Are biomarkers associated with sensitivity to physical activity?
Bibliographic record
Abstract
BACKGROUND: Activity-based treatments play an integral role in managing musculoskeletal conditions including low back pain. However, while therapeutic exercise has been shown to reduce pain in such conditions, certain individuals experience a paradoxical pain increase in response to exercise. The physiological processes underlying this sensitivity to physical activity (SPA) are not fully understood, however stress and inflammation have been shown to contribute to SPA. The present cross-sectional study investigated whether physiological indicators of stress (cortisol) and inflammation (IL-6) help explain SPA. METHODS: Twenty-seven patients with chronic low back pain and 21 healthy controls completed a 1-h exercise session of standardized physical tasks. SPA was calculated from the difference between post- and pre-exercise pain levels. Participant's saliva was collected at several timepoints for cortisol and IL-6 levels quantification. Their waking cortisol response was calculated to reflect their cortisol regulation. Reactivity of IL-6 and cortisol was calculated to reflect changes in these measures during exercise. RESULTS: IL-6 reactivity was significantly and positively correlated with SPA among participants with low back pain. In contrast, neither cortisol waking response nor cortisol reactivity was significantly correlated within the low back pain group. No significant differences in IL-6 reactivity, cortisol reactivity or cortisol waking response were observed. CONCLUSION: These findings are the first to link SPA to an objective biomarker among people with low back pain. These findings help describe the physiological mechanisms of SPA and can support new clinical research that targets the inflammatory response of patients with chronic low-back pain and elevated SPA. SIGNIFICANCE: This study reveals a correlation between SPA and an objective salivary biomarker of IL-6 in people with low back pain, improving our understanding of this clinically relevant subjective experience.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".