Prediabetes
Conditions
Keywords
Prediabetes, Glucose control, Feedback, Health education, Artificial intelligence, Behavioral analysis
Brief summary
The objective of this project is to deliver a behavior health education video series that combines wearable continuous glucose monitoring (CGM) with smartphone feedback and video clips generated by artificial intelligence (AI) software to improve glycemic control among individuals with prediabetes. The goal is to prevent transition to type 2 diabetes and advanced metabolic complications.
Detailed description
A health education video series, ¡Mi Control!, led by community health workers known as Promotores de Salud (PdS), will be integrated with continuous glucose monitoring (CGM) to support self-monitoring of glucose levels and excursions. Study G2 is a Phase 0 feasibility study enrolling 30 Spanish- and/or English-speaking PdS who test positive for prediabetes via a finger-prick A1c% screening. Participants will wear CGM devices for 20 days, with glucose data masked for the first 10 days and unmasked for the next 10 days. During the unmasked phase, participants will receive daily health education videos on their smartphones. The study will compare the duration of glucose excursions between the masked and unmasked phases to assess the impact of CGM feedback and health education on glucose regulation.
Interventions
DEVICECGM
The CGM sensor is worn on the back of the arm and glucose levels are recorded continuously but levels are not shown on the participant's phone app in phase A.
Sponsors
David S Black, PhD
National Institute on Minority Health and Health Disparities (NIMHD)
Study design
Allocation
NA
Intervention model
SEQUENTIAL
Primary purpose
PREVENTION
Masking
NONE
Masking description
Participants are blinded to glucose values during Phase A. The CGM system generates automated records of glucose and does not require observation from an outcomes assessor. The investigator does not engage with participants and becomes aware of unmasking after trial is completed.
Intervention model description
An AB sequential condition design with CGM automated continuous glucose recording with glucose level masked and unmasked phases.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Prediabetes by finger prick blood A1C% * Centers for Disease Control and Prevention (CDC) prediabetes risk test score of 5 or higher * Willingness to wear CGM sensor * Community health worker
Exclusion criteria
* Currently pregnant * Less than 18 years of age, which is adult in California * Diagnosed with any disorder that interferes with glucose * Influential medical disorder/event affecting ability to participate in study * Incompatible smartphone device not pairing with Dexcom G6 app
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Glucose levels | Maximum of 20 days of continuous wear | Mean glucose in mg/dL |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Time out of range > 140 mg/dL (TOR) | Maximum of 20 days of continuous wear | Time out of range \> 140 mg/dL (TOR) |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATORDavid S Black, PhD MPH
University of Southern California Keck School of Medicine
Outcome results
None listed