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Record W2183584396

Sprint interval exercise elicits near maximal peak VO2 during repeated bouts with a rapid recovery within 2 minutes.

2014· article· en· W2183584396 on OpenAlexaff
Tom J. Hazell, T. Dylan Olver, Rebecca E. K. MacPherson, Craig D. Hamilton, Peter W.R. Lemon

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsSprintHeart rateVO2 maxCardiorespiratory fitnessInterval trainingAnimal scienceMedicineInternal medicineCardiologyPhysical therapyBlood pressureBiology
DOInot available

Abstract

fetched live from OpenAlex

AIM: We investigated the cardiorespiratory response during acute sprint interval exercise (SIE; 4 x 30 sec maximal efforts, each separated by 4 min recovery) vs. continuous endurance exercise (CEE; 30 min) at 70% VO2max. METHODS: Oxygen consumption (VO2) and heart rate were measured in 8 males (age: 23±2.3 y, height: 181±6.4 cm, body mass: 78±8.6 kg, VO2max: 52±3.1 ml·kg-1·min-1, mean±SD). Pre-exercise diet was controlled. RESULTS AND CONCLUSION: Total VO2 was greater with CEE vs. SIE (87.6±13.1 vs. 35.1±4.4 L O2) with small differences (P=0.06) in average heart rates (CEE: 157±10 bpm vs. SIE: 149±6 bpm) and peak heart rates (CEE: 166±10 vs. SIE: 173±6; P=0.14). VO2 increased during the sprint bouts (53-72% of VO2max) and attained near maximal values (84-96%) in the immediate recovery period (within 20 sec). Thereafter a rapid decrease occurred so that at 2 min of recovery VO2 was ~1.5 L/min (~38% VO2max). During the remaining 2 min of recovery VO2 declined more slowly to ~1.3 L/min or ~33% of VO2max. Similar heart rate responses with CEE and SIE and a greater VO2 during SIE suggest increased muscle oxygen extraction with SIE, which might explain the greater peripheral adaptations, observed previously with sprint vs. continuous training. The potential value of shorter recovery durations to SIE needs to be examined.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.952
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.190
Teacher spread0.180 · 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

Citations17
Published2014
Admission routes1
Has abstractyes

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