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Neural adaptations to fatigue: implications for muscle strength and training

2001· article· en· W2028043967 on OpenAlexaff
David A. Gabriel, Jeffrey R. Basford, Kai‐Nan An

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2001
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsBrock University
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsIsometric exerciseBicepsElectromyographyElbowElbow flexionMedicinePhysical medicine and rehabilitationPhysical therapyMuscle fatigueIntraclass correlationBiceps brachii muscleAnatomy

Abstract

fetched live from OpenAlex

PURPOSE: This paper investigates the neural mechanisms responsible for the increase in strength that occurs during serial isometric contractions. METHODS: A three-session design was used. Thirteen subjects (N = 13) were asked to perform five maximal isometric elbow extension strength trials to serve as baseline. After a 5-min rest, the subjects were administered a 30-trial fatigue protocol. This process was repeated two more times at 2-wk intervals. Elbow extension torque and surface electromyography (EMG) of the triceps and biceps brachii were monitored concurrently. The criterion measures were elbow extension torque, root-mean-square EMG amplitude, and mean power frequency (MPF). RESULTS: Intraclass reliability ranged from good to excellent. Within each experimental session, the fatigue protocol resulted in a decrease in maximal isometric elbow extension torque as well as biceps and triceps EMG amplitude and MPF (P < 0.05). However, the mean of the 30 trials and the magnitude of the linear decrease in elbow extension torque increased across the three sessions (P < 0.05). Biceps and triceps EMG amplitude increased and MPF decreased as the number of sessions increased (P < 0.05). CONCLUSIONS: These findings suggest that the fatigue protocol served as a training stimulus to down regulate motor-unit firing frequency.

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 score0.928
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.040
GPT teacher head0.295
Teacher spread0.255 · 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

Citations39
Published2001
Admission routes1
Has abstractyes

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