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.
Multimedia Understanding Group, School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Nutrients. 2019 Mar 20;11(3):672. doi: 10.3390/nu11030672.
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 ( = 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 ( = 0.91). Real-life eating rate was 33.5 g/min (±14.8), which was significantly higher ( = 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 ( = 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.
大量的食物份量和高进食速度与高能量摄入和肥胖有关。大多数人在食物和环境的影响下，仍然保持着他们的食物摄入量（克）和进食速度（克/分钟）与同龄人相关的排名。单次用餐的测量结果可以识别出“大量进食者”和“快速进食者”，从而发现有肥胖风险的个体。本研究的目的是基于一顿学校午餐来预测实际的食物摄入量和进食速度。24 名高中生的平均年龄（±标准差）为 16.8 岁（±0.7），体重指数为 21.9（±4.1），没有排除标准。食物摄入量和进食速度首先由自己评估（“少于”、“平均”或“多于同龄人”），然后在一顿学校午餐中客观记录（以克为单位记录消耗的食物的绝对重量）。之后，参与者在至少两周的时间内，使用蓝牙连接的体重秤和智能手机应用程序，尽可能多地记录真实生活中的主要餐食（早餐、午餐和晚餐）。在研究期间，参与者平均记录了 18.9（7.3）餐。实际的食物摄入量为 327.4 克（±110.6），明显低于（=0.027）单顿学校午餐的 367.4 克（±167.2）。当比较客观记录的真实生活和学校午餐的食物摄入量之间的组内相关系数时，相关性非常好（=0.91）。实际的进食速度为 33.5 克/分钟（±14.8），明显高于（=0.010）单顿学校午餐的 27.7 克/分钟（±13.3）。记录的进食率在真实生活和学校午餐之间的组内相关系数非常大（=0.74）。参与者记录的食物摄入量和进食速度分为三分之一，然后与学校午餐和真实生活进行比较，具有中等或更高的一致性（κ=0.75 和 κ=0.54）。相比之下，自我评估和真实生活中食物摄入量和进食率的排名之间几乎没有一致性（κ=0.09 和 κ=0.08）。本研究提供的证据表明，每个人在真实生活中的每顿饭的食物摄入量和进食速度都有很大的差异。然而，根据这些行为，大多数学生可以根据单顿学校午餐正确地与同龄人进行分类。相比之下，自我报告的食物摄入量和进食速度是真实生活测量的较差预测指标。最后，根据记录的真实生活中食物摄入量和进食率的个体变异性，不建议根据真实生活环境中单次记录的结果对个人进行排名。
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