A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 10 Issue 1
Jan.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Contents
U. Lee, G. Jung, E.-Y. Ma, J. S. Kim, H. Kim, J. Alikhanov, Y. Noh, and H. Kim, “Toward data-driven digital therapeutics analytics: Literature review and research directions,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 42–66, Jan. 2023. doi: 10.1109/JAS.2023.123015
Citation: U. Lee, G. Jung, E.-Y. Ma, J. S. Kim, H. Kim, J. Alikhanov, Y. Noh, and H. Kim, “Toward data-driven digital therapeutics analytics: Literature review and research directions,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 42–66, Jan. 2023. doi: 10.1109/JAS.2023.123015

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

doi: 10.1109/JAS.2023.123015
Funds:  This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (2020R1A4A1018774)
More Information
  • 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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    • Data-driven DTx analytics enable contextual analysis and causal inference
    • Data-driven DTx analytics offer novel ways for improving DTx and behavior engagement
    • The key components and processes of data-driven DTx analytics are reviewed
    • New research directions are discussed to innovate DTx and behavior engagement


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