Evidence map›Paper›PMID 29956216›Full record

ArticleMedical & biological engineering & computing2018

Nonparametric dynamical model of cardiorespiratory responses at the onset and offset of treadmill exercises.

Hairong Yu, Lin Ye, Ganesh R Naik, Rong Song, Hung T Nguyen, Steven W Su

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Article in Medical & biological engineering & computing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Hairong YuSchool of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia.
Lin YeSchool of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia.
Ganesh R NaikMarcs Institute For Brain, Behaviour & Development, Western Sydney University, Sydney, NSW, 2751, Australia.
Rong SongSchool of Biomedical Engineering, Sun Yat-sen University, Guangzhou, People's Republic of China.
Hung T NguyenFaculty of Science, Engineering & Technology, Swinburne University of Technology, Melbourne, VIC, Australia.
Steven W SuSchool of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia. Steven.Su@uts.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper applies a nonparametric modelling method with kernel-based regularization to estimate the carbon dioxide production during jogging exercises. The kernel selection and regularization strategies have been discussed; several commonly used kernels are compared regarding the goodness-of-fit, sensitivity, and stability. Based on that, the most appropriate kernel is then selected for the construction of the regularization term. Both the onset and offset of the jogging exercises are investigated. We compare the identified nonparametric models, which include both impulse response models and step response models for the two periods, as well as the relationship between oxygen consumption and carbon dioxide production. The result statistically indicates that the steady-state gain of the carbon dioxide production in the onset of exercise is bigger than that in the offset while the response time of both onset and offset are similar. Compared with oxygen consumption, the response speed of carbon dioxide production is slightly slower in both onset and offset period while its steady-state gains are similar for both periods. The effectiveness of the kernel-based method for the dynamic modelling of cardiorespiratory response to exercise is also well demonstrated. Graphical Abstract Comparison between VO

Indexed as

Exercise TestModels, CardiovascularAdultBiomechanical PhenomenaCarbon DioxideComputer SimulationHumansMaleOxygen ConsumptionCarbon DioxideCarbon dioxide productionCardiorespiratory response to exerciseNonparametric modellingTreadmill exercise

Identifiers

PMID29956216

What OpenQuestion holds

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Registered trials

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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.