Exploring the Perceived Difficulty and Importance of Lower Limb Physical Activities for People With and Without Osteoarthritis: A Discrete Choice Experiment
Bibliographic record
Abstract
BACKGROUND: Many outcome measures used in lower-limb osteoarthritis (OA) present ceiling effects. This compromises the ability of those measures to accurately assess people with higher levels of physical function. Understanding of the difficulty and importance of physical activities would enable the inclusion of challenging and meaningful activities in new outcome measures. PURPOSE: To explore the perceived difficulty and importance of 40 physical activities by people with and without lower limb OA. METHODS: We conducted a discrete choice experiment (DCE) using 1000minds software. We recruited people with and without OA using OA databases and social media. Participants were asked to complete two comparison tasks, first about the relative difficulty and then about the importance of the physical activities. Pairwise comparisons were presented (i.e., two alternatives at a time), and participants selected the most difficult/important. RESULTS: We analysed data from 613 participants, of whom 215 had OA. Rankings of difficulty and importance were obtained. No major differences existed in the difficulty ranking between people with and without OA. People with OA rated activities like kneeling and balancing activities as more important than those without OA. In contrast, people without OA rated jogging, squatting, and running as more important than those with OA. Challenging activities were generally rated as less important. CONCLUSIONS: A DCE ranked 40 different lower limb physical activities in terms of difficulty and importance. Challenging activities were found to be less important than easier ones. People with OA gave more importance to easier activities than people without 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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".