Exercise and behaviour change support for individuals living with and beyond cancer: Interim results and program satisfaction of the EXCEL study
Bibliographic record
Abstract
Purpose: Examine the impact of the EXercise for Cancer to Enhance Living Well's (EXCEL) 10-12-week exercise and behaviour change support intervention on secondary effectiveness outcomes, including patient-reported outcomes, physical function, and program satisfaction. Methods: Individuals with cancer up to 3 years post treatment with any tumour type were eligible. Outcomes were measured at baseline and immediately following the 10-12-week intervention. Patient-reported outcomes included participant characteristics, overall well-being, cognition, fatigue, symptom severity, exercise barrier self-efficacy, and program satisfaction. Physical function included shoulder flexion, 30-s sit to stand, sit and reach, 2-min step test or 6-min walk test (in-person only), and single leg balance. Wilcoxon signed-rank tests were used to assess changes in patient-reported outcomes and physical function assessments from baseline to 12-weeks. Results: = 0.34) assessments. Participants reported high satisfaction with program staff (average = 4.5/5) and that the program was beneficial and enjoyable (average = 4.6/5). Conclusion: EXCEL's group-based exercise program with behaviour change support, delivered in an online supervised setting to individuals living with cancer, may improve patient-reported outcomes and physical function and is associated with high participant satisfaction.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".