对饮食数据收集方法进行系统审查,以确定饮食多样性指标
Subeg Mahal1, Christopher Kucha2, Ebenezer M Kwofie1
1Department of Bioresource Engineering, McGill University, Ste-Anne-de-Bellevue, QC, Canada.
Frontiers in nutrition
|April 5, 2024
概括
这项研究审查了饮食数据收集方法,以获取饮食多样性指标. 智能手机应用程序在克服回忆偏差和与传统方法相关的高成本等局限性方面表现有前途.
科学领域:
- 营养科学 营养科学
- 公共卫生研究 公共卫生研究
- 数字健康干预措施 数字健康干预措施
背景情况:
- 饮食多样性指标对于评估人口营养状况至关重要.
- 传统的饮食数据收集方法存在重大限制,影响数据的准确性和效率.
- 现有的MDD-W,IYCF-MDD和HDDS等指标的方法需要进行批判性评估.
研究的目的:
- 批判性地评估饮食数据收集在饮食多样性指标中的现有方法.
- 识别和分析传统数据收集方法中的差距和局限性.
- 提出智能手机应用程序作为一个可行的解决方案,以提高饮食数据收集.
主要方法:
- 研究人员对来自38个国家的80项研究进行了系统审查.
- 研究根据研究目标,设计,目标受众,数据收集方法,样本大小,数据类型,频率和位置进行评估.
- 专注于用于女性最低饮食多样性 (MDD-W),婴儿和幼儿养实践 (IYCF-MDD) 和家庭饮食多样性得分 (HDDS) 的方法.
主要成果:
- 大多数审查的研究使用面试官管理的24小时回忆来评估饮食多样性.
- 在传统方法中发现了显著的局限性,包括回忆偏差,社会可取性偏差和广泛的面试官培训要求.
- 费用和时间限制也被认为是传统饮食数据收集的主要缺点.
结论:
- 智能手机应用程序提供了一个有希望的技术解决方案,以减轻传统饮食数据收集方法的局限性.
- 拟议的数字方法有可能减少回忆和社会可取性偏见,减少对训练有素面试官的依赖,并优化资源配置.
- 整合移动技术可以显著提高饮食多样性评估的准确性,效率和可扩展性.
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