Prediction of daily food intake as a function of measurement modality and restriction status

Abstract

Objective: Research on eating relies on various indices (e.g., stable, momentary, neural) to accurately reflect food-related reactivity (e.g., disinhibition) and regulation (e.g., restraint) outside the laboratory. The degree to which they differentially predict real-world consumption remains unclear. Further, the predictive validity of these indices might vary depending on whether an individual is actively restricting intake.

Methods: We assessed food craving reactivity and regulation in 46 healthy participants (30 women, 18-30 years) using standard measurements in three modalities: a) self-reported (stable) traits using surveys popular in the eating literature, and b) momentary craving ratings and c) neural activation using aggregated functional magnetic resonance imaging data gathered during a food reactivity-and-regulation task. We then used these data to predict variance in real-world consumption of craved energy-dense "target" foods across 2 weeks among normal-weight participants randomly assigned to restrict or monitor target food intake.

Results: The predictive validity of four indices varied significantly by restriction. When participants were not restricting intake, momentary (B = 0.21, standard error [SE] = 0.05) and neural (B = 0.08, SE = 0.04) reactivity positively predicted consumption, and stable (B = -0.22, SE = 0.05) and momentary (B = -0.24, SE = 0.05) regulation negatively predicted consumption. When restricting, stable (B = 0.36, SE = 0.12) and neural (B = 0.51, SE = 0.12) regulation positively predicted consumption.

Conclusions: Commonly-used indices of regulation and reactivity differentially relate to an ecologically-valid eating measurement, depending on the presence of restriction goals, and thus have strong implications for predicting real-world behaviors.

Figure 1

Experimental design. Independent variables were gathered in an initial laboratory session: stable, momentary, and neural indices of food reactivity and regulation. Participants were then randomly assigned to either restrict or monitor their intake of a craved target food. The primary dependent variable, food intake (in total number of target food servings consumed), was measured across two weeks of text messaging.

Figure 2

Graphs representing the relationship between Stable, Momentary, and Neural Reactivity (a) and Regulation (b) and Predicted Total Target Food Servings Consumed. Participants instructed to restrict target food consumption are shown in gray, participants instructed to simply monitor consumption (control) are shown in black. The x-axis scales represent the degree of reactivity (top row) or regulation (bottom row) in standard units, and reflect a composite of the measures based on independent components analyses as described in the text; y-axis scales are the predicted total number of target food servings consumed across the two-week sampling period. * ME p < 0.05; ^ interaction p < 0.05