Evidence map›Paper›PMID 32078033›Full record

ArticleEuropean journal of applied physiology2020

A regression method for the power-duration relationship when both variables are subject to error.

Giovanni Vinetti, Anna Taboni, Guido Ferretti

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Article in European journal of applied physiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Giovanni VinettiDepartment of Molecular and Translational Medicine, University of Brescia, Viale Europa 11, 25123, Brescia, Italy. g.vinetti001@unibs.it.ORCID http://orcid.org/0000-0003-4743-532X
Anna TaboniDepartment of Anesthesiology, Pharmacology, Intensive Care and Emergencies, University of Geneva, 1 rue Michel Servet, 1211, Geneva 4, Switzerland.
Guido FerrettiDepartment of Molecular and Translational Medicine, University of Brescia, Viale Europa 11, 25123, Brescia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe power-duration relationship has been variously modelled, although duration must be acknowledged as the dependent variable and is supposed to represent the only source of experimental error. However, there are certain situations, namely extremely high power outputs or outdoor field conditions, in which the error in power output measurement may not remain negligible. The geometric mean (GM) regression method deals with the assumption that also the independent variable is subject to a certain amount of experimental error, but has never been utilized in this context.

methodsWe applied the GM regression method for the two- and three-parameter critical power models and tested it against the usual weighted least square (WLS) procedure with our previous published data.

resultsThere were no significant differences between parameter estimates of WLS and GM. Bias and limit of agreements between the two methods were low, while correlation coefficients were high (0.85-1.00).

conclusionsGM provided equivalent results with respect to WLS in fitting the critical power model to experimental data and for its conceptual characteristics must be preferred wherever concerns on the precision of P measurement are present, such as for in-field power meters.

Indexed as

Regression AnalysisPhysiologyCurve fittingCyclingHyperbolic modelModel II nonlinear regressionReduced major axis regressionSport

Identifiers

PMID32078033

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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.