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Record W4412159099 · doi:10.1177/10711813251357939

Effects of an Active and a Passive Work Condition on Musculoskeletal Discomfort and Computer Typing Performance for Computer-Based Work

2025· article· en· W4412159099 on OpenAlexaff
Amarjeet Mohanty, Cristina G. Banks, Gourab Kar

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2025
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsWorkplace Health, Safety and Compensation Commission
Fundersnot available
KeywordsWork (physics)TypingComputer scienceHuman–computer interactionPhysical medicine and rehabilitationArtificial intelligenceMedicineSpeech recognitionEngineering

Abstract

fetched live from OpenAlex

A growing body of research suggests that excessive chair-seated work is negatively associated with health and wellbeing of office workers. Replacing chair-seated work with “active rest” postures may offer beneficial health and productivity outcomes. Therefore, this pilot study compared musculoskeletal-discomfort (MSD) and computer-typing performance between an active (20-min floor-seated work followed by 10-min of standing work) and a passive (30-min of chair-seated work) condition in an office-like laboratory environment. Using a repeated measures experimental design twelve participants performed computer-typing tasks for 60 minutes each in the active and passive work conditions. Results indicate that change in MSD was significantly lower in the active work condition compared to passive work condition; there was no significant difference in computer-typing performance between work conditions. Findings suggest that replacing chair-seated work with a combination of floor-seated and standing work can attenuate MSD without negatively impacting computer-typing performance.

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 score0.320
Threshold uncertainty score0.732

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 head0.008
GPT teacher head0.259
Teacher spread0.252 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Human Factors and Ergonomics Society Annual MeetingSame topicErgonomics and Musculoskeletal DisordersFrench-language works237,207