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Low-frequency fatigue at maximal and submaximal muscle contractions

2009· article· en· W2156121516 on OpenAlexaff
Rafael Reimann Baptista, Eduardo Mendonça Scheeren, Brian R. MacIntosh, Marco Aurélio Vaz

Bibliographic record

VenueBrazilian Journal of Medical and Biological Research · 2009
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Calgary
FundersUniversidade Federal do Rio Grande do SulConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsIsometric exerciseConcentricEccentricContraction (grammar)MedicineAnalysis of varianceMuscle fatigueMuscle contractionCardiologyInternal medicineStimulationRepeated measures designPhysical medicine and rehabilitationElectromyographyMathematicsPhysics

Abstract

fetched live from OpenAlex

Skeletal muscle force production following repetitive contractions is preferentially reduced when muscle is evaluated with low-frequency stimulation. This selective impairment in force generation is called low-frequency fatigue (LFF) and could be dependent on the contraction type. The purpose of this study was to compare LFF after concentric and eccentric maximal and submaximal contractions of knee extensor muscles. Ten healthy male subjects (age: 23.6 +/- 4.2 years; weight: 73.8 +/- 7.7 kg; height: 1.79 +/- 0.05 m) executed maximal voluntary contractions that were measured before a fatigue test (pre-exercise), immediately after (after-exercise) and after 1 h of recovery (after-recovery). The fatigue test consisted of 60 maximal (100%) or submaximal (40%) dynamic concentric or eccentric knee extensions at an angular velocity of 60 degrees /s. The isometric torque produced by low- (20 Hz) and high- (100 Hz) frequency stimulation was also measured at these times and the 20:100 Hz ratio was calculated to assess LFF. One-way ANOVA for repeated measures followed by the Newman-Keuls post hoc test was used to determine significant (P < 0.05) differences. LFF was evident after-recovery in all trials except following submaximal eccentric contractions. LFF was not evident after-exercise, regardless of exercise intensity or contraction type. Our results suggest that low-frequency fatigue was evident after submaximal concentric but not submaximal eccentric contractions and was more pronounced after 1-h of recovery.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.971
Threshold uncertainty score0.398

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 integrity0.0000.001
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 head0.066
GPT teacher head0.338
Teacher spread0.271 · 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

Citations31
Published2009
Admission routes1
Has abstractyes

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