SCC-PG: Building capacity for data-driven physical activity interventions in communities with depression and obesity hotspots
SCC-PG: Building capacity for data-driven physical activity interventions in communities with depression and obesity hotspots
批准号:
1951378
负责人:
Andrea Hartzler
金额:
$15.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-10-01 至 2022-09-30
中文摘要
点击翻译按钮获取中文摘要
英文摘要
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.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
Geospatial divide in real-world EHR data: Analytical workflow to assess regional biases and potential impact on health equity.
现实世界 EHR 数据中的地理空间鸿沟:评估区域偏差和对健康公平的潜在影响的分析工作流程。
DOI:
--
发表时间:
2023
期刊:
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
影响因子:
--
作者:
[Xie,SerenaJinchen, Kapos,FlaviaP, Mooney,StephenJ, Mooney,Sean, Stephens,KariA, Chen,Cynthia, Hartzler,AndreaL, Pratap,Abhishek]
通讯作者:
Pratap,Abhishek
Community-tailored physical activity interventions for people with obesity and depression: Overcoming social vulnerabilities and neighborhood barriers
针对肥胖和抑郁症患者的社区定制体育活动干预措施:克服社会脆弱性和邻里障碍
DOI:
--
发表时间:
2022
期刊:
AMIA Clinical Informatics Conference
影响因子:
--
作者:
[Cano-Calhoun C, Sangameswaran S]
通讯作者:
Cano-Calhoun C, Sangameswaran S
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