Increased sensitivity to physical activity among individuals with knee osteoarthritis: Relation to pain outcomes, psychological factors, and responses to quantitative sensory testing
Bibliographic record
Abstract
Recent findings suggest that certain individuals with musculoskeletal pain conditions have increased sensitivity to physical activity (SPA) and respond to activities of stable intensity with increasingly severe pain. This study aimed to determine the degree to which individuals with knee osteoarthritis (OA) show heightened SPA in response to a standardized walking task and whether SPA cross-sectionally predicts psychological factors, responses to quantitative sensory testing (QST), and different OA-related outcomes. One hundred seven adults with chronic knee OA completed self-report measures of pain, function, and psychological factors, underwent QST, and performed a 6-min walk test. Participants rated their discomfort levels throughout the walking task; an index of SPA was created by subtracting first ratings from peak ratings. Repeated-measure analysis of variance revealed that levels of discomfort significantly increased throughout the walking task. A series of hierarchical regression analyses determined that after controlling for significant covariates, psychological factors, and measures of mechanical pain sensitivity, individual variance in SPA predicted self-report pain and function and performance on the walking task. Analyses also revealed that both pain catastrophizing and the temporal summation of mechanical pain were significant predictors of SPA and that SPA mediated the relationship between catastrophizing and self-reported pain and physical function. The discussion addresses the potential processes contributing to SPA and the role it may play in predicting responses to different interventions for musculoskeletal pain conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".