摘要
背景：要解决饮食等慢性疾病的关键行为风险因素，需要采用创新方法来客观衡量饮食模式及其上游决定因素，特别是饮食环境。虽然地理信息系统（GIS）技术通过绘制食品销售点的可用性图推动了这一领域的发展，但它们往往将食品获取动态简化为家庭住址附近的情况，可能会误判邻里效应。利用谷歌位置历史时间轴（GLH）数据提供了一种新方法，可在个人层面评估食品店利用的长期模式，从而深入了解食品环境相互作用、饮食质量和健康结果之间的关系：我们利用了之前从华盛顿州双胞胎登记（WSTR）参与者子集中收集的 GLH 数据。GLH 包括来自 357 名参与者的超过 2.87 亿条位置记录。我们开发了一些方法，利用应用于 InfoUSA 食品店位置数据的特定食品店缓冲区来识别食品店访问。这种方法包括应用最短和最长停留时间以及重访间隔。我们从 GLH 数据中计算出指标，以检测对健康有重要影响的不同食品店分类（如杂货店、快餐店、便利店）的访问频率。我们还进行了几项敏感性分析，以检验我们的食品店指标的稳健性，并检验居民点 1 公里和 2.5 公里范围内的访问情况：我们为 357 名研究参与者确定了 156,405 次特定的食品店访问。其中 60% 为提供全面服务的餐馆，15% 为提供有限服务的餐馆，16% 为超市。每人每月光顾食品店的平均次数为 12.795 次。只有 8%、10% 和 11% 的全套服务餐馆、有限服务餐馆和超市位于居民点 1 公里范围内：GLH数据为评估个人层面的食物利用行为提供了一种新方法。Background: Addressing key behavioral risk factors for chronic diseases, such as diet, requires innovative methods to objectively measure dietary patterns and their upstream determinants, notably the food environment. Although GIS techniques have pushed the boundaries by mapping food outlet availability, they often simplify food access dynamics to the vicinity of home addresses, possibly misclassifying neighborhood effects. Leveraging Google Location History Timeline (GLH) data offers a novel approach to assess long-term patterns of food outlet utilization at an individual level, providing insights into the relationship between food environment interactions, diet quality, and health outcomes.
Methods: We leveraged GLH data previously collected from a sub-set of participants in the Washington State Twin Registry (WSTR). GLH included more than 287 million location records from 357 participants. We developed methods to identify visits to food outlets using outlet-specific buffer zones applied to the InfoUSA data on food outlet locations. This methodology involved the application of minimum and maximum stay durations, along with revisit intervals. We calculated metrics from the GLH data to detect frequency of visits to different food outlet classifications (e.g. grocery stores, fast food, convenience stores) important to health. Several sensitivity analyses were conducted to examine the robustness of our food outlet metrics and to examine visits occurring within 1 and 2.5 km of residential locations.
Results: We identified 156,405 specific food outlet visits for the 357 study participants. 60% were full-service restaurants, 15% limited-service restaurants, and 16% supermarkets. Mean visits per person per month to any food outlet was 12.795. Only 8, 10 and 11% of full-service restaurants, limited-service restaurants, and supermarkets, respectively, occurred within 1 km of residential locations.
Conclusions: GLH data presents a novel method to assess individual-level food utilization behaviors.
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