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

Abstract

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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 (yongzhenwang@dlut.edu.cn), Xiaozhong Liu (xliu14@wpi.edu), or Jun Lin (linjun.lj@alibaba-inc.com) if there are questions or concerns.