基于传感器捕捉的饮食场合的范围审查,可以用于实时增强个性化的营养干预措施
Leanne Wang1, Margaret Allman-Farinelli1, Eric Hekler2
1Discipline of Nutrition and Dietetics, Susan Wakil School of Nursing and Midwifery, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.
Advances in nutrition (Bethesda, Md.)
|December 22, 2025
概括
基于传感器的设备显示出自动饮食评估的前景,但目前没有一个满足所有现实世界的可行性标准. 用户舒适度和处理能力的近期进步表明了个性化营养护理的未来潜力.
科学领域:
- 营养科学 营养科学
- 生物医学工程 生物医学工程
- 可穿戴技术可穿戴技术
背景情况:
- 传统的饮食评估方法受到自我报告错误和负担的限制.
- 基于传感器的设备和机器学习提供自动化的饮食行为检测.
- 这些技术旨在提高饮食评估准确性和个性化干预措施.
研究的目的:
- 对基于传感器的检测饮食设备进行范围审查.
- 评估设备可行性的最新进展,用于现实世界营养应用.
主要方法:
- 使用PRISMA-ScR框架进行范围审查.
- 包括2022年1月至2025年9月间发表的研究.
- 根据六个可行性标准对设备进行评估:准确性,食物自由性,社会接受性,电池寿命,实时检测和饮食检测.
主要成果:
- 包括52个设备,没有一个符合所有六个可行性标准.
- 最常见的未满足标准是电池寿命 (43 个设备) 和实时处理 (37 个设备).
- 其他限制包括各种测试的食物/行为,检测饮食,社会接受度和准确性.
结论:
- 目前,没有基于传感器的设备符合饮食评估中实际可行性的所有标准.
- 在社会接受度和计算效率方面的进步表明了进步.
- 未来的设计可能会支持自动饮食评估和个性化的营养护理.
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