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Record W4310235050 · doi:10.1080/00140139.2022.2152113

Effect of office chair design features on lumbar spine posture, muscle activity and perceived pain during prolonged sitting

2022· article· en· W4310235050 on OpenAlexafffund
Diana De Carvalho, Jack P. Callaghan

Bibliographic record

VenueErgonomics · 2022
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsUniversity of WaterlooMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsSittingPhysical therapyLow back painLumbarMedicinePhysical medicine and rehabilitationAsymptomaticPopulationBack painConfoundingSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Chair design features are typically compared using multiple seats, which can lead to confounding effects. Using a single chair, configurable to four designs (control, lumbar support, seat pan tilt and scapular relief), we investigated the effect of chair design on spine posture and movement, muscle activity and perceived pain in a sample of 31 asymptomatic adults. A total of 39% of the population were classified as pain developers, having significantly higher peak pain levels across most body regions. The lumbar support and seat pan tilt condition resulted in more neutral spine and pelvic postures. Greater muscle activity was found in the seat pan condition and non-pain developers displayed lower spine muscle activation levels overall. Despite some improvements in spine posture, sitting-induced pain was present in the study sample at similar proportions to those reported previously. Future studies may consider investigating interventions targeted to sitting-induced pain developers as opposed to the general population. Practitioner summary: Four office chair configurations were tested. The lumbar support and seat pan tilt conditions resulted in the most neutral back posture but did not mitigate the clinically significant levels of sitting-induced pain experienced by a large portion of the tested sample. Future work should target interventions to these individuals.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.253
Teacher spread0.245 · 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.

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

Citations14
Published2022
Admission routes2
Has abstractyes

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