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

Brenda Y Miao; Madhumita Sushil; Ava Xu; Michelle Wang; Douglas Arneson; Ellen Berkley; Meera Subash; Rohit Vashisht; Vivek Rudrapatna; Atul J Butte

doi:10.1016/S2589-7500(23)00244-3

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

Lancet Digit Health. 2024 Mar;6(3):e222-e229. doi: 10.1016/S2589-7500(23)00244-3.

Authors

Affiliations

¹ Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA. Electronic address: [email protected].
² Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.
³ Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.
⁴ Department of Clinical Pharmacy, School of Pharmacy, University of California, San Francisco, CA, USA; UC Davis Health, University of California, Sacramento, CA, USA.
⁵ Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA; School of Biomedical Informatics, UTHealth Houston, Houston, TX, USA.
⁶ Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA; Division of Gastroenterology and Hepatology, Department of Medicine, University of California, San Francisco, CA, USA.
⁷ Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA; University of California Health, Oakland, CA, USA.

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.

Publication types

Systematic Review
Review

MeSH terms

Clinical Trials as Topic*
Humans
Natural Language Processing*
United States
United States Food and Drug Administration

Abstract

Publication types

MeSH terms

Grants and funding