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Open AccessArticle

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

1
Innovative Use of Mobile Phones to Promote Physical Activity and Nutrition across the Lifespan (the IMPACT) Research Group, Department of Biosciences and Nutrition, Karolinska Institutet, 14152 Stockholm, Sweden
2
Multimedia Understanding Group, School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
3
Laboratory of Medical Informatics, School of Medicine, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
4
Mandometer Clinics, 14104 Stockholm, Sweden
*
Author to whom correspondence should be addressed.
Nutrients 2019, 11(3), 672; https://doi.org/10.3390/nu11030672
Received: 30 January 2019 / Revised: 13 March 2019 / Accepted: 13 March 2019 / Published: 20 March 2019
(This article belongs to the Special Issue The Portion Size Effect and Strategies for Portion Control)
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. View Full-Text
Keywords: Cohen’s kappa; sensory science; eating behaviour; nutrition; novel technology; overweight; meal duration; recording frequency; confidence interval; reliability Cohen’s kappa; sensory science; eating behaviour; nutrition; novel technology; overweight; meal duration; recording frequency; confidence interval; reliability
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Langlet, B.; Fagerberg, P.; Delopoulos, A.; Papapanagiotou, V.; Diou, C.; Maramis, C.; Maglaveras, N.; Anvret, A.; Ioakimidis, I. Predicting Real-Life Eating Behaviours Using Single School Lunches in Adolescents. Nutrients 2019, 11, 672.

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