《在数字时代下的膳食评估:不断寻求更好的方式》

  • 来源专题:食物与营养
  • 编译者: niexiuping
  • 发布时间:2018-05-30
  • 在自由生活的人群中测量饮食摄入量是困难的,但对于理解饮食在健康和疾病中的重要作用至关重要。这些研究面临的诸多挑战之一是摄入量的日常变化以及参与者记忆食物消耗的能力。 基于访问者的24小时召回和称量食物记录被认为是评估实际每日摄入量的最佳方法,并且已被用于国家监督和临床试验。食物频率问卷(FFQs)包括用于评估消费频率的食物列表,包括或不包含份量大小,在流行病学研究中最常用作为评估成千上万参与者膳食摄入和模式的成本效益方法(1)。 事实上,FFQs在长期流行病学研究中一直是膳食评估的主流,已被证明在个人膳食暴露的广泛范围排序消费中是有效的。

相关报告
  • 《评价学校膳食数据收集过程》

    • 来源专题:食物与营养
    • 编译者:李晓妍
    • 发布时间:2020-03-30
    • 《学校膳食数据收集程序的评估》研究描述并评估了学校、学校食品当局(SFA)和州机构用于收集和报告联邦政府使用的三种食品和营养服务(FNS)表格数据的方法和过程,包括“学校计划运营报告”(FNS-10),“ SFA验证收集报告”(FNS-742)和“国家机构直接认证率数据元素报告”(FNS-834)。 除了描述过程外,该研究还确定了完成这三种表格时潜在的错误来源,并提供了改进数据收集过程的有用实践和建议。
  • 《SPADE, 一个新的统计程序,评估来自众多食物来源和膳食补充剂的习惯性膳食摄入》

    • 来源专题:食物与营养
    • 编译者:潘淑春
    • 发布时间:2015-01-13
    • 2014 美国营养学会。 Background: For the evaluation of both the adequacy of intakes and the risk of excessive intakes of micronutrients, all potential sources should be included. In addition to micronutrients naturally present in foods, micronutrients can also be derived from fortified foods and dietary supplements. In the estimation of the habitual intake, this may cause specific challenges such as multimodal distributions and heterogeneous variances between the sources. Objective: We present the Statistical Program to Assess Dietary Exposure (SPADE) that was developed to cope with these challenges in one single program. Method: Similar to other methods, SPADE can model habitual intake of daily and episodically consumed dietary components. In addition, SPADE has the option to model habitual intake from dietary supplements. Moreover, SPADE offers models to estimate habitual intake distributions from different sources (e.g., foods and dietary supplements) separately and adds these habitual intakes to get the overall habitual intake distribution. The habitual intake distribution is modeled as a function of age, and this distribution can directly be compared with cutoff values to estimate the proportion above or below. Uncertainty in the habitual intake distribution and in the proportion below or above a cutoff value is quantified with ready-for-use bootstrap and provides 95% CIs.