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Modeling the oxygen uptake kinetics during exercise testing of patients with chronic obstructive pulmonary diseases using nonlinear mixed models

Overview of attention for article published in BMC Medical Research Methodology, June 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

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1 news outlet
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1 tweeter

Citations

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8 Dimensions

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33 Mendeley
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Title
Modeling the oxygen uptake kinetics during exercise testing of patients with chronic obstructive pulmonary diseases using nonlinear mixed models
Published in
BMC Medical Research Methodology, June 2016
DOI 10.1186/s12874-016-0173-8
Pubmed ID
Authors

Florent Baty, Christian Ritz, Arnoldus van Gestel, Martin Brutsche, Daniel Gerhard

Abstract

The six-minute walk test (6MWT) is commonly used to quantify exercise capacity in patients with several cardio-pulmonary diseases. Oxygen uptake ([Formula: see text]O2) kinetics during 6MWT typically follow 3 distinct phases (rest, exercise, recovery) that can be modeled by nonlinear regression. Simultaneous modeling of multiple kinetics requires nonlinear mixed models methodology. To the best of our knowledge, no such curve-fitting approach has been used to analyze multiple [Formula: see text]O2 kinetics in both research and clinical practice so far. In the present study, we describe functionality of the R package medrc that extends the framework of the commonly used packages drc and nlme and allows fitting nonlinear mixed effects models for automated nonlinear regression modeling. The methodology was applied to a data set including 6MWT [Formula: see text]O2 kinetics from 61 patients with chronic obstructive pulmonary disease (disease severity stage II to IV). The mixed effects approach was compared to a traditional curve-by-curve approach. A six-parameter nonlinear regression model was jointly fitted to the set of [Formula: see text]O2 kinetics. Significant differences between disease stages were found regarding steady state [Formula: see text]O2 during exercise, [Formula: see text]O2 level after recovery and [Formula: see text]O2 inflection point in the recovery phase. Estimates obtained by the mixed effects approach showed standard errors that were consistently lower as compared to the curve-by-curve approach. Hereby we demonstrate the novelty and usefulness of this methodology in the context of physiological exercise testing.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 3%
Unknown 32 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 15%
Student > Ph. D. Student 4 12%
Professor 3 9%
Other 3 9%
Researcher 3 9%
Other 6 18%
Unknown 9 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 15%
Nursing and Health Professions 5 15%
Medicine and Dentistry 3 9%
Computer Science 2 6%
Unspecified 1 3%
Other 5 15%
Unknown 12 36%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 August 2020.
All research outputs
#2,491,437
of 18,707,006 outputs
Outputs from BMC Medical Research Methodology
#426
of 1,695 outputs
Outputs of similar age
#46,643
of 275,506 outputs
Outputs of similar age from BMC Medical Research Methodology
#2
of 6 outputs
Altmetric has tracked 18,707,006 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,695 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.1. This one has gotten more attention than average, scoring higher than 74% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 275,506 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.