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Record W4401828886 · doi:10.1080/10803548.2024.2387484

The relationship between core endurance, physical activity level and balance in office workers

2024· article· en· W4401828886 on OpenAlexaboutno aff
Songül Bağlan Yentür, Ezgi Yaraşır

Bibliographic record

VenueInternational Journal of Occupational Safety and Ergonomics · 2024
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsWaistPhysical therapyCore (optical fiber)Balance (ability)TrunkMedicineCircumferenceHuman factors and ergonomicsPhysical medicine and rehabilitationPoison controlEngineeringObesityInternal medicineEnvironmental healthMathematics

Abstract

fetched live from OpenAlex

Objectives. The aim of this study was to evaluate the relationship between core endurance tests and physical activity level, balance, ergonomics and pain in office workers. Methods. The study included 57 office workers who had been employed for at least 1 year. Core endurance was assessed using McGill core endurance tests. Physical activity, balance, pain and ergonomic risks were evaluated with the international physical activity questionnaire (IPAQ), timed up and go (TUG), visual analog scale (VAS) and rapid office strain assessment (ROSA), respectively. Results. A significant correlation was found between balance and static core endurance tests. However, no significant correlation was found between ergonomics and physical activity level and core endurance tests except for trunk extension and prone bridge tests. In addition, there was a significant difference in core endurance tests for patients with and without regular exercise habits. Waist circumference and hip circumference measurements were found to be significantly negatively associated with static core tests. Conclusion. Core endurance was found to be associated with exercise habits, balance, hip and waist circumference and ergonomics in office workers. Improving core endurance may be beneficial for preventing musculoskeletal risks in office workers.

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 score0.245
Threshold uncertainty score0.344

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 head0.082
GPT teacher head0.384
Teacher spread0.302 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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Same venueInternational Journal of Occupational Safety and ErgonomicsSame topicErgonomics and Musculoskeletal DisordersFrench-language works237,207