Evidence map›Paper›PMID 33948242›Full record

ArticleJournal of clinical and translational science2020

The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data.

Brinnae Bent, Ke Wang, Emilia Grzesiak, Chentian Jiang, Yuankai Qi, Yihang Jiang, Peter Cho, Kyle Zingler, Felix Ikponmwosa Ogbeide, Arthur Zhao and 3 more

Abstract read
In one paragraph

Article in Journal of clinical and translational science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing 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

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

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Applications and prospects of biomaterials in diabetes management.Frontiers in bioengineering and biotechnology · 2025
    Review
  10. Review
  11. Article
  12. Artificial Intelligence in Cardiovascular Clinical Trials.Journal of the American College of Cardiology · 2024
    Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Review
  19. DISPEL: A Python Framework for Developing Measures From Digital Health Technologies.IEEE open journal of engineering in medicine and biology · 2024
    Article
  20. Article
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

13 authors.

Brinnae BentDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.ORCID https://orcid.org/0000-0002-7039-0177
Ke WangDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.
Emilia GrzesiakDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.
Chentian JiangDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.
Yuankai QiDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.
Yihang JiangDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.
Peter ChoDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.
Kyle ZinglerDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.
Felix Ikponmwosa OgbeideDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.
Arthur ZhaoDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.
Ryan RungeSchool of Medicine, Stanford University, Palo Alto, CA, USA.
Ida SimDepartment of Medicine, University of California, San Francisco, CA, USA.
Jessilyn DunnDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.ORCID https://orcid.org/0000-0002-3241-8183

Funding

Open mHealth: Community-Based Data and Metadata Standards for Mobile HealthR24EB025845 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SIM, IDA · 2017 to 2019
$1.1M
NIBIB NIH HHS R24 EB025845
6 · The paper itself

Abstract

introductionDigital health is rapidly expanding due to surging healthcare costs, deteriorating health outcomes, and the growing prevalence and accessibility of mobile health (mHealth) and wearable technology. Data from Biometric Monitoring Technologies (BioMeTs), including mHealth and wearables, can be transformed into

methodsIn order to provide a collaborative, standardized space for digital biomarker research and validation, we present the first comprehensive, open-source software platform for end-to-end digital biomarker development:

resultsHere, we detail the general DBDP framework as well as three robust modules within the DBDP that have been developed for specific digital biomarker discovery use cases.

conclusionsThe clear need for such a platform will accelerate the DBDP's adoption as the industry standard for digital biomarker development and will support its role as the epicenter of digital biomarker collaboration and exploration.

Indexed as

Biomedical informaticsdigital healthdigital medicinehealth information managementmHealthopen sourcewearable sensors

Identifiers

PMID33948242
PMCPMC8057397

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.