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Whole‐body Heat Exchange in Young and Middle‐Aged Men during Constant‐ and Variable‐Intensity Work of Equivalent Metabolic Demand in Dry Heat

2020· article· en· W3016784873 on OpenAlexaffabout
Sean R. Notley, Robert D. Meade, Andrew W. D’Souza, Maura M. Rutherford, Jung‐Hyun Kim, Glen P. Kenny

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsWork (physics)Intensity (physics)Constant (computer programming)Work IntensityChemistryThermodynamicsMechanicsMathematicsAnimal scienceDemographyPhysicsBiologyComputer science

Abstract

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Current occupational heat stress guidelines incorporate time‐weighted averaging to quantify a workers’ metabolic rate during tasks of varied intensity. Since such guidelines originate from data obtained during work at a constant (fixed) intensity, their validity relies on the assumption that constant‐ and variable‐intensity work eliciting the same time‐weighted average metabolic rate will evoke similar heat strain. We recently confirmed the veracity of this assumption in young men. However, it remains uncertain whether this holds for the rising number of middle‐aged workers, who display age‐related impairments in whole‐body total heat loss (evaporative+dry heat loss) during constant, moderate work that worsen at higher work intensities. It is possible that variable‐intensity work involving brief periods at higher intensities may exacerbate age‐related impairments in heat loss in these individuals, thereby worsening body heat storage relative to constant‐intensity work. To evaluate this hypothesis, 7 middle‐aged (mean (SD): 60 (4) years) and 8 young (25 (4) years) men completed four trials involving 90‐min of work (cycling) at an average metabolic heat production of 200 W/m 2 in dry‐heat (40°C, 20% relative humidity). Trials were administered in a random order (each separated by >48 hours) and differed only in the pattern of work performed, with one involving constant external work (40 W/m 2 ) and the others involving 10‐min cycles of varying external work: (i) 5 min at 15 W/m 2 then 5‐min at 60 W/m 2 ; (ii) 6 min at 15 W/m 2 then 4 min at 70 W/m 2 ; and (iii) 7 min at 15 W/m 2 then 3 min at 80 W/m 2 . Metabolic heat production and whole‐body total heat loss were measured via indirect and direct calorimetry (respectively), and used to derive body heat storage (summation of heat production and total heat loss). Data were averaged over each 90‐min period and compared between groups and across conditions using a mixed‐model ANOVA. Heat production was similar between groups and across conditions (group: p =.80; condition: p =.53; interaction: p =.84), averaging 205 (9) W/m 2 and 206 (7) W/m 2 in middle‐aged and young men, respectively. However, contrary to our hypothesis, total heat loss did not differ significantly between groups or across conditions (group: p =.23; condition: p =.87; interaction: p =.97), averaging 180 (8) W/m 2 and 185 (12) W/m 2 in middle‐aged and young men, respectively. As such, body heat storage was also similar between groups and across conditions (group: p =.22; condition: p =.35; interaction: p =.75), averaging 25 (9) W/m 2 and 21 (9) W/m 2 in middle‐aged and young men, respectively. In conclusion, whole‐body total heat loss and body heat storage were not significantly influenced by the partitioning of work intensity in either middle‐aged or young men, indicating that time‐weighted averaging appears to be an appropriate means of quantifying metabolic demand to assess occupational heat stress. Support or Funding Information The Government of Ontario, Canada.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.261
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes2
Has abstractyes

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