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 of 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.Comment: This paper has been accepted by the IEEE/CAA Journal of Automatica
Sinic