Dynamically Tailored Behavioral Interventions in Diabetes

NCT ID: NCT04226027

Last Updated: 2025-07-30

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 300 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2020-01-17
• Study Completion Date: 2025-03-30

Brief Summary

In this project, the investigators will evaluate the efficacy of a novel approach to personalizing behavioral interventions for self-management of type 2 diabetes (T2DM) to individuals' behavioral and glycemic profiles discovered using computational learning and self-monitoring data. This study is a two-arm randomized controlled trial with n=280 participants recruited from the participating Federally Qualified Health Centers (FQHCs). The participants will be randomly assigned to the intervention group and the usual care (control) group with 1-1 allocation ratio. Half of the participants (n=140) will be randomly assigned to a usual care (control) group. Both groups will receive standard diabetes education at their respective FQHC site. In addition, the experimental group will receive instructions to use T2.coach for a minimum of 6 months.

Detailed Description

One of the main difficulties in managing diabetes is that each affected individual requires personally tailored combination of diet, exercise, and medication to effectively control their blood sugar. Rather than strictly following a doctor's prescription, individuals need to carefully examine their lifestyle choices and their impact on their health. Independent learning, experimentation and problem solving become of great importance. However, they can be challenging for individuals with diabetes. In this project, the investigators will refine and evaluate a novel intervention for diabetes self-management that uses computational analysis of self-monitoring data to help individuals with type 2 diabetes identify what daily activities, including consumption of meals, physical activity, and sleep, have impact on blood glucose levels, and suggest modifications to these daily activities to improve blood glucose levels.

Growing evidence highlights significant differences in glycemic function and cultural, social, and economical circumstances of individuals with type 2 diabetes (T2DM) that impact their self-management. Precision medicine strives to personalize medical treatment to an individual's genetic makeup, computationally discovered clinical phenotypes and lifestyle. Studies showed the benefits of tailoring not only medical treatment, but also behavioral interventions. Yet, currently, personalization of self-management in T2DM requires each individual to engage in discovery, reflection, and problem-solving-critical but cognitively demanding activities-or to rely on their healthcare providers. Both of these may present considerable barriers to individuals from medically under-served low income communities. Mobile health (mHealth) solutions in T2DM bring promise of reaching wider populations in need of self-management; however, few such solutions provide assistance with personalizing self-management behaviors. Ongoing efforts on personalizing behavioral interventions outside of T2DM focus on tailoring behavior modification techniques to individuals' psycho-social characteristics, such as self-efficacy ), and tailoring delivery of intervention to individuals' context rather than on personalizing self-management strategies.

The ongoing focus of this research is on developing informatics interventions for diabetes self-management, with a specific focus on discovery with self-monitoring data and on problem-solving for improving glycemic control. In the proposed research the investigators introduce T2.coach, an mHealth intervention that uses computational analysis of self-monitoring data to identify behavioral patterns associated with poor glycemic control and formulate personalized behavioral goals for changing problematic behaviors. This study will evaluate T2.coach's efficacy in a two-arm RCT with stratified randomization conducted with Clinical Directors Network (CDN), a well-recognized primary care practice-based research network (PBRN) of Federally Qualified Health Centers (FQHCs), and Agency for Healthcare Research and Quality (AHRQ)-designated Center of Excellence (P30) for Practice-based Research and Learning.

Conditions

Type 2 Diabetes

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: OTHER
• Blinding Strategy: NONE

Study Groups

T2.coach

Participants receive standard care (diabetes self-management education provided by their Federally Qualified Community Health Center) and are asked to use T2.coach for 6 months.

Group Type: EXPERIMENTAL

T2.coach

Intervention Type: BEHAVIORAL

T2.coach is a smartphone app for low-burden capture of diet and blood glucose (BG) levels and for reviewing past records, integrated with FitBit for captured of physical activity and sleep. All captured data are sent to the computational inference engine that uses machine learning methods and expert system to formulate personalized behavioral goals. Examples of behavioral goals include the following: "For high carbohydrate breakfasts, reduce your carbs to be about 1 carb choice. Examples of 1 carb choice are 1 slice of whole wheat toast, 1 cup of oatmeal, or 1 apple." The T2.coach chatbot companion uses text messages to help individuals set goals that are consistent with evidence based guidelines for diabetes self-management, inferences on data captured with T2.coach, and their own preferences, as well as send individuals goal reminders and prompts for reflection on goal achievement.

Control

Participants receive standard care (diabetes self-management education provided by their Federally Qualified Community Health Center).

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

T2.coach

T2.coach is a smartphone app for low-burden capture of diet and blood glucose (BG) levels and for reviewing past records, integrated with FitBit for captured of physical activity and sleep. All captured data are sent to the computational inference engine that uses machine learning methods and expert system to formulate personalized behavioral goals. Examples of behavioral goals include the following: "For high carbohydrate breakfasts, reduce your carbs to be about 1 carb choice. Examples of 1 carb choice are 1 slice of whole wheat toast, 1 cup of oatmeal, or 1 apple." The T2.coach chatbot companion uses text messages to help individuals set goals that are consistent with evidence based guidelines for diabetes self-management, inferences on data captured with T2.coach, and their own preferences, as well as send individuals goal reminders and prompts for reflection on goal achievement.

Intervention Type: BEHAVIORAL

Eligibility Criteria

Inclusion Criteria

• Patient of the health center for ≥ 6 months and a diagnosis of T2DM
• HbA1c ≥ 8.0,
• Aged 18 to 65 years
• Attends diabetes education program at the health center
• Owns a basic mobile phone
• Proficient in either English or Spanish

Exclusion Criteria

• Pregnant
• Presence of severe cognitive impairment (recorded in patient chart),
• Existence of other serious illnesses (e.g. cancer diagnosis with active treatment, advanced stage heart failure, dialysis, multiple sclerosis, advanced retinopathy, recorded in patient chart),
• Plans for leaving the FQHC in the next 12 months,
• Participation in the previous trial of diabetes self-management technologies

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Clinical Directors Network

NETWORK

Sponsor Role: collaborator

University of Colorado, Denver

OTHER

Sponsor Role: collaborator

National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

NIH

Sponsor Role: collaborator

Columbia University

OTHER

Sponsor Role: lead

Responsible Party

Olena Mamykina, PhD

Associate Professor of Biomedical Informatics

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Olena Mamykina, PhD

Role: PRINCIPAL_INVESTIGATOR

Columbia University

Locations

Clinical Directors Network

New York, New York, United States

Columbia University Irving Medical Center

New York, New York, United States

Countries

United States

Other Identifiers

R01DK113189

Identifier Type: NIH

Identifier Source: secondary_id

View Link

AAAS5528

Identifier Type: -

Identifier Source: org_study_id