Mobile Regulation of Craving Training (mROC-T) to Improve Dietary Intake in Rural Adolescent Girls
Mobile Regulation of Craving Training (mROC-T) to Improve Dietary Intake in Rural Adolescent Girls
The goal of this clinical trial is to test if a regulation of craving training intervention in the form of a mobile phone app can increase fruit and vegetable intake in adolescent girls ages 14-18 years of age. The main questions it aims to answer are:
Randomization: Participants will be randomized to either an active mROC-T group (mROC-T+ or mROC-Tc) or a craving rating only (CRO) control group stratified by race/ethnicity and BMI%. We will use urn randomization for participant assignment.
Baseline and 1-year follow up in-person visit measures Oral glucose tolerance test (OGTT): We will use a standard NHANES oral glucose tolerance test (OGTT) to assess the participants' glucose disposal. Participants will be fasted for at least eight hours. A fasted blood draw will be performed via finger stick. We will analyze glucose concentration using the HemoCue Glucose 201 analyzer and HbA1c using Abbott Afinion™ HbA1c analyzer. The participants will then drink a glucose bolus dosed at 1.75g glucose/kg body weight. Subsequent samples will be taken at 30, 60, 90, and 120-minutes. During which time the participant will play the mROC-T game and record their diet recall of the previous day. Participants will complete the puberty development scale (PDS), which has shown good epidemiological validity and is a suitable control variable. We will also measure height, weight, waist circumference using NHANES protocols. We will measure trait food craving using the Food Craving Questionnaire (trait).
Overall model for aims 1 and 2: For both aims we will use mixed-effect models (MEM, allows us to model random intercepts for participants by location and random slopes for the intervention by time) and estimated marginal means (EMM, corrects for potential imbalanced data). Covariates (COV) are baseline PDS, FCQT, race/ethnicity, and age. Intervention is a categorical variable representing intervention group.
Aim1 assesses the effect of the intervention over 1 year compared to baseline in the mROC-T and CRO groups on HEI calculated monthly. Fixed effects will be considered significant at p≤ 0.02. HEI1-year ~ Intervention*x(time) + HEIbaseline + COV; where x() is the time function (see note below)
Time note: we plan on modeling time using linear, power polynomials (square and cubic), and logistic functions. We will compare the different time powered models using ANOVAs and consider p ≤ 0.05 a significant difference between models, and Akaike information criterion (AIC) to assess model parsimony.
Aim 1 expected results: Based on a 6% increase in HEI after a similar 3-month intervention, we hypothesize a clinically meaningful 10% increase in HEI after 1 year in the mROC-T groups regardless of valance, and thus a 6% reduction in T2D risk. Dr. Kober has not seen a difference between the positive or negative mROC-T conditions on eating behavior, therefore we do not expect a difference.
Aim2 assesses the effect of the mROC-T and CRO intervention over 1 year on change in anthropometric (BMI%, BMIz, WC) and HbA1c measures in participants (n=40) with overweight or obesity (BMI% > 85th) and compares them to recommended weight participants matched for age, race/ethnicity, and PDS (n=40).
Absolute change in Outcome1-year ~ Intervention* BMI group(baseline) + Outcomebaseline + COV; where Outcome is BMI%, BMIz, WC, or HbA1c; COV includes baseline BMI%, WC, and/or HbA1c if not the outcome
Aim 2 Expected Results: We hypothesize a 0.25 reduction in BMIz (about a 2% decrease in BMI%) and a 3% decrease in WC in the intervention group only. A decrease of 0.25 BMIz is related to improved insulin sensitivity in adolescents. Given the expected improvement in HEI and BMIz associated with improved insulin sensitivity/lower T2D risk we hypothesize a clinically meaningful reduction in HbA1c of 0.5%.
Exploratory analysis: We will also use the model from aim 2 to explore glucose AUC and 1-hr glucose value. We will test the effect of the intervention on glucose curves using binary logistic regression.
Inclusion Criteria:
Exclusion Criteria:
Biologically male
Self-identify as male
BMI percentile (for age and sex) < 5th%
Diagnosis from a medical profession of any of the following conditions, syndromes, diseases that may affect growth, glucose metabolism, blood clotting, cognitive development*: