Does musculoskeletal pain interfere with motor learning in a gait adaptation task? A proof-of-concept study
Bibliographic record
Abstract
BACKGROUND: Experimental pain during gait has been shown to interfere with learning a new locomotor task. However, very few studies have investigated the impact of clinical pain on motor learning due to the challenges associated with clinical populations. OBJECTIVE: The first objective of this proof-of-concept study was to determine the feasibility to obtain two groups of participants with chronic ankle pathology with or without residual pain while walking. The second objective was to evaluate the impact of clinical musculoskeletal pain on motor learning during gait. METHODS: Participants with chronic isolated ankle pathology were recruited and their personal and clinical characteristics were collected (functional performance, dorsiflexion maximal strength, range of motion). To assess motor acquisition (Day 1) and retention (Day 2), participants performed an adaptation task on two consecutive days that consisted of walking while experiencing a perturbing force applied to the ankle. The level of pain during the task was measured, and participants who reported pain were attributed to the Pain group and participants without pain to the No Pain group. Learning performance was assessed by measuring ankle kinematics (Mean plantarflexion absolute error) and learning strategy was assessed by measuring the Relative timing of error and the tibialis anterior (TA) electromyographic activity. RESULTS: Twenty-five participants took part in the experiment. Eight (32%) were excluded because they could not be included in either the Pain or No Pain group due to the intermittent pain, leaving eight participants in the Pain group and nine in the No Pain group. Both groups were similar in terms of baseline characteristics. Musculoskeletal pain had no influence on learning performance, but the learning strategy were different between the two groups. The No Pain group showed a TA activity reduction before perturbation between the days, while the Pain group did not. CONCLUSION: Some barriers were identified in studying musculoskeletal pain including the high rates of participants' exclusion, leading to a small sample size. However, we showed that it is feasible to investigate clinical pain and motor learning. From the results of this study, musculoskeletal pain has no influence on motor learning performance but influences the learning strategy.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".