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

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.

Automated summary

With the rise of digital therapeutics, the development of software as a medical device for mobile and wearable devices has become increasingly important. Current evaluations of digital therapeutics primarily focus on effectiveness, but to gain a deeper understanding of engagement and adherence, analysis of contextual and interaction data from these devices is necessary. This review of data-driven analytics provides researchers and practitioners with guidance on exploring and analyzing digital therapeutic datasets, examining contextual patterns, and establishing the relationship between engagement and adherence.