一个平行,随机对照试验的协议,关于人工智能在现实世界的场景中对人工智能的新型个性化营养方法的影响
Jingyuan Feng1, Hongwei Liu1, Shupeng Mai2
1School of Public Health, Fudan University, Shanghai, China.
BMC public health
|September 2, 2023
概括
这项研究探讨了中国新的人工智能驱动的个性化营养 (PN) 方法. 人工智能工具为餐饮营养提供实时反,旨在改善饮食模式和健康结果.
科学领域:
- 营养科学 营养科学
- 医疗保健中的人工智能
- 公共卫生干预 公共卫生干预
背景情况:
- 在中国,营养服务需求很大.
- 以前的研究证实了个性化营养 (PN) 干预措施的有效性.
- 整合人工智能 (AI) 的新型PN方法需要评估.
研究的目的:
- 确定一种新的AI支持的个性化营养 (PN) 方法的有效性和可行性.
- 评估基于人工智能的实时营养反对饮食选择和健康的影响.
- 评估人工智能驱动的营养干预对现实世界的转化潜力.
主要方法:
- 一个双臂并行,随机对照试验,在现实世界的工作人员食堂环境中进行.
- 第一阶段:170名参与者使用智能手机应用程序3个月,接受基于AI的菜营养和PN评估.
- 第二阶段:800名食客使用该小程序一年,并使用通用线性混合模型分析饮食,人体测量和代谢变化.
主要成果:
- 这款由人工智能驱动的小程序提供实时,基于人工智能的菜营养评估和个性化的营养 (PN) 评估.
- 该干预措施旨在帮助用户做出明智的食物选择,以改善健康.
- 预期的结果包括优化的饮食模式,体重,血压和正常化的身体新陈代谢.
结论:
- 这种由人工智能驱动的新方法提供实时的营养洞察力,以指导食物选择.
- 该研究的现实场景设计支持干预的实际转化.
- 这些发现将为基于人工智能的有效个性化营养策略的开发和实施提供信息.
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