Evidence map›Paper›PMID 31304393›Full record

ArticleNPJ digital medicine2019

A systematic review of feasibility studies promoting the use of mobile technologies in clinical research.

Jessie P Bakker, Jennifer C Goldsack, Michael Clarke, Andrea Coravos, Cynthia Geoghegan, Alan Godfrey, Matthew G Heasley, Daniel R Karlin, Christine Manta, Barry Peterson and 8 more

Abstract read
In one paragraph

Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 2 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

33 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  19. Translational precision medicine: an industry perspective.Journal of translational medicine · 2021
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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

18 authors.

Jessie P Bakker1Philips, Monroeville, PA USA.
Jennifer C GoldsackClinical Trials Transformation Initiative, Durham, NC USA.
Michael Clarke5Queen's University Belfast, Belfast, UK.
Andrea CoravosDigital Medicine Society (DiMe), Boston, MA USA.ORCID 0000-0001-5379-3540
Cynthia GeogheganPatient and Partners, Madison, CT USA.
Alan Godfrey9Northumbria University, Newcastle upon Tyne, UK.ORCID 0000-0003-4049-9291
Matthew G Heasley10GlaxoSmithKline, Stevenage, UK.ORCID 0000-0002-6520-8369
Daniel R KarlinDigital Medicine Society (DiMe), Boston, MA USA.
Christine MantaIndependent Consultant, Philidelphia, PA USA.
Barry PetersonIndependent Consultant, Charlotte, NC USA.
Ernesto Ramirez14Evidation Health, San Mateo, CA USA.ORCID 0000-0002-3121-2837
Nirav ShethMicroMedicine, Inc., Waltham, MA USA.
Antonia Bruno16Ross University School of Medicine, Bridgetown, Barbados.
Emilia Bullis1723andMe Inc., Mountain View, CA USA.
Kirsten Wareham18PRA Health Sciences, Blue Bell, PA USA.
Noah Zimmerman19Icahn School of Medicine at Mount Sinai, New York, NY USA.ORCID 0000-0003-3391-2623
Annemarie ForrestClinical Trials Transformation Initiative, Durham, NC USA.
William A Wood20University of North Carolina, Chapel Hill, NC USA.

Funding

Increasing the Quality and Efficiency of Clinical Trials (R18)R18FD005292 · FDA · DUKE UNIVERSITY · PI LI, JENNIFER S · 2014 to 2019
$26.8M
FDA HHS R18 FD005292Medical Research Council G0901530
6 · The paper itself

Abstract

Mobile technologies, such as smart phone applications, wearables, ingestibles, and implantables, are increasingly used in clinical research to capture study endpoints. On behalf of the Clinical Trials Transformation Initiative, we aimed to conduct a systematic scoping review and compile a database summarizing pilot studies addressing mobile technology sensor performance, algorithm development, software performance, and/or operational feasibility, in order to provide a resource for guiding decisions about which technology is most suitable for a particular trial. Our systematic search identified 275 publications meeting inclusion criteria. From these papers, we extracted data including the medical condition, concept of interest captured by the mobile technology, outcomes captured by the digital measurement, and details regarding the sensors, algorithms, and study sample. Sixty-seven percent of the technologies identified were wearable sensors, with the remainder including tablets, smartphones, implanted sensors, and cameras. We noted substantial variability in terms of reporting completeness and terminology used. The data have been compiled into an online database maintained by the Clinical Trials Transformation Initiative that can be filtered and searched electronically, enabling a user to find information most relevant to their work. Our long-term goal is to maintain and update the online database, in order to promote standardization of methods and reporting, encourage collaboration, and avoid redundant studies, thereby contributing to the design and implementation of efficient, high-quality trials.

Indexed as

Clinical trialsMedical research

Identifiers

PMID31304393
PMCPMC6554345

What OpenQuestion holds

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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.