Novel uses of healthcare technology for individuals with mild to moderate hip or knee osteoarthritis: The technology, exercise and activity prescription for enhanced mobility (TEAM) study randomized controlled trial protocol
Bibliographic record
Abstract
Objectives: Patient education, physical activity, and exercise are recommended as first-line treatments for mild to moderate hip and knee osteoarthritis (OA). We developed two novel healthcare interventions: an electronic medical record-embedded physical activity prescription tool (PARx) for physicians, and a free, online educational platform (Joint Management (JM)) with exercise programming and optional telerehabilitation with a physiotherapist for patients. Objectives: 1) Determine the effectiveness of PARx ± JM on patient-reported outcomes, physical activity levels, and performance-based functional outcomes in individuals with mild to moderate hip or knee OA, versus usual care; 2) evaluate engagement and adherence to PARx + JM; and 3) explore the feasibility of PARx and PARx + JM. Registration: NCT04544904. Methods: Randomized controlled trial (type 1 hybrid implementation effectiveness). We will recruit 339 (113/group) participants ≥40 years old with mild to moderate hip or knee OA and randomize them into three groups: PARx, PARx + JM, or control (usual care). Follow-up appointments will be completed at 2-, 6-, and 12-months. Primary outcome: Knee Injury/Hip Disability and OA Outcome Score. Secondary outcomes: physical activity levels, anthropometric measurements, physical function, and other patient-reported outcomes. We will assess intervention feasibility and hold focus groups with patients and providers to explore perceptions of the interventions. Conclusion: Two novel healthcare interventions will be used to provide physical activity and exercise programming for individuals with mild-moderate knee and hip OA. This study will allow us to determine the effectiveness of these interventions on patient-reported outcomes, physical activity levels, and performance-based functional outcomes.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".