Evidence map›Paper›PMID 39583103›Full record

ArticlePeerJ2024

Examining the practical importance of nonstationary cardio-respiratory coupling detection in breathing training: a methodological appraisal.

Jinfeng Li, Yong Fan, Wenbin Shi, Mengwei Li, Lixuan Li, Wei Yan, Muyang Yan, Zhengbo Zhang, Chien-Hung Yeh

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Jinfeng Li *School of Information and Electronics, Beijing Institute of Technology, Beijing, China.
Yong Fan *Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, Chinese PLA General Hospital, Beijing, China.
Wenbin ShiSchool of Information and Electronics, Beijing Institute of Technology, Beijing, China.
Mengwei LiCenter Medical School of Chinese PLA, Beijing, China.
Lixuan LiCenter for Artificial Intelligence in Medicine, Medical Innovation Research Department, Chinese PLA General Hospital, Beijing, China.
Wei YanCenter Department of Hyperbaric Oxygen, the First Medical Center, Chinese PLA General Hospital, Beijing, China.
Muyang YanCenter Department of Hyperbaric Oxygen, the First Medical Center, Chinese PLA General Hospital, Beijing, China.
Zhengbo ZhangCenter for Artificial Intelligence in Medicine, Medical Innovation Research Department, Chinese PLA General Hospital, Beijing, China.
Chien-Hung YehSchool of Information and Electronics, Beijing Institute of Technology, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates changes in cardiorespiratory coupling during clinic breathing training and its impact on autonomic nervous functioning compared with heart rate variability (HRV). A total of 39 subjects undergoing dynamic electrocardiogram-recorded breathing training were analyzed. Subjects were divided into early- and late-training periods, and further categorized based on changes in HRV indexes. Subtypes were identified using time-frequency cardiorespiratory coupling diagrams. Significant differences were observed in the high-frequency (HF) index between training stages in the subgroup with increasing HF-HRV (

Indexed as

Autonomic Nervous SystemBreathing ExercisesElectrocardiographyHeart RateAdultFemaleHumansMaleMiddle AgedRespirationYoung AdultAutonomic nervous systemBreathing trainingCardiorespiratory couplingHeart rate variabilityNonstationary signals

Identifiers

PMID39583103
PMCPMC11583904

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.