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Record W4376138664 · doi:10.3390/app13105908

Reducing Lumbar Flexion in a Repetitive Lifting Task: Comparison of Leukotape and Kinesio Tape and Their Effect on Lumbar Proprioception

2023· article· en· W4376138664 on OpenAlexaff
Kim Grütters, Susanne Narciss, Shawn M. Beaudette, Luca Oppici

Bibliographic record

VenueApplied Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsBrock University
Fundersnot available
KeywordsLumbarProprioceptionTrunkPhysical medicine and rehabilitationMedicineSagittal planeReduction (mathematics)Physical therapySurgeryAnatomyMathematics

Abstract

fetched live from OpenAlex

Rigid leukotape applied to the skin of the trunk dorsum, superficial to the lumbar paraspinals, has been shown to reduce lumbar flexion in repetitive lifting, with the potential to reduce the risk of injury in jobs requiring the handling of material. It is unclear which mechanism underpins this reduction, and whether a tape with more elastic properties (i.e., kinesio tape) can yield similar results. In this study, twelve participants were randomly allocated into two groups, and practiced a repetitive lifting task with either leukotape or kinesio tape applied to the skin of their trunk dorsum. The participants also performed a sagittal plane repositioning task to assess changes in lumbar proprioception. The results showed a small reduction in lumbar flexion in the kinesio tape group and a moderate reduction in the leukotape group, and suggested a reduction in repositioning errors in the kinesio tape group only. We suggest that leukotape may correct the movement and improve performance during a flexion-based task, while kinesio tape may improve lumbar proprioception and promote learning. These results have implications for the choice and use of wearable textiles aiming to reduce injury risks in the manual handling industry.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.315
Teacher spread0.296 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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