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基于网络的图形化食物频率评估系统:设计、开发与可用性指标

A Web-Based Graphical Food Frequency Assessment System: Design, Development and Usability Metrics.

作者信息

Franco Rodrigo Zenun, Alawadhi Balqees, Fallaize Rosalind, Lovegrove Julie A, Hwang Faustina

机构信息

Biomedical Engineering, School of Biological Sciences, University of Reading, Reading, United Kingdom.

Hugh Sinclair Unit of Human Nutrition and Institute for Cardiovascular and Metabolic Research, Department of Food and Nutritional Sciences, University of Reading, Reading, United Kingdom.

出版信息

JMIR Hum Factors. 2017 May 8;4(2):e13. doi: 10.2196/humanfactors.7287.

DOI:10.2196/humanfactors.7287
PMID:28483746
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5440732/
Abstract

BACKGROUND

Food frequency questionnaires (FFQs) are well established in the nutrition field, but there remain important questions around how to develop online tools in a way that can facilitate wider uptake. Also, FFQ user acceptance and evaluation have not been investigated extensively.

OBJECTIVE

This paper presents a Web-based graphical food frequency assessment system that addresses challenges of reproducibility, scalability, mobile friendliness, security, and usability and also presents the utilization metrics and user feedback from a deployment study.

METHODS

The application design employs a single-page application Web architecture with back-end services (database, authentication, and authorization) provided by Google Firebase's free plan. Its design and responsiveness take advantage of the Bootstrap framework. The FFQ was deployed in Kuwait as part of the EatWellQ8 study during 2016. The EatWellQ8 FFQ contains 146 food items (including drinks). Participants were recruited in Kuwait without financial incentive. Completion time was based on browser timestamps and usability was measured using the System Usability Scale (SUS), scoring between 0 and 100. Products with a SUS higher than 70 are considered to be good.

RESULTS

A total of 235 participants created accounts in the system, and 163 completed the FFQ. Of those 163 participants, 142 reported their gender (93 female, 49 male) and 144 reported their date of birth (mean age of 35 years, range from 18-65 years). The mean completion time for all FFQs (n=163), excluding periods of interruption, was 14.2 minutes (95% CI 13.3-15.1 minutes). Female participants (n=93) completed in 14.1 minutes (95% CI 12.9-15.3 minutes) and male participants (n=49) completed in 14.3 minutes (95% CI 12.6-15.9 minutes). Participants using laptops or desktops (n=69) completed the FFQ in an average of 13.9 minutes (95% CI 12.6-15.1 minutes) and participants using smartphones or tablets (n=91) completed in an average of 14.5 minutes (95% CI 13.2-15.8 minutes). The median SUS score (n=141) was 75.0 (interquartile range [IQR] 12.5), and 84% of the participants who completed the SUS classified the system either "good" (n=50) or "excellent" (n=69). Considering only participants using smartphones or tablets (n=80), the median score was 72.5 (IQR 12.5), slightly below the SUS median for desktops and laptops (n=58), which was 75.0 (IQR 12.5). No significant differences were found between genders or age groups (below and above the median) for the SUS or completion time.

CONCLUSIONS

Taking into account all the requirements, the deployment used professional cloud computing at no cost, and the resulting system had good user acceptance. The results for smartphones/tablets were comparable with desktops/laptops. This work has potential to promote wider uptake of online tools that can assess dietary intake at scale.

摘要

背景

食物频率问卷(FFQ)在营养领域已得到广泛应用,但围绕如何开发能促进更广泛采用的在线工具仍存在重要问题。此外,FFQ的用户接受度和评估尚未得到广泛研究。

目的

本文介绍了一种基于网络的图形化食物频率评估系统,该系统解决了可重复性、可扩展性、移动友好性、安全性和可用性等挑战,并展示了一项部署研究的使用指标和用户反馈。

方法

应用程序设计采用单页应用程序网络架构,后端服务(数据库、身份验证和授权)由谷歌Firebase的免费计划提供。其设计和响应能力利用了Bootstrap框架。FFQ于2016年作为EatWellQ8研究的一部分在科威特部署。EatWellQ8 FFQ包含146种食物(包括饮料)。在科威特招募参与者,不提供经济激励。完成时间基于浏览器时间戳,可用性使用系统可用性量表(SUS)进行测量,得分在0到100之间。SUS得分高于70的产品被认为是良好的。

结果

共有235名参与者在系统中创建了账户,163人完成了FFQ。在这163名参与者中,142人报告了他们的性别(93名女性,49名男性),144人报告了他们的出生日期(平均年龄35岁,范围为18 - 65岁)。所有FFQ(n = 163)的平均完成时间(不包括中断时间)为14.2分钟(95%置信区间13.3 - 15.1分钟)。女性参与者(n = 93)在14.1分钟内完成(95%置信区间12.9 - 15.3分钟),男性参与者(n = 49)在14.3分钟内完成(95%置信区间12.6 - 15.9分钟)。使用笔记本电脑或台式机的参与者(n = 69)平均在13.9分钟内完成FFQ(95%置信区间12.6 - 15.1分钟),使用智能手机或平板电脑的参与者(n = 91)平均在14.5分钟内完成(9,5%置信区间13.2 - 15.8分钟)。SUS中位数得分(n = 141)为75.0(四分位间距[IQR]12.5),完成SUS的参与者中有84%将系统评为“良好”(n = 50)或“优秀”(n = 69)。仅考虑使用智能手机或平板电脑的参与者(n = 80),中位数得分为72.5(IQR 12.5),略低于台式机和笔记本电脑的SUS中位数(n = 58),后者为75.0(IQR 12.5)。在SUS或完成时间方面,性别或年龄组(中位数上下)之间未发现显著差异。

结论

考虑到所有要求,此次部署免费使用了专业云计算,并且最终系统具有良好的用户接受度。智能手机/平板电脑的结果与台式机/笔记本电脑相当。这项工作有可能促进能大规模评估饮食摄入量的在线工具得到更广泛的采用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ce/5440732/657ce2449149/humanfactors_v4i2e13_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ce/5440732/ec8db0f2ab32/humanfactors_v4i2e13_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ce/5440732/d01412c23c2f/humanfactors_v4i2e13_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ce/5440732/2655d2ba26de/humanfactors_v4i2e13_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ce/5440732/c6722d76e670/humanfactors_v4i2e13_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ce/5440732/e4d5d057fe99/humanfactors_v4i2e13_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ce/5440732/657ce2449149/humanfactors_v4i2e13_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ce/5440732/ec8db0f2ab32/humanfactors_v4i2e13_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ce/5440732/d01412c23c2f/humanfactors_v4i2e13_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ce/5440732/2655d2ba26de/humanfactors_v4i2e13_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ce/5440732/c6722d76e670/humanfactors_v4i2e13_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ce/5440732/e4d5d057fe99/humanfactors_v4i2e13_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ce/5440732/657ce2449149/humanfactors_v4i2e13_fig6.jpg

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