Evidence map›Paper›PMID 32175520›Full record

ArticleDigital biomarkers

Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research.

Reham Badawy, Farhan Hameed, Lauren Bataille, Max A Little, Kasper Claes, Suchi Saria, Jesse M Cedarbaum, Diane Stephenson, Jon Neville, Walter Maetzler and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Digital biomarkers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

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

  1. Pooled it
  2. Article
  3. Developing digital health technologies for frontotemporal degeneration.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Review
  4. Examining the healthy human microbiome concept.Nature reviews. Microbiology · 2025
    Review
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Digital Phenotyping in Clinical Neurology.Seminars in neurology · 2022
    Article
  14. Review
  15. Article
  16. Article
  17. Review
  18. Review
  19. Review
  20. 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

14 authors.

Reham BadawySchool of Computer Science, University of Birmingham, Birmingham, United Kingdom.
Farhan HameedDigital Medicine and Pfizer Innovation Research Lab, Early Clinical Development, Pfizer, Inc., Cambridge, Massachusetts, USA.
Lauren BatailleThe Michael J. Fox Foundation for Parkinson's Research, New York, New York, USA.
Max A LittleSchool of Computer Science, University of Birmingham, Birmingham, United Kingdom.
Kasper ClaesUCB Biopharma, Brussels, Belgium.
Suchi SariaMachine Learning and Healthcare Laboratory, Departments of Computer Science, Statistics, and Health Policy, Malone Center for Engineering in Healthcare, and Armstrong Institute for Patient Safety and Quality, Johns Hopkins University, Baltimore, Maryland, USA.
Jesse M CedarbaumBiogen, Cambridge, Massachusetts, USA.
Diane StephensonCritical Path Institute, Tucson, Arizona, USA.
Jon NevilleClinical Data Interchange Standards Consortium, Austin, Texas, USA.
Walter MaetzlerDepartment of Neurology, Christian Albrecht University, Kiel, Germany.
Alberto J EspayJames J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, Ohio, USA.
Bastiaan R BloemDepartment of Neurology, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands.
Tanya SimuniDepartment of Neurology, Gardner Center for Parkinson's Disease and Movement Disorders, UC Gardner Neuroscience Institute, University of Cincinnati, Cincinnati, Ohio, USA.
Daniel R KarlinTufts University School of Medicine, Boston, Massachusetts, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual's disease symptoms, both in clinical care and in research. Nonetheless, such digital health technologies have yet to widely demonstrate value in clinical research due to insufficient data interpretability and lack of regulatory acceptance. Metadata, i.e., data that accompany and describe the primary data, can be utilized to better understand the context of the sensor data and can assist in data management, data sharing, and subsequent data analysis. The need for data and metadata standards for digital health technologies has been raised in academic and industry research communities and has also been noted by regulatory authorities. Therefore, to address this unmet need, we here propose a metadata set that reflects regulatory guidelines and that can serve as a conceptual map to (1) inform researchers on the metadata they should collect in digital health studies, aiming to increase the interpretability and exchangeability of their data, and (2) direct standard development organizations on how to extend their existing standards to incorporate digital health technologies. The proposed metadata set is informed by existing standards pertaining to clinical trials and medical devices, in addition to existing schemas that have supported digital health technology studies. We illustrate this specifically in the context of Parkinson's disease, as a model for a wide range of other chronic conditions for which remote monitoring would be useful in both care and science. We invite the scientific and clinical research communities to apply the proposed metadata set to ongoing and planned research. Where the proposed metadata fall short, we ask users to contribute to its ongoing revision so that an adequate degree of consensus can be maintained in a rapidly evolving technology landscape.

Indexed as

Digital health technologyMetadataObjective monitoring of motor symptomsParkinson's diseaseSensors

Identifiers

PMID32175520
PMCPMC7046173

What OpenQuestion holds

Textmetadata
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.