Gait Adaptation to a Phase-Specific Nociceptive Electrical Stimulation Applied at the Ankle: A Model to Study Musculoskeletal-Like Pain
Bibliographic record
Abstract
Introduction: Lower limb pain, whether induced experimentally or as a result of a musculoskeletal injury, can impair motor control, leading to gait adaptations such as increased muscle stiffness or modified load distribution around joints. These adaptations may initially reduce pain but can also lead to longer-term maladaptive plasticity and to the development of chronic pain. In humans, many current experimental musculoskeletal-like pain models are invasive, and most don’t accurately reproduce the movement-related characteristics of musculoskeletal pain. The main objective of this study was to measure pain adaptation strategies during gait of a musculoskeletal-like experimental pain protocol induced by phase-specific, non-invasive electrical stimulation. Methods: Sixteen healthy participants walked on a treadmill at 4 km/h for three consecutive periods (BASELINE, PAIN, and POST-PAIN). Painful electrical stimulations were delivered at heel strike for the duration of heel contact (HC) using electrodes placed around the right lateral malleolus to mimic ankle sprains. Gait adaptations were quantified bilaterally using instrumented pressure-sensitive insoles. One-way ANOVAs and group time course analyses were performed to characterize the impact of electrical stimulation on heel and forefoot contact pressure and contact duration. Results: During the first few painful strides, peak HC pressure decreased on the painful side (8.6 ± 1.0%, p < 0.0001) and increased on the non-stimulated side (11.9 ± 0.9%, p < 0.0001) while HC duration was significantly reduced bilaterally (painful: 12.1 ± 0.9%, p < 0.0001; non-stimulated: 4.8 ± 0.8%, p < 0.0001). No clinically meaningful modifications were observed for the forefoot. One minute after the onset of painful stimulation, perceived pain levels stabilized and peak HC pressure remained significantly decreased on the painful side, while the other gait adaptations returned to pre-stimulation values. Discussion: These results demonstrate that a non-invasive, phase-specific pain can produce a stable painful gait pattern. Therefore, this protocol will be useful to study musculoskeletal pain locomotor adaptation strategies under controlled conditions.
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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 | 0.000 |
| 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) | 0.001 | 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".