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Changes In Muscle Activation Patterns And Kinematics While Learning The Bilateral Barbell Squat

2016· article· en· W2498507180 on OpenAlexaff
Jeremy W. Noble, Katherine M. Lantz

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2016
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsSquatting positionSquatKinematicsSagittal planePhysical medicine and rehabilitationElectromyographyAnkleWork (physics)Physical therapyMotion (physics)MathematicsMedicinePsychologyComputer scienceAnatomyEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

While the barbell squat has been subjected to many biomechanical analyses, it is not known how the kinematics and muscle activation profiles will change as an individual learns how to perform the movement. Previous work using different motion patterns have noted decreased variability in movement patterns between repetitions as an individual learns a new movement pattern. PURPOSE: The goal of this work was to document changes in variability of kinematic and muscle activity patterns while individuals learn to perform a standard bilateral barbell squat. METHODS: Ten university age females with no prior barbell squatting experience were recruited. The participants attended four weekly sessions where they performed 4 sets of 10 squats at 50% of their one-repetition maximum (1RM) while receiving feedback on their squatting technique. During the squats the participants had their motion captured in the sagittal plane using a high speed video camera and electromyography (EMG) was recorded from 8 lower-limb muscles that were involved in the squatting motion from the right lower-limb. Prior to the first session and at the end of the last session a 1RM test was performed to determine the maximum capacity for the barbell squat. RESULTS: Despite using a load where a strength adaptation would not be expected, the participants demonstrated a significant increase in 1RM across the study (50.1±8.6 kg vs 57.1 ± 10.0 kg; p < 0.05). Interestingly, most changes in kinematic variability occurred at the ankle joint, where increased variability was observed across the 4 sessions (p < 0.05). It was also observed that the participants were squatting deeper as sessions progressed, as measured by the angle of the thigh segment (p < 0.05). Rectus Femoris (RF) showed decreased variability across the sessions (p < 0.05). CONCLUSIONS: Although an increase in 1RM was found, this improvement is likely due to changes in technique rather than strength. Our findings that the ankle increased in variability may be supported by the uncontrolled manifold hypothesis, which states since attentional focus was on the hips in this study (due to the nature of our feedback), that variability may increase in other areas. Changes in muscle activity of RF may indicate that the participants became more adept at coordinating the hip and knee joints during the squat.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.024
GPT teacher head0.279
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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