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Local metabolic rate during whole body vibration

2013· article· en· W1990045939 on OpenAlexafffund
Bernd Friesenbichler, Benno M. Nigg, Jeff F. Dunn

Bibliographic record

VenueJournal of Applied Physiology · 2013
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsWhole body vibrationMetabolic rateVastus medialisMetabolic costIsometric exerciseMedicinePhysical medicine and rehabilitationAnatomyPhysical therapyInternal medicineVibrationElectromyographyPhysics

Abstract

fetched live from OpenAlex

Whole body vibration (WBV) platforms are currently used for muscle training and rehabilitation. However, the effectiveness of WBV training remains elusive, since scientific studies vary largely in the vibration parameters used. The origin of this issue may be related to a lack in understanding of the training intensity that is imposed on individual muscles by WBV. Therefore, this study evaluates the training intensity in terms of metabolic rate of two lower-extremity muscles during WBV under different vibration parameters. Fourteen healthy male subjects were randomly exposed to 0 (control)-, 10-, 17-, and 28-Hz vibrations while standing upright on a vibration platform. A near-infrared spectrometer was used to determine the gastrocnemius medialis (GM) and vastus lateralis (VL) muscles' metabolic rates during arterial occlusion. The metabolic rates during each vibration condition were significantly higher compared with control for both muscles (P < 0.05). Each increase in vibration frequency translated into a significantly higher metabolic rate than the previous lower frequency (P < 0.05) for both muscles. The current study showed that the local metabolic rate during WBV at 28 Hz was on average 5.4 times (GM) and 3.7 times (VL) of the control metabolic rate. The substantial changes in local metabolic rate indicate that WBV may represent a significant local training stimulus for particular leg muscles.

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 score0.546
Threshold uncertainty score0.402

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 head0.008
GPT teacher head0.268
Teacher spread0.260 · 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

Citations7
Published2013
Admission routes2
Has abstractyes

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