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Record W2980309035 · doi:10.1177/0018720819879310

Does Reducing Sedentarity With Standing Desks Hinder Cognitive Performance?

2019· article· en· W2980309035 on OpenAlexaff
Élise Labonté-LeMoyne, Marc-Antoine Jutras, Pierre‐Majorique Léger, Sylvain Sénécal, Marc Frédette, Mickaël Begon, Marie-Eve Mathieu

Bibliographic record

VenueHuman Factors The Journal of the Human Factors and Ergonomics Society · 2019
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsUniversité de MontréalHEC Montréal
Fundersnot available
KeywordsDeskSittingTask (project management)CognitionWork (physics)PsychologyApplied psychologyPerceptionProductivityAffect (linguistics)Working memoryComputer scienceMedicineEngineeringCommunication

Abstract

fetched live from OpenAlex

OBJECTIVE: The goal of this study was to determine if using a standing desk would affect the productivity of workers, based on the type of work they perform. BACKGROUND: Standing desks are a promising new health intervention in the workplace, but users and employers often require more specific recommendations related to productivity, such as the type of work that is more suited for the standing desk. METHOD: Thirty-seven young and healthy adults performed eight cognitive tasks in a 2 × 2 × 2 within-subject design of the following independent variables: posture (sitting/standing), task difficulty (easy/hard), and input device (computer mouse/tactile screen) in a counterbalanced order. RESULTS: = .001). CONCLUSION: We conclude that users of standing desks can freely stand for any level of task difficulty for work that involves working memory. However, more research is needed to generalize these results to other types of cognitive abilities and prolonged use of standing desks. APPLICATION: Our results simplify recommendations for workers as they do not need to worry about the type of work they are performing when using a standing desk.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.924

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.271
Teacher spread0.248 · 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

Citations20
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueHuman Factors The Journal of the Human Factors and Ergonomics SocietySame topicErgonomics and Musculoskeletal DisordersFrench-language works237,207