Suppr超能文献

技术驱动的能量摄入估算方法的系统评价

A Systematic Review of Technology-Driven Methodologies for Estimation of Energy Intake.

作者信息

Doulah Abul, McCrory Megan A, Higgins Janine A, Sazonov Edward

机构信息

Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA.

Department of Health Sciences, Boston University, MA 02215, USA.

出版信息

IEEE Access. 2019;7:49653-49668. doi: 10.1109/access.2019.2910308. Epub 2019 Apr 11.

Abstract

Accurate measurement of energy intake (EI) is important for estimation of energy balance, and, correspondingly, body weight dynamics. Traditional measurements of EI rely on self-report, which may be inaccurate and underestimate EI. The imperfections in traditional methodologies such as 24-hour dietary recall, dietary record, and food frequency questionnaire stipulate development of technology-driven methods that rely on wearable sensors and imaging devices to achieve an objective and accurate assessment of EI. The aim of this research was to systematically review and examine peer-reviewed papers that cover the estimation of EI in humans, with the focus on emerging technology-driven methodologies. Five major electronic databases were searched for articles published from January 2005 to August 2017: Pubmed, Science Direct, IEEE Xplore, ACM library, and Google Scholar. Twenty-six eligible studies were retrieved that met the inclusion criteria. The review identified that while the current methods of estimating EI show promise, accurate estimation of EI in free-living individuals presents many challenges and opportunities. The most accurate result identified for EI (kcal) estimation had an average accuracy of 94%. However, collectively, the results were obtained from a limited number of food items (i.e., 19), small sample sizes (i.e., 45 meal images), and primarily controlled conditions. Therefore, new methods that accurately estimate EI over long time periods in free-living conditions are needed.

摘要

准确测量能量摄入(EI)对于评估能量平衡以及相应的体重动态变化至关重要。传统的EI测量方法依赖自我报告,这可能不准确且会低估EI。诸如24小时饮食回顾、饮食记录和食物频率问卷等传统方法存在缺陷,这就需要开发基于可穿戴传感器和成像设备的技术驱动方法,以实现对EI的客观准确评估。本研究的目的是系统回顾和审视同行评审的论文,这些论文涵盖了人类EI的估计,重点是新兴的技术驱动方法。在五个主要电子数据库中搜索了2005年1月至2017年8月发表的文章:PubMed、Science Direct、IEEE Xplore、ACM图书馆和谷歌学术。检索到26项符合纳入标准的合格研究。该综述指出,虽然当前估计EI的方法显示出前景,但在自由生活个体中准确估计EI仍面临诸多挑战和机遇。EI(千卡)估计的最准确结果平均准确率为94%。然而,总体而言,这些结果是从有限数量的食物项目(即19种)、小样本量(即45张膳食图像)以及主要是受控条件下获得的。因此,需要新的方法来在自由生活条件下长时间准确估计EI。

相似文献

1
A Systematic Review of Technology-Driven Methodologies for Estimation of Energy Intake.
IEEE Access. 2019;7:49653-49668. doi: 10.1109/access.2019.2910308. Epub 2019 Apr 11.
4
An objective estimate of energy intake during weight gain using the intake-balance method.
Am J Clin Nutr. 2014 Sep;100(3):806-12. doi: 10.3945/ajcn.114.087122. Epub 2014 Jul 23.
5
6
8
Can a Web-based food record accurately assess energy intake in overweight and obese women? A pilot study.
J Hum Nutr Diet. 2013 Jul;26 Suppl 1:140-4. doi: 10.1111/jhn.12094. Epub 2013 Mar 17.
9

引用本文的文献

1
A Systematic Review of Sensor-Based Methods for Measurement of Eating Behavior.
Sensors (Basel). 2025 May 8;25(10):2966. doi: 10.3390/s25102966.
2
Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review.
J Med Internet Res. 2024 Nov 15;26:e51432. doi: 10.2196/51432.
3
Automated Artificial Intelligence-Based Thai Food Dietary Assessment System: Development and Validation.
Curr Dev Nutr. 2024 Apr 4;8(5):102154. doi: 10.1016/j.cdnut.2024.102154. eCollection 2024 May.
4
AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review.
Ann Med. 2023;55(2):2273497. doi: 10.1080/07853890.2023.2273497. Epub 2023 Dec 7.
5
Advancing sustainability in the food and nutrition system: a review of artificial intelligence applications.
Front Nutr. 2023 Nov 16;10:1295241. doi: 10.3389/fnut.2023.1295241. eCollection 2023.
6
Criterion validity of wrist accelerometry for assessing energy intake via the intake-balance technique.
Int J Behav Nutr Phys Act. 2023 Sep 25;20(1):115. doi: 10.1186/s12966-023-01515-0.
7
Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature Review.
Sensors (Basel). 2023 Jul 4;23(13):6137. doi: 10.3390/s23136137.
8
Development of a Web-Based Diabetes Prevention Program (DPP) for Chinese Americans: A Formative Evaluation Approach.
Int J Environ Res Public Health. 2022 Dec 29;20(1):599. doi: 10.3390/ijerph20010599.
9
A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation.
Sensors (Basel). 2022 Feb 15;22(4):1493. doi: 10.3390/s22041493.

本文引用的文献

1
The importance of field experiments in testing of sensors for dietary assessment and eating behavior monitoring.
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:5759-5762. doi: 10.1109/EMBC.2018.8513623.
2
Narrative Review of New Methods for Assessing Food and Energy Intake.
Nutrients. 2018 Aug 10;10(8):1064. doi: 10.3390/nu10081064.
4
Speech2Health: A Mobile Framework for Monitoring Dietary Composition From Spoken Data.
IEEE J Biomed Health Inform. 2018 Jan;22(1):252-264. doi: 10.1109/JBHI.2017.2709333.
5
Automatic Measurement of Chew Count and Chewing Rate during Food Intake.
Electronics (Basel). 2016;5(4). doi: 10.3390/electronics5040062. Epub 2016 Sep 23.
6
Predicting food nutrition facts using pocket-size near-infrared sensor.
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:742-745. doi: 10.1109/EMBC.2017.8036931.
7
Meal Microstructure Characterization from Sensor-Based Food Intake Detection.
Front Nutr. 2017 Jul 17;4:31. doi: 10.3389/fnut.2017.00031. eCollection 2017.
9
Doubly labelled water assessment of energy expenditure: principle, practice, and promise.
Eur J Appl Physiol. 2017 Jul;117(7):1277-1285. doi: 10.1007/s00421-017-3641-x. Epub 2017 May 15.
10
Measurement Methods for Physical Activity and Energy Expenditure: a Review.
Clin Nutr Res. 2017 Apr;6(2):68-80. doi: 10.7762/cnr.2017.6.2.68. Epub 2017 Apr 28.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验