The influence of pain and kinesiophobia on motor control of the upper limb: how pointing task paradigms can point to new avenues of understanding
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".