Abstract
Large portion sizes and a high eating rate are associated with high energy intake and obesity. Most individuals maintain their food intake weight (g) and eating rate (g/min) rank in relation to their peers, despite food and environmental manipulations. Single meal measures may enable identification of “large portion eaters” and “fast eaters,” finding individuals at risk of developing obesity. The aim of this study was to predict real-life food intake weight and eating rate based on one school lunch. Twenty-four high-school students with a mean (±SD) age of 16.8 yr (±0.7) and body mass index of 21.9 (±4.1) were recruited, using no exclusion criteria. Food intake weight and eating rate was first self-rated (“Less,” “Average” or “More than peers”), then objectively recorded during one school lunch (absolute weight of consumed food in grams). Afterwards, subjects recorded as many main meals (breakfasts, lunches and dinners) as possible in real-life for a period of at least two weeks, using a Bluetooth connected weight scale and a smartphone application. On average participants recorded 18.9 (7.3) meals during the study. Real-life food intake weight was 327.4 g (±110.6), which was significantly lower (p = 0.027) than the single school lunch, at 367.4 g (±167.2). When the intra-class correlation of food weight intake between the objectively recorded real-life and school lunch meals was compared, the correlation was excellent (R = 0.91). Real-life eating rate was 33.5 g/min (±14.8), which was significantly higher (p = 0.010) than the single school lunch, at 27.7 g/min (±13.3). The intra-class correlation of the recorded eating rate between real-life and school lunch meals was very large (R = 0.74). The participants’ recorded food intake weights and eating rates were divided into terciles and compared between school lunches and real-life, with moderate or higher agreement (κ = 0.75 and κ = 0.54, respectively). In contrast, almost no agreement was observed between self-rated and real-life recorded rankings of food intake weight and eating rate (κ = 0.09 and κ = 0.08, respectively). The current study provides evidence that both food intake weight and eating rates per meal vary considerably in real-life per individual. However, based on these behaviours, most students can be correctly classified in regard to their peers based on single school lunches. In contrast, self-reported food intake weight and eating rate are poor predictors of real-life measures. Finally, based on the recorded individual variability of real-life food intake weight and eating rate, it is not advised to rank individuals based on single recordings collected in real-life settings.
Highlights
The “Ending Childhood Obesity” WHO committee report of 2016, identifies prevention and control of overweight and obesity as a core priority in combating the obesity epidemic.the report recommends that researchers should investigate the effect eating behaviour has on weight gain [1].In the laboratory, food properties, sensory experience and the environment are all factors which have been shown to alter eating behaviour parameters and in turn, short term energy intake [2,3,4].Two of the eating behaviour parameters which are often associated with increased energy intake are high eating rates and large portion sizes [5,6]
A systematic review showed how reducing the amount of food eaten per unit of time by verbal and electronic feedback and by manipulating food properties can significantly reduce energy intake [6]
With the current sample size (n = 24), food intake weight values from single meal recordings in real-life produced more accurate group means than eating rate, which likely requires a larger sample size
Summary
Food properties, sensory experience and the environment are all factors which have been shown to alter eating behaviour parameters and in turn, short term energy intake [2,3,4]. Two of the eating behaviour parameters which are often associated with increased energy intake are high eating rates and large portion sizes [5,6]. A Cochrane review recently concluded that reducing portion size could potentially reduce daily energy intake between 8.5% and 13.5% [5]. A systematic review showed how reducing the amount of food eaten per unit of time (eating rate, g/min) by verbal and electronic feedback and by manipulating food properties can significantly reduce energy intake [6]. In line with the laboratory studies, real-life studies have shown a lower prevalence of overweight and obesity in individuals with a low eating rate [7,8]
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