Update on the Relation Between Pain and Movement: Consequences for Clinical Practice
Bibliographic record
Abstract
It is generally thought that exercise is beneficial to alleviate pain. However, prolonged movement may lead to the development of painful injuries, because of the overload of low-threshold motor units. Especially in individuals with a pain condition, exercise prescription and the impact of fatigue is less clear. This may be because of the dual effects, aggravation and relief, which fatigue has on pain. The purpose of this review is to ascertain the relation between pain and the motor system, both in the development and management of pain. Recent studies show that fatigue alters pain-induced increases in corticomotor excitability and leads to within and between-muscle adaptations. Studies of acute pain have shown complex adaptations such as increased movement variability, which may be because of a search for motor solutions to prolong overall task performance. In contrast, chronic pain seems to limit movement duration, speed, and variability which could be protective in the short term but also counterproductive over time. Owing to these adaptations in movement strategies, pain chronicity may help to dictate exercise prescription. For example, the correct dosage of multimuscle, dynamic exercises would act to promote movement variability. Thus, it seems that exercise involving the use of different movement strategies could be effective in helping people to obtain exercise-induced benefits while avoiding injury and pain reaggravation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".