An unsupervised online Tai Chi program for people with knee osteoarthritis (“My Joint Tai Chi”): Study protocol for the RETREAT randomised controlled trial
Bibliographic record
Abstract
Background: Knee osteoarthritis (OA) is a leading contributor to global disability, with exercise proven to be an effective treatment. Tai Chi is a recommended type of exercise, but it is primarily done in person which imposes an accessibility issue. This study aims to evaluate the effects of an online unsupervised program, when provided with online educational information and exercise adherence support, on changes in knee pain and physical function, when compared to online education control for people with knee OA. Methods: A two-arm, superiority parallel-design, pragmatic randomised controlled trial will be conducted involving 178 people with a clinical diagnosis of knee OA. After completing baseline assessment, participants will be randomly assigned to either: i) "My Joint Education", an education control website containing OA information only; or ii) "My Joint Tai Chi", an intervention website containing the same information as the control, a 12-week unsupervised online Tai Chi program to be undertaken at home 3 times a week, and information about an exercise adherence support app. All participants will be reassessed at 12 weeks after randomisation. Primary outcomes are overall knee pain during walking and physical function using the Western Ontario and McMaster Universities Osteoarthritis Index subscale. Discussion: This randomised controlled trial will provide evidence about the effectiveness of the "My Joint Tai Chi" website compared to "My Joint Education" website on self-reported knee pain and physical function for people with knee OA. Trial registration: July 2023.
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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 | 0.029 | 0.027 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.103 | 0.017 |
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".