CTSI Pilot: Improving Adherence to Diabetic Diet

NCT04051008 | Completed Results

• 2 other identifiers: STUDY00003054UL1TR001412
• Study Type: interventional
• Target: 65
• Locations: 1 country
• Sites: 1

Brief Summary

425 million adults live with diabetes worldwide, and the prevalence of Type 2 diabetes is rising. Dietary approaches are recommended for weight control and diabetes management, but modern environments, characterized by plentiful, unhealthy foods, pose challenges to selecting a healthy diet. Behavioral economics offers a framework for modifying the food environment to encourage individuals with diabetes to select low-calorie and low-sugar foods. The goal of this study is to test novel approaches informed by behavioral economics to promote healthier grocery shopping among diabetic patients. Adults who have Type 2 diabetes or who are at risk for developing Type 2 diabetes will be recruited. Participants will be randomly assigned to 1 of 2 interventions or a control group in which they will shop in-person as usual. The Online intervention will utilize online grocery shopping to promote healthier purchasing. The Defaults intervention will augment this intervention, showing participants a default shopping cart pre-filled with items that correspond to the DASH diet and diabetic diet goals, which they may modify as they like. Receipt data will be collected to quantify the alignment of purchases with diabetic diet goals before, during, and after interventions. Purchases lower in calories, carbohydrates, and sugar and higher in nutritional quality (DASH diet score) are expected in the Defaults group; the Online group is expected to have intermediary results between Defaults and Controls. The investigators will also explore effects of the interventions on spending and dietary intake. This study is intended to demonstrate the efficacy of strategies that leverage behavioral economics principles to make the purchasing of healthier foods easier. The strategies have translational significance as they could be incorporated into clinical treatment, with the potential to improve dietary intake, glucose regulation, weight, and medication needs among diabetic patients.

Conditions

Diabetes Mellitus, Type 2; Diet, Healthy; Pre-diabetes

Keywords

grocery shopping; online shopping; diabetes; behavioral economics; diet

Outcome Measures

Primary Outcomes (12)

• Calories Purchased

  Receipt data will be entered into Nutritionist Pro software and will be used to calculate total calories purchased from target grocery stores.

  At post-test (week 5)

• Calories Purchased

  Receipt data are entered into Nutritionist Pro software and used to calculate total calories purchased from target grocery stores each week. For this analysis, the weekly values were aggregated, such that the least squares means reported herein represent average calories purchased across all 5 weeks, in the context of the repeated measures ANOVA model.

  Weeks 1-5 (testing overall main effect of study group)

• Change in Calories Purchased

  Receipt data will be entered into Nutritionist Pro and will be used to calculate changes in total calories purchased from target grocery stores.

  Baseline (week 1), intervention (weeks 2-4), and post-test (week 5)

• Carbohydrates Purchased

  Receipt data will be entered into Nutritionist Pro and will be used to calculate carbohydrates purchased from target grocery stores.

  At post-test (week 5)

• Carbohydrates Purchased

  Receipt data are entered into Nutritionist Pro and used to calculate carbohydrates purchased from target grocery stores each week. For this analysis, the weekly values were aggregated, such that the least squares means reported herein represent average carbohydrates (grams) purchased across all 5 weeks, in the context of the repeated measures ANOVA model.

  Weeks 1-5 (testing overall main effect of study group)

• Change in Carbohydrates Purchased

  Receipt data will be entered into Nutritionist Pro and will be used to calculate changes in carbohydrates purchased from target grocery stores.

  Baseline (week 1), intervention (weeks 2-4), and post-test (week 5)

• Sugars Purchased

  Receipt data will be entered into Nutritionist Pro and will be used to calculate sugars purchased from target grocery stores.

  At post-test (week 5)

• Sugars Purchased

  Receipt data are entered into Nutritionist Pro software and used to calculate total sugar purchased from target grocery stores each week. For this analysis, the weekly values were aggregated, such that the least squares means reported herein represent average sugar (grams) purchased across all 5 weeks, in the context of the repeated measures ANOVA model.

  Weeks 1-5 (testing overall main effect of study group)

• Change in Sugars Purchased

  Receipt data will be entered into Nutritionist Pro and will be used to calculate changes in sugars purchased from target grocery stores.

  Baseline (week 1), intervention (weeks 2-4), and post-test (week 5)

• Nutritional Quality of Purchases

  Nutritional quality of grocery purchases was created using indicators adapted from Fung 2008: fruit, vegetables, nuts/legumes, whole grains, low-fat dairy, sodium, red/processed meat, carbohydrates. Quintiles were calculated for each, using the number of items from a food group or sodium \[mg\] and carbohydrates \[g\] purchased that week. Resulting scores from 1-5 indicate whether participants were lower or higher on each indicator (red meat, sodium, carbohydrates reverse scored). The 8 scores were summed for a total nutritional quality score ranging from 8 to 40 (40=highest nutritional quality).

  At post-test (week 5)

• Nutritional Quality of Purchases

  Nutritional quality of grocery purchases was created using indicators adapted from Fung 2008: fruit, vegetables, nuts/legumes, whole grains, low-fat dairy, sodium, red/processed meat, carbohydrates. Quintiles were calculated for each, using the number of items from a food group or sodium \[mg\] and carbohydrates \[g\] purchased that week. Resulting scores from 1-5 indicate whether participants were lower or higher on each indicator (red meat, sodium, carbohydrates reverse scored). The 8 scores were summed for a total nutritional quality score ranging from 8 to 40 (40=highest nutritional quality).

  Weeks 1-5 (testing overall main effect of study group)

• Change in Nutritional Quality of Purchases

  Nutritional quality of grocery purchases was created using indicators adapted from Fung 2008: fruit, vegetables, nuts/legumes, whole grains, low-fat dairy, sodium, red/processed meat, carbohydrates. Quintiles were calculated for each, using the number of items from a food group or sodium \[mg\] and carbohydrates \[g\] purchased that week. Resulting scores from 1-5 indicate whether participants were lower or higher on each indicator (red meat, sodium, carbohydrates reverse scored). The 8 scores were summed for a total nutritional quality score ranging from 8 to 40 (40=highest nutritional quality)

  Baseline (week 1), intervention (weeks 2-4), and post-test (week 5)

Secondary Outcomes (4)

• Dollars Spent

  At post-test (week 5)

• Dollars Spent

  Weeks 1-5 (testing overall main effect of study group)

• Change in Dollars Spent

  Baseline (week 1), intervention (weeks 2-4), and post-test (week 5)

• Nutritional Quality of Foods Consumed

  At post-test (week 5)

Study Arms (3)

Group 1 - Control

NO INTERVENTION

No intervention - participants will shop in-person as they usually would. Participants will receive recipe cards that follow the evidence-based DASH diet and correspond to dietary recommendations for diabetic patients.

Group 2 - Online

EXPERIMENTAL

Participants will utilize online grocery shopping (shopping at a local grocery store via an online platform). Participants will also receive recipe cards that follow the evidence-based DASH diet and correspond to dietary recommendations for diabetic patients.

Behavioral: Online - use of online grocery shopping platform

Group 3 - Default

EXPERIMENTAL

The default intervention will augment the Online intervention, showing participants a default cart when they log into their online grocery shopping accounts. They will be told that their cart has been filled with items that conform to a diabetic diet and can be used to make recipes from the provided recipe cards, and that they can modify it as they like. Participants will also receive recipe cards that follow the evidence-based DASH diet and correspond to dietary recommendations for diabetic patients.

Behavioral: Default - use of online grocery shopping platform with default shopping carts

Interventions

Online - use of online grocery shopping platform BEHAVIORAL

The Online intervention will utilize online grocery shopping (shopping at a local grocery store via online platform).

Group 2 - Online

Default - use of online grocery shopping platform with default shopping carts BEHAVIORAL

The Defaults intervention, will augment the Online intervention, showing participants a default cart when they log into their online grocery shopping accounts. They will be told that their cart has been filled with items that conform to a diabetic diet and can be used to make the recipes from provided recipe cards, and that they may modify their cart as they like.

Group 3 - Default

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Must be at least 18 years old
• Must have been diagnosed diabetes or demonstrate diabetes risk as defined by the American Diabetes Association
• Must shop in-person at select local grocery stores at least weekly, and at least 75% of the grocery shopping for the household must be done at these stores
• Must do at least 75% of the grocery shopping for the household
• Must speak English

You may not qualify if:

• Is under 18 years of age
• Does not meet diabetes/risk for diabetes criteria
• Does not shop in-person at select local grocery stores at least weekly
• Does not live in a household in which 75% of groceries come from select local grocery stores
• Does not do at least 75% of the grocery shopping for the household
• Does not speak English
• Has dietary restrictions or preferences that would not allow them to reasonably partake in the study (i.e. they would not be willing or able to buy/eat many of the staple foods included in default carts/recipe cards)

Sponsors & Collaborators

• State University of New York at Buffalo: lead
• National Center for Advancing Translational Sciences (NCATS): collaborator

Study Sites (1)

State University of New York at Buffalo

Buffalo, New York, 14214, United States

Location

Related Publications (1)

• Hollis-Hansen K, Tan S, Bargnesi S, McGovern L, Drozdowsky J, Epstein LH, Leone LA, Mak E, Masci J, Anzman-Frasca S. Feasibility and implementation of a grocery shopping intervention for adults diagnosed with or at-risk for type 2 diabetes. Public Health Nutr. 2023 Oct;26(10):2118-2129. doi: 10.1017/S1368980023001453. Epub 2023 Jul 27.

  PMID: 37496394 DERIVED

MeSH Terms

Conditions

Diabetes Mellitus, Type 2; Glucose Intolerance; Diabetes Mellitus

Condition Hierarchy (Ancestors)

Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Hyperglycemia

Results Point of Contact

• Title: Stephanie Anzman-Frasca
• Organization: SUNY Buffalo

safrasca@buffalo.edu

17168296692

Study Officials

• Stephanie Anzman Frasca, PhD

  Associate Professor

  PRINCIPAL INVESTIGATOR

Publication Agreements

• PI is Sponsor Employee: No
• Restrictive Agreement: No

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: July 31, 2019
• First Posted: August 9, 2019
• Study Start: October 14, 2019
• Primary Completion: December 3, 2020
• Study Completion: December 3, 2020
• Last Updated: December 13, 2023
• Results First Posted: December 13, 2023

Record last verified: 2023-11

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)