Department of Bioresource Engineering, McGill University, Macdonald Campus, 21111 Lakeshore Road, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada.
Department of Food Science and Technology, University of Georgia, 100 Cedar St., Athens, GA 30602, USA.
Nutrients. 2023 Jun 27;15(13):2901. doi: 10.3390/nu15132901.
The purpose of the current study was to describe the design, development, and validation of the 'Diet DQ Tracker'. The 'Diet DQ Tracker' is the first self-administered smartphone app designed to collect dietary data for diet diversity indicators. The main objective of the app was to replace the traditional methods of dietary data collection, such as in-person or telephone 24 h recall via pen and paper questionnaire or tablets. The real-time meal recording, extensive food database, and automatic score calculations and visualizations for MDD-W, IYCF-MDD, and HDDS have the potential to overcome the drawbacks of 24 h recalls. Recall depends on respondent memory, food expertise, and time consumption and demands skilled interviewers. Further, SAIN, LIM recommendations in the app prompt users to diversify diets with healthy foods. The pilot study determined the acceptability, feasibility, and relative validity of the 'Diet DQ Tracker' with a 24 h dietary recall. The results demonstrated minimal differences in dietary scores by both methodologies. The app, being convenient, easy to use, less time-consuming, and enjoyable, was preferred by the entire study sample over 24 h recall. The app will be continually updated with foods from different cultures for validating in large-scale studies. The future studies will help to improve the subsequent versions of the app.
本研究旨在描述“饮食 DQ 追踪器”的设计、开发和验证。“饮食 DQ 追踪器”是第一个专为收集饮食多样性指标的饮食数据而设计的自我管理智能手机应用程序。该应用程序的主要目的是取代传统的饮食数据收集方法,如通过纸笔问卷或平板电脑进行面对面或电话 24 小时回顾。实时记录膳食、广泛的食物数据库以及 MDD-W、IYCF-MDD 和 HDDS 的自动评分计算和可视化,有可能克服 24 小时回顾的缺点。回忆取决于受访者的记忆、食物专业知识以及时间消耗和对熟练访谈者的需求。此外,应用程序中的 SAIN 和 LIM 建议提示用户用健康食品来多样化饮食。该试点研究通过 24 小时饮食回顾确定了“饮食 DQ 追踪器”的可接受性、可行性和相对有效性。结果表明,两种方法的饮食评分差异极小。该应用程序方便、易于使用、耗时更少且有趣,因此受到整个研究样本的青睐,而不是 24 小时回顾。该应用程序将不断更新来自不同文化的食物,以在大规模研究中进行验证。未来的研究将有助于改进应用程序的后续版本。