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Record W81124232 · doi:10.1123/jab.29.6.712

The Effect of Two Movement Strategies on Shoulder Resultant Joint Moment During Elastic Resistance Exercise

2013· article· en· W81124232 on OpenAlexaff
Gregory Neil Hodges, Dean Kriellaars

Bibliographic record

VenueJournal of Applied Biomechanics · 2013
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of ManitobaHealth Sciences Centre
Fundersnot available
KeywordsMoment (physics)Resistance trainingMovement (music)Joint (building)Physical medicine and rehabilitationShoulder jointMedicineMathematicsOrthodonticsPhysical therapyPhysicsEngineeringStructural engineeringClassical mechanicsAnatomyAcoustics

Abstract

fetched live from OpenAlex

Despite the common use of elastic resistance in training, only the static loading characteristics have been studied, whereas the dynamic components remain undetermined. The purpose was to determine the effect of two movement strategies on the shoulder resultant joint moment (RJM) during internal/external rotation exercise with elastic load. Ten healthy subjects performed sweep and step movement strategies over a constant range of motion and cadence (1:1). Shoulder RJM was determined using a Newtonian model with elastic force measured by force transducer, joint angle by electrogoniometer, and limb acceleration by accelerometer. Relative to the sweep strategy, the step strategy revealed a 49% increase in angle-specific RJM during the initial phase, RJM was reduced to 67-69% during midrange, and increased to over 110% at the end of the repetition. These RJM differences were wholly attributable to strategy-dependent changes in limb acceleration. Shoulder RJM in the sweep strategy was almost entirely explained by moment of elastic force. Movement strategy can substantially alter shoulder loading despite constant range of motion and cadence, impacting the magnitude and nature of the stimulus for neuromuscular adaptation. These acceleration-dependent changes in shoulder RJM may be important to consider for exercise efficacy and safety.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.426

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 head0.006
GPT teacher head0.206
Teacher spread0.201 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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