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| ID | Type | Description | Link |
|---|---|---|---|
| 11-22-JDFN-03 | Other Grant/Funding Number | American Diabetes Association |
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| Name | Class |
|---|---|
| American Diabetes Association | OTHER |
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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 Afinionâ„¢ 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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Positive regulation of craving training | Experimental | Participants randomized into the active mROC-T intervention (mROC-T+) will:
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| Critical regulation of craving training | Experimental | Participants randomized into the active mROC-T intervention (mROC-Tc) will:
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| Control | Sham Comparator | Participants randomized to the control (CRO) group will:
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Regulation of craving training | Behavioral | Participants randomized into the active mROC-T intervention (mROC-T+ or mROC-Tc) will: 1. Read a brief essay about either the social justice benefits of eating healthy foods (POSITIVE, mROCT+) or critical of social justice aspects of unhealthy foods (CRITICAL, mROC-Tc). 2. Participants complete six free-response questions to ensure that they understood the essays. 3. Participants are instructed to use the information from the essay. They are shown a cue matching the essay they read either: "Think Positive" or "Think Critically". 4. They are shown an image of a healthy food (no added sugar and < 2g/serving saturated fat) OR an image of an unhealthy food (contain added sugar and/or >4.5g/serving saturated fat). 5. Finally, the participant is asked to rate how strong their craving is for the pictured food. The participant indicates their craving on a VAS from 1 (No craving at all) to 5 (Very high craving) |
| Measure | Description | Time Frame |
|---|---|---|
| Healthy eating index (HEI) | Participants will provide diet recalls twice per month. The participants will complete the diet recalls using the Automated Self Administered 24 hour recall (ASA-24) platform. The ASA-24 computes the necessary variables to calculate the healthy eating index (HEI). HEI is a measure of overall diet quality according to the Dietary Guidelines for Americans and is scored from 0-100 (100 being the best possible diet). HEI scores will be calculated per month (mean of diet recalls per person) using the most recent HEI scoring algorithm as recommended for intervention studies. | 1 year |
| Measure | Description | Time Frame |
|---|---|---|
| Body mass index | We will measure height (cm) and weight (kg) in triplicate to the nearest 0.1. Using the height and weight measurement we will calculate body mass index (BMI). We will then use the participants' age and biological sex to derive BMI percentile (BMI%) for age and sex and BMI z-score (BMIz) for age and sex. | 1 year |
| Measure | Description | Time Frame |
|---|---|---|
| Blood sugar dynamics | To measure blood sugar dynamics we will calculate glucose curves will be calculated over the two-hour oral glucose tolerance test period. We will use the mono and biphasic definitions from Olivieri et al. | 1 year |
| Insulin sensitivity |
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*:
Currently we are studying participants who are biologically female and who consider themselves female.
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| Name | Affiliation | Role |
|---|---|---|
| Grace Shearrer, PhD | University of Wyoming | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Wyoming | Laramie | Wyoming | 80270 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 27842263 | Background | Jensen CD, Duraccio KM, Barnett KA, Stevens KS. Appropriateness of the food-pics image database for experimental eating and appetite research with adolescents. Eat Behav. 2016 Dec;23:195-199. doi: 10.1016/j.eatbeh.2016.10.007. Epub 2016 Oct 27. | |
| 26644270 | Background | Boswell RG, Kober H. Food cue reactivity and craving predict eating and weight gain: a meta-analytic review. Obes Rev. 2016 Feb;17(2):159-77. doi: 10.1111/obr.12354. Epub 2015 Dec 8. |
| Label | URL |
|---|---|
| Preregistration | View source |
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We will share our data 6-months after the publication of our main effects paper. Access to trial IPD can be requested by qualified researchers engaging in independent scientific research, and will be provided following review and approval of a research proposal and Statistical Analysis Plan (SAP) and execution of a Data Sharing Agreement (DSA). Requests can be made to Dr. Shearrer.
We will share our data 6-months after the publication of our main effects paper for up to 24 months
Data that will be shared: All of the individual participant data collected during the trial, after de-identification. Other documents that will be available: Study Protocol, Statistical Analysis Plan, Informed Consent Form, Clinical Study Report, Analytic Code Timeframe: Beginning 6 months and ending 24 months following article publication Who is able to access the data: Researchers who provide a methodologically sound proposal with delineation of publication expectations and authorship outlined.
Types of analysis: To achieve aims in the approved proposal. Mechanism data will be made available: Proposals should be directed to Dr. Shearrer. To gain access, data requestors will need to sign a data access agreement and submit a proposal.
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| ID | Term |
|---|---|
| D009765 | Obesity |
| D003924 | Diabetes Mellitus, Type 2 |
| ID | Term |
|---|---|
| D050177 | Overweight |
| D044343 | Overnutrition |
| D009748 | Nutrition Disorders |
| D009750 | Nutritional and Metabolic Diseases |
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| Blood sugar |
We will measure blood sugar using hemoglobin A1c (HbA1c). HbA1c reflects an individual's average blood sugar over the past 2-3 months. |
| 1 year |
We will use the quantitative insulin-sensitivity check index (QUICKI) to approximate insulin sensitivity using the fasted values (before glucose consumption).
| 1 year |
| 31235156 | Background | Miri SF, Javadi M, Lin CY, Griffiths MD, Bjork M, Pakpour AH. Effectiveness of cognitive-behavioral therapy on nutrition improvement and weight of overweight and obese adolescents: A randomized controlled trial. Diabetes Metab Syndr. 2019 May-Jun;13(3):2190-2197. doi: 10.1016/j.dsx.2019.05.010. Epub 2019 May 22. |
| 30420496 | Background | Boswell RG, Sun W, Suzuki S, Kober H. Training in cognitive strategies reduces eating and improves food choice. Proc Natl Acad Sci U S A. 2018 Nov 27;115(48):E11238-E11247. doi: 10.1073/pnas.1717092115. Epub 2018 Nov 12. |
| 27621440 | Background | Bryan CJ, Yeager DS, Hinojosa CP, Chabot A, Bergen H, Kawamura M, Steubing F. Harnessing adolescent values to motivate healthier eating. Proc Natl Acad Sci U S A. 2016 Sep 27;113(39):10830-5. doi: 10.1073/pnas.1604586113. Epub 2016 Sep 12. |
| Data safety monitoring plan | View source |
| D001835 |
| Body Weight |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D004700 | Endocrine System Diseases |