Smartphone and Wearable Device-Based Digital Phenotyping to Understand Substance use and its Syndemics

Abstract

Digital phenotyping is a process that allows researchers to leverage smartphone and wearable data to explore how technology use relates to behavioral health outcomes. In this Research Concepts article, we provide background on prior research that has employed digital phenotyping; the fundamentals of how digital phenotyping works, using examples from participant data; the application of digital phenotyping in the context of substance use and its syndemics; and the ethical, legal and social implications of digital phenotyping. We discuss applications for digital phenotyping in medical toxicology, as well as potential uses for digital phenotyping in future research. We also highlight the importance of obtaining ground truth annotation in order to identify and establish digital phenotypes of key behaviors of interest. Finally, there are many potential roles for medical toxicologists to leverage digital phenotyping both in research and in the future as a clinical tool to better understand the contextual features associated with drug poisoning and overdose. This article demonstrates how medical toxicologists and researchers can progress through phases of a research trajectory using digital phenotyping to better understand behavior and its association with smartphone usage.

Keywords: Digital Phenotyping; Smartphone; Substance use; Syndemics; Wearable.

Fig. 1

Digital phenotyping collects passive data from smartphones or wearables with a target biobehavioral outcome in mind. Large amounts of data are collected from devices, and then analyzed using various machine learning techniques to develop algorithms than can be piloted among populations of interest

Fig. 2

Clustering of GPS data into geographic regions. Here the stars indicate the center-most points of each individual cluster. Each color shown on the map below represents the centroid that cluster belongs to and is labeled with the corresponding predicted activity. These GPS data have been anonymized for publication

Fig. 3

Identification of screen states captured through user interactions with a phone. Here the different markers indicate what screen state was captured and the lines are colored with the corresponding location the user was at when the activity was captured

Fig. 4

Digital phenotyping research trajectory flow diagram