PED-PHAM: An Automated and Scalable Spatial Tool That Predicts and Monetizes Health Impacts of the Built, Natural, and Social Environment

PED-PHAM：一种自动化且可扩展的空间工具，可预测建筑、自然和社会环境对健康的影响并从中获利

• 批准号: 10761396
• 负责人: James Chapman
• 金额: $ 27.57万
• 依托单位: URBAN DESIGN 4 HEALTH, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10761396
• 关键词: Accelerometer; Accounting; Adult; Age; Air Pollution; American; Area; Arizona; Artificial Intelligence; Baltimore; Big Data; Body mass index; Censuses; Chronic Disease; Cities; Communities; Computer Vision Systems; Computer software; Control Groups; Data; Data Set; Databases; Demographic Factors; Detection; Diabetes Mellitus; Disease Outcome; Elements; Environment; Exposure to; Funding; Geography; Goals; Grant; Green space; Health; Health Benefit; Health Personnel; Health behavior; Heart Diseases; Home; Human; Hypertension; Image; Imagery; Income; Individual; Institution; Intervention; Investments; Legal patent; Life Style; Lighting; Location; Malignant Neoplasms; Measurable; Measures; Methodology; Modeling; Neighborhoods; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Outcome; Participant; Patient Self-Report; Peer Review; Performance; Persons; Phase; Physical activity; Policies; Population; Predictive Factor; Privatization; Public Health; Recreation; Reporting; Research; Resistance; Risk Factors; Scientific Advances and Accomplishments; Signal Transduction; Social Environment; Standardization; Statistical Data Interpretation; Statistical Models; Stroke; Sum; Testing; Training; Translating; Transportation; Travel; United States National Institutes of Health; Universities; Walking; Weight; Work; built environment; cardiometabolism; commercialization; community planning; cost effective; demographics; density; design; detection method; digital; digital imaging; evidence base; health assessment; improved; indexing; innovation; land use; meetings; meter; physical conditioning; physical inactivity; predictive modeling; predictive tools; prototype; sedentary; social factors; suburb; success; tool; urban setting; walkability; web app; web based software; willingness

The proposed PEDestrian Public Health Assessment Model (PED-PHAM) uses proven Artificial Intelligence (AI) detection methods to derive pedestrian environment features (PEF), such as sidewalks, crosswalks, and lighting, from digital images. It will add these PEFs to Urban Design 4 Health's existing peer reviewed National- PHAM that predicts health outcomes. PED-PHAM will better enable analysts and decision makers at public planning agencies, consulting firms, land developers, health care providers, lending institutions, and research and big data entities to account for health benefits of modifiable and cost-effective design features known to predict physical activity and BMI. Urban Design 4 Health's and Arizona State University's aims are: 1: Evaluate Degree AI Models Detect PEFs, 2: Create and Optimize Block Group Level AI-derived PEF Indices, and 3: Evaluate PEF enhanced Models. Create PED-PHAM. Physical inactivity is a primary risk factor for obesity, heart disease, stroke, and Type II diabetes. Most adults are inactive. This physical activity (PA) deficiency has not changed meaningfully for the US population in the last two decades. Lack of PA is partially due to hostile pedestrian environments, sedentary car-dependent lifestyles, and sprawling urban environments. Significant relationships have been documented between the built environment, PA, body mass index (BMI), diabetes, and overall cardiometabolic health. Few peer-reviewed evidence-based tools quantify and predict physical activity and health impacts of community-based transportation investments, land use, and community design decisions, and none capture PEFs. We will calculate PEFs for 2,173 participant home locations in two NIH- funded R01 studies, create new objective physical activity and reported BMI models, and integrate the results into the N-PHAM tool. The following steps will be taken [1] Objectively detect PEF presence using trained and validated AI computer vision models applied to Google Street View omnidirectional imagery every 15 meters along roads in census block groups containing 2173 participants in the Baltimore, Phoenix, San Diego, and Seattle regions from two NIH funded studies, [2] Construct block group level metrics for each detected PEF, [3] Optimize block group level PEF indices by translating PEF metrics into summed standardized distributions, [4] Spatially join new PEFs, macro walkability, greenspace, and demographic measures with objectively assessed and self reported PA and BMI into a combined person-level database. [5] Conduct statistical analysis to determine which PEFs (individually and in combined indices) and weights best explain PA and BMI when adjusting for demographics, walkability, and greenspace, and [6] Add new statistical models to the existing N- PHAM platform creating PED-PHAM. Phase 2 will scale PED-PHAM for national application and commercialization and further account for air pollution exposure to create optimized community design place- based prescriptions to increase PA and reduce chronic diseases for user-selected locations across the US.

拟议的行人公共卫生评估模型（PED-PHAM）使用了经过验证的人工智能 （AI）得出行人环境特征（PEF）的检测方法，例如人行道，人行横道和 照明，来自数字图像。它将将这些PEF添加到Urban Design 4 Health现有的同行评审国家 - PHAM可以预测健康结果。 Ped-Pham将更好地使分析师和决策者公开 规划机构，咨询公司，土地开发商，卫生保健提供者，贷款机构和研究 和大数据实体，以考虑可修改和成本效益的设计功能的健康益处 预测体育活动和BMI。城市设计4 Health's和Arizona State University的目标是：1：评估 程度AI模型检测PEFS，2：创建和优化块组级别AI衍生的PEF指数，3： 评估PEF增强模型。创建Ped-Pham。身体不活跃是肥胖的主要危险因素， 心脏病，中风和II型糖尿病。大多数成年人都是不活跃的。这种体育活动（PA）缺乏 在过去的二十年中，美国人口没有任何有意义的变化。 PA的缺乏部分是由于敌对的 行人环境，久坐的汽车依赖生活方式和庞大的城市环境。重要的 已经记录了建筑环境，PA，体重指数（BMI），糖尿病， 和整体心脏代谢健康。很少有同行评审的循证工具可以量化和预测物理 基于社区的运输投资，土地使用和社区设计的活动和健康影响 决策，没有捕获PEF。我们将在两个NIH- 资助R01研究，创建新的客观体育活动并报告了BMI模型，并整合了结果 进入N-PHAM工具。将采取以下步骤[1]使用训练有素的和 经过验证的AI计算机视觉模型应用于Google街道视图全向图像每15米 沿着人口普查街区的道路，其中包含2173位参与者，位于巴尔的摩，凤凰城，圣地亚哥和 来自两项NIH资助研究的西雅图地区，[2]每个检测到的PEF的构建块组水平指标，[3] 通过将PEF指标转换为求和标准化分布，[4]来优化块组级PEF指标。 通过空间加入新的PEF，宏观步行性，绿地和人口统计措施，并通过客观评估 自我将PA和BMI报告为一个组合人级数据库。 [5]将统计分析进行 确定哪些PEF（单独和组合索引）和权重最好解释PA和BMI 调整人口统计学，步行性和绿地，[6]在现有n-中添加新的统计模型 PHAM平台创建PED-PHAM。第2阶段将对国家申请进行规模扩展，并 商业化并进一步说明空气污染的暴露，以创建优化的社区设计地点 - 基于增加PA并减少美国各地用户选择位置的慢性疾病的处方。