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Record W2117358473 · doi:10.1139/h04-048

Resting Mechanomyography After Aerobic Exercise

2004· article· en· W2117358473 on OpenAlexaff
William P. S. McKay, Philip D. Chilibeck, Karen Chad, Brian L. F. Daku

Bibliographic record

VenueCanadian Journal of Applied Physiology · 2004
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsExercise intensityOxygenElectrical impedance myographyVO2 maxInternal medicineIntensity (physics)Cycle ergometerAerobic exerciseMedicineCardiologyChemistryEndocrinologyPhysical therapyHeart rateBlood pressurePhysics

Abstract

fetched live from OpenAlex

A number of mechanisms have been proposed for the elevation in oxygen consumption following exercise. Biochemical processes that return muscle to its preexercise state do not account for all the oxygen consumed after exercise. It is possible that mechanical activity in resting muscle, which produces low frequency vibrations (i.e., muscle sounds: mechano-myographic [MMG] activity), could contribute to the excess postexercise oxygen consumption. Therefore the purpose of this study was to determine whether the resting MMG amplitude changes after exercise, and whether the change is related to the elevation in oxygen consumption (VO2). Ten young male subjects (22.9 yrs) performed 30 minutes of exercise on a cycle ergometer at an intensity corresponding to 70% peak VO2. Oxygen consumption was measured by indirect calorimetry, and MMG by an accelerometer placed over the mid-quadriceps before exercise and for 5.5 hours after exercise. MMG activity, expressed as mean absolute acceleration, was significantly elevated for the 5.5 hours of measurement after exercise (p < 0.05). MMG and VO2 decayed exponentially after exercise with time constants of 7.2 minutes and 7.4 minutes, respectively. We conclude that muscle is mechanically active following exercise and that this may contribute to an elevated VO2.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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

Citations23
Published2004
Admission routes1
Has abstractyes

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