Evidence map›Paper›PMID 38269372›Full record

ArticleDigital health

Personalized and longitudinal electronic informed consent in clinical trials: How to move the needle?

Evelien De Sutter, Liese Barbier, Pascal Borry, David Geerts, John P A Ioannidis, Isabelle Huys

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
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

6 authors.

Evelien De SutterClinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.ORCID https://orcid.org/0000-0001-6575-2033
Liese BarbierClinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.ORCID https://orcid.org/0000-0003-1786-2080
Pascal BorryCentre for Biomedical Ethics and Law, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium.
David GeertsKU Leuven Digital Society Institute, KU Leuven, Leuven, Belgium.
John P A IoannidisMeta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA.
Isabelle HuysClinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Changes in the clinical trials landscape have been driven by advancements in digital technology. The use of electronic informed consent to inform research participants and to obtain their consent electronically has the potential to improve participant-researcher interactions over time, facilitate clinical trial participation, and increase efficiency in clinical trial conduct. A personalized electronic informed consent platform that enables long-term interactions with the research team could function as a tool to empower participant engagement in clinical trials. However, significant challenges persist impeding successful and widespread implementation. This Perspective provides insights into the opportunities and challenges for the implementation of electronic informed consent in clinical trials. It sets out key recommendations to promote the implementation of this innovative approach to the informed consent process, including the creation of uniform electronic informed consent platforms at regional and national level.

Indexed as

clinical researchimplementationinformed consentregulatoryTechnologytrial

Identifiers

PMID38269372
PMCPMC10807334

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.