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

根据疾病控制和预防中心的数据，超过40%的患有抑郁症的成年人患有肥胖症，而且患有抑郁症的成年人比没有抑郁症的成年人更容易肥胖。抑郁症和肥胖症加在一起，是美国最常见的两个公共卫生问题。然而，帮助人们减肥和应对抑郁症状的有效运动计划并不总是到达最有需求的社区。通过电子健康记录(EHR)和数字健康技术挖掘数据可以通过识别受肥胖症和抑郁症影响最严重的社区来帮助扩大覆盖范围。一旦确定，让这些社区参与对于提供将被采用的当地量身定做的干预措施至关重要。该项目的目的是建设将健康数据分析与社区参与努力相结合的能力，以确定面临肥胖和抑郁症的社区并为其设计更好的干预措施。该项目(1)集成了不同的数据源、机器学习和地理空间分析，以确定称为社区热点的抑郁和肥胖程度高的地理区域，以及(2)动员来自热点的社区成员共同设计所需的体力活动干预。项目成果将通过基于各种真实世界数据和社区级干预设计产生热点检测的新方法来推动该领域的发展，同时为未来的成功奠定必要的技术和合作基础。技术贡献包括新颖的数据分析方法、数据源和使用地区性EHR数据进行热点定位的指标。通过该项目建立的协作伙伴关系将指导今后将参与扩展到更多的社区热点。更广泛地说，这项工作将通过数据驱动、社区合作的方法造福社会，通过解决美国最重要的两个健康问题：抑郁症和肥胖来改善公民的健康和福祉。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.