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Aerobic Fitness Modulates Whole‐body Heat Loss Responses in Young Adult Males during Exercise in Hot Dry Conditions

2016· article· en· W4389024443 on OpenAlexafffundabout
Jeff C. Louie, Dallon T. Lamarche, Sean R. Notley, Martin P. Poirier, Sarah Y. Zhang, Y.Y. Lee, Glen P. Kenny

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnimal scienceCalorimetryChemistryThermoregulationHeat stressAerobic capacityVO2 maxMedicineInternal medicineHeart rateThermodynamicsBiology

Abstract

fetched live from OpenAlex

It is well established that aerobic fitness (as defined by peak oxygen uptake) improves the body's ability to dissipate heat during exercise in hot conditions. However, it currently remains unclear at what level of heat stress differences in aerobic fitness may alter the body's physiological capacity to dissipate heat. Therefore, in this pilot work, we examined whole‐body heat loss using direct calorimetry in endurance (n=4, 63.5±1.5 mlO 2 /kg/min) and non‐endurance trained (n=4; 37.3±0.3 mlO 2 /kg/min, P=0.002) healthy young adult males (22±4 years) matched for body surface area (endurance: 1.89±0.14; non‐endurance: 1.87±0.16 m 2 , P=0.104). Participants performed three 30‐min bouts of semi‐recumbent cycling at fixed rates of metabolic heat production equal to 300 (Ex1), 400 (Ex2), and 500 W (Ex3) (an equivalent external workload of approximately 45, 75 and 100 W, respectively), each separated by a 15‐min recovery period in dry heat (40°C, 14% relative humidity). These fixed rates of heat production were employed to ensure a similar stimulus for whole‐body sweating. The rates of whole‐body heat loss (evaporative heat loss and dry heat exchange) and metabolic heat production were measured by direct and indirect calorimetry, respectively. Whole‐body heat content was measured as the cumulative summation of heat production and heat loss. We show that the rate of dry heat gain ( i.e. , from the environment), and therefore the net heat load ( i.e. , metabolic heat production plus dry heat gain), was similar between groups throughout the intermittent exercise protocol (Ex1, Ex2, Ex3, all P≥0.207). Although not statistically significant at all exercise bouts, the rate of whole‐body evaporative heat loss tended to be lower in the non‐endurance males during Ex1 (non‐endurance: 357±17 vs. endurance: 378±9 W, P=0.047), Ex2 (non‐endurance: 438±17 vs. endurance: 481±18 W, P=0.059), and Ex3 (non‐endurance: 513±29 vs. endurance: 573±26 W, P=0.113) compared to their endurance trained counterparts. Consequently, whole‐body heat content trended towards being greater in the non‐endurance group during Ex1 (non‐endurance: 127±50 vs. 112±36 kJ, P=0.163), Ex2 (non‐endurance: 138±38 vs. 86±25 kJ, P=0.038), and Ex3 (non‐endurance: 227±47 vs. 157±34 kJ, P=0.122) relative to the endurance trained group. Our early findings demonstrate that aerobic fitness may modulate the body's physiological capacity to dissipate heat at metabolic heat loads ≥300 W in young adult males differing in peak oxygen uptake by as much as ~26 mlO 2 /kg/min. Therefore, our data supports that maintaining a high level of aerobic fitness may facilitate an enhanced capacity to dissipate heat and thereby minimize the incidence of experiencing heat related illness during exercise in the heat. Support or Funding Information This study was supported by grants from the Natural Sciences and Engineering Research Council of Canada (Discover grant, RGPIN‐06313‐2014; Discovery Grants Program ‐ Accelerator Supplement, RGPAS‐462252‐2014; funds held by Dr. Glen P. Kenny).

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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)0.000
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
Metaresearch0.0000.000
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)0.0020.000

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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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

Citations1
Published2016
Admission routes3
Has abstractyes

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