ArticleCommunications medicine2025
Digital health technology use in clinical trials of rare diseases: a systematic review.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Artificial Intelligence Readiness in Clinical Trial Operations: A Narrative Review and Site-Level Governance Framework.Healthcare (Basel, Switzerland) · 2026Review
- New model for remote symptom management and self-care of cancer patients based on digital health platforms: a narrative review.Translational cancer research · 2026Review
- First published report of the FLCN c.1222 C > T (p.Gln408Ter) variant in a Chinese family with Birt-Hogg-Dubé syndrome and literature review.BMC medical genomics · 2026Review
- Digital health technology empowering China's community full life cycle health management.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundRare disease clinical trials face unique challenges, resulting in low enrollment and limited retention. Digital health technologies (DHTs), when applied through a patient-centric lens, offer promising solutions to these challenges by enhancing data collection efficiency, improving patient engagement, and supporting long-term health care.
methodsThis study investigates DHT application in clinical studies across the ten most-studied rare diseases and was registered with PROSPERO (registration number: CRD420251088025). Up to June 26 2024, a total of 262 studies were identified through ClinicalTrials.gov, the International Clinical Trials Registry Platform, Cochrane Central Register of Controlled Trials, PubMed, and Web of Science. DHT applications were classified into patient recruitment, digital treatment, data monitoring and collection, outcome assessment, remote follow-up, and long-term care management based on scenarios and functionalities.
resultsAmong all DHT applications, data monitoring and collection was the most prevalent (31.3%), serving primarily to enable continuous tracking of physiological parameters relevant to specific rare disease conditions. Digital treatment is featured in 57 studies (21.8%), most commonly as digital physiotherapy. A notable increase in DHT adoption is observed from 2017-2020 to 2021-2024 across nearly all ten diseases. Between 2021 and 2024, cystic fibrosis shows the highest proportion of DHT-enabled trials relative to all studies conducted for that disease (29.7%).
conclusionsOur findings reveal a growing trend toward digital integration in rare disease trials to support decentralized, scalable, and patient-centered research models. We propose a "4A" conceptual framework-Accessibility, Agility, Awareness, and Adaptability to accelerate therapeutic development and expand access to care for these underserved rare disease populations.
Identifiers
What OpenQuestion holds
Registered trials
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.