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Record W2082413809 · doi:10.1519/jsc.0b013e3181e07585

Effects of Wearing a Cooling Vest During the Warm-Up on 10-km Run Performance

2011· article· en· W2082413809 on OpenAlexaff
Alicja B. Stannard, Jason P. Brandenburg, William A. Pitney, Judith M. Lukaszuk

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2011
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsVESTRating of perceived exertionThermal sensationTreadmillAnimal scienceHeart rateMedicinePerceived exertionPhysical therapyInternal medicinePhysicsMathematicsMeteorologyThermal comfortBlood pressureBiology

Abstract

fetched live from OpenAlex

The purpose of this study was to examine whether wearing a cooling vest during an active warm-up would improve the 10-km time trial (TT) performance of endurance runners. Seven male runners completed 3 10-km TTs (1 familiarization and 2 experimental) on a treadmill after a 30-minute warm-up. During the warm-up of the experimental TTs, runners wore either a t-shirt (control [C]) or a cooling vest (V), the order of which was randomized. No differences were found between the C and V conditions for the 10-km TT times (2,533 ± 144 and 2,543 ± 149 seconds, respectively) (p = 0.746) or any of the 2-km split times. Heart rate (HR) at the start of the TT equaled 90 ± 17 b·min for C and 94 ± 16 b·min for V. The HR peaked at 184 ± 20 b·min in C and 181 ± 19 b·min in V. At the start of the TT Tc was 37.65 ± .72°C in C and 37.29 ± .73°C in V (p = 0.067). In C, Tc gradually increased until 39.34 ± 0.43°C while in V is reached 39.18 ± 0.72°C (p = 0.621). Although rating of perceived exertion (RPE) and Thermal sensation (TS) increased during both experimental TTs, there were no differences between V and C. Findings suggest wearing a cooling vest during a warm-up does not improve 10-km performance. The use of cooling vests during the warm-up did not produce any physiological (HR and Tc) or psychological (RPE and TS) benefit, perhaps accounting for the lack of improvement.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.053
GPT teacher head0.332
Teacher spread0.279 · 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 designBench or experimental
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

Citations24
Published2011
Admission routes1
Has abstractyes

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