Five-year Evolution Patterns of Physical Activity and Sedentary Behavior in Patients with Lower-limb Osteoarthritis and Their Sociodemographic and Clinical Correlates
Bibliographic record
Abstract
OBJECTIVE: The present study aimed to identify trajectories of physical activity (PA) components (frequency, duration, intensity, and type) and screen-based sedentary behavior (SB) as well as baseline predictors of each trajectory in patients with hip and/or knee osteoarthritis (OA). METHODS: We included 878 patients with a 5-year follow-up from the KHOALA cohort. PA and SB were measured by the Modifiable Activity Questionnaire. We used group-based trajectory analysis to identify the trajectories of PA components and screen-based SB, and multivariable logistic regression to determine predictors of the identified trajectories. RESULTS: Two groups of trajectories were identified for each PA component and 3 for SB. High and decreasing PA duration was associated with female sex (OR 0.3, 95% CI 0.1-0.5) as was low and stable, more so than high and decreasing prevalence of weight-bearing activities (OR 0.6, 95% CI 0.4-0.9). Patients with impaired patient-reported outcome measures and obese patients often featured low versus high and decreasing prevalence of weight-bearing activities. Predictors of moderate and high versus low and slightly increasing screen-based SB trajectories were male sex, age < 60 years, single status (OR 1.5, 95% CI 1.1-2.1), obesity (OR 2.1, 95% CI 1.4-3.1), smoking (OR 2.0, 95% CI 1.1-3.7), and less physical jobs. Predictors of moderate and high versus low screen-based SB trajectories were all sociodemographic: male sex, age < 60 years, single status, obesity, smoking, and less physical jobs. CONCLUSION: Sociodemographic and clinical predictors of trajectories vary between PA components; they are associated mainly with PA frequency and type. No clinical characteristics were associated with screen-based SB.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".