Evidence map›Paper›PMID 39850202›Full record

ArticleFrontiers in digital health2024

Building an open-source community to enhance autonomic nervous system signal analysis: DBDP-autonomic.

Jessilyn Dunn, Varun Mishra, Md Mobashir Hasan Shandhi, Hayoung Jeong, Natasha Yamane, Yuna Watanabe, Bill Chen, Matthew S Goodwin

Abstract read
In one paragraph

Article in Frontiers in digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

8 authors.

Jessilyn Dunn *Department of Biomedical Engineering, Duke University, Durham, NC, United States.
Varun Mishra *Khoury College of Computer Sciences and Bouvé College of Health Sciences, Northeastern University, Boston, MA, United States.
Md Mobashir Hasan ShandhiDepartment of Biomedical Engineering, Duke University, Durham, NC, United States.
Hayoung JeongDepartment of Biomedical Engineering, Duke University, Durham, NC, United States.
Natasha YamaneKhoury College of Computer Sciences and Bouvé College of Health Sciences, Northeastern University, Boston, MA, United States.
Yuna WatanabeKhoury College of Computer Sciences and Bouvé College of Health Sciences, Northeastern University, Boston, MA, United States.
Bill ChenDepartment of Biomedical Engineering, Duke University, Durham, NC, United States.
Matthew S GoodwinKhoury College of Computer Sciences and Bouvé College of Health Sciences, Northeastern University, Boston, MA, United States.

Funding

Treatment Development & Evaluation CoreP30DA029926 · NIDA · DARTMOUTH COLLEGE · PI Lisa A. Marsch · 2011 to 2026
$21.5M
Mobile technologies to screen for prediabetes and type 2 diabetes in asymptomatic adultsR01DK133531 · NIDDK · DUKE UNIVERSITY · PI JESSILYN P DUNN · 2023 to 2026
$2.5M
SCH: Enhancing Automated Prediction of Challenging Behavior in Individuals with Autism Using Biosensor Data and Machine LearningR01LM014191 · NLM · NORTHEASTERN UNIVERSITY · PI GOODWIN, MATTHEW SCOTT · 2022 to 2025
$1.2M
NIDA NIH HHS P30 DA029926NIDDK NIH HHS R01 DK133531NLM NIH HHS R01 LM014191
6 · The paper itself

Abstract

Smartphones and wearable sensors offer an unprecedented ability to collect peripheral psychophysiological signals across diverse timescales, settings, populations, and modalities. However, open-source software development has yet to keep pace with rapid advancements in hardware technology and availability, creating an analytical barrier that limits the scientific usefulness of acquired data. We propose a community-driven, open-source peripheral psychophysiological signal pre-processing and analysis software framework that could advance biobehavioral health by enabling more robust, transparent, and reproducible inferences involving autonomic nervous system data.

Indexed as

autonomic signalsdigital phenotypingopen-sourcephysiological signalspsychophysiologysignal processing

Identifiers

PMID39850202
PMCPMC11754217

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.