Evidence map›Paper›PMID 17805234›Full record

ArticleJournal of exposure science & environmental epidemiology2008

Modeling energy expenditure and oxygen consumption in human exposure models: accounting for fatigue and EPOC.

Kristin Isaacs, Graham Glen, Thomas Mccurdy, Luther Smith

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of exposure science & environmental epidemiology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  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.

Kristin IsaacsAlion Science and Technology Inc., Research Triangle Park, North Carolina 27709, USA. kisaacs@alionscience.com
Graham Glen
Thomas Mccurdy
Luther Smith

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human exposure and dose models often require a quantification of oxygen consumption for a simulated individual. Oxygen consumption is dependent on the modeled individual's physical activity level as described in an activity diary. Activity level is quantified via standardized values of metabolic equivalents of work (METS) for the activity being performed and converted into activity-specific oxygen consumption estimates. However, oxygen consumption remains elevated after a moderate- or high-intensity activity is completed. This effect, which is termed excess post-exercise oxygen consumption (EPOC), requires upward adjustment of the METS estimates that follow high-energy expenditure events, to model subsequent increased ventilation and intake dose rates. In addition, since an individual's capacity for work decreases during extended activity, methods are also required to adjust downward those METS estimates that exceed physiologically realistic limits over time. A unified method for simultaneously performing these adjustments is developed. The method simulates a cumulative oxygen deficit for each individual and uses it to impose appropriate time-dependent reductions in the METS time series and additions for EPOC. The relationships between the oxygen deficit and METS limits are nonlinear and are derived from published data on work capacity and oxygen consumption. These modifications result in improved modeling of ventilation patterns, and should improve intake dose estimates associated with exposure to airborne environmental contaminants.

Indexed as

FatigueAir Pollution, IndoorEnergy MetabolismEnvironmental ExposureExerciseHumansInhalation ExposureModels, BiologicalMotor ActivityOxygen ConsumptionTime FactorsVentilation

Identifiers

PMID17805234

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.