Physical activity in solid organ transplant recipients: Participation, predictors, barriers, and facilitators
Bibliographic record
Abstract
BACKGROUND: Our objectives were to describe the physical activity (PA) levels, predictors, barriers, and facilitators to PA in solid organ transplant (SOT) recipients. METHODS: A web-based questionnaire was sent to members of the Canadian Transplant Association including the Physical Activity Scale for the Elderly (PASE), and questions regarding barriers and facilitators of PA. RESULTS: One hundred and thirteen SOT recipients completed the survey. The median PASE score was 164.5 (24.6-482.7). Re-transplantation was the only statistically significant predictor of levels of PA. The most common facilitators of PA included a feeling of health from activity (94%), motivation (88%), social support (76%), knowledge and confidence about exercise (74%) and physician recommendation (59%). Influential barriers were cost of fitness centers (42%), side effects post-transplant or from medications (41%), insufficient exercise guidelines (37%), and feelings of less strength post-transplant (37%). CONCLUSION: There is a large variation in PA levels among SOT recipients. Multiple factors may explain the variance in PA levels in SOT recipients. Identification of facilitators and barriers to PA can inform the development of health and educational promotion strategies to improve participation among SOT recipients with low activity levels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".