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Record W2073310678 · doi:10.3138/ptc.2012-04

The Relationship of Knee-Extensor Strength and Rate of Torque Development to Sit-to-Stand Performance in Older Adults

2013· article· en· W2073310678 on OpenAlexaffvenue
Katie Crockett, Kimberly Ardell, Marlyn Hermanson, Andrea Penner, Joel L. Lanovaz, Jonathan P. Farthing, Cathy M. Arnold

Bibliographic record

VenuePhysiotherapy Canada · 2013
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsConcentricEccentricPhysical therapyMedicineKnee flexionPhysical medicine and rehabilitationAnalysis of varianceMathematicsPhysicsInternal medicineGeometry

Abstract

fetched live from OpenAlex

PURPOSE: To investigate the association of knee-extensor strength and power to performance in the 30-second sit-to-stand test (30sSTS) in healthy older adults. METHOD: In a cross-sectional study of 29 healthy older adults aged 60-82 years (12 male, 17 female), hierarchical regression was used to determine the relationship of knee-extensor concentric and eccentric strength, peak rate of torque development (peak RTD) using isokinetic dynamometry, and momentum variables with the number of sit-to-stand repetitions completed in 30 seconds (30sSTSreps). RESULTS: Concentric (180°/s) and eccentric (90°/s) knee-extensor strength were significant independent predictors of 30sSTSreps after controlling for physical activity level, height and weight (adjusted R (2)=0.425, p=0.004; adjusted R (2)=0.427, p=0.004 respectively), as was concentric (90°/s) knee-extensor peak RTD (adjusted R (2)=0.424, p=0.004). Peak linear vertical momentum (PLVM) (adjusted R (2)=0.615, p<0.001) accounted for 36% of the variance. CONCLUSIONS: Generation of PLVM is an important predictor of 30sSTSreps; knee-extensor concentric and eccentric strength and power are associated with improved performance in this common functional task. Focusing on these parameters in exercise interventions may improve functional performance and give insight into specific factors related to success on the test.

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 score0.240
Threshold uncertainty score0.874

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 head0.012
GPT teacher head0.298
Teacher spread0.286 · 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

Citations49
Published2013
Admission routes2
Has abstractyes

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