Association of Pain and Steps Per Day in Persons With Mild‐to‐Moderate, Symptomatic Knee Osteoarthritis: A Mixed‐Effects Models Analysis of Multiple Measurements Over Three Years
Bibliographic record
Abstract
OBJECTIVE: Pain is a consistently reported barrier to physical activity by persons with knee osteoarthritis (OA). Nonetheless, few studies of knee OA have investigated the association of pain with daily walking levels. The current study assessed the relationship of 2 distinct measures of knee pain with objectively measured physical activity in adults with knee OA. METHODS: ) with clinical knee OA. Data were collected every 3 months for up to 3 years. Physical activity was characterized as the average steps per day taken over at least 3 days, mea-sured by accelerometry. Pain was measured using 2 patient-administered questionnaires: the pain subscale of the Knee Injury and Osteoarthritis Outcome Score (KOOS-pain) and the P4 pain scale (P4-pain). Mixed-effects models determined the association between pain and physical activity levels (over covariates) among adults with knee OA (α = 0.05). RESULTS: All covariates (age [β = -3.65, P < 0.001], body mass index [β = -3.06, P < 0.001], season [spring/fall β = -6.91, P = 0.002; winter β = -14.92, P < 0.001]) were predictors of physical activity. Neither the inverted KOOS-pain (β = 0.04, P = 0.717) nor P4-pain (β = -0.37, P = 0.264) was associated with physical activity. CONCLUSION: Knee pain is not associated with daily walking levels in persons with mild-to-moderate, symptomatic knee OA. While pain management remains an important target of interventions, strategies to increase steps per day in this population should focus on overcoming potentially more crucial barriers to activity participation.
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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 | 0.032 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".