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Isometric Force-Frequency and Dynamic Power-Frequency Relationships in Human Knee Extensors

2023· article· en· W4378675073 on OpenAlexaff
Alexander M. Zero, Michael T. Paris, Charles L. Rice

Bibliographic record

VenuePhysiology · 2023
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsWestern University
Fundersnot available
KeywordsIsometric exerciseConcentricDynamometerElectromyographyContraction (grammar)Knee JointTorqueShouldersAnatomyPhysical medicine and rehabilitationMathematicsMedicinePhysicsPhysical therapySurgeryInternal medicineGeometryEngineering

Abstract

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Under voluntary control the neuromuscular system grades contractile output of recruited motor units by modulating discharge frequency. Electrically evoked (i.e., involuntary) contractions have been used to assess relationships between frequency input and contractile output. During an isometric contraction (i.e., no joint rotation) it is well-established that the force-frequency relationship is sigmoidal. However, there are few data describing the relationship between evoked input and dynamic (i.e., joint rotation) contractile parameters. Thus, the purpose was to examine the relationships between stimulated frequencies and dynamic power (i.e., product of concentric torque and angular velocity) during an isotonic contraction in comparison to an isometric force-frequency relationship. Knee extensors of 7 healthy young adult males were tested in a Cybex dynamometer. The right knee was at 90° and hip at 110°. Seat belts secured to the dynamometer were strapped across the hips and shoulders, and a non-elastic strap was secured across the thigh to avoid extraneous movement. Two stimulation electrodes, which consisted of aluminum wrapped in a conductive gel-soaked cloth were secured transversely to the proximal and distal portion of the knee extensors. For dynamic contractions the dynamometer was set to the “isotonic mode” and range of motion was 60°. The muscles were stimulated at 1, 5, 7.5, 10, 12, 15, 17, 20, 25, 30, 40, 50, 75 and 100 Hz. Besides the 1 Hz (i.e., single pulse) all frequencies during isometric contractions were 2s in duration. During isotonic contractions with the load set to 7.5% maximal voluntary contraction (MVC), pulses were programmed to terminate once range of motion was complete or until full range of motion was unachievable. Electrical current for all frequencies was determined from that required at 100 Hz to produce 50% isometric MVC force. Stimulation was done in ascending order with 20s rest between. The order between contraction modes was randomized and a 5-minute rest was given between modes. Results are presented as a percentage of relative peak force or peak power for 100 Hz during isometric or isotonic contractions, respectively. Both isometric force-frequency and dynamic power-frequency relationships were sigmoidal. During stimulation of increasing frequencies peak power was lower than isometric force by ~82%, ~47%, ~43%, ~38%, ~32%, ~25%, ~30%, ~23%, ~17%, ~12%, ~8% at ascending frequencies from 1 Hz to 40Hz, respectively. From 50 Hz to 100 Hz there were minimal differences (0-4%). These data indicate for the same stimulation frequencies the power-frequency relationship of a moderately loaded isotonic contraction is shifted rightward relative to an isometric force-frequency relationship. Therefore, isometric force-frequency and dynamic power-frequency relationships are not equivalent, and assessments of both relationships may be required to adequately characterize contractile function depending on the task. Supported by NSERC. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

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 score0.897
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.018
GPT teacher head0.246
Teacher spread0.228 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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