The influence of sociodemographic and health factors on adherence to home-based rehabilitation after fast-track total knee arthroplasty: secondary analysis of a randomized controlled trial
Bibliographic record
Abstract
Purpose Adherence to home rehabilitation following total knee arthroplasty (TKA) is essential to reach optimal functional outcomes, especially in fast-track procedures. The aim of this study is to identify which sociodemographic and health factors significantly affect adherence in this context.Methods This is a secondary analysis of a randomized controlled trial with 52 patients. Adherence was measured as the percentage of completed exercises. Two statistical analyses were performed, one on the entire population and another on the telerehabilitation group only, to study which factors significantly affects adherence.Results The analysis included the 42 patients with adherence data (23 TRH, 19 control). In Analysis I (n = 42), six variables were statistically significant: history of depression (p = 0.00026), educational level (p = 0.00151), social support (p = 0.00157), treatment group (p = 0.0081), history of diabetes (p = 0.01153), and ASA score (p = 0.02752). In Analysis II (TRH, n = 23), three variables were significant: history of depression (p = 0.003), educational level (p = 0.006), and history of hypertension (p = 0.047).Conclusion There are sociodemographic and health factors affecting adherence to home rehabilitation post-TKA. Depression stands out as a negative factor, while high educational level and social support improve adherence. Telerehabilitation has positive effects and reduces the influence of social and economic factors.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".