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Record W3209347195 · doi:10.1016/j.apergo.2021.103605

Different sit:stand time ratios within a 30-minute cycle change perceptions related to musculoskeletal disorders

2021· article· en· W3209347195 on OpenAlexafffund
Nancy L. Black, Mathieu Tremblay, Fandresena Ranaivosoa

Bibliographic record

VenueApplied Ergonomics · 2021
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsUniversité du Québec à RimouskiUniversité de Moncton
FundersEuropean Regional Development FundNatural Sciences and Engineering Research Council of CanadaUniversité de Moncton
KeywordsPerceptionPhysical therapyPsychologyPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Regular standing interruptions to sedentary work are recommended, but their dosage is understudied. To measure perception variations associated with different sit:stand ratios, 16 people used six ratios (30:0, 27:3, 24:6, 21:9, 18:12 and 15:15) within 30-min cycles in their normal office environment. At start and end of each workday, study participants recorded their perception of 11 factors on a 10-point scale. Musculoskeletal discomfort in 10 body regions was measured before and after exposure to sit-stand ratios. Overall preferred ratios were recorded. Sit:stand ratio affected all perceived factors, with impact varying. Standing at least 6 min improved results most overall; however, individual perceived factors were least impacted by any of 30:0, 27:3, 24:6 or 21:9. Preferred sit:stand ratios were 15:15, 18:12 and 21:9. Typically, least liked ratios involved briefest standing (30:0, 27:3, 24:6) although two participants least liked 15:15. Understanding these variations contributes to appropriate standing dosage recommendations.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
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)0.0040.005

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 head0.011
GPT teacher head0.267
Teacher spread0.256 · 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; both teacher heads agree on what is shown here.

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

Citations7
Published2021
Admission routes2
Has abstractyes

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