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Record W2947474415 · doi:10.1080/00140139.2019.1626491

Postural and muscular adaptations to repetitive simulated work

2019· article· en· W2947474415 on OpenAlexafffund
Samantha E. Pritchard, Calvin T.F. Tse, Alison C. McDonald, Peter J. Keir

Bibliographic record

VenueErgonomics · 2019
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcMaster University
FundersAUTO21 Network of Centres of ExcellenceNatural Sciences and Engineering Research Council of Canada
KeywordsElectromyographyPhysical medicine and rehabilitationWork (physics)TrunkKinematicsPhysical therapyElbowUpper limbUpper trunkMuscle fatigueMotor unit recruitmentMedicinePsychologyAnatomyEngineering

Abstract

fetched live from OpenAlex

Complex repetitive tasks are common in the workplace and have been associated with upper extremity disorders. The purpose of this study was to examine the progressive effects of highly repetitive work on joint kinematics and muscle activity of the trunk and upper extremity. Fifteen healthy men performed 60 one-minute cycles of 4 simulated automotive-related tasks. Electromyography of eight muscles and kinematics of the trunk and right upper extremity were collected. Data were analysed at 12-min intervals and divided into a complete work cycle. The time to complete the work cycle decreased by 6.3 s over the trials. Peak shoulder flexion decreased and peak elbow flexion increased during the work cycle. Muscle activity magnitude and variability was influenced by time during the repetitive tasks. This study found adaptations to highly repetitive but light work in only 1 h; redistributing muscle demands within the shoulder over time may reduce muscle fatigue development.Practitioner Summary: While the work was not strenuous, we were able to demonstrate muscular and postural adaptations in a single hour of simulated work. By evaluating both the whole work cycle and the sub-tasks, we aim to develop new methods for evaluating the risk of complex tasks in prolonged repetitive work.

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 score0.304
Threshold uncertainty score0.263

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 head0.007
GPT teacher head0.247
Teacher spread0.240 · 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

Citations13
Published2019
Admission routes2
Has abstractyes

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