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Record W4398190609 · doi:10.1016/j.apergo.2024.104310

Traditional versus dynamic sitting: Lumbar spine kinematics and pain during computer work and activity guided tasks

2024· article· en· W4398190609 on OpenAlexafffund
J. Davidson, Jackie D. Zehr, Paolo B. Dominelli, Jack P. Callaghan

Bibliographic record

VenueApplied Ergonomics · 2024
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSittingKinematicsPelvisLumbarLow back painSagittal planePhysical medicine and rehabilitationPhysical therapyWork (physics)Back painMedicineLumbar spineSurgeryEngineeringAnatomyPhysics

Abstract

fetched live from OpenAlex

Dynamic sitting may mitigate low back pain during prolonged seated work. The current study compared pelvis and lumbar spine kinematics, pain, and work productivity, in traditional and dynamic sitting. Sixteen participants completed three 20-min blocks of computer work and activity guided tasks in a traditional office chair or backless and multiaxial rotating seat pan while kinematics were measured from accelerometers on the low back. Pain ratings were recorded on a visual analogue scale every 10 min. Similar pelvis and lumbar kinematics emerged when performing computer work in traditional and dynamic sitting. Pelvis and lumbar sagittal and frontal plane shifts and fidgets were largest for dynamic sitting in the activity guided tasks. Buttocks pain was higher in dynamic sitting, but low back pain and work productivity were unaffected. Dynamic sitting increased spine movement during activity guided tasks, without negatively impacting lumbar kinematics, low back pain, or productivity during seated computer 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.552

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 head0.017
GPT teacher head0.257
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

Citations10
Published2024
Admission routes2
Has abstractyes

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