Evidence map›Paper›PMID 38027988›Full record

ArticleHeliyon2023

Estimation of peak oxygen pulse from body mass, resting heart rate, age, gender and systolic blood pressure in Chinese adults aged 20-39.

Feng Zhanpeng, Jiang Yan, Zhou Fengrong, Tan Sijie

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07049718 (Effect of Whole-body Vibration Training on Bone Mineral Density and Physical Performance in Patient With Renal Dialysis), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.8field-weighted citation impact, top 21% of its field
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.

NCT07049718 nacompletednot on this mapstarted 2025, after this paper: background citation

Effect of Whole-body Vibration Training on Bone Mineral Density and Physical Performance in Patient With Renal Dialysis

TypeinterventionalSponsorCairo UniversityRan2025 to 2025Enrolled60ConditionsRenal DialysisArmswhole body vibration
3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

4 authors at 2 institutions in 1 country.

Feng ZhanpengTianjin Institute of Physical Education Sports and Health College, Tianjin University of Sport, Tianjin, China.
Jiang YanCollege of Physical Science, Tianjin Normal University, Tianjin, China.
Zhou FengrongSports Department, Tianjin College of Media & Arts, Tianjin, China.
Tan SijieTianjin Institute of Physical Education Sports and Health College, Tianjin University of Sport, Tianjin, China.
Tianjin University of Sport · CNTianjin Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Peak oxygen pulse (O Objectives: The aim of the present cross-sectional study was to establish a prediction equation to estimate O Methods: 252 adults (20-39 years old) were recruited and randomly allocated to the validation group (n = 226) and the cross-validation group (n = 26). To be included in the study, subjects were required to be healthy, none-professional sports experience (healthy individuals who are not athletes or have had experience as athletes), and no medication taken recently. Subjects with cardiovascular diseases, lung disease and musculoskeletal diseases were excluded. The subjects' anthropometric and cardiovascular variables were measured and each subject performed a maximal exercise test on an electromagnetic cycle ergometer. Results: The O Conclusions: The established equation has high effectiveness and reliability to predict O

Indexed as

AdultsCardiovascular functionMaximum oxygen uptakePeak oxygen pulsePrediction equation

Identifiers

PMID38027988
PMCPMC10663905
OpenAlexW4388366299

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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.