Improving environmental measures in obesity research using innovative technology

利用创新技术改善肥胖研究中的环境措施

• 批准号: 9052141
• 负责人: NATALIE COLABIANCHI
• 金额: $ 17.47万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-09 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9052141
• 关键词: Accelerometer; Adult; Area; Cereals; Characteristics; Child; Code; Communities; Data; Development; Elderly; Environment; Epidemic; Funding; Future; Goals; Gold; Grant; Health; Health behavior; Height; Housing; Individual; Intervention; Knowledge; Life; Longitudinal Studies; Low income; Maps; Measures; Mechanics; Methodology; Methods; Minority; Neighborhoods; Not Hispanic or Latino; Obesity; Online Systems; Outcome; Outcome Measure; Patient Self-Report; Physical activity; Policies; Population; Process; Public Health; Race; Research; Research Infrastructure; Resolution; Resources; Robot; Sampling; Source; System; Technology; Testing; Time; Training; United States National Institutes of Health; Validity and Reliability; Walking; Weight; Woman; base; built environment; cohort; cost; cost effective; experience; improved; innovative technologies; interest; outcome prediction; physical inactivity; population based; tool

 DESCRIPTION (provided by applicant): Obesity and physical inactivity are significant public health issues, particularly in low-income and minority populations. Recent research has highlighted the built environment as a potential causal factor for obesity and physical inactivity. A number of methods can be used to measure built environment characteristics including self-report measures, archival data, direct observation and, more recently, web-based audits (e.g., Google Street View); however, each of these methods has notable limitations. In the proposed study, we seek to test the use of an innovative technology for measuring the built environment, namely GigaPan. GigaPan is a robot system that automates obtaining numerous photos of an area using a basic camera housed within its apparatus. The resulting photos are then stitched together to form a single high-resolution photo that is highly navigable. Using GigaPan, we will document and characterize features of the built environment on 683 street segments and in 287 target areas across 19 park/playgrounds. These measures will be compared to measures obtained via direct observation (collected as part of a separate grant) and web-based audits (i.e., Google Street View for street segments and Bing Maps for parks/playgrounds). We hypothesize that the GigaPan technology will embody most of the benefits of direct observation while significantly reducing the time and cost burden. In addition, we hypothesize that measures obtained via GigaPan will be more valid than measures obtained from Google Street View and better suited for studies of built environment change as GigaPan data are time sensitive. The study will capitalize on individual-level data from a population-based cohort of adults (N=842) living in low-income minority communities that are experiencing significant built environment changes. Individual data include objectively measured height and weight, physical activity levels from both accelerometry and self-report, and demographic data. The specific aims of the grant are as follows: 1) To assess the reliability of coding environmental constructs from GigaPan photos across multiple raters, including a subsample of photos rated by Amazon Mechanical Turk Workers; 2) To determine the validity of measures obtained via GigaPan by comparing them to the same measures obtained through direct observation (gold- standard) and web-based audits (less time- and cost-intensive); 3) To determine the predictive validity of GigaPan by examining the association between built environment characteristics defined through GigaPan and accelerometer-based physical activity levels, self-reported walking levels, objectively-measured obesity status and park use; and 4) To document the costs of obtaining built environment data via GigaPan, direct observation, and web-based audits. We believe that this study will demonstrate GigaPan's promise as a relevant tool for future multi-regional and longitudinal studies examining the effect of built environment changes on health behavior and will serve as an important intermediate step in automating the process of coding built environment characteristics.

 描述（由申请人提供）：肥胖和缺乏身体活动是重大的公共卫生问题，特别是在低收入和少数族裔群体中。最近的研究强调建筑环境是导致肥胖和缺乏身体活动的潜在因素。 可以使用多种方法来测量建筑环境特征，包括自我报告测量、档案数据、直接观察以及最近的基于网络的审计（例如谷歌街景）；然而，这些方法都有明显的局限性。在拟议的研究中，我们试图测试测量建筑环境的创新技术（即 GigaPan）的使用。 GigaPan 是一种机器人系统，可使用其设备内的基本相机自动获取某个区域的大量照片。然后将生成的照片拼接在一起，形成一张易于导航的高分辨率照片。使用 GigaPan，我们将记录和描述 19 个公园/游乐场的 683 个街道段和 287 个目标区域的建筑环境特征。这些措施将与通过直接观察（作为单独拨款的一部分收集）和基于网络的审计（即街道段的 Google 街景和公园/游乐场的 Bing 地图）获得的措施进行比较。我们假设 GigaPan 技术将体现直接观察的大部分好处，同时显着减少时间和成本负担。此外，我们假设通过 GigaPan 获得的测量结果比从 Google 街景获得的测量结果更有效，并且更适合研究建筑环境变化，因为 GigaPan 数据对时间敏感。该研究将利用居住在正在经历重大建筑环境变化的低收入少数族裔社区的基于人口的成年人群 (N=842) 的个人层面数据。个人数据包括客观测量的身高和体重、加速度测量和自我报告的身体活动水平以及人口统计数据。该赠款的具体目标如下： 1) 评估多个评估者对 GigaPan 照片编码环境构造的可靠性，包括由 Amazon Mechanical Turk Workers 评估的照片子样本； 2) 通过将通过 GigaPan 获得的测量值与通过直接观察（黄金标准）和基于网络的审计（时间和成本较少）获得的相同测量值进行比较，确定其有效性； 3) 通过检查 GigaPan 定义的建筑环境特征与基于加速度计的体力活动水平、自我报告的步行水平、客观测量的肥胖状况和公园使用之间的关联，确定 GigaPan 的预测有效性； 4) 记录通过 GigaPan、直接观察和基于网络的审计获取建筑环境数据的成本。我们相信，这项研究将证明 GigaPan 作为未来多区域和纵向研究的相关工具的前景，以检查建筑环境变化对健康行为的影响，并将作为建筑环境特征编码过程自动化的重要中间步骤。