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

    Year: 2023

    Authors: Lee, Uichin; Jung, Gyuwon; Ma, Eun-Yeol; Kim, Jin San; Kim, Heepyung; Alikhanov, Jumabek; Noh, Youngtae; Kim, Heeyoung

    Keywords: Behavioral sciences; Causal inference; Data privacy; Data visualization; data-driven analytics framework; digital therapeutics (DTx); Medical devices; mobile and wearable data; Software; Surveillance; technical and behavioral engagement; Wearable computers

    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 data-driven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets, which helps to iteratively improve the receptivity of existing DTx.
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