Predictors of adherence to a 12‐week exercise program among men treated for prostate cancer: ENGAGE study
Bibliographic record
Abstract
Understanding the factors that influence adherence to exercise programs is necessary to develop effective interventions for people with cancer. We examined the predictors of adherence to a supervised exercise program for participants in the ENGAGE study - a cluster randomized controlled trial that assessed the efficacy of a clinician-referred 12-week exercise program among men treated for prostate cancer. Demographic, clinical, behavioral, and psychosocial data from 52 participants in the intervention group were collected at baseline through self-report and medical records. Adherence to the supervised exercise program was assessed through objective attendance records. Adherence to the supervised exercise program was 80.3%. In the univariate analyses, cancer-specific quality of life subscales (role functioning r = 0.37, P = 0.01; sexual activity r = 0.26, P = 0.06; fatigue r = -0.26, P = 0.06, and hormonal symptoms r = -0.31, P = 0.03) and education (d = -0.60, P = 0.011) were associated with adherence. In the subsequent multivariate analysis, role functioning (B = 0.309, P = 0.019) and hormonal symptoms (B = -0.483, P = 0.054) independently predicted adherence. Men who experienced more severe hormonal symptoms had lower levels of adherence to the exercise program. Those who experienced more positive perceptions of their ability to perform daily tasks and leisure activities had higher levels of adherence to the exercise program. Hormonal symptoms and role functioning need to be considered when conducting exercise programs for men who have been treated for prostate cancer.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".