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EFFECT OF BODY HYPOHYDRATION ON AEROBIC PERFORMANCE OF BOYS WHO EXERCISE IN THE HEAT

2002· article· en· W2013783245 on OpenAlexaff
B. Wilk, H Yuxiu, Oded Bar‐Or

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2002
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAerobic exerciseDehydrationHeart rateAnimal scienceMedicineHeat stressBody weightTreadmillRelative humidityPhysical therapyInternal medicineChemistryBiologyBlood pressure

Abstract

fetched live from OpenAlex

PURPOSE: To assess the effect of various levels of body hypohydration on aerobic performance in boys. METHODS: Seven 10- to 12-year-old boys attended three sessions in which they first dehydrated through exercise (6×10-min bouts at 40–45% VO2max) in a climatic chamber, then rested for 45 min in a thermoneutral room and finally performed in 35°C, 50% relative humidity an all-out cycling test at 90% VO2max. Dehydration was aimed to induce hypohydration of 0%, 1% or 2% of initial body weight (BW). These were achieved through individualized combinations of climatic heat stress and drinking regimens. Session sequence was counterbalanced. BW changes, total work to exhaustion (TW) in kJ and heart rate (HR) were recorded. RESULTS: As intended, initial BW changes (−0.02 ± 0.02 %BW, −1.08 ± 0.06 %BW, −2.12 ± 0.05 %BW, for 0%, 1% and 2%, respectively) were significantly different (p < 0.001) among the sessions. TW was significantly lower −49.2 ± 10.0 kJ (p < 0.05) and 40.2 ± 7.8 kJ (p < 0.05) – in 1% and 2%, respectively, than in 0% (55.5 ± 11.3 kJ). HR at the end of the test reached a similar level (on average, 189–193 bpm, 95–98 %HRmax) in all sessions. CONCLUSION: Even mild (1%BW) to moderate (2%BW) hypohydration causes significant reduction in aerobic performance of boys at 35 °C, 50% relative humidity. Therefore, dehydration should be prevented to maintain performance in the heat. Supported by Gatorade Sports Science Institute

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.067
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.274
Teacher spread0.260 · 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 teacher head, 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

Citations30
Published2002
Admission routes1
Has abstractyes

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