MétaCan
Menu
Back to cohort

On the effects of constant and variable work of equivalent average intensity on whole‐body heat exchange

2019· article· en· W3173852486 on OpenAlexafffundabout
Robert D. Meade, Sean R. Notley, Andrew W. D’Souza, Maura M. Rutherford, Glen P. Kenny

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Ontario
KeywordsIntensity (physics)Work (physics)CyclingCalorimetryRelative humidityChemistryWork rateHumidityAnimal scienceThermodynamicsHeart ratePhysicsMedicineInternal medicineBiology

Abstract

fetched live from OpenAlex

Currently recommended heat exposure guidelines prescribe work‐rest allocations based on the level of heat stress originating from the environment and estimated work intensity with the primary goal of maintaining body core temperature within safe limits. Due to the nature of most occupations, these parameters are rarely, if ever, constant. For this reason, ambient conditions (e.g., temperature and humidity) and metabolic rate (endogenous heat production) are quantified as a time‐weighted average over a given work period. What is unknown, however, is whether time‐dependent alterations in work intensity influence whole‐body heat exchange and cardiovascular strain during work eliciting an equivalent time‐weighted average heat production. On four occasions, six young (27 years [SD 4]) males performed 90‐min of semi‐recumbent cycling at a time‐weighted average rate of heat production (~200 W·m −2 ) in dry heat (40°C, ~20% relative humidity) elicited via cycling at a constant external work rate of 40 W·m −2 (CON) or variable intensity cycling performed in 10‐min cycles: 1) 5 min at an external work rate of 15 W·m −2 followed by 5‐min at 60 W·m −2 (VAR‐LOW); 2) 6 min at 15 W·m −2 followed by 4 min at 70 W·m −2 (VAR‐MOD); and 3) 7 min at 15 W·m −2 followed by 3 min at 80 W·m −2 (VAR‐HIGH). Heat production was measured via indirect calorimetry while whole‐body evaporative heat loss and dry heat gain were evaluated with direct calorimetry. Body heat storage was calculated as the temporal‐summation of heat production and net heat exchange (evaporative heat loss minus dry heat gain). Calorimetric data were expressed as a time‐weighted average (W·m −2 ) except for heat storage, which was presented as a cumulative value (kJ). Rectal temperature and heart rate were monitored continuously and reported as an average of the final 10‐min of exercise (i.e., final cycle) as well as a peak 30‐s average during this period. Across conditions, there were no differences in evaporative heat loss (241 [20] W·m −2 , P=0.20 ), dry heat gain (59 [13] W·m −2 , P=0.52 ), net heat exchange (281 [10] W·m −2 , P=0.54 ) or body heat storage (224 [55] kJ, P=0.87) . Likewise, rectal temperature was not influenced by varying work intensity (average: 37.79 [0.23]°C; peak: 37.84 [0.22]°C; both P≥0.65 ). However, compared to CON (115 [8] beats·min −1 ), average heart rate over the final 10 min of exercise was elevated in VAR‐LOW (124 [12] beats·min −1 ; P=0.01 ), VAR‐MOD (122 [10] beats·min −1 ; P=0.04 ) and VAR‐HIGH (124 [11] beats·min −1 ; P=0.01 ), but similar between the variable work conditions (all P≥0.17 ). Similarly, peak heart rate was elevated in each of the variable work conditions compared to CON (120 [10] beats·min −1 ; all P<0.01 ) and in VAR‐HIGH (154 [11] beats·min −1 ) compared to both VAR‐LOW (140 [12] beats·min −1 ; P<0.01 ) and VAR‐MOD (143 [12] beats·min −1 ; P=0.01 ). These preliminary data suggest that time‐dependent alterations in work intensity eliciting equivalent time‐weighted rates of metabolic heat production do not influence whole‐body heat exchange or thermal strain, but exacerbate cardiovascular strain compared to constant‐intensity work. Support or Funding Information This project was funded by the Government of Ontario and the Natural Sciences and Engineering Research Council of Canada. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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".

Quick stats

Citations0
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicThermoregulation and physiological responsesFrench-language works237,207