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Characterisation of digital therapeutic clinical trials: a systematic review with natural language processing

Year: 2024
Type: review
Study design: Systematic Review
Abstract: Digital therapeutics (DTx) are a somewhat novel class of US Food and Drug Administration-regulated software that help patients prevent, manage, or treat disease. Here, we use natural language processing to characterise registered DTx clinical trials and provide insights into the clinical development landscape for these novel therapeutics. We identified 449 DTx clinical trials, initiated or expected to be initiated between 2010 and 2030, from ClinicalTrials.gov using 27 search terms, and available data were analysed, including trial durations, locations, MeSH categories, enrolment, and sponsor types. Topic modelling of eligibility criteria, done with BERTopic, showed that DTx trials frequently exclude patients on the basis of age, comorbidities, pregnancy, language barriers, and digital determinants of health, including smartphone or data plan access. Our comprehensive overview of the DTx development landscape highlights challenges in designing inclusive DTx clinical trials and presents opportunities for clinicians and researchers to address these challenges. Finally, we provide an interactive dashboard for readers to conduct their own analyses.
DOI: 10.1016/s2589-7500(23)00244-3
Authors: Brenda Y Miao, Madhumita Sushil, Ava Xu, Michelle Wang, Douglas Arneson, Ellen Berkley, Meera Subash, Rohit Vashisht, Vivek Rudrapatna, Atul J Butte
Language: English
Field: Psychology
Open Access status: gold
Estimated APC paid: $5,780
Funders: California Institute for Regenerative Medicine, March of Dimes Foundation, Alnylam Pharmaceuticals, Stryker, Leon Lowenstein Foundation, National Institutes of Health, U.S. Food and Drug Administration, Genentech, University of California, San Francisco, Center of Excellence in Regulatory Science and Innovation, Clinical and Translational Science Institute, Boston University, National Institute of General Medical Sciences, U.S. National Library of Medicine, National Center for Advancing Translational Sciences
Awards: A data science framework for transforming electronic health records into real-world evidence, Maximizing Opportunities for Research Excellence, G3897007235, BMI Bioinformatics Training Grant, G6245908341, G7120641930, Clinical and Translational Science Institute
OpenAlex ID: W4392017198
FWCI: 1.817
Cites: 26
Cited by: 30
Related to: 10