The Automatic Context Measurement Tool: bringing environmental data to non-specialists

项目摘要/摘要 最近重申美国地理差异的研究表明，这些差异并没有得到充分的解释 通过社会经济差异，表明环境或环境对健康有很大影响。然而，关于 环境对健康的影响，特别是行为干预的环境影响， 一直受到计算特定主体的环境背景衡量标准的成本的限制。我们建议 通过以下方式将环境措施置于非专业研究人员和健康促进专家的能力范围内 构建并验证自动上下文测量工具(ACMT)。ACMT是一种软件工具， 研究人员和从业者可以使用来高效地编译、归因于个人和分析环境 测量数据来自免费的全国性数据集，如美国人口普查和国家土地覆盖 数据库。在建立ACMT之后，我们将分五个步骤对其进行验证和推广。首先，我们将量化如何 这些国家可用的测量方法通过以下方式捕捉研究参与者环境中与健康相关的方面 比较国家可用的体力活动预测环境指标与当地可用的环境指标 环境测量对华盛顿州金县的一个队列的体力活动进行预测。第二，我们将 通过对比环境指标的预测性，展示ACMT如何应用于多站点试验 金县的体力活动与盐湖城、德克萨斯大学和俄勒冈州波特兰的预测体力活动的人相同。 第三，我们将使用来自Kaiser Permanente的大型电子健康记录(EHR)数据集的ACMT 华盛顿(原团体健康)患者和华盛顿大学医学院患者探讨哪些环境 测量方法最好地预测健康成年人的BMI轨迹。第四，我们将确定ACMT可用于 通过比较健康干预效果的环境预测因素来确定健康干预效果的环境修饰者 接受减肥手术的成年人与未接受手术的肥胖成年人之间的BMI轨迹。 最后，我们将确保ACMT在网络上可用，其界面可供非专家使用， 从体重管理计划和其他患者护理项目中招募项目协调员以提高可用性 测试ACMT并提供反馈，以便我们改进它。一旦向公众开放，ACMT将 为没有经过地理空间培训的研究人员和从业者开放环境措施的使用 以前受到收集和分析所需的大量专业知识(和相关费用)的阻碍 潜在的环境对健康的影响。我们将采取的验证ACMT的步骤还将提供 对环境对体力活动和肥胖的影响的更多洞察。最后，互补性 包括课程作业、结构化指导和体验式学习在内的培训计划将使我发展 作为一名独立科学家开始我的职业生涯的技能在信息学和 流行病学。

PROJECT SUMMARY/ABSTRACT Recent studies reaffirming geographic disparities in the United States showed that they are not fully explained by socioeconomic differences, suggesting context or environment strongly impacts health. Yet research on environmental influences on health, particularly environmental effect modification of behavioral interventions, has been limited by the cost of computing subject-specific measures of environmental context. We propose to put environmental measures within the reach of non-specialist researchers and health promotion experts by building and validating the Automatic Context Measurement Tool (ACMT). ACMT is a software tool that researchers and practitioners can use to efficiently compile, attribute to individuals, and analyze environmental measures drawn from free and nationally available datasets such as US Census and the National Land Cover Database. After building ACMT, we will take five steps to validate and promote it. First, we will quantify how well these nationally available measures capture health-relevant aspects of study participants' environments by comparing nationally available environment measures predictive of physical activity to locally-available environment measures predictive of physical activity for a cohort based in King County, WA. Second, we will demonstrate how ACMT might apply to multi-site trials by contrasting environment measures predictive of physical activity in King County with those predictive of physical activity in Salt Lake City, UT and Portland, OR. Third, we will use ACMT with large electronic health record (EHR) datasets from Kaiser Permanente Washington (formerly Group Health) patients and UW Medicine patients to explore which environmental measures best predict BMI trajectories in healthy adults. Fourth, we will establish that ACMT can be used to identify environmental modifiers of health intervention effectiveness by comparing environmental predictors of BMI trajectories among adults receiving bariatric surgery compared with obese adults not receiving surgery. Finally, we will ensure ACMT is available on the web with an interface that is usable by non-specialists, recruiting project coordinators from weight management programs and other patient care projects to usability test ACMT and provide feedback allowing us to improve it. Once made publically available, the ACMT will unlock the use of environment measures for researchers and practitioners without geospatial training who had previously been hindered by the considerable expertise (and related expense) required to collect and analyze potential environmental influences on health. The steps we will take to validate ACMT will also provide additional insight into environmental influences on physical activity and obesity. Finally, the complementary training plan comprising coursework, structured mentoring, and experiential learning will let me develop the skills to launch my career as an independent scientist working at the intersection of informatics and epidemiology.