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Record W3215019797 · doi:10.1080/00140139.2021.2007290

Effect of a ‘spine offloading’ chair design on seated height and posture

2021· article· en· W3215019797 on OpenAlexafffund
Mona Frey, Adam R. Blanchard, Ian Skinner, Diana De Carvalho

Bibliographic record

VenueErgonomics · 2021
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSittingSimulationBack painMedicinePhysical therapyPhysical medicine and rehabilitationComputer science

Abstract

fetched live from OpenAlex

A prototype chair with anterior chest and arm supports has been designed to reduce compressive spine loads. The purpose of this study was to compare the effects of this offloading design on seated height compared to a control configuration of the same chair. 20 males sat on each configuration for 1 hour. Seated height, perceived pain, spine angles, seat pressure, and participant experience were measured. Spine height loss was significantly reduced in the offloading (–0.75 ± 3.79 mm) compared to the control configuration (–6.16 ± 4.27 mm, p < 0.001), and participants sat significantly more anterior on the seat pan in the offloading (20.56 ± 1.67 cm) compared to control configuration (18.03 ± 1.92 cm, p < 0.001). There were no differences in spine angles or perceived back and gluteal pain between configurations. This design appears to be a promising approach to protecting the back during sitting when engaging in forward leaning tasks where the offloading effect of a backrest may be minimised. Practitioner summary: A prototype chair with anterior chest and arm supports designed to offload the spine was shown to significantly reduce seated height loss during 1-hour of sitting compared to a control configuration. While participants perceived the offloading design to be more supportive, no differences in perceived pain or posture were found.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score0.290

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 head0.258
Teacher spread0.251 · 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 designBench or experimental
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

Citations1
Published2021
Admission routes2
Has abstractyes

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