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Record W2094540898 · doi:10.1139/h05-017

Excess post-exercise oxygen consumption in untrained males: effects of intermittent durations of arm ergometry

2006· article· en· W2094540898 on OpenAlexvenueno aff
Scott Lyons, Mark T. Richardson, Phillip A. Bishop, J. F. Smith, Hank Heath, J. Martin Giesen

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2006
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePhysical therapyPhysical exerciseVO2 maxExercise physiologyEnergy expenditureCardiologyHeart rateAnaerobic exercisePhysical medicine and rehabilitationInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate excess post-exercise oxygen consumption (EPOC) following a continuous 30 min bout of upper-body exercise (UBE) compared with 3 consecutive 10 min bouts of UBE. Ten male subjects (age (mean +/- standard deviation), 25.7 +/- 5.83 years; arm VO(2) (peak), 2.2 +/- 0.25 L x min(-1), on separate days (48 h between trials) and in counterbalanced order, performed a continuous 30 min bout of arm exercise at 60% of arm VO2 peak and 3 separate 10 min bouts of arm exercise at 60% of arm VO(2) (peak). Subjects reported to the laboratory rested and after a 12 h fast. Each test was preceded by a 30 min baseline test to determine resting metabolic rate. Post-exercise VO2 was continuously monitored until baseline was re-established. Results showed that the combined magnitude of the EPOCs from the intermittent exercise sessions was significantly (p > .05) greater (4.47 +/- 1.58 L O2) than that elicited from the continuous exercise session (1.54 +/- 1.25 L O2). These data indicate that separating a continuous 30 min arm exercise into 3 equal 10 min arm exercises will elicit a small but significantly higher EPOC, and thus result in greater post-exercise energy expenditure. This could be beneficial for those unable to perform lower-body exercise (LBE), or for those with limited exercise capacities.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.007
GPT teacher head0.235
Teacher spread0.229 · 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

Citations23
Published2006
Admission routes1
Has abstractyes

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