Dataset.

Coronary Heart Disease (CHD) is the leading cause of death in the United States, affecting over 20.5 million adults. Previous studies link health behaviors – such as dietary behavior, physical activity, smoking, and alcohol consumption – to CHD risk. These studies typically use surveys and interviews, which, despite their benefits, are resource-intensive and limited by small sample sizes. Using large-scale national level anonymized smartphone-based location data, our study examines whether health behaviors that are proxy measured by place visitation are associated with CHD prevalence across US census tracts. This study utilized data from multiple sources, including demographic and socioeconomic characteristics, health outcomes, and smartphone-based place visitation data. Health behavior measures were derived from aggregated smartphone location data at the census tract level, focusing on categories such as food retails, drinking places, and physical activity locations. Three sets of regression analyses were conducted: one using only demographic variables, the second including socioeconomic variables, and another incorporating the derived health behavior measures. Linear and spatial regression analyses were employed to assess the relationship between neighborhood-level CHD prevalence and these behaviors. Findings indicate a significant association between health behaviors that are proxy measured by place visitation data and the prevalence of CHD at the neighborhood level. The models incorporating these behaviors demonstrated improved fitness and highlighted specific behavioral factors such as increased visits to physical activity facilities and healthy food retail associated with lower CHD rates. Conversely, higher visits to less healthy food retail were associated with increased CHD rates. Smartphone-based visitation data offers a novel method to assess health behaviors at a large scale, providing valuable insights for targeting CHD interventions more effectively at the neighborhood level. This approach could enhance our understanding and management of CHD, informing public health strategies and interventions to mitigate this major health challenge.冠心病（Coronary Heart Disease, CHD）是美国民众的首要致死原因，影响超过2050万成年人。既往研究已证实，饮食行为、身体活动、吸烟与饮酒等健康行为与冠心病发病风险密切相关。此类研究多采用问卷与访谈方法，虽具备一定优势，但存在资源消耗量大、样本规模受限的缺陷。本研究依托大规模国家级匿名智能手机位置数据，探究以场所访问作为代理指标的健康行为，是否与美国各人口普查街区的冠心病患病率存在关联。本研究整合多源数据，涵盖人口统计学与社会经济特征、健康结局指标，以及基于智能手机的场所访问数据。健康行为指标从人口普查街区层级的聚合智能手机位置数据中衍生，聚焦食品零售场所、饮酒场所与身体活动场所等类别。研究共开展三组回归分析：第一组仅纳入人口统计学变量，第二组加入社会经济变量，第三组则纳入衍生的健康行为指标。本研究采用线性回归与空间回归分析方法，评估邻里层面冠心病患病率与上述健康行为间的关联。研究结果显示，以场所访问数据代理测量的健康行为，与邻里层面的冠心病患病率存在显著关联。纳入健康行为指标的模型拟合效果更优，且明确了特定行为关联：增加身体活动设施与健康食品零售场所的访问频次，与更低的冠心病发病率相关；反之，增加非健康食品零售场所的访问频次，则与更高的冠心病发病率相关。基于智能手机的访问数据为大规模评估健康行为提供了全新方法，可为更高效地在邻里层面开展冠心病干预提供宝贵参考。该研究方法有助于深化我们对冠心病的认知与管理，为应对这一重大公共卫生挑战的公共卫生策略与干预措施提供决策依据。