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The influence of pain and kinesiophobia on motor control of the upper limb: how pointing task paradigms can point to new avenues of understanding

2024· article· en· W4392951730 on OpenAlexaff
Arnaud Duport, Pierre Morel, Guillaume Léonard, H. Devanne

Bibliographic record

VenuePain · 2024
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTranscranial magnetic stimulationPhysical medicine and rehabilitationDeltoid curveMotor controlUpper limbPsychologyKinematicsElectromyographyMedicinePhysical therapyNeuroscienceStimulationAnatomy

Abstract

fetched live from OpenAlex

ABSTRACT: People experiencing kinesiophobia are more likely to develop persistent disabilities and chronic pain. However, the impact of kinesiophobia on the motor system remains poorly understood. We investigated whether kinesiophobia could modulate shoulder pain-induced changes in (1) kinematic parameters and muscle activation during functional movement and (2) corticospinal excitability. Thirty healthy, pain-free subjects took part in the study. Shoulder, elbow, and finger kinematics, as well as electromyographic activity of the upper trapezius and anterior deltoid muscles, were recorded while subjects performed a pointing task before and during pain induced by capsaicin at the shoulder. Anterior deltoid cortical changes in excitability were assessed through the slope of transcranial magnetic stimulation input-output curves obtained before and during pain. Results revealed that pain reduced shoulder electromyographic activity and had a variable effect on finger kinematics, with individuals with higher kinesiophobia showing greater reduction in finger target traveled distance. Kinesiophobia scores were also correlated with the changes in deltoid corticospinal excitability, suggesting that the latter can influence motor activity as soon as the motor signal emerges. Taken together, these results suggest that pain and kinesiophobia interact with motor control adaptation.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.867
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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