Dynamically Tailored Behavioral Interventions in Diabetes

Dynamically Tailoring Interventions for Problem-Solving in Diabetes Self-Management Using Self-Monitoring Data - a Randomized Controlled Trial (RCT)

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.

Study Overview

• Status: Recruiting
• Conditions: Type 2 Diabetes
• Intervention / Treatment: Behavioral: T2.coach

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.

• Study Type: Interventional
• Enrollment (Anticipated): 280
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • New York
    • New York, New York, United States, 10032
      • Recruiting
      • Columbia University Irving Medical Center
      • Contact: Olena Mamykina, PhD; Phone Number: 212-305-3923; Email: om2196@cumc.columbia.edu
    • New York, New York, United States, 10018
      • Recruiting
      • Clinical Directors Network
      • Contact: Andrea Cassells, MPH; Phone Number: 212-382-0699; Email: acass@cdnetwork.org
      • Sub-Investigator: Andrea Cassells, MPH
      • Principal Investigator: Jonathan Tobin, PhD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 65 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: 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.	Behavioral: 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.
No Intervention: Control; Participants receive standard care (diabetes self-management education provided by their Federally Qualified Community Health Center).

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Change in HbA1c value	Hemoglobin A1c	Baseline, 6 months, 12 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
DPSI Score	Diabetes Problem-Solving Inventory (DPSI) is a 9-item, open-ended questionnaire. Answers are coded on a Likert 5-point scale (1-very poor strategy; 5-excellent strategy). The final score ranges from 1 (lowest) to 5 (highest) and an overall score ≤3 indicates poor diabetes problem solving, so a higher score indicates a better outcome.	Baseline, 6 months, 12 months
SCA-I Score	Diabetes Self-Care Inventory (SCA-I) is a 15-item 5-point Likert scale (1-never engage; 5-always engage) for measuring different aspects of diabetes self-care. The final score ranges from 1 (lowest) to 5 (highest) with a higher score indicating better self-care (better outcome).	Baseline, 6 months, 12 months
DSES Score	Diabetes Self-Efficacy Scale (DSES) is a 15-item 10-point Likert scale (1-not at all confident; 4-totally confident) that measures the belief that one can self-manage one's own health, adapted to diabetes. The final score ranges from 1 (lowest) to 4 (highest) with a lower score indicating poor self-efficacy (worse outcome).	Baseline, 6 months, 12 months
PAID Score	Problem Areas in Diabetes (PAID) is a 20-item 5-point Likert scale (0-not a problem; 4-very serious problem) that measures the emotional aspect of living with diabetes. The final score ranges from 0 (lowest) to 80 (highest), with a higher score indicating greater emotional discomfort (worse outcome).	Baseline, 6 months, 12 months

Collaborators and Investigators

• Sponsor: Columbia University
• Collaborators: University of Colorado, Denver; National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK); Clinical Directors Network
• Investigators: Principal Investigator: Olena Mamykina, PhD, Columbia University

Study record dates

Study Major Dates

• Study Start (Actual): January 17, 2020
• Primary Completion (Anticipated): September 30, 2023
• Study Completion (Anticipated): September 30, 2023

Study Registration Dates

• First Submitted: January 9, 2020
• First Submitted That Met QC Criteria: January 9, 2020
• First Posted (Actual): January 13, 2020

Study Record Updates

• Last Update Posted (Actual): April 6, 2022
• Last Update Submitted That Met QC Criteria: April 4, 2022
• Last Verified: April 1, 2022

More Information

Terms related to this study

• Keywords: Mobile health; Smartphone app; Mhealth; T2.coach
• Additional Relevant MeSH Terms: Glucose Metabolism Disorders; Metabolic Diseases; Endocrine System Diseases; Diabetes Mellitus
• Other Study ID Numbers: AAAS5528; R01DK113189 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No
• IPD Plan Description: Due to the sensitive nature of individual participant data (IPD) collected in this study, the dataset will only be made available for other potential users under a data-sharing agreement that provides for: (1) a commitment to using the data only for research purposes and not to identify any individual participant; (2) a commitment to securing the data using appropriate computer technology; and (3) a commitment to destroying or returning the data after analyses are completed.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No