Consumer‐Focused Osteoarthritis e‐Learning to Complement Physiotherapy Care in People With Knee Osteoarthritis: Protocol for the PEEKO Randomised Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: To investigate whether the addition of a consumer e-learning course to verbal information on OA and a home strengthening exercise programme prescribed by a physiotherapist enhances pain and/or physical function outcomes at 24 weeks in people with knee OA. Primary outcomes are (i) severity of knee pain while walking (11-point numeric rating scale) at 24 weeks and (ii) physical function at 24 weeks using the Western Ontario and McMaster Universities Osteoarthritis Index subscale. Secondary outcomes include measures of knee-related quality of life; sport and recreation; self-efficacy for exercise; pain self-efficacy; physical activity levels; OA knowledge; perceptions of OA (illness perceptions); fear of movement; belief in the inevitability of needing a knee joint replacement; global rating of change; satisfaction with treatment; exercise adherence; and use of recommended OA self-management approaches and oral pain medication. METHODS: Two-arm parallel-design superiority randomised controlled trial. One hundred thirty-six community dwelling Australian adults with clinically diagnosed knee OA will be randomly allocated into one of two groups: (i) verbal OA education and a home strengthening programme (control) or (ii) the same intervention plus access to a freely available 4-module e-learning course on OA and its recommended management (e-learning group). Both groups will receive the same scripted verbal OA information and a lower limb strengthening programme over two 30-min video consultations with a physiotherapist over six weeks. Those in the e-learning group will receive access to the e-learning course from their physiotherapist. Primary outcomes are (i) severity of knee pain while walking (11-point numeric rating scale) at 24 weeks and (ii) physical function at 24 weeks using the Western Ontario and McMaster Universities Osteoarthritis Index subscale. Secondary outcomes include measures of knee-related quality of life; sport and recreation; self-efficacy for exercise; pain self-efficacy; physical activity levels; OA knowledge; perceptions of OA (illness perceptions); fear of movement; belief in the inevitability of needing a knee joint replacement; global rating of change; satisfaction with treatment; exercise adherence; and use of recommended OA self-management approaches and oral pain medication. CONCLUSION: This RCT evaluates whether supplementing physiotherapy care with consumer-focused e-learning improves outcomes for people with knee OA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".