Altering visual feedback during reaching: no mitigating effect on pain for individuals with complex regional pain syndrome, but visuomotor adaptation is preserved
Bibliographic record
Abstract
Introduction Complex regional pain syndrome (CRPS) is often associated with pain-related fear of movement, and virtual reality has been proposed as a potential rehabilitation intervention to overcome this issue. Therefore, this cross-sectional study aimed to test whether altering the visual feedback (VF) on movement could mitigate pain and promote movement. Methods Fifteen participants with upper-limb CRPS and 15 age- and gender-matched pain-free participants undertook a target reaching task in the Kinarm exoskeleton, with two VF conditions being tested (Per Exposure: GREATER or SMALLER VF; two separate sessions), preceded and followed by reaching movements without VF (Pre-/Post-Exposure). Proprioception was assessed with a Limb Position Sense task, and a Movement Accuracy outcome was derived from the Pre-Exposure reaching movements. Results Contrary to our hypothesis, pain intensity was not influenced by VF conditions but increased over Time ( p < 0.001). Analyses performed on kinematics data showed that participants displayed visuomotor adaptation both Per Exposure, and Pre-/Post-Exposure to altered VF (VF condition * Time: p < 0.001). Per Exposure analyses revealed that CRPS participants tended to adapt their Movement Length to a lesser extent than pain-free participants (Group * VF condition: p = 0.048). Pre-/Post-Exposure analyses revealed that CRPS participants consistently performed larger movements than pain-free participants ( p = 0.002). Both groups performed similarly for the Limb Position Sense task, but CRPS participants displayed significantly larger errors for Movement Accuracy, suggesting impaired proprioceptive integration in the CRPS group. Discussion These findings support the idea that visuomotor adaptation is preserved in CRPS and can be used to promote movement.
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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 | 0.000 |
| 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".