Evidence map›Paper›PMID 38395542›Full record

SynthesisThe Lancet. Digital health2024

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

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in The Lancet. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Digital Therapeutics in China: Comprehensive Review.Journal of medical Internet research · 2025
    Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Brenda Y MiaoBakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA. Electronic address: brenda.miao@ucsf.edu.
Madhumita SushilBakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.
Ava XuBakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.
Michelle WangBakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.
Douglas ArnesonBakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.
Ellen BerkleyDepartment of Clinical Pharmacy, School of Pharmacy, University of California, San Francisco, CA, USA; UC Davis Health, University of California, Sacramento, CA, USA.
Meera SubashDivision of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA; School of Biomedical Informatics, UTHealth Houston, Houston, TX, USA.
Rohit VashishtBakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.
Vivek RudrapatnaBakar 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.
Atul J ButteBakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA; University of California Health, Oakland, CA, USA.

Funding

Clinical and Translational Science InstituteUL1TR001872 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI COLLARD, HAROLD R, JACOBY, VANESSA · 2016 to 2025
$112.1M
UCSF-Stanford Center of Excellence in Regulatory Science and InnovationU01FD005978 · FDA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ALTMAN, RUSS BIAGIO, GIACOMINI, KATHLEEN M · 2016 to 2025
$34.5M
Maximizing Opportunities for Research Excellence - Evaluation Training SupplementR25GM056847 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI DUNCAN, D'ANNE S., SELLO, JASON K · 1998 to 2023
$14.1M
BMI Bioinformatics Training GrantsT32GM067547 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HERNANDEZ, RYAN D. · 2003 to 2022
$6.6M
A data science framework for transforming electronic health records into real-world evidenceK99LM014099 · NLM · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI RUDRAPATNA, VIVEK ASHOK · 2023 to 2024
$178k
FDA HHS U01 FD005978NCATS NIH HHS UL1 TR001872NIGMS NIH HHS R25 GM056847NIGMS NIH HHS T32 GM067547NLM NIH HHS K99 LM014099
6 · The paper itself

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.

Indexed as

Clinical Trials as TopicNatural Language ProcessingHumansUnited StatesUnited States Food and Drug Administration

Identifiers

PMID38395542
PMCPMC11074920

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.