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

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          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.

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          Most cited references46

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Diagnostic systems of several kinds are used to distinguish between two classes of events, essentially "signals" and "noise". For them, analysis in terms of the "relative operating characteristic" of signal detection theory provides a precise and valid measure of diagnostic accuracy. It is the only measure available that is uninfluenced by decision biases and prior probabilities, and it places the performances of diverse systems on a common, easily interpreted scale. Representative values of this measure are reported here for systems in medical imaging, materials testing, weather forecasting, information retrieval, polygraph lie detection, and aptitude testing. Though the measure itself is sound, the values obtained from tests of diagnostic systems often require qualification because the test data on which they are based are of unsure quality. A common set of problems in testing is faced in all fields. How well these problems are handled, or can be handled in a given field, determines the degree of confidence that can be placed in a measured value of accuracy. Some fields fare much better than others.
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                Author and article information

                Journal
                Digit Health
                Digit Health
                DHJ
                spdhj
                Digital Health
                SAGE Publications (Sage UK: London, England )
                2055-2076
                28 March 2022
                Jan-Dec 2022
                : 8
                : 20552076221089092
                Affiliations
                [1 ]Institute of Science of Science and S&T Management, Ringgold 12399, universityDalian University of Technology; , Dalian, China
                [2 ]Department of Computer Science, Ringgold 8718, universityWorcester Polytechnic Institute; , Worcester, MA, USA
                [3 ]School of Informatics, Computing and Engineering, Ringgold 1771, universityIndiana University; , Bloomington, IN, USA
                [4 ]Ringgold 573754, universityAlibaba DAMO Academy; , Hangzhou, China
                Author notes
                [*]Yongzhen Wang, Institute of Science of Science and S&T Management, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian 116024, China. Email: yongzhenwang@ 123456dlut.edu.cn
                [*]Xiaozhong Liu, Department of Computer Science, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USA. Email: xliu14@ 123456wpi.edu
                Author information
                https://orcid.org/0000-0001-7306-1291
                Article
                10.1177_20552076221089092
                10.1177/20552076221089092
                8966098
                7884172e-11b0-4e37-bc7e-b8b0811201ae
                © The Author(s) 2022

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 31 August 2021
                : 6 March 2022
                Funding
                Funded by: Fundamental Research Funds for the Central Universities;
                Award ID: DUT21RC(3)068
                Categories
                Original Research
                Custom metadata
                ts19
                January-December 2022

                online shopping behavior,lifestyle risk factor,chronic disease risk prediction,depression,type 2 diabetes

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