JMIR Publications

ABSTRACT

Background:

Digital phenotyping is the real-time collection of individual-level active and passive data from users in naturalistic and free-living settings via personal digital devices, such as mobile phones and wearable devices. Given the novelty of research in the field, there is heterogeneity in the clinical use cases, types of data collected, modes of data collection, data analysis methods, and outcomes measured.

Objective:

The primary aim of this scoping review was to map the published research on digital phenotyping and to outline study characteristics, data collection and analysis methods, artificial intelligence (AI) and machine learning approaches used, and future implications.

Methods:

We utilized an a priori approach for the literature search and data extraction and charting process, guided by the PRISMA Extension for Scoping Reviews. We identified relevant studies published in 2020, 2021, and 2022 on PubMed and Google Scholar using search terms related to digital phenotyping. The titles, abstracts, and keywords were screened during the first stage of the screening process, and the second stage involved screening the full texts of the shortlisted articles. We extracted and charted the descriptive characteristics of the final studies. The descriptive characteristics were countries of origin, designs, clinical areas, active and/or passive data collected, modes of data collection, data analysis approaches, and limitations.

Results:

A total of 454 articles on PubMed and Google Scholar were identified through search terms associated with digital phenotyping, and 46 articles were deemed eligible for inclusion in this scoping review. Most studies evaluated wearable data and originated from the Americas. The most dominant study design was observational, followed by the randomized trials, and most studies focused on psychiatric disorders, mental health disorders, and neurological diseases. Seven studies used machine learning approaches for data analysis, with random forest, logistic regression, and support vector machines being the most common ones.

Conclusions:

Our review provides foundational as well as application-oriented approaches towards digital phenotyping in health. Future work should focus on more prospective, longitudinal studies that include larger data sets from diverse populations; addressing privacy and ethical concerns around data collection from consumer technologies; and building “digital phenotypes” to personalize digital health interventions and treatment plans.