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Record W4292567359 · doi:10.3390/jfmk7030060

Looped Elastic Resistance during Squats: How Do Band Position and Stiffness Affect Hip Myoelectric Activity?

2022· article· en· W4292567359 on OpenAlexaff
Eduardo Campos Martins, Lucas B. Steffen, Diogo Almeida Gomes, Walter Herzog, Alessandro Haupenthal, Heiliane de Brito Fontana

Bibliographic record

VenueJournal of Functional Morphology and Kinesiology · 2022
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Calgary
FundersFundação de Amparo à Pesquisa e Inovação do Estado de Santa CatarinaConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsSquatMediusThighBicepsAnatomyFascia lataMedicineFasciaVastus medialisSquatting positionElectromyographyOrthodonticsPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Looped elastic bands around the thigh are commonly used during squats and result in increased hip activation. Due to the closed kinetic chain nature of the squat exercise, one may expect that placing the elastic band on distal segments, close to the floor contact, may not result in the same increase in hip muscle activation as that achieved with a looped band around the thigh. We analyzed the effects of band position (thigh, lower leg, and forefoot) and band stiffness on the myoelectric activity of the tensor fascia latae, gluteus medius, gluteus maximus, biceps femoris, vastus medialis, and vastus lateralis during squats in 35 healthy subjects (18 men and 17 women). The greatest myoelectric activity of hip muscles was observed when the stiffest band was positioned around the forefeet with an increase in 24% for the tensor fascia latae, 83% for the gluteus medius, and 68% for the gluteus maximus compared to free (without resistance band) squatting. Contrary to previous thinking, the use of elastic bands around the forefeet during squats can elicit increased myoelectric activity of hip muscles, with a magnitude often greater than when the band is placed around the thigh segments.

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 score0.979
Threshold uncertainty score0.522

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 head0.195
Teacher spread0.187 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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