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Record W1982945467 · doi:10.1080/00140139.2014.926396

Cycle to cycle variability in a repetitive upper extremity task

2014· article· en· W1982945467 on OpenAlexaff
Jimmy Tat, Michael W.R. Holmes, Peter J. Keir

Bibliographic record

VenueErgonomics · 2014
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcMaster UniversityUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsBicepsForearmRest (music)Physical medicine and rehabilitationCoefficient of variationWork (physics)Deltoid muscleAnatomyElectromyographyMedicinePhysical therapyMathematicsInternal medicineStatisticsPhysics

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the variability in muscle activity at rest and work during a repetitive task. A total of 20 participants performed a bimanual push task using three frequencies (4, 8, 16 pushes/min), three loads (1 kg, 2 kg, 4 kg) and two grip conditions (no grip, 30% maximum). The coefficient of variation (CoV) of muscle activity was determined for the anterior deltoid, biceps brachii, extensor digitorum and flexor digitorum superficialis. Faster push frequencies and heavier loads had lower work-rest ratio CoV and higher mean muscle activity (p < 0.01). Sixteen pushes per minute produced the lowest CoV for the anterior deltoid (p < 0.01), while the 1- kg load produced the lowest CoV for the extensor digitorum and flexor digitorum superficialis (p < 0.01). Changes were driven by the rest phase rather than by the work phase, except for grip decreasing forearm muscle CoV. These findings underscore the importance of variability at rest and indicate that low variability of muscle activity is associated with ergonomic risk factors. PRACTITIONER SUMMARY: Decreased motor variability has been associated with pain and injury. A cyclical push task, evaluated in terms of work and rest phases, found that greater workloads increased variability primarily due to changes in the rest phase. Muscle variability, especially for the rest phase, may provide insight into injury risk.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.173
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.005
GPT teacher head0.251
Teacher spread0.246 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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