Brief time-based activity pacing instruction as a singular behavioral intervention was not effective in participants with symptomatic osteoarthritis
Bibliographic record
Abstract
Osteoarthritis (OA) of the lower extremities is a prevalent cause of disability in which symptoms interfere with mobility and activity participation. Behavioral self-management for OA symptomatology is commonly recommended; but these interventions are underutilized, unstandardized in application, and at times, unavailable in the context of clinical care. For people with chronic pain, rehabilitation professionals may select to apply activity pacing instruction as one behavioral strategy to manage symptoms. Activity pacing is widely used in combination with other pharmacological and behavioral interventions but has not been studied as a singular behavioral intervention for people with OA. The purpose of this study was to evaluate the effectiveness of an occupational therapist-delivered, time-based activity pacing program for treatment of pain, fatigue, and physical function in people with symptomatic knee or hip OA. A 3-arm randomized controlled trial was conducted in which 193 people were randomized into tailored activity pacing, general activity pacing, or usual care arms. Assessments were done at 10 weeks and 6 months after baseline. Using linear mixed models, Western Ontario and McMaster Universities Osteoarthritis Index pain scores changed over time, decreasing the most in the general and usual care groups; only the usual care group had decreased pain over 6 months. The tailored and general activity pacing groups reported higher frequency of pacing behaviors than the usual care group at 10 weeks, but pacing was not sustained at 6 months. This trial does not support the use of time-based pacing as a singular behavioral strategy for people with knee or hip OA.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".