Leveraging online shopping behaviors as a proxy for personal lifestyle choices: New insights into chronic disease prevention literacy

<strong>Abstract</strong> <strong>Objective: </strong>Ubiquitous internet access is reshaping the way we live, but it is accompanied by unprecedented challenges in preventing chronic diseases that are usually planted by long exposure to unhealthy lifestyles. This paper proposes leveraging online shopping behaviors as a proxy for personal lifestyle choices to improve chronic disease prevention literacy, targeted for times when e-commerce user experience has been assimilated into most people's everyday lives. <strong>Methods:</strong> Longitudinal query logs and purchase records from 15 million online shoppers were accessed, constructing a broad spectrum of lifestyle features covering various product categories and buyer personas. Using the lifestyle-related information preceding online shoppers’ first purchases of specific prescription drugs, we could determine associations between their past lifestyle choices and whether they suffered from a particular chronic disease. <strong>Results:</strong> Novel lifestyle risk factors were discovered in two exemplars—depression and type 2 diabetes, most of which showed reasonable consistency with existing healthcare knowledge. Further, such empirical findings could be adopted to locate online shoppers at higher risk of these chronic diseases with decent accuracy [i.e., (area under the receiver operating characteristic curve) AUC=0.68 for depression and AUC=0.70 for type 2 diabetes], closely matching the performance of screening surveys benchmarked against medical diagnosis. <strong>Conclusions:</strong> Mining online shopping behaviors can point medical experts to a series of lifestyle issues associated with chronic diseases that are less explored to date. Hopefully, unobtrusive chronic disease surveillance via e-commerce sites can grant consenting individuals a privilege to be connected more readily with the medical profession and sophistication. This repository provides data access for our newly discovered lifestyle risk factors. Note that: 1) For ordinal coding, results are rescaled by dividing the mean by the max and by dividing the standard deviation by the mean. 2) For nominal coding, category information is confidential, and we only report the distribution of online shoppers on the control category and the rest. Contact Yongzhen Wang ([e-mail]), Xiaozhong Liu ([e-mail]), or Jun Lin ([e-mail]) if there are questions or concerns.**摘要** **研究目的**：泛在互联网接入正在重塑人类的生活方式，但与此同时，预防因长期暴露于不健康生活习惯而诱发的慢性病面临着前所未有的挑战。本文提出以网购行为作为个人生活方式选择的代理变量，以提升慢性病预防素养，该方案适配于电商用户体验已融入多数人日常生活的时代背景。 **研究方法**：本研究获取了1500万在线购物者的纵向查询日志与购买记录，构建了覆盖各类商品品类与买家画像的多维度生活方式特征集。结合在线购物者首次购买特定处方药前的生活方式相关信息，我们可分析其过往生活方式选择与是否罹患特定慢性病之间的关联。 **研究结果**：本研究针对抑郁症与2型糖尿病两个典型慢性病案例，发现了全新的生活方式风险因素，其中多数结论与现有医疗健康知识具有合理的一致性。进一步研究表明，基于该实证发现可精准定位慢性病高风险在线购物者，模型性能优异——抑郁症预测的受试者工作特征曲线下面积（Area Under the Receiver Operating Characteristic Curve，AUC）为0.68，2型糖尿病预测的AUC为0.70，其表现与以医学诊断为基准的筛查问卷性能相当。 **研究结论**：挖掘在线购物行为，可为医疗专家提供一系列迄今尚未被充分探索的、与慢性病相关的生活方式关联问题。未来有望通过电商平台实现隐蔽的慢性病监测，使自愿参与的个体能够更便捷地对接专业医疗资源。本数据集仓库提供了我们新发现的生活方式风险因素的数据获取途径。 注意事项： 1） 对于序数编码（ordinal coding），结果通过「均值除以最大值」与「标准差除以均值」进行重缩放。 2） 对于名义编码（nominal coding），品类信息属于保密内容，我们仅报告在线购物者在对照类别与其余类别中的分布情况。 如有疑问或相关建议，请联系王永珍（[e-mail]）、刘晓钟（[e-mail]）或林俊（[e-mail]）。