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Enregistrement W2408881733 · doi:10.1249/mss.0000000000000638

On the Maintenance of Human Heat Balance during Cold and Warm Fluid Ingestion

2015· letter· en· W2408881733 sur OpenAlexaffabout
Anthony R. Bain, Nathan B. Morris, Matthew N. Cramer, Ollie Jay

Notice bibliographique

RevueMedicine & Science in Sports & Exercise · 2015
Typeletter
Langueen
DomaineMedicine
ThématiqueThermoregulation and physiological responses
Établissements canadiensUniversity of OttawaUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésCalorimetryIngestionThermodynamicsChemistryThermal energy storagePhysicsBiochemistry

Résumé

récupéré en direct d'OpenAlex

Dear Editor-in-Chief, The influence of ingested fluid temperature on thermoregulatory responses during exercise has attracted considerable attention during the past decade. To date, however, it has remained unclear whether body heat storage is truly altered. A thermometric model is generally considered inaccurate for estimating heat storage during exercise (2). Therefore, we previously assessed the influence of ingested fluid temperature on this parameter using partitional calorimetry (1). In a follow-up study, we demonstrated that thermoreceptors in the abdomen—not mouth—might independently mediate fluid-temperature-dependent alterations in sweating (4). A recent study by Lamarche et al. (3), published in Medicine & Science in Sports & Exercise®, duplicated the design of our earlier study but importantly assessed heat storage using direct, rather than partitional, calorimetry—the former, ostensibly, being more accurate. Their précis suggested observations different from our two previous studies and questioned the validity of employing partitional calorimetry. However, upon closer examination, their study seemingly yielded similar findings, with one important exception that helps highlight the limitations of partitional calorimetry. Arguably, the most practically relevant finding from our previous study (1) was that ingestion of cold fluids (10°C or 1.5°C), compared to thermoneutral (37°C) fluids, during exercise does not lead to lower body heat storage due to a reduction in evaporation that is proportional to the heat energy exchanged internally with ingested fluids. Lamarche et al. (3) reproduced the same finding (for 1.5°C), thus demonstrating that partitional calorimetry provides an apparently reliable assessment of heat storage at least during cold fluid ingestion. No statistical difference in forehead sweat rate was observed between ingestion of 50°C water and ingestion of 1.5°C water, whereas upper back sweat rate was only statistically different after the third bolus ingestion (3). Together, these observations were reported to contradict our other previous study (4). However, a clear separation between conditions appears evident—particularly on the forehead (Fig. 3D in [3])—after ingestion, indicating that statistical significance may have been attained with a larger sample size. With direct calorimetry, our previous conclusion of a disproportionately greater increase in evaporation from the skin relative to the heat gained internally with ingestion of 50°C water (1) is now shown to be potentially incorrect (3). These conflicting reports can probably be explained by a better maintenance of sweating efficiency with circulating airflow in a direct calorimeter, as opposed to our forward-facing airflow. Although the accuracy of both calorimetric methods is limited to combinations of activity and climate that induce complete evaporation, the present study indicates that the upper limit of these conditions is likely cooler and drier for a given metabolic rate when employing partitional calorimetry. These limits can probably be expanded though with the addition of a sideways-facing fan. Collectively, these studies conclusively demonstrate that humans physiologically compensate for internal heat exchange with cold (and probably warm) fluid ingestion by altering sweating activity and thus evaporation from the skin. As such, when complete sweat evaporation is permitted, no differences in heat storage occur, irrespective of drink temperature. However, colder fluid is probably beneficial from a human heat balance perspective when sweat begins dripping. Anthony R. Bain Center for Heart, Lung and Vascular Health University of British Columbia Kelowna, BC, CANADA Nathan B. Morris Exercise and Sport Science Faculty of Health Sciences University of Sydney Lidcombe, AUSTRALIA Matthew N. Cramer School of Human Kinetics University of Ottawa Ottawa, ON, CANADA Ollie Jay Exercise and Sport Science Faculty of Health Sciences University of Sydney Lidcombe, AUSTRALIA School of Human Kinetics University of Ottawa Ottawa, ON, CANADA [email protected]

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,011
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,033

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,011
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0020,001
Intégrité de la recherche0,0040,005
Charge utile insuffisante (le modèle a refusé de juger)0,0100,005

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.

Tête enseignante Opus0,029
Tête enseignante GPT0,294
Écart entre enseignants0,265 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2015
Routes d'admission2
Résumé présentoui

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