在个性化的营养数字健康干预中整合食物偏好分析,行为改变策略和机器学习以预防心血管疾病:概念管道开发和原则证明研究:概念管道开发和原则证明研究
Hana Fitria Navratilova1,2, Anthony David Whetton1,3, Nophar Geifman3,4
1School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Leggett Building, Manor Park, Daphne Jackson Road, Guildford, GU2 7WG, United Kingdom.
这项研究开发了个性化营养的数字健康干预 (DHI),使用食物偏好来预测心血管疾病 (CVD) 风险并提供量身定制的建议. 该DHI的目标是使健康饮食更容易获得,并降低心血管疾病风险.
科学领域:
- 数字健康干预措施 数字健康干预措施
- 个性化的营养 个性化的营养
- 心血管疾病风险预测预测
背景情况:
- 个性化的饮食建议必须平衡健康风险与个人食物偏好.
- 根据个人口味定制推,可以提高坚持和有效性.
研究的目的:
- 开发一个数字健康干预 (DHI),提供个性化的营养建议.
- 建立一个基于个人食物偏好 (FPP) 和心血管疾病 (CVD) 风险的系统.
主要方法:
- 利用来自61,229名参与者的英国生物库数据来开发DHI管道.
- 使用食物偏好配置和机器学习算法创建了一个CVD预测模型.
- 整合了行为变化轮来设计干预功能和行为变化技术.
主要成果:
- 确定了14个关键的食物项目,用于有效的食物偏好概况.
- 食品偏好概况组实现了与传统方法相比的预测准确性.
- 选择了四个DHI功能:食物/份量信息,食谱,推系统和社区平台.
结论:
- 证明了DHI管道的原则证明,整合了个人需求和偏好.
- DHI有可能改善饮食选择,降低心血管疾病风险.
- 让健康饮食更容易获得和可持续,可以显著影响公共健康.
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