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)：肥胖和缺乏运动是重大的公共卫生问题，特别是在低收入和少数群体中。最近的研究强调，建筑环境是肥胖和缺乏运动的潜在原因。 可以使用许多方法来衡量建筑环境特征，包括自我报告测量、档案数据、直接观察和最近的基于网络的审计(例如，谷歌街景)；然而，这些方法每一种都有明显的局限性。在拟议的研究中，我们试图测试使用一种创新的技术来测量建筑环境，即GigaPan.GigaPAN是一个机器人系统，它使用设备中安装的基本摄像头自动获取一个区域的大量照片。然后，将生成的照片拼接在一起，形成一张高度可导航的高分辨率照片。我们将使用GigaPAN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(作为单独赠款的一部分收集)和基于网络的审计(即街道路段的谷歌街景和公园/游乐场的必应地图)获得的措施进行比较。我们假设，GigaPann技术将体现直接观测的大部分好处，同时显著减少时间和成本负担。此外，我们假设，通过GigaPann获得的测量结果将比从Google Street View获得的测量结果更有效，更适合于建筑环境变化的研究，因为GigaPanan数据具有时间敏感性。这项研究将利用基于人口的成人队列(N=842)的个人水平数据，这些成年人生活在正在经历重大建筑环境变化的低收入少数群体中。个人数据包括客观测量的身高和体重，来自加速计和自我报告的体力活动水平，以及人口统计数据。赠款的具体目标如下：1)评估从多个评分者的GigaPan照片中编码环境结构的可靠性，包括亚马逊土耳其机械工人评分的照片的子样本；2)通过将通过GigaPanTM获得的测量结果与通过直接观察(黄金标准)和基于网络的审计(耗时和成本较低)获得的相同测量结果进行比较，确定这些测量结果的有效性；3)通过检查GigaPann定义的建成环境特征与基于加速计的体力活动水平、自我报告的步行水平、客观测量的肥胖状态和公园使用之间的关联，确定GigaPann的预测有效性；以及4)记录通过GigaPAN、直接观察和基于网络的审计获得建筑环境数据的成本。我们相信，这项研究将展示GigaPAN作为未来多区域和纵向研究建筑环境变化对健康行为影响的相关工具的前景，并将作为自动化建筑环境特征编码过程的重要中间步骤。