Effect of experimental muscle pain on the acquisition and retention of locomotor adaptation: different motor strategies for a similar performance
Bibliographic record
Abstract
As individuals with musculoskeletal disorders often experience motor impairments, contemporary rehabilitation relies heavily on the use of motor learning principles. However, motor impairments are often associated with pain. Although there is substantial evidence that muscle pain interferes with motor control, much less is known on its impact on motor learning. The objective of the present study was to assess the effects of muscle pain on locomotor learning. Two groups (Pain and Control) of healthy participants performed a locomotor adaptation task (robotized ankle-foot orthosis perturbing ankle movements during swing) on two consecutive days. On day 1 (acquisition), hypertonic saline was injected in the tibialis anterior (TA) muscle of the Pain group participants, while Control group participants were pain free. All participants were pain free on day 2 (retention). Changes in movement errors caused by the perturbation were assessed as an indicator of motor performance. Detailed analysis of kinematic and electromyographic data provided information about motor strategies. No between-group differences were observed on motor performance measured during the acquisition and retention phases. However, Pain group participants had a residual movement error later in the swing phase and smaller early TA activation than Control group participants, thereby suggesting a reduction in the use of anticipatory motor strategies to overcome the perturbation. Muscle pain did not interfere with global motor performance during locomotor adaptation. The different motor strategies used in the presence of muscle pain may reflect a diminished ability to anticipate the consequences of a perturbation. NEW & NOTEWORTHY This study shows that experimental muscle pain does not influence global motor performance during the acquisition or next-day retention phases of locomotor learning. This contrasts with previous results obtained with cutaneous pain, emphasizing the risk of directly extrapolating from one pain modality to another. Muscle pain affected motor strategies used when performing the task, however: it reduced the ability to use increased feedforward control to overcome the force field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".