Comparing Novel and Existing Measures of Sensitivity to Physical Activity Among People With Chronic Musculoskeletal Pain
Bibliographic record
Abstract
OBJECTIVES: Increasing pain during physical activity is an important, but often poorly assessed, barrier to engaging in activity-based rehabilitation among people with chronic musculoskeletal pain. Preliminary work has addressed this problem by developing new clinical measures of sensitivity to physical activity (SPA). Indices of SPA are generated by evaluating how pain changes in relation to brief physical tasks. Three strategies have been identified for structuring SPA-related physical tasks (self-paced, standardized, and tailored). This cross-sectional study aimed to comparatively estimate the extent of the 3 SPA tasks' evoked pain responses, predictive value of pain severity and pain interference, and their underlying psychological and sensory constructs, among 116 adults with chronic musculoskeletal pain. MATERIALS AND METHODS: Testing included questionnaires, quantitative sensory testing, and the 3 SPA measures (self-paced, standardized, and tailored). The primary analysis estimated the predictive value of each SPA measure for pain severity and pain interference. Correlational analyses were first conducted between all variables of interest to determine what variables will be included in the hierarchical regression analysis, which in turn was conducted for each outcome. RESULTS: Analyses revealed that the tailored SPA index was most effective at evoking activity-related pain, was uniquely associated with temporal summation of pain, and was a unique predictor of pain and pain-related interference, even when controlling for established psychological and sensory risk factors. DISCUSSION: This study further emphasizes SPA as an important and unique attribute of the pain experience and reveals the added value of using a tailored approach to assess SPA.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".