Abstract：
Globally, the adoption and implementation of policies to improve the healthiness of food environments and prevent population weight gain have been inadequate. This is partly because of the complexity associated with monitoring dynamic food environments. Crowdsourcing is a citizen science approach that can increase the extent and nature of food environment data collection by engaging citizens as sensors or volunteered computing experts. There has been no literature synthesis to guide the application of crowdsourcing to food environment monitoring. We systematically conducted a scoping review to address this gap. Forty-two articles met our eligibility criteria. Photovoice techniques were the most employed methodological approaches (n = 25 studies), commonly used to understand overall access to healthy food. A small number of studies made purpose-built apps to collect price or nutritional composition data and were scaled to receive large amounts of data points. Twenty-nine studies crowdsourced food environment data by engaging priority populations (e.g., households receiving low incomes). There is growing potential to develop scalable crowdsourcing platforms to understand food environments through the eyes of everyday people. Such crowdsourced data may improve public and policy engagement with equitable food policy actions.
摘要：
全球范围内,改善食物环境健康和防止人口体重增加的政策的采用和实施是不够的。部分原因是与监测动态食物环境相关的复杂性。众包是一种公民科学方法，可以通过让公民作为传感器或志愿计算专家来增加食物环境数据收集的程度和性质。一直没有文献综合来指导众包在食品环境监测中的应用。我们系统地进行了范围审查，以解决这一差距。42篇文章符合我们的资格标准。语音技术是最常用的方法学方法(n=25项研究)，通常用于了解健康食品的整体获取。少数研究开发了专门构建的应用程序来收集价格或营养成分数据，并进行了扩展以接收大量数据点。29项研究通过参与优先人群(例如，低收入家庭)。开发可扩展的众包平台以通过普通人的眼睛了解食物环境的潜力越来越大。此类众包数据可以改善公众和政策与公平食品政策行动的参与。
