针对个性化的饮食规划进行动态营养成分分析的全面系统审查:元分析和基于PRISMA的证据综合
Mohammad Hasan Molooy Zada1, Da Pan1, Guiju Sun1
1Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Nutrition and Food Hygiene, School of Public Health, Southeast University, 87 Ding Jia Qiao Road, Nanjing 210009, China.
动态营养成分分析显著改善饮食质量和坚持,导致更好的临床结果,如减肥. 人工智能增强系统显示出卓越的效率,但标准化和可访问性仍然是关键的研究领域.
科学领域:
- 营养科学 营养科学
- 生物医学信息学 生物医学信息学
- 个性化医疗是个性化的医疗.
背景情况:
- 动态营养成分分析将实时数据与人工智能集成,以实现个性化的营养.
- 它代表了朝着个性化饮食建议的转变.
- 本次审查评估了当前的方法,有效性和结果.
研究的目的:
- 系统地审查和元分析动态营养成分分析方法,以实现个性化的饮食规划.
- 评估它们的有效性,方法质量和临床结果.
- 确定当前的局限性和未来的研究方向.
主要方法:
- 按照PRISMA 2020指南进行系统审查和元分析.
- 全面的数据库搜索 (PubMed,Scopus,科学网,IEEE Xplore,谷歌学者) 截至2024年12月.
- 使用经过验证的工具 (RoB 2,ROBINS-I,纽卡斯尔-太华尺度) 和随机效应元分析进行质量评估.
主要成果:
- 分析了117项研究 (45,672名参与者),使用算法,生物标志物或人工智能增强方法.
- 在饮食质量 (SMD=1.24),坚持 (RR=1.34) 和减肥 (MD=-2.8公斤) 中观察到显著改善.
- 人工智能增强系统的有效性比算法方法 (SMD=1.08) 高 (SMD=1.67),尽管存在相当大的异质性.
结论:
- 动态营养成分分析显示,个性化的营养干预措施具有很强的前景.
- 方法标准化,长期验证和成本效益分析是关键的研究重点.
- 人工智能和多omics数据的整合是这个领域的未来方向.
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