Objective quantification of the food proximity effect on grapes, chocolate and cracker consumption in a Swedish high school. A temporal analysis

Abstract

Close food proximity leads to increased short-term energy intake, potentially contributing to the long-term development of obesity. However, its precise effects on eating behaviour are still unclear, especially with food available for extended periods of time. This study involved two similar high school student groups (15–17 years old), which had ad libitum access to grapes, chocolates and crackers during an hour-long experimental session. In the distal condition the foods were placed 6 meters away from the students (n = 24), in contrast to the proximal condition (n = 17) were the food was placed near the students. The identification of the type and the quantification of the amount of each food selected, for each individual serving, was facilitated through use of food scales and video recording. In the proximal condition individuals served themselves grapes and crackers more often and consumed more chocolate than in the distal condition. In total, participants in the proximal condition ingested significantly more energy (726 kcal vs. 504 kcal; p = 0.029), without reporting higher fullness. Food proximity also affected the temporal distribution of servings, with the first five minutes of the sessions corresponding to 53.1% and 45.6% of the total energy intake for the distal and proximal conditions, respectively. After the first five minutes, the servings in the distal condition were strongly clustered in time, with many students getting food together. In the proximal condition however, students displayed an unstructured pattern of servings over time. In conclusion, this study strengthens past evidence regarding the important role of food proximity on individual energy intake and, for the first time, it associates continuous food proximity to the emergence of unstructured eating over time. These conclusions, expanded upon by future studies, could support the creation of meaningful intervention strategies based on spatially and temporally controlled food availability.

Healthy and unhealthy eating amongst stressed students: considering the influence of mindfulness on eating choices and consumption

“…Chocolate pieces of Galaxy Minstrels were chosen as the unhealthy option as research indicates that individuals snack on chocolate more when compared to crisps and biscuits (Wallis & Hetherington, 2009). Whilst grapes do contain high levels of natural sugars, compared to chocolates grapes contain less fat and more protein and are consequently considered to be more "healthy" than chocolates (Langlet, Fagerberg, Glossner, & Ioakimidis, 2017;Zellner et al, 2006). Both grapes and chocolates were chosen due to them being "sweet" foods, with research indicating that students consume more sweet foods when stressed, in comparison to other food types (Emond et al, 2016;Kim, Yang, Kim, & Lim, 2013 116 health psychology report…”

Validation of a Deep Learning System for the Full Automation of Bite and Meal Duration Analysis of Experimental Meal Videos

“…Efforts to use off-the-shelf wearable technologies (e.g., smartwatches) for meal analyses are promising [35], but they are currently not widely available for large-scale use. Thus, the predominant methodology for objective, non-invasive behavioral analysis of meals is the use of video observation, especially for studies performed in controlled [16] and semi-controlled settings [36]. Further, video meal analysis is usually the "ground truth" for the development of other objective analysis methodologies.…”

Predicting Real-Life Eating Behaviours Using Single School Lunches in Adolescents

“…On an individual level, the large difference in confidence interval ranges across individuals of both food intake weight and eating rate, suggests measuring manipulation/treatment response in real-life requires more measurements in certain individuals. It has been previously reported that meal-specific food intake is affected by different environmental parameters, just like food proximity [31] and accessibility [32], social environment [33]. In other cases, restricted time during the meal [34] has been shown to affect both eating rate and food intake weight in semi-controlled meals.…”