Dissociating Biophysical and Training-Related Determinants of Core Temperature
Notice bibliographique
Résumé
Dear Editor-in-Chief: In a recent review, Dr. Mora-Rodriguez (5) concluded that core temperature is predicted by the percentage of peak oxygen uptake (%V˙O2peak) in physiologically compensable conditions and absolute heat production in uncompensable conditions (see Fig. 4 in (5)). Heat balance calculations (3) and recent evidence from our laboratory (4) suggest otherwise. High (HI) and low (LO) V˙O2peak groups matched for mass and body surface area (BSA), exercising at 540 W heat production in compensable conditions, showed similar changes in rectal temperature (Tre) and whole-body sweat losses despite vastly different relative intensities (39.7% vs 57.6% V˙O2peak) (4). Furthermore, absolute end-exercise Tre was ∼0.2°C lower in the HI group simply because of lower preexercise values. In contrast, exercise at 60% V˙O2peak (heat production, 844 vs 600 W) yielded greater changes in Tre and absolute end-exercise Tre values in the HI group, and whole-body sweat losses were greater in the HI group because of higher evaporative heat balance requirements (Ereq) (4). In compensable conditions, these findings suggest the following after eliminating differences in mass and BSA: (i) changes in Tre are determined by heat production, not %V˙O2peak; (ii) any differences in end-exercise absolute Tre between fitness groups only arise because of differences in preexercise Tre; and (iii) sweating is not altered by a high V˙O2peak. We further suggested that groups heterogeneous for body morphology may be compared for changes in Tre using a fixed heat production per unit mass (W·kg–1) in compensable environments. This approach explains the greater Tre changes in trained subjects at 40%V˙O2peak (8.2 vs 6.1 W·kg–1) (6), with these greater changes compensated by different preexercise Tre values, leading to similar absolute end-exercise temperatures between training groups. By definition, uncompensable conditions arise when Ereq exceeds the maximum possible evaporation rate (Emax). Dr. Mora-Rodriguez suggests that Ereq > Emax at a similar %V˙O2peak in trained and untrained groups (see Fig. 4 in (5)). However, at a given %V˙O2peak, Ereq is lower in untrained individuals because of their lower heat production, and the primary reason that Ereq > Emax at the same V˙O2peak in the proposed model is the lower maximum skin wettedness (ωmax) assigned to untrained individuals (ωmax = 0.85). Although maximum sweat rate is probably different (1), such large ωmax adjustments as a function of training status do not seem justified by the literature. A ωmax of 0.85 and 1.00 were proposed originally for nonheat-acclimated and heat-acclimated individuals, respectively (2), but physical training only imparts partial acclimation (7). Even if ωmax differences between training groups are as large as proposed, heat balance calculations (3) show the %V˙O2peak at which Ereq > Emax still should be greater in unfit/untrained subjects with the same BSA/mass ratio. The %V˙O2peak at which Ereq > Emax declines with decreasing BSA/mass ratio. Because the BSA/mass ratio of the author’s untrained group (6) was lower, it appears that a combination of different physical characteristics and assigned ωmax values led to a conclusion with restricted validity. A more robust descriptor of the reported differences in Tre between training groups at high relative exercise intensities (6) may be the difference between Ereq and Emax expressed in W·kg–1. Matthew N. Cramer Nathan B. Morris Ollie Jay Thermal Ergonomics Laboratory School of Human Kinetics University of Ottawa Ottawa, Ontario, Canada
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».