Exercise training augments brain function and reduces pain perception in adults with chronic pain: A systematic review of intervention studies
Bibliographic record
Abstract
Introduction: Chronic pain (CP) is a leading cause of disability worldwide. Pain may be measured using subjective questionnaires, but understanding the underlying physiology, such as brain function, could improve prognosis. Further, there has been a shift towards cost-effective lifestyle modification for the management of CP. Methods: We conducted a systematic review (Registration: #CRD42022331870) using articles retrieved from four databases (Pubmed, EMBASE, AMED, and CINAHL) to assess the effect of exercise on brain function and pain perception/quality of life in adults with CP. Results: Our search yielded 1879 articles; after exclusion, ten were included in the final review. Study participants were diagnosed with either osteoarthritis or fibromyalgia. However, two studies included "fibromyalgia and low back pain" or "fibromyalgia, back, and complex regional pain." Exercise interventions that were 12 weeks or longer (n = 8/10) altered brain function and improved pain and/or quality of life outcomes. The cortico-limbic pathway, default-mode network, and dorsolateral prefrontal cortex were key regions that experienced alterations post-intervention. All studies that reported an improvement in brain function also demonstrated an improvement in pain perception and/or quality of life. Discussion: Our review suggests that alterations in brain function, notably the cortico-limbic, default-mode and dorsolateral prefrontal cortex, may be responsible for the downstream improvements in the subjective experience of CP. Through appropriate programming (i.e., length of intervention), exercise may represent a viable option to manage CP via its positive influence on brain health.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".