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Personalized Dietary Management in Type 2 Diabetes
Sponsor: NYU Langone Health
Summary
In a randomized trial of 255 participants with early-stage T2D, participants will be randomized to 1 of 3 groups: Standardized, Personalized, or a Usual Care Control (UCC). In the first phase, participants will be randomized with equal allocation to these 3 groups. In the second phase (current phase), the remaining participants will be randomized with equal allocation to the Standardized and UCC groups.
Key Details
Gender
All
Age Range
21 Years - 80 Years
Study Type
INTERVENTIONAL
Enrollment
294
Start Date
2021-12-14
Completion Date
2025-11-30
Last Updated
2025-09-18
Healthy Volunteers
No
Conditions
Interventions
Standardized
Participants are instructed to follow a Mediterranean-style diet. Dietary counseling is paired with SCT-based behavioral counseling, which focuses on the role played by self-referent thought in the maintenance of behavior change. Self-efficacy (e.g., the participant's confidence in their ability to engage in healthier behavior) is derived from four major sources of information: mastery experiences, social modeling, verbal persuasion, and physiological states. Participants self-monitor their diet using a mobile app and receive real-time feedback from the app on macronutrient distribution.
Usual Care Control (UCC)
Participants are instructed to follow a Mediterranean-style diet,
Personalized Guidance to Minimize Postprandial Glycemic Response (PPGR)
Participants are instructed to follow a Mediterranean-style diet. Dietary counseling is paired with SCT-based behavioral counseling, which focuses on the role played by self-referent thought in the maintenance of behavior change. Self-efficacy (e.g., the participant's confidence in their ability to engage in healthier behavior) is derived from four major sources of information: mastery experiences, social modeling, verbal persuasion, and physiological states. Participants self-monitor their diet using a mobile app and receive real-time feedback from the app on their predicted PPGR to meals and snacks at the time they enter them into their smart phone. PPGR predictions will be generated from a gut microbiome-based machine learning algorithm.
Locations (1)
NYU Langone Health
New York, New York, United States