must own a basic cell phone

Exclusion Criteria:

* Pregnancy
* Presence of serious illness (e.g. cancer diagnosis with active treatment, advanced stage heart failure, multiple sclerosis)
* Presence of cognitive impairment
* Plans for leaving their healthcare provider in the next 12 months
* Does not have a computer and/or Internet access

Primary outcomes:
measure: Change in score on Summary of Diabetes Self-Care Activities Questionnaire (SDSCA)
timeFrame: From Baseline to 4 weeks
Secondary outcomes:
Other outcomes:
Locations:
city: New York
country: United States
facility: Clinical Directors Network
state: New York
city: New York
country: United States
facility: Columbia University Medical Center
state: New York
Study officials:
affiliation: Columbia University
name: Olena Mamykina, Ph.D.
role: PRINCIPAL_INVESTIGATOR
References:
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