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识别超重和肥胖研究志愿者膳食中的常见食物选择:对饮食建议的启示。

Identifying usual food choices at meals in overweight and obese study volunteers: implications for dietary advice.

机构信息

1School of Medicine, Faculty of Science, Medicine and Health,University of Wollongong,Wollongong,NSW 2522,Australia.

2Illawarra Health and Medical Research Institute,University of Wollongong,Wollongong,NSW 2522,Australia.

出版信息

Br J Nutr. 2018 Aug;120(4):472-480. doi: 10.1017/S0007114518001587. Epub 2018 Jul 17.

DOI:10.1017/S0007114518001587
PMID:30015604
Abstract

Understanding food choices made for meals in overweight and obese individuals may aid strategies for weight loss tailored to their eating habits. However, limited studies have explored food choices at meal occasions. The aim of this study was to identify the usual food choices for meals of overweight and obese volunteers for a weight-loss trial. A cross-sectional analysis was performed using screening diet history data from a 12-month weight-loss trial (the HealthTrack study). A descriptive data mining tool, the Apriori algorithm of association rules, was applied to identify food choices at meal occasions using a nested hierarchical food group classification system. Overall, 432 breakfasts, 428 lunches, 432 dinners and 433 others (meals) were identified from the intake data (n 433 participants). A total of 142 items of closely related food clusters were identified at three food group levels. At the first sub-food group level, bread emerged as central to food combinations at lunch, but unprocessed meat appeared for this at dinner. The dinner meal was characterised by more varieties of vegetables and of foods in general. The definitions of food groups played a pivotal role in identifying food choice patterns at main meals. Given the large number of foods available, having an understanding of eating patterns in which key foods drive overall meal content can help translate and develop novel dietary strategies for weight loss at the individual level.

摘要

了解超重和肥胖个体的膳食选择可能有助于针对他们的饮食习惯制定减肥策略。然而,有限的研究探讨了餐时的食物选择。本研究旨在确定参加减肥试验的超重和肥胖志愿者的常规膳食食物选择。使用为期 12 个月的减肥试验(HealthTrack 研究)的筛选饮食史数据进行了横断面分析。应用关联规则的 Apriori 算法这一描述性数据挖掘工具,使用嵌套分层食物组分类系统来识别餐时的食物选择。从摄入数据(n=433 名参与者)中总共确定了 432 份早餐、428 份午餐、432 份晚餐和 433 份其他(餐)。在三个食物组水平上共确定了 142 个密切相关的食物群。在第一亚食物组水平,面包成为午餐食物组合的核心,但晚餐时出现的是未经加工的肉类。晚餐的特点是蔬菜和一般食物的种类更多。食物组的定义在确定主要膳食的食物选择模式方面发挥了关键作用。鉴于可食用食物的种类繁多,了解哪些关键食物主导整体膳食内容的饮食模式可以帮助在个体层面上转化和开发新的减肥饮食策略。

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