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Record W4322495708 · doi:10.1080/00140139.2023.2183850

Is there a u-shaped relationship between load levels and fatigue and recovery? An examination of possible mechanisms

2023· article· en· W4322495708 on OpenAlexaff
Marcus Yung, Linda M. Rose, Patrick Neumann, Amin Yazdani, Jay Kapellusch

Bibliographic record

VenueErgonomics · 2023
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsToronto Metropolitan UniversityConestoga College
Fundersnot available
KeywordsChronic fatigueMechanism (biology)PhenomenonPsychologyWork (physics)MedicinePhysical medicine and rehabilitationPhysical therapyChronic fatigue syndromeEngineering

Abstract

fetched live from OpenAlex

In a previous study, an unexpected u-shaped relationship was observed between load level and fatigue/recovery responses. Moderate load levels resulted in lower perceived discomfort, pain, and fatigue, and shorter recovery times compared to either low or high load levels. This phenomenon has been reported in other studies, but no article has examined the possible mechanisms that might explain this u-shaped relationship. In this paper, we re-examined the previously published data and found that the phenomenon does not appear to be due to the experimental artefact; the u-shape may be due to unexpectedly lower fatigue effects at moderate loads, and higher fatigue effects at lower loads. We then conducted a literature review and identified several possible physiological, perceptual, and biomechanical explanatory mechanisms. No single mechanism explains the entirety of the phenomenon. Further research is needed on the relationship between work exposures, fatigue, and recovery, and the mechanisms related to the u-shaped relationship.Practitioner summary: We examine a previously observed u-shaped relationship between load level and fatigue/recovery, where moderate force resulted in lower perceived fatigue and shorter recovery times. A u-shaped fatigue response suggests that simply minimising load levels might not be an optimal approach to reduce the risk of workplace injuries.

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 score0.199
Threshold uncertainty score0.296

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 head0.072
GPT teacher head0.319
Teacher spread0.247 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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