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Menstrual Cycle Modulates the Contribution of Dry Heat Loss to Total Heat Loss During Exercise in Warm‐Dry Conditions in Young, Recreationally Active Females: Preliminary Findings

2022· article· en· W4225321605 on OpenAlexafffund
Nathalie V. Kirby, Sean R. Notley, Robert D. Meade, Brodie J. Richards, Glen P. Kenny

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDry heatMenstrual cycleMedicineAnimal sciencePhysical therapyEndocrinologyBiologyMaterials science

Abstract

fetched live from OpenAlex

The mid‐luteal phase of the menstrual cycle is characterized by an upward shift in basal body core temperature secondary to elevated circulating estradiol and progesterone levels as compared to the early‐follicular phase. This elevation in body core temperature, perhaps together with increased estradiol‐mediated cutaneous vasodilation, may increase convective and radiative (dry) heat loss by improving core‐to‐skin heat transfer. This increase in dry heat loss may, in turn, reduce the need for sweat secretion (evaporative heat loss) without necessarily altering the relative contribution of these avenues to total heat exchange. Thus, the purpose of this study was to evaluate the hypothesis that females in the mid‐luteal phase would exhibit a greater contribution of dry heat loss to total heat loss as compared to the early‐follicular phase during exercise in warm‐dry conditions. Seven young, recreationally active, ovulating females (3 using hormonal intrauterine devices, mean (SD), 24 (3) years, V̇O 2peak 40.5 (3.4) mL kg ‐1 min ‐1 ) completed two 45‐min bouts of semi‐recumbent cycling at a low (175 W m ‐2 ; ~40% V̇O 2peak ) and high (275 W m ‐2 ; ~65% V̇O 2peak ) rate of metabolic heat production, interspersed by 15‐min rest, in warm‐dry conditions (30.0 (0.2) °C, (25 (9) % relative humidity) in the early‐follicular (cycle days 2‐6) and mid‐luteal phase (days 19‐23). Metabolic heat production and dry and evaporative heat loss were measured via indirect and direct calorimetry, respectively. The contribution of dry heat loss was expressed as a percentage of total heat loss. Body core temperature (esophageal, n =6) and mean skin temperature (8 sites) were measured continuously. Averages of the final 5‐min of exercise in each heat load were compared between phases using dependent, two‐tailed t‐tests. As anticipated, resting body core temperature in the mid‐luteal phase was 0.2 °C [95% CI: 0.1, 0.3] higher than in the early‐follicular phase ( p <0.001). Core temperature remained 0.2 °C [0.1, 0.5] higher at the low heat load (37.8 (0.2) vs. 37.6 (0.3) °C, p <0.01), but initial differences in core temperature were not observed in the high heat load (38.4 (0.3) vs. 38.3 (0.4) °C, p =0.83). Skin temperature and core‐to‐skin gradient were similar between phases throughout (all p ≥0.21). Total heat loss was not different between phases at the low (162 (8) vs. 162 (13) W m ‐2 , p =0.93) or high (255 (10) vs. 252 (7) W m ‐2 , p =0.39) heat loads. However, the contribution of dry heat loss to total heat loss was 3% [1, 4] greater in the mid‐luteal phase at the low heat load (20 (8) vs. 17 (9) %, p <0.01). This difference was not observed at the high heat load (12 (8) vs. 10 (9) %, p =0.16). In this preliminary analysis, females in the mid‐luteal phase exhibited a higher body core temperature and greater contribution of dry heat loss to total heat loss at low exercise‐induced heat loads in warm‐dry conditions. However, these differences were not observed at higher heat loads. These data provide novel mechanistic insight into the shifts in the contribution of the main avenues of heat loss in ovulating females across the menstrual cycle.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.015
GPT teacher head0.280
Teacher spread0.265 · 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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Citations1
Published2022
Admission routes2
Has abstractyes

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