Faculty of Life Sciences: Food, Nutrition and Health, University of Bayreuth, Kulmbach, Germany.
Behavioural Science Group, Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Health Psychol Rev. 2022 Dec;16(4):526-550. doi: 10.1080/17437199.2021.2016066. Epub 2021 Dec 21.
Smartphones have become popular in assessing eating behaviour in real-life and real-time. This systematic review provides a comprehensive overview of smartphone-based dietary assessment tools, focusing on how dietary data is assessed and its completeness ensured. Seven databases from behavioural, social and computer science were searched in March 2020. All observational, experimental or intervention studies and study protocols using a smartphone-based assessment tool for dietary intake were included if they reported data collected by adults and were published in English. Out of 21,722 records initially screened, 117 publications using 129 tools were included. Five core assessment features were identified: photo-based assessment (48.8% of tools), assessed serving/ portion sizes (48.8%), free-text descriptions of food intake (42.6%), food databases (30.2%), and classification systems (27.9%). On average, a tool used two features. The majority of studies did not implement any features to improve completeness of the records. This review provides a comprehensive overview and framework of smartphone-based dietary assessment tools to help researchers identify suitable assessment tools for their studies. Future research needs to address the potential impact of specific dietary assessment methods on data quality and participants' willingness to record their behaviour to ultimately improve the quality of smartphone-based dietary assessment for health research.
智能手机在实时、真实环境下评估饮食行为已经变得流行起来。本系统综述全面概述了基于智能手机的膳食评估工具,重点介绍了如何评估膳食数据并确保其完整性。2020 年 3 月,我们在行为、社会和计算机科学的七个数据库中进行了搜索。如果研究报告了成年人采集的数据,并且使用基于智能手机的评估工具评估饮食摄入,且研究为观察性、实验性或干预性研究或研究方案,并以英文发表,那么这些研究或方案均被纳入。在最初筛选的 21722 条记录中,有 117 篇使用 129 种工具的出版物被纳入。确定了五个核心评估特征:基于照片的评估(48.8%的工具)、评估服务/份量(48.8%)、食物摄入量的自由文本描述(42.6%)、食物数据库(30.2%)和分类系统(27.9%)。平均而言,一种工具使用了两个特征。大多数研究没有实施任何提高记录完整性的特征。本综述提供了基于智能手机的膳食评估工具的全面概述和框架,以帮助研究人员为他们的研究确定合适的评估工具。未来的研究需要解决特定膳食评估方法对数据质量和参与者记录行为意愿的潜在影响,以最终提高基于智能手机的膳食评估在健康研究中的质量。