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 PEDestrian 公共卫生评估模型 (PED-PHAM) 使用经过验证的人工智能 (AI) 检测方法来导出行人环境特征 (PEF)，例如人行道、人行横道和 照明，来自数字图像。它将把这些 PEF 添加到 Urban Design 4 Health 现有的同行评审的国家- 预测健康结果的 PHAM。 PED-PHAM 将更好地帮助分析师和决策者公开 规划机构、咨询公司、土地开发商、医疗保健提供者、贷款机构和研究机构 和大数据实体来解释众所周知的可修改且具有成本效益的设计功能的健康益处 预测身体活动和体重指数。 Urban Design 4 Health 和亚利桑那州立大学的目标是： 1：评估 程度 AI 模型检测 PEF，2：创建和优化区块组级别 AI 衍生的 PEF 指数，以及 3： 评估 PEF 增强模型。创建 PED-PHAM。缺乏身体活动是肥胖的主要危险因素， 心脏病、中风和 II 型糖尿病。大多数成年人都不活跃。这种体力活动（PA）缺乏症 在过去的二十年里，美国人口并没有发生有意义的变化。缺乏 PA 部分是由于敌意 行人环境、久坐的汽车生活方式以及广阔的城市环境。重要的 建筑环境、PA、体重指数 (BMI)、糖尿病、 和整体心脏代谢健康。很少有经过同行评审的基于证据的工具可以量化和预测身体状况 基于社区的交通投资、土地使用和社区设计对活动和健康的影响 决策，但没有一个捕获 PEF。我们将计算两个 NIH 的 2,173 个参与者家庭位置的 PEF： 资助 R01 研究，创建新的客观身体活动和报告的 BMI 模型，并整合结果 进入 N-PHAM 工具。将采取以下步骤 [1] 使用经过培训和培训的人员客观地检测 PEF 的存在 经过验证的 AI 计算机视觉模型应用于 Google 街景每 15 米全方位图像 人口普查街区组沿路进行，共有 2173 名参与者，分布在巴尔的摩、菲尼克斯、圣地亚哥和 来自 NIH 资助的两项研究的西雅图地区，[2] 为每个检测到的 PEF 构建块组级别指标，[3] 通过将 PEF 指标转换为标准化分布总和来优化块组级别 PEF 指数，[4] 在空间上加入新的 PEF、宏观步行性、绿地和人口统计指标并进行客观评估 并将自我报告的 PA 和 BMI 存入个人级别的综合数据库中。 [5] 进行统计分析 确定哪些 PEF（单独和组合指数）和权重最能解释 PA 和 BMI 调整人口统计、步行适宜性和绿地，以及 [6] 向现有的 N- 中添加新的统计模型 PHAM 平台创建 PED-PHAM。第二阶段将扩展 PED-PHAM 以供全国应用和 商业化并进一步考虑空气污染暴露，以创建优化的社区设计场所 - 为美国各地用户选择的地点提供基于处方的药物，以增加 PA 并减少慢性疾病。