Evidence map›Paper›PMID 38977928›Full record

ArticleScientific reports2024

Validity of predictive equations for total energy expenditure against doubly labeled water.

Olalla Prado-Nóvoa, Kristen R Howard, Eleni Laskaridou, Guillermo Zorrilla-Revilla, Glen R Reid, Elaina L Marinik, Brenda M Davy, Marina Stamatiou, Catherine Hambly, John R Speakman and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. 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

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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

11 authors.

Olalla Prado-NóvoaDepartment of Human Nutrition, Foods, and Exercise, Human Integrative Physiology Laboratory, Virginia Tech, Blacksburg, VA, USA. prado.novoa.olalla@gmail.com.
Kristen R HowardDepartment of Human Nutrition, Foods, and Exercise, Human Integrative Physiology Laboratory, Virginia Tech, Blacksburg, VA, USA.
Eleni LaskaridouDepartment of Human Nutrition, Foods, and Exercise, Human Integrative Physiology Laboratory, Virginia Tech, Blacksburg, VA, USA.
Guillermo Zorrilla-RevillaDepartment of Human Nutrition, Foods, and Exercise, Human Integrative Physiology Laboratory, Virginia Tech, Blacksburg, VA, USA.
Glen R ReidDepartment of Human Nutrition, Foods, and Exercise, Human Integrative Physiology Laboratory, Virginia Tech, Blacksburg, VA, USA.
Elaina L MarinikDepartment of Human Nutrition, Foods, and Exercise, Human Integrative Physiology Laboratory, Virginia Tech, Blacksburg, VA, USA.
Brenda M DavyDepartment of Human Nutrition, Foods, and Exercise, Human Integrative Physiology Laboratory, Virginia Tech, Blacksburg, VA, USA.
Marina StamatiouInstitute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 2TZ, Scotland, UK.
Catherine HamblyInstitute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 2TZ, Scotland, UK.
John R SpeakmanInstitute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 2TZ, Scotland, UK.
Kevin P DavyDepartment of Human Nutrition, Foods, and Exercise, Human Integrative Physiology Laboratory, Virginia Tech, Blacksburg, VA, USA. kdavy@vt.edu.

Funding

NextGeneration EU funds Margarita Salas Postdoctoral FellowshipNIH HHS AG075390Virginia Polytechnic Institute and State University Human Nutrition Foods and Exercise postdoctorate fellowshipVirginia Polytechnic Institute and State University Presidential Postdoctoral FellowshipVirginia Polytechnic Institute and State University Translational Obesity Research Interdisciplinary Graduate Education Predoctoral Fellowship
6 · The paper itself

Abstract

Variations in physical activity energy expenditure can make accurate prediction of total energy expenditure (TEE) challenging. The purpose of the present study was to determine the accuracy of available equations to predict TEE in individuals varying in physical activity (PA) levels. TEE was measured by DLW in 56 adults varying in PA levels which were monitored by accelerometry. Ten different models were used to predict TEE and their accuracy and precision were evaluated, considering the effect of sex and PA. The models generally underestimated the TEE in this population. An equation published by Plucker was the most accurate in predicting the TEE in our entire sample. The Pontzer and Vinken models were the most accurate for those with lower PA levels. Despite the levels of accuracy of some equations, there were sizable errors (low precision) at an individual level. Future studies are needed to develop and validate these equations.

Indexed as

Energy MetabolismAccelerometryAdultExerciseFemaleHumansMaleMiddle AgedReproducibility of ResultsWaterYoung AdultWaterDoubly labeled waterPhysical activityPredictive equationsTotal energy expenditure

Identifiers

PMID38977928
PMCPMC11231257

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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.