SCC-PG: Building capacity for data-driven physical activity interventions in communities with depression and obesity hotspots

根据美国疾病控制与预防中心（Centers for Disease Control and Prevention）的数据，超过40%患有抑郁症的成年人肥胖，而患有抑郁症的成年人比没有抑郁症的成年人更容易肥胖。抑郁症和肥胖症是美国最常见的两大公共健康问题。然而，帮助人们减肥和应对抑郁症状的有效运动项目并不总能达到最需要的社区。通过电子健康记录（EHR）和数字卫生技术挖掘数据，可以通过确定受肥胖和抑郁症影响最大的社区，帮助扩大覆盖范围。一旦确定，让这些社区参与进来对于告知将采用的因地制宜的干预措施至关重要。这个项目的目的是建立能力，将卫生数据分析与社区参与工作结合起来，为面临肥胖和抑郁症的社区确定和设计更好的干预措施。该项目(1)整合了异构数据源、机器学习和地理空间分析，以确定抑郁和肥胖高水平的地理区域，称为社区热点；(2)从热点地区吸引社区成员共同设计所需的体育活动干预措施。项目成果将通过提供基于多种真实数据和社区层面干预设计的热点检测新方法，推动该领域的发展，同时为未来的成功奠定必要的技术和协作基础。技术贡献包括新颖的数据分析方法、数据源和使用区域电子病历数据进行热点定位的度量。通过该项目建立的合作伙伴关系将指导今后将参与范围扩大到更多的社区热点。更广泛地说，这项工作将通过数据驱动、社区合作的方式造福社会，通过解决美国最重要的两个健康问题：抑郁和肥胖，改善公民的健康和福祉。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

According to the Centers for Disease Control and Prevention, more than 40% of adults with depression are obese and adults with depression are more likely to be obese than adults without depression. Combined together, depression and obesity are two of the most common public health problems in the United States. Yet, effective exercise programs that help people to lose weight and cope with depression symptoms do not always reach communities with the greatest need. Mining data through electronic health records (EHR) and digital health technology can help to improve the reach by identifying communities most affected by obesity and depression. Once identified, engaging those communities is critical to informing locally-tailored interventions that will be adopted. The purpose of this project is to build capacity for combining health data analytics with community engagement efforts to identify, and design better interventions for, communities facing obesity and depression. This project (1) integrates heterogeneous data sources, machine learning, and geospatial analysis to identify geographical areas with high levels of depression and obesity called community hotspots, and (2) engages community members from hotspots to co-design desired physical activity interventions. Project findings will advance the field by yielding new ways for hotspot detection based on diverse real-world data and community-level intervention design while laying the technical and collaborative foundations necessary for future success. Technical contributions include novel data analytic methods, data sources, and metrics for hot-spotting using regional EHR data. Collaborative partnerships developed through this project will guide future extension of engagement to a broader number of community hotspots. More broadly, this work will benefit society through a data-driven, community-partnered approach that improves the health and wellbeing of citizens by addressing two of the most significant US health problems: depression and obesity.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.