JMIR Publications

ABSTRACT

The COVID pandemic has required transitioning many clinical addictions programs to telephonic or virtual visits. Novel solutions are needed to enhance substance use treatment during a time when many patients are disconnected from clinical care and social supports. Digital phenotyping, which leverages the vast functionality of smartphones sensors (GPS, social behavior, and typing patterns), can buttress clinical treatment in a remote, scalable fashion. Specifically, digital phenotyping can improve relapse prediction, improve relapse detection, and allow for more timely intervention. Digital phenotyping can enhance relapse prediction through coupling machine learning tools with the enormous wealth of collected behavioral data. Activity based analysis can be used to prevent relapse by warning substance users when they approach locational triggers such as bars or liquor stores. Wearable devices detect when someone has relapsed to substances through measuring physiological changes such as electrodermal activity and locomotion. Despite its initial promise, privacy, security and barriers to access are important issues to address.