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Record W2289448932 · doi:10.1080/02640414.2016.1154978

Net joint moments and muscle activation in barbell squats without and with restricted anterior leg rotation

2016· article· en· W2289448932 on OpenAlexaff
Loren Z.F. Chiu, Gabriella L. vonGaza, Liane M.Y. Jean

Bibliographic record

VenueJournal of Sports Sciences · 2016
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSquatAnkleRange of motionMedicineVastus medialisPhysical medicine and rehabilitationKnee JointExternal rotationElectromyographyOrthodonticsPhysical therapyAnatomySurgery

Abstract

fetched live from OpenAlex

Muscle utilisation in squat exercise depends on technique. The purpose of this study was to compare net joint moments (NJMs) and muscle activation during squats without and with restricted leg dorsiflexion. Experienced men (n = 5) and women (n = 4) performed full squats at 80% one repetition maximum. 3D motion analysis, force platform and (EMG) data were collected. Restricting anterior leg rotation reduced anterior leg (P = 0.001) and posterior thigh (P < 0.001) rotations, resulting in a smaller knee flexion range of motion (P < 0.001). At maximum squat depth, ankle plantar flexor (P < 0.001) and knee extensor (P < 0.001) NJM were higher in unrestricted squats. Hip extensor NJM (P = 0.14) was not different between squat types at maximum squat depth. Vastus lateralis (P > 0.05), vastus medialis (P > 0.05) and rectus femoris (P > 0.05) EMG were not different between squat types. Unrestricted squats have higher ankle plantar flexor and knee extensor NJM than previously reported from jumping and landing. However, ankle plantar flexor and knee extensor NJM are lower in restricted squats than previous studies of jumping and landing. The high NJM in unrestricted squat exercise performed through a full range of motion suggests this squat type would be more effective to stimulate adaptations in the lower extremity musculature than restricted squats.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.124

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 communication0.0000.001
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 head0.025
GPT teacher head0.276
Teacher spread0.251 · 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

Citations24
Published2016
Admission routes1
Has abstractyes

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