Clinical trial NCT04226027: Dynamically Tailored Behavioral Interventions in Diabetes
Official title: Dynamically Tailoring Interventions for Problem-Solving in Diabetes Self-Management Using Self-Monitoring Data - a Randomized Controlled Trial (RCT)
Study type: INTERVENTIONAL
Overall status: ACTIVE_NOT_RECRUITING
Phase: NA
Conditions: Type 2 Diabetes
Keywords: Mobile health
Mhealth
T2.coach
Smartphone app
Lead sponsor: Columbia University
Sponsor class: OTHER
Collaborators: name: Clinical Directors Network
name: University of Colorado, Denver
name: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
First posted: 2020-01-13
Enrollment: 280
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Two-arm RCT with 1:1 randomization at participant level, with stratified randomization to balance by clinical site, sex, and language, evaluate the efficacy of the T2.coach intervention
Primary purpose: OTHER

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.

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

Primary outcomes:
measure: Change in HbA1c value
timeFrame: Baseline, 6 months, 12 months
Secondary outcomes:
measure: DPSI Score
timeFrame: Baseline, 6 months, 12 months
measure: SCA-I Score
timeFrame: Baseline, 6 months, 12 months
measure: DSES Score
timeFrame: Baseline, 6 months, 12 months
measure: PAID Score
timeFrame: Baseline, 6 months, 12 months
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 Irving Medical Center
state: New York
Study officials:
affiliation: Columbia University
name: Olena Mamykina, PhD
role: PRINCIPAL_INVESTIGATOR
References: