Participant Characteristics Associated with High Responsiveness to Personalized Healthy Food Incentives: a Secondary Analysis of the Randomized Controlled Crossover Smart Cart Study

Abstract

Background: Personalized dietary behavioral interventions could be enhanced by understanding factors accounting for individual variability in dietary decisions.

Objective: This study was a secondary analysis of the Smart Cart randomized controlled trial to determine whether participant characteristics predicted high responsiveness to personalized grocery incentives for purchasing healthy food.

Methods: This secondary analysis of a 9-mo crossover study included 192 regular shoppers (86%) from a Rhode Island supermarket. To analyze whether health, behavioral, and/or sociodemographic characteristics predicted responsiveness to a personalized grocery incentive intervention, participants were divided into 3 categories [high (n = 47), moderate (n = 50), and unresponsive (n = 95)] based on percentage changes in their Grocery Purchase Quality Index scores during the intervention versus control period calculated from sales data. We tested whether participant characteristics, including individual, household, and intervention-related factors, differed across responsiveness groups using ANOVA and whether they predicted the likelihood of being high responsive versus unresponsive or moderate responsive using logistic regression.

Results: Participants had a mean (SD) age of 56.0 (13.8) y and were 89% female. Education, self-reported BMI, income, diet-related medical condition, food insecurity, cooking adequacy, and value consciousness differed across responsiveness categories (P < 0.1). High versus moderate and unresponsive participants increased their percentage of spending on targeted foods (P < 0.0001) and purchased fewer unique items (P = 0.01). In multinomial adjusted models, the odds of being high versus unresponsive or moderate responsive were lower for participants with a BMI (in kg/m2) <25 versus ≥25 (OR: 0.41; 95% CI: 0.19, 0.90) and higher with a diet-related medical condition present (OR: 3.75; 95% CI: 1.20, 11.8). Other characteristics were not associated with responsiveness.

Conclusions: Findings demonstrated that a BMI ≥25 and having a diet-related medical condition within the household predicted high responsiveness to a personalized grocery purchasing intervention, suggesting that personalized dietary interventions may be particularly effective for households with higher health risk. This trial is registered at www.clinicaltrials.gov as NCT03748056.

Keywords: Smart Cart Study; dietary intervention; grocery purchase quality; individual variation in effectiveness; intervention responsiveness; personalized behavioral interventions; precision nutrition.

FIGURE 1

Changes in GPQI component scores in the intervention vs. control periods among high, moderate, and unresponsive participants. For each responsiveness group, the radar chart depicts the % change in GPQI component scores between the intervention and control periods. Higher component scores indicate better purchasing quality. Positive change values (i.e., all components for the high-responsive group) indicate improvement during the intervention period, whereas negative change values (i.e., all components for the unresponsive group) indicate better scores during the control (vs. intervention) periods. An asterisk (*) next to the component label indicates a statistically significant difference between the 3 responsiveness groups (P < 0.01) using ANOVA. GPQI, Grocery Purchase Quality Index.