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Hand and Forearm, But Not Neck Cooling, Reduces Thermophysiological and Perceptual Strain Following Passive Hyperthermia

2015· article· en· W2467362704 on OpenAlexaff
Ross A. Sherman, Mackenzie L. Abeare, Samantha C. Orr, Stephen S. Cheung

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2015
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsBrock University
Fundersnot available
KeywordsHyperthermiaForearmThermal sensationRectal temperatureSkin temperatureHeart rateImmersion (mathematics)Heat stressMedicineThermoregulationPerceived exertionAnesthesiaMaterials scienceAnimal scienceSurgeryChemistryBiomedical engineeringThermal comfortInternal medicineMathematicsMeteorologyGeometryBlood pressure

Abstract

fetched live from OpenAlex

Combined hand and forearm cooling effectively attenuates hyperthermia during exercise, and improves recovery between bouts of exercise in an uncompensable heat stress environment. Neck cooling can also be effective in reducing thermoregulatory strain during exercise in high ambient temperatures, or when magnitude of cooling is sufficient. PURPOSE: To determine the effect of active cooling on temperature, heart rate and thermal sensation following passive hyperthermia. METHODS: Eleven healthy participants (22±5 y; 173±10 cm; 71.8±15.1 kg) were passively heated to 39°C rectal temperature (Tre) by 40°C whole-body immersion. They were then removed from the water and sat quietly in a room (24.6±0.8°C and 49.8±6.3% rh) and used either a 2.6 m2 commercially available cooling towel wrapped against the surface of their neck (NT), hand and forearm immersion in 10.5±1.3°C water (H) or cooled passively (C) until Tre reached 38°C. Heart rate, Tre, mean skin temperature (Tsk), and thermal sensation (TS) were measured pre and post whole-body immersion, and every 5 min during cooling. Nude body mass was measured before and after each trial. One and two way repeated measures ANOVA were used to determine differences across time and between groups. RESULTS: Time to cool was faster (p<0.01) with H (24±7 min) compared to C and NT (C: 37±13 min; NT: 38±9 min). There were Tre interaction effects (time and condition, p<0.01) at 10 min cooling with H (38.7±0.3°C) compared to NT (39.0±0.2°C) and C (38.9±0.2°C), and at 15 min cooling with H (38.4±0.4°C) when compared to NT (38.7±0.2°C) and C (38.7±0.3°C). There was also a lower Tsk (p<0.05) when using H (34.5±2.7°C) compared to NT (34.9±2.9°C) and C (34.7±2.8°C). Mean heart rate during recovery was lower (p<0.01) when using hand cooling (96±19 beats·min-1) compared to both neck towel cooling (107±20 beats·min-1) and control (105±22 beats·min-1). Perceived TS was found to be lower (p<0.01) with H (4.1±1.7) compared to NT and C (NT 4.7±1.4; C 4.6±1.5). Across the duration of each of the trials, there was no significant difference in body mass change. CONCLUSION: Neck towel cooling was found to be an ineffective hyperthermia recovery strategy. However, hand and forearm cooling effectively reduced thermal strain and recovery time, along with decreasing heart rate and improving perceptual responses.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

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)0.0030.000

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 head0.044
GPT teacher head0.314
Teacher spread0.270 · 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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Citations0
Published2015
Admission routes1
Has abstractyes

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