Toward Data-Driven Digital Therapeutics Analytics:Literature Review and Research Directions  

作　　者：Uichin Lee Gyuwon Jung Eun-Yeol Ma Jin San Kim Heepyung Kim Jumabek Alikhanov Youngtae Noh Heeyoung Kim 

机构地区：[1]Korea Advanced Institute of Science and Technology,Daejeon 34141,Republic of Korea [2]Inha University,Incheon 402-751,Republic of Korea [3]KENTECH,Naju-si,Republic of Korea

出　　处：《IEEE/CAA Journal of Automatica Sinica》2023年第1期42-66,共25页自动化学报（英文版）

基　　金：supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Korea government(MSIT)(2020R1A4A1018774)。

摘　　要：With the advent of digital therapeutics(DTx),the development of software as a medical device(SaMD)for mobile and wearable devices has gained significant attention in recent years.Existing DTx evaluations,such as randomized clinical trials,mostly focus on verifying the effectiveness of DTx products.To acquire a deeper understanding of DTx engagement and behavioral adherence,beyond efficacy,a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis.In this work,the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets,to investigate contextual patterns associated with DTx usage,and to establish the(causal)relationship between DTx engagement and behavioral adherence.This review of the key components of datadriven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets,which helps to iteratively improve the receptivity of existing DTx.

关 键 词：Causal inference data-driven analytics framework digital therapeutics(DTx) mobile and wearable data technical and behavioral engagement 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]