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Record W2779551779 · doi:10.1002/tsm2.5

Loading intensity of jumping exercises in post-menopausal women: Implications for osteogenic training

2017· article· en· W2779551779 on OpenAlexaff
Kenneth B. Smale, Lisa H. Hansen, Kristensen Jk, Mette Kreutzfeldt Zebis, Christoffer H. Andersen, Daniel L. Benoit, Eva Wulff Helge, Tine Alkjær

Bibliographic record

VenueTranslational Sports Medicine · 2017
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGround reaction forceJumpingJumpForce platformCountermovementBone mineralMedicineKinematicsOsteoporosisPhysical therapyVertical jumpMathematicsPhysical medicine and rehabilitationOrthodonticsPhysicsInternal medicine

Abstract

fetched live from OpenAlex

Post-menopausal women frequently exhibit low bone mineral density, and therefore, evidence-based exercises that induce osteogenic loading and prevent osteoporosis are often essential. The purpose of this study was to investigate the loading intensity of 3 different jumping exercises in post-menopausal women. Fourteen post-menopausal women participated in this study and completed a series of countermovement jumps, drop jumps, and hard landings. A full-body kinematic and kinetic analysis was performed to estimate the load intensity. Peak hip extensor moment and rate of moment change were significantly greater (P < .05; η2 = 0.483-0.693) in the first landing of the drop jump than the countermovement jump and hard landing. Hip stiffness approached significance (P = .067), while peak vertical ground reaction force, vertical ground reaction force loading rate, and vertical ground reaction force index (peak*loading rate) were significantly greater (P < .01; η2 = 0.259-0.864) during the hard landing. The drop jump and hard landing appear to generate the greatest loads at the highest rates and therefore are likely to have the largest osteogenic impact. Thus, future rehabilitation programs aimed at enhancing osteogenesis in post-menopausal women are encouraged to include these easily implemented jumping exercises.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.432

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 head0.049
GPT teacher head0.285
Teacher spread0.236 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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