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This study aims to use the Multiphase Optimization Strategy (MOST) to build and optimize a multi-component intervention that improves diet quality. The investigators will evaluate the effects of evidence-based public health interventions on consumers' diet quality via a web-based grocery store "NUSMart" and then identify a multi-component intervention that includes only those interventions meaningfully affecting diet quality.
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The important role that diet plays in health and disease is well established. Excessive intake of energy, saturated fat and sodium increase the risk of heart disease, diabetes and certain cancers. As a result, interventions aimed at encouraging healthier food consumption have been pursued by many countries. These can be broadly grouped into the following categories: price manipulations, food labeling, and behavioral nudges.
No study has previously assessed the potentially interactive effects of a multi-component intervention that incorporates the strongest features of each intervention component while discarding those that do not meaningfully contribute to healthier consumption. That is the goal of this effort.
To this end, the investigators chose a full-factorial design because this experimental design allows us to estimate not only the independent (main) effects of the interventions but also their interaction effects. The full-factorial design includes all possible combinations of the interventions' status. Because the investigators have four interventions, each of which has two levels (intervention On or Off), there are 16 (i.e. 2^4) experimental conditions/arms in total. The four interventions for this study are outlined below:
Foods and Beverages: The investigators will impose a 20% tax on sales price of the food items.
Participants will be randomly assigned to one of the 16 arms and instructed to perform a one-time hypothetical grocery shopping on NUSMart.
The investigators will collect participants' demographic and health characteristics as well as hunger at the time of the survey and their self-control in the baseline survey. The collected data will be used to precisely estimate the causal effect of the interventions and address their underlying mechanism to change consumers' food choices.
Our hypotheses about the effects of the interventions on diet quality, measured by the weighted average Nutri-Score (primary) of finalized shopping baskets, are as follows:
We will also run models both with and without including covariates that include demographic variables (e.g., age, minority status, income, BMI, sex, and household size) and measurements of self-control, hunger, and health-status. To test the moderating effects of hunger, self-control, health-status, and education level, we will include interaction terms between the intervention arms and these variables.
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756 participants in 16 patient groups
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Data sourced from clinicaltrials.gov
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