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Record W1498563596 · doi:10.1123/jab.25.1.73

Torso and Hip Muscle Activity and Resulting Spine Load and Stability while Using the ProFitter 3-D Cross Trainer

2009· article· en· W1498563596 on OpenAlexafffund
Priyanka Banerjee, Stephen H.M. Brown, Samuel J. Howarth, Stuart M. McGill

Bibliographic record

VenueJournal of Applied Biomechanics · 2009
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Waterloo
FundersMcGill University
KeywordsTorsoElectromyographyPhysical medicine and rehabilitationBiomechanicsCore stabilityBalance (ability)TrunkBackpackCompression (physics)Functional movementMedicinePhysical therapyComputer scienceOrthodonticsAnatomyEngineeringMaterials scienceStructural engineering

Abstract

fetched live from OpenAlex

The ProFitter 3-D Cross Trainer is a labile surface device used in the clinic and claimed to train spine stability. The purpose of this study was to quantify the spine mechanics (compression and shear forces and stability), together with muscle activation mechanics (surface electromyography) of the torso and hip, during three ProFitter exercises. Trunk muscle activity was relatively low while exercising on the device (<25%MVC). Gluteus medius activity was phasic with the horizontal sliding position, especially for an experienced participant. Sufficient spinal stability was achieved in all three exercise conditions. Peak spinal compression values were below 3400 N (maximum 3188 N) and peak shear values were correspondingly low (under 500 N). The exercises challenge whole-body dynamic balance while producing very conservative spine loads. The motion simultaneously integrates hip and torso muscles in a way that appears to ensure stabilizing motor patterns in the spine. This information will assist with clinical decision making about the utility of the device and exercise technique in rehabilitation and training programs.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.026
GPT teacher head0.298
Teacher spread0.272 · 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 designBench or experimental
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
Published2009
Admission routes2
Has abstractyes

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