Department of Information Systems and Analytics, National University of Singapore, Singapore, Singapore.
JMIR Mhealth Uhealth. 2019 Jan 21;7(1):e11312. doi: 10.2196/11312.
Mobile apps are being widely used for delivering health interventions, with their ubiquitous access and sensing capabilities. One such use is the delivery of interventions for healthy eating behavior.
The aim of this study was to provide a comprehensive view of the literature on the use of mobile interventions for eating behavior change. We synthesized the studies with such interventions and mapped out their input methods, interventions, and outcomes.
We conducted a scoping literature search in PubMed/MEDLINE, Association for Computing Machinery Digital Library, and PsycINFO databases to identify relevant papers published between January 2013 and April 2018. We also hand-searched relevant themes of journals in the Journal of Medical Internet Research and registered protocols. Studies were included if they provided and assessed mobile-based interventions for dietary behavior changes and/or health outcomes.
The search resulted in 30 studies that we classified by 3 main aspects: input methods, mobile-based interventions, and dietary behavior changes and health outcomes. First, regarding input methods, 5 studies allowed photo/voice/video inputs of diet information, whereas text input methods were used in the remaining studies. Other than diet information, the content of the input data in the mobile apps included user's demographics, medication, health behaviors, and goals. Second, we identified 6 categories of intervention contents, that is, self-monitoring, feedback, gamification, goal reviews, social support, and educational information. Although all 30 studies included self-monitoring as a key component of their intervention, personalized feedback was a component in 18 studies, gamification was used in 10 studies, goal reviews in 5 studies, social support in 3 studies, and educational information in 2 studies. Finally, we found that 13 studies directly examined the effects of interventions on health outcomes and 12 studies examined the effects on dietary behavior changes, whereas only 5 studies observed the effects both on dietary behavior changes and health outcomes. Regarding the type of studies, although two-thirds of the included studies conducted diverse forms of randomized control trials, the other 10 studies used field studies, surveys, protocols, qualitative interviews, propensity score matching method, and test and reference method.
This scoping review identified and classified studies on mobile-based interventions for dietary behavior change as per the input methods, nature of intervention, and outcomes examined. Our findings indicated that dietary behavior changes, although playing a mediating role in improving health outcomes, have not been adequately examined in the literature. Dietary behavior change as a mechanism for the relationship between mobile-based intervention and health outcomes needs to be further investigated. Our review provides guidance for future research in this promising mobile health area.
移动应用程序正被广泛应用于提供健康干预措施，因其具有无处不在的访问和感知能力。其中一种应用是为健康饮食行为提供干预措施。
本研究旨在提供一个关于使用移动干预措施改变饮食行为的文献综述。我们综合了这些干预措施的研究，并对其输入方法、干预措施和结果进行了梳理。
我们在 PubMed/MEDLINE、美国计算机协会数字图书馆和 PsycINFO 数据库中进行了范围广泛的文献检索，以确定 2013 年 1 月至 2018 年 4 月期间发表的相关论文。我们还对《医学互联网研究杂志》和注册方案中相关主题的期刊进行了手工搜索。如果研究提供并评估了基于移动的饮食行为改变和/或健康结果干预措施，则将其纳入研究。
检索结果得到 30 项研究，我们根据 3 个主要方面对其进行了分类：输入方法、基于移动的干预措施以及饮食行为改变和健康结果。首先，关于输入方法，有 5 项研究允许对饮食信息进行照片/语音/视频输入，而其余研究则使用文本输入方法。除了饮食信息外，移动应用程序中输入数据的内容还包括用户的人口统计学信息、药物、健康行为和目标。其次，我们确定了 6 类干预内容，即自我监测、反馈、游戏化、目标审查、社会支持和教育信息。虽然所有 30 项研究都将自我监测作为干预的关键组成部分，但 18 项研究中包含个性化反馈，10 项研究使用了游戏化，5 项研究进行了目标审查，3 项研究提供了社会支持，2 项研究提供了教育信息。最后，我们发现 13 项研究直接考察了干预措施对健康结果的影响，12 项研究考察了对饮食行为改变的影响，而只有 5 项研究观察了饮食行为改变和健康结果的影响。关于研究类型，虽然纳入研究中有三分之二进行了各种形式的随机对照试验，但另外 10 项研究采用了现场研究、调查、方案、定性访谈、倾向评分匹配法和测试与参考法。
本范围综述根据输入方法、干预性质和考察结果对基于移动的饮食行为改变干预措施进行了识别和分类。我们的研究结果表明，尽管饮食行为改变在改善健康结果方面发挥了中介作用，但在文献中并未得到充分考察。饮食行为改变作为移动干预措施与健康结果之间关系的机制需要进一步研究。我们的综述为这一充满前景的移动健康领域的未来研究提供了指导。
